Online corrosion monitoring for the combined combustion of coal and chlorine-rich biomasses in pulverised fuel and circulating fluidised bed systems (ONCORD) : final report.
Date: 2019-02-25
Institutions: Directorate-General for Research and Innovation
Subjects: biomass, chemical process, chlorine, coal, corrosion, fuel, industrial research, power plant, report, research project, testing

Online corrosion monitoring for 
the combined combustion of coal 
and chlorine-rich biomasses in 
pulverised fuel and circulating 
fluidised bed systems

(ONCORD) 

FINAL REPORT 

Research and 
Innovation 

EUR 29520 EN

 
Online corrosion monitoring for the combined combustion of coal and chlorine-rich 
biomasses in pulverised fuel and circulating fluidised bed systems (ONCORD) 
European Commission 
Directorate-General for Research and Innovation 
Directorate D - Industrial Technologies 
Unit D.4 — Coal and Steel 
Contact   Hervé Martin 
E-mail   RTD-PUBLICATIONS@ec.europa.eu 

European Commission 
B-1049 Brussels 

Manuscript completed in 2018. 

This  document  has  been  prepared  for  the  European  Commission  however  it  reflects  the  views  only  of  the  authors,  and  the 
Commission cannot be held responsible for any use which may be made of the information contained therein. 

More information on the European Union is available on the internet (http://europa.eu). 

Luxembourg: Publications Office of the European Union, 2018 

PDF  

ISBN 978-92-79-98298-9  

ISSN 1831-9424 

doi:10.2777/072410 

KI-NA-29-520-EN-N 

© European Union, 2018. 
Reuse is authorised provided the  source  is acknowledged. The reuse policy of European Commission  documents  is regulated by 
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For any use  or reproduction of  photos  or other material that is not  under the EU copyright, permission must  be sought directl y 
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All pictures, figures and graphs © Technische Universität München – TUM, RFCR-CT-2014-00010 ONCORD 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
European Commission 

Research Fund for Coal and Steel 
Online corrosion monitoring for the 
combined combustion of coal and 
chlorine-rich biomasses in pulverised fuel 
and circulating fluidised bed systems 
(ONCORD) 

VTT Technical research centre of Finland Ltd - VTT 
Koivurannantie 1, PO Box 1603, 40101 Jyväskylä, Finland 

Abo Akademi - ABO 
Piispankatu 8, 20500 Abo, Finland 

Universität Augsburg - UAU 
Universitätsstr. 1, 86159 Augsburg, Germany 

Valmet Technologies Oy - Valmet 
Lentokentänkatu 11, 33101 Tampere, Finland 

Enel Ingegneria e Ricerca SPA - Enel 
Via Andrea Pisano 120, 56122 Pisa, Italy 

Doosan Power Systems Ltd. - DBB 
PAA8UW Renfrew, United Kingdom 

Babcock Borsig Steinmueller GmbH – BBS 
Europaallee 1, 46047 Oberhausen, Germany 

Fondazione Internazionale per la ricerca sulla Combustione – Onlus – IFRF 
Via Salvatore Orlando 5, 57123 Livorno, Italy 

Universitaet Stuttgart 
Keplerstr. 7, 70174 Stuttgart, Germany 

Grant Agreement RFCR-CT-2014-00010 
01.07.2014 – 30.06.2017 

Final report 

Directorate-General for Research and Innovation 

2018 

EUR 29520 EN

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
European Commission 

Research Programme of the Research Fund for Coal and Steel 

Technical Group: TGC3 

“Online corrosion monitoring for the combined combustion 
of coal and chlorine-rich biomasses in pulverised fuel and 
circulating fluidised bed systems” 

OnCord 

1 

2 

3 

Technische Universität München – TUM 
(Coordinator) 
Chair of Energy Systems 
Boltzmannstraße 15 
85747 Garching 
Germany 
VTT Technical research centre of Finland Ltd - 
VTT 
Combustion processes 
Koivurannantie 1, PO Box 1603 
40101 Jyväskylä 
Finland  

Abo Akademi - ABO 
Laboratory of Inorganic Chemistry 
Piispankatu 8 
20500 Abo 
Finland 

4  Universität Augsburg - UAU 

Institut für Physik 
Universitätsstr. 1 
86159 Augsburg 
Germany 

6 

7 

8 

9 

Enel Ingegneria e Ricerca SPA - Enel 
Technical Research Area 
Via Andrea Pisano 120 
56122 Pisa 
Italy 

Doosan Power Systems Ltd. - DBB 
Research and Development Centre 
PAA8UW Renfrew 
United Kingdom 

Babcock Borsig Steinmueller GmbH – BBS 
Europaallee 1 
46047 Oberhausen 
Germany 

Fondazione Internazionale per la ricerca sulla 
Combustione – Onlus – IFRF 
Via Salvatore Orlando 5 
57123 Livorno 
Italy 

5 

Valmet Technologies Oy - Valmet 
Lentokentänkatu 11 
33101 Tampere 
Finland 

10  Universitaet Stuttgart 

Keplerstr. 7 
70174 Stuttgart 
Germany 

Grant Agreement number: RFCR-CT-2014-00010 

Project Period: 01.07.2014 – 30.06.2017 

Final Report 

3 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Coordinators name: 

Prof. Dr. Hartmut Spliethoff (TUM) 

Authors: 

Christian Wolf (TUM) 
Andreas Stephan (TUM) 
Martti Aho (VTT) 
Timo Leino (VTT) 
Patrik Yrjas (Abo) 
Merja Hedman (Valmet) 
Paul Kilgallon (Doosan) 
Barbara Waldmann (BBS) 
Manoj Paneru (USTUTT) 
Chiara Galetti (IFRF) 
Silvia Masci (Enel) 
Daniel Ott (University of Augsburg) 

4 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table of Contents 

Table of Contents ...................................................................................................................... 5 

Abstract ................................................................................................................................... 6 

1. 

2. 

4.1 

4.2 

Final summary ................................................................................................................ 7 

Scientific and technical description of the results ............................................................... 17 

Objectives of the project ............................................................................................. 20 

Description of activities and discussion ......................................................................... 21 

4.2.1  WP1: Fuel selection and characterisation ................................................................... 21 

4.2.2  WP2: Corrosion sensor preparation and adaption (BBS) .............................................. 23 

4.2.3  WP3: Measurements in PF systems (Enel) ................................................................. 24 

4.2.4  WP4: Measurements in CFB systems (VTT) ................................................................ 48 

4.2.5  WP5: Data evaluation, comparison and assessment (TUM) .......................................... 67 

4.2.6  WP6: Modelling (IFRF) ............................................................................................ 72 

4.2.7  WP7: Development of corrosion mitigation concepts (Valmet) ...................................... 76 

4.3 

4.4 

Conclusions ............................................................................................................... 84 

Exploitation and impact of the research results .............................................................. 85 

Abbreviations .......................................................................................................................... 88 

List of figures .......................................................................................................................... 89 

List of tables ........................................................................................................................... 91 

References ............................................................................................................................. 92 

Appendix ................................................................................................................................ 93 

Results standard and advanced fuel characterisation ................................................................. 93 

5 

 
 
 
 
 
 
 
Abstract 

The main goal of the project was to develop online corrosion sensors in pulverised (PF) and circulating 
fluidised bed (CFB) combustion systems in order to fire biomass and other low-grade fuels safely at high 
portions in power plants. Another important goal was to find protective effects from coal in power plant 
furnaces during co-firing biomass and other low-grade fuels. The obtained results helped significantly to 
understand the information  derived  from  corrosion  sensors,  which  is essential to their  integration for 
power plant process control. The studied sensors already in their present form have potential for power 
plant control. Due to the complicated character of signal formation and some needs to change particular 
details in the sensor construction, further development work would be useful. Optimising the sensitivity 
and availability to long-lasting reliable operation in very difficult environments would be key issues.  
The project results can also be utilised to re-consider the role of coal in energy production. The new idea 
to use coal as a protective agent ensuring biomass firing at very high portions instead of considering 
coal only as a co-firing fuel, can improve acceptance and public opinion to the use of coal in biomass 
power plants. The addition of coal in many biomass power plants has been forbidden so far due to fossil 
CO2  formation,  which  can  prevent  the  utilisation  of  many  potential  biomass  fuels  in  those  plants. 
Experimental  work  was  first  conducted  with  pilot-scale  pulverised  fuel  and  circulating  fluidised  bed 
reactors with various sizes. The obtained results, refined further with modelling tools, were successfully 
used to conduct the power plant tests. 

6 

 
 
 
1.  Final summary 

The main results of the OnCord project are: 

 

 

 

Improved understanding of signals from two different corrosion sensors enabling their further 
development. The need for such sensors is high to guarantee safe combustion of biomass at high 
portions in electricity and combined heat and power production. 
The role of coal in co-combustion with biomass can be changed from co-firing fuel to protective 
fuel with aid of the obtained results improving public acceptance of coal firing in power plants 
PF  and  CFB  techniques  were  compared  in  a  new  and  thorough  way  in  co-firing  of  coal  and 
biomass. Completing work with pilot-scale reactors with capacity to burn biomass up to 100% 
portion and with good prediction ability to power plant scale (confirmed here by scale-up studies) 
brought important additional value to the results. 

Two  firing  systems  were  investigated  during  the  OnCord  project:  pulverised  fuel  combustion  and 
circulating fluidised bed combustion. Experiments were performed in small-scale test rigs with a wider 
range of fuels, in mid-scale test rigs with a narrower fuel selection (compared to small-scale tests) and 
at full-scale in operating power stations. The designated naming of the test rigs and the utilised fuels 
are shown in Figure 1 in chapter 4. 

Two online corrosion sensors were used throughout the project: 

  Online corrosion sensor of Babcock Borsig Steinmüller using Linear polarization resistance (LPR) 

technique called BBS sensor. 

  Online corrosion sensor of Savcor Oy (subcontractor to Valmet Technologies Oy) using LPR to 
measure corrosion online and the so-called Wenner method to judge deposit electrical resistivity 
(ER), called Savcor sensor. Two materials can be mounted on this sensor at the same time. 

Table 1 summarises the investigated materials and set material temperatures of the corrosion sensors 
in each test rig. 

Table 1: Materials used on the corrosion sensors in the different test rigs / power stations. 

Firing 
System 

Test rig 

Material 

Sensor 

Method 

10CrMo9-10 

BBS sensor 

10CrMo9-10 

550 

BBS sensor 

TUM  pilot-scale  PF 
test rig 
USTUTT  industrial-
scale PF test rig 

Studstrup 
station PF 

power 

power 

Fusina 
station 
VTT pilot-scale CFB 
test rig 
Valmet 
scale CFB test rig 
CFB  power  plant 
Dunaujvaros 

industrial 

PF 

CFB 

10CrMo9-10 

10CrMo9-10, 
Super304H 
10CrMo9-10 

10CrMo9-10, 
AISI310S 
10CrMo9-10, 
TP310H 
10CrMo9-10 
10CrMo9-10, 
TP310H 

Material T 
[°C] 
530 

600,  
T-Variation 
550, 600 

600 

560 

560 

LPR 

LPR 

LPR 

BBS sensor 

Savcor sensor 

LPR, ER 

BBS sensor 

LPR 

Savcor sensor 

LPR, ER 

Savcor sensor 

LPR, ER 

560 
560, 520 

BBS sensor 
Savcor sensor 

LPR 
LPR, ER 

The main project objective was the utilisation of low-grade fuels (biomass, residual fuels etc.) in highly 
efficient power plants through the intelligent combination with coal and enhanced control of the boiler 
performance  via  monitoring  with  online  corrosion  sensors.  State-of-the-art  online  corrosion  sensors 
together  with  other  complementary  measurement  techniques  were  employed.  Sulphur  and  Alumino-
Silicates from the coal are used to prevent a high amount of deposits on the superheater and corrosion 
is usually observed during combustion of pure low-grade fuels. 

7 

 
 
 
Work package 0: Project coordination (TUM) 

Task 0.1 

Objectives: 

Project coordination 

  Management and coordination of the project 
  Organisation of project meetings 
  Reporting and interaction with the European Commission (EC) 
 
  Dissemination activities: Organisation of journal publications and conference participations 
  Monitor possible patents and provide guidelines for patent processing 

Providing communication tools and setting up a project website 

Results obtained, conclusions and applications: 
The project was carried out and coordinated efficiently. In total six successful project meetings were 
organized during the project time, which enabled the consortium to discuss and organise the technical 
and  scientific  content.  The  project  progress  was  presented  to  the  European  Commission  in  three 
Technical Group meetings. All required reports and deliverables have been submitted. At the beginning 
of  the  project  a  secure  data-sharing  system  was  set-up  by  TUM  for  project  partners  to  transfer 
important data and documents. Additionally, a project website was established (www.oncord.eu). 
The Consortium submitted two Amendments to the EC:  
Content of Amendment No. 1: 

  Addition of new partner University of Stuttgart (USTUTT) to the Consortium 
 

Transfer  of  Rights  and  Obligations  from  partner  Corrmoran  GmbH  to  Babcock  Borsig 
Steinmüller GmbH 
Transfer of Rights and Obligation from Valmet Power Oy to Valmet Technologies Oy 
Transfer of Rights and Obligations from VTT to VTT Oy 

 
 
  Several budget shifts 
  Adjustment of the Technical Annex, the current version dated to 01.03.2016 can be found in 

the Annex attached to this document. 

Content of Amendment No. 2: 

 

Transfer  of  Rights  and  Obligations  from  partner  ENEL  Ingegneria  e  Ricerca  SPA  to  ENEL 
Produzione SPA. 

Both Amendments were officially accepted by the EC. 

Three journal publications were published and: 

 

Yrjas  P.,  Lindén  D.,  Sevonius  C.,  “Capture  of  harmful  gaseous  KCl  with  coal  fly  ash  under 
conditions typical for fluidised beds”,  International  Technical  Conference  on  Clean  Coal and 
Fuel Systems, 41st, Clearwater, USA, June 2016, 

  Wolf  C.,  Stephan  A.,  Fendt  S.,  Spliethoff  H.,  “Measuring  gaseous  HCl  emissions  during 
pulverised co-combustion of high shares of straw in an entrained flow reactor”, 11th European 
Conference on Industrial Furnaces and Boilers, Energy Procedia 2017, 

  Stephan A., Wolf C., Fendt S., Spliethoff H., “Online corrosion measurements in small- and 
mid-scale  during  pulverised  biomass/coal  co-combustion”,  11th  European  Conference  on 
Industrial Furnaces and Boilers, Energy Procedia 2017. 

  Wolf  C.,  Leino  T.  J.,  Stephan  A.  R.,  Aho  M.  J.,  and  Spliethoff  H.,  “Online  corrosion 
measurements in combination with deposit and aerosol analysis during the co-firing of straw 
with  coal  in  electrically  heated,  small-scale  pulverized  fuel  and  circulating  fluidized  bed 
systems,” Energy Fuels, 2018. 

Project results were presented at different conferences and at a final workshop organized by TUM in 
Munich on the 29th of March 2017 (held instead of the summer school). Key players from the industry 
(e.g. Dong Energy, Engie Laborelec etc.) and academia (Technical University of Denmark, University 
of Darmstadt etc.) participated.  
No patent applications were filed during the project. 

8 

 
 
 
 
 
 
 
Work package 1: Fuel selection and provision (ABO) 

Task 1.1 

Objectives: 

Fuel selection and provision 

 

Fuel selection and provision to relevant partners 

Results obtained, conclusions and applications: 
Fuels were selected based on their composition and availability. Several coals were suggested by the 
industrial partners. Four coals with significantly different sulphur and aluminium content were selected 
in order to evaluate the influence  of the  different  interaction mechanisms between low-grade fuels 
and coal during combustion. After a screening for biomass materials in all European countries involved 
in the project, two low-grade fuels were chosen for the tests at the small- and mid-scale. All fuels 
were delivered to the relevant test rigs and prepared accordingly to the requirements of the different 
firing systems. 

Task 1.2 

Objectives: 

Standard fuel characterisation 

  Use standard fuel characterisation methods in order to evaluate possible fuels 
 
 
 
 

Proximate, ultimate, heating value analysis, ash composition and ash melting behaviour 
Input data for experiments and modelling 
First indication of corrosion potential based on fuel indices 
Frequent sampling and analysis of power plant fuels and ashes 

Results obtained, conclusions and applications: 
Seven coals and six biomasses were analysed by standard characterisation methods. More biomass 
materials  were  investigated  for  their  chlorine  content  during  the  screening  process.  Based  on  the 
results fuels were selected in T1.1. All results are summarised in a database available to all partners. 
The data was used to calculate combustion settings (e.g. air flows, blending ratios, fuel input etc.) in 
the different test rigs. Additionally, the analysis results served as input for thermodynamic equilibrium 
calculations and computational fluid dynamics simulations conducted in work package (WP) 6. 
A literature review on existing fuel indices assessing the corrosion potential of fuels based on their 
composition was performed. One of the most regular used ones is the ratio of the sulphur content and 
the chlorine content (S/Cl) indicating the sulphation potential of the fuels. Fuel indices of the selected 
fuels and their blends were calculated and used in WP5 to compare the online corrosion signals derived 
with the fuel composition and critical blending ratios. 
Three power plants had fuels and fly ashes sampled on a regular basis during the long-term corrosion 
measurements in order to correlate the fuel composition to the online corrosion signals. Standard fuel 
characterisation was performed on several samples with the main focus on ash composition, sulphur 
content in the coals and chlorine content in the co-combusted low-grade materials. 

Task 1.3 

Objectives: 

Advanced fuel characterisation 

  Use advanced fuel characterisation methods (Computer controlled scanning microscopy and 

chemical fractionation) on selected fuels 
Laboratory material tests in order to select suitable alloys for corrosion tests 
Thermogravimetry tests in conjunction with gas analysis equipment in order to investigate the 
release of critical species 
Laboratory tests to study the capacity of coal ashes to absorb alkali vapours 

 
 

 

Results obtained, conclusions and applications: 
Computer controlled scanning electron microscopy (CCSEM) was used on two coals to evaluate their 
mineral composition in more detail. While one coal ash consisted mainly of non-reactive quartz in the 
other one, a larger share of reactive Al-Silicates was present. Chemical fractionation was performed 
on in total 11 fuels. Information on the occurrence (water -leachable, organically bound, acid leachable 
and non-soluble) of the ash forming species was generated.  
Four alloys were investigated in a laboratory oven exposed to an artificial deposit and gas atmosphere 
in  order  to  select  suitable  materials  for  the  online  corrosion  tests.  The  oxide  layer  thickness  was 
measured after the exposure to indicate the level of corrosion on each alloy. The medium alloyed steel 
10CrMo9-10  was  selected  to  be  used  to  assure  significant  corrosion  in  the  small-scales  tests  after 
reasonable experiment durations.  
A thermogravimetric balance and a heated oven were used in combination with a mass spectrometer 
to assess the release of HCl during combustion of blends of low-grade fuels with coal. From online gas 
measurements  and  solid  fuel  analysis  it  was  observed  that  chlorine  is  released  mainly  during  the 
pyrolysis  phase.  Additionally  the  amount  of  Cl  released  could  be  correlated  to  the  amount  of  Cl 
leachable in water measured by the chemical fractionation analysis.  

9 

 
In a further laboratory test rig the capability of different ashes and kaolin to absorb alkali vapours at 
conditions relevant for CFB applications was investigated. All in all, fly ashes showed a better capability 
compared to cyclone ash from the VTT CFB test rig. A higher temperature resulted in an increased 
amount of captured potassium. 

Work package 2: Corrosion sensor preparation and adaption (BBS) 

Task 2.1 

Objectives: 

Adaption pilot scale 

  Adaption of online corrosion sensors for small- & mid-scale test rigs 
 

Preparation of further measurement equipment 

Results obtained, conclusions and applications: 
Three sensors of Babcock Borsig Steinmüller (BBS) were prepared for the tests in TUM’s entrained 
flow reactor (EFR). One sensor was shortened and the sensor head was equipped three times with 
10CrMo9-10  samples.  One  sensor  of  BBS  was  prepared  for  the  tests  in  the  mid-scale  test  rig  of 
USTUTT. 
A smaller version of the Savcor sensor than the one usually used in power plants was utilised in the 
VTT test rig. A new sensor head, equipped with AISI310 and 10CrMo9-10 samples, was used in each 
experiment. One Savcor sensor was utilised in the mid-scale test rig of Valmet.  
Additional to the online sensors, deposit and material loss probes were prepared individually in each 
test  rig.  Further  prepared  measurement  equipment  comprised  of:  Valmet  Corrored  sensor,  Valmet 
KCl-laser, electrical low pressure impactor and Dekati low pressure impactor as well as gas analysis 
devices. 

Task 2.2 

Objectives: 

Adaption power plant 

  Adaption of online corrosion sensors for power plant measurements 

Results obtained, conclusions and applications: 
The  online  corrosion  sensors  of  the  OnCord  project  were  implemented  in  three  different  power 
stations.  The  BBS  sensor  was  used  in  Studstrup  power  station  that  was  co-firing  straw  with  coal, 
Fusina power station that was co-firing Refuse Derived Fuel (RDF) with coal and the CFB power station 
in Dunaujvaros  that was co-firing different low quality fuels with coal. The Savcor online corrosion 
sensor was used in Studstrup and Dunaujvaros during the same time in order to enable a comparison 
of both sensors. Measurement locations were defined and manholes adjusted for the different tests. 
Sensors and necessary evaluation units were deployed to the power stations. Additionally, a material 
loss probe was prepared and used in Studstrup power station. 

Task 2.3 

Objectives: 

Optimisation 

  Optimisation and solutions for problems occurring during all measurements 

Results obtained, conclusions and applications: 
The Consortium logged all problems of the online corrosion sensors occurring during the project. Based 
on the collected problems, several lessons learned were developed. It was observed that the sensors 
require a short period of time to give a constant signal in the power stations, therefore the sensors 
have to be controlled carefully during the first week after implementation. The sensor diameter and/or 
the distance between the insulation rings influence the sensitivity of the sensor. Therefore, a small 
sensor head may lead to bridging of the insulation between the measurement rings and this can result 
in an instable signal. Large temperature gradients over the circumferences of the sensor need to be 
avoided in order to neglect leakage of cooling air in the sensor head. For a correct evaluation of the 
signal it is crucial to have the same timelines in the boiler data logging unit and the evaluation unit of 
the  online  corrosion  unit.  The  interpretation  of  the  derived  signals  of  higher  alloyed  steels  was 
observed to be difficult. It is assumed that for higher alloyed steels the signal may be influenced by 
reactions  inside  the  deposits.  For  an  online  access  to  the  data  of  the  online  sensors,  a  SIM  card 
connection was used.  In general,  both sensors worked  well  in  different co-firing environments  and 
withstood high flue gas temperatures. Sensors should usually be used in the superheater area of co-
firing power stations. 

10 

 
 
 
 
 
Work package 3: Measurements in PF systems (Enel) 

Task 3.1 

Objectives: 

PF electrically stabilised pilot-scale measurements 

Implementation of the BBS online corrosion sensor in TUM’s entrained flow reactor 

 
  Utilisation of gas analysis equipment, deposit & material loss probes and the electrical low 

pressure impactor to measure fine particles 
Investigation of interaction between coal and biomass during co-combustion 
Identification of critical co-firing shares for different coals in the blend 

 
 
  Assess the online corrosion sensor in the small-scale 

Results obtained, conclusions and applications: 
Three  measurement  campaigns  were  performed  at  the  entrained  flow  reactor  of  TUM.  Three  fuel 
blends  consisting  of  3  coals  and  1  biomass  (straw  with  high  chlorine  content)  were  investigated. 
Blending ratios were set to 0, 10, 25, 40, 60 and 100 % straw in the fuel blend on an energy basis 
(e.b.). Each blend (16 in total) was combusted for 8 h. The online corrosion sensor was implemented 
at a flue gas temperature window between 700 - 900 °C. The reactor temperature was set to 1200 °C, 
the  air-to-fuel  ratio  to  ~1.13  –  1.20,  resembling  power  plant  conditions.  For  each  blend,  flue  gas 
composition, fine particle number/composition, deposit and corrosion behaviour were examined. 
During the combustion of the coal with the lowest Al content in ash, the online corrosion sensor showed 
a signal indicating corrosion at 60 % e.b. straw in the blend while for the other coals (with a higher 
Al content) a signal was derived only during pure straw combustion. Results from other measurements 
also  supported  the  finding,  that  the  Columbian  coal  showed  a  lower  corrosion  mitigation  potential 
compared to the South African and El Cerrejon coals. In general, the sensor reacted very quickly to 
changes in the fuel blend composition. 
Additionally, it could be shown that, at the conditions present in the test rig, the embedding of alkalis 
in  Al-Silicates is  most  likely  the  predominant  mechanism  to  mitigate  gaseous  alkali  vapours,  while 
sulphation is only of minor relevance. 
The results were used for the development of corrosion mitigation strategies. 

Task 3.2 

Objectives: 

PF industrial scale measurements 

Increase exposure time of the BBS online corrosion sensor to 24h per blend 
Test the online corrosion sensor in a larger test rig with higher fuel input and temperatures 
Investigate the interaction of one coal with one straw with a real-flame combustion 

 
 
 
  Compare data to TUM’s data from the smaller scale 

Results obtained, conclusions and applications: 
One measurement campaign with one fuel blend (El Cerrejon coal/straw) was performed at the test 
rig of the University of Stuttgart. Each of the five straw shares (0, 25, 40, 60, 100 % e.b.) were co-
fired for 24 hours. The air-to-fuel ratio was set to 1.15; no external heating was necessary. Carbon 
conversion > 99 % was achieved. Conducted measurements comprise of cooled/uncooled deposits, 
fuel/fly ash sampling, fine particle number/composition, flue gas analysis, temperature measurements 
and the BBS online corrosion sensor.  
The online corrosion  sensor worked well  also in these  conditions.  A  signal  indicating  corrosion  was 
derived only at pure straw combustion, which is in line with measurements at TUM’s test rig. However, 
the  derived  signals  were  much  higher  than  in  the  small  scale,  possibly  due  to  the  higher  flue  gas 
temperature  at  the  measurement  location  (~1000 °C).  All  other  measurement  results  are  very 
comparable to the ones obtained at TUM. From the results of the fine particle measurements, it could 
be  concluded  that  the  embedding  reactions  were  also  predominant  under  the  mid-scale  test 
conditions. 

Task 3.3 

Objectives: 

PF power plant measurements 

Long-term exposure of online corrosion sensors in real-scale power stations 

 
  Comparison of online corrosion data with boiler data 
  Short-term measurements with gas analysis, deposit probing and fine particles measurements 
 
 

Implementation of one long-term material loss probe in Studstrup power station 
Investigation of the influence of different co-fired low-grade materials 

Results obtained, conclusions and applications: 
Three different power plant measurement campaigns in pulverised fuel  power stations were carried 
out:  One  investigating  the  co-firing  of  straw  with  coal  in  Studstrup  power  station  (Denmark);  one 
examining the co-firing of RDF with coal in Fusina power station (Italy) and one in a petcoke/coal co-
combustion  environment  in  Compostilla  power  station  (Spain).  Both  online  corrosion  sensors  were 

11 

 
implemented in a comparable cross-section in Studstrup power station. The signals showed a similar 
behaviour when compared to boiler data. The main influencing parameter on the online signals was 
the boiler load. Due to the low amount of co-fired straw, a change in the fuel blend did not have an 
effect  on  the  signals.  Further  investigations  focused  on  the  signal  behaviour  after  a  shutdown  and 
during soot-blowing periods. Additionally, the material temperature of the BBS sensor head was varied 
confirming the expected exponential behaviour of the corrosion signal with increasing temperature. 
Results of the online signals were compared with measured material loss from the material loss probe. 
A more difficult interpretation of the signal derived for higher alloyed steels could be observed. Further 
short-term  measurements  included  the  testing  of  Valmet’s  Corrored  sensor  and  e.g.  fine  particle 
measurements.  
The BBS online sensor was implemented in Fusina power station in order to investigate the influence 
of a different low-grade fuel on the signal. A similar behaviour as found during straw combustion was 
monitored.  However,  a  longer  starting  phase  and  a  higher  signal  value  were  observed,  probably 
attributed to the higher temperature at the measurement location. Nevertheless, a robust signal was 
derived even during highly flexible boiler operation. 
Four offline corrosion probes were implemented in the membrane wall of Compostilla power station. 
In-depth analysis of the material coupons revealed sulphur-dominated corrosion. The derived results 
helped  the  boiler  operator  to  improve  the  availability  of  the  power  plant  and  added  value  to  the 
knowledge of corrosion processes during pet-coke/coal combustion. 

Work package 4: Measurements in CFB systems (VTT) 

Task 4.1 

Objectives: 

CFB electrically stabilised pilot scale measurements 

Implementation of the Savcor online corrosion sensor in VTT’s electrical stabilised test rig 
Improve understanding of the online corrosion signal 

 
 
  Utilisation  of  gas  analysis  equipment  (including  KCl-laser),  fine  particle  measurements, 

deposit & material loss probes 
Identification of critical shares of straw and solid residual fuel (SRF) in blends with two coals 
Identification of corrosion mitigation capability of both coals 

 
 

Results obtained, conclusions and applications: 
Four blends consisting of two coals and two low-grade materials were combusted in VTT’s test rig. 
The coals showed a significant difference in the S and Al-content in order to investigate the interaction 
behaviour.  One  coal  was  similar  to  one  used  at  TUM.  The  same  straw  as  in  TUM’s  test  rig  was 
combusted, additionally a SRF from Finland was chosen as the second low-grade fuel. The reactor was 
heated to maintain a bed temperature of 850 °C, fuel input was set to 50 kW total, combustion took 
place with air staging at an air-to-fuel ratio of ~1.26. Gas temperatures and residence time profiles 
were comparable to large-scale applications. Blending ratios were set to 0, 10, 25, 40, 60 and 100 % 
e.b. low-grade fuel in the blends. Limestone addition was investigated for selected fuel mixtures. 
Tests  started  with  so-called  scanning  tests  in  order  to  evaluate  flue  gas  compositions  and  the 
behaviour of the online corrosion sensor. KCl concentrations increased with increasing shares of low-
grade fuels. During the longer tests (8-14 h per blending ratio) a new sensor head was used for each 
blend.  Russian  coal  with  a  higher  Al-content  showed  a  lower  amount  of  fine  particles  containing 
corrosive  elements  during  co-combustion  with  straw  compared  to  the  Columbian  coal.  The  sensor 
measured corrosive activities on the metal surface quickly after the ratio reached 40 % e.b. in blends 
with both coals. Limestone addition lead to an increased share of KCl in the flue gas due to the reduced 
amount of reactive SO3 in the flue gas. SRF revealed to be less corrosive when compared to straw. 
The online sensors detected no corrosion activity during 40 % e.b. SRF in both blends. 

Task 4.2 

Objectives: 

CFB industrial scale measurements 

 

Testing and developing of Savcor online corrosion sensor at mid-scale with increased exposure 
time 

  Verification  and  comparison  to  further  online  corrosion  measurements  (namely  Corrored 

analyser and KCl laser) 
Investigate interaction of one coal with two low-grade fuels 

 
  Cross-check with data from VTT’s test rig 
Results obtained, conclusions and applications: 
Two  fuel  mixtures  consisting  of  Russian  coal  with  straw  and  SRF  were  combusted.  Addition  of 
limestone was investigated for selected blends. In total 11 blends were co-fired during three weeks. 
Fuel input was set to 3.8 MW, air-to-fuel ratio was set to ~1.4 with air-staging, the bed temperature 

12 

 
 
was controlled to maintain at 850 °C without external heating. Similar measurements as in VTT’s test 
rig  were  performed:  Gas  analysis  including  HCl  and  KCl  measurements,  deposit  &  material  loss 
measurements  and  fine  particle  sampling.  Additionally  Valmet’s  Corrored  analyser  was  used 
measuring S/Cl in gas and particles ratio online by titration. 
For the SRF/Coal blend a critical share of ~ 65 % e.b. was identified. The critical share of straw in the 
blend  was  found  to  be  40 %  e.b.  The  difference  was  attributed  to  a  different  release  behaviour  of 
chlorine from SRF compared to straw. While Cl in straw is mainly released as gaseous KCl, Cl from 
SRF is released more in the form of HCl. The derived data from the measurements were in line with 
results from VTT’s test rig. KCl laser and Corrored sensor were tested and worked well in the present 
conditions. The utilised real-scale Savcor sensor reacted quickly to changes in both fuel blends  but 
was influenced by the deposited material already present on the surface. 

Task 4.3 

Objectives: 

CFB power plant measurements 

 

Full scale measurement campaign in order to validate reliability and operability of both online 
corrosion sensors in CFB systems 

  Compare online corrosion signals with boiler data, especially the fuel mixture 
  Gather further measurement data to verify the derived online signals 

Results obtained, conclusions and applications: 
Extensive measurements took place in a CFB power station located in Hungary: Hamburger Hungária 
Erömü Kft CFB-boiler, Dunaujvaros. Both short-term (flue gas analysis, deposit probes) and long-term 
measurements (Savcor & BBS online corrosion sensor, material loss probe, fuel & ash sampling) were 
performed.  The  co-combusted  fuel  mixture  contained  coal,  wood,  plastic  rejects,  fibre  rejects  and 
biogas. The signal of the Savcor sensor indicated corrosive processes during periods where low shares 
of coal and high shares of wood were combusted in combination with plastic rejects. The sensor signal 
collapsed however after a short time period in those conditions. An occurring “short circuit” between 
the measurement electrodes was indicated as one possible reason. The BBS sensor showed a slower 
but more stable signal behaviour. Corrosive activities at the metal surface were indicated during four 
periods with low coal but high wood and plastic rejects share in the fuel blend. The signal returned to 
its base value after the fuel mixture was changed to a safer mix. This might indicate that the active 
oxidation cycle causing excessive metal loss due to chlorine corrosion could not be established in this 
short time. This kind of information can be of high value for boiler operators. Chlorine was found in 
short-term deposit samples of critical fuel blends verifying the signals derived by the online sensors. 

