November 2014 G-1



# Appendix G: Illustrative Biogenic Process Attribute s

Table of Contents

1. Introduction ......................................

................................................... ................................................... ..................... G-2
2. Framework Representation of Feedstock Carbon Losses and Products for Forest- and Agriculture-derived Feedstocks ....................

................................................... ............................................. G-3
2.1. Equations Underlying P and L

.................................................. ................................................... ................ G-4
2.1.1. P

.................................................. ................................................... ................................................... ............. G-4
2.1.2. L

.................................................. ................................................... ................................................... ............. G-5
3. Evaluation of Post-Conversion Products ............

................................................... .......................................... G-5
3.1. Co-firing in Electric Generating Units (EGUs) .....

................................................... .............................. G-6
3.1.1. Estimated P

Term Values ......................................

................................................... ........................... G-7
3.1.2. Discussion ........................................

................................................... ................................................... .... G-8
3.2. Pyrolysis .........................................

................................................... ................................................... ................ G-9
3.2.1. Estimated P

Term Values ......................................

................................................... ........................ G-10
3.2.2. Discussion ........................................

................................................... ................................................... . G-11 3.3 Conversion of Forest-Derived Industrial Byprodu cts ............................................... ............................. G-11
3.3.1 Estimated P

Term Values ......................................

................................................... ........................ G-12
3.3.2 Discussion ........................................

................................................... ................................................... . G-13
4. Evaluation of Pre-conversion Products and Losses Du ring Transportation, Storage, and Processing ........................................

................................................... ................................................... .............. G-13
4.1. Feedstock Losses during Transportation, Storage and Processing ....................................... ... G-14
4.2. Possible Methods for Developing Illustrative LOSS Values for Onsite Storage and Processing ........................................

................................................... ................................................... .......... G-15
4.3. Representative Values for Storage Losses ..........

................................................... ............................. G-17
4.4. Discussion of Other Losses: Transportation and Harv est ............................................... ............. G-20
4.4.1. Transportation ....................................

................................................... ............................................... G-2 0
4.4.2. Harvest ...........................................

................................................... ................................................... .... G-21
4.5. Representative Factor Discussion ..................

................................................... ..................................... G-21
5. References ........................................

................................................... ................................................... .................... G-21
6. Addendum I. Literature Reviewed for Storage and Pro cessing at Stationary Sources .............. G-25
7. Addendum II. Literature Review of Dry Matter Losses from Woody Biogenic Material Storage ...................

................................................... ........................................ G-27November 2014 G-2

7.1. Introduction ......................................

................................................... ................................................... ........ G-27
7.2. Storage Losses ....................................

................................................... ................................................... ...... G-27
7.2.1. Forest Biomass Storage Methods ....................

................................................... ........................... G-28
7.2.1. Storage Time and Challenges in Tracking Biomass Inv entory ......................................... G- 33
7.3. References ........................................

................................................... ................................................... .......... G-34


## List of Tables Table G-1. Carbon Content of Different Biogenic Fee dstocks. .......................................... ................................ G-6 Table G-2. Sample P for Ash Information for Low Eff iciency Boiler. ................................... ........................... G-8 Table G-3. Example Estimated Range of P Values for Co-Firing Biomass in Electrical Generating Units. ......................

................................................... ................................................... . G-8 Table G-4. Sample P Information for Pyrolysis of Co rn Stover. ........................................ ............................. G-10 Table G-5. Example Representative Values for Storag e Losses for Woodchips in the Northeast (based on Figure G-3). ............................

................................................... ................................................... .. G-19 Table G-6. Dataset Sources and Cases by Source for Storage and Processing at Stationary Sources. .................

................................................... .......................................... G-25 

## List of Figures Figure G-1. Diagram of Pyrolysis Process. .........

................................................... ................................................... .. G-9 Figure G-2. Data Points for Herbaceous Crops Storag e for Western (W), Southeastern (SE), Northeastern (NE) and Southwestern (SW) Regions. St orage losses are not clearly distinguishable by region or storage type (N = 69).

.................................................. ......................... G-18 Figure G-3. Data Points and Trendlines for Storage Losses for Woodchips in the Northeast (N = 29). ........................

................................................... ................................................... G-18 Figure G-4. Simplified Principle of Degradation Beh avior (Wihersaari, 2005) .......................... ............ G-31 

## 1. Introduction
The purpose of this appendix is to describe possibl e methods for developing values for biogenic process attributes, which are attributes that refle ct feedstock carbon procured by a stationary source that exits assessment through pathways other than emissions from the stack. These biogenic process attributes can include feedstock carbon tha t is deviated from the process prior to conversion 1 to bioenergy (such as products produced from feeds tock material or feedstock losses during transport, storing, and other processing) or feedstock carbon in products that exit the stationary source bioenergy conversion process (suc h as ethanol or ash).
For demonstrative purposes in this appendix, illust rative values are presented by general stationary source technology and/or process type fo r specific feedstocks. Values for products can depend on the specific process used at a stationary source, as well as the type of feedstock used.

1 Conversion refers to technologies or processes tha t convert biomass into energy directly, in the form of heat or electricity, or may convert it to another form, such as liquid biofuel or combustible biogas. Examp les of biomass conversion processes include biomass-fired or co-fired boilers, biomass gasification or pyroly sis systems, and anaerobic digesters.November 2014 G-3

Values for losses can depend on the type and durati on of transport, storage, and processing. In cases where specific information is not available, or if the framework is being applied at a scale larger than stationary-source specific, estimated v alues for these terms may need to be generalized representative factors. The appendix also provides a detailed technical discussion of the data sources and analytical methods used to develop illu strative values.


## 2. Framework Representation of Feedstock Carbon Losses and Products for Forest- and Agriculture-derived Feedst ocks
Feedstock carbon from forest- and agriculture-deriv ed feedstocks can be transformed into products or exit the supply chain (pre- and post-conversion) through means other than the stack. T he biogenic assessment factor ( BAF ) equation is designed to transform a measurable or estimated quantity (the carbon content of biomass feedstock u sed at the point of assessment, represented by potential gross emissions, PGE ) into a quantity that cannot be directly measured (the net atmospheric contribution of biogenic carbon resulti ng from use of the quantity of biogenic feedstock that the entity is responsible for, repre sented by net biogenic emissions, NBE ). The framework equation, as discussed in the main report as well as Appendix F, is:

 = 

 = (
 +  +  + ) (  ) () (EQ. G.1)
The terms below each play a specific role in this e quation:

• PGE is carbon content of the biogenic feedstock used b y a specific entity (or generally consumed). This is a quantity that could be measure d or estimated at different points of assessment (e.g., at the boiler mouth, stationary s ource gate, feedstock production site, or at the stack: wherever the point of assessment needs t o be. Thus, this term can have different values indicated by subscripts, representing differ ent points along the supply chain).

• L is a unitless adjustment factor greater than or eq ual to 1 that represents biogenic feedstock carbon that leaves the supply chain (e.g. , via transit or decomposition, deviated for use as a product) between the feedstock product ion site and input into the conversion process at a stationary source. L scales PGE , as it was measured at the point of assessment, up to account for any losses during transportation or storage between the feedstock production site and the point of assessment. PGE times L is thus the carbon content of the biomass that was grown at the feedstock production site in order to deliver the quantity of feedstock measured at the point of assessment.

