Mitsubishi Heavy Industries Technical Review Vol. 57 No. 1 (March 2020)
1Virtual Measurement and Monitoring Technologies forPlant Water Quality using Software Sensors

TETSUYA SAWATSUBASHI*1 SHINNOSUKE KAJI*1
AYAKO URESHINO*1

SHUJI FUJII*2

RYUICHI MATSUBARA*3

KIMITO FUNATSU*4

Mitsubishi Heavy Industries, Ltd. (MHI) examined the feasibility of the virtual measurementof power plant water quality using software sensors developed in collaboration with the Universityof Tokyo. MHI developed a software sensor model that follows changes in plant operation thatcombines genetic algorithm-based variable selection, a nonlinear regression model and anadaptive model, and verified that the chloride ion concentration, which had been difficult tocontinuously measure, could be virtually measured through analysis using actual plant data.
The Mitsubishi Heavy Industries Group has been expanding its digital solution business thatprovides maintenance and operation supports using AI and IoT. Going forward, MHI will develop
applications that incorporate software sensors to build a power plant water quality diagnosis
system that can monitor state quantities regardless of operator skill.

|1. Introduction
Due to an increase in independent power producer (IPP) projects and a shortage of skilled
operators, there has been increasing need for water quality management support for the stable
operation of power plants(1). Plant water quality is managed using sensor measurement values andcomposition analysis values. However, problems such as bursting, corrosion and scaling ofevaporator tubes may occur. To prevent these problems, continuous monitoring of importantcomposition and concentration values is effective, but there is no specified monitoring device thatachieves such monitoring at reasonable installation and maintenance costs. In addition, if humanand material resources are insufficient, there is a limit to manual management.
Recently, the technological progress of AI and IoT has been remarkable. Virtual
measurement technologies using so-called software sensors, which simultaneously analyze various
types of data, have been actively developed, and their practical use in the chemical and petroleum
industries, semiconductor industry, steel industry and pharmaceutical industry has beenprogressing(2).
As such, we use these software sensors to continuously virtual monitor the importantcomposition and concentration values of the plant water based on the existing plant data, and aredeveloping virtual measurement technologies with the aim of optimizing operation in terms of theperformance and running cost. Figure 1 schematically represents the situation before and after theapplication of the software sensors.
This paper introduces an example of examining the concentration prediction of chloride ions(Cl), which affects the corrosion, etc., of equipment.

*1

Chemical Research Department, Research & Innovation Center*2

Manager, Chemical_Research_Department, Research & Innovation Center*3

Research Manager, Chemical_Research_Department, Research & Innovation Center*4

Professor, School of Engineering, The University of Tokyo

Mitsubishi Heavy Industries Technical Review Vol. 57 No.1 (March 2020)
2Figure 1

Water quality monitoring before and after application of software sensors|2. Software sensors
2.1

Software sensors

Software sensors(3),(4) construct in advance a numerical model based on the correlation
between explanatory variables such as temperature and pressure that can be easily measured
directly by online sensors and objective variables that can be obtained only irregularly by manual
analysis. These software sensors then use the numerical model to estimate the value of theobjective variable to be monitored from the explanatory variables to be continuously measured.
Figure 2 illustrates a conceptual diagram thereof(5). By using the software sensors, it is possible toutilize objective variables as if it were virtually being continuously measured using existing sensor
values.

Figure 22.2

Conceptual diagram of software sensor

Means for selecting explanatory variables in consideration of time delay

When constructing a numerical model using software sensors, it is important to selectexplanatory variables that have a high correlation with the objective variable to improve theprediction accuracy. When measuring a fluid, however, a given sensor value can change due to thetime delay accompanying the movement of the fluid. The delay depends on the position of thesensor in the system and the response delay of the sensor, so when an analysis is performed using
multiple sensor indication values at the same time, problems such as significant errors or failure tofind a correlation occur. If the relationship between the objective variable and the explanatory
variable is known from the physical model or experience, a good model can be constructed byadjusting the time to maximize the correlation. However, when multiple data samples are used orwhen the correlation is unclear, it is difficult to consider the time delay.
As such, this time we adopted a method that considers dynamic characteristics using a
genetic algorithm and selects variables simultaneously (Genetic Algorithm-based Process Variables
and Dynamics Selection, or GAVDS(6)). Figure 3 presents the concept of the time delay in theplant. When predicting variables downstream of the plant (y in Figure 3) from multiple upstreamvariables (x1 and x2 in Figure 3), a model with higher prediction accuracy can be constructed byMitsubishi Heavy Industries Technical Review Vol. 57 No.1 (March 2020)3

predicting y(t) at time t from x1(t-t1) or x2(t-t2) obtained at the same time or a combination of thetwo, rather than from x1(t) or x2(t). However, when a numerical model is constructed including themeasured values of past explanatory variables, the number of explanatory variables becomesenormous, and the processing time becomes huge. Accordingly, by using a genetic algorithm,
which is one of the optimization methods, a combination of an explanatory variable and a timedelay suitable for predicting objective variables was extracted.
In this way, it was made possible to select variables in consideration of the delay of eachsensor reading by using GAVDS.