Work package 5: Data evaluation, comparison and assessment (TUM) 

Task 5.1 

Objectives: 

Database 

  Collecting and combining all data from experiments in a database 
  Assure comparability and clarity of results 
Results obtained, conclusions and applications: 
A database including all relevant data from the fuel analysis conducted in WP1 has been established 
in form of an excel-table. A second database was  set-up in form of an excel-table summarizing all 
derived  project  results  from  the  different  test  rigs  in  diagrams.  Suitable  diagrams  were  discussed 
during  the  project  meetings  and  created  by  all  partners.  All  partners  of  the  project  can  enter  the 
databases via the data-sharing system.  

Task 5.2 

Objectives: 

Evaluation of results 

 
 

In-depth evaluation of data from experiments and preparation for modelling and reporting 
Identification of correlations between  fuel composition, blending ratios, material losses  and 
online corrosion signals 

  Verification of fuel indices for co-combustion cases 
  Comparisons between firing systems (PF & CFB) and test rig sizes 

Results obtained, conclusions and applications: 
Data from all measurement campaigns was evaluated and prepared as input parameters for modelling 
activities.  The  aluminium  content  in  the  coal  ash  could  be  identified  as  one  possible  parameter 
indicating the corrosion mitigation potential of different coals next to the sulphur content. A higher Al-
content possibly enables better embedding reactions  with gaseous alkali chlorides and leads to the 
formation of Alkali-Al-Silicates. Chlorine in deposits could be clearly correlated to the derived online 
corrosion  signals.  The  comparison  between  the  co-combustion  tests  at  TUM  (PF)  and  VTT  (CFB) 

13 

 
 
revealed the higher importance of embedding reactions in conditions present in PF systems. However, 
sulphation reactions were found to be  of  higher importance  in CFB  applications.  Most  likely,  this is 
caused by the different temperatures existing in the different firing systems.  

Task 5.3 

Objectives: 

Microscopy 

  Use electron microscopy to investigate deposit and fine particle compositions 
  Use electron microscopy to measure oxide layer thicknesses of material rings 

Results obtained, conclusions and applications: 
Electron microscopy (SEM) was used to evaluate the oxide layer thickness of corrosion rings and the 
morphology  of  fine  particles  sampled.  Energy  dispersive  X-ray  spectroscopy  (EDX)  was  used  to 
evaluate  the  composition  of  the  sampled  particles  and  the  collected  deposits.  The  methods  and 
procedures of the project partners involved were summarised and discussed. 

Work package 6: Modelling (IFRF) 

Task 6.1 

Objectives: 

Model setup pilot-scale 

 

  Computational fluid dynamic calculations of the small- and mid-scale PF test rigs in order to 
generate temperature and velocity fields, gas concentration fields and particle trajectories 
Thermodynamic equilibrium calculations to understand and explain phenomena taking place 
in the gas-phase and in the deposit 
Results obtained, conclusions and applications: 
The  entrained  flow  reactor  of  TUM  was  modelled  using  ANSYS  FLUENT.  A  computational  grid  was 
generated and simulations started with the calculation of the pure straw and  coal case in order to 
evaluate  flue gas composition, particle  trajectories as well as temperature and velocity fields. In a 
subsequent  step,  the  combustion  of  two  blends  was  simulated.  The  test  rig  of  the  University  of 
Stuttgart was modelled by IFRF. A three-dimensional grid was set-up and simulations started with the 
pure straw case. A model predicting the concentrations of aerosol precursors and a reactor network 
model  describing  the  condensation  of  inorganic  aerosols  was  implemented  in  the  simulation. 
Additionally, equilibrium calculations were performed by VTT investigating the flue gas composition 
and the deposit melting process. 

Task 6.2 

Objectives: 

Model development 

Investigation of the reactions taking place during co-combustion of low-grade fuels with coal 

  Model development for the release of precursor species 
 
  Development of a mechanistic model to predict aerosol formation 
  Describe and explain sensor signals by thermodynamic equilibrium calculations of the deposit  

Results obtained, conclusions and applications: 
Different models were developed. The release of precursor species relevant for corrosion was regarded 
in experiments in WP1. Additionally, an in-depth literature review was performed. Based on the results 
a release model was implemented in the simulation of USTUTT’s test rig describing the distribution of 
KCl and K2SO4 in the reactor.  
The  understanding  of  the  factors  leading  to  increased  flue  gas  corrosivity  was  assessed  by  VTT.  A 
model based on the global thermodynamic equilibrium approach was developed. It describes the flue 
gas atmosphere and the composition of fine particulate matter. By determining so-called reactivity 
coefficients of relevant elements, the prediction of KCl concentrations in the flue gas was improved 
significantly. It was discovered, that the reactivity coefficients are comparable to the reactivity of the 
components measured by chemical fractionation. 
Thermodynamic  equilibrium  calculations  were  used  as  well  by  TUM  to  describe  the  measured  HCl 
concentrations for the different coal/straw blends combusted in the EFR. Modelled HCl concentrations 
were verified by measured values.  
A model describing the formation of aerosols in PF applications were implemented in the simulation of 
the  USTUTT  test  rig  by  IFRF.  The  model  is  based  on  a  reactor  network  analysis  that  is  applied  to 
computational fluid dynamics results, in order to allow the use of detailed kinetics required to evaluate 
the formation of aerosol precursors. This tool is complemented by a gas-to-particle model based on 
nucleation, coagulation and particle growth. 
Additionally, VTT used chemical equilibrium calculations to describe the share of melt fraction in the 
deposits and correlated this to the measured electric resistivity signal of the Savcor sensor. 

14 

 
 
Task 6.3 

Objectives: 

Model test power plant 

  Modelling of one PF power station by computational  fluid  dynamics to describe phenomena 

taking place 

Results obtained, conclusions and applications: 
Fusina power station was modelled by Enel. Thermal flow, gas temperature and flue gas fields were 
derived.  The  model  was  used  to  verify  operational  data  and  to  evaluate  the  level  of  slagging  and 
fouling  of  heat  exchange  surfaces.  Furthermore,  the  results  of  modelling  were  used  for  process 
optimization of the combustion system. Additionally the results were used to evaluate the behaviour 
of the BBS online sensor in Fusina in more detail. 

Work package 7: Development of corrosion mitigation concepts (Valmet) 

Task 7.1 

Objectives: 

Sensor development 

  Adapt sensors for very high temperature environments and for high heat fluxes 
  Suggestions for proper use of online corrosion sensors in power plants 
  Critical assessment of the measuring principle and sensor sensitivity 

Results obtained, conclusions and applications: 
Both sensors worked well in Studstrup power station with temperature up to ~1000 °C. Similar signal 
trends  of  both  sensors  were  measured  in  Studstrup  supporting  the  reliability  of  the  measurement 
principle in large-scale applications. Both sensors reacted similarly to changes in the boiler data, e.g. 
load changes. Difficulties were observed during the measurement campaign in the CFB power station 
in Dunaujvaros. During combustion of a difficult fuel mixture and high load periods the Savcor sensor 
signal  drifted  out  of  the  interpretable  measuring  range.  This  was  investigated  thoroughly  and 
attributed to the higher sensitivity of the Savcor sensor. A balance between sensitivity and robustness 
was  found  to  be  crucial  for  a  reliable  signal,  especially  in  difficult  environments.  In  general,  the 
measurement principles of the sensors were shown to work well (both linear polarization resistance 
and electrical resistivity method). 

Task 7.2 

Objectives: 

Measurement system 

  Suggestions for measurement locations and handling of online corrosion sensors 
 
  Development of a holistic and reliable online corrosion measurement system 

Providing guidelines for operators for signal interpretation 

Results obtained, conclusions and applications: 
Online  corrosion  sensors  investigated  during  the  OnCord  project  are  ideally  implemented  in  the 
superheater section, both in CFB and PF power stations. The utilisation of two or more online sensors 
can be reasonable in case of known flame misalignment resulting in an uneven distribution of corrosive 
species inside the boiler. Another reasonable case for multiple online sensors can be present, if the 
superheaters are constructed of different materials. The material of the superheater needs to be used 
on the sensor to get accurate results. 
The signal derived from the online corrosion sensor can be of high value for operators. It indicates if 
the  corrosive  processes  directly  at  the  metal  surface  could  be  stopped  by  corrosion  mitigation 
procedures. Chlorine in deposits can cause drastic metal loss due to the formation of the so-called 
active oxidation cycle, even after the flue gas atmosphere could be changed to a safer composition. 
The online sensors can indicate if the formation of this cycle has been avoided. 
Based on the OnCord measurements correlations between chlorine in deposits and the measured flue 
gas corrosivity (indicated by the S/Cl ratio measured with Valmet’s Corrored analyser) were derived. 
Consequently, thresholds of the measured S/Cl ratio were defined especially for CFB systems, advising 
boiler operators to perform corrosion mitigation procedures developed in T7.3.  
A reliable online corrosion measurement system was developed. It is proposed to combine Valmet’s 
Corrored sensor, Valmet’s KCl laser (which is currently not commercially available and only used in 
scientific  experiments)  and  an  online  corrosion  sensor  in  order  to  have  detailed  knowledge  of  the 
corrosivity of the combusted fuel mixture.  
Both the gas atmosphere as well as the metal surface are under surveillance. The system is designed 
to be redundant in case one application fails. 

15 

 
 
 
The  developed  online  corrosion  system  can  help  boiler  operators  to  improve  the  reliability  and 
operability of their power station, to minimize unplanned outages, optimise their outage planning and 
give very valuable information about the state of the superheater. 

Task 7.3 

Objectives: 

Operational strategy 

  Development  of  strategies  for  intelligent  fuel  blending  based  on  standard  characterisation 

methods in order to mitigate corrosion 

Implementation of the online corrosion strategies into the daily business of boiler operators 

  Assessment of further corrosion mitigation strategies 
 
  Guidelines for boiler operators to realise the suggested strategies 
 

Estimation of cost savings for boiler operator 
Results obtained, conclusions and applications: 
Strategies to mitigate corrosion during the co-firing of high shares of low-grade fuels with coal were 
developed separately for pulverised and circulating fluidised bed systems.  
Considering  the  case  of  a  PF  system  where  coal  blends  are  usually  combusted.  When  corrosion  is 
detected by the online corrosion measurement system the share of the coal with the highest Al-content 
can be increased to enhance alkali capture. A procedure for power plant operators to react to a signal 
indicating corrosion  was developed throughout the project. Next  to intelligent  fuel blending further 
corrosion mitigation strategies in PF systems were evaluated and integrated into the procedure. 
Results from the project have been used in Valmet superheater KCl corrosion management solution 
(FuelDiet®) to optimise the monitoring of corrosion and superheater lifetime predictions. The tool can 
be used to increase the share of difficult fuels in CFB systems and gives boiler operators full knowledge 
on the state of the superheater. Additionally, the dosing of the so-called CorroStop additive can be 
managed. 
Cost savings resulting from the enhanced surveillance, extended superheater lifetimes and cheaper 
fuels now possible to be combusted, were estimated for one example 140 MW fluidised bed boiler. 
The utilisation of a cheaper fuel due to corrosion mitigation can save up to 700 k€/year. Furthermore, 
the lifetime extension of the implemented superheaters gives a cost saving potential of ~ 200 k€/year. 
In  combination  with  further  improvements  (e.g.  due  to  better  planning  of  maintenance  periods, 
reduction  of  unplanned  shutdowns  and  savings  in  fuel  analysis  costs)  a  potential  cost  saving  of 
~ 400 k€/year  is  possible  by  implementing  Valmet’s  KCl  corrosion  management  system.  The 
amortisation time of the corrosion measurement and monitoring system for such a case is estimated 
to be approx. one year. 

16 

Online corrosion measurement systemOnline corrosionsensorKCl analyzer(not commerciallyavailable)Corrored analyzer 
 
 
 
2.  Scientific and technical description of the results 

The  following  sections  describe  the  activities  performed  during  the  project  and  the  derived  results  in 
more detail. A short introduction summarises the most important theory behind the co-combustion of 
low-grade  fuels  with  coal  and  the  online  corrosion  sensors  in  order  to  give  basic  information  to 
understand the derived  results.  The main  objectives of  the  OnCord  project  are  listed  in the  following 
section.  The  deviations from  the initial  work  plan  are  then  presented, followed by a description of all 
activities focussed on the innovation made. All achievements of the OnCord project are summarised in 
the conclusion section. Finally, the impact of the project together with the performed dissemination and 
exploitation activities are presented. 

Introduction 
Interaction of low-grade fuels with coal: 

Two  firing  systems  were  investigated  during  the  OnCord  project:  pulverised  fuel  combustion  and 
circulating fluidised bed combustion. An overview of the test rigs and fuels used is presented in Figure 
1. 

Figure 1: Designated names of test rigs/power stations and utilised fuels in the OnCord project. 

One project goal was the in-depth investigation of the interaction of coal with low-grade fuels during co-
combustion in order to mitigate corrosion and deposit formation by intelligent fuel blending. For example 
herbaceous fuels such as straw can contain high shares of volatile alkali compounds and chlorine. High 
concentrations of gaseous alkali salts in  the  flue gas can  cause  enhanced deposit formation and high 
temperature  chlorine  induced  corrosion  in  the  superheater  area.  Volatilised  alkali-salts  may  form 
deposits via direct condensation on heat exchanger surfaces or by the formation of fine aerosol particles, 
which then deposit via thermophoresis, eddy deposition and/or diffusion. A high share of chlorine in the 
deposits can lead to a corrosive atmosphere on the surface of the superheater tubes. This process is 
usually  referred  to  as  high-temperature  chlorine  induced  corrosion  or  active  oxidation.  Nielsen  et  al. 
describe the process extensively with other possible corrosion phenomena occurring during co-firing of 
biomass with coal [1]. 

The prevention of the aforementioned problems has been investigated thoroughly for both co-firing of 
low shares of biomass and pure biomass combustion in fluidised bed and pulverised fuel firing systems. 
The goal is the limitation of the amount of gaseous  alkali chlorides in the flue gas. Two main ways of 
alkali reduction that take advantage of chemical reactions in the flue gas have been established. 

a)  Sulphation of alkali chlorides: 

2KCl(g,s) + SO2(g) + 1/2O2(g) + H2O(g)  K2SO4(s) + 2HCl(g) 

2KCl(g,s) + SO3(g) + H2O(g)  K2SO4(s) + 2HCl(g) 

(1) 

(2) 

Both reactions convert alkali salts to less dangerous alkali sulphates and gaseous HCl. Reaction (2) is 
reported to be an order of magnitude faster than reaction (1). The gaseous oxidation of SO2 to SO3 is a 
required step in order to achieve a satisfying sulphation. In co-firing applications the sulphur is usually 
contributed by the coal. 

17 

PFCFBTUM, Munich,15 kWthUSTUTT, 300 kWthEnel, Compostilla,SpainEnel,Fusina,ItalyVTT, Jyväskylä,50 kWthValmet, Tampere,4 MWthValmetDunaujvarosHungaryFusinacoal+ RDFStudstrupcoal + Straw DENCoal, plasticrejects,Biomass, sludgeetc.Compostilla coal+ pet-cokeDong,Studstrup, DenmarkColumbiancoal+ Straw ITColumbiancoal + Straw ITElCerrejonCoal+ StrawITRussiancoal + Straw ITRussiancoal + SRF FINElCerrejonCoal+ StrawITRussiancoal + Straw ITRussiancoal + SRF FINSouth African coal + Straw IT 
 
 
 
 
 
 
 
The  second  strategy  to  lower  the  amount  of  gaseous  alkali  chlorides  is  by  alkali  capture  in  alumina-
silicates. Equation (3) describes the overall reaction: 

b)  Embedding/capture of alkali in Aluminium-Silicates: 

Al2O3·2SiO2(s) + 2KCl(g) + H2O(g)  K2O·Al2O3·2SiO2(s) + 2HCl(g). 

(3) 

The alkalis (for straw usually potassium) are embedded in the matrix of reactive alumina-silicates with 
simultaneous  release  of  gaseous  HCl.  During  co-firing,  the  alumina-silicates are  provided  by  the  coal 
ash. Depending on the coal type, different minerals are present. 

During the OnCord project, the influence of the coal composition on the alkali reduction capability was 
investigated for different coals both in CFB and PF applications for very high shares of low-grade fuels. 

Online corrosion sensors: Measurement methods and theory 

Two online corrosion sensors were used throughout the project (see chapter 2 for a short description) in 
order to test them in co-firing atmospheres and to assess the corrosivity of the different fuel blends. The 
theory behind both corrosion measurement methods is described shortly in the following text. 

Linear  Polarization  Resistance  Method:  LPR  indicates  the  state  of  oxide  layer  on  the  studied  metal 
surface.  The  LPR  method  takes  advantage  of  the  electrochemical  nature  of  corrosive  processes.  The 
electrochemical  corrosion  process  consists  in  principle  of  two  sub-processes,  which  take  place 
simultaneously: an anodic and a cathodic process. During these processes, iron atoms leave the metal 
as positive charged metal ions and electrons stay in the bulk material. This leads to charge separation 
at the material surface. Figure 2 shows a simplified outline of this process. Without external influences, 
the two sub-processes are in equilibrium and the flow of electrons from each reaction is balanced so that 
there is no net current between the charged sections.  

Figure 2: Simplified outline of the processes dominating the high temperature chlorine corrosion. 

The LPR method uses the charge separation caused by the corrosion process (see Figure 2). As there 
are charged particles, the system can be influenced by applying an external voltage (polarization). This 
disturbs the equilibrium of the system and provokes an ionic current through the ionically conducting 
layers  of  the  corrosion  product  between  the  electrodes.  The  correlation  between  applied  voltage  and 
resulting current  shows  an exponential behaviour.  The  slope of the  linear part of  the  voltage-current 
characteristic around the so called free corrosion potential is directly proportional to the current corrosion 
rate  (see  Figure  3a)  (Faraday’s  law).  During  the  measurement,  a  current-voltage  characteristic  is 
registered every 4 minutes (BBS sensor). Each measurement of an I-V-curve  takes 1,5 minutes.  The 
gradient  of  the  characteristic  is  determined  by  linear  regression  of  the  measured  values.  The 
measurement of the characteristic voltages and currents is made possible by a specially designed sensor 
head  with  different  material  rings  which  are  electrically  connected.  The  probe  head  has  a  modular 
construction and consists of measuring electrodes 1, 2, 3 (see Figure 3b) and a so called calibration ring 
M.  The  material  of  the  measuring  electrodes  can  be  chosen  freely  according  to  the  material  of  the 
superheater. 

18 

1•The ferric atom is split by the corrosion process into a positive charged ion and several electrons according to its valency2•The positive charged ferric ion Fex+leaves the steel while the electrons remain3•An electrochemical double layer will be brought up -> without external influence a potential raises and the corrosion process slows downAnodic reaction: Metal dissolutionCathodic reaction: Electron consumption1•Removal of the excess electrons by reducing species of the electrolyte or the environment –e.g. reduction of chlorine radicals2•Potential decreases3•The corrosion process is balanced and its speed depends on the equilibrium of both processesSteelElectrolyte with ferric ions Fex+Negative charged layere-Fe2+FeCl2Cl-Cl2ICorr 
 
 
 
a) 

b) 

Figure 3a): Measured current as a function of the applied overvoltage with a linear fit. The gradient of 
the straight line determines the corrosion resistance (or the inverse value called corrosion conductance) 
or rather the polarization resistance and therefore the corrosion rate; b) Sensor head with material rings 
enabling the measurement of the characteristic voltages and currents.  

Resistivity by Wenner method (so called ER method applied only by the Savcor Sensor): The so-called 
Wenner method enables the measurement of the electrical resistivity (ER) of the deposit building up on 
the sensor. A higher conductivity of the deposit leads to a decreasing ER-value. The measured ER signal 
can give information about the present state of the deposit on the metal surface: Molten or partial molten 
deposit result in a higher conductivity and therefore decreasing ER signal. Molten deposits are known to 
cause excessive metal loss due to faster corrosive reactions compared to lower reaction rates in solid 
deposits. 

After this short introduction, the specific project objectives of the OnCord project are summarised. 

19 

U [V]I [mA]0Slope = Corrosion ConductanceCorrosion rate = C * Corrosion Conductance 
 
 
 
 
4.1  Objectives of the project 

The main objective of the project was to use coal as a protective agent against corrosive deposits on 
heat  exchanging  surfaces  in  PF  and  CFB  systems  in  combination  with  cutting  edge  online  corrosion 
monitoring technologies supported by state-of-the-art (real time) flue gas analytics. Figure 4 shows a 
graphical explanation of the approach of the project. 

Figure 4: Graphical explanation of the research approach of the OnCord project. 

The specific project objectives were: 

•  The utilisation of coal, and coal ash as an additive, enabling biomass firing, and co-firing in PF- 

and CFB-systems at substantially higher shares. 

•  The enabling of higher levels of fuel flexibility in biomass boilers and in biomass co-firing projects. 
Increase the use of coal in CFB-systems and strengthen the position and reputation of solid fuel 
• 
fired power plants. 

•  The reduction of the risks of excessive metal loss by high temperature corrosion. 
•  The preparation of design and technical guidelines on the complex biomass-coal ash chemistries 

and on the selection of coal and biomass combinations for particular applications. 

•  The provision of test data at plant scale of the corrosion rates, using two different online sensors 

and material loss probes, 

•  The  development  of  an  improved  understanding  of  the  corrosion  mechanisms  by  correlating 
corrosion rates to fuel composition, gaseous- and particulate-phase in conjunction with deposit 
analysis and thermodynamic equilibrium calculations. 

•  The  development  of  a  model  predicting  corrosion  precursors  and  their  distribution  within  the 

boiler using thermodynamic equilibrium calculations and computational fluid dynamics, 

•  The development and demonstration of online corrosion monitoring sensors for PF-systems with 

high steam temperatures and long-term exposures. 

•  The development of an operational strategy for boiler operators for the mitigation of corrosion 

when firing and co-firing difficult biomass materials. 

The OnCord project used a bottom-up approach to investigate co-firing and corrosion in a broad spectrum 
of experiments (see Figure 1) in order to achieve the set objectives. Accompanying modelling activities 
helped to understand the ongoing processes and to interpret the derived online corrosion signals. Finally, 
strategies to mitigate corrosion and a holistic online corrosion measurement system were developed. 

The following chapter indicates deviations between the proposed work plan and the realisation. 

20 

++STOPLow-grade fuelSuitablecoalOnline corrosionmeasurementStopcorrosivedepositsEnhanceoperability 
 
 
 
4.2  Description of activities and discussion 

In  this  section  the  scientific  and  technical  activities  performed  during  the  entire  project  period  is 
presented per work package. Each work package section further subdivided by work package tasks. Due 
to the very large amount of results generated the sections focus on the most important output from each 
task, the main experimental work was conducted in  WP3 and WP4. At the end of each work package 
section the innovative content is summarised. 

4.2.1  WP1: Fuel selection and characterisation 

In this work package fuels were selected and characterised. The data was used as input for experiments, 
expected  corrosion  rates  as  well  as  modelling.  Fuels  were  selected  based  on  the  performed 
characterisations. 

Task 1.1: Fuel selection and provision 
Fuels were selected out of: 

  Seven coals with varying sulphur and aluminium content, 
  Six biomasses & low-grade fuels with varying chlorine and potassium content. 

Fuels have been selected and delivered to the small- and mid-scale test rigs. The chosen fuels are shown 
in Table 2. 

Table 2: Selected fuel in small- and mid-scale test rigs 

Firing system 

Test rig 
TUM entrained flow reactor 

Combusted fuels 
South African Coal 
Columbian Coal 
El Cerrejon Coal 
Straw Italy 
El Cerrejon Coal 
Straw Italy 

USTUTT mid-scale test rig 

VTT electrical stabilised test rig  Columbian Coal 

Valmet mid-scale test rig 

Russian Coal 
Straw Italy 
Solid residual fuel Finland 
Russian Coal 
Straw Italy 
Solid residual fuel Finland 

PF 

CFB 

Task 1.2: Standard characterisation 
Analysis and characterisation of fuels used in the pilot-scale test rigs by standard methods (proximate, 
ultimate, heating value, ash analysis by XRF and ICP-OES, ash melting behaviour) was performed. Fuel 
indices  (e.g.  S/Cl)  were  calculated  in  order  to  correlate  the  online  corrosion  signals  to  the  fuel 
composition. 

Figure 65a-d in the appendix show the results of the proximate and ultimate analysis of the main coals 
and biomasses used in the small- and mid-scale test rigs on dry basis (d.b.). 

Fuel and ash samples were taken frequently at: 

•  Studstrup power station  from  November 2015  until  March  2016. Fuel samples (straw & coal) 
were taken every two weeks directly from the feeding line, fly ash samples from the electrical 
precipitator.  
Fusina power station from September 2016 until February 2017. Fuel samples (RDF & coal) were 
taken every 15 days. One fly ash sample was taken to evaluate its composition.  

• 

•  Dunaujvaros power station during the short-term and long-term test campaigns from September 
2016 until January 2017. Fuel samples (wood, coal, plastic reject, fibre reject) and ash samples 
taken during the short-term tests were analysed extensively.  

Correlations of the online corrosion sensor signal of both sensors to the respective fuel composition are 
discussed in chapter 4.2.3 and 4.2.4.  

21 

 
 
Task 1.3: Advanced characterisation 
A variety of different characterisation methods and lab-scale tests were performed in task 1.3. Conducted 
analysis methods and lab-scale test and their according objective are underlined. 

Advanced fuel characterisation: 

Abo Akademi did chemical fractionation of in total eleven fuels. Chemical fractionation gives information 
on  the  chemical  bonding  of  important  ash  forming  elements  in  the  fuels.  Results  from  the  chemical 
fractionation analysis of the fuels used in the small-and mid-scale can be found in the Annex (Figure 
66). A high share of water-soluble K and Cl was detected in the utilised straw. K and Cl form KCl during 
the combustion process and KCl is known to cause corrosion and deposition problems.  

Additionally to the leaching analysis, a computer-controlled scanning microscopy analysis of the South 
African and the Columbian coal was performed to evaluate the distribution of minerals within the organic 
matrix.  Figure  67  and  Figure  68  in  the  Annex  show  the  mineral  distribution  per  particle  size.  It  was 
observed that Columbian coal has a high share of in-reactive quartz present in the mineral phase, while 
in South African coal the mineral phase is dominated by Montmorillonite, a mineral capable to capture 
potassium by embedding reactions. 

Lab-scale tests: 

The high-temperature laboratory material tests were performed to investigate the potential influence of 
potassium chloride (KCl), a salt often regarded as the main cause for corrosion in biomass fired boilers, 
on material alternatives as a sensor material for the corrosion probe. The test specimens were machined 
to a size of approximately 20x20 mm and a thickness of 5 mm. Before the tests, the specimens were 
pre-oxidized in a furnace for 24 h at 200 °C and then covered with KCl (0.25 g/specimen). After this, 
the  material  samples  were  exposed  to  high  temperature  in  a  horizontal  tube  furnace  with  a  pre-
determined gas atmosphere for 168 h (7 days). After the corrosion test, the specimens were allowed to 
cool down to room temperature outside the furnace. The samples were then placed in a mould and cast 
in epoxy, then cut off in the middle to reveal the specimen’s cross-section. The samples were polished 
and then analysed with Scanning Electron Microscope/Energy Dispersive X-ray (SEM/EDX) using back 
scatter electron mode to identify various chemical elements. The corrosion products were identified using 
X-ray images. The corrosion layer thickness was determined using scanning electron microscope back-
scatter images. The corrosion layer was defined as the thickness of the oxide layer and the corrosion 
attack was expressed as a mean thickness of the oxide layer. The test materials were 10CrMo9-10, T11, 
SA210  and  TP310.  From  the  test  results  and  confirmed  by  optical  observations  and  a  more  detailed 
SEM/EDX image analysis (to confirm no grain boundary, no pitting corrosion, or spallation of oxide layer) 
it was concluded that the best options for the sensor material would be either 10CrMo or SA210. 

Based on thermogravimetric tests with different fuel blends on the interaction of biomass and coal during 
pyrolysis and combustion, further tests in a lab-scale heated oven were performed at TUM investigating 
the release of precursor species. 1 gram of milled and dried fuel (Experiments conducted with straw IT 
and  Columbian  coal)  was  placed  in  a  wire  mesh  and  inserted  in  a  heated  pipe  oven.  Two  different 
measurements were conducted: 

  HCl release measured by mass spectroscopy, 
  Chlorine release at different temperatures via ash/char analysis. 

For the second set of experiments the residues (char/ash) after the pyrolysis/combustion in the heated 
oven  were  investigated  by  a  Vario  Macro  Cube  at  the  TUM  laboratory  for  their  chlorine  content. 
Subsequently the total chlorine release could be calculated. 

To evaluate the chlorine content after pyrolysis and combustion the surrounding gas atmosphere inside 
the oven was adjusted in two phases: 

  20 minutes long pyrolysis phase with inert N2 atmosphere, mass flow controlled by a mass flow 

controller. 

  Combustion  phase  with  a  20 %/80 %  mixture  of  O2/N2,  mass  flows  controlled  by  mass  flow 

controllers, air mixture added after the pyrolysis phase. 

22 

 
Figure 5a shows the share of chlorine released from the investigated fuel blends during pyrolysis and 
combustion. In Figure 5b the share of chlorine leachable in water and ammonium acetate is displayed 
(50/50 blend calculated on a mass basis from pure fuels). 

a) 

b) 

Figure 5a) Ratio of released chlorine during pyrolysis and combustion of three different fuel blends at 
800 °C; b) Ratio of leachable chlorine of the original fuels in water and ammonium acetate (results from 
chemical fractionation tests of Abo Akademi). 

It can be seen, that most of the chlorine is released during the pyrolysis phase, while only a small share 
is  further  released  in  the  combustion  environment.  Shares  of  H2O  leachable  chlorine  from  leaching 
analysis are in very good agreement with shares of chlorine released in an inert atmosphere supporting 
the  assumption  that  species,  which  can  be  leached  out  by  water,  can  be  regarded  as  the  reactive 
components of the fuel matrix. 

Abo Akademi investigated the alkali absorption ability of coal ashes in a specially designed lab-scale test 
rig. Reaction (3) shown on page 24 is investigated at  800, 850, 900 and 950 °C.  Coal ash originating 
from the small-scale circulating fluidised bed test rig at VTT from WP4 has been studied for its ability to 
capture gaseous KCl. The experiments were done in a laboratory reactor by introducing KCl to the heated 
reactor, whereas the salt started to evaporate and was led through a sample holder with a coal ash bed. 
The ash bed was kept in place by a metallic net. The carrier gas consisted of 15 % water and air. After 
each experiment the weight of the remaining KCl was determined by dissolving it in water in the sample 
holder  and  analysing  the  solution  for  chlorine  ions  by  means  of  ion  chromatography.  The  difference 
between the introduced KCl and the remaining Cl-ions give the KCl gaseous concentration during each 
test  when  the  gas  flow  and  temperature  are  known.  The  solid  sample  (absorbent)  was  weighed  and 
analysed for K (wet chemical analysis). A part of the sample was casted in epoxy, cut and polished and 
further studied with a SEM/EDX (elemental mapping, imaging, etc.). 

The results showed that the kaolin-type minerals in coal fly ash were as effective as kaolin to react with 
potassium, while cyclone ash was not as effective. It was also shown that a reaction temperature increase 
from  900  to  950 °C  significantly  increased  the  potassium  capture.  However,  the  limitations  of  the 
apparatus´ setup did not allow for tests at higher temperatures than 950 °C. 

Innovative outcome of WP1 
Results from standard and advanced characterisation methods helped with the interpretation of online 
corrosion signals in WP3 and WP4. Based on the analysis a new fuel index describing the corrosivity of 
a low-grade/coal blend with regard to high temperature chlorine corrosion was suggested. It is presented 
in section 4.2.5 of this report.  

The conducted material tests helped to select  a suitable alloy for online corrosion tests. 10CrMo was 
chosen to derive reasonable results also in small-scale test rigs at short experiment durations. 

The correlation of the released chlorine to the water soluble share derived by chemical fractionation was 
shown in the literature before but could be strengthened by the conducted heated oven tests.  

All results from tests of alkali capture were presented at the Clearwater Clean Coal Conference 2016 and 
included in a paper by Abo Akademi, which was published in the Conference Proceedings. 

4.2.2  WP2: Corrosion sensor preparation and adaption (BBS) 
Two sensors (BBS &  Savcor online corrosion  sensor)  were  used throughout the  project  with  different 
materials mounted on the sensor head. Both sensors use the linear polarization resistance (LPR) method 
23 

0%10%20%30%40%50%60%70%80%90%100%Straw_100Straw_Col_50_50CoalShare of Cl-releasePyrolysisCombustion0%10%20%30%40%50%60%70%80%90%100%Straw_100Straw_Col_50_50CoalShare of ClLeachable in H20Leachable in NH4Ac 
 
 
to measure the corrosion progress by electrochemical means. The Savcor sensor is additionally capable 
of measuring the resistivity (ER) of the deposit on the sensor by the Wenner-method. An overview of 
the used materials and chosen temperatures for each test rig is shown in Table 1. 