• P is a unitless adjustment factor between zero and on e, equal to the share of the carbon content of the feedstock at the point of assessment that is emitted to the atmosphere by a stationary source (versus that which is embedded in products). In effect, this term also reflects the share of carbon that remains in produc ts, that is either not emitted to the atmosphere or is sold and eventually emitted to the atmosphere by a downstream user.November 2014 G-4



### 2.1. Equations Underlying P and L

2.1.1. P

P accounts for the carbon content of the feedstock m aterial that is emitted to the atmosphere and therefore also accounts for biogenic carbon in prod ucts that is not emitted from the stack and leaves the producing entity (pre- and post-conversi on) for downstream use or disposal. Examples of products that may exit the supply chain prior to conversion include:

• Wood material in products (lumber, wood pulp, panel products);

• Mill residues sold/transferred to a separate statio nary source for use as raw material or fuel;

• Bark sold/transferred to a separate stationary sour ce for fuel;

• Bark sold for mulch;

• Agricultural by-products (e.g., stover, stalks, str aws, husks, hulls, etc.) sold/transferred to a separate stationary source for use as fuel; or

• Pulping by-products (tall oil, turpentine).
Examples of products that may exit the supply chain after conversion include:

• Dried distillers grains (from ethanol production);

• Ethanol; or

• Bottom ash, flyash, or biochar (e.g., materials con taining unburned carbon).
Equation F.7 from Appendix F defines P as calculated in this example.

 =  + ∑ 
 

    (EQ. G.2)

PROD i represents the sum of all pre- and post-conversion products and PGE j is PGE evaluated at point of assessment (

j ). Note that for many stationary sources, biogenic feedstocks will be used to produce a number of final products, so the summatio n in Equation G.2 simply sums over all i products that are produced prior to the bioenergy c onversion process with biogenic CO 2 stack emissions, indexed from 1 to S , where S is the last point on the carbon trail and represen ts the stack. For example, if PROD 2 is the only product, this summation would simply b e equal to PROD 2 . Since PROD i is always negative and the absolute value of the su m of all PROD i must be less than PGE j , the sum of all PROD i divided by PGE j will be a fraction that has a negative value, and P as calculated above will be a positive number between 0 and 1 (technically this expression is 1 plus a negative number). In effect, this calculation takes away the portion of carbon the products are responsible for, and attributing the remainder to t he stationary source. Although this is a relatively simple example, the mathematical representation of this term and more complicated scenarios are illustrated in Appendix F.
The framework recognizes that some post-conversion materials—such as fly ash—can potentially be sold and used for a number of commercial purpose s (i.e., cement manufacturing) or disposed of. Either way, such post-conversion materials are cons idered under the P term because they passNovember 2014 G-5

through the stationary source, exiting through mean s other than emissions out the stack. There is limited data on some post-conversion materials: lim ited and/or emerging scientific data in some cases (such as biochar) and publically available pr oduction and market data (e.g., end uses, sales of such materials). Also, whether or not these materia ls are sold, the carbon contained in this material can in some circumstances remain sequestered for so me duration of time.

2.1.2. L

The L term reflects biogenic feedstock losses in transpor tation to, storage, and processing at a stationary source and any products that exit the su pply chain prior to the conversion process. This value facilitates the link between the quantity of feedstock received at the stationary source, the quantity of feedstock that enters the stationary so urce process, and the quantity of feedstock grown on or removed from (e.g., in the case of crop resid ues) the land. Some stationary sources may measure both feedstock delivered at the gate and at the point of entry into the conversion process (e.g., boiler mouth) and thus have the data to calc ulate its onsite L term. Depending on the data that stationary sources have, PGE could be estimated, measured (i.e., the mass of th e biogenic feedstock as it enters a stationary source process and conver ting that mass into its CO 2 equivalents), or perhaps back calculated from the direct process emissions at the stationary source. An estimate of losses associated with storage at the stationary so urce and any pre-conversion products plays a role in the estimation of the amount of feedstock a ctually delivered to the site versus that entering the supply chain at the production landscape.
The purpose of L here is twofold. First, L represents the transformation of PGE 0 ( PGE evaluated at the feedstock production/harvest site) to PGE j ( PGE evaluated at point of assessment j ), implicitly capturing any losses ( LOSS i ) and any pre-conversion products ( PROD i ) that occur between these two PGEs. Note that this fraction will result in a positive number. Second, if considered in combination with the landscape effects term, it ser ves to bring the landscape effects in relation to PGE j .  =   

   (EQ. G.3)
Calculations presented in Section 4 of this documen t generate illustrative loss terms for when such site or supply chain-specific calculations are not possible or when representative factors are necessary within a specific program or policy appli cation (e.g., regional application of the framework).


## 3. Evaluation of Post-Conversion Products

In addition to information on the carbon content of the input feedstock, the methodology applied in this appendix also requires knowledge of the relati onship between input and output. This information is often necessary to properly express P as demonstrated in the equations below:
P Equation

 =  + ∑ (!""	#$%&#'()*  ×!&,#-	)#-*.-*	#$	%&#'()*  )  (!""	$..'"*#)/	-%(*×!&,#-	)#-*.-*	#$	$..'"*#)/	 -%(*)November 2014 G-6

Thus:

 =  + ∑ 
     (EQ. G.4)

Where:


  = (!""	#$	%&#'()*  × !&,#-	)#-*.-*	#$	%&#'()*  ) (EQ. G.5)

  = ( !""	$..'"*#)/	-%(* × !&,#-	)#-*.-*	#$	$..'"*#)/	- %(* ) !*	*0.	%#-*	#$	!"".""1.-*	 As explained in Appendix F, P is 1 minus the share of PGE j that remains in products or by-products and is either not emitted to the atmosphere or is s old and eventually emitted to the atmosphere by a different entity. (Note that because PROD is negative, technically this expression is 1 plus a negative number.) Because of the variability of dif ferent conversion technologies (e.g., boiler types)
, this appendix relies on mass balance data to establ ish the relationship between input and output and then uses the equations above to calculate exam ple values for P

. The following section explores methods that could b e used to estimate PROD i parameters contributing to P . The example process and stationary source combina tions below are addressed in this appendix for use in case study applications pe r different baselines:

• Co-firing in electric generating units (EGUs) (usin g forest-derived feedstocks); and

• Pyrolysis chambers in pyrolysis facilities (using c orn stover).
Carbon content values for agricultural feedstocks a re taken from Spokas (2010), while values for forest-derived feedstocks are from Skog (2008). Tab le G-1 shows values for each, expressed as percent carbon on a dry weight basis.
Table G-1. Carbon Content of Different Biogenic Feedst ocks.

Input Feedstocks Percent C by Dry Weight Roundwood 50 Logging Residue 50 Corn Stover 2 44



### 3.1. Co-firing in Electric Generating Units (EGUs)
Some EGUs are designed for 100% biomass combustion (Johansson et al., 2003). However, most EGUs are optimized for coal, natural gas, or oil. Y et, some EGUs can co-fire a certain percentage of biogenic fuel to be used in addition to the fossil- based feedstock (Demirbas, 2005), depending on the biogenic fuel and boiler design (e.g., solid-fu el boilers may be able to co-fire solid biomass). Generally, any type of biogenic feedstock can be us ed, but most often in the U.S., feedstocks are

2 Although presented more specifically in examples a s 44% in this appendix, in Appendix L, the carbon c ontent of corn stover is assumed to be 50% for consistency wi th other FABA feedstock case studies.November 2014 G-7

forest-derived (e.g., roundwood, logging, or mill r esidues), often converted to pellets or chips (Demirbas, 2005). Regardless of feedstock blend (i. e., percentage of biomass in fuel input), an EGU combusts the feedstock in a boiler to heat water in to steam, which is used to drive turbines and generate electricity.