Figure 3

Example of variable selection by GAVDS(Method to automatically select variables necessary and their time domain for predicting y with highaccuracy)

2.3

Prediction modelA prediction model was constructed using the selected model construction sample(combination of sensor values). In this analysis, we compared three methods: partial least squares
regression (PLS(7)), which is a linear regression method, support vector regression (SVR(8)) andrandom forest (RF(9)) which are a nonlinear regression method.

2.4

Sample selection of data for model creation

In a plant, the relationship between the objective variable to be predicted and the explanatory
variable used for prediction changes over time due to the deterioration of the equipment, theadhesion of scale to the pipes, etc. For this reason, the accuracy of the prediction model createdfrom the initial model construction sample may decrease over time.
To address this problem, software sensors that follow changes in plant operation (hereinafterreferred to as adaptive software sensors) were applied. When using adaptive software sensors,
prediction is performed while selecting and updating the sample used for creating the prediction
model using a predetermined method. As methods for this, the MW (Moving Window) method(10)and the JIT (Just in Time) method(11) have been studied. Figure 4 gives the concept of the MW
method and the JIT method. The MW method selects several highly-correlated samples close to therequired time, constructs a prediction model, and changes the samples one after another over time.
This method has a feature that can easily cope with model deterioration. On the other hand, the JIT
method selects several samples close to the current plant state from the accumulated past samples
and constructs a prediction model. In this analysis, we adopted the MW method, which easily
follows changes over time during operation, as a method for selecting the samples used to construct
the prediction model.

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4

Figure 4

Comparison of adactive models|3. Examination of impurity concentration prediction
3.1

System diagram of examination target and prediction data location

If seawater enters the system for some reason and Cl ions contained in the seawater enter thecirculating water system, Cl ions get mixed in with the steam via the condensing system, feed watersystem, boiler system, steam system, etc., and in the worst case, they are deposited on the steamturbine. For this reason, the Cl concentration of high-pressure drum water was selected as asampling point for detecting seawater leakage from the condenser. Figure 5 indicates thecontamination range at the time when a seawater leak occurs and the prediction target point.
Generally, when the Cl concentration at the outlet of the condensate pump near the condenser canbe detected, a seawater leak can be detected early. However, since the target of this examination isa trace seawater leak with a concentration level that cannot be detected through the electricalconductivity or acid conductivity of the condensate pump outlet water, the Cl concentration in thehigh-pressure drum water, which is concentrated by the evaporation of water in the drum, is used as
the prediction target. By estimating the Cl concentration in the high-pressure drum water, it ispossible to make an early decision on whether to continue plant operation based on the state ofimpurity contamination in order to minimize the risk of steam turbine corrosion due to trace
seawater leaks.

Figure 5

Prediction data locations of Cl concentration at the time when seawater leak occurs

Mitsubishi Heavy Industries Technical Review Vol. 57 No.1 (March 2020)
53.2

Examination target data

This examination used the water quality data of the condensate and feed water systems of anatural gas-fired gas turbine combined cycle thermal power plant. Table 1 lists the data used as
explanatory variables and objective variables. The explanatory variables are electrical conductivity,
pH, etc., the measured values of which can be continuously obtained online from sensors installedin the plant. The objective variable is the Cl concentration of the high-pressure drum water, whichis an optimal variable for understanding the mixing of Cl ions into the circulating water system as
described above.
Table 1

Variables of examination target

Explanatory variables
Objective variables
Continuous data (2344 points)
Manual analysis data (134 points)① Condensate pump outlet CC, EC⑤ High-pressure drum water Cl② Low-pressure economizer inlet CC, EC, DO, pH, N2H4
③ Low-pressure drum water EC, pH, Si④ Intermediate-pressure drum water EC, pH, Si⑤ High-pressure drum water EC, pH, Si⑥ Low-pressure superheater outlet steam CC⑦ Gas turbine combustor inlet steam CC⑧ Reheater outlet steam CC⑨ High-pressure superheater outlet steam CC
CC: acid conductivity, EC: electric conductivity, DO: dissolved oxygen, pH: Potential of hydrogen,
N2H4: hydrazine, Si: silica, Cl: Chloride ion