Task 2.1: Adaption pilot scale 
T2.1  was  dedicated  to  the  preparation  of  online  corrosion  sensors  for  the  pilot-scale  tests  (including 
constructive adjustments,  mounting of the chosen  materials on the sensor heads, deployment  to the 
test rigs and installation). Additional material and deposit probes were prepared as well, to be able to 
correlate  deposit  composition  and  built-up  oxide  layer  thicknesses  to  the  online  signal.  Supporting 
measurement equipment (Corrored sensor, KCl laser, electrical low pressure impactor (ELPI) etc.) was 
prepared for the respective tests. All sensors were deployed to the test rigs. Deposit/material loss probes 
were equipped with similar material rings and prepared for the tests in the respective test rigs. 

Task 2.2: Adaption power plant 
Similar to Task 2.1 both corrosion probes (BBS, Savcor) were adapted to be deployed in the respective 
large-scale power plants (Studstrup, Fusina, CFB power station Dunaujvaros). Additionally, a long-term 
material  loss  probe  of  project  partner  Doosan  used  at  Studstrup  power  station  and  offline  corrosion 
probes were prepared for the tests in Compostilla power station. The relevant partners installed online 
corrosion  probes  in  the  respective  power  stations  in  the  superheater  section.  Necessary  adjustments 
(construction of manholes, installation of cooling & evaluation equipment, coordination with the power 
plant  operators  etc.)  were  performed.  Additionally,  the  Doosan  longterm  material  loss  probe  was 
installed in Studstrup power station. RSE, a subcontractor of Enel within the OnCord project, provided 4 
offline corrosion probes, equipped with A210 samples, which were exposed in the combustion chamber 
(most critical area for corrosion due to pet-coke). 

Task 2.3: Optimisation 
Further optimisation of the utilised online  corrosion sensors was one  main objective  of WP2.  Lessons 
learned from the power plant tests for the sensor utilisation and signal interpretation are: 

 

 

  An  initial  starting  phase  until  a  valid  sensor  signal  needs  to  be  taken  into  account.  Careful 
observation of the signal in the beginning is required, especially in large-scale applications. 
The location where the sensor is inserted into the boiler needs to be chosen according to the 
needs of the power stations. Internals and externals near the insertion point need to be taken 
into account. 
The size (diameter and distance between the measuring electrodes) of the sensor head can have 
an  influence  on  its  sensitivity  and  durability.  A  larger  sensor  head  showed  a  more  robust 
behaviour to difficult flue gas conditions. 
Temperature gradients along the sensor diameter and length can result in difficulties. Thermal 
stresses may occur leading to leakages in the air cooling stream. This needs to be avoided. 
For a correct comparison  of the  sensor  signals to boiler data timelines have to be congruent. 
Time shifts can lead to misinterpretations. 

 

 

  Connectivity of the evaluation unit with the internet via UMTS was critical in some boilers. Local 
SIM cards  usually  work better compared to  ones from  abroad.  Sometimes different providers 
have to be checked. 

  After  dismounting,  the  sensor  head  needs  to  be  protected  to  assure  good  conditions  for 

subsequent analysis with electron microscope methods. 

  Different  type  of  materials  on  the  sensor  head  give  different  signals.  Careful  evaluation  is 

required especially for higher alloyed steels. 

Innovative outcome of WP2 
Especially the influence of the sensor head size on the sensitivity and the robustness of the signal was 
not  known  prior  to  the  OnCord  tests.  A  balance  between  sensor  sensitivity  and  robustness  must  be 
assured to derive accurate and reliable data. Furthermore, the influence of the mounted material was 
shown for the first time. Signals derived for higher alloyed steels like Super304H are more difficult to 
interpret than lower alloyed ones like 10CrMo9-10. 

4.2.3  WP3: Measurements in PF systems (Enel) 
WP3 focuses on the experimental online & offline corrosion sensor research with pulverised fuel firing 
systems (PF) at different scales:  

 

8 kWfuel entrained flow reactor at TUM,  

24 

 
  300 kWfuel combustion test rig (KSVA) at the University of Stuttgart 
  Studstrup power station co-firing biomass/coal,  
 
  Compostilla power station co-firing pet-coke/coal.  

Fusina power station co-firing residual derived fuel (RDF)/coal and  

Corrosion sensor measurements are completed with several other measurements: flue gas analysis, fine 
particle sampling, analysis of collected deposits, and surface analysis of the deposited probe material 
samples. 

Task 3.1: PF electrically stabilised pilot scale measurements 
Experiments  in  the  PF  small-scale  are  carried  out  at  the  electrically  stabilised  entrained  flow  reactor 
(EFR) of the Chair for Energy Systems at TUM. The BBS corrosion sensor is used in the EFR. 

The realized test matrix can be seen in Table 3. The utilised measurement equipment comprised of the 
following: 

  Online flue gas measurement by conventional IR methods (CO, CO2, NOx, SO2 electrochemical: 

O2; Siemens ULTRAMAT) in the radiation section, 

  Second online flue gas analysis with a heated extraction line by IR methods (CO, CO2, NOx, H2O, 

HCl, paramagnetic: O2; SICK MCS300P) after the cyclone and in the radiation section, 

  Wet chemical HCl measurements according to DIN EN 1911 with washing flasks after the cyclone 

to cross-check HCl results of the SICK flue gas analyser, 

Fine particle sampling via ELPI with subsequent SEM/EDX evaluation, 

  Online fine particle number analysis by electrical low pressure impactor (ELPI), 
 
  Bilfinger Online corrosion sensor equipped with alloy 10CrMo9-10, material temperature set to 
530 °C (due to the low fuel input in the small scale a higher temperature could not be stabilised) 
using the Linear Polarization Resistance method (LPR), 

  Deposition  probes  mimicking  superheater  tubes,  material:  10CrMo9-10,  temperature  set  to 

530 °C, 

  Holistic ash analysis of fly and bottom ash (ash oxides, chlorine content, particle size distribution 

etc.). 

The tests started with the collection of fine particle matter in the convective section of the reactor at flue 
gas temperatures of approx. 700 – 850 °C. The electrical low pressure impactor of TUM was used for 
this.  Each  test  collecting  particles  lasted  for  approx.  1 hour.  Collected  particles  were  investigated  by 
SEM/EDX to examine their elemental composition and morphology. Additionally, flue gas measurements 
were performed at port level 3 to assure stable conditions during the particle sampling. 

For each of the three long-term measurements a new online corrosion sensor was used. The sensors 
were introduced at the same position in the convective part of the reactor for each blend together with 
additional deposit probes. The Bilfinger sensor needs an initial deposit layer on its sensor head surface 
to give a stable corrosion signal. Therefore, the sensors were introduced for 16 h to respectively pure 
Columbian, South African and El Cerrejon coal combustion environment before the actual tests started. 
The sensor signals kept at a constant low during these preparation tests. 

Much longer tests durations (8 h for each blend) compared to the ELPI tests were realized to investigate 
the influence  of the  different  blending  ratios on the  corrosion processes taking place. During the test 
duration, flue gas measurements and HCl measurements were performed after the cyclone and at port 3. 
HCl content in the flue gas was examined by both conventional IR methods and wet chemical analysis. 
Furthermore, ash samples of both fly and bottom ash were collected. 

At the end of the South African measurement campaign, the test team ran out of milled straw. Therefore, 
the final blend containing 100 % (e.b.) straw lasted only two hours. 

25 

 
 
 
 
 
 
Table 3: Test matrix Corrosion tests at TUM’s EFR in the OnCord project. 

The third measurement campaign was performed with a blend of El Cerrejon coal (same coal as used in 
the  USTUTT  tests)  with  a  similar  straw  (a  milled  batch  was  received  by  TUM  from  the  University  of 
Stuttgart). Both long-term and ELPI test were conducted. A blend of 91 % e.b. coal was not investigated 
because  this was not fired in  the  Stuttgart test rig  as well. A variation of the reactor temperature to 
1400 °C during the last two hours of 100 % straw firing was done in the end of the long-term tests. The 
tests finished with the performance of the fine particle tests for the El Cerrejon/Straw blend. 

Due to the long measurement duration the reactor was affected by fouling in the transition region from 
the heated section to the convective part. The occurring deposits were removed during the campaign 
and did not influence the measurements. No other severe problems occurred. 

Figure 6a) and Figure 6b) show the sampling locations and the utilised measurement equipment during 
the fine particulate measurements and during the long-term measurements. Due to the limited amount 
of space within the reactor, the fine particulate matter measurements had to be performed separately. 
The reactor consists of an approx.  2 m long radiation section and  a subsequent  convective  part. The 
radiation  section  is  heated  externally  by  five  electrical  heaters.  The  reactor  temperature  was  set  to 
1200 °C.  The  fuel  input  was  set  to  8 kWfuel  and  kept  constant  for  all  blends  weighted  with  the  lower 
heating value on a dry basis. 

26 

DateTest #DurationColumbian Coal - % e.b.South African Coal - % e.b.El Cerrejon - % e.b.Straw - % e.b.TemperatureMeasurements11h1000120021h919120031h7426120041h6060120051h4040120061h0100120071h1000120081h919120091h74261200101h60401200111h40601200121h01001200138h10001200148h9191200158h74261200168h60401200178h40601200188h01001200198h10001200208h74261200218h60401200228h40601200232h01001200248h10001200258h74261200268h60401200278h40601200284h01001200292h01001400301h10001200311h74261200321h60401200331h40601200341h01001200Online corrosion sensor, deposit probes, flue gas measurements, HCl analysis, ash probingWeek 50ELPI PSD & collection, Flue gasWeek 49Online corrosion sensor, deposit probes, flue gas measurements, HCl analysis, ash probingWeek 20Week 16Week 17ELPI PSD & collection, Flue gasELPI PSD & collection, Flue gasWeek 19Online corrosion sensor, deposit probes, flue gas measurements, HCl analysis, ash probing 
 
 
a) ELPI tests (1-12, 30-34) 

b) Long-term measurements (13 – 29) 

Figure 6: Schematic picture of the reactor with measurement locations and utilised equipment 

Therefore, all fuel shares are given on an energy basis. Air flows were adjusted to an excess ratio of 3 – 
5 vol-% measured at port 3 (target equivalence ratio = ~0.87 or lambda = ~1.15). All fuels were grinded 
and  sieved  to  a  pulverised  state  (d50  diameters:  Columbian  =  24 µm;  South  African=  16 µm,  El 
Cerrejon= 29 µm, Straw = 280 µm). Blending ratios are selected according to tests conducted in the 
circulating fluidised bed system of VTT (T4.1) and tests conducted at the University of Stuttgart (T3.2) 
to assure comparability. 

The main fuel characteristics are shown in Table 4. The straw has a high chlorine and potassium content. 
Both components are almost completely soluble in water (leaching experiments from WP1) which leads 
to the assumption that they are capable to form gaseous KCl. Major differences between the coals are 
visible for the aluminium and calcium content. A  higher share of aluminium in the ash may lead to a 
higher share of K-Al-Silicates formed during combustion lowering the share of gaseous KCl and therefore 
the corrosivity. Calcium may react with available sulphur to Ca-Sulphate reducing the share of reactive 
sulphur oxides to reduce gaseous KCl by sulphation in the flue gas. 

Table 4: Fuel properties of fuels used in the EFR experiments 

Properties 

Unit 

Fuel (abbreviation) 

Columbian Coal 
(COL) 

South African Coal 
(SA) 

El Cerrejon 
(El_C) 

Italian Straw 
(Straw) 

Lower heating value 

Total moisture 

MJ/kg 

wt-% 

29.7 

4.6 

Volatile matter 

wt-% dry 

37.1 

Ash content (815/550 °C) 

wt-% dry 

11.3 

Major components 

C 

H 

N 

S 

O (by difference) 

wt-% dry 

72.4 

wt-% dry 

wt-% dry 

wt-% dry 

wt-% dry 

5.6 

1.5 

1.2 

8.0 

26.8 

3.3 

25.9 

15.0 

69.4 

3.9 

1.6 

0.8 

9.3 

Important minor components by XRF (ratio leachable in 
H2O in %) 

Si 

Al 

K 

Cl 

Ca 

mg/kg, dry 

31440 (0 %) 

36660 (0 %) 

mg/kg, dry 

13840 (0 %) 

25980 (0 %) 

mg/kg, dry 

2660 (0 %) 

mg/kg, dry 

430 (0 %) 

850 (0 %) 

110 (0 %) 

mg/kg, dry 

960 (39 %) 

6680 (7 %) 

27.7 

3.0 

33.9 

11.6 

70.6 

5.3 

1.6 

0.8 

10.2 

28940 

16100 

1600 

250 

3200 

16.6 

8.6 

73.3 

9.0 

46.0 

5.8 

0.6 

0.3 

38.3 

28500 (5 %) 

1990 (14 %) 

11090 (98 %) 

3570 (100 %) 

4540 (26 %) 

27 

ELPIFluegas (CO2, O2, NOx, CO, SO2)ConvectivesectionOnline corrosionsensor530°C, Depositprobes530°CFluegas (CO2, O2, NOx, CO, SO2)Fluegas (CO2, O2, NOx, CO, HCl, H2O)Convectivesection 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
In order to evaluate the temperature at the measurement location, results from a computational fluid 
dynamics  (CFD)  simulation  are  taken  to  generate  the  graphs  visible  in  Figure  7.  Measured  flue  gas 
temperature values (by a suction pyrometer) at the position of the online corrosion sensor were slightly 
lower. 

Figure 7: Temperature-residence time distribution of the EFR firing pure coal/straw, red dot indicates 
the sensor position, square dot indicates the measured temperature by a suction pyrometer during pure 
straw combustion. 

A large temperature increase can be seen in the combustion zone for both fuels. The temperature peak 
is slightly shifted downstream for pure straw combustion, possibly due to a larger particle size and a 
higher share of  volatiles in the fuel. Both  temperature  profiles show a stable temperature of approx. 
1200 °C at port 3 (reactor temperature). The temperature decreases after the flue gas leaves the heated 
section.  Simulated  temperature  at  the  position  of  the  online  sensor  is  around  810 °C;  the  measured 
temperature  is  around  720 °C.  All  in  all  the  simulations  give  an  approximate  indication  of  the 
temperature profile within the reactor. 

In  the  following  section,  all  investigated  fuel  blends  are  presented  by  comparing  the  results  of  the 
different measurements from each blend, starting with the presentation of flue gas data (mainly HCl, 
SO2). To get a deeper insight on the distribution of chlorine containing species, the results of the fine 
particulate  matter  measurements  are  presented.  Those  particles  are  capable  of  depositing  on 
superheater surfaces, therefore the results of the deposit probing are shown subsequently. In the end, 
the signal behaviour of the online corrosion sensor is displayed and discussed shortly with regards to the 
other measurements.  

Emission  measurements  of  HCl  were  performed  at  port  3  and  after  the  cyclone.  Figure  8  shows  the 
behaviour of HCl measured after the cyclone and SO2 at port 3 of all coals co-combusted with straw. 
Very high burnout was achieved for all combusted blends. 

SO2 concentrations decrease for higher straw shares for all coals due to the lower sulphur content of the 
straw. The overall measured SO2 concentration during the combustion of the South African Coal is lower 
compared to Columbian Coal due to the lower S-content in the fuel, while for the El Cerrejon coal the 
values  are  in  between  the  other  coals.  This  is  in  line  with  the  results  for  the  S-content  in  the  coals 
measured by the fuel analysis.  

HCl concentrations show an increasing behaviour for all coals with an increasing share of straw in the 
fuel blend until a maximum is reached. For the Columbian coal the HCl share in the flue gas increases 
until a straw share of approx. 40 % e.b. and decreases for higher shares. The South African coal shows 
a  maximum  for  60 %  e.b.  straw  in  the  blend  with  a  decreasing  value  for  higher  straw  shares.  The 
maximum may be attributed to the better alkali capture ability according to reaction (3) of the South 
African coal compared to the Columbian coal due to the higher aluminium content in the fuel ash. HCl 
concentrations measured after the cyclone during  El Cerrejon combustion increase for a higher straw 
share in the fuel blend. By contrast to the other fuel blends, the HCl emission did not decrease during 

28 

00,511,522,533,502004006008001000120014001600Residence time [s]Temperature [°C]100_Colum100_StrawOnlineSensor100_Straw_Pyro 
 
this measurement campaign. The value measured by wet chemical analysis even increased while it was 
almost  constant  compared  to  the  60_S  case  for  IR  measurements.  No  clear  explanation  for  this 
phenomenon was found.  

The lower HCl concentration at pure straw combustion, while the mass flow of chlorine into the reactor 
is increasing, leads to the assumption that the rest of Cl is present in a different form, most probably as 
KCl. A comparison of the measured values with results from chemical equilibrium calculations can be 
found in chapter 4.2.6 in T6.2. 

Columbian Coal 

South African Coal 

El Cerrejon Coal 

Figure 8: HCl (measured by IR and wet chemical method – after cyclone) and SO2 concentrations (port 
3) as function of straw in fuel blend of all coals/straw blends. 

The  measurements  of  the  HCl  concentrations  of  all  coal/straw  blends  gave  a  first  idea  on  the  alkali 
capture  ability  of  the  different  coals.  For  a  further  assessment,  the  results  of  the  fine  particle 
measurements are presented next. 

Fine particles were measured in the convective part at an approx. flue gas temperature of 700 – 850 °C 
in separate experiments (simultaneous measurement during the long-term campaigns was not possible 
due to the limited space in the reactor). Fine particles were sucked out of the reactor at the position of 
the online corrosion sensor by a heated probe and diluted with pre-heated nitrogen to avoid unwanted 
particle nucleation. Particles < 10 µm were separated by a heated cyclone before the sample flue gas 
stream entered the impactor unit. Main species contributing to corrosive processes are Na, K, S and Cl, 
mainly in the form of vaporised salts. Figure 9 displays the amount of those elements in PM1 matter for 
all coal/straw blends. The composition of the  fine  particles was measured by  EDX.  Values  have been 
calculated to an excess oxygen of 6 vol-%. 

29 

020040060080010001200140016001800050100150200250300350020406080100SO2 [mg/Nm³ @ 6% O2 dry]HCl [mg/Nm³ @ 6% O2 dry]Straw share on energy basisColumbian Coal / StrawIRWetSO2020040060080010001200140016001800050100150200250300350020406080100SO2 [mg/Nm³ @ 6% O2 dry]HCl [mg/Nm³ @ 6% O2 dry]Straw share on energy basisSouth African Coal / StrawIRWetSO2020040060080010001200140016001800050100150200250300350020406080100SO2 [mg/Nm³ @ 6% O2 dry]HCl [mg/Nm³ @ 6% O2 dry]Straw share on energy basisEl Cerrejon Coal / StrawIRWetSO2 
 
 
 
 
 
Columbian Coal 

South African Coal 

El Cerrejon Coal 

Figure  9:  Shares  of  K,  Na,  Cl,  S  in  fine  particles  <  0.8µm  from  electrical  low  pressure  impactor 
measurements – co-firing of all coals with Straw in EFR. 

The amount of K, Na, Cl and S in PM1 matter for 100_Col, 10_S, 25_S and 40_S is close to zero. The 
amount of these species increases for the 60_S and 100_S blend. The high amount of K in the 60_S 
blend must be judged critically. Though the K content of Columbian Coal is higher compared to the other 
coals (see Table 4) it is not clear how such a high amount can be explained. A small share of chlorine 
occurs in 60_S blend indicating a small share of KCl in the flue gas. The amount of Cl drastically increases 
for pure  straw  combustion. This might result  in a  very corrosive  atmosphere during  the 100_S case. 
Only one measurement of the pure straw case was performed. The result is shown in all graphs.  

Due to the higher HCl concentration during 60 % straw/South African coal combustion a lower ratio of 
Cl is expected in the fine particulate matter during this case. Fine particles were again analysed by EDX 
for the chemical composition. Almost no chlorine was detected even in the 60_S case. This is in line with 
expectations and supports the assumption of good alkali capture by aluminium silicates and therefore 
chlorine release as HCl for the South African coal. 

Similar evaluations of the fine particle matter as for Columbian and South African coal were performed 
for the El Cerrejon coal. No K, Na, S and Cl were detected in the PM1 matter for pure coal and the 25_S 
case. A very low amount of Cl was found for the 40_S case. Both the K and the Cl ratio increased for the 
60_S and the 100_S blend. The amount of chlorine detected in the 60_S blend is comparable to the one 
measured for the 40 %Columbian/60% Straw blend. The amount of potassium however is lower, which 
can possibly be explained by a lower share of K in the coal.  

Major  differences  between  the  fine  particle  compositions  generated  during  the  combustion  of  the 
different coal/straw blends were evaluated. Additionally it can be seen that the amount of sulphur in the 
fine particles is low for all coals. This indicates only minor sulphation reactions (see equations (1) and 
(2)) are taking place and the embedding reactions in alumina-silicates (reaction (3)) are most likely the 
more important mechanism. This was investigated in more detail in a paper of TUM presented at the 
INFUB  2017.  Fine  particles  have  a  high  impact  on  the  deposit  built-up  on  heat  exchanger  surfaces. 
Therefore, the results of the deposit probing is presented next. 

To be able to judge the deposition behaviour and composition of the deposits on superheater surfaces, 
deposit probes were implemented into the reactor next to the online corrosion probe. The material used 
was 10CrMo9-10, the temperature was set to 530 °C. Three rings can be mounted on each probe. One 
ring was embedded in epoxy resin, grinded and polished for subsequent SEM analysis of the oxide-layer 
thickness. The other rings were used for the investigation of the deposit composition. Table 5 summarises 
the  average  (from  windward,  45°  to  windward  and  lee  side)  K  and  Cl  content  on  the  surface  of  the 
deposit rings of the Columbian and El Cerrejon coal. No deposit testing is available for the 100_Col, 10_S 

30 

000012177002349229050100150200250300350400450500100_Col10_S25_S40_S60_S100_SFine particles<0.8 µm mg/Nm³@6%O2 dryFuel blendSNaKCl000117710416229050100150200250300350400450500100_SA25_S40_S60_S100_SFine particles < 0.8µm mg/Nm³@6%O2 dryFuel blendSNaKCl002717700310229050100150200250300350400450500100_El_C25_S40_S60_S100_SFine particles< 0.8 µm mg/Nm³@6%O2 dryFuel blendSNaKCl 
 
 
 
and the 25_S cases of the Columbian coal and for the South African coal. The results show that almost 
no chlorine could be detected in the deposits except for the pure straw combustion case. Ratios of K are 
increasing with a higher straw share. In the deposits taken during the El Cerrejon campaign no chlorine 
was  detected  for  all  blends  containing  coal.  The  potassium  content  increased  also  with  an  increasing 
straw share. Results are comparable to the ones derived for the Columbian Coal and the ones from the 
experiments conducted at the University of Stuttgart with the same coal. 

Table 5: Share of Cl and K in deposit for Columbian & El Cerrejon/Straw blends in the EFR measured by 
EDX. 

Share of Cl and K in deposit [wt-%] – Columbian Coal 

100_Col 

10_S 

25_S 

Cl  - 
K 
- 
Share of Cl and K in deposit [wt-%] – El Cerrejon coal 

- 
- 

- 
- 

100_El_C, 
T=500°C 

Cl  0 
K  2 

- 

- 
- 

25_S, 
T=500°C 
0,1 
5 

40_S, 
T=500°C 
0 
7 

40_S, 
T=530°C 
< 1 
9 

60_S, 
T=530°C 
< 1 
11 

60_S, 
T=500°C 
0 
16 

100_S, 
T=500°C 
19 
29 

100_S, 
T=500°C 
19 
29 

The  oxide  layer  thickness  can  be  regarded  as  an  indicator  for  the  status  of  the  corrosion  process.  A 
thicker oxide layer thickness implies that more metal atoms (mostly iron/chromium) are present in their 
oxidized  form.  Depending  on  the  alloy  used  the  oxide  layer  thickness  can  either  have  a  protective 
function or, if it gets brittle, can lead to enhanced corrosion due to flaking of material. In  Table 6 the 
measured oxide layer thicknesses for all investigated blends from SEM pictures are summarised. 

Table 6: Oxide layer thickness of embedded material rings for Coal/Straw blends in the EFR measured 
by SEM, T-material = 530°C, duration in hours. 

25_S, 8h 
12 

10_S, 8h 
13 

Oxide layer thickness [µm], duration– Columbian Coal 
100_Col, 8h 
10 
Oxide layer thickness [µm], duration – South African Coal 
40_S, 8h 
100_SA, 8h 
12 
20 
Oxide layer thickness [µm], duration – El Cerrejon Coal 
100_El_C, 8h 
9 

25_S, 8h 
19 

40_S, 8h 
12 

40_S, 8h 
13 

25_S, 8h 
10 

60_S, 8h 
19 

60_S, 7h 
19 

60_S, 8h 
16 

100_S, 7h 
22 

100_S, 7h 
22 

100_S, 7h 
22 

A  similar  oxide  layer  thickness  was  detected  for the  pure  Columbian  coal,  10_S,  25_S  and  the  40_S 
cases.  The  oxide-layer  growth  thicker  for  the  60_S  and  the  100_S  cases  indicating  enhanced  metal 
oxidation on the metal surface during this flue gas atmospheres. A faster oxide layer growth should lead 
to an increase in the online corrosion sensor signal due to the increased electron flow (see Figure 3). 
The  measured  oxide  layer  thickness  built-up  during  South  African  /Straw  blend  combustion,  already 
increased  for  the  25_S  straw  blend  and  is  almost  constant  for  an  increasing  straw  share.  This  is  in 
contradiction  to  the  expected  behaviour.  All  possible  sources  of  error  (insufficient  cooling,  different 
position of the probe in the reactor, different state of the material before exposure to the flue gas etc.) 
were avoided. Therefore, it is unclear why a thicker oxide layer is measure already for 25 % e.b. in the 
fuel blend. The oxide layer thickness of the materials rings after exposure to the flue gas atmosphere of 
the El Cerrejon/Straw blends gets thicker for higher share of straw.  

The measured oxide layer thicknesses could not be correlated perfectly to the derived online corrosion 
signals, which are presented in the next section. The results from the composition analysis of the deposits 
lead to the assumption that especially chlorine in the deposit does cause an increased online corrosion 
signal. 

31 

 
 
 
 
 
 
 
 
 
 
 
Figure  10  shows  the  behaviour  of  the  online  corrosion  signal  during  the  all  measurement  campaigns 
during the entire duration (48h) for the all Coal/Straw blends. 

Columbian Coal 

South African Coal 

El Cerrejon Coal 

Figure 10: Online corrosion measurement all Coals/Straw: The online signal (yellow), sensor (grey) and 
sensor tip (orange) temperature over the test period are shown. Dotted lines indicate the blend changes. 

The  displayed  sensor  head  temperature  signal  (measured  on  the  tip  of  the  sensor  head  giving  an 
indication  of  the  flue  gas  temperature)  is  fluctuating  over  time  for  all  coal/straw  blends.  This  can  be 
attributed to the fouling happening in the convective section. Rapid increases of the temperature are 
visible  after  the  deposited  material  has  been  removed  from  the  convective  part.  The  material 
temperature  of  530 °C  was  held  constant  during  the  whole  measurement  duration  for  all  coal/straw 
blends. 

A low sensor signal with no changes is detected until the start of the 60 % e.b. Columbian coal/straw 
blend.  During  the  combustion  of  60%  straw  in  the  reactor,  the  signal  increases  moderately.  After 
switching to pure straw combustion the signal increases rapidly. By comparing the sensor signal with 
measured data of the HCl, fine particle and deposition analysis it can be seen that it is highly probable 
that  it  correlates  with  an  increased  share  of  gaseous  KCl  in  the  flue  gas  and  in  the  deposit.  A  more 
detailed evaluation of all data is be performed in section 4.2.5 of this report. 

A  very  low,  almost  constant  signal  was  measured  for  all  fuel  blends  containing  South  African  coal.  A 
steep increase can be seen after the pure straw combustion started. The pure straw combustion was 
conducted only for 2 h because the test team ran out of the milled and sieved straw. 

The online corrosion signal also stayed at a constant low for all blends containing El Cerrejon coal and 
increased after the pure straw combustion was started. A rapid increase both of the measured flue gas 
temperature and the online corrosion signal can be seen for the final hour after the reactor temperature 
was set to 1400 °C.  

Discussion of results from EFR tests 
Three coals with different compositions were co-combusted with straw during three campaigns at the 
TUM entrained flow reactor. The corrosivity of the flue gas from the different fuel blends was assessed 
by a variety of measurement techniques. Main parameters influencing the corrosivity were identified as:  

32 

 
 
 
 
Ash  content  of  coals,  Reactivity  of  ash  minerals of  coals,  Aluminium  content in  ash  of  coals,  Sulphur 
content in coals, Chlorine/Potassium content in straw.  
Additionally, an online corrosion sensor of Babcock Borsig Steinmueller was used for the first time during 
co-combustion of straw with coal. The following conclusions were drawn: 

  A higher concentration of K and Cl in the flue gas leads to an increasing online corrosion signal. 
The sensor reacts very fast to a change of the flue gas atmosphere in case a sufficient amount 
 
of KCl is present. 

  Different critical co-firing ratios for the different combusted coals were identified: approx. 60% 
straw e.b. with Columbian Coal, while more than 60 % is possible for South African Coal and El 
Cerrejon coal. 
The embedding reactions of alkalis (especially K) in coal minerals was observed to most likely 
be the more important of the two alkali reduction mechanisms. 

 

Two  scientific  papers  were  written  based  on  the  experiments,  which  were  presented  at  the  11th 
International Conference on Furnaces and Boilers in Albufeira, Portugal on the 17th of April 2017 and are 
published in a special issue of Energy Procedia. 

Task 3.2: PF industrial pilot scale measurements 
The main objective of the Task 3.2 is to investigate the corrosion phenomena in a mid-scale combustion 
rig.  The  fuel  fed  will  be  coal  and  biomass  at  different  percentages  in  order  to  observe  the  corrosive 
behaviour of the respective fuel blends. 

Tests in task 3.2 were performed in the combustion test rig (KSVA) of the University of Stuttgart during 
one week. The combustion chamber consists of six cylindrical segments with a total length of 7.000 mm 
and an inner diameter of 800 mm. Refractory lining covers the inner surface of the upper four segments 
of  the  combustion  chamber  to  a  distance  of  4.000 mm  from  the  burner.  Numerous  measurement 
openings are located along the reactor wall. Figure 11 shows the combustion test rig (KSVA) and relevant 
measurement sampling locations for the test discussed here. 

Figure  11a)  Temperature  profile  along  the  centreline  of  combustion  chamber,  b)  the  sketch  of 
combustion chamber with indication of conducted measurements 

a) 

b) 

A  bituminous  coal  from  Columbia  (El  Cerrejon)  available  at  the  University  of  Stuttgart  was  used  in 
combination with a high-chlorine straw for combustion tests. Both fuels were milled to a pulverised state 
prior to combustion. The combustion test included mono-firing of coal and straw as well as co-firing with 
25 %, 40 % and 60 % thermal share (dry basis) of straw. In total five different firing scenarios were 
run, each  firing  scenario  last  maximum  up  to 24 hours.  The  fuel/fuel  blend-feeding  rate  is  chosen to 
keep the total thermal output of 300 kW. The total combustion air was chosen to have an excess O2 of 
3 vol.-%. The combustion was continuously monitored by measuring the flue gas concentration at the 
end of combustion chamber (L26). 

Table 7 shows the fuel composition and mass/air flows for each blend used in the tests in the KSVA. 

33 

 
 
 
 
 
Table 7: Fuel composition and mass/air flows during combustion in the KSVA. 

Fuel 

W 

A 

V 

Fix-C 

C 

H 

N 

S 

Cl 

LHV 

[%, ad]  [%, db] [%, daf] [%, daf] [%, daf] [%, daf] [%, daf] [%, daf] [%, daf]  [MJ/kg, an] 

Coal (EC) 

Straw (S) 

3.3 

9.1 

12.7 

35.3 

64.7 

80.5 

10.4 

79.3 

20.7 

49.3 

4.9 

6.0 

1.7 

0.77 

0.03 

27.65 

0.91 

0.31 

0.41 

15.51 

Coal [kg/h] 
Biomass [kg/h] 
Total Primary Air [Nm3/h] 
as carrier air 
Total Secondary Air [Nm3/h] 
Cooling Air [Nm3/h] 

100EC 
39 
-- 

63 

221 
10 

25S 
29 
17 

60 

218 
10 

40S 
24 
27 

62 

214 
10 

60S 
15 
43 

65 

228 
10 

100S 
-- 
66 

65 

231 
10 

Measurement equipment comprised of: 

  Cooled deposit probing simulating superheater tubes, 10CrMo9-10 was used as a material, rings 
were  cooled  to  a  temperature  of  550 °C,  deposit  were  collected  at  level  15  for  a  duration  of 
approx. 8 hours 

  Uncooled deposit probe connected to a weighing cell measuring the mass of the built-up deposit 

online at level 11 and 15 
Fly ash sampling by an ejection system at level 11 and 15 of the test rig 
Fine particle number and sampling by an electrical low pressure impactor at level 18 

 
 
  BBS online corrosion sensor equipped with 10CrMo9-10 sensor rings, temperature set to 550 °C 

at level 18 

  Online flue gas analysis of standard gas components (CO2 by NDIR, CO by NDIR, SO2 by NDIR, 
NOx by Chemiluminesence, O2 by paramagnetism) at level 26 and HCl (wet chemical analysis a 
level 18 and by FTIR at level 17 and 23) 

  Continuous fly ash sampling at different filter in the flue gas duct and at level 27 to evaluate the 

 

overall burnout 
Temperature measurements by a suction pyrometer over the entire reactor height (see Figure 
11) 

Evaluation of results start with an analysis of un-burned carbon in the sampled ash from level 27 and 
from the bottom ash hopper. A very low ratio of un-burned carbon was found at both sampling locations 
(< 1.2 %). The unburned-carbon shows a tendency to decrease with increasing straw share. In case of 
100 % straw case, the un-burned carbon is slightly higher and out of the trend, however is still lower 
than for the 100 % coal case. 