3.1.1. Estimated P

Term Values Application of the framework could first reflect th e relevant stationary source technologies and processes used at EGUs, then the applicable biogeni c feedstock inputs and residual outputs. The process of co-firing biomass at an EGU does not pro duce non-energy/heat commercial products other than post-combustion materials like ash. As n oted above, an EGU combusts the feedstock in a boiler to heat water and produce steam, which is us ed to drive turbines and generate electricity. 3

To calculate P

in this illustrative scenario, the amount of carbo n in the post-process materials produced can be expressed as a proportion of carbon in the input feedstock. This value differs depending on the boiler type and efficiency. Boiler s vary from facility to facility, because there are a number of different boiler types to choose from, in cluding fluidized bed boilers, stoker boilers, or cyclone boilers. Each functions at a different effi ciency, and some can accept higher percentages of biogenic feedstock in the fuel blend. Differences a mong boilers in efficiency or temperature of combustion can result in different amounts of post- process materials, or different carbon content of the materials (Tarelho et al., 2011). Literature su ggests boiler type is an important predictor of unburnt carbon remaining, but the feedstock blend c ould impact efficiency of combustion and thus result in more or less post-process materials (e.g. , ash) or a higher or lower carbon content of the materials (Demirbas, 2005). Generally, agricultural biomass features higher ash content than woody biomass (Cassidy and Ashton, 2007).
Regardless of process, the amount of unburnt carbon remaining in the ash is typically expected to be low compared to the amount of feedstock carbon e ntering the conversion process (Kaufmann et al., 2000), but, as it depends on the conversation process efficiency, rates can vary substantially. Compared with modern, high-efficiency boilers, trad itional grate boilers often result in less efficien t combustion—and therefore feature higher levels of u nburnt carbon. Tollin (2000) suggests carbon content of ash can approach 50% in low efficiency g rate boilers. Biomass stoker boilers may have 30–40% carbon in the ash (Gustafson and Raffaeli, 2 009). Pitman (2006) reports average carbon content in wood ash to range from 7 to 50% (average 26%) in commercial boilers in the eastern U.S.
Those values pertain to carbon content of the ash i tself, not the proportion of incoming feedstock carbon remaining. The amount of ash generated in th e combustion process is typically ≈10% (by weight) of incoming feedstocks. Table G-2 shows an example calculation based on a feedstock with 50% carbon content, as well as other hypothetical a ssumption values based on Table G-1.

3 Theoretically, if combustion in an EGU was 100% eff icient in converting biogenic carbon to CO 2 , then P could be set to P = 1 to reflect that there is no carbon remaining i n post-combustion material (ash) and that there are no products produced from the feedstock used in the EG U (i.e., PROD = 0).November 2014 G-8

Table G-2. Sample P

for Ash Information for Low Efficiency Boiler.

Variable Value Incoming feedstock 1 ton Carbon content of feedstock 50% PGE j : Total incoming carbon 0.5 tons Ash content of feedstock 10% Ash remaining post-combustion 0.1 tons Carbon content of ash 26% Total remaining carbon post-combustion 0.026 tons PROD: Proportion of initial carbon remaining post-c ombustion to PGEj −0.052 1 P 0.948 2
Notes: 1. Carbon in products is subtracted (i.e., has a negat ive value) because product carbon does not enter th e atmosphere with the biogenic CO 2 from the conversion process. PROD = -(0.026/0.5) 2. P = 1 + (-0.052) = 0.948

PROD (ash) values would be lower for modern, high-effic iency boilers, leading to higher P terms. Nussbaumer and Hasler (1999) and Johansson et al. ( 2003) report fly ash carbon concentrations of 1–10% in modern boilers. Demirbas (2000) uses an av erage ash content of 0.5%. Kaufmann et al. (2000) analyzed ash samples and determined carbon c ontent to be no more than 0.05%. Fluidizedbed boilers typically achieve nearly 100% combustio n, meaning practically 0% carbon remains in ash (EPA, 2007; Gustafson and Raffaeli, 2009). As a bove, the mass of the ash itself is a small fractio n of total incoming feedstock, meaning the remaining carbon is only a small percentage of the total incoming carbon. Tarelho et al. (2011) present the best data on this, showing total unburnt carbon left in post-combustion residuals to be only 0.7−2. 8% of the original feedstock carbon (i.e., 0.007−0.028 tons per ton of feedstock carbon input)

. The value varies depending on the efficiency of the boiler, but generally the majority of the incoming carbon will be released via combustion as CO 2 . Table G-3 shows example illustrative values of the P

term based on boiler efficiency. Actual values for P are expected to vary depending on boiler technology being used at a facility and f eedstock type.
Table G-3. Example Estimated Range of P Values for Co-Firing Biomass in Electrical Generat ing Units.
Co-firing Boiler Type P High Efficiency 0.972 1 to 0.993 2 Low Efficiency (as shown in Table G-2 above) 0.948

1 P = 1 + (- PROD / PGE

) = 1 + (-0.028 tons C in ash/ton of C input in fee dstock) = 0.972

2 P = 1 + (- PROD / PGE

) = 1 + (-0.007 tons C in ash/ton of C input in fee dstock) = 0.993

3.1.2. Discussion Serious data gaps exist in developing representativ e P values for co-firing boilers at EGUs. Overall, little information exists about carbon content of p ost-combustion materials. Because of this, determining representative P values is difficult, and considerable uncertainty remains.November 2014 G-9

More detailed data from proximate and ultimate anal ysis of different biogenic feedstocks are required, as are engineering analyses related to co mbustion using different technologies. For example, ash content will impact boiler heat surfac e fouling, which will impact the completeness of combustion—and by extension the P value for different combustion technology—for diff erent feedstock combinations. As noted above, the process of co-firing biomass at an EGU can be done with a number of different biogenic feedstocks and a number of different boilers. Furthermore, the fossil fuel/biomass blend can vary greatly dependin g on cost, supply, boiler specifics, and a number of other factors.
However, despite the lack of data, it is clear that in most scenarios much of the carbon is emitted as CO 2 during combustion. Therefore, although the carbon content of post-combustion materials may vary, the average is expected to be low.



### 3.2. Pyrolysis
The pyrolysis process involves the thermal destruct ion of organic materials in an environment void of oxygen (Demirbas, 2005). When the biomass is hea ted in a pyrolysis chamber, it produces a hydrocarbon rich gas mixture (syngas), an oil-like liquid (bio-oil), and a carbon rich solid material (biochar). The relative amounts of each product dep end on whether the facility employs high heat fast pyrolysis or low heat slow pyrolysis. Typical mass yield ranges for fast pyrolysis are bio-oil 50

– 70%, syngas 10–30%, and biochar 10–25%. Typical mas s yield ranges for slow pyrolysis are: bio-oil 20–50%, syngas 20–50%, biochar 25–35% (UK Biochar R esearch Center (UK BRC), 2009). In most pyrolysis processes, the syngas is used to fuel the system (Mullen et al., 2010). Figure G-1 shows the pyrolysis process in the context of the carbon cycl e.