3.3
(1)

Examination results and considerations
Selection of main parameters using GAVDS
Figure 6 gives the results of GAVDS analysis. On the horizontal axis of this heat map,
zero is the current time, and a negative value on the left corresponds to a past time. The verticalaxis indicates explanatory variables. The portions shown in yellow on this map indicate theexplanatory variables with a high correlation with the Cl concentration of the high-pressure
drum water, which is the objective variable, and their time delays. From the results of thisanalysis, it was found that the electrical conductivity (EC) of the high-pressure drum water at
the time 0 to 10 minutes before and after has the highest correlation with the Cl concentrationof the high-pressure drum water at the current time, and that it is an important explanatory
variable and time delay for predicting the objective variable.

Figure 6

Example of GAVDS analysis results

Mitsubishi Heavy Industries Technical Review Vol. 57 No.1 (March 2020)
6(2)Prediction of Cl concentration in high-pressure drum water
Mainly using the explanatory variables selected in the GAVDS analysis, as shown in
Figure 7, the first half of the dataset was used as the learning data and the second half as theverification data, and the Cl concentration in the second half was predicted.

Figure 7

Learning period and verification period of data to be examined

MW, which is a method that can deal with plant data drift, was used as an applicable model,and PLS, SVR, and RF were used as regression models. Table 2 presents the analysis results.
Within the verified range, it was confirmed that the prediction accuracy became highest when RF
was used as the regression model, and that the Cl concentration could be predicted with acoefficient of determination of R2 = 0.68. Figure 8 shows the trend of Cl concentration in which
the predicted value and the actually measured value are displayed in a superimposed manner.
Table 2SampleMWMWMWCI concentration prediction resultsRegression model
SVRRFPLS

R2
0.62
0.68
0.63

RMSE
0.03
0.03
0.03

R2: Coefficient of determination
RMSE: Root mean square error
MW: Moving Window
PLS: Partial Least Squares Regression
SVR: Support Vector Regression
RF: Random Forest

Figure 8 Trend comparison between measured and predicted Cl
concentration values (analysis method: MW-RF)

Generally, the electric conductivity of high-pressure drum water is used to control theconcentration of chemicals injected, so it was thought that the electric conductivity meter could
detect salt contamination only in the case of a relatively large leak of seawater and that a change inelectrical conductivity due to a trace seawater leak could not be distinguished from that due tochemical injection. As a result of this analysis, it was made possible to predict the Cl concentration

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in high-pressure drum water without being affected by disturbances due to chemical injection byproperly extracting and modeling explanatory variables using GAVDS. We are currently
accumulating data and verifying models with other plant water qualities to further improve theaccuracy of the models.

3.4

System development for monitoring and diagnosing state quantity of plantwater quality

The Mitsubishi Heavy Industries Group is expanding its digital solution business thatprovides plant maintenance and operation supports, and is developing various applications andpromoting their implementation in plants. Figure 9 provides an example of a water quality
diagnosis flowchart combining software sensors. We are developing a hybrid water quality
diagnosis system that uses a state monitoring system and software sensors that is equipped with awater quality monitoring and abnormality diagnosis model that does not depend on operator skill.
By combining conventional water quality monitoring based on empirical rules and physical models
with virtual measurement and water quality diagnosis using software sensors, we will build asystem that contributes to plant operation control.
With a view to future power plant operation using AI and IoT, we will support the continuous
operation of power plants by contributing to reducing the risk of unplanned plant shutdowns andensuring product reliability by utilizing and applying technologies such as software sensors.

Figure 9Example of water quality diagnosis flowchart combining software sensors|4. Conclusion
In recent years, digital solution businesses have been developed to provide maintenance andoperation supports utilizing AI and IoT for thermal power plants, chemical plants, the steel industry
and environmental equipment. Under this situation, the Mitsubishi Heavy Industries Group isdeveloping various applications and promoting their implementation in plants.
This paper summarized the concept and research results of a virtual measurement technology
for plant water quality using software sensors developed in collaboration with the University ofTokyo. At present, we are examining model improvement and combination with abnormality
detection by accumulating data and verification using other plant water qualities, and working on
the implementation of this technology in a plant water quality diagnosis system. We plan to apply
this technology to thermal and nuclear power plants, which will lead to safe operation andmaintenance activities of the equipment.

Mitsubishi Heavy Industries Technical Review Vol. 57 No.1 (March 2020)
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