As a next step, the results of the HCl and SO2 concentration measurement for each blend are presented. 
Figure 12 shows the concentrations of HCl (measured by FTIR at level 17 and wet chemical method at 
level 18) and SO2 from level 26 as a function of the share of straw in the fuel blend. Values are given in 
mg/Nm³ and were converted to 6 vol-% oxygen in dry flue gas. 

34 

 
 
 
 
 
Figure  12:  HCl  (measured  by  FTIR  and  wet  chemical  method)  and  SO2  concentrations  as  function  of 
straw in fuel blend with El Cerrejon coal. 

The  decrease  in  SO2  concentration  is  related  to  the  decreased  input  of  fuel-S  with  increasing  straw 
shares. Moreover, due to abundance of SO2 capturing species, like calcium and potassium in biomass 
ash, the SO2 concentration in the flue gas decreases.  

The increase in HCl concentration with increasing co-firing straw share can be explained by increasing 
fuel-Cl content. Despite of highest fuel-Cl content for 100S case, the HCl content is not the highest. It 
indicates that the release of fuel-Cl as HCl is hindered during 100S case. In straw, during combustion 
most of the chlorine is expected to release as KCl in gas phase.  Sulphation is one of the mechanisms 
that  releases  chlorine  as  HCl.  Due  to  low  fuel-S  amount  in  the  straw  sulphation  is  probably  limited. 
Additionally,  interaction  of  KCl  with  ash  species  especially  alumina-silicate  is  another  mechanism  to 
release chlorine as HCl, however such ash species are largely limited in straw ash. Large fraction of the 
fuel-Cl in straw remains as KCl and finally end up in fly ash and/or in deposits. Co-firing straw with coals 
facilitates the release of fuel-Cl as HCl as the fuel-S content is significant in  coal as well as  alumina-
silicate ash species are significantly present in coal ash.  

Additionally,  to  the  HCl  measurements  by  FTIR  at  level  17  the  results  of  the  wet  chemical  method 
performed by TUM are displayed. Measured values are lower as values from FTIR measurements for all 
fuel  blends.  This  might  be  caused  by  incomplete  scrubbing  of  the  flue  gas  in  the  wash  water. 
Nevertheless, the trend with an increasing HCl concentration until 60% straw and a drop for 100% straw 
is resembled. This strengthens the above-mentioned interpretation of a stronger KCl release during pure 
biomass combustion and therefore a higher corrosion risk. 

Measurements  of  gaseous  HCl  emissions  are  in  line  with  measurements  of  fine  particles  by  an  ELPI. 
Measurements were performed at level 18 at an approx. flue gas temperature of 850- 950 °C for different 
straw shares. Similar to the results of  the TUM  test rig,  the PM1 particles  were  investigated  for their 
chemical composition by SEM/EDX. Figure 13 shows the ratios of S, Na, K and Cl (species mostly relevant 
for corrosion during biomass/coal co-firing) in the PM1 particulate matter. Remaining species not shown 
were mainly C, O, Si and Al. Due to the semi-quantitative nature of the EDX measurements the shown 
numbers have to be seen critically but overall ratios give an idea of the composition. 

35 

020040060080010001200140016001800050100150200250300020406080100SO2 [mg/Nm³ @ 6% O2 dry]HCl [mg/Nm³ @ 6% O2 dry]Straw share on energy basisEl Cerrejon / Straw ItalyHCl_Wet_ChemicalHCl_FTIR_IFKSO2 
 
Figure  13:  Shares  of  K,  Na,  Cl,  S  in  fine  particles  <  0.8µm  from  electrical  low  pressure  impactor 
measurements – co-firing of El Cerrejon with Straw in USTUTT test rig. 

No species containing K or Cl were found in blends firing pure El Cerrejon and 25 % e.b. straw with El 
Cerrejon.  The  share  of  K  and  Cl  increased  with  a  higher  amount  of  straw  in  the  fuel  blend.  Around 
100 mg/Nm³  at  6%  O2  dry  composed of  K  and  Cl  are  emitted  during  pure  straw  combustion.  For  all 
blends the shares of S and Na in the PM1 matter is very low. The ratio of emitted K to Cl for pure coal is 
calculated to 56/43 = 1.3. By comparing this to the ratio of the molar masses of both elements (approx. 
39/36 = 1.09) it can be concluded that most of the present K and Cl is in the form of KCl (stoichiometry 
1:1) with a slight surplus of K (probably in form of K-silicates due to small amount of Si present in PM1 
matter). The ratio of the amount of K to Cl is rising with an decreasing share of straw in the fuel blend 
(60S: 13/9 ≈1.44 and 40S: 12/5 ≈ 2.4). This can attributed to the rising share of Si/Al in PM1 matter 
(not shown) able to bind K in the form of K-silicates and K-Al-Silicates. In summary, it can be concluded 
both from HCl and fine particle measurements that a high amount of corrosive species (namely gaseous 
KCl) is emitted during pure straw combustion while the amount is approx. five times lower already for 
60 % straw in the fuel blend. The ratio of corrosive species in the flue gas is further decreasing for higher 
coal shares in the fuel blend.  

To complete the analysis of the corrosivity of the flue gas both cooled and uncooled deposit probes were 
placed  in  the  flue  gas  stream.  The  uncooled  probe  was  connected  with  a  weighing  unit  enabling  the 
online measurement of deposit built-up. Cooled probes were used to collect deposits at realistic material 
surface  temperatures  mimicking  superheater  tubes  in  a  power  station.  Collected  deposits  was 
investigated  afterwards  by  SEM/EDX  analysis.  Additionally,  a  part  of  the  material  ring  was  cut  and 
embedded in epoxy resin to be able to analyse the oxide layer thickness. 

Figure 14 shows the deposited mass [g] vs time [min] at L11 and L15 over a period of ~8 h with different 
straw shares. It can be seen that the deposit build-up is increasing for a higher share of straw in the fuel 
blend. 

Figure 14: Online deposition measurement at L11 (left) and L15 (right) with different straw shares. 

The cooled deposit probe was placed at level 15 at flue gas temperature of approx. 1000 °C for a duration 
of  ~8 h.  Material  rings  of  10CrMo9-10  were  used  to  enable  comparison  with  the  rings  of  the  online 
corrosion sensor. Material temperature was set to approx. 550 °C. Chlorine in the deposits is known to 
enhance  corrosion.  Therefore,  Table  8  shows  the  share  of  Cl  and  K  in  the  material  deposited  on  the 
36 

00594301121356020406080100100_El_C25_S40_S60_S100_SFine particles < 0.8 µm mg/Nm³@6%O2 dryFuel blendS KNaKK KClK 
 
 
 
cooled deposit probes. It can be seen that almost no Cl is found for all blends except during pure straw 
combustion. This is in line with the measurements of the amount of fine particulate matter presented 
above. Those particles mainly deposit via condensation and/or thermophoresis. Due to the low amount 
of emitted PM1 matter composed of K and Cl during all co-firing cases only a very small amount of those 
components are deposited on the surface of the cooled deposit probe. In the case of pure straw firing a 
much higher amount of PM1 matter is emitted leading to a significant share of Cl on the material surface. 
Additionally a high amount of K is also found in the deposited material. Therefore, the deposited material 
may contain a high share of KCl which can lead to severe corrosion of the material. 

Table 8: Share of Cl in deposit of the cooled deposit probe of level 16 measured by EDX. 

Share of Cl and K in deposit [wt-%] 

100_El_C 

Cl  < 1 
K  1 

25_S 
< 1 
7 

40_S 
< 1 
14 

60_S 
<1 
20 

100_S 
~12 
24 

Results of the evaluation of the oxide layer thickness are shown in Table 9. The measurement approach 
was similar to the one described in T3.1. 

Table 9: Oxide layer thickness of embedded material rings measured by SEM. 

Oxide layer thickness [µm] 
100_El_C 
23 

25_S 
31 

40_S 
- 

60_S 
- 

100_S 
66 

The oxide layer thickness  increases with a higher share of straw in the fuel blend indicating a higher 
corrosivity  of  the  flue  gas.  Additionally,  an  already  flaking  oxide  layer  thickness  was  observed  after 
exposing the material ring to the flue gas of the pure straw combustion. 

By combining all above mentioned measurements the behaviour of the online corrosion signal from the 
BBS sensor shown in Figure 15 can be explained. The sensor was inserted at level 18 using 10CrMo9-10 
material  rings  at  a  material  temperature  of  550 °C.  Flue  gas  temperature  was  approx.  900 °C.  One 
sensor  was  used  throughout  the  whole  measurement  campaign.  A  higher  signal  indicates  higher 
corrosivity / faster growing oxide layer thickness. 

a) 

b) 

Figure 15: Overview of the online corrosion measurement: The total test run (left) with the online signal 
(black), sensor temperature (grey) and sensor tip temperature (orange) over the test period. The grey 
dotted lines indicate the blend changes. The right graph shows the average sensor signal for each blend. 

The signal is very low during pure coal and 25 % e.b. straw /75 % coal combustion. Is slightly increases 
after the share of straw is set to 40 % but stays almost constant even during the combustion phase of 
the 60 % straw blend. After the fuel is changed to pure straw, the signal increased steeply and reacted 
very fast. The signal kept rising during the whole period. By averaging the signal for the duration of all 
blends, an exponential behaviour is observed.  

37 

01002003004005006007008000,00,10,20,30,40,50,60,70,80,91,027.6.16 0:0027.6.16 12:0028.6.16 0:0028.6.16 12:0029.6.16 0:0029.6.16 12:0030.6.16 0:0030.6.16 12:001.7.16 0:001.7.16 12:00Temperature [°C]Corrosion Value [mA/V]Cor_ValueT_HeadT_Maty = 0,0001e1,4549xR² = 0,96820,000,050,100,150,200,250,30100% Col_Coal26 % Straw IT40 % Straw IT60 % Straw IT100% Straw ITAverage Corrosionvalue [mV/A] 
 
 
 
 
 
 
Discussion of results from KSVA tests 
By comparing the measured online signal to the other measurements the following conclusions can be 
drawn: 

 

The increase of the online corrosion signal is correlated with the amount of fine particulate matter 
containing K  and Cl. A higher concentration  of those elements in  the flue gas leads  to higher 
amount of them in the deposits. 

  A high share of chlorine in the deposits in a flue gas atmosphere leads to a steep signal increase. 

 

The sensor reacts very fast to this change. 
The  behaviour  of  the  online  signal  is  supported  by  the  measurements  of  the  oxide  layer 
thickness. 

  A corrosive share of gaseous KCl in the flue gas and on the deposits could be avoided even at 
60 % straw in the coal/straw blend. Most of the chlorine leaves the boiler in the state of gaseous 
HCl. 
The  beneficial  interaction  between  coal  minerals  and  the  KCl  originating  from  the  straw  was 
proven by several measurements. 

 

Results of this measurement campaign were compared to the El Cerrejon/Straw campaign conducted at 
the TUM EFR and supported the transferability of small-scale results to larger test rigs.  

Task 3.3: PF power plant measurements 
Three pulverised fuel power plant measurement campaigns were performed in Task 3.3. Three different 
types of low-grade fuel/coal co-combustion were investigated in order to evaluate corrosion mechanisms 
and phenomena. Figure 16 shows the location and main fuels of the respective plants. 

Figure 16: Overview PF power stations in the OnCord project. 

Main results of all three power plant campaigns are presented here. 

1)  Studstrup campaign 

Overall goals of this measurement campaign were: 

 

Long-term measurement with two online corrosion sensors in the large scale to evaluate their 
behaviour. 

  Holistic characterisation of the conditions at the measurement location of the online sensors with 

regard to corrosivity in order to explain corrosion signals derived by the online sensors. 

  Correlation of the sensor signals to fuel and boiler data in order to examine their influence on 

possible corrosion during co-firing of straw. 

Measurements at Studstrup power station comprised of several long- and short-term equipment. Table 
10 summarises the utilised measurement equipment. 

Four burners of 24 are adjusted for the co-firing of  straw. The maximum amount of co-fired straw is 
approx. 15 % on an energy basis depending on the load of the power station. 

38 

Compostilla power station•Pet-Coke/Coalcofiring•Ponferrada, SpainStudstrup power station•Straw/Coalcofiring•Aarhus, DenmarkFusina power station•RDF/Coalcofiring•Fusina, Italy 
 
 
 
Table 10: Overview measurements at Studstrup power station in the OnCord project. 

Long-term measurements 

Measurement 

Responsible partner 

Measurement period 

Online Corrosion Sensor BBS 

Babcock Borsig Steinmüller 

08.12.2015 – 25.03.2016 

Online Corrosion Sensor Savcor 

Material loss probe 

Valmet Technologies / 
Subcontractor Savcor Oy 
Doosan 

Frequent fuel and fly ash evaluation 

TUM 

Relevant boiler data 

TUM 

Short-term measurements 

28.01.2016 – 25.03.2016 

15.12.2015 – end of march 2016 

01.12.2015 – 01.03.2016 

01.12.2015 – 31.03.2016 

Measurement 

Corrored sensor 

Deposition probes 

Flue gas measurements 

Suction pyrometer 

Fine particle measurement (ELPI) 

Responsible partner 

Measurement period 

Valmet Technologies 

22.01.2016 – 29.01.2016 

TUM 

TUM 

TUM 

TUM 

22.01.2016 – 29.01.2016 

22.01.2016 – 29.01.2016 

22.01.2016 – 29.01.2016 

22.01.2016 – 29.01.2016 

The authors will present here  the  main  results from the long-term measurement campaign using the 
both online corrosion sensors (BBS & Savcor) and the Doosan material loss probe. 

BBS corrosion probe data 
The  BBS  online  corrosion  sensor  was  implemented  in  the  region  of  the  4th  superheater  (flue  gas 
temperature  measured  by  a  suction  pyrometer:  approx.  820 °C  near  to  the  boiler  wall)  in  the  same 
vertical cross-section of the boiler as the Savcor sensor.  

During  the  approx.  3  months’  measurement  period,  several  investigations  were  conducted  and  are 
summarised in this chapter. Figure 17 shows the behaviour of the corrosion signal (here: corrosion value 
in mA/V) and the material temperature. After a short initial period where the material temperature was 
set to 350 °C the temperature was increased to 600 °C. The material temperature was stable for the 
whole measurement period with few exceptions during several short power plant shutdowns (e.g. around 
the 16.03.2016). Additionally, a variation of the material temperature was performed during the TUM 
short term measurement week to investigate the influence of the material temperature on the corrosion 
signal. Further analyses included  the investigation of  the sensor  signal  after soot-blowing nearby the 
sensor  and  the  behaviour  after  one  shutdown.  The  longest  period  of  the  measurement  with  stable 
process  conditions  was  achieved  for  1  month  from  the  06.02.2016  –  06.03.2016.  The  measured 
corrosion  values,  temperatures  and  boiler  data  from  this  period  were  used  to  perform  a  statistical 
analysis examining the correlation coefficients of e.g. the straw share and the corrosion value.  

Furthermore, both sensors (Bilfinger & Savcor) were implemented simultaneously in the boiler during 
the time from the 28.1.2016 – 25.03.2016. Therefore, both derived signals are compared for this period. 

Figure 17: Corrosion value (with moving average in green) of the BBS online corrosion sensor and the 
material temperature over the whole measurement period. 

39 

010020030040050060070000,020,040,060,080,10,120,140,1607.12.1527.12.1516.01.1605.02.1625.02.1616.03.16Temperature [°C]Corrosionvalue[mA/V]Corrosion valueTemp.TemperaturevariationMeasurement periodforstatisticalanalysis 
 
 
 
Influence of soot-blowing and shutdowns on the corrosion signal 
The derived data from Studstrup power stations enables two additional analyses of the sensor signal. 
On the one hand, the influence of soot-blowing in the region nearby the BBS sensor was assessed. On 
the other hand, the behaviour of the signal after a power plant shutdown is investigated. Both analysis 
can help to understand the sensor signal and can identify possible overlapping influences with the actual 
signal.  In  Figure  18  the  trend  of  the  sensor  signal  and  the  sensor  head  temperature  after  one  soot-
blowing event is displayed. Both a measurement point of the sensor signal and the head temperature is 
derived every 10 minutes. No change of the sensor signal after the soot-blowing is visible for approx. 
90 minutes after the event. A similar behaviour was observed for further soot-blowing events all over 
the boiler. Therefore, it is assumed that soot blowing has only a minor influence on the corrosion signal. 

Figure 18: Behaviour of the sensor signal and the sensor head temperature after soot-blowing next to 
the BBS sensor. 

From the 14.03.2016 08:00 until the 15.03.2016 16:30 the power station was shut down for approx. 
24 h. The measured sensor signal and the sensor temperatures during this time period are shown in 
Figure  19.  During  the  shutdown  the  temperatures  dropped  to  approx.  200 °C.  The  sensor  signal 
decreased  to  almost  zero  with  one  short  and  very  sharp  increase,  which  may  be  caused  by  e.g. 
moisture dropping on the sensor head from above. 

After the restart of the power station both the sensor signal and the temperatures increased quickly 
to values comparable to before the shutdown. A small overshoot is visible but does not disturb the 
signal trend. 

Figure  19:  Behaviour  of  the  sensor  signal  and  the  sensor  head/material  temperature  after  a  24h 
shutdown. 

Overall, the soot-blowing as well as the shutdown event were clearly reflected by the sensor signal 
as it had to be expected. The signal behaviour shows the changed corrosion rate during both events 
and returns to the state before. Therefore, the signal can be judged quite robust against such events. 

40 

6726736746756766776786796806816820,0410,04150,0420,04250,0430,04350,0440,04450,0450,04553:073:213:363:504:044:194:334:485:025:165:31Temperature in [°C]Corrosion value [mA/V]TimeCorrosion valueTemp. HeadSootblowingeventnearsensor010020030040050060070080000,010,020,030,040,050,060,070,080,090,119:1219:1219:1219:1219:1219:1219:12Temperature in [°C]Corrosion value [mA/V]TimeCorrosion valueTemp. HeadMaterial Temp.Shutdown 
 
 
 
Material temperature variation 
During the TUM measurement week (22.01.16 – 29.01.16) the material temperature was varied from 
350 °C  to  600 °C  with  a  step  width  of  50 °C.  Each  temperature  was  set  for  24h.  This  temperature 
variation was performed at the power station instead at TUM’s entrained flow reactor due to much more 
stable conditions and longer measurement times. 

Corrosion is a thermally activated process. Therefore, an exponential behaviour of the corrosion signal 
(indicating higher corrosion risk) with an increasing material temperature was expected.  

a) 

b) 

Figure 20: Material temperature variation of the BBS online corrosion sensor, a) Average values of 24h 
time period, b) Arrhenius plot. 

Figure 20a shows the averaged corrosion value in mA/V of the whole  24 h measurement period of all 
temperatures. A clear exponential trend is visible with an R-square value of the trend line very close to 
one.  The  standard  deviation  is  low  with  an  increasing  trend  for  higher  temperatures.  Due  to  its 
exponential  behaviour  over the temperature the signal can be  fitted in  an  Arrhenius type graph (see 
Figure 20b). The averaged corrosion signal in mA/V is plotted over the inverse material temperature in 
1/K. A straight line is derived with an R-square value also very close to one. A typical Arrhenius equation 
(equation 4): 

𝑟 = 𝐴 ∗ 𝑒(−

) 
where r is typically a reaction rate, A is called the pre-exponential factor, Ea is the activation energy of 
the reaction, R is the universal gas constant and T the temperature in Kelvin is applied. By comparing 
the coefficien Behaviour of the sensor signal and the sensor head temperature after soot-blowing next 
to the BBS sensor.ts of the plotted linear equation in Figure 20b with the logarithmic version of equation 
2.1 the following Arrhenius coefficients can be derived (see equation 5).  

(4) 

𝐸𝑎
𝑅∗𝑇

𝐴 = 𝑒6,2174,    𝐸𝑎 = 7664.2 ∗ 8.314 

(5) 

To ensure a better comparability they have been normalized by the surface area of the working electrode 
of the BBS sensor (see Table 11). 

Table 11: Arrhenius parameters of the online corrosion signal. 

Variable 
Surface area working electrode 
Pre-exponential factor A 
Activation energy Ea 

Value 
15.08 
33.25 
4225.6 

The derived exponential behaviour of the online corrosion signal is very well in line with the theoretical 
description of corrosion processes. The calculated Arrhenius constant can be used for a comparison to 
other studies on online corrosion signals.  However, transferability is judged difficult because the data 
was derived for specific gas atmosphere in a specific pulverised fuel power station. 

41 

y = 0,0014e0,6963xR² = 0,99170,000,010,020,030,040,050,060,070,080,090,10350400450500550600Averaged Corrosion value [mV/A]T_Material [°C]y = -7664,2x + 6,2174R² = 0,9969-8-7-6-5-4-3-2-100,0010,00120,00140,00160,0018Ln (Corrosion value) 1/T_Material in [1/K] 
 
 
 
 
 
 
Comparison: BBS and Savcor sensor signal 
During the period from the 28.01.2016 – 25.03.2016 both sensors measured at Studstrup power station. 
To be able to perform a comparison, the BBS sensor signal was converted by a calculation to the same 
unit  as  the  Savcor  sensor.  While  the  BBS  signal  is  given  in  mA/V  the  Savcor  sensor  measures  in 
Ohm*cm². While using the Savcor unit, it has to be taken into account that a lower value indicates a 
higher corrosion risk. The conversion formula is stated in the equation below: 

𝑥 𝑖𝑛 𝑂ℎ𝑚 ∗ 𝑐𝑚² =

1000 ∗  15,07 𝑐𝑚²
𝐵𝑖𝑙𝑓𝑖𝑛𝑔𝑒𝑟 𝑐𝑜𝑟𝑟𝑜𝑠𝑖𝑜𝑛 𝑣𝑎𝑙𝑢𝑒

. 

(6) 

The surface area of the working electrode was used to normalize the derived signal. 

Figure  21  shows  the  signal  behaviour  of  both  sensors  (in  Ohm*cm²)  over  the  whole,  simultaneous 
measurement  period.  Two  outages  are  visible  through  a  steep  increase  in  both  signals.  Both  signals 
behave  similarly  with  constant  signals  over  the  whole  period.  However,  the  Savcor  signal  is  lower 
compared  to  the  signal  value  derived  from  the  BBS  sensor  despite  the  lower  material  temperature. 
Furthermore, it can be shown that the Savcor sensor shows higher small-scale fluctuations than the BBS 
sensor (see Table 12). The ratio between the average value and the standard deviation is higher for the 
Savcor sensor signal. Due to the high values of the signal and the overall constant behaviour, this does 
not influence the signal quality. Overall, the Savcor sensor seems to be more sensitive compared to the 
BBS sensor. The main reason for this might be the different construction of the sensor head of the Savcor 
sensor. 

a) Comparison whole measurement period: 
1.2.16 – 25.3.16  

b) Comparison from 20.3.16 – 26.3.16, both 
sensors cooled to 600°C material T 

Figure 21: BBS online corrosion signal, unit converted to Savcor sensor unit. 

After the first shutdown (around the 07.02.16) a higher fluctuation can be seen in both graphs while 
after this period both signals became very stable. After the temperature increase of the Savcor sensor 
to 600 °C a signal drop is observed. All in all, no large deviations between both sensors are observed 
and both indicate a low corrosion risk. 

Table 12: Statistical analysis of the sensor signals (10.02.16 – 10.03.16). 

Sensor 

Savcor 
BBS 
Correlation coefficient between both signals: 0.44 

Average signal 
[Ohm*cm²] 
127500 
371500 

Standard deviation 
[Ohm*cm²] 
39200 
79800 

Ratio STD/AVE 

0.31 
0.21 

42 

5005205405605806006201101001000100001000001000000100000001000000001E+0921.01.1631.01.1610.02.1620.02.1601.03.1611.03.1621.03.1631.03.16Temperature [°C]Corrosion value [Ohm*cm²]Savcor 10CrMoBBS 10CrMoT_SavcorT_BBS110100100010000100000100000022.03.1622.03.1623.03.1623.03.1624.03.1624.03.1625.03.1625.03.1626.03.16Corrosion value [Ohm*cm²]Savcor 10CrMoBBS 10CrMo 
 
 
 
 
 
 
 
 
A further analysis of both signals was performed by a statistical correlation of the signals with the main 
influencing boiler parameter comprising of: 

  Boiler load expressed as the generator load, 
  Coal flow into the boiler, 
  Straw flow into the boiler. 

Correlation coefficients of the online corrosion signal with boiler data were calculated according to: 

𝐾𝑜𝑟(𝑋, 𝑌) =

𝐶𝑜𝑣(𝑋, 𝑌)

√𝑉𝑎𝑟(𝑋)𝑉𝑎𝑟(𝑌)

(7) 

where X and Y are stochastic variables (here the corrosion signal and the respective boiler data), Cov is 
the covariance and Var is the Variance. Calculations were performed in Matlab. Correlation coefficients 
are interpreted in the following way: 

  Correlation coefficient = 1, total positive linear correlation, 
  Correlation coefficient = 0, no correlation, 
  Correlation coefficient = -1, total negative linear correlation. 

The  results  are  presented  in  Figure  22.  The  color  pattern  looks  very  similar  with  a  high  correlation 
between the coal flow and the generator load. A decreasing indicates higher corrosive activities when 
the signal is displayed in Ohm*cm². Therefore, a high invers correlation is detected for both signals to 
the boiler load and the coal flow to the boiler.  

a)  BBS sensor 

b)  Savcor sensor 

Figure  22:  Correlation  matrix  of  the  online  corrosion  signal  with  most  important  boiler  data  for  both 
sensors calculated for the period from the 06.02.16 – 06.03.16. 

The  lowest  correlation  is  calculated  between  the  straw  flow  and  the  corrosion  signals.  The  low  straw 
input into the boiler (< 15 % energy basis) does not influence the corrosion signal. 

Doosan Babcock material loss probe data 
The material loss probe was constructed of 11 cylindrical probe sections that fitted to a carrier system 
that  provided  air  cooling.  Six  of  the  probe  sections  were  made  from  Super304H  and  the  rest  from 
10CrMo9-10.  Each  probe  section  had  accurate  wall  thickness  measurements  conducted  around  the 
circumference  and  had  a  reference  point.  The  Super304H  probe  sections  had  three  thermocouples 
positioned around the inside surface. The probe was installed at Studstrup power station on the 15th 
December 2015 and removed on the 6th April 2016. 

The variability in the boiler load and other factors resulted in relatively complex temperature-time plots 
for  test  sections  with  thermocouples.  Histograms  in  which  the  total  time  that  the  probe  section 
thermocouples  spent  at  a  particular  temperature  range  plotted  against  the  temperature  ranges  were 
created, an example of which is shown in Figure 23. 

The  histograms  showed  that  the  ranges  of  temperatures  experienced by  the  probe  sections  over  the 
period of exposure when the  boiler was operating  at reasonable load levels were quite wide. In each 
case, three temperatures were defined as follows: 

43 

 
 
 
 
 
 
a. 

b. 

c. 

The Peak temperature, i.e. the probe temperature at which the maximum number of hours 
were spent 
The  Maximum  temperature,  i.e.  the  maximum  temperature  recorded  for  5  hours  or  more 
during exposure, ignoring isolated outliers 
The Full Load Average Temperature (FLAT), which is the average temperature seen weighted 
by a thermocouple between the Peak and Maximum temperatures 

The metal loss and oxide layer thickness of all rings were measured and used for a comparison with the 
measured online corrosion signal of both alloys from the Savcor sensor. 

Figure 23: Example temperature histogram for SP7 

Averaged values of the signals of the Savcor sensor of 10CrMo and Super304H and of important data of 
the Doosan material  loss probe  derived  at Studstrup power station are  shown in  Table  13. Averaged 
values for the Doosan material loss probe are derived by averaging results from Ring 1 (Super304H) 
and  Ring  2  (10CrMo)  over  the  whole  circumferences.  By  comparing  the  averaged  LPR  signal  (in 
Ohm*cm², which means a lower signal indicates higher corrosion at the metal surface) of both materials 
to the measured metal loss from the Doosan material probe it can be concluded that: 

  Although the averaged metal loss of 10CrMo is much higher (53.1 µm to 7.1 µm) compared to 

Super304H the averaged LPR signal is lower. 

  A  higher  averaged  oxide  layer  thickness  was  measured  for  Super304H.  The  averaged  value 
however gives an inappropriate result of the actual state of the oxide layer. For  Super304H at 
one  position  a  very  thick  oxide  layer  was  detected  while  at  other  positions  it  was  almost 
negligible.  Therefore,  interpretation  of  the  LPR  value  for  high  alloyed  steels  like  Super304H 
requires special considerations.  

Table  13:  Averaged  values  of  different  data  points  for  10CrMo  and  Super304H  of  the  Savcor  online 
sensor and Doosan material loss probe from Studstrup power station to compare the material influence. 

Savcor online sensor 
Material T 

LPR value  

Probe: 
Averaged 
values: 
Unit 
Time period:  7.2.16 – 
14.3.16 
173000 
105160 

10CrMo: 
Super304H: 

[Ohm*cm²] 

[°C] 
7.2.16 – 
14.3.16 
550 
550 

Doosan material loss probe 

Metal loss  

[µm] 
15.12.15 – 
06.04.16  
53.3 
7.1 

Oxide layer 
thickness 
[µm] 
15.12.15 – 
06.04.16  
66.5 
81.0 

Material T 
(FLAT) 
[°C] 
15.12.15 – 
06.04.16  
~550 
547 

The  shown  data  supports  the  experience  of  the  different  behaviour  of  the  sensor  signal  for  different 
materials gained throughout the project. Knowledge of the used alloy on the sensor head is crucial for 
correct interpretation. Additionally the corrosion data derived by the Doosan material loss probe can be 
used  to  improve  existing  or  create  new  corrosion  models  for  use  in  plant  design,  plant  integrity  and 
assessing the impact of a change of fuel or operation. 

Fuel and ash sampling 
Samples of the combusted coal, straw and ash from the electric precipitator were frequently collected 
and analysed for their composition at the laboratory at TUM. No clear correlation was obtained, mostly 

44 

 
 
 
because the frequency of the chlorine measurement is for too small to be related to the online signal. A 
reasonable frequency results is a very high effort, which was not possible to be performed during this 
project. 

Discussion of results from Studstrup power station 
An in-depth analysis of the online corrosion signals was performed. Due to the low straw share co-fired 
no distinct peaks could be derived. Operability was shown even during soot-blowing and after shutdowns 
and both sensors showed comparable results. Comparisons of the online corrosion signal with material 
loss  data  of  the  long-term  probe  support  the  result  that  higher  alloyed  steels  are  more  difficult  to 
interpret and possibly other reactions influence the derived corrosion signal. 

2)  Fusina campaign 

For  the  measurement  at  Fusina  Power  Plant,  BBS  manufactured  a  corrosion  probe  with  a  sensor 
consisting of 10CrMo9-10 electrodes. Measurements took place until 15.03.2017 for approx. 3800 hours. 
The installation point was located in the convective area, near the superheater bundles. Table 14 gives 
an overview of the conducted measurements. 

Table 14: Overview measurements at Fusina power station in the OnCord project 

Long-term measurements 
Measurement 

Online Corrosion Sensor 
BBS 

Responsible company 

Measurement period 

Babcock Borsig Steinmüller  28.09.2016 – 15.03.2016 

The material temperature was set to 500°C during October 2016 and increased to 600°C on the 4th of 
November 2016. The probe was installed in the superheater section at a flue gas temperature of approx. 
900 – 1000 °C. The online corrosion signal was very high in the beginning of the measurement period 
and stabilised only after the temperature change to 600°C. No clear reason for this behaviour was found. 
May be a protecting layer of some kind has built up at the steel surface. A comparison with boiler data 
was performed for the period from 1.12.16 – 28.02.17. Figure 24a) and b) show the online corrosion 
signal together with the boiler load (electrical).  

Figure 24: Corrosion value and boiler load Fusina power station, a) 26.01.16 – 28.02.17, b) 09.02.17 – 
28.02.17 

a) 

b) 

The boiler load is fluctuating due to a night and day shift. It can be seen how the sensor signal follows 
the  load  changes.  The  overall  signal  value  is  much  higher  when  compared  to  the  data  derived  at 
Studstrup power station. There are two possible explanation to this suggested by the authors: 

1.)  The  flue  gas  temperature  is  higher  at  the  measurement  location  in  Fusina  power  station 
compared to the flue gas temperature at the measurement location in Studstrup power station 
(approx. 1000 °C compared to approx. 830 °C). The flue gas temperature has a high impact on 
the corrosion value. 

2.)  The very high signal from the beginning of the measurement period indicates a generally higher 

corrosion rate in Fusina. 

45 

050100150200250300350012345678926.11.201616.12.201605.01.201725.01.201714.02.201706.03.2017Boiler Load [MW_el]Corrosion Value [mA/V]Corrosion ValueBoiler_Load05010015020025030035000,511,522,508.02.201713.02.201718.02.201723.02.201728.02.2017Boiler Load [MW_el]Corrosion Value [mA/V]Corrosion ValueBoiler_Load 
 
 
 
 
Fuel and ash sampling 
Samples of the combusted coal, RDF and ash were frequently collected and analysed. No clear correlation 
was obtained. 

Discussion of results from Fusina power station 
One online corrosion sensor of BBS was installed in Fusina power station operated by Enel. After some 
difficulties  during  the  first  measurement  month,  the  signal  stabilised  and  a  similar  behaviour  as  in 
Studstrup  power  station  was  observed.  Due  to  the  low  share  of  the  critical  fuel  in  the  fuel  blend 
combusted no distinct influence of the co-combustion on the signal could be indicated. Nevertheless, one 
important result is that the sensor signal can stabilise at high values. 