Figure G-1. Diagram of Pyrolysis Process.November 2014 G-10

3.2.1. Estimated P

Term Values For purposes of this framework, biochar, bio-oil, a nd syngas can be considered under the P term. Values are estimated for both fast and slow process es, based on the sample mass balance information in Table G-4. Table G-4 uses Mullen et al. (2010) numbers for fast pyrolysis and Ringer et al. (2006) numbers for slow pyrolysis. Carbon co ntent of biochar is derived from Roberts et al. (2010) and Brown et al. (2010). Corn stover is used as an example feedstock, assuming a carbon content of 44%.
Note that carbon content of bio-oil and syngas is t he same for both processes, the differences lie merely in the amount of each material produced (Mul len et al., 2010).
Table G-4. Sample P

Information for Pyrolysis of Corn Stover.

Sample P Information Fast Pyrolysis (high heat, 600°C) Slow Pyrolysis (low heat, 300°C) % Input feedstock (Corn stover) 100 100 Bio-oil 61.6 (includes water content) 30 a Syngas 21.9 35 a Biochar 17.0 35 1 Carbon Content of Bio-oil 53.97 53.97 2 Carbon Content of Syngas 16.7 16.7 3 Carbon Content of Biochar 57.29 67.68 c

1 Ringer et al. (2006).

2 Mullen et al. (2010).

3 Roberts et al. (2010). p. 9.
This illustrative stover pyrolysis example assumes that the assessment boundary encapsulates the actual pyrolysis process chamber including the syng as boiler and represents a pyrolysis facility where all of the syngas is used onsite for process energy (instead of some of it being shipped offsite). In the syngas boiler stage, the syngas is fully combusted and therefore P (syngas) = 0. Thus, the P value in this example is only calculated for the b io-oil and biochar products. As explained in Appendix F, P

is 1 minus the share of PGE j that remains in products or by-products and is eit her not emitted to the atmosphere or is sold and eventually emitted to the atmosphere by a different entity. (Note that because PROD is negative, technically this expression is 1 plus a negative number.)
For fast pyrolysis, P

values can be calculated as follows per ton of fee dstock input:

P Equation for Fast Pyrolysis

 =  + ∑ ( !""	#$%&#'()*  × !&,#-	)#-*.-*	#$	%&#'()*  )

 ( !""	$..'"*#)/	-%(* × !&,#-	)#-*.-*	#$	$..'"*#)/	- %(* ) =  + 2− 4 ( !""	,#

- #5×	-	,#

- #5 ) 4	(!""	,#)0!&×	-	,#)0!&)

(!""	#$	)#&-	"*#6.&×!&,#-	)#-*.-*	#$	)#&-	"*#6.&) 7 (EQ. G.6)

 =  + 8 −[ + ( . ;; × . <=>? ) + ( . ? × . <?@> ) ] ÷ (  × . CC ) DNovember 2014 G-11

 =  + ( −. >?;> ) = 	. @= For slow pyrolysis P

values can be calculated as follows per ton of fee dstock input:

P Equation for Slow Pyrolysis

 =  + ∑ ( !""	#$%&#'()*  × !&,#-	)#-*.-*	#$	%&#'()*  )

 ( !""	$..'"*#)/	-%(* × !&,#-	)#-*.-*	#$	$..'"*#)/	- %(* ) =  + 2− 4 ( !""	,#

- #5×	-	,#

- #5 ) 4	(!""	,#)0!&×	-	,#)0!&)

(!""	#$	)#&-	"*#6.&×!&,#-	)#-*.-*	#$	)#&-	"*#6.&) 7 (EQ. G.7)

 =  + 8 −[ + ( . = × . <=>? ) + ( . =< × . ;?;E ) ] ÷ (  × . CC ) D

 =  + ( −. >;= ) = 	. >=?

3.2.2. Discussion The values estimated in this appendix are meant to be illustrative examples only. Pyrolysis is not yet being undertaken on a commercial scale; as a re sult, it is difficult to derive robust values. Hammond (2011) notes that “pyrolysis has not yet be en demonstrated at ... large scales,” and that “due to the immature state of [pyrolysis] technolog y ... there is a lack of good quality datasets and therefore greater uncertainty in the data than is d esirable.” This is similarly true for stationary sources using dedicated energy crops (e.g., switchg rass) and short-rotation woody crops (e.g., poplar) as a feedstock for pyrolysis.
For example, Ringer et al. (2006), Enders et al. (2 012), and Wright et al. (2008) show the production percentage of biochar can range from 0–7 7%. Much of this range is the result of differences in process, namely the decision to empl oy fast pyrolysis or slow pyrolysis. Slow pyrolysis produces more biochar (35%); fast pyrolys is produces less (17%). Table G-4 combines values from Enders et al. (2012), Wright et al. (20 08), and Ringer et al. (2006).
Spokas (2010) notes that biochar “variability is ba sed on the conditions of pyrolysis and the biomass parent material, with biochar spanning the range of various forms of black carbon. Thereby, this variability induces a broad spectrum in the observed rates of reactivity and, correspondingly, the overall chemical and microbial stability.” Furthermore, the literature is complicated by the lack of uniformity when it comes to the nomenclature for the products of biomass conversion. Char, charcoal, soot, graphitic carbon, ash, coal, and black carbon have all been used to describe the solid residual products.” As a result, the potential values of P for pyrolysis techniques could vary from facility to facility. Re presentative values could be calculated using ranges, averages or median values, as done in the e xample above. 

### 3.3 Conversion of Forest-Derived Industrial Byproduc ts Producers of industrial forest-derived products and by-products may use roundwood or wood chips as raw material to manufacture paper or wood products. Various wood product manufacturing processes can result in the productio n of mill residues including bark, saw dust, sander dust, and panel trim, which may be sold down stream to other users as products or used onsite as boiler fuel. The mass of wood converted t o products is dependent on the specific productsNovember 2014 G-12

being produced and production processes used. The a mount of mill residues used as onsite boiler fuel instead of being sold for use as a raw materia l or fuel at a separate entity is highly site-speci fic.
This section provides an example of a hypothetical wood products facility that converts roundwood into veneer for onsite plywood manufacture, uses th e cores remaining after peeling of veneer to produce lumber, and produces additional lumber from logs. This illustrative facility burns 60% of the mill residues it generates (bark, plywood trim, hog fuel, and sanderdust) in an onsite boiler and sells the remaining 40% of mill residues (sawdust a nd planar shavings) to other downstream users for fuel or use in other products (e.g., pellets, p articleboard).

3.3.1 Estimated P

Term Values

To calculate P

for this example wood products facility, the amoun t of carbon in the products produced can be expressed as a proportion of carbon in the input feedstock. For each dry ton of carbon in the input feedstock, in this hypothetical example, a site-specific estimate of 0.2 dry tons of carbon in mill residues are generated, of which 0.6 x 0.2 = 0.12 dry tons of carbon are used onsite as boiler fuel and 0.4 x 0.2 = 0.08 dry tons of carbon are sold as products. Mill residues are collected from various points in the production process and a re either routed to onsite boiler fuel storage (0.12 dry tons of carbon per dry ton of carbon in t he input feedstock) or to loadout bins for transfer to other entities (0.08 dry tons of carbon per dry ton of carbon in the input feedstock). Mill residue s collected for boiler fuel at this example mill are used within a few days so boiler fuel storage losse s are negligible. In addition, this hypothetical exam ple includes a site-specific estimate of 0.00012 tons of carbon remaining in the boiler ash followin g combustion of mill residues.
Because the boiler is supporting various different equipment for wood products manufacturing, it is separate from the plywood and lumber production lin es. The mass of mill residues combusted in the boiler is not measured at every mill residue ge neration point in the facility, but instead is determined at the boiler. Similarly, the mass of mi ll residues (sawdust and planar shavings) sold as a product is determined at the loadout bins rather than being measured at every saw and planar within the facility. It may not be necessary to dis tinguish between pre- and post-conversion products because the facility can calculate the fol lowing dry mass amounts on a per-ton-of-carboninput basis:

• Mass of carbon in the incoming logs ( PGE j = 1 ton of C input);

• Mass of carbon retained in the plywood and lumber p roducts ( PROD 1 = 0.8 tons of C in plywood and lumber);

• Mass of carbon in mill residues sold ( PROD 2 = 0.08 tons of C in residuals sold);

• Mass of carbon in mill residues burned in the boile r (0.12 tons of C burned); and

• Mass of carbon remaining in the boiler ash ( PROD 3 = 0.00012 tons of C remaining in ash).
All products may be considered within the PROD and P terms. P can be calculated as follows:

 =  + ∑ 
 

    =  + F G(
  4
 @ 4
 = )   H (EQ. G.8)

 =  + I

G ( .E4.E4.@ ) *#-"		-	%&#'()*"

	*#-		-	*0.	$..'"*#)/	-%(* J = . @November 2014 G-13

Carbon in products is subtracted (i.e., has a negat ive value) because product carbon does not enter the atmosphere with the biogenic CO 2 from the conversion process at this wood products facility.

3.3.2 Discussion The hypothetical example above shows that, in the c ase of a wood products facility, the majority of the carbon entering the facility can remain in the wood products produced by the facility, including mill residues sold to other entities. The example a lso illustrates inclusion of all products ( PROD ) in the P term when it is not clear whether the products are pre- or post-conversion products because the material is collected from various points withi n the production process that are not necessarily upstream or downstream from the bioenergy conversio n process (i.e., the boiler). In this case, use of the point of assessment ( PGE j ) as the feedstock carbon input to the facility (ro undwood) allowed for the P calculation to be done without distinguishing betw een pre- and post-conversion products in the facility.
As shown in Equation G.3 above, L in this example would be calculated as the ratio o f PGE o (at the feedstock production site) and PGE j (roundwood entering the facility). Thus, for the c alculation of P in this example, it is assumed that there are no ph ysical feedstock losses ( LOSS ) prior to the residues entering the conversion process and theref ore no need to distinguish products generated within the facility as pre- or post-conversion for purposes of calculating L

. 4


## 4. Evaluation of Pre-conversion Products and Losses Du ring Transportation, Storage, and Processing
There are few established methods for tracking and/ or calculating feedstock losses along the supply chain, especially for raw herbaceous feedsto ck (Miranowski et al., 2010). However, when conducting a mass balance-based calculation, having some means to estimate the flows of biogenic feedstock carbon from feedstock production to eithe r emissions out of the stationary source stack or post-conversion products, some assessment of the se supply chain flows can relate biogenic CO 2 emissions to feedstock production. This framework e quation includes a term ( L ), which scales PGE as it is estimated or measured at the point of asse ssment up to account for any pre-conversion products exiting the supply chain or feedstock loss es during transportation, storage, or processing between the feedstock production site (e.g., forest , farm) and the point of assessment.
As the feedstock moves through the supply chain, de viated or lost feedstock carbon may occur due to pre-conversion products or losses during transpo rt, storage, and processing/handling between or within different stages of the supply chain. The L term actually refers to two separate loss components: one that reflects feedstock carbon take n from the supply chain for other purposes such as pre-conversion products (e.g., lumber, bark for mulch) and another that reflects physical losses between the feedstock production site and th e point of assessment. The volume of physical losses can depend on feedstock type, technologies a pplied, storage length and type, and several other factors. Therefore, measuring direct stack CO 2 emissions at the stationary source would likely

4 Losses and only the pre-conversion products occurr ing between PGE o and PGE

j are included in L

.November 2014 G-14

be an underestimate of the total net biogenic CO 2 emissions associated with procurement and use of the biogenic feedstock. The adjustment of PGE , the carbon content of the biomass feedstock used by a specific entity at different points of the ass essment, by the term L can vary for each supply chain, unless generalizations are made to create re presentative values.
Representative values for losses could depend on id entification of different storage scenarios and transportation scenarios by feedstock type and coul d also take the region into consideration if appropriate (e.g., effects of regional climate on s tored feedstock). Methods for calculating representative feedstock loss values related to tra nsport, storage, and processing that could be developed for stationary sources to use, in lieu of case-specific, customized calculations, are shown below. This appendix does not develop values for possible products coming out of the supp ly chain between the feedstock production site and point of assessment because of the many different product possibilities and permutations that exist. More detailed discussion of the underlying mathematical computations related to how products c an be accounted for in Appendix F. The reminder of this appendix focuses on the physical l osses component of L

. 

### 4.1. Feedstock Losses during Transportation, Storage and Processing
Weighing the feedstock at the sale transaction poin t is considered common practice. In commercial operations, this is usually done at a stationary so urce’s delivery point scale. The delivered moisture content can be used to calculate the dry weight in the load, and the payment is normally on a dry weight basis. The material is usually placed in sto rage prior to being introduced into the process. The conditions for storage, including feedstock typ e, moisture content, time of storage, climate conditions, and the amount of protection against ad ded moisture offered by the stationary source, can affect feedstock storage losses. Feedstocks tak en immediately from the scale to the process or into storage that is dry and cool are likely to hav e fewer losses than feedstocks stored in uncovered piles on the ground in a warm, moist climate for se veral months (see below for research findings relating to such losses).
As described above, a portion of the L term represents the CO 2 equivalent of feedstock losses (e.g., decomposition, too dirty or destroyed to use in boi ler), or LOSS i 5 , that occur between the weigh-in point of the feedstock at the stationary source and the entry point of the production process (boiler or other processor). If the feedstock is processed immediately by the stationary source, there may be minimal, or zero, losses in storage. In many sit uations, however, the feedstock must be stored prior to utilization, because harvest and delivery schedules are cyclical, whereas biogenic feedstock demand and utilization needs would be continuous. T hose losses are due to the decomposition in a storage system, such as a chip pile and onsite stor age, which is likely to last one year or less. Specific applications of the framework could includ e calculations of the emissions related to decay: however, the illustrative calculations in this appe ndix count those losses as immediate and do not include any long-term decay rates (see section belo w on onsite storage losses).

5 There can be various points of losses along the su pply chain. These losses can be given individual lo ss terms (e.g., LOSS 1 , LOSS 2
, LOSS 3 ).November 2014 G-15

In contrast to stationary source onsite storage los ses that can happen subsequent to the feedstock weighing, potential harvest and transport losses co uld also occur and thus could be considered for treatment in the framework, depending on available data. For some feedstocks, losses may occur during transportation between the feedstock product ion site and the stationary source. For example, some of the material harvested from a farm (e.g., switchgrass) may be lost from the truck while in transit, or decay might occur while the fe edstock is in storage at the stationary source (Qin et al., 2006). Such losses are not typically calcul ated or included in the delivered weight; thus, few data are available on this aspect of losses. These losses are expected to be rather minor and likely depend on the biogenic feedstock material that is h arvested or collected.