3)  Compostilla campaign 

RSE  corrosion  monitoring  system  had  been  installed  at  “Compostilla  II”  power  plant  (Spain)  for 
characterising the metallic materials in the membrane wall of the boiler when pet-coke and coal are fired 
in co-combustion in the plant, with the aim of evaluating pet-coke influence on corrosion behaviour of 
waterwall  tubes.  For  this  purpose,  a  long-term  campaign  has  been  launched,  exposing  corrosion 
monitoring probes into the boiler. The corrosion measurement at Compostilla power station is an offline 
one, consisting  in the installation  of  4 offline  corrosion monitoring probes, developed and realized by 
RSE, equipped with samples of the material of interest (SA-210 carbon steel). 

During week 29/2016 the corrosion monitoring system had been assembled on plant, set up and started 
up. The probes had been installed in the combustion chamber, just above the refractory area, where the 
material is most subject to corrosion problems. Table 15 shows the measurements and the responsible 
partner. 

Table 15: Overview measurements at Compostilla power station in the OnCord project 

Long-term measurements 
Measurement 

Responsible company 

Measurement period 

Offline corrosion 
measurements 

RSE (Enel subcontractor) 

19.07.2016 – 30.03.2017 
(approximately) 

The schematic layout of the 4 probe positions in the combustion chamber is shown in Figure 25a), with 
probes number 3 and number 4 at the same elevation, about 2 m and 3 m higher than probe number 2 
and number 1, respectively. Suitable devices have been placed on the outer surface of the boiler wall to 
hold probes (see Figure 25). 

Figure  25:  a)  Schematic  representation  of  probes  mutual  position,  b)  Prepared  access  for  probes 
number 2 and number 1. 

a) 

b) 

46 

 
 
 
 
The target temperature of probe samples were set to 400 °C, since that is the thermal working condition 
of tubes constituting the boiler waterwall in the same area where probes had been installed. The metal 
samples mounted on corrosion probes are made with the same material – namely SA-210-C steel – that 
the boiler waterwall tubes are made of. 

During the test, which lasted seven months, the samples underwent the desired test conditions for about 
4300 hours; at the end of this period the probes had been removed and samples had been dismounted 
and sent to the laboratory, where metallographic examinations had been performed. The samples were 
labelled as sample R453/1, R453/2, R453/3 and R453/4. The passive part of the samples and the inert 
central pins were covered by a yellow-reddish layer of products, with localized detachment of products 
in some areas. Figure 26 shows three images related to R453/1 specimen: the aspect of the “as received” 
specimen,  a  macro  view  of  specimen  with  platinum  pin  in  the  middle  and  a  light  optical  microscope 
(LOM) micrograph of the etched specimen. 

Vickers hardness measurements (HV10) were performed on the passive metal close to external surface 
and close to internal surface on both sides of the inert central pin and the results are summarized in 
Table 18 as average value with standard deviation. 

Figure 26: Aspect of as received R453/1 specimen, macro view of the metallographic section and LOM 
micrograph of etched R453/1 specimen. 

Table 16: Hardness measurements 

R453/1 

R453/2 

R453/3 

R453/4 

Ext. 
surface 

Int. 
surface 

Ext. 
surface 

Int. 
surface 

Ext. 
surface 

Int. 
surface 

Ext. 
surface 

Int. 
surface 

Average 
value 

108 ± 1 

108 ± 1 

105 ± 3 

108 ± 2 

117 ± 2 

116 ± 2 

109 ± 1 

109 ± 1 

Discussion of results from Fusina power station 
Performed analysis on all four samples comprised of: 

  Distance of the passive metal surface from the pin reference line, 
  Vickers hardness measurements, 
 

Light optical (LOM) and scanning electron microscope (SEM) characterisation in order to analyse 
the morphology and the composition of the deposited material. 

The  metallographic  investigation  shows  a  regular  oxidation  and  sulphur  assisted  corrosion  of  passive 
metal SA210, with the formation of a compact layer of iron oxide adherent to base metal. Iron oxide is 
externally covered by a thick layer of alumino-silicate including Ca, K, S, Fe that is probably related to 
solid particles present in flue gas. No relevant sign of degradation was detected in the metal structure. 
For the four different points of boiler investigated, no significant difference was found. 

The temperature of the 4 probes had been recorded during the whole test period. Deviations from settled 
temperature are common to all temperature plots recorded and this behaviour is due to changes of the 
boiler working conditions, whereas other deviations depend on local reduction of heat exchange due to 
deposition of material on the single sample. 

47 

 
 
 
 
 
 
Innovative outcome of WP3 
Experiments conducted in the small-scale enabled the evaluation of the corrosion mitigation potential of 
different coals during co-firing. The Al-content and amount of reactive ash containing alumina-silicates 
were observed to have a significant effect. Maximum co-firing shares of straw without corrosion were 
identified.  The  interaction  mechanism  lowering  the  amount  of  gaseous  alkali  in  the  flue  gas  was 
investigated. It was observed that at the conditions usually present at pulverised fuel combustion the 
embedding  in  alumina-silicates  is  most  likely  the  dominant  reaction  while  sulphation  is  only  of  lower 
relevance. 
The  online  corrosion  sensors  used  in  the  small-scale  showed  a  quick  reaction  to  changes  in  the 
combusted fuel mixture. Differences between coals could be observed. A selection of results of the small-
scale tests was published in two papers presented at the INFUB 2017. 
Experiments in  the  PF mid-scale confirmed  the findings of the  small-scale  tests for  one  fuel  mixture. 
Critical co-firing share with straw was identified with > 60 % e.b. The online corrosion sensors reacted 
quickly to changes in the fuel gas atmosphere, too. 
The operability of online corrosion sensors in large-scale PF power stations combusting fuel mixtures of 
coal with low-grade fuel has been shown in the OnCord project. Sensors reacted robust to soot-blowing 
near  their  measurement  location  as  well  as  to  a  shutdown.  Additionally,  the  corrosion  kinetics  were 
investigated  by  a  temperature  variation.  The  corrosion  signal  behaved  according  to  the  expected 
exponential  trend.  The  signals  stayed  stable  during  longer  periods  without  large  external  influences. 
Main  impact  on  the  sensor  signals  was  the  boiler  load,  while  the  low  co-firing  share  did  not  show  a 
significant  effect.  The  comparison  of  long-term  material  loss  tests  with  the  online  corrosion  signal 
revealed the different behaviour of the signal for different alloys mounted on the sensor. Signals derived 
for higher alloyed steels were observed to be more difficult to interpret. This was observed for the first 
time during the OnCord project. 
The behaviour of one online corrosion signal in a different power station with a different fuel mixture 
revealed  the  low  influence  of  the  co-combusted  fuel  when  co-fired  in  low-shares.  Additionally  it  was 
observed that the period after the sensor implementation requires careful observation. 
Corrosion  tests  in  a  pet-coke/coal  power  station  improved  the  availability  of  the  investigated  power 
station and contributed to the understanding of corrosion processes in a high-sulphur environment. 

4.2.4  WP4: Measurements in CFB systems (VTT) 
WP4 focuses to experimental online corrosion sensor  research with circulating fluidised beds (CFB) of 
different sizes: 

  50 kWfuel (so called electrically stabilised pilot reactor at VTT),  
  4 MWfuel (so called industrial pilot plant at Valmet) and  
  158 MWfuel (full scale power plant in Dunaujvaros, Hungary).  

Task 4.1: CFB electrically stabilised pilot scale measurements 
The main objectives of the experiments at the VTT’s 50 kWfuel pilot-scale CFB in Jyväskylä were: 

 

 

To  improve  understanding  of  the  signals  of  the  corrosion  sensor  enabling  its  development  to 
power plant use 
To  find  out  maximum  safe  shares  of  straw  and  SRF  in  blends  with  two  coals  and  to  find 
differences  in  protecting  powers  of  these  coals.  Term  safe  means  combustion  without 
superheater corrosion. 

The experiments were carried out at VTT’s 50 kW circulating fluidised bed reactor (Figure 27). In spite 
of the small size, this unique reactor creates temperature vs. residence time profiles similar to full scale 
CFB and air staging can be carried out as in power plants. In addition, bed material circulation is similar 
to power plants. The fuels selected in WP1 for pilot scale CFB tests were: 

  Columbian bituminous coal (by ENEL) 
  Russian bituminous coal (by Valmet) 
  Straw pellet (also cereal straw IT through IFRF from Italy) 
  SRF (from Finland by Lassila & Tikanoja) 

The basic analyses of the fuels are shown in Table 17. Cl concentrations in the used bituminous coals 
were very low. Coal can sometimes contain up to 0.1 w-% Cl. Low sulphur content in the Russian coal 
may lead to low capability to destroy corrosive alkali chlorides in the furnace. However, the goal of the 
48 

 
 
project was to test two coals with clearly different sulphur concentrations, and therefore the obtained 
coal samples met well this goal. Free alumina silicates in coal ash may destroy effectively corrosive alkali 
chlorides resulting in a good protecting power even without additional contribution from fuel-S.  

Table 17: Main fuel analysis of fuels used in VTT test rig. 

Measurement equipment comprised of: 

  Basic on-line flue gas analysis by conventional methods (IR: CO2, CO, SO2, NOx; paramagnetic: 

O2) (real time)  

Fine particle number analysis (ELPI – Electrical Low Pressure Impactor) (real time) 

  Completing on-line flue gas analysis (HCl, N2O, NO2 etc.) by FTIR-spectroscopy (real time) 
 
  Savcor  Online  corrosivity  sensor  measuring  electrical  resistance  of  deposit  (ER)  and  linear 
polarization  resistance  (LPR)  of  two  metals  (10CrMo9-10  and  AlSI310S)  kept  at  metal 
temperatures simulating the most critical positions of superheaters (real time) 
  Valmet’s KCl Laser (CPFAAS) to measure KCl concentration in flue gas (real time) 
  Deposition  probes  (superheater  simulators)  collecting  deposits  on  metal  rings  kept  at  metal 

temperatures simulating most critical positions of superheaters 

  Concentration of corrosive elements and sulphur in vapours and aerosolic particles (Cl, S, Na, 
K) (fine fly ash fractionation by Low Pressure Impactor (LPI) completed by elemental analysis) 

The realised test matrix consisted of four test sets with different fuels blends and measurements (see 
Table 18). The test set 1 with Columbian bituminous coal – SRF blends with short (1-2 hours) stable 
periods  was  called  “scan  test”.  Such  short  tests  give  concentration  data  of  the  measured  flue  gas 
components  assisting  in  planning  new  sets  of  experiments  with  long-lasting  stable  periods.  Gas 
concentration reach usually balance, whereas the corrosivity sensor may need nearly ten hours to detect 
risk  to  metal  surface,  if  corrosivity  of  the  flue  gases  is  rather  low  (due  to  low  alkali  chloride 
concentrations). The results from these tests are not included into final report as they are not comparable 
with other test sets. 

The  test  sets  2-4  with  Columbian  coal  +  Straw,  Russian  coal  +  Straw  and  Russian  coal  +  SRF 
concentrated more on the performance of the corrosion probe system. Therefore, duration of each test 
with  the  probe  was  much  longer  than  in  scan  tests  (13-21 hours  depending  on  the  fuel  blend).  The 
insertion time of the corrosion sensor was about 14 hours during combustion of coal-straw blends and 
about  8  hours  during  combustion  of  coal-SRF  blends.  Every  experimental  point  was  started  with  a 
changed (new) sensors. The metal temperature of the top of the corrosivity probe was 560 °C. Probe 
was inserted through a port locating between return and secondary cyclones, where the gas temperature 
was about 800 °C (Figure 27). In addition to the corrosivity and flue gas analysis deposits were collected 
on the corrosivity probe and on two “traditional” deposit probes (with metal temperatures of 500 °C and 
450 °C, see their locations in Figure 27). 

Furnace temperature has been presented as function of gas residence time in Figure 28 enabling direct 
comparison to larger reactors and power plants. The conditions in the 50 kW CFB reactor simulate well 
those in CFB power plant furnaces. 

49 

AnalysisunitColombian coalRussian coalStraw pelletSRFHHVMJ/kg, dry30.328.41822.1LHVMJ/kg, dry29.327.416.820.7Ash 815 Cwt% dry8.916.38.312.1Ash 550 Cwt% dry9.116.88.513.8Cwt% dry74.168.344.749.6Hwt% dry4.94.65.46.6Nwt% dry1.72.220.660.8Swt% dry0.880.360.080.34Clwt% dry0.0080.0050.280.59 
 
 
 
Table 18: Tests and blend compositions. 

Figure 27: 50 kW CFB reactor: Samplings and their location in test sets 2-4 with long (12-24 h) stable 
periods. 

50 

Coal 1 - en-%Coal 2 - en-%Biomass 1 - en-%Biomass 2 - en-%Colombian bituminous coalRussian bituminous coalStraw pelletSRFScan-12100 %0 %Scan-2169 %31 %Scan-3150 %50 %Scan-4142 %58 %Scan-5132 %68 %Scan-6121 %79 %Scan-710 %100 %Scan-8                             (bed material change)80 %100 %Scan-910 %100 %Scan-10120 %80 %Scan-11141 %59 %Scan-12150 %50 %Scan-13169 %31 %13100 %0 %21991 %9 %32074 %26 %42160 %40 %52241 %59 %610 %100 %73100 %0 %81987 %13 %91974 %26 %102059 %41 %112141 %59 %121058 %42 %yes13375 %25 %141459 %41 %151330 %70 %16242 %58 %171459 %41 %181342 %58 %19330 %70 %Week 23 -2015Week 38 -2015Week 42 -2015Week 43 -2015noSet 4yesyesnonoSet 3 Set 2Set 1 - ScanTest#Limestone addedDuration - hnoPrimaryair throughgridFluegasfanPrimarycycloneSecondarycycloneGascoolerSamplingports–Beforecooler1.KCl-laser2.Depositprobe450  C / ELPI + DLPIFueland limestonefeedBaghousefilterLoopsealSecondaryand tertiaryairsSamplingports–Betweencyclones3.Depositprobe500  C / Corrosionprobe560  C4.FTIR 
 
 
 
a) – Test set 2 Col / Straw 

b) - Test set 3 Russian / Straw +limestone 

c) – Test set 4 Russian / SRF + limestone 

Figure 28: Temperature vs. residence time data during the tests. 

Key flue gas composition results on corrosion viewpoint have been compared to Cl, S, K and Na mass 
flows in Figure 29. In contrast to HCl, KCl can accumulate Cl to deposits. SO2 is indicator to the presence 
of  SO3.  SO3  sulphates  fast  KCl  and  converts  KCl  to  HCl,  but  SO2  is  slow  in  sulphation.  SO3  can  be 
measured directly only with very high effort. 

51 

00.511.522.533.54650700750800850900950Flue gas recidence time, sTemperature, oC100% Colombian Coal91% Coal - 9% Straw74% Coal - 26% Straw60% Coal - 40% Straw41% Coal - 59% Straw100% Straw pelletBefore cooler:‒ KCl-laser ‒ ELPI / DLPI ‒ deposit probe 450  CBetween cyclones:‒ FTIR‒ corrosion probe 560  C‒ deposit probe 500  C 00.511.522.533.54650700750800850900950Flue gas recidence time, sTemperature, oC100% Russian Coal87% Coal - 13% Straw74% Coal - 26% Straw59% Coal - 41% Straw58% Coal - 42% Straw + Lime41% Coal - 59% StrawBefore cooler: ‒ KCl-laser ‒ ELPI / DLPI ‒ deposit probe 450  CBetween cyclones: ‒ FTIR‒ corrosion probe 560  C‒ deposit probe 500  C 00.511.522.533.54650700750800850900950Flue gas recidence time, sTemperature, oC75% Russian Coal + 25% SRF59% Coal + 41% SRF59% Coal + 41% SRF + Lime42% Coal + 58% SRF42% Coal + 58% SRF + Lime30% Coal + 70% SRF30% Coal + 70% SRF + LimeBetween cyclones: ‒ FTIR‒ corrosion probe 560  C‒ deposit probe 500  C Before cooler:‒ KCl-laser ‒ ELPI / DLPI ‒ deposit probe 450  C010203040506070809010002004006008001000120014001600180020000102030405060708090100KCl (% from max)SO2/ HCl (mg/Nm3dry @ 6% O2)Straw (% in energy)SO2HClKClSO2Emission limit = 150mg/Nm30033501121130021202551020406080100120140160180200220mg/Nm3dry, red. 6% O2SNaKClin <0.62 mm particles010203040506070809010002004006008001000120014001600180020000102030405060708090100KCl (% from max)SO2/ HCl (mg/Nm3dry @ 6% O2)Straw (% in energy)SO2SO2 (limestone)HClHCl (limestone)KClKCl (limestone)SO2Emission limit = 150mg/Nm3213161001845240012122231020406080100120140160180200220mg/Nm3dry, red. 6% O2SNaKClin <0.62 mm particles 
 
 
 
 
 
 
 
 
Figure 29: KCl, HCl, SO2 concentrations and mass impactor sampling followed by analysis of Cl, S, Na 
and K  in under 0.6 µm  particles  during the conducted tests with  Columbian  bituminous coal + Straw 
pellet (up), Russian bituminous coal + Straw pellet (middle) and Russian bituminous coal + SRF (low) 
fuel blends. 

The lower sulphur concentration in Russian coal compared to Columbian coal (see Table 17) produced 
lower  SO2  concentration  to  the  furnace  (Figure  29).  However,  the  increase  in  KCl  concentration  with 
increasing straw portion was similar or maybe even slightly weaker with Russian coal inside range 0-
30 % straw compared to Columbian coal ‒ straw blends. KCl increase in range 0-40 % SRF was rather 
similar with blends containing Russian or Columbian coal. Role of free alumina silicates to destroy alkali 
chlorides during combustion can be clearly more dominating with Russian coal balancing the protection 
capacity of the two coals against Cl corrosion. Limestone increased clearly KCl concentrations with straw-
coal blends. The effect was weaker in the SRF-coal tests. Although limestone can weaken only sulphation 
of alkali chlorides, not alkali capture by alumina silicates. 

The trends of KCl concentrations obtained from impactor sampling were in good agreement with trends 
from direct KCl measurements (in Figure 29): increase in KCl concentration was rather weak in the range 
26-40 % straw (in energy) but a strong increase was found between 40-59 % straw. However, detection 
limit of KCl analyser seems to be lower compared to impactor sampling plus K and Cl analysis to observe 
KCl from  the furnace.  Alkali sulphates found (Figure  29) are  probably sulphation products of KCl and 
NaCl (equation (2), NaCl reacts similarly). Sulphates formed directly from the fuel and after reactions 
with Ca and Mg are present in larger particles than the range shown in Figure 29 (<0.6 µm originating 
from vapours and sulphation products). The measurements with corrosion probe starting always with a 
new sensor revealed the following with Straw + coal blends (Figures 30 - 31): 

 

LPR  signals  maintained  their  maximum  levels  during  combustion  of  26 %  straw,  but  small 
decrease in ER signal after about twelve hours measuring time with Russian coal + straw blend 
(Figure  31)  indicates  some  melt  formation  in  the  deposit  bringing  theoretic  risk  to  the  metal 
damage. 

  All  signals  started  to  decrease  during  combustion  of  40 %  straw,  where  relative  KCl 
concentration (normalized % value) was still rather low. 310S should resist better corrosion than 
10CrMo9-10. Therefore, it is important to analyse metal surfaces and come back to this topic 
later in WP5. 

  All signals started to decrease during combustion of 59 % straw, but the decrease started much 
faster (after ~ 1 h) compared to test with 40 % straw. In addition, the decreasing rate with time 
was much faster than during combustion of 40 % straw. 

  Addition of limestone during feeding of the same straw portion resulted in rather fast drop in all 
signals. This finding is in agreement with increased KCl formation during limestone addition 

52 

010203040506070809010002004006008001000120014001600180020000102030405060708090100KCl (% from max)SO2/ HCl (mg/Nm3dry @ 6% O2)SRF (% in energy)SO2SO2 (limestone)HClHCl (limestone)KClKCl (limestone)SO2Emission limit = 150mg/Nm318151814040200000102030405060708090100110120mg/Nm3dry, red. 6% O2SNaKClin <0.62 mm particles 
 
 
Figure 30: Online corrosion data during the conducted tests with Columbian bituminous coal + Straw 
pellet fuel blends. 

Figure 31: Online corrosion data during the conducted tests with Russian bituminous coal + Straw pellet 
fuel blends. 

53 

 
 
 
 
 
 
 
 
 
 
 
In the tests with SRF + coal blends (Figure 32):  

  Corrosion  probe  signals  maintained  on  their  maximum  level  during  combustion  of  blend 
containing  41 %  SRF  without  limestone  addition  giving  extra  evidence  on  strong  protection 
ability of Russian coal against Cl corrosion despite its low sulphur content. However, addition of 
limestone  generated  some  melt  in  the  deposit  without  changes  in  LPR  signals  (oxide  layers 
untouched). 

  All signals started to decrease fast during combustion of 70 % SRF. 

ER signal started to drop fast during limestone feeding when combusting a blend containing 58 % SRF. 
The decrease in ER signal as function of time was approximate as fast as during combustion of 70 % 
straw without limestone addition, but decrease in LPR signals was slower during 58 % SRF with limestone 
addition compared to 70 % SRF without limestone. 

Figure 32: Online corrosion data during the conducted with Russian bituminous coal + SRF fuel blends. 

Discussion results from tests at VTT’s electrical stabilised test rig 
Four blends consisting of two coals and two low-grade materials were combusted in VTT’s test rig. The 
two coals showed a different protection  ability  against  Cl  corrosion: Coal from  Russia showed a good 
alkali reduction potential despite its low sulphur content indicating the importance of alkali embedding 
in Alumino-Silicates also in CFB systems. Two low-grade materials were investigated for their corrosivity. 
Higher shares of SRF were possible to combust in both blends when compared to straw/coal blends. This 
might  be  attributed  to  the  different  release  behaviour  of  Cl  from  both  low-grade  fuels.  Higher 
concentrations of HCl were measured during SRF co-combustion indicating a lower corrosiveness of the 
flue  gas.  The  results  were  supported  by  measurements  of  the  online  corrosion  sensors.  The  Savcor 
sensor  reacted  quickly  to  changes  in  the  fuel  mixture.  Limestone  addition  resulted  in  more  alkali-
chlorides in the flue gas due to a reduced amount of available sulphur. VTT’s CFB results obtained with 
Columbian  coal  –  straw  blends  were  compared  to  pulverised  fuel  reactor  results  in  TUM  because  the 
same or similar fuels were combusted. Results are found in section 4.2.5. 

Task 4.2: CFB industrial pilot scale measurements 
After the 50 kW combustion tests with VTTs electrically stabilised CFB pilot were carried out, scale-up to 
Valmet’s  4 MW  industrial  scale  CFB-pilot.  The  focus  of  the  combustion  tests  was  on  developing  and 
testing  online  corrosion  measurement  techniques  (Savcor  online  corrosion  sensor),  for  which  other 
measurement data was also gathered for verification.  

54 

 
 
 
 
 
 
The fuels used in the co-combustion tests were Russian bituminous coal, straw (cereal straw IT) and 
solid recovered fuel (SRF) (produced in Finland). The same fuels were also co-fired in the VTT pilot tests. 
A  scheme  of  the  pilot  boiler  is  shown  in  Figure  33.  The  delivered  Russian  bituminous  coal  required 
crushing and the SRF produced by a Finnish supplier needed to be crushed and pelletized. The Straw 
was obtained from Italy (via IFRF) in pellet form and only required unloading at the pilot plant. 

Several measurements were done during the tests and extensive solid sampling carried out for further 
analysis. The measurements and samplings done during the combustion tests comprised of: 

  Deposit probes (Tmat 500 / 560 °C, Tmat 300/350 °C) to evaluate the composition of the initial 

deposit layer, 

LPI, DLPI for fine particle number and composition, 

  Savcor corrosion sensor to derive ER, LPR signals for deposit properties and corrosion, 
  Online gas analysis by FTIR (HCl, SO2, NO, NO2, N2O, CO and O2), 
 
  KCl analyser to derive KCl concentrations online, 
  Valmet Corrored sensor to evaluate the S/Cl ratio online in order to judge on the corrosion risk, 
 

Fuel and ash (fly & bottom) sampled frequently. 

Figure 33: Valmet Pilot boiler 4 MW CFB / 2 MW BFB. 

The tests were divided into two periods: Russian bituminous coal and SRF co-combustion and Russian 
bituminous coal and straw co-combustion. The realized test points carried out during these periods are 
listed in Table 19. Respective test points (fuel shares close enough) than in VTT tests were carried out 
both during SRF and straw co-combustion periods. During the SRF tests, the duration of a test point was 
2.5 – 6 hours, which is sufficient to collect the needed samples and measurement data. The duration of 
the straw test points were approximately 20 hours to ensure proper balancing of the influences caused 
by the agricultural fuel (mainly regarding the fluidised bed interactions). 

Table 19: Test matrix of Valmet 4 MW pilot test. 

Coal share [e.b.] 

SRF share [e.b] 

Use of in-furnace 
limestone 

100 
79 
47 
25 
25 
47 
77 
Coal share [e.b] 

0 
21 
53 
75 
75 
53 
23 
Straw share [e.b] 

100 
81 
81 
62 
41 
21 

0 
19 
19 
38 
59 
79 

55 

X 
X 
X 
Use of in-furnace 
limestone 
X 
X 

Test point abbreviation 

C100 
SRF21 
SRF53 
SRF75 
SRF75L 
SRF53L 
SRF23L 
Test point abbreviation 

C100L 
Str19L 
Str19 
Str38 
Str59 
Str79 

 
 
 
 
 
 
 
 
 
 
 
Fuels  have  analysed  in  detail  also  for  this  measurement  campaign.  Table  20  shows  the  main  fuel 
properties of the three combusted fuels. 

Table 20: Fuel properties of fuels combusted in Valmet test rig. 

Properties 
Moisture 
C 
H 
S 
O 
N 
Cl 
Ash 
LHV, dry 

wt-% 
wt-%, dry 
wt-%, dry 
wt-%, dry 
wt-%, dry 
wt-%, dry 
wt-%, dry 
wt-%, dry 
MJ/kg, dry 

Coal 
8.9 
64.3 
4.4 
0.4 
16.3 
1.9 
0.008 
12.7 
27.0 

SRF 
3.2 
49.7 
7.0 
0.1 
32.2 
0.8 
0.296 
9.9 
22.4 

Straw 
8.0 
41.8 
5.3 
0.1 
43.9 
0.6 
0.236 
8.0 
15.0 

During the test points the pilot boiler was operated with 3.8 MWfuel on average. The bed temperature 
level  was  kept  at  ~850 °C.  During  the  SRF  co-combustion  also  the  residence  time  requirement 
2 s/850 °C was fulfilled.  The profile for SRF blends is very similar with VTT lab scale tests, for straw 
blends temperature levels are 30 – 50 °C lower in the upper part of the furnace. Valmet pilot temperature 
profile for SRF and straw blends is comparable to full scale CFB boiler furnaces. Furnace conditions are 
shown in Figure 34. 

Figure 34: Temperature versus residence time profiles during Coal – SRF (left side diagram) and Coal -  
straw (right side diagram) co-combustion test weeks. The red circle includes measurement zone for 
deposit probes 560 °C and 500 °C, VTT’s ELPI, DLPI & FTIR measurements, KCL-laser and Savcor 
corrosion probe. 

Some problems were encountered in the feeding of SRF. The pelletized fuel feeding was not as stable as 
desired and the fluctuation in the feeding rate caused high CO peaks. This may further on relate to e.g. 
SO2 reduction and concentration in flue gas. The effect of CO peaks was considered in data processing 
and  analysis  of  the  results.  In  the  beginning  of  the  first  test  week  with  SRF,  some  difficulties  were 
encountered in KCl analyser operation. These were fixed and reliable results were obtained from SRF-
shares  of  50 %  (without  limestone)  forward.  Corrored  analyser,  measuring  the  ratio  of  gaseous  and 
56 

 
 
 
 
aerosolic sulphur and chlorine compounds and further on corrosion risk, was operating during all test 
points. 

For online corrosion probe, Savcor, a sufficient layer of deposit is needed to be formed in order to gain 
reliable results. In short 1 – 2 week test runs this may have an effect on the measurement results as 
the test points are rather short compared to long term boiler operation and respective deposit layers. 
Also, as the changes in flue gas atmosphere may be observed after a delay with the online probe,  the 
short test points (such as for the SRF-combustion) may be too fast for this measurements. However, 
these aspects were recognized prior to test runs and the probe was operating as expected and giving 
data in line with other supporting measurements. 

Results are presented separately for coal-SRF mixture and coal-straw mixture. 

The shown KCl value (% - of max) is defined as the test point measurement result, or test period average 
value, divided by the maximum test point average value obtained with coal – SRF or coal – straw mixture, 
defined separately. During SRF – coal test period Corrored analyser sampling probe was blocked and no 
data was recorded. Valid data was obtained from coal and straw co-combustion after the probe type was 
changed and operated differently in higher flue gas temperature at ~350 °C.  

Coal – SRF combustion 

As in the other tests the distribution of the main species affecting superheater corrosion (SO2, HCl and 
KCl) are presented in dependence on the share of SRF in the fuel blend with and without the addition of 
limestone (see Figure 35 
Figure 35). KCl concentrations are presented as relative (% - of max) value. The maximum KCl average 
value was measured at 75 e-% SRF with limestone (SRF75L). 

a) Russian Coal /SRF 

b) Russian Coal / SRF + limestone 

Figure 35 a) SRF co-combustion SO2, HCl & KCl levels, no limestone; b) SRF co-combustion SO2, HCl & 
KCl levels with limestone in use. 

A similar behaviour to the results from the VTT test rig (see Figure 29) is observed. HCl and KCl emissions 
increase with an increasing share of SRF in the fuel blend while SO2 emissions are decreasing. 

Fine particle sampling and measurements were performed by Reteres Oy. Data is shown in  Figure 36 
and Figure 37. The results from water soluble material analysis indicate that only during the combustion 
of the blends SRF75 and SRF75L fine particle (< 0.62 µm) mass exists and contains alkali chlorides. The 
chlorine found in particle sizes > 0.62 µm is in substances not formed from alkali chloride vapours. More 
likely chlorine salt is formed in the sampling phase with Ca. 

57 

 
 
 
 
 
 
 
 
 
Figure 36: Fine particle measurement analysis results for water soluble elements of test points SRF20 = 
SRF21, SRF50= SRF53 and SRF70 = SRF75 without limestone injection. 

Figure 37: Fine particle measurement analysis results for water soluble elements of test points SRF20L 
= SRF23L, SRF50L = SRF53L and SRF70 = SRF75L with limestone injection. 

Different cooled deposit probes with different material temperatures have been used. Results shown in 
Figure 38 display the composition of deposits at the windside of a deposit probe ring cooled to 560 °C 
both of the SRF and the straw co-firing tests. Deposit composition was analysed by SEM/EDX. 

Figure  38:  Deposit  probe  SEM-EDS  analysis  results  of  wind  side  deposits  of  probe  at  controlled 
temperature 560 °C. 

The Savcor probe was installed to the measurement level at the channel after the cyclone. The signals 
for  electrical  resistivity  (ER)  and  linear  polarization  resistance  (LPR)  from  SRF  combustion  period  is 
shown  in  Figure  39.  The  value  is  presented  as  scaled  exponent  values  (log10X)  to  maximum  level 
(log10Xmax) at the start on the 9.11.2016. The signal levels are decreasing while the SRF share increase, 

58 

 
 
 
 
first without limestone. The slope of signals downwards is sinking in the period when the SRF share is 
53 e-% and increases to 75 e-%. The TP310H signal is below the 10CrMo9-10 level during the whole 
week.  This  indicates  higher  corrosion  rate  for  TP310H.  With  limestone  injection  at  SRF75L  the  signal 
drop  even  more  achieving  its  absolute  minimum  level  the  test  point.  While  the  SRF  share  starts  to 
decrease after SRF75L, simultaneously the signals begin to recovery slowly. 

Figure 39: On-line ER and LPR signals during SRF and coal co-combustion, SRF energy shares [e-%] 
also presented. 

Coal – Straw combustion 

Results for the co-combustion tests of Russian coal with straw are presented in the similar structure as 
before, starting with the flue gas measurements, followed by fine particulate matter analysis and deposit 
composition,  finishing  with  measured  online  corrosion  signal.  Additionally,  some  results  of  Valmet’s 
Corrored Sensor are presented. 

The  measured  concentrations  of  SO2,  HCl  and  KCl  for  straw  and  coal  co-combustion  fuel  blends  are 
shown in Figure 40. KCl concentrations are presented as relative (% - of max) value. The maximum KCl 
average value was measured at 79 e-% straw (Str79). 

Figure 40: Straw co-combustion SO2, HCl & KCl levels, with limestone: solid line & solid symbols; No 
limestone: dotted line with hollow symbols. 

Lower HCl emissions compared to SRF/coal co-firing are measured. Furthermore, the HCl emissions are 
slightly decreasing for a share of 79 % e.b. of straw in the fuel blend. The behaviour of SO2 and KCl is 
similar to the one during SRF/Coal combustion. 

59 

 
 
 
 
 
 
The mass of fine particle mode (< 0.62 µm) contains alkalis and chlorides in straw shares and test points 
beyond Str38. In test point Str19 no alkali chlorides could be detected. The amounts are approximately 
ten times higher than in case of SRF blends.  

Figure 41: Fine particle measurement analysis results for water soluble elements of test points 
Straw40=Str38, Straw60=Str59 and Straw80 = Str79, without limestone injection. 