### 4.2. Possible Methods for Developing Illustrative LOSS Values for Onsite Storage and Processing
This section describes a methodology for calculatin g representative values for the relevant loss factors ( LOSS

i ) included in the L term. Biomass types considered below include herba ceous (switchgrass, hay, etc.), woody biomass (logging re sidues, etc.), and agricultural residues (corn stover, etc.).
In general, of all the biomass losses occurring acr oss a given supply chain, the storage losses (e.g., decomposition, too dirty or destroyed to use in boi ler) are potentially the most significant. Even for long-distance supply chains including international shipping, Hamelinck et al. (2005) estimated a total loss throughout the international supply chai n of 15% of dry matter; 13% of these losses were associated with storage, 2% with processing, and <1 % with handling.
One way to calculate onsite storage losses is to de termine the feedstock delivered to the stationary source versus the amount that enters into the conve rsion process. The difference between the delivered weight and the pre-conversion input weigh t represents the onsite loss. This preconversion input weight can also be compared with t he direct emissions (stack emissions) from the process to help determine accuracy of the calculati on. However, such multi-point measurement onsite is currently not common practice. If the poi nt of assessment, where PGE is calculated, is when the feedstock is entering the conversion proce ss (e.g., after any storage losses have occurred), the L term (which includes losses represented by LOSS i ) is needed in the equation that converts PGE
to NBE , to represent any losses (and pre-conversion produ cts) that could have occurred prior to initial calculation of PGE

. Biogenic feedstocks contain a significant amount of water which can change as the material dries in storage and/or absorbs water through precipitation. Dry matter refers to the weight of material without water (i.e., the weight of material when it is completely dried). Dry matter loss in storage (e.g., due to decay, physical losses) is a function of several factors that will differ between statio nary sources. The factors include (1) the type and packa ging (loose, bundles, etc.) of feedstock; (2) the moisture content of the stored feedstock; (3) the t ype of storage facility at the stationary source; ( 4) the length of time the material is stored; and (5) the climatic conditions (e.g., temperature, humidity) during the storage period.November 2014 G-16

Several studies give estimates of dry matter loss f or different types of feedstocks. For woody biomass, losses of 12% were found in green chips st ored in a large pile for 7 months (Thornqvist and Jirjis, 1990). Although little to no dry matter losses can be expected when storing logs, bundles of logging residues exhibit higher dry matter losse s (Jirjis, 1995). Afzal et al. (2010), for instance
, observed higher dry matter losses in woodchip piles (8–27%) than in bundles (≈3%) over one year. Jirjis and Nordén (in Eriksson and Gustavsson, 2010 ; 2002) observed only 5.8% dry matter loss of green logging residue bundles after 51⁄2 months in Sw eden.
Moisture is the most important factor in dry matter loss, because it directly affects the microbial activity that produces the loss (Ashton, 2008). Stu dies of switchgrass losses show results ranging from zero for a year’s inside protected storage to 13% for unprotected bales stored outside for 6 months (Sanderson et al., 1997). Buckmaster (1992) showed dry matter losses in baled hay under indoor storage ranging from near zero dry matter lo ss at 12% moisture to around 7% dry matter loss at 25% moisture at baling. Studies of indoor h ay storage indicate that, to reduce the risk of severe heat production and fire in the storage stat ionary source, moisture content of incoming bales should be 25% or less (Buckmaster, 1992).
The type of storage facility (indoor, outdoor unpro tected, outdoor protected, etc.) and the underlying foundation (sod, concrete, gravel, etc.) affect storage losses , primarily through protection from increased moisture and differences in aeration (Sanderson et al., 1997). For instance, in a Swedish study on forest residue bale s, the 10 month dry matter losses due to biological activity were highest in the outdoor-unc overed stack, which had an average total dry matter loss of 18.5%, while the indoor stacks had a dry matter loss of 14% (Eriksson and Gustavsson, 2010; Jirjis, 2003). For Atlantic Canad a conditions, Afzal et al. (2010) estimate 6.6 to 15.6% dry matter losses during 6 months for uncover ed birch chip piles of (naturally) dried and fresh forest residue, respectively.
Besides initial moisture content of feedstock, type of storage facility (indoor, outdoor unprotected, outdoor protected, etc.) and use of an appropriate foundation, storage losses also strongly depend on the “packaging” of the feedstock. For instance, losses are higher in chipped material than in loose residue piles, because chipping increases the amoun t of exposed surfaces on which microbial action can occur and releases soluble contents of plant ce lls that provide nutrition to microbes (Richardson et al., 2002). Small chips also reduce air flow in piles and prevent heat dissipation and moisture release.
The length of storage time affects losses in combin ation with moisture and facility type. The decay process starts as soon as 1 week, with the highest dry matter losses in the first weeks of storage (Wihersaari, 2005; for Finland). Indoor storage may result in moisture content going from 25% to around 12% after 2 months or more of storage (Buckm aster, 1992), at which point microbial activity will be minimized.
These studies indicate that, if necessary, it may b e possible to develop a representative estimate that could be applied to a given stationary source on the basis of the five variables (type of biomass and particle size, initial moisture content, type o f storage, average length of storage period, and regional climate conditions). Emissions will be com posed almost entirely of CO 2 except in storageNovember 2014 G-17

facilities where aeration is limited. For example, one study of logging residues stored in laboratory vessels found maximum concentrations of CO 2 , CO, and CH 4 to be 13.8%, 0.16%, and 0.15%, respectively, over a 35-day period (He et al., 2011 ).

A literature search identified studies that investi gate dry matter loss over time for various types of biomass, including those with original research on dry matter loss. Based on 22 studies, a dataset containing 112 cases of reported dry matter loss ov er time was produced (see Addendum I in Section 6, with case categories including source, o rigin, region, biomass type, storage type, original moisture content, storage length, and dry matter lo ss). If data were presented from abroad, the study location was matched by climatic zones with o ne of the U.S. regions described in Smith and Heath (2002).
Addendum II (see Section 7) expands upon the litera ture review presented in this Section and in Addendum I, focusing on dry matter losses from stor age of woody biogenic materials.



### 4.3. Representative Values for Storage Losses
Analysis of the dataset (see Addendum I in Section 6 below) revealed no clear trends in storage loss by biomass type. Even herbaceous crops that were as sumed to produce comparable results across studies (usually baled and pre-dried) did not produ ce notable and distinguishable trend lines by region or storage type (see Figure G-2). Additional data and statistical analysis could provide more insights. Based on the available data, trend lines describing the storage losses for each of the three storage types (outdoor, uncovered; outdoor, covered ; indoor) were produced for one feedstock (woodchips) (see Figure G-3). Because of the limite d research on indoor storage losses for woodchips, all indoor storage data were aggregated irrespective of region and biomass type and a “worst-case” scenario was used, assuming rapid stor age losses.November 2014 G-18

Figure G-2. Data Points for Herbaceous Crops Storage for Western (W), Southeastern (SE), Northeastern (NE) and Southwestern (SW) Regions. St orage losses are not clearly distinguishable by region or storage type (N = 69).

Figure G-3. Data Points and Trendlines for Storage Losses for Woodchips in the Northeast (N = 29).November 2014 G-19

Table G-5 describes the trend lines shown in Figure G-3 and is a first approximation for a storage loss term lookup table for woodchips in the Northea st provided for illustrative purposes only.
Table G-5. Example Representative Values for Storag e Losses for Woodchips in the Northeast (based on Figure G-3).
Storage Duration (months) Outdoor, Uncovered Outdoor, Covered Indoor

1 11% 4% 2% 2 14% 5% 3% 3 16% 6% 3% 4 17% 6% 4% 5 18% 6% 4% 6 19% 7% 4% 7 20% 7% 4% 8 21% 7% 4% 9 21% 8% 4% 10 22% 8% 4% 11 22% 8% 4% 12 23% 8% 5%

Based on the data identified in the figures and tab les above, four options for developing representative LOSS

values are explored (though other options could be used in applications of the framework):

1. Develop a “maximum” L term for each feedstock type by region. This could reflect a “highest possible case” system (see black line in F igure G-3) i.e., outside, uncovered, onground, long-term (>6 months) storage (assuming 50% initial moisture, which results in>20% loss ( LOSS 1 => 0.2). 2. Provide “loss deductions” that could be used to red uce the maximum losses in situations in which stationary sources use improved management (e .g., a system that stores material at a moisture content of 20% indoors on concrete for < 1 month could lead to an estimate of a zero loss). 3. Provide for a stationary source-specific loss calcu lation method that could be used to estimate the LOSS

term for a particular stationary source (Sale poin t dry weight (DW) − Process point DW) / Process point DW = L ). 4. Provide one single representative LOSS value (at a broader scale than an individual stationary source, e.g., region) that presents aver age losses for herbaceous and woody biomass for an average storage time.
The following example discusses development of a re gional representative LOSS value for illustrative purposes: 10% loss ( LOSS = 0.1) for woody and herbaceous feedstocks, includ ing stover, is discussed and calculated below using the Northea stern data.
It is assumed that stationary sources consuming woo dy biomass usually store a 3-month supply uncovered on their premises. Averaging covered and uncovered outdoor storage data for woodchips in the Northeast (Figure G-3), this would equal a loss of 11%.November 2014 G-20

There are limited data on storage-related data for stover and agricultural residues, but trends for these feedstocks were similar to herbaceous crops ( Figure G-2), so both feedstock categories were combined. In terms of storage time for herbaceous f eedstocks, this example assumes a 4-month harvest window. It is also assumed that during this harvest window, feedstocks are immediately converted to usable heat without storage losses. An average storage time of 2.7 months for feedstock was calculated based on an assumed 8 mont hs of off-harvest season. This average storage time takes into account that some of the feedstock is burnt immediately after harvest (during the harvest months), while other sections of the feedst ock are stored in decreasing quantities for up to 8 months until the next harvest window. The data po ints for a 3-month outdoor storage (Figure G2) suggest a loss of 3 to 15% with an average of ar ound 9 to 10% across all regions because no separate regional trends are discernible with curre nt data.
The dataset contained no reports on storage losses for roundwood, and only one case was recorded for wood pellets. Because of this lack of data, thi s appendix does not provide illustrative examples of storage losses for these feedstock categories.
Potential feedstocks could also be oil seed crops a nd sugar-based feedstocks such as corn kernels or sweet sorghum. Sugar-based feedstocks can experienc e rapid storage losses in a matter of days (e.g., Bennett and Anex, 2009; Jasberg et al., 1984 ), which is usually not measured in dry matter losses and is therefore not compatible with the app roach followed in the dataset described above.



### 4.4. Discussion of Other Losses: Transportation and Harv est

4.4.1. Transportation Dry matter lost in transportation is almost wholly due to mechanical losses. Chemical losses, such as the degradation of dry-matter, generally occur d uring storage and at a much larger timescale than that which occurs during transport (weeks to m onths of sitting in storage vs. hours to days in transit). Although mechanical losses occur in the l oading and unloading of dry matter, these losses are small and are considered more or less as inevit able with few mitigation options available.
Transport losses are often less that 1% (Sanderson et al., 1997) even in the case of long-distance supply chains for woody biomass (Hamelinck et al., 2005). One study found that bale weight changes and biomass loss of switchgrass during hand ling and transportation from the field resulted in a total dry matter loss of 0.4% (Sanderson et al ., 1997). In the case of roundwood, transport losses are generally not expected to occur. As an e xample, developing a lookup table for transport losses could be done by using research specifying r epresentative values by transport type (truck, vessel, train) and distance (e.g. <50 mi, 50–100 mi , >100 mi). If they are included, transport losses for one crop should be added together to provide an exhaustive view to systematically quantify total losses (Hamelinck, 2005). For instance, if bi omass is transported by truck (e.g., 1% loss) followed by train (1% loss), total transport losses could equal 2%. However, due to lack of data, no such illustrative example is included in this appen dix.November 2014 G-21

4.4.2. Harvest Harvest losses are not included in the example calc ulations in this appendix, because such losses are not often measured or included in the delivered product weight and thus not typically calculated in yield computations. However, harvest losses could be significant in the case of specific feedstocks or for vertically integrated stationary source operations that oversee farm/forest operations. Harvesting efficiencies for crop residu es using conventional multipass harvesting systems are relatively low, with only one-third to two-thirds of the available crop residues actually collected (Hess et al., 2009). For instance, Monti et al. (2009) note that “Unlike storage, major biomass losses 6 occurred during harvest [of hay], either due to th e biomass not picked-up by the baler machine (up to 17%), and uncut biomass by the mower machine (up to 29%). Overall, of the potentially harvestable biomass, only 64% was actua lly baled.” However, Hess et al. (2009) noted that for most farmers, it is general agricultural p ractice to intentionally leave a certain amount of crop residue on the landscape to ensure soil health . The amount of crop residue left behind is somewhere between 40% and 70%, with an average amou nt of biomass left onsite of 60% (Hess et al., 2009). Nevertheless, these harvesting values a re for specific crop yields and are highly variant in nature; more data would be required to estimate suc h losses.


### 4.5. Representative Factor Discussion
There may be cases where information on supply chai n losses is unavailable between the feedstock production site (e.g., farm, forest) and the feedst ock conversion facility. For example, if an intermediate supplier or primary processing facilit y supplies feedstock (e.g., woody mill residues) to the stationary source, there may be little infor mation about the transportation and storage losses incurred between the landscape where the feedstock is grown and the stationary source. Representative values for losses may be determined based on a review of literature as described in Section 4.3 above. Alternatively, a representative factor derived from the literature may be assigned for a particular feedstock type in the absence of r efined information. 

## 5. References
Afzal, M., A. Bedane, S. Sokhansanj, and W. Mahmood . 2010. Storage of comminuted and uncomminuted forest biomass and its effect on fuel quality. BioResources 5(1):55-69. Ashton, S.F. Dry-Matter Loss . 2008. Forest Encyclopedia Network. Retrieved June 6, 2012 from http://www.forestencyclopedia.net/p/p1308

. Bennett, A.S., and R.P. Anex. 2009. Production, tra nsportation and milling costs of sweet sorghum as a feedstock for centralized bioethanol production i n the upper Midwest. Bioresource Technology 100(4):1595-1607. Brown, T.R., M.M. Wright, and R.C. Brown. 2010. Est imating profitability of two biochar production scenarios: slow pyrolysis vs. fast pyrolysis. Bioguels, Bioproducts and Biorefining 5(1).