High  amounts  of  K  and  Cl  are  detected  in  the  Str59  and  Str79  cases.  The  high  amount  of  particles 
< 0.62 µm containing most probably KCl leads to a higher share of Cl in the deposits (see Figure 38).  

The signals for electrical resistivity (ER) and linear polarization resistance (LPR) from straw combustion 
period is shown in Figure 42. The starting level of signals has remained in the level after the previous 
SRF test week.  The signal levels of  ER and  LPR  of  10CrMo9-10 are  decreasing  while the  straw  share 
increase. The step downwards in the signals after the straw share increase to 80 e-% is relatively minor. 
10CrMo9-10 sinks below the TP310H signal at test point Str38. This indicates higher corrosion rate for 
10CrMo9-10 at the end of the test period. 

Figure 42: On-line ER and LPR signals during straw and coal co-combustion, straw energy shares [e-%] 
also presented. 

Additionally to the other measurements the Corrored analyser of Valmet (measuring gaseous S & Cl and 
S & Cl in fine particles online by titration) was used during the coal/straw campaign. Corrored analyser 
measurement results are presented below in Figure 43. The measured chorine level shows the increase 
of chlorine in flue gases, both in gas phase and in fine particles. The total amount in Str19L and Str19 
is app. 80 % of total chlorine calculated based on balance. In Str38 and Str59 the amount decreases to 
app. 50 %, respectively. 

60 

 
 
 
 
 
 
Figure 43: Corrored analyser & KCl laser results during coal-straw co-combustion. 

Discussion of results from 4 MW CFB tests 
The target of the test periods of coal – SRF and coal – straw co-combustion in the Valmet’s 4 MW CFB 
pilot plant was to obtain data of the corrosiveness of the environment to superheater materials. High 
temperature superheater corrosion is induced mainly by alkali chlorides. Fuel blends tested were selected 
based on preliminary evaluation calculations and VTT laboratory scale pilot test results presented at D4.2 
(“Data acquisition CFB pilot scale”).  
Valmet  operated  two  new  on-line  measurements,  KCl-laser  and  Corrored-analyser,  which  produced 
successfully  data  of  corrosiveness.  Also,  Savcor  corrosion  probe  continuous  on-line  signal  for  deposit 
and  metal  oxide  layer  activity  was  measured.  To  verify  on-line  measurement  data  fine  particle 
measurements were performed by Reteres and deposit probes were exploited. To ensure satisfactory 
operation and combustion performance of the pilot plant FTIR measurement of the flue gas composition 
was also made. 
The  measurement  and  analysis  methods  used  in  combination  gave  an  idea  of  progress  of  the 
corrosiveness while SRF or straw share was added to the blend. For coal-SRF the critical SRF shares to 
cause high temperature corrosion and enhanced fouling for traditional CFB design were found to be in a 
range  65  ±  10 e-%.  With  furnace  limestone  injection  in  use,  the  safe  SRF  share  should  be  ~10 e-% 
lower for respective CFB design. Savcor corrosion probe signal levels were indicating corrosive conditions 
with  SRF75  &  SRF75L  blends,  which  was  not  that  obviously  indicated  with  other  corrosiveness 
measurement values. For coal – straw fuel blend the corresponding critical share of straw is in a range 
30  ±  10 e-%.  The  CFB  boiler  structure  enables  the  final  superheater  to  locate  in  circulation  material 
return leg after the cyclone, which improves corrosion mitigation capability even more. 
Since  the  fuel  amounts  are  enormous  the  quality  of  fuels  is  fluctuating  continuously  in  full  scale 
commercial  boiler,  as  it  was  seen  even  in  4 MW  industrial  pilot  scale,  the  importance  of  on-line 
measurement  capability  is  evident.  Also  the  need  for  optimise  the  fuel  blend  economically  causes 
continuous changes to fuel procurement and quality. 

61 

 
 
 
 
Task 4.3: CFB power plant measurements 
The  CFB-side  measurements  were  carried  out  in  Hamburger  Hungária  Erömü  Kft  power  plant 
(158 MWfuel),  Dunaujvaros,  Hungary  which  generates  process  steam  and  electricity  to  the  adjacent 
recycled containerboard mill. Various fuels are combusted including coal, SRF, biomass, sludge, wood, 
biogas etc.  

Extensive measurements were conducted during the short-term campaign and the long term corrosion 
measurements was conducted before and after the short-term campaign. The focus in the combustion 
tests was on developing and testing online corrosion measurement techniques (Savcor and BBS online 
corrosion sensor), for which other measurement data is also gathered for verification. Additionally, other 
aspects relating to co-firing of coal with biomass and plastic reject are considered. A similar measurement 
set-up,  but  a  lot  narrower  to  that  of  VTT  pilot  tests  and  Valmet’s  4 MW  CFB  pilot  test  was  used  for 
ensuring the scale-up.  

Several measurements were done during the tests and extensive solid sampling carried out for further 
analysis.  The  measurements  and  sampling  done  during  the  combustion  tests  are  listed  in  Figure  44. 
Location of the measurements are presented as well. 

Boiler data: 

  158 MWfuel 
  62 kg/s steam 
  113 bar 
  520 °C 

Shortterm measurements (week 43/2016): 

Equipment: 

Measurement 

Location 

Note 

FTIR 

Flue gas 
composition 

KCl laser 

KCl conc. 

CP7R 

CP44L & 
CP44R 

Deposit 1 

Deposit 2 

Thermo-
couples 

Deposit 
composition 

Deposit 
composition 

CP43L1 

T = 500°C 

CP35L1 

T = 560°C 

Temperature 

Flue gas T 

Longterm measurements (approx. Sept. 16 – Feb. 17): 

Savcor 

BBS 

Corrosion 
probe 

Online 
corrosion 

Online 
corrosion 

Offline 
corrosion 

CP43L3 

T = 560°C 

CP43L4 

T = 560°C 

CP43L1 

Figure 44: Measurements and their location in the CFB boiler, main boiler data of the used power station. 

All short-term measurements were performed according to the plan. Main results from long-term online 
corrosion tests are presented here.  

The  boiler  operated  on  full  load  mainly  the  whole  measurement  period,  during  which  the  flue  gas 
temperature  after  the  cyclone  and  oxygen  content  were  somewhat  constant,  i.e.  ~870 °C  and 
~3.8 vol % dry. Three remarkable partial load operation periods were seen at the end of 2016 and in 
early 2017 (see Figure 45). Fuel mixture varied a lot during the long-term measurement period. Coal 
and biomass clearly dominated; however, coal being the most often used fuel in the given period. Plastic 
and fibre reject as well as biogas supply or generation from the container board mill were quite stable.  

62 

 
 
 
 
 
 
Figure 45: Boiler operated mainly in maximum load during the long-term measurement period, during 
which  flue  gas  temperature  and  oxygen  were  constant.  Oxygen  content  was  higher  in  partial  load, 
respectively flue gas temperature was lower. 

The recorded boiler data fuel input data were used to interpret the derived online signals. Additionally, 
the  online  signals  were  compared  to  the  results  from  short-and  long-term  deposit  measurements. 
Deposited material on cooled rings was evaluated for its composition. 

The  signals  for  electrical  resistivity  (ER)  and  linear  polarization  resistance  (LPR)  from  long-term  test 
period  are  shown  in  Figure  46.  The  signal  levels  were  stable  at  constant  level  during  ~  21.9.  – 
14.10.2016. After that a rapid drop of all signals was seen on 18.10. and the probe was damaged. A 
new probe set-up was assembled on 25.10. On 27.10. the probe temperature was elevated to 520 °C 
and the ER and LPR signals dropped and stabilised to a lower level. Until 2.11. the probe was in operation. 
Again, a dramatic drop of all signals was seen and no reliable data was obtained after this. A common 
factor for these two total signal collapse and “short circuit” states is that there has been preceding low 
coal  (≤45 e -%)  –  high  bio  and  plastics  periods  for  2-3  days.  The  Savcor  sensor  head  design  is  a 
compromise of both sensitivity and durability. The current design of probe measurement sensor structure 
could be too sensitive for this type of atmosphere and exposure times. 

Figure 46: On-line ER and LPR signals of long term measurement test period. 

Long-term online corrosion measurements with the BBS online corrosion sensor were performed from 
the 11.10.16 – 24.01.17.  

63 

 
 
 
The sensor signal was compared to the main boiler data consisting of: 

  Overall boiler load, 
 
 
 

Energy input of coal into the boiler in MW, 
Energy input of biomass (wood) into the boiler in MW, 
Energy input of reject (plastics and fibre combined) into the boiler in MW. 

Figure 47 shows the online corrosion signal in comparison with different boiler data for the time period 
from 13.10.16 – 18.12.16 (data interruption between the 18.12.16 until the 21.12.16). The black  line 
indicates the online corrosion signal while the orange lines show the different fuel inputs. The total load 
was  constant  for  the  whole  period  (see  Figure  47a).  The  red  line  in  Figure  47b)  indicates  the  border 
where the wood input is larger than 45 % energy basis of the total fuel input. Usually the coal input was 
reduced while the wood input was increased (compare Figure 47b) and c)). The reject input fluctuates 
independently from the other fuel input (Figure 47d). Two distinct peaks and one slight increase of the 
online corrosion signal are visible during this period. From the diagrams, it can be seen that the peaks 
correlate well with the decrease of coal and a simultaneous increase of the wood share. The reject input 
is kept almost constant.  In short-term deposit tests, it was found that chlorine is present in deposits 
sampled  during  a  fuel  blend  consisting  of  29 %  coal,  47 %  wood,  14 %  reject  and  2 %  sludge  (rest 
biogas, all energy basis). This fuel blend is comparable to the fuel mixtures during the signal increase 
detected by the BBS sensor. Therefore, it is assumed that a significant share of chlorine in the deposit 
causes the signal increase. During a wood share larger than 45 % e.b. during a low coal input and a high 
reject  share  (supplying  the  chlorine  to  the  boiler)  the  coal  amount  is  too  low  to  reduce  the  share  of 
gaseous alkali chlorides sufficiently. Increased corrosion at the metal surface detected by the sensor is 
the consequence. 

a)  Total load 

b)  Biomass load 

c)  Coal load 

d)  Reject load 

Figure  47:  Comparison  of  the  BBS  online  corrosion  signal  with  the  fuel  mix  in  the  period  from  the 
13.10.16 – 18.12.16. 

A  similar  behaviour  was  observed  for  two  more  peaks  in  the  BBS  signal  during  the  period  from  the 
21.12.16 – 21.01.2017. The first one appeared during a low load period around the 21.12.16. The total 
load was reduced to approx. 85 MW. The share of wood during this period was higher than 45% e.b. and 
the online corrosion signal increased in the same manner as during the full load period. The second peak 
appeared also during a high wood/low coal phase with a constant amount of rejects.  

64 

02040608010012014016018020000,511,522,5307.10.201617.10.201627.10.201606.11.201616.11.201626.11.201606.12.201616.12.201626.12.2016Load [MW]Corrosion value [mA/V]Corrosion ValueLoad02040608010012014016000,511,522,5307.10.201617.10.201627.10.201606.11.201616.11.201626.11.201606.12.201616.12.201626.12.2016Bio load [MW]Corrosion value [mA/V]Corrosion ValueBio_Load45% woodsharein fuelmix (~80 MW)02040608010012014016018000,511,522,5307.10.201617.10.201627.10.201606.11.201616.11.201626.11.201606.12.201616.12.201626.12.2016Coal load [MW]Corrosion value [mA/V]Corrosion ValueCoal_Load01020304050607000,511,522,5307.10.201617.10.201627.10.201606.11.201616.11.201626.11.201606.12.201616.12.201626.12.2016Reject load [MW]Corrosion value [mA/V]Corrosion ValueReject_Load 
 
 
 
 
Two general behaviours could be observed for the signals for the whole measurement period. Firstly, the 
sensor signal reacted delayed to the change of the fuel composition. Figure 48 shows the peak during 
the 7.12.16 – 15.12.16 in more detail. The sensor signal increased with a delay of approx. 3 days. The 
authors think that this is due to the deposit already present on the sensor head. The chemical species 
causing the corrosion have to diffuse through the deposit layer to the metal surface. Nevertheless, the 
sensor gives important information on the processes on the metal surface.  A corrosion measurement 
system was developed based on these results presented in section 4.2.7. 

Secondly, it can be observed that the sensor signal returns to its base value after all peaks. This indicates 
that the corrosive processes are most likely stopped after the share of coal has been increased again. It 
is therefore assumed that the circular process of the so-called active oxidation (see e.g. [1]) is not yet 
established in such a short time period. This information can be very valuable to boiler operators. 

Figure 48: Detail of peak of sensor signal during the period from the 07.12.16 – 15.12.16. 

Discussion of results from Dunaujvaros power station 
Varying shares of fuel blends used in the boiler (coal, woody biomass, plastic reject and  fibre sludge) 
were combusted during the short-term measurements leading to seven test points. The coal share was 
varied from 29 e-% to 63 e-% and biomass share from 18 e-% to 54 e-%. Also, limestone was used for 
sulphur reduction in furnace during some of the test points. The coal ash content was 14.5 wt.-% d.b. 
and sulphur content 0.55 wt.-% d.b. on average. 

Chlorine was present in deposit samples of 500 °C material temperature during all test points except for 
a  test point  with  63 e-% share  of  coal.  However,  in  deposit  samples of  560 °C  material  temperature 
chlorine  was  neither  found  in  test  point  with  45 e-%  coal.  Savcor  corrosion  probe  signal  levels  were 
decreased indicating somewhat elevated corrosive conditions in case of coal ≤45 e-% blends. Also, the 
corrosion  probe  samples  indicated  corrosion  for  10CrMo9-10  material.  Thus,  depending  on  the 
superheater  material  temperatures  and  grades,  corrosion  might  be  expected  during  the  tested  fuel 
blends of coal ≤45 e-%. 

The BBS online corrosion was implemented in the superheater section. 10CrMo was used as the sensor 
material  at  a  material  temperature  of  560°C.  Four  distinct  peaks  could  be  observed  during  the 
measurement period. All peaks occurred during a period where approx. over 45% e.b. wood was fired 
while the input of coal  was reduced  and  the  input  of rejects was constant. By comparing the deposit 
composition of the short-term deposit tests with the online signal it can be concluded that during those 
periods the share of chlorine in the deposits might be increased causing corrosion at the metal surface. 
This could be indicated by the online corrosion signal. The online corrosion signal showed a short time 
delay after the share of wood was increased. This is probably caused by the time the corrosive species 
need to diffuse  to  the metal  surface. The  sensor  signal returned back to the value before the peaks. 
Therefore, the conclusion was drawn that the amount of chlorine in the deposit was still low enough to 
avoid the formation of the circular process of the so-called active oxidation. 

65 

02040608010012000,20,40,60,811,230.11.201605.12.201610.12.201615.12.2016Biomass [MW]Corrosion Vlaue [mA/V]TimeTime delay~ 3 days45% woodsharein fuelmix 
 
 
Innovative content of WP4 
Online corrosion data and analysis results of the samples taken during pilot-scale co-combustion tests 
of coals with two low-grade fuels.  Combining all the data improved significantly understanding of the 
signals (ER and LPR) from the corrosion sensor. In contrast to earlier way of thinking, deposit properties 
(composition,  share  of  melt,  etc.)  can  participate  to  the  formation  of  both  ER  and  LPR  signals.  This 
finding is essential and can be utilised in further development of the corrosion sensors. Pilot plant results 
were completed with power plant tests including use of two different corrosion sensors.  

In the 50 kW CFB, where each test was started with a clean sensor top, signs of corrosion were measured 
after a critical limit in the portion of biomass in the studied blends. KCl was simultaneously found in the 
flue  gas.  The  used  Russian  coal  showed  a  good  corrosion  mitigation  potential  despite  its  rather  low 
sulphur  content.  In  consequence,  the  ash-related  reactions  (contributed  mostly  by  active  aluminium 
silicates) can dominate over sulphation in destruction of alkali chlorides during co-firing biomass with 
coal. The studied SRF might be less corrosive compared to the studied straw due to a lower release of 
alkali chlorides from the SRF sample during combustion. Limestone addition lead to an increased KCl 
formation and increased risk to corrosion due to decreased concentration of active sulphur in the flue 
gas for sulphation of alkali chlorides.  

In the industrial scale (4 MW) CFB pilot plant, Valmet Corrored Analyser was tested in combination with 
the KCl-laser and the Savcor online corrosion sensor. This sensor had larger dimensions compared to 
the sensor in the 50 kW tests and its top was kept  untouched  when changing biomass portion in the 
blends.  These  tests  have  been  described  in  section  4.2.7.  One  coal  and  two  low-grade  fuels  were 
combusted. A similar behaviour of the low-grade fuels compared to the 50 kW CFB  was  found: straw 
was  indicated  to  be  more  corrosive  than  the  SRF  studied  due  to  higher  measured  concentration  of 
chlorine in the collected deposits. 

During  the  CFB  power  plant  tests  a  holistic  measurement  campaign  in  a  difficult  environment  was 
performed.  Two  online  corrosion  sensors  were  implemented.  In  contrast  to  pilot-scale  findings,  the 
Savcor sensor design was observed to be problematic during combustion of very difficult fuel mixtures 
in  power  plant  scale.  The  results  will  be  used  for  future  optimisation  of  the  sensor.  The  BBS  sensor 
measured a critical co-fired wood share of approx. 45 % e.b. with a simultaneous low coal share and 
high reject share. After a short delay caused by diffusive effects due to the already deposited material, 
the sensor signal indicated corrosive processes on the metal surface. The sensor signal returned to the 
base value after the fuel mixtures was changed to a safe composition. The formation of the cycle process 
of high temperature chlorine corrosion could be avoided. This result was used for the development of 
the corrosion mitigation strategies presented in section 4.2.7. 

66 

 
 
 
 
 
 
4.2.5  WP5: Data evaluation, comparison and assessment (TUM) 
All experimental results of WP1, 2, 3 and 4 are collected in WP5. 

Task 5.1: Database 
All data from fuel analysis conducted in WP1 was included in the first part of the database. A second 
database has been established by TUM and includes all relevant results from tests from WP3 and WP4 
in the form of diagrams. Suitable diagrams (see e.g. section 4.2.3) were evaluated during one project 
meeting and set-up by the relevant partners. The databases is available through the data-sharing system 
to every partner. Both databases were created in the form of an Excel-File.  

Task 5.2: Evaluation of results 
Data from all conducted experiments was used to identify correlations between fuel indices and blending 
ratios to the online corrosion signals. Here results from the tests at TUM are summarised. Additionally, 
the influence of chlorine in the deposit on the online corrosion signals in VTT’s test rig is presented here. 
Finally, selected results from different firing systems and scales were compared in order to evaluate the 
impact of combustion technology on the corrosion. 

Correlation of fuel composition with online corrosion signals 
For an in-depth investigation of the fuel composition on the online signals derived in TUM’S EFR the so-
called change rate of the online corrosion signal after a blend change was calculated. It describes how 
fast the derived online signal is changing after the fuel mixtures has been adjusted. Figure 49 shows the 
calculation scheme and the results for all straw/coal blends (online signals are shown in Figure 10). 

Figure 49: a) Calculation of the change rate of the online corrosion signal in TUM’s EFR experiments after 
a blend change; b) Calculated change rates for the different fuel mixtures. 

a) 

b) 

A  comparable  change  rate  after  the  change  to  pure  straw  combustion  in  all  three  measurement 
campaigns was observed. The change rate was used to compare different selected fuel indices to the 
online corrosion signal and therefore to assess the corrosivity of the fuel blends. One of the most known 
fuel  indices  is  the  ratio  of  the  Sulphur  to  chlorine  content  in  the  fuel  on  a  molar  basis  developed  by 
Salmenoja  [2]. It assesses the sulphation potential (reaction (1)  and (2)) of  a fuel.  In  Figure  50a) a 
decreasing behaviour with a higher straw share can be observed for all blends. Usually a value below 2 
is associated with occurring corrosion in the superheater region. Very high S/Cl ratios are derived for 
the pure coals (the S/Cl ratio is usually not applied to coals but for higher chlorine fuels). A slightly better 
sulphation of the Columbian Coal in the blends was calculated due to its higher S-content. 

67 

C2C1t2t1mA/VtCorrosionValue𝐶ℎ𝑎𝑛𝑔𝑒 𝑟𝑎 𝑒= 𝐶2 𝐶1 2  101002003004005006007008000,00,10,20,30,40,50,60,70,80,91,027.6.16 0:0027.6.16 12:0028.6.16 0:0028.6.16 12:0029.6.16 0:0029.6.16 12:0030.6.16 0:0030.6.16 12:001.7.16 0:001.7.16 12:00Temperature [°C]Corrosion Value [mA/V]Cor_ValueT_HeadT_MatCalculationforinitial 2h periodper blend.00,0050,010,0150,020,0250,03ColumbianSouth AfricanEl CerrejonChange rate Corrosion value [mA/V per h]1007560400 
 
 
Figure 50: a) S/Cl ratio calculated for all combusted fuel blends in TUM’s EFR; b) Calculated Cl/Al ratio. 

a) 

b) 

As a second fuel index the Cl/Al ratio in the fuel on a molar basis was suggested in the project in order 
to  evaluate  the  alkali  embedding  potential  of  the  coals  and  fuel  blends.  An  increasing  value  was 
calculated (see Figure 50b) with an increasing straw share indicating a lower available amount of reactive 
Al-Silicates per KCl. The South African Coal showed the lowest values indicating the most available Al 
per Cl. The change rate was correlated according to equation (7) to the derived results of the fuel indices. 
Results are listed in Table 21. Additionally, the correlation to the Al-content in the dry fuel is shown. 

Table 21: Correlation coefficients of selected fuel data to the change rate of the online corrosion signal. 

Columbian 
-0.71 
Change rate to Al-content 
-0.33 
Change rate to S/Cl ratio 
Change rate to Cl/Al content  0.97 

South African 
-0.72 
-0.28 
0.99 

El Cerrejon 
-0.73 
-0.33 
0.98 

The  first  two  investigated  correlations  show  negative  correlation  coefficient  indicating  an  opposite 
behaviour. The correlation coefficient of the Al-content to the signal change rate is closer to -1 then the 
one of the S/Cl ratio. This leads to the conclusion, that the Al-content has a higher influence than the 
S/Cl ratio and is therefore most likely more suitable when assessing the corrosivity of a fuel blend in PF 
systems. The newly introduced parameter Cl/Al shows a correlation value very close to +1 indicating a 
very good correlation. 

Comparison deposit composition to online corrosion signals 
For a further assessment on the reliability of the online corrosion signal, the composition of the deposit 
sampled during the  different combusted blends  was compared to the signal  behaviour.  Here selected 
results from VTT tests are summarised.  

Cl deposition is often strongest on side position of the superheater tube. Therefore, deposit compositions 
at  that  position  of  the  material  sleeves  for  selected  elements  (including  Cl)  are  presented  here  as 
comparison  to  the  measured  LPR  signals.  Alloy  310S  has  known  to  have  high  corrosion  resistance 
whereas  10CrMo  (as  a  cheaper  alloy)  is  more  sensitive  to  corrosion  under  conditions  where  deposit 
composition is risky. 

For Columbian coal- straw blends 13 – 26 % straw in energy content did not bring Cl on the deposits 
(see Figure 51). Therefore, ER did not detect electrically conducting deposit (melt) on the sensors nor 
LPR  signals  showed  corrosion  on  10CrMo  and  310S  sensors  (see  Figure  30).  40 %  straw  in  energy 
accumulated some Cl (0.4 wt-%) in the deposit (see Figure 51) and caused a drop in both ER and LPR 
signals  (see  Figure  31).  Conducting  deposit  clearly  started  some  reactions  on  the  sensor  surface. 
Increasing straw energy portion to 59 % led to clearly stronger Cl deposition (3.6 wt-% at side position) 
and clearly faster drop of both ER and LPR signals compared to those for 40 % straw (see Figure 31). 

For Russian coal - straw blends, low Cl deposition was also found for a straw energy portion of 41 % 
(see Figure 51). However, the deposit Cl content was ~20 % of the value obtained with Columbian coal. 
59 % of straw with Russian coal in the fuel blend seemed to accumulate more Cl in the deposit than with 
the  Columbian  coal.  Limestone  addition  clearly  led to  more  risky  deposit  because  then  Cl  was  rather 
strongly deposited at 42 % straw portion. Correspondingly both ER and LPR signals showed fast decrease 
as function of time during 59 % straw without limestone addition and during 42 % straw with limestone 
addition. Limestone decreased effectively SO2 concentration in the flue gas, which probably led to lower 
SO3  concentration  and  weaker  sulphation.  LPR  signals  remained  at  maximum  level  for  other  straw 
68 

01020304050607080ColumbianSouth AfricanEl CerrejonS/Cl [mol/mol]10075604000,00,20,40,60,81,01,21,41,6ColumbianSouth AfricanEl CerrejonCl/Al [mol/mol]1007560400 
 
 
 
portions studied, although 10CrMo showed some instability during combustion of straw at 40 % energy 
portion.  

Columbian Coal/Straw blend 

Russian Coal/Straw blend 

Figure  51:  Left:  SEM/EDX  45°  side  deposit  analyses  from  co-firing  of  different  ratios  of  Columbian 
coal/straw blend; Right: Russian coal/straw blend. 

Table 22 shows results of oxide layer thickness measurements from the used corrosivity sensor alloys 
10CrMo9-10 and 310S after each test.  

Table  22:  Corrosion  layer  thickness  measurements  of  the  two  alloys:  10CrMo9-10  and  310S  after 
combustion tests with different Columbian Coal-straw and Russian coal-straw mixtures 

Fuel 

Corrosion layer, µm 

Corrosion layer thickness 
mm/week (normalized)* 

Exp. 
No. 

Columbian 
coal 

Russian 
coal 

Straw  10CrMo9-10  

310S  10Crmo9-10  310S 

average  max 

2 
3 
4 
5 

8 
9 
10 
11 
12* 

91 
74 
60 
41 

0 
0 
0 
0 
0 

0 
0 
0 
0 

87 
74 
59 
41 
58 

9 
26 
40 
59 

13 
26 
41 
59 
42 

NM 
NM 
30 
20 

NM 
15 
90 
100 
70 

- 
NM 
NM 
60 
30 
** 
- 
NM 
20 
160 
140 
90 

NM 
NM 
NM 
NM 

NM 
NM 
NM 
NM 
NM 

0 
0 
0,6 
0,4 

0 
0,2 
1,9 
1,7 
1,5 

0 
0 
0 
0 

0 
0 
0 
0 
0 

* Linear extrapolation. The corrosion (oxide) layer thickness is NOT equal to material loss. 
** Shut down after 2.5 h from the start due to bed agglomeration. Therefore, not measured. 

Missing oxide layer on alloy 310S in spite of the LPR signal decrease is a significant finding indicating 
great importance of deposit properties to LPR signal formation. 

In general, it was observed that the online corrosion signals derived in VTT’s test rig reacted quickly to 
chlorine present in the deposits. The sensors were shown to be capable to detect corrosion on the metal 
surface reliably and even resolve difference between the different co-combusted fuels.  

Comparison between TUM pulverised fuel tests to VTT circulating fluidised bed tests 
The  bottom-up  approach  of  the  project  (see  Figure  1)  enabled  the  comparison  between  scales  and 
different  firing  systems.  Special  focus  was  on  the  comparison  of  the  small-scale  tests  of  TUM  using 
pulverised fuel combustion to the small-scale tests in the circulating fluidised test rig at VTT. Selected 
results are presented here. 

Due to the different temperature distributions in the EFR and VTT’s CFB test rig (see Figure 7 and Figure 
27)  a  different  interaction  behaviour  between  the  combusted  Columbian  coal  and  straw  is  expected. 
Higher  combustion  temperatures  are  expected  to  favour  embedding  reactions  while  in  the  CFB 
temperature window sulphation might be of higher importance.  

69 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
In a first step the flue gas measurements, especially the concentrations of HCl and SO2 of the different 
combusted blends are compared. Figure 52 shows the trend of the concentrations over the straw share 
in the fuel blend. 

TUM PF test rig 

VTT CFB test rig 

Figure  52:  SO2  and  HCl  concentrations  of  the  different  blends  combusted  in  TUM  and  VTT  test  rigs. 
Measured KCl concentration given for VTT’s test rig, KCl concentrations simulated by FactSage for TUM’s 
test rig. 

The left picture shows the concentration of SO2, HCl measured in the EFR of TUM during pulverised fuel 
combustion at a temperature of approx. 1200 °C. Additionally, the KCl concentration in the flue gas was 
calculated by thermodynamic equilibrium calculations conducted with the Software tool FactSage. The 
given values are normed by the maximum value of KCl received for pure straw combustion. An increase 
of the KCl concentration in the flue gas for higher shares than 40 % straw in the blend is visible. 

The right picture shows concentration trends of SO2, HCl and KCl measured in the electrical stabilised 
test  rig  of  VTT  at  a  temperature  of  approx.  800 °C.  KCl  was  measured  by  the  KCl  laser  of  Valmet 
technologies at approx. 730 °C. The overall trends are comparable to the ones of TUM’s test rig. 
The following differences can be observed: 

  SO2 concentrations are slightly lower in the CFB test rig for all measurement points. 
  HCl concentrations are constantly increasing in the CFB test rig, by contrast to the PF system, 

 

where a distinct maximum at 40 % straw is detected. 
The trend for KCl concentrations looks similar for both test rigs with a slightly higher normed 
value for 40 % straw blend in the CFB system and a slightly higher value for the 60 % straw 
blend in the PF systems. However, due to the lack of quantitative values a comparison is difficult.  

Deviations in the SO2 concentrations might be caused by different S content in the combusted coals. 
Comparisons between fuel analysis of the coals combusted at both test rigs directly from the feeding 
lines  show a slightly higher measured  Sulphur  content of  the  coal used  in  the PF systems (1,2 wt-% 
compared  to  0,88 wt-%  in  the  CFB  test  rig).  Additionally,  Sulphur  capture  in  the  bed  material  and 
remaining ash in the fluidised bed system might decrease the measured concentrations. Nevertheless, 
due to the lower temperature in CFB systems the sulphation process of KCl might be enhanced in this 
firing system leading to a consumption of SO2 and production of K2SO4. 

Differences  between  the  trends  of  the  HCl  concentrations  might  be  caused  also  by  the  different 
combustion temperature. Dayton et. al. report a higher HCl concentration and lower KCl concentrations 
for a lower combustion temperature (during switchgrass combustion) in their release studies by using 
an  Molecular  Beam  Mass  Spectrometer  [3].  This  might  be  an  explanation  for  the  higher  HCl 
concentrations  in  the  CFB  test  rig  at  60 %  straw  and  100 %  straw  in  the  blend.  Additionally,  more 
pronounced sulphation could happen at the temperatures present in CFB systems even during pure straw 
combustion. Differences in the fuel composition (Cl content measured at TUM: 0.36 compared to 0.27 wt-
% dry basis at VTT) can furthermore influence the HCl release. 

Secondly, the measured aerosol mass flows and their composition were compared. Particles smaller than 
approx. 1 µm are assumed to be formed from vapours in the sampling line by homogenous condensation 
(also called nucleation) [4]. Most important elements are K, Cl, Na, and S. Flue gas temperature at the 
measurement location in the TUM system was 750 – 850 °C, in the VTT test rig 720 – 730 °C. 

70 

010203040506070809010002004006008001000120014001600180020000102030405060708090100KCl simulated in FACTSAGE [ % from max.]SO2 & HCl [mg/Nm³ @ 6% O2 dry]Straw share on energy basisHClSO2KCl simulated010203040506070809010002004006008001000120014001600180020000102030405060708090100KCl (% from max)SO2/ HCl (mg/Nm3dry @ 6% O2)Straw (% in energy)SO2HClKClSO2Emission limit = 150mg/Nm3 
 
 
Figure 53 shows the measured mass flows and composition of the very fine particle fraction (< 0.8 µm 
for TUM and 0.62 µm for VTT due to different calibrations of the impactor cascade). The amount of the 
main fine particle forming elements are displayed. 

TUM PF test rig 

VTT CFB test rig 

Figure  53:  Fine  particle  (<  0.8  µm)  mass  flows  and  their  composition  of  the  different  blends  with 
Columbian coal and straw combusted in TUM and VTT test rigs. Ratios of the main elements S, Na, Cl 
and K are given; more elements have been detected. 

In the TUM test rig, very low fine particle mass flow were detected for 10 %, 25 % and 40 % straw in 
the blend. The mass flow increased to approx. 77 mg/Nm³ @6% O2 dry for the 60 % straw blend. Results 
are  similar  for  10  and  25 %  straw  in  the  blend  in  the  VTT  test  rig.  The  40 %  shows  slightly  higher 
concentrations while for the 60 % straw blend the mass flow is 211 mg/Nm³ @6% O2, and approx. 5 
times higher compared to the TUM test rig. Measurements were performed very carefully but deviations 
are possible. Next to measurement differences, another reason might be the different alkali embedding 
behaviour in the PF and CFB systems. At the temperatures present in the PF rig embedding reactions in 
alumino-silicates can be the more important mechanism for the reduction of alkali  vapours in the flue 
gas. Sulphation however leads to the formation of K2SO4, which is capable of forming fine particles as 
well.  

In both test rigs, the main component of the very fine particle fraction is K. The amount of Cl and Na is 
comparable in both test rigs. A much higher share of S was measured in the VTT fine particles compared 
to the ones measured at TUM (approx. 25 % to 11 % in the S, Na, K, Cl domain). This strengthens the 
assumption of more distinct sulphation processes in the CFB test rig. 

From the comparison between the PF and CFB tests the assumption is derived that sulphation of alkali 
chlorides might be more dominating in the CFB firing systems than in PF systems due to the different 
temperature  distribution.  Consequently,  the  embedding  reactions  might  be  the  main  alkali  mitigation 
mechanism in PF systems. More comparisons of data derived at TUM and VTT were performed. 

Task 5.3: Microscopy 
A very high amount of deposit probes and sampled fine particulate matter from PF and CFB experiments 
was investigated by electron microscopy. Secondary electron microscopy (SEM) was used to evaluate 
the  oxide  layer  thickness  of  material  rings.  Energy  dispersive  X-ray  spectroscopy  (EDX)  was  used  to 
examine the composition of  deposits on the material  rings and sampled fine particulate matter. Main 
work was performed at the electron microscopes at TUM, University of Augsburg and Abo Akademi. The 
consortium gathered an overview of the used electron microscope devices, the evaluation methods and 
detailed information of all conducted measurements. 

Innovative content of WP5 
The correlation of the derived online corrosion signals to the fuel composition revealed a number of new 
insights on the interaction of low-grade fuels with coal. It was observed that the S/Cl ratio is not suitable 
to describe the corrosion risk in pulverised fuel systems for co-firing of straw with coal. Therefore, a new 
index,  the  Cl/Al-ratio,  was  suggested  describing  the  embedding  behaviour  of  alkali  in  the  coal  ash 
minerals during co-combustion. A high correlation to the derived online corrosion data was shown for 
the first time. 
In order to evaluate the reliability of the online corrosion sensor signals a comparison of the chlorine 
content detected in the deposits to the signal behaviour was conducted. It could be shown that the signal 
indicated corrosion in case of chlorine in the deposit. Furthermore, the sensor could resolve differences 
71 

00012023490003011805010015020010_S25_S40_S60_SFine particles<0.8 µm mg/Nm³@6%O2 dryFuel blendSNaKCl 
 
 
between the low-grade fuels and the coals. All in all the reliability of the sensor could be shown in the 
small- and mid-scale. 
A comparison between tests with similar fuels in the PF and CFB small-scale test rig was done for the 
first time in the OnCord project. A different interaction behaviour of the straw with Columbian coal was 
observed, most likely due to the different temperature distribution in the test rigs. 
Comparisons between the different scales (TUM to USTUTT tests in PF and VTT to Valmet tests in CFB 
combustion) supported the received results and derived conclusions. Results are shown in section 4.2.3 
and 4.2.4. 

4.2.6  WP6: Modelling (IFRF) 
The development of several models for a better understanding of co-firing and corrosion characteristics 
and behaviour was performed in WP6. The following objectives were planned to be achieved: 

Investigation of the release of precursor species. 

 
  Development of a mechanistic model to predict inorganic aerosol formation 
 
 

Thermodynamic equilibrium calculations to understand mechanisms taking place 
Thermodynamic equilibrium calculations to judge on deposit phase & composition (molten, solid, 
mixture etc.) 

Pulverised  fuel  test  rigs  (TUM  EFR,  USTUTT  KSVA,  Fusina  power  station)  have  been  modelled  by 
computational fluid dynamic (CFD) methods. Additionally thermodynamic equilibrium calculations were 
performed. 

Task 6.1: Model setup pilot scale 
A model for the TUM entrained flow reactor and the industrial scale PF test rig (USTUTT 300 kWth test 
rig) was set-up. This included the collection of geometry, boundary conditions etc. A three-dimensional 
model of the test rigs was generated using the commercial software ICEM and ANSYS FLUENT. 

A detailed thermo/-fluid dynamic PF combustion model of  the  Entrained  Flow  Reactor located at TUM 
was  set-up.  The  proposed  CFD  model  can  be  used  to  calculate  thermo/fluid-dynamic  field,  flue  gas 
composition  and  fuel  conversion.  Critical  parameters  needed  for  simulations  were  identified  and 
summarised. Calculations of the combustion of 100% straw at a reactor temperature of 1200 °C, an air-
ratio  of  1.17  and  a  thermal  input  of  8 kW  were  performed.  Additionally,  the  pure  coal  case  and  two 
blends were simulated. The generated data helped to evaluate the combustion behaviour of the different 
fuel blends. 

A  computational  model  of  the  test  rig  of  the  University  of  Stuttgart  has  been  set-up  by  IFRF  using 
software  licensed  by  the  University  of  Pisa.  Relevant  geometrical  data  was  provided  by  University  of 
Stuttgart.  The  burner  was  modelled  with  a  block-structured  grid,  a  grid  independency  study  was 
performed. The calculated flow field was used as an inlet boundary condition for the furnace. The grid 
independency study was performed for the furnace region as well as the burner. Only a quarter of the 
furnace  was modelled to save  computational time  assuming axial  symmetry.  The combustion of pure 
straw was modelled. 

72 

 
Figure 54: a) Grid for burner – KSVA, b) Grid for furnace – KSVA, c) T-field - KSVA, d) CO2 field - KSVA. 

The  results  of  the  calculation  were  used  to  implement  a  model  describing  the  formation  of  aerosol 
particles during combustion. 

In addition to the CFD calculations, thermodynamic equilibrium calculations were performed by VTT and 
TUM using the software FactSage. Results are shown in the next task T6.2. 

Task 6.2: Model development 
Several models were developed and evaluated throughout the OnCord project: 

Investigation and modelling of the release of precursor species, 

 
  Development of a mechanistic model to predict inorganic aerosol formation, 
 
 

Thermodynamic equilibrium calculations to understand mechanisms taking place, 
Thermodynamic equilibrium calculations to judge on deposit phase & composition (molten, solid, 
mixture etc.). 

The developed models are described briefly in the following. Main work was performed by VTT, Valmet 
for CFB firing systems and TUM, IFRF and Enel for PF firing systems. 

1)  Modelling of the release of precursor species:  

To be able to predict the concentrations and the distribution of species relevant for corrosion (K, S and 
Cl and their respective compounds) in the PF pilot scale systems (EFR & KSVA) it is necessary to model 
the release of them. Main release was observed during the devolatilisation phase in the tests in WP1 
T1.3. This result is supported by findings from an extensive literature review. Therefore, the release of 
potassium and chlorine was included in the devolatilisation reaction (for pure straw combustion): 

C1.04H1.92O0.92S0.0004K0.007N0.034Cl0.0015+0.1O2 →  
0.092C6H5OH+0.092CH2O+0.0015KCl+0.005KOH+0.54H2O+0.4CO+0.0004SO2+0.034NH3 

Volatiles  are  treated  as  a  lumped  species  whose  formula  is  derived  from  the  biomass  proximate  and 
ultimate  analysis.  This  reaction  was  used  in  the  simulation  of  the  KSVA.  The  gas-phase  reaction 
mechanism  of  Glarborg  et.  Al.  [5]  was  used  to  model  the  formation  of  sulphates  relevant  for  the 
formation of aerosol species. 

2)  Model to predict aerosol formation 

A mechanistic model to describe the aerosol formation process was developed by IFRF using sub-models 
developed by the University of Pisa. From the CFD simulation a reactor network model (RNA) was applied 
to model the chemical reactions taking place. In RNA the furnace is divided into a small number (NR) of 
connected ideal reactors (perfectly stirred reactors). CFD cells close in composition-thermal space are 
grouped together into NR reactors; the mass fluxes through the network are determined from the CFD 
solution. 

73 

 
 
An energy equation is not solved in the reactor network. Instead, by default, the temperature in each 
reactor is calculated from  the equation of state.  The  reactor pressure is fixed and determined as the 
mass-averaged pressure of the CFD cells in the reactor. The density of each reactor is fixed since both 
the reactor volume and the reactor mass are constant. This approach ensures that heat loss (or gain) in 
the CFD simulation is appropriately accounted for in the reactor network. The impact of the number of 
reactors used was investigated in a parametric sensitivity study. 

The  overall  kinetic  model  used  to  describe  the  entire  combustion  process,  and  thus  the  formation  of 
aerosol precursors in the gas phase was obtained from the union of different modules. Totally, about 
200 species and 3000 reactions were considered; moreover, the species required the definition of proper 
thermodynamic data. 

Figure 55 shows the concentration of KCl and K2SO4 in the burner near region from the calculation for 
the pure straw combustion. 

a)  KCl 
Figure 55: Distribution of KCl and K2SO4 in the furnace. 

b)  K2SO4 

After  calculating  the  distribution  of  relevant  precursor  species,  the  gas-to-particle  conversion  was 
evaluated. By solving the so-called General Dynamics Equation for nucleation, coagulation and superficial 
growth, the concentration of aerosol particles were calculated. 

3)  Development of a reaction scheme to describe flue gas corrosivity in CFB applications 

VTT used thermodynamic equilibrium calculations (using the tool FactSage 6.4) to predict the reactivity 
of Na, K, Ca, Al and Si in the flue gas. Two models were set up to predict the KCl concentration in the 
flue  gas;  model  1  calculating  the  concentration  from  the  overall  fuel  composition  at  the  combustion 
conditions described in T4.1. Model 2 uses so-called optimised reactivity coefficients (ratio of the reactive 
part  of  a  fuel  element)  to  calculate  the  amount  of  KCl  formed.  The  predictive  power  of  the  model  is 
acceptable, with increasing accuracy from Russian Coal – SRF, Columbian Coal – SRF, Columbian Coal 
Straw, and Russian Coal – Straw. 

The calculated reactivity coefficients were additionally compared to results from leaching experiments 
from WP1. The coefficients are consistent with the measured reactivity of these elements using chemical 
fractionation:  trends  of  calculated  reactivity  coefficients  follow  trends  of  results  from  chemical 
fractionation. 

Thermodynamic equilibrium calculations were performed at TUM as well (using FactSage 7.0) to describe 
the  HCl  emissions  measured  in  T3.1.  Figure  56  shows  the  trends  of  calculated  HCl  emissions  in 
comparison with measured values of all three coals. Additionally, the trend of gaseous KCl emissions is 
displayed. Flue gas compositions have been simulated for different blends by changing the share of straw 

74 

 
 
 
in the blend in 5 % point steps from 100 % coal to 100 % straw combustion. Boundary conditions were 
similar as were the databases employed. The following results are visible: 

  All coals show an increasing share of HCl in the flue gas until a certain straw share. For 

Columbian coal the maximum HCl emissions are calculated at a share of approx. 49 %, while 
the share for South African coal is approx. 70 % and 60 % for El Cerrejon coal. 

  After the maximum in HCl emissions the concentration drops quickly for higher straw shares 

with a simultaneous increase of KCl emissions. 

  HCl emissions increase slightly after the minimum when the share of straw reaches 100 %. 

Measured values at port 3 (filled dot) are in fairly good agreement with calculated values. For all blends 
only a small increase of the HCl emissions between port 3 and port 5 (white squares) is observed. 

a)  Columbian Coal 

b)  South African Coal 

Figure  56: Comparison of  measured  HCl  values (by  IR method) at  Port 3 and  Port  5 (see T3.1) with 
chemical  equilibrium  calculations  (simulated  for  1200 °C  and  air/fuel  ratio  of  1.17):  a)  South  African 
Coal / Straw blend; b) Columbian Coal / Straw blend. 

c)  El Cerrejon Coal 

In summary, the difference is small for most of the blends indicating only minor HCl production after the 
heated section in the reactor. This might be caused by the short residence times in the convective parts 
at temperatures enabling gaseous sulphation reactions. Therefore, the conclusion is drawn that under 
the present conditions (resembling pulverised fuel power plant conditions), the embedding of K from KCl 
in alumina-silicates is the main mechanism mitigating the formation of gaseous alkali chlorides.  

Model to understand deposit behaviour on Savcor online corrosion sensor 

VTT set-up a second model using chemical equilibrium calculations to describe the signal behaviour of 
the Savcor probe during the experiments of T4.1. Using the model the melt fraction was calculated and 
correlated with the final resistivity signals measured by the probes. Melt fraction did not correlate with 
the  signals  of  the  probe,  in  contrast  to  expectations  that  molten/liquid  phase  enhance  ionic  charge 
transfer. Statistical analysis provides insight in the key components of the deposits affecting the signal: 
Si, Al, Mg, Ni, Fe  promotes high  values of  resistivity,  while K, Cl and  S reduced  the resistivity  of the 
deposit. 

Task 6.3: Model test power plant 
The objective of this task is to develop a mathematical model of the large-scale pulverised fuel boiler 
used for the test of WP3 Task 3.3. It was decided that Fusina power station is modelled by Enel. 

75 

0100200300400500600700800050100150200250300350400020406080100KCl [mg/Nm³ @ 6% O2 dry]HCl [mg/Nm³ @ 6% O2 dry]Straw share on energy basisColumbian Coal / StrawHCl_IR_Port_3HCl_IR_Port_5HCl_FactSageKCl_FactSage0100200300400500600700800050100150200250300350400020406080100KCl [mg/Nm³ @ 6% O2 dry]HCl [mg/Nm³ @ 6% O2 dry]Straw share on energy basisSouth African Coal / StrawHCl_IR_Port_3HCl_IR_Port_5HCl_FactSageKCl_FactSage0100200300400500600700800050100150200250300350400020406080100KCl [mg/Nm³ @ 6% O2 dry]HCl [mg/Nm³ @ 6% O2 dry]Straw share on energy basisEl Cerrejon Coal / StrawHCl_IR_Port_3HCl_IR_Port_5HCl_FactSageKCl_FactSage 
 
 
 
Enel modelled Fusina power station (co-firing of SRF with coal) starting with the 1-D code PROATESTM to 
verify operational data and give average data as an input for the 3D-CFD simulation using the software 
tool IPSE. Focus was on the evaluation of the boiler behaviour during different loads. Gas temperatures, 
flue  gas  composition  and  velocity  as  well  as  slagging  on  the  boiler  walls  was  investigated  for  three 
different thermal loads (310 MWe (97 %), 250 MWe (78 %), 160 MWe (47 %)).  

Unit 4 of Fusina power station is used to co-fire a share of approx. 5 % e.b. RDF with coal. Unit 4 is a 
tangentially fired dry-bottom  boiler with  a Low NOx Concentric firing system. The  unit  consist  of five 
burner levels. Air staging is realized via secondary air inlets and separated levels of Overfire Air. The 
combustion chamber cross-section is 12.8 m x 10.3 m with a height of 42.7 m. RDF is injected in the 
combustion chamber mixed with the coal in burner level 2 and 3. Figure 57 shows the cross-section of 
the  utilised  computational  grid  and  as  an  example  results  for  the  temperature  field  combined  with 
velocity vectors for the different loads. 

Figure 57: Left: Computational grid with burner levels; Right: Velocity (m/s) – temperature (°C) maps 
in centre line section in y-z direction: (a) 310 MWe; (b) 250 MWe; (c) 160 MWe. 

The data from the simulation  was used to further assess the derived online corrosion signal from the 
measurements at Fusina in T3.3. 

Innovative content of WP6 
Several  models  were  developed  during  the  OnCord  project  helping  to  understand  and  describe  the 
ongoing processes during co-firing of low-grade fuels with coal as well as the derived online corrosion 
signals. The flue gas corrosivity was assessed by chemical equilibrium calculations. Additionally a model 
describing the distribution of corrosive species and particle formation  by  computational  fluid  dynamic 
calculations was developed. 

4.2.7  WP7: Development of corrosion mitigation concepts (Valmet) 
Results from all previous work packages were used in WP7 for the development of corrosion mitigation 
strategies. A combination of a reliable online corrosion monitoring system with intelligent fuel blending 
was used  to develop operational strategies for  highly-efficient power stations co-firing  high  shares  of 
critical fuels. 

76 

 
 
 
 
 
 
 
Task 7.1: Sensor development 
The following conclusions on sensor development and reliability were derived from the conducted power 
plant tests:  

  Comparison  of  the  sensor  signals  derived  by  the  Savcor  and  BBS  sensor  in  Studstrup  power 
station (see section 4.2.3): A similar trend of signals was observed for both sensors, while the 
signal values differed most likely due to the higher sensitivity of the Savcor sensor. Influencing 
boiler data on the corrosion signals was similar for both sensors.  

  Comparison of the signals derived by the BBS sensor in Studstrup and Fusina power station (see 
section 4.2.3): The derived signals (only BBS sensor) differed most likely due to a different start-
up phase of the sensors and a different load behaviour of the power station. No influence of the 
type of the co-combusted fuel could be found. 

Additionally,  both  online  sensors  were  implemented  in  the  CFB  power  station  simultaneously. 
Information on both signals converted to the same unit (Ohm*cm²) and on the combusted fuel mixture 
are shown in Figure 58. 

a)  Sensor signals 

b)  Combusted fuel mix – share of wood and 

rejects 

c)  Savcor sensor signal and wood share 

d)  BBS sensor signal and wood share 

Figure 58: Sensor signals and fuel mixture in Dunaujvaros power station. 

The signals of both sensors are difficult to be compared due to the drift of the Savcor sensor already 
described. Nevertheless, both sensors reacted to a change in the fuel mixture, most likely resulting in 
an increased share of chlorine in the deposit due to a high share of wood and rejects in the fuel mix. A 
balance between robustness and sensitivity needs to be assured in order to avoid drifts but assure an 
accurate signal.  

Online  corrosion  signals  derived  in  the  4 MW  test  rig  of  Valmet  could  be  correlated  very  well  to  the 
measured KCl concentrations in the flue gas. Therefore, the conclusion was drawn that a combination of 
both measurement techniques can give an accurate measurement of the corrosive atmosphere in the 
boiler and corrosive processes on the metal surface of the superheater tubes.  
Both  online  corrosion  sensors  were  able  to  measure  in  large-scale  applications  for  long-term  periods 
even at difficult flue gas atmospheres. Future development options can be the optimisation of robustness 
and sensitivity of the sensor by adjusting the electrode surface area.  

77 

1101001000100001000001000000100000001000000001E+0912.10.201617.10.201622.10.201627.10.201601.11.201606.11.201611.11.201616.11.201621.11.2016LPR [Ohm*cm²]LPR-Signal BBSLPR Signal SavcorNew Savcor sensorimplemented010203040506070809010012.10.201617.10.201622.10.201627.10.201601.11.201606.11.201611.11.201616.11.201621.11.2016Share in combusted fuel mix [% e.b.]Wood shareReject share01020304050607080901001101001000100001000001000000100000001000000001E+0912.10.201617.10.201622.10.201627.10.201601.11.201606.11.201611.11.201616.11.201621.11.2016Share in combusted fuel mix [% e.b.]LPR [Ohm*cm²]LPR Signal SavcorWood shareNew Savcor sensorimplemented01020304050607080901001101001000100001000001000000100000001000000001E+0912.10.201617.10.201622.10.201627.10.201601.11.201606.11.201611.11.201616.11.201621.11.2016Share in combusted fuel mix [% e.b.]LPR [Ohm*cm²]LPR-Signal BBSWood share 
 
 
 
 
 
 
 
 
 
 
Task 7.2: Measurement system 
A holistic and reliable online corrosion measurement system was suggested based on the outcome of all 
measurements conducted during the OnCord project. Different tests have shown the functionality of the 
online corrosion sensors in  PF and CFB system in co-combustion atmospheres (see  section 4.2.3  and 
4.2.4). The online corrosion sensors were capable to detect the critical share of biomass in the fuel mix 
and can help boiler operators to monitor the state of the superheater in their power station. 

However, to enable complete monitoring of the corrosive processes inside the boiler, the sensors are 
combined  with  other  online  measurement  techniques  of  Valmet  Technologies.  Figure  59  shows  the 
desired combination and lists the main features of each device. 

Figure 59: Graphical explanation of the online corrosion monitoring system. 

The Corrored analyser of Valmet Technologies measures the S/Cl ratio in the flue gas online and fine 
particles  online.  This  can  be  used  to  control  the  flue  gas  atmosphere  e.g.  by  adding  additives  or 
combusting a suitable coal in order to optimise the sulphation processes to mitigate gaseous KCl in the 
boiler. This is of higher importance in CFB power stations, however the online measurement of Cl in the 
flue gas can help controlling the flue gas corrosivity also in PF power stations. The KCl analyser (not yet 
commercially available, only used for scientific experiments) of Valmet Technologies can be used to get 
directly feedback on the share of corrosive species in the boiler and can act as a redundant measurement. 
The online corrosion sensors (Savcor & BBS) can help assuring that corrosion at the metal surface has 
been  stopped  e.g.  by  adjusting  the  fuel  mix.  Due  to  their  reaction  delay  an  additional,  faster 
measurement technique is recommended to be used in combination with them. 

The developed online corrosion system can help boiler operators to improve the reliability and operability 
of  their  power  station,  to minimize  unplanned  outages,  optimise their outage planning  and  give  very 
valuable information about the state of the superheater. Cost information on the different devices are 
available on request. The typical utilisation time of each component depends on the corrosivity of the 
flue  gas,  typically  the  online  corrosion  sensors  are  planned  to  be  used  for  approx.  one  year  before 
maintenance  of the sensor head is required.  In  case of  severe corrosion,  utilisation durations can be 
shorter. 

Suggestions for measurement locations and handling 
The  online  corrosion  sensors  should  be  installed  into  the  flue  gas  temperature  region  where  the 
superheaters they simulate are located. When alkali chloride induced corrosion risk is in the focus, this 
typically means flue gas temperatures > 700 °C. For the KCl-analyser, the location is also > 700 °C flue 
gas temperatures. Examples of suitable locations in CFB, BFB and PF- boilers are indicated in Figure 60. 

Since  the  Corrored  analyser  can  exploit  also  condensed  alkali  chlorides  and  sulphates  in  the  titration 
analysis, the measurement temperature can be < 700 °C. The recommended flue gas temperature range 

78 

Online corrosion measurement systemOnline corrosionsensor:-Directmeasurementofthemetalstate-Indicationifcorrosionongoing/stoppedon metalsurface-DifferencesbetweenalloysdetectableKCl analyzer:-Veryquick measurementofKCl concentrationin fluegas-Directfeedbackifcorrosiveatmospherein boiler-Not commerciallyavailableCorroredanalyzer:-Online measurementofS/Cl-ratio in fluegas andfineparticles-Control ofadditive feedingorfuelblending 
 
 
is 500 – 700 °C. In temperatures < 500 °C the ash and condensed vapours can initiate fouling and cause 
plugging on the cooled sampling probe tip. 

A 

B 

A 

B 

B 

A 

a) 

b) 

c) 

Figure  60:  CFB-boiler  (a),  BFB-boiler  (b)  and  PF  boiler  (c)  with  indications  of  suitable  measurement 
locations  for  online  corrosion  sensors  when  monitoring  alkali  chloride  induced  corrosion  (A)  and  for 
Corrored analyser (B). 

In pulverised fuel boilers, flue gas temperatures in the superheater region can be higher compared to 
CFB boilers. Therefore, the BBS sensor was adjusted to withstand temperatures of up to 1100 °C. The 
cooling air distribution inside the sensor head was optimised to assure a uniform temperature distribution 
of the material rings to avoid leakage. The sensor was used at flue gas temperatures up to 1100 °C in 
Fusina power station. 

Depending  on  the  size  of  the  boiler,  a  second  online  corrosion  sensor  can  be  helpful  to  get  a  more 
detailed analysis of the ongoing processes in the superheater region. A non-uniform flow-field inside the 
boiler can lead to local maxima e.g. KCl or CO concentrations. This is usually more likely to happen in 
PF  boilers  caused  by  a  misalignment  of  the  flames  or  by  problems  with  the  fuel  feeding  system. 
Additionally, the temperature field changes during low-load periods. However, during the OnCord project 
none of such influences was observed. In case that the superheater is composed of different alloys, it 
might be necessary to use multiple sensors with different materials mounted on the sensor heads to get 
an accurate signal for the different regions. 

79 

 
 
 
 
 
 
Task 7.3: Operational strategy 
The  development  of  operational  strategies  for  boiler  operators  to  enable  the  co-combustion  of  high 
shares of low-grade fuels with low corrosion risk and at high plant availability was one of the main goals 
of the project. Intelligent fuel blending was proposed to be used in order to mitigate corrosion in the 
superheater area. Results are presented separately for PF and CFB systems. 

Operational strategies for PF power stations 
Based on the results derived in the experiments a fuel blending strategy was developed. Additionally, 
further corrosion mitigation strategies were evaluated. 

The aluminium content in the coals could be identified to have a high influence on the mitigation of alkali 
chlorides in the flue gas and therefore the corrosion risk in PF systems. During the combustion of the 
Columbian  coal  with  the  lowest  aluminium  content in  combination  with  straw,  a  corrosion  signal  was 
detected  already  at  60 %  e.b.  straw  in  the  blend  while  for  the  other  coals  an  increasing  signal  (BBS 
sensor in mA/V) was detected only for 100 % straw combustion. 

Therefore, it is proposed to adjust the combusted coal in a power station based on its aluminium content. 
In  case  of  detected  corrosion  by  the  online  corrosion  measurement  system,  (see  section  4.2.7)  the 
following procedure is suggested: The coal storage yard is organized in a way that the Al-content of each 
stored coal is known. This requires very good organization of the coal yard and detailed knowledge on 
the composition of the stored coals. Power plant operators need to assure that no “rogue” coal is stored 
and plan the storing locations in advance. Furthermore, a sufficient amount each coal is required to be 
stored to be able to adjust the combusted coal. An exact amount of coal to be changed is difficult to 
calculate. It is recommended to change the coal in a way that no KCl is anymore detected. 

In case of a detected corrosive atmosphere (e.g. by an increased amount of KCl in the flue gas detected 
by the KCl analyser) the combusted coal is adjusted so that the alkali capture ability is increased and 
the KCl content in the flue gas is decreased. If the online corrosion sensor signal stays constant at its 
base value, no enhanced corrosive activities at the metal surface are ongoing. If the signal changes, the 
corrosive processes at the metal surface are enhanced. In this case, it is proposed that the operator may 
wait for approx. one week if the signal is going back to the base value, which means that the corrosion 
at the metal surface has been stopped. If the signal keeps high or is even increasing, further mitigation 
strategies must be performed. The procedure is summarised in Figure 61. 

Figure 61: Flow diagram for corrosion mitigation in case of detected corrosive flue gas atmosphere (for 
BBS sensor value). 

Further mitigation strategies are summarised in the following text. 

80 

Corrosiveatmosspheredetectedbyonline corrosionmeasurementsystems.Adjustmentofcoalin fuelblend–> enhancedalkalicaptureability.OCS signaldecreasestobasevalueafter time?YesNoFurther mitigationstrategiesrequired.Nocorrosiveactivites/ corrosiveactivitiesstoppedon metalsurface.Online corrosionsensorsignalconstantat basevalue.00,20,40,60,811,200,20,40,60,811,200,20,40,60,811,2 
 
 
Reduction of share of co-fired low-grade fuel: 

A reduction of the co-fired share leads to a reduced release of corrosive species and therefore to reduced 
corrosion. However, a high share of low-grade material is usually desired to be combusted due to lower 
fuel costs and possible incentives. Therefore, this solution can be seen as a final solution if the boiler is 
endangered. 

Intensified soot-blowing in affected areas: 

Soot-blowing can remove the deposited material on the superheater tubes. This can also remove the 
corrosive materials in case it is performed at the right time. Usually soot-blowing is performed either on 
a regular basis or in case the steam production is reduced. The online corrosion sensor can help to plan 
the soot-blowing sequences. In case of a detected corrosive activity on the metal surface soot-blowers 
can  activated.  However  these  measure  needs  to  be  combined  with  a  strategy,  which  reduces  the 
concentration  of  gaseous  alkali  chlorides  in  the  flue  gas,  otherwise  the  alkali  chlorides  may  deposit 
directly on the blank metal surface causing possibly even higher corrosion rates. 

Insertion of additives: 

In some cases, the addition of specially designed additives can help reducing the share of gaseous alkali 
chlorides in the flue gas. There are usually two types of additives based on reaction (1) and (3). The first 
group (e.g. Ammonium Sulphate also known as ChlorOut@, Ferric Sulphate or pure Sulphur etc. known 
as  Valmet  Corrostop®  and  FuelDiet®)  releases  Sulphur  in  the  flue  gas  enabling  enhanced  sulphation 
reactions. The second group (e.g. coal fly ash, kaolin etc.) adds reactive aluminium silicates in the boiler 
enabling embedding reactions of alkali chlorides. This strategy however requires further equipment for 
additive  dosing  and  insertion  and  is  usually  performed  during  pure  biomass  combustion  or  waste 
incineration. 

Fuel pre-treatment to lower share of corrosive species: 

The properties of the co-combusted low-grade fuel can be adjusted by pre-treatment methods to lower 
their corrosive potential. This includes e.g. leaching out alkali chloride in straw by leaving it on the field 
after  harvest  until  it  rains.  Further  possibilities  are  newly  developed  pre-treatment  technologies  like 
torrefaction,  hydrothermal  carbonization  and  steam  explosion.  These  technologies  are  currently 
investigated e.g. in the Horizon2020 project Biofficiency. 

Operational strategies for CFB power stations 
As described in section 4.2.4, in the full-scale CFB measurement campaign a decrease of the coal share 
in the fuel blend was observed to increase the corrosion risk based on the online corrosion sensor signal. 
In case higher steam parameters are desired in boilers using similar type of blends, a corrosion mitigation 
system utilising an online sensor can be implemented to mitigate corrosion risk and optimise the fuel 
blend. 
In the description and figures below the fundamentals of Valmet Superheater KCl corrosion management 
solution (FuelDiet®) are presented, which was further tested and optimised during the OnCord project. 
This  includes  fuel  blending,  sulphate  based  Corrostop®  additive,  and  Corrored  boiler  atmosphere 
measurement based corrosion risk calculations and rate predictions. 

The  FuelDiet®  KCl  Corrosion  solution  provides  tools  to  control  the  risk  of  alkali  chloride  induced 
superheater  corrosion,  save  in  fuel  costs,  increase  transparency  in  power  plant’s  daily  fuel  and 
combustion management and gives real time estimate of the remaining lifetime of the superheaters. 
The “Action based on real-time sensing”- solution consists of the following blocks: 

1.) 
2.) 
3.) 

FuelDiet® knowhow and software platform for data analyses and decision-making. 
New type of sensors to gather information of the process atmosphere. 
Measures  and  remedies  like  FuelDiet®  control  or  additive  feeding  system  and  additives  to 
control the challenge of the high temperature corrosion. 

Monitors visualize the corrosion risk and give information of fuel chlorine content by internally combining 
the signals provided by sensor (Corrored etc.) and plant measurements. Coal properties such as kaolinite 
content  can  be  included  in  the  procedure.  Controlling  of  the  corrosion  risk  is  handled  by  CorroStop® 
additive  or  management  of  the  fuels  by  the  FuelDiet®.  There  is  also  a  solution  variant  for  carbon 
monoxide (CO) control. Additionally, the remaining lifetime of superheater based on corrosion rate per 

81 

 
material grade of individual superheater sections can be predicted. Specialist’s reporting assesses the 
link between fuel quality, process parameters and corrosion. Access to benchmark database (both fuel 
and power plants) is available during this procedure. 

The calculated corrosion risk estimate is based on S/Cl ratio including Cl & SO4 levels, also in aerosols, 
and data from boiler superheater properties and operation. Optionally, other measurements can be also 
added to the calculation procedure. As an example, the obtained corrosion risk value during coal - straw 
co-combustion test in the 4 MW pilot is shown in Figure 62. 

Figure 62: Valmet Corrored signal based Corrosion risk and corrosion rate calculation for two material 
grades during coal-straw co-combustion in the 4 MW test rig. 

Figure 63 and Figure 64 show the concept and two monitors of the KCl Corrosion management system 
of Valmet. 

Figure 63: Valmet FuelDiet® KCl – corrosion mitigation software platform. 

82 

 
 
 
 
Figure 64: a) Valmet FuelDiet® KCl – corrosion superheater lifetime Monitor; b) Valmet FuelDiet® fuel 
blend optimiser Monitor. 

a) 

b) 

Innovative content of WP7 

The corrosion mitigation capability of different coals was shown in WP3 and WP4 in both firing systems. 
Based on the experimental results, different corrosion mitigation strategies were developed during the 
OnCord project. The main result is an intelligent fuel blending strategy based on the fuel compositions. 
In the case of severe corrosion, the co-combusted coal is adjusted to enhance reaction (1) or (3). In PF 
combustion systems coals are chosen based on their content of reactive alumina-silicates, while in CFB 
combustion systems the sulphur content in the coal is also judged to have an impact on the corrosion 
mitigation capability. During the co-firing of suitable coals, very high shares of difficult fuels with a high 
chlorine  content  could  be realised.  This  might  be  used  to  change  the  role  of  coal  in  combustion  with 
biomass from a co-firing fuel to protective fuel and help to improve public acceptance of coal firing in 
power plants. 

The success of the mitigation strategy can be monitored by the online corrosion measurement system 
developed in T7.2. It enables a very detailed and fast assessment of the corrosiveness of the flue gas 
atmosphere and corrosivity  on  the  metal surface.  A systematic guideline for boiler  operators,  how to 
interpret the derived signals and how to react in case of occurring corrosion, was developed during the 
project. 

Valmet  Superheater  KCl  corrosion  management  solution  (FuelDiet®)  corrosion  mitigation  system, 
originally developed for FB-boilers, was further tested and optimised during this project. It gives direct 
information on superheater lifetime predictions and can be used to optimise the fuel mixture in terms of 
costs  and  corrosion  risk.  A  detailed  overview  of  the  cost  saving  potential  is  shown  in  the  following 
chapter. 

83 

 
 
 
 
 
4.3 

Conclusions 

A variety of conclusions can be drawn from the OnCord project: 

  Conclusion on the utilisation of online corrosion sensors in co-firing applications: 

Online  corrosion  sensors  reacted  fast  (even  in  less  than  one  hour)  in  form  of  signal  change  to  the 
appearance of alkali chlorides in the furnace in small and mid-scale PF and CFB test rigs. Alkali chlorides 
bring corrosive Cl to superheater deposits. We suggest that corrosion signals with constant values from 
the beginning of use mean safe conditions to superheater surfaces, which information would be essential 
to safe power plant use. So, these probes might be suitable to such a use even without further product 
development. Instead, the  signal  change  is rather  complicated to explain because  it can mean either 
starting metal damage or undesired changes in deposit composition. On the other hand, these changes 
can  lead  eventually  to  metal  loss.  In  this  view  use  of  signal  change  data  to  power  plant  control  (for 
example by stopping the signal change by fast protective actions) might be the correct way to operate. 
However, the signals may not  always reach  the  original level after removing the corrosive conditions 
from the furnace. Shift of the signals to the original level would clearly mean risk removal, but stop in 
signal change or weak signal shift to safe direction can mean either change of furnace conditions from 
risky to safe or progressing (slow) corrosion. There is still need to develop further this technology and 
to further increase understanding of the corrosivity signals. Especially with larger corrosion probes, fast 
signal change requires presence of sufficient thickness of deposit on the probe surface which collection 
can  last  several  days.  Results  from  corrosion  probes  obtained  in  power  plants  were  more  difficult  to 
explain than results obtained in pilot scale. Nevertheless, both online corrosion sensors showed a robust 
behaviour during the power plant measurements. Due to low shares of co-fired low-grade materials (< 
10 % e.b.) low corrosion was detected in PF power stations, which is in line with results from small/mid-
scale tests. 

  Conclusion on the critical shares of biomass to be co-fired with different coals and fuel blending 

strategies: 

CFB systems: Most likely higher shares of SRF are possible to be co-fired due to the different behaviour 
of Cl species during combustion compared to Straw. Furthermore, Russian coal showed a better alkali 
capture ability compared to the Columbian coal. Limestone addition led to a higher share of Cl containing 
fine particles. 

PF systems: Shares of up to 60% straw were possible to be co-fired with South African and El Cerrejon 
coal in small/mid-scale systems without detection of corrosion by the online corrosion sensor. During 
the  combustion  of  Columbian  coal  the  sensor  signal  increased  already  during  60%  coal  combustion. 
Feasible shares of straw are higher compared to CFB systems possibly due to the higher temperatures 
leading to better alkali capture. 

Those results might be used to change the role of coal in combustion with biomass from a co-firing fuel 
to protective fuel and help to improve public acceptance of coal firing in power plants. Detailed results 
achieved in both firing systems will help power plant operators to plan and optimise their fuel mixture 
concerning cost savings and corrosion mitigation. 

  Conclusion on developed online corrosion measurement system: 

The developed and presented online corrosion measurement system is capable to detect corrosion risk 
and ongoing processes both in the flue gas and at the metal surface. During the project, the applicability 
and reliability were shown. It can be used to control the suggested fuel blending strategies online and 
help to increase the share of biomass in the power system. 

  Conclusion on modelling activities: 

The  modelling  activities  supported  the  results  from  experiments.  Especially  chemical  equilibrium 
calculations proved to be very valuable when describing different occurring phenomena. The corrosion 
mitigation capability of different coals could be identified. Furthermore, models describing the flue gas 
composition with focus on corrosive species were developed and further optimised. 

84 

 
 
 
 
4.4 

Exploitation and impact of the research results 

The project results were exploited in various ways presented in this section. 

Actual applications: 
Two online corrosion sensors, one from project partner Babcock Borsig Steinmüller (Germany) and one 
from Savcor Oy, a subcontractor to Valmet Technologies (both Finland), were tested intensively during 
the OnCord project. Both sensors were shown to be ready for utilisation in large-scale power stations. 
In  addition,  testing  validated  the  suitability  of  both  the  BBS  Online  corrosion  measurement  and  the 
Savcor sensor also for applications with low corrosion rates. This sensitivity of the corrosion sensors, can 
be  used  in  coal-fired  power  plants  to  monitor  the  influence  of  fuel  changes,  for  example,  switching 
between different coals or to biomass co-firing. Both sensors demonstrated a fast response to changes 
in the boiler flue gas atmosphere, which could enable their use in fuel blend control. Overall, it has been 
demonstrated that the field of application of the sensors includes a wide range of power plants. However, 
currently  the  Savcor  sensor  may  be  too  sensitive  for  the  more  challenging  atmospheres  such  as 
combustion of a fuel mix with high share of recovered fuel. 
The tests with the  Doosan material loss probe  generated corrosion  data from  cooled  probes exposed 
under  real  plant  conditions.  This  data  is  essential  in  the  understanding  of  fireside  corrosion  and  for 
development  of  an  ability  to  predict  it.  These  predictions  are  one  of  the  key  elements  for  material 
selection and improved plant integrity. With improved reliability, availability and maintainability there is 
a clear economic benefit to the plant operators. 
The offline corrosion tests  in the pet-coke/coal  co-firing  power station  increased  the  plant  availability 
and  contributed  to  the  understanding  of  the  corrosion  processes  in  a  high-sulphur  combustion 
environment. 
As one of the main outcomes of the project a holistic online corrosion measurement system for alkali—
chloride induced corrosion was developed. The combination of two different measurement techniques, 
namely Valmet Corrored analyser, and an online corrosion sensor, was demonstrated to be suitable for 
monitoring the corrosivity of the flue gas atmosphere and the ongoing processes on the metal surface. 
The whole system was shown to be reliable in several experiments.  
Based on the results of the experiments, a number of corrosion mitigation strategies for co-combustion 
environments have been developed that boiler operators can use both for PF and CFB firing systems. 
Valmet’s KCl corrosion management solution (FuelDiet®) has been improved to give the best available 
predictions of superheater lifetimes even for cases where the share of low-grade fuel has increased. This 
can  be  used  to  optimise  asset  integrity  management,  for  example,  ensuring  that  the  replacement  of 
superheater  elements  occurs  during  an  appropriate  planned  shutdown  to  avoid  unexpected  losses  of 
operation time. 
Small-scale experiments were shown to be useful as a concept to find maximum low-grade fuel shares 
with  different  coals.  On  the  one  hand,  this  can  be  used  in  the  future  to  assess  the  compatibility  of 
different fuel blends and their suitability for PF or CFB combustion applications. On the other hand, the 
concept  is  suitable  to  evaluate  the  corrosion  mitigation  capability  of  different  coals  giving  valuable 
information  to  power  plant  operators.  Small-scale  tests  can  be  offered  by  research  institutes  to 
characterize different fuel blends prior to their utilisation in power plants. 

Technical and economic potential for the use of the results: 
The project has made a very valuable contribution to the understanding of the processes occurring during 
the co-combustion of low-grade fuels with coal. Boiler operators interested in realising co-firing projects 
can use the derived conclusions to inform their decision making.  
The results from the OnCord project are transferable to a certain extent to the interaction of corrosion 
reducing additives used in pure biomass combustion. As the current situation in Europe is shifting more 
to pure biomass combustion in large-scale applications, the project outcomes can also benefit biomass-
fired power plant operators [6]. 

The cost saving  potential of  the  corrosion management solution is stated  in  Table  23  for an  example 
~140 MW CFB boiler. The payback time for the investment is approximately 1 year. 

85 

 
 
 
 
Table 23: Valmet Superheater KCl corrosion management solution (FuelDiet™) cost saving potential. 

Customer values;  
The savings potential in figures is calculated based on following conditions;  
Unit size and steam parameters:  
Fuels:  

140 MWth 500 °C, 80 bar  
Solid recovered fuel (SRF), Woody biomass 

Operating time:  
Fuel consumption:  
Fuel prices:  

Yearly fuel cost:  
Electricity price:  
Valmet Superheater KCl corrosion management solution 

6000 h/year  
780 GWh/a  
Woody biomass  = 18 €/MWh,  
Peat = 19 €/MWh 
14 M€, if 100 % biomass  
35 €/MWh  
400 k€ 

Savings Potential 
Potential to save on yearly fuel purchasing cost 200 – 700 k€ of 14 M€ per year. 

Extended lifetime of superheater from 10 to 15 years. Potential saving 200 k€ per year 

Increased availability and predictability of superheater lifetime: one unexpected shutdown due superheater leakage will take to 
fix four days. In addition, repair work cost and other indirect cost. 
Potential to save on fuel chlorine analysing cost ~20 k€ per year 
Total potential: 400 k€ per year 

Payback for Valmet Superheater KCl corrosion management solution: ~ 1 year 

The  economic  potential  of  co-firing  in  large-scale  applications  is  strongly  influenced  by  the  subsidy 
situation. Due to the current incentive scheme in different European countries the current trend is to 
retro-fit power stations for the combustion of pure biomass fuels, e.g. wood pellets. China has stated in 
its current 5-Year-Plan (2016-2020) the enhancement of its co-firing activities opening a new market 
for co-firing technologies [7]. 

Any possible patent filing: 
No patent applications were filed during the project. 

86 

 
 
 
 
 
 
 
Publications / conference presentations resulting from the project: 

Wolf  C.,  Spliethoff  H.,  “The  OnCord  project  –  investigation  of  online  corrosion  monitoring  for  the 
combined  combustion  of  coal  with  biomass”,  VGB-Workshop  High  Temperature  Corrosion  in  Biomass 
Power Plants, Oberhausen, 2015 

Yrjas  P.,  Lindén  D.,  Sevonius  C.,  “Capture  of  harmful  gaseous  KCl  with  coal  fly  ash  under  conditions 
typical  for  fluidized  beds”,  International  Technical  Conference  on  Clean  Coal  and  Fuel  Systems,  41st, 
Clearwater, USA, June 2016 

Wolf C., Stephan A., Fendt S., Spliethoff H., “Measuring gaseous HCl emissions during pulverised co-
combustion of high shares of straw in an entrained flow reactor”, 11th European Conference on Industrial 
Furnaces and Boilers, Special Issue Energy Procedia 2017 

Stephan A., Wolf  C, Fendt  S., Spliethoff  H.,  “Online  corrosion measurements in small-  and mid-scale 
during pulverised biomass/coal co-combustion”, 11th European Conference on Industrial Furnaces and 
Boilers, Special Issue Energy Procedia 2017 

Wolf  C.,  Leino  T.  J.,  Stephan  A.  R.,  Aho  M.  J.,  and  Spliethoff  H.,  “Online  corrosion  measurements  in 
combination  with  deposit  and  aerosol  analysis  during  the  co-firing  of  straw  with  coal  in  electrically 
heated, small-scale pulverized fuel and circulating fluidized bed systems,” Energy Fuels, 2018. 

Any other aspects concerning the dissemination of results: 
In March 2017, a one-day workshop was organized in Munich presenting the outcome of the project to 
a  public  audience.  Key  players  of  the  European  industry  currently  involved  in  co-firing  and  biomass 
combustion  projects  joined  the  meeting  (e.g.  Dong  Energy,  Engie  Laborelec,  Valmet  Technologies) 
together with well-known experts from academia (e.g. Technical University of Denmark, University of 
Erlangen etc.). 
Based on the experience and strong collaboration during the project some partners submitted a project 
proposal  to  the  call  H2020-LCE-2016-RES-CCS-RIA.  The  proposal  was  evaluated  positively  by  the 
European Commission and has started on the 01.11.2016. More information can be found on the official 
homepage: www.biofficiency.eu. 
The university partners involved in the project used the project time to supervise bachelor and master 
students. In total seven Bachelor theses and two master theses were successfully finished. 

87 

 
 
 
 
 
 
 
Abbreviations 

BFB 

CCSEM 

CFB 

CFD 

COV 

CPFAAS 

CPU 

D 

d.b. 

DLPI 

e-% 

EDX 

e.b. 

EFR 

ELPI 

ER  

FTIR 

ICP-MS 

ICP-OES 

IR 

KOR 

KSVA 

LOM 

LPI 

LPR 

MS 

PF 

PM1 

RDF 

RNA 

SEM 

SRF 

UMTS 

VAR 

WP 

XRF 

Bubbling Fluidised Bed 

Computer-Controlled Scanning Electron Microscopy 

Circulating Fluidised Bed 

Computational Fluid Dynamics 

Covariance (statistical) 

Collinear Photo-Fragmentation and Atomic Adsorption 
Spectroscopy 
Central Processing Unit 

Deliverable 

dry basis 

Dekati Low Pressure Impactor 

Energy % 

Energy Dispersive X-ray Spectroscopy 

energy basis 

Entrained Flow Reactor 

Electric Low Pressure Impactor 

Electrical Resistivity 

Fourier Transform Infrared Spectroscopy 

Inductively Coupled Plasma Mass Spectrometry 

Inductively Coupled Plasma Optical Emission Spectrometry 

Infrared 

Correlation Coefficient 

Kohlestaubverbrennungsanlage 

Light Optical Microscope 

Low Pressure Impactor 

Linear Polarization Resistance 

Mass Spectrometer 

Pulverised Fuel 

Particulate Matter < 1 µm 

Refuse Derived Fuel 

Reactor Network Approach 

Scanning Electron Microscope 

Solid Recovered Fuel 

Universal Mobile Telecommunications System 

Variance (statistical) 

Work Package 

X-Ray Flourescence Analysis 

88 

 
 
 
 
List of figures 

Figure 1: Designated names of test rigs/power stations and utilised fuels in the OnCord project. ....... 17 
Figure 2: Simplified outline of the processes dominating the high temperature chlorine corrosion. ..... 18 
Figure 3a): Measured current as a function of the applied overvoltage with a linear fit. The gradient of 
the straight line determines the corrosion resistance (or the inverse value called corrosion conductance) 
or rather the polarization resistance and therefore the corrosion rate; b) Sensor head with material rings 
enabling the measurement of the characteristic voltages and currents. .......................................... 19 
Figure 4: Graphical explanation of the research approach of the OnCord project. ............................ 20 
Figure 5a) Ratio of released chlorine during pyrolysis and combustion of three different fuel blends at 
800 °C; b) Ratio of leachable chlorine of the original fuels in water and ammonium acetate (results from 
chemical fractionation tests of Abo Akademi). ............................................................................. 23 
Figure 6: Schematic picture of the reactor with measurement locations and utilised equipment ......... 27 
Figure 7: Temperature-residence time distribution of the EFR firing pure coal/straw, red dot indicates 
the sensor position, square dot indicates the measured temperature by a suction pyrometer during pure 
straw combustion. ................................................................................................................... 28 
Figure 8: HCl (measured by IR and wet chemical method – after cyclone) and SO2 concentrations (port 
3) as function of straw in fuel blend of all coals/straw blends. ....................................................... 29 
Figure  9:  Shares  of  K,  Na,  Cl,  S  in  fine  particles  <  0.8µm  from  electrical  low  pressure  impactor 
measurements – co-firing of all coals with Straw in EFR. .............................................................. 30 
Figure 10: Online corrosion measurement all Coals/Straw: The online signal (yellow), sensor (grey) and 
sensor tip (orange) temperature over the test period are shown. Dotted lines indicate the blend changes.
 ............................................................................................................................................. 32 
Figure  11a)  Temperature  profile  along  the  centreline  of  combustion  chamber,  b)  the  sketch  of 
combustion chamber with indication of conducted measurements.................................................. 33 
Figure  12:  HCl  (measured  by  FTIR  and  wet  chemical  method)  and  SO2  concentrations  as  function  of 
straw in fuel blend with El Cerrejon coal. .................................................................................... 35 
Figure  13:  Shares  of  K,  Na,  Cl,  S  in  fine  particles  <  0.8µm  from  electrical  low  pressure  impactor 
measurements – co-firing of El Cerrejon with Straw in USTUTT test rig. ......................................... 36 
Figure 14: Online deposition measurement at L11 (left) and L15 (right) with different straw shares. . 36 
Figure 15: Overview of the online corrosion measurement: The total test run (left) with the online signal 
(black), sensor temperature (grey) and sensor tip temperature (orange) over the test period. The grey 
dotted lines indicate the blend changes. The right graph shows the average sensor signal for each blend.
 ............................................................................................................................................. 37 
Figure 16: Overview PF power stations in the OnCord project. ...................................................... 38 
Figure 17: Corrosion value (with moving average in green) of the BBS online corrosion sensor and the 
material temperature over the whole measurement period. .......................................................... 39 
Figure 18: Behaviour of the sensor signal and the sensor head temperature after soot-blowing next to 
the BBS sensor. ...................................................................................................................... 40 
Figure  19:  Behaviour  of  the  sensor  signal  and  the  sensor  head/material  temperature  after  a  24h 
shutdown. .............................................................................................................................. 40 
Figure 20: Material temperature variation of the BBS online corrosion sensor, a) Average values of 24h 
time period, b) Arrhenius plot. .................................................................................................. 41 
Figure 21: BBS online corrosion signal, unit converted to Savcor sensor unit. ................................. 42 
Figure  22:  Correlation  matrix  of  the  online  corrosion  signal  with  most  important  boiler  data  for  both 
sensors calculated for the period from the 06.02.16 – 06.03.16. ................................................... 43 
Figure 23: Example temperature histogram for SP7 ..................................................................... 44 
Figure 24: Corrosion value and boiler load Fusina power station, a) 26.01.16 – 28.02.17, b) 09.02.17 – 
28.02.17 ................................................................................................................................ 45 
Figure  25:  a)  Schematic  representation  of  probes  mutual  position,  b)  Prepared  access  for  probes 
number 2 and number 1. ......................................................................................................... 46 
Figure 26: Aspect of as received R453/1 specimen, macro view of the metallographic section and LOM 
micrograph of etched R453/1 specimen. .................................................................................... 47 
Figure 27: 50 kW CFB reactor: Samplings and their location in test sets 2-4 with long (12-24 h) stable 
periods. ................................................................................................................................. 50 
Figure 28: Temperature vs. residence time data during the tests. ................................................. 51 
Figure 29: KCl, HCl, SO2 concentrations and mass impactor sampling followed by analysis of Cl, S, Na 
and K  in under 0.6 µm particles during the  conducted tests with Columbian bituminous coal +  Straw 

89 

 
pellet (up), Russian bituminous coal + Straw pellet (middle) and Russian bituminous coal + SRF (low) 
fuel blends. ............................................................................................................................ 52 
Figure 30: Online corrosion data during the conducted tests with Columbian bituminous coal + Straw 
pellet fuel blends. .................................................................................................................... 53 
Figure 31: Online corrosion data during the conducted tests with Russian bituminous coal + Straw pellet 
fuel blends. ............................................................................................................................ 53 
Figure 32: Online corrosion data during the conducted with Russian bituminous coal + SRF fuel blends.
 ............................................................................................................................................. 54 
Figure 33: Valmet Pilot boiler 4 MW CFB / 2 MW BFB. .................................................................. 55 
Figure 34: Temperature versus residence time profiles during Coal – SRF (left side diagram) and Coal -
 ............................................................................................................................................. 56 
Figure 35 a) SRF co-combustion SO2, HCl & KCl levels, no limestone; b) SRF co-combustion SO2, HCl & 
KCl levels with limestone in use. ............................................................................................... 57 
Figure 36: Fine particle measurement analysis results for water soluble elements of test points SRF20 = 
SRF21, SRF50= SRF53 and SRF70 = SRF75 without limestone injection. ....................................... 58 
Figure 37: Fine particle measurement analysis results for water soluble elements of test points SRF20L 
= SRF23L, SRF50L = SRF53L and SRF70 = SRF75L with limestone injection. .................................. 58 
Figure  38:  Deposit  probe  SEM-EDS  analysis  results  of  wind  side  deposits  of  probe  at  controlled 
temperature 560 °C. ................................................................................................................ 58 
Figure 39: On-line ER and LPR signals during SRF and coal co-combustion, SRF energy shares [e-%] 
also presented. ....................................................................................................................... 59 
Figure 40: Straw co-combustion SO2, HCl & KCl levels, with limestone: solid line & solid symbols; No 
limestone: dotted line with hollow symbols. ................................................................................ 59 
Figure 41: Fine particle measurement analysis results for water soluble elements of test points ........ 60 
Figure 42: On-line ER and LPR signals during straw and coal co-combustion, straw energy shares [e-%] 
also presented. ....................................................................................................................... 60 
Figure 43: Corrored analyser & KCl laser results during coal-straw co-combustion. .......................... 61 
Figure 44: Measurements and their location in the CFB boiler, main boiler data of the used power station.
 ............................................................................................................................................. 62 
Figure 45: Boiler operated mainly in maximum load during the long-term measurement period, during 
which  flue  gas  temperature  and  oxygen  were  constant.  Oxygen  content  was  higher  in  partial  load, 
respectively flue gas temperature was lower. .............................................................................. 63 
Figure 46: On-line ER and LPR signals of long term measurement test period. ................................ 63 
Figure  47:  Comparison  of  the  BBS  online  corrosion  signal  with  the  fuel  mix  in  the  period  from  the 
13.10.16 – 18.12.16. ............................................................................................................... 64 
Figure 48: Detail of peak of sensor signal during the period from the 07.12.16 – 15.12.16. .............. 65 
Figure 49: a) Calculation of the change rate of the online corrosion signal in TUM’s EFR experiments after 
a blend change; b) Calculated change rates for the different fuel mixtures. .................................... 67 
Figure 50: a) S/Cl ratio calculated for all combusted fuel blends in TUM’s EFR; b) Calculated Cl/Al ratio.
 ............................................................................................................................................. 68 
Figure  51:  Left:  SEM/EDX  45°  side  deposit  analyses  from  co-firing  of  different  ratios  of  Columbian 
coal/straw blend; Right: Russian coal/straw blend. ...................................................................... 69 
Figure  52:  SO2  and  HCl  concentrations  of  the  different  blends  combusted  in  TUM  and  VTT  test  rigs. 
Measured KCl concentration given for VTT’s test rig, KCl concentrations simulated by FactSage for TUM’s 
test rig. .................................................................................................................................. 70 
Figure  53:  Fine  particle  (<  0.8  µm)  mass  flows  and  their  composition  of  the  different  blends  with 
Columbian coal and straw combusted in TUM and VTT test rigs. Ratios of the main elements S, Na, Cl 
and K are given; more elements have been detected. .................................................................. 71 
Figure 54: a) Grid for burner – KSVA, b) Grid for furnace – KSVA, c) T-field - KSVA, d) CO2 field - KSVA.
 ............................................................................................................................................. 73 
Figure 55: Distribution of KCl and K2SO4 in the furnace. ............................................................... 74 
Figure 56: Comparison of  measured HCl  values (by  IR method) at  Port 3 and Port 5 (see T3.1) with 
chemical  equilibrium  calculations  (simulated  for  1200 °C  and  air/fuel  ratio  of  1.17):  a)  South  African 
Coal / Straw blend; b) Columbian Coal / Straw blend. .................................................................. 75 
Figure 57: Left: Computational grid with burner levels; Right: Velocity (m/s) – temperature (°C) maps 
in centre line section in y-z direction: (a) 310 MWe; (b) 250 MWe; (c) 160 MWe. ............................. 76 
Figure 58: Sensor signals and fuel mixture in Dunaujvaros power station. ...................................... 77 
Figure 59: Graphical explanation of the online corrosion monitoring system.................................... 78 

90 

 
Figure  60:  CFB-boiler  (a),  BFB-boiler  (b)  and  PF  boiler  (c)  with  indications  of  suitable  measurement 
locations  for  online  corrosion  sensors  when  monitoring  alkali  chloride  induced  corrosion  (A)  and  for 
Corrored analyser (B). ............................................................................................................. 79 
Figure 61: Flow diagram for corrosion mitigation in case of detected corrosive flue gas atmosphere (for 
BBS sensor value). .................................................................................................................. 80 
Figure 62: Valmet Corrored signal based Corrosion risk and corrosion rate calculation for two material 
grades during coal-straw co-combustion in the 4 MW test rig. ....................................................... 82 
Figure 63: Valmet FuelDiet® KCl – corrosion mitigation software platform. ..................................... 82 
Figure 64: a) Valmet FuelDiet® KCl – corrosion superheater lifetime Monitor; b) Valmet FuelDiet® fuel 
blend optimiser Monitor. .......................................................................................................... 83 
Figure 65: Proximate and Ultimate analysis results of the main coals and biomasses used in the small- 
and mid-scale experiments, analysis conducted at TUM laboratory. ............................................... 93 
Figure 66: Results from chemical fractionation analysis of fuel used in small- and mid-scale tests. .... 93 
Figure 67: Distribution of minerals (incl. & excl.) South African coal in wt-% on mineral basis. ......... 94 
Figure 68: Distribution of minerals (incl. & excl.) Columbian coal in wt-% on mineral basis .............. 94 

List of tables 

Table 1: Materials used on the corrosion sensors in the different test rigs / power stations. ................ 7 
Table 2: Selected fuel in small- and mid-scale test rigs ................................................................ 21 
Table 3: Test matrix Corrosion tests at TUM’s EFR in the OnCord project. ....................................... 26 
Table 4: Fuel properties of fuels used in the EFR experiments ....................................................... 27 
Table 5: Share of Cl and K in deposit for Columbian & El Cerrejon/Straw blends in the EFR measured by 
EDX. ...................................................................................................................................... 31 
Table 6: Oxide layer thickness of embedded material rings for Coal/Straw blends in the EFR measured 
by SEM, T-material = 530°C, duration in hours. .......................................................................... 31 
Table 7: Fuel composition and mass/air flows during combustion in the KSVA. ................................ 34 
Table 8: Share of Cl in deposit of the cooled deposit probe of level 16 measured by EDX. ................ 37 
Table 9: Oxide layer thickness of embedded material rings measured by SEM. ................................ 37 
Table 10: Overview measurements at Studstrup power station in the OnCord project. ..................... 39 
Table 11: Arrhenius parameters of the online corrosion signal....................................................... 41 
Table 12: Statistical analysis of the sensor signals (10.02.16 – 10.03.16). ..................................... 42 
Table  13:  Averaged  values  of  different  data  points  for  10CrMo  and  Super304H  of  the  Savcor  online 
sensor and Doosan material loss probe from Studstrup power station to compare the material influence.
 ............................................................................................................................................. 44 
Table 14: Overview measurements at Fusina power station in the OnCord project ........................... 45 
Table 15: Overview measurements at Compostilla power station in the OnCord project.................... 46 
Table 16: Hardness measurements ............................................................................................ 47 
Table 17: Main fuel analysis of fuels used in VTT test rig. ............................................................. 49 
Table 18: Tests and blend compositions. .................................................................................... 50 
Table 19: Test matrix of Valmet 4 MW pilot test. ......................................................................... 55 
Table 20: Fuel properties of fuels combusted in Valmet test rig. .................................................... 56 
Table 21: Correlation coefficients of selected fuel data to the change rate of the online corrosion signal.
 ............................................................................................................................................. 68 
Table  22:  Corrosion  layer  thickness  measurements  of  the  two  alloys:  10CrMo9-10  and  310S  after 
combustion tests with different Columbian Coal-straw and Russian coal-straw mixtures ................... 69 
Table 23: Valmet Superheater KCl corrosion management solution (FuelDiet™) cost saving potential.86 

91 

 
 
 
 
 
References 

[1]  H.  P.  Nielsen,  F.  J.  Frandsen,  K.  Dam-Johansen  and  L.  L.  Baxter,  “The  implications  of  chlorine-
associated corrosion on the operation of biomass-fired boilers,” Progress in Energy and Combustion 
Science, no. 26, pp. 283-298, 2000.  

[2]  K. Salmenoja, Field and laboratory studies on chlorine-induced superheater corrosion in boilers fired 

with biofuels. Ph. D Thesis, Turku: Abo Akademi, Faculty of Chemical Engineering, 2000.  

[3]  D.  Dayton,  R.  French  and  T.  Milne,  “Direct  Observation  of  Alkali  Vapor  Release  during  Biomass 
Combustion and Gasification. 1. Application of Molecular Beam/Mass Spectrometry to Switchgrass 
Combustion,” Energy & Fuels, pp. 855-865, 1995.  

[4]  F.  Frandsen,  Ash  formation,  Deposition  and  Corrosion  when  Utilizing  Straw  for  Heat  and  Power 

Production, Lyngby: DTU Chemical Engineering, 2011.  

[5]  P. Glarborg and P. Marshall, “Mechanism and modeling of the formation of gaseous alkali sulfates,” 

Combustion and Flame, no. 141, pp. 22-39, 2005.  

[6]  W. Livingston, The status of large scale biomass firing, IEA Bionenergy, 2016.  

[7]  L. Xiang, “Technical Routes and Policy Research for Cofiring Biomass with Coal in China,” IEA, Italy, 

2016. 

92 

 
 
 
 
 
 
Appendix 

Results standard and advanced fuel characterisation 

a) 

c) 

b) 

d) 

Figure 65: Proximate and Ultimate analysis results of the main coals and biomasses used in the small- 
and mid-scale experiments, analysis conducted at TUM laboratory. 

Figure 66: Results from chemical fractionation analysis of fuel used in small- and mid-scale tests. 

93 

59,00%51,57%57,70%25,90%37,10%31,30%15,00%11,30%54,50%33,90%11,60%11,00%Russian Coal100100Columbian CoalEl Cerrejon CoalSouth African Coal100100Content in wt-%100%AshFixed-CVolatiles15,00%11,32%11,00%11,60%9,20%8,00%3,30%10,20%1,60%1,47%2,50%1,60%3,90%5,63%5,20%5,30%0,00%0,80%1,19%0,80%Russian Coal0,03%1000,60%10070,60%77,30%Columbian Coal0,00%72,40%South African Coal100100%Content in [wt%]69,40%El Cerrejon Coal1000,01%CHClOSAshN17,65%73,30%74,77%9,00%18,91%6,32%Content in [wt%]100%Straw ITSRF100100VolatilesFixed-CAsh9,00%18,90%38,30%20,10%5,70%6,70%0,80%0,30%Content in [wt%100%SRFStraw IT52,80%0,36%1000,70%0,60%1010,60%46,00%AshClOSNHC05000100001500020000250003000035000SiAlFeTiMnCaMgPNaKSClmg/kg dry fuelSouth African CoalRest fraction, analysedLeached in HClLeached in AcetateLeached in H2OUntreated Fuel05000100001500020000250003000035000SiAlFeTiMnCaMgPNaKSClmg/kg dry fuelColombian CoalRest fraction, analysedLeached in HClLeached in AcetateLeached in H2OUntreated Fuel02000400060008000100001200014000160001800020000220002400026000SiAlFeTiMnCaMgPNaKSClmg/kg dry fuelSRF (Finnish)Rest fraction, analysedLeached in HClLeached in AcetateLeached in H2OUntreated Fuel02000400060008000100001200014000160001800020000220002400026000SiAlFeTiMnCaMgPNaKSClmg/kg ds fuelStraw ITRest fraction, analysedLeached in HClLeached in AcetateLeached in H2OUntreated Fuel05000100001500020000250003000035000SiAlFeTiMnCaMgPNaKSClmg/kg fuelRussian coalLeached in H2OLeached in AcetateLeached in HClRest fraction, analysedUntreated Fuel 
 
 
 
 
 
 
Figure 67: Distribution of minerals (incl. & excl.) South African coal in wt-% on mineral basis. 

Figure 68: Distribution of minerals (incl. & excl.) Columbian coal in wt-% on mineral basis 

94 

0510152025303540UNCLASSIFIED13CA-RICH3SI-RICH1APATITE3OXIDIZED PYRRHOTITE0PYRRHOTITE0CALCITE10Iron Oxide1Quartz130KAOLINITEwt% on mineral basisMinerals10PYRITE2ALUMINOSILICATE2FE AL-SILICATE0K AL-SILICATE1MONTMORILLONITE33DOLOMITE5MIXED AL-SILICATES22.0-46.04.6-10.02.2-4.61.0-2.210.0-22.046.0-400.0051015202530354014ALUMINOSILICATE3FE AL-SILICATE4K AL-SILICATE349MONTMORILLONITE7Iron Oxide2Quartz1MineralsUNCLASSIFIED15SI-RICH8OXIDIZED PYRRHOTITE2PYRRHOTITE6PYRITEMIXED AL-SILICATESwt% on mineral basis0APATITE00CALCITE0DOLOMITECA-RICHKAOLINITE46.0-400.010.0-22.022.0-46.04.6-10.02.2-4.61.0-2.2 
 
 
 
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The main goal of the project was to develop online corrosion 
sensors in pulverised (PF) and circulating fluidised bed (CFB) 
combustion systems in order to fire biomass and other low-
grade fuels safely at high portions in power plants. Another 
important  goal  was  to  find  protective  effects  from  coal  in 
power  plant  furnaces  during  co-firing  biomass  and  other 
low-grade fuels. The obtained results helped significantly to 
understand  the  information  derived  from  corrosion  sensors, 
which is essential to their integration for power plant process 
control.  The  studied  sensors  already  in  their  present  form 
have  potential  for  power  plant  control.  Due  to  the 
complicated  character  of  signal  formation  and  some  needs 
to  change  particular  details  in  the  sensor  construction, 
further  development  work  would  be  useful.  Optimising  the 
sensitivity  and  availability  to  long-lasting  reliable  operation 
in very difficult environments would be key issues.  
The project results can also be utilised to re-consider the role 
of coal in energy production. The new idea to use coal as a 
protective  agent  ensuring  biomass  firing  at  very  high 
portions instead of considering coal only as a co-firing fuel, 
can improve acceptance and public opinion to the use of coal 
in  biomass  power  plants.  The  addition  of  coal  in  many 
biomass  power  plants  has  been  forbidden  so  far  due  to 
fossil  CO2  formation,  which  can  prevent  the  utilisation  of 
many  potential  biomass  fuels  in  those  plants.  Experimental 
work was first conducted with pilot-scale pulverised fuel and 
circulating  fluidised  bed  reactors  with  various  sizes.  The 
obtained  results,  refined  further  with  modelling  tools,  were 
successfully used to conduct the power plant tests. 

Studies and reports