6 The word “losses” in this quote refers to biomass left behind as opposed to the definition being used in the rest of this appendix.November 2014 G-22

Buckmaster, D.R. 1992. Indoor hay storage: Dry matter loss and quality cha nges , Fact Sheet PSU/92. State College, PA: Agricultural and Biological Engi neering Department, Pennsylvania State University. Cassidy, P., and S. Ashton. 2007. Ash Content Fact Sheet : Sustainable Forestry for Biogenergy and Bio-Based Products. Demirbas, A. 2000. Recent advances in biomass conve rsion technologies. Energy Education Science and Technology

6:19-40. Demirbas, A. 2005. Potential applications of renewa ble energy sources, biomass combustion problems in boiler power systems and combustion rel ated environmental issues. Progress in Energy and Combustion Science 31(2):171-192. Enders, A., K. Hanley, T. Whitman, S. Joseph, et al . 2012. Characterization of biochars to evaluate recalcitrance and agronomic performance. Bioresource Technology 114:644-653.
EPA. 2007. Biomass Combined Heat and Power Catalog of Technolo gies : Combined Heat and Power Partnership. http://www.epa.gov/chp/documents/biomass_chp_catalo g.pdf

. Eriksson, L., and L. Gustavsson. 2010. Comparative analysis of wood chips and bundles – Costs, carbon dioxide emissions, dry-matter losses and all ergic reactions. Biomass and Bioenergy
, 34(1):80-92. Gustafson, R., and N. Raffaeli. 2009. Washington State Pulp and Paper Mill Boilers: Curre nt and Potential Renewable Energy Production . Seattle, WA: University of Washington, School of Forest Resources, Department of Ecology. https://fortress.wa.gov/ecy/publications/publicatio ns/0907048.pdf. Hamelinck, C.N., R.A.A. Suurs, and A.P.C. Faaij. 20 05. International bioenergy transport costs and energy balance. Biomass and Bioenergy 29(2):114-134. Hammond, J., S. Shackley, S. Sohi, and P. Brownsort . 2011. Prospective life cycle carbon abatement for pyrolysis biochar systems in the UK. Energy Policy 39(5):2646-2655. He, X., A.K. Lau, S. Sokhansanj, C.J. Xiaotao, et a l. 2011. Dry matter losses in combination with gaseous emissions during the storage of forest resi dues. Fuel 95:662-664. Hess J.R., et al. 2009. Uniform-format solid feedst ock supply system: a commodity-scale design to produce an infrastructure-compatible bulk solid fro m lignocellulosic biomass. INL/EXT-08- 14752 Jasberg, B.K., R.R. Montgomery, and A. R.A. 1984. Preservation of Sweet Sorghum Biomass . Peoria, IL: U. S. Department of Agriculture, Agricultural Resea rch Service, Northern Regional Research Center. Jirjis, R. 1995. Storage and drying of wood fuel. Biomass and Bioenergy 9:181-190. Jirjis, R., and B. Nordén. 2002. Lagring av buntat skogsbränsle – små substansförluster, inget arbetsmiljöproblem [Stockpiling of composite residu e logs (CRLs) – small biomass losses and no health problems]. Skogforsk Resultat 12:1-2. Jirjis, R. 2003. Storage of forest residues in bales . Uppsala, Sweeden: Swedish University of Agricultural Sciences, Department of Bioenergy.November 2014 G-23

Johansson, L.S., C. Tullin, B. Leckner, and P. Sjöv all. 2003. Particle emissions from biomass combustion in small combustors. Biomass and Bioenergy 25(4):435-446. Kaufmann, H., T. Nussbaumer, L. Baxter, and N. Yang . 2000. Deposit formation on a single cylinder during combustion of herbaceous biomass. Fuel 79:141-151. Miranowski, J., M. Khanna, J. Hess. Suntainable Feedstocks for Advanced Biofuels - Sust ainable Alternative Fuel Feedstock Opportunities, Challenge s and Roadmaps for Six U.S. Regions . Soil and Water Conservation Society, Proceedings of the Sust ainable Feedstocks for Advance Biofuels Workshop - Atlanta, GA. September 28-30, 2010. Chap ter 11 - Economics of Feedstock Production, Harvest, Storage, and Transport. Monti, A., S. Fazio, and G. Venturi. 2009. The disc repancy between plot and field yields: Harvest and storage losses of switchgrass. Biomass and Bioenergy 33(5):841-847. Mullen, C.A., A.A. Boateng, N.M. Goldberg, I.M. Lim a, et al. 2010. Bio-oil and biochar production from corn cobs and stover by fast pyrolysis. Biomass and Bioenergy 34(1):67-74. Nussbaumer, T., and P. Hasler. 1999. Formation and properties of aerosols from wood combustion. Raw Wood Materials

57:13-22. Pitman, R.M. 2006. Wood ash use in forestry – a rev iew of the environmental impacts. Forestry 79(5). Richardson, J., R. Bjorheden, P. Hakkila, A.T. Lowe , et al., (eds.). 2002. Bioenergy from sustainable forestry: guiding principles and practice . Dordrecht, The Netherlands: Kluwer Academic Publishers. Ringer, M., V. Putsche, and J. Scahill. 2006. Large-scale pyrolysis oil production: A technology assessment and economic analysis . Golden, CO: National Renewable Energy Laboratory. Roberts , K.G., B.A. Gloy, S. Joseph, N.R. Scott, e t al. 2010. Life cycle assessment of biochar system s: Estimating the energetic, economic and climate chan ge potential. Environmental Science & Technology

12(2):107-115. Sanderson, M.A., R.P. Egg, and A.E. Wiselogel. 1997 . Biomass losses during harvest and storage of switchgrass. Biomass and Bioenergy 12(2):107-115. Skog, K.E. 2008. Sequestration of carbon in harvest ed wood products for the United States. Forest Products Journal

58(6):56-72. Smith, J.E., and L.S. Heath. 2002. A model of forest floor carbon mass for United Stat es forest types

. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northeastern Research Station. Spokas, K.A. 2010. Review of the stability of bioch ar in soils: predictability of O:C molar ratios. Carbon Management

1(2):289-303. Tarelho, L.A.C., D.S.F. Neves, and M.A.A. Matos. 20 11. Forest biomass waste combustion in a pilot scale bubbling fluidized bed combustor. Biomass and Bioenergy 35(4):1511-1523. Thornqvist, T., and R. Jirjis. 1990. Changes in fuel chips during storage in large piles , Report No. 219. Uppsala, Sweden: Swedish University of Agricultural Sciences, Department of Forest Products. Tollin, J. 2000. Reburning biomass fly ash. CADDET Renewable Energy Newsletter , 13. UK Biochar Research Center (UK BRC). 2009. Working Paper

.November 2014 G-24

Wihersaari, M. 2005. Greenhouse gas emissions from final harvest fuel chip production in Finland. Biomass and Bioenergy

28(5):435-443. Wright, M.M., R.C. Brown, and A.A. Boateng. 2008. D istributed processing of biomass to bio-oil for subsequent production of Fischer-Tropsch liquids. Biofuels, Bioprocessing, and Biorefining 2:229-238.November 2014 G-25



## 6. Addendum I. Literature Reviewed for Storage and Pro cessing at Stationary Sources Facilities

Table G-6. Dataset Sources and Cases by Source for Storage and Processing at Stationary Sources.

Source No of
cases Afzal, M., A. Bedane, S. Sokhansanj, and W. Mahmood . 2010. Storage of comminuted and uncomminuted forest biomass and its effect on fuel quality. BioResources , 5(1):55-69. 6 Buckmaster, D.R. 1992. Indoor hay storage: Dry matter loss and quality cha nges , Fact Sheet PSU/92. State College, PA: Agricultural and Biologi cal Engineering Department, Pennsylvania State University. 2
Collins, M., D. Dirtsch, J.C. Henning, L.W. Turner, S. Isaacs, and G.D. Lacefield 1997. Round Bale Hay Storage in Kentucky. AGR-171, Kentucky Cooperat ive Extension Service. 8 Filbakk, T., HØibØ, O.A., Dibdiakova, J., Nurmi, J. 2011. Modelling moisture content and dry matter loss during storage of logging residues for energy. Scandinavian Journal of Forest Research , 26 (3): 267-277 2
Fredriksson, H. and Rutegård, G. 1985. Storage of c hunkwood and fuel chips in bins. Res. Notes no. 151, Dept of For. Prod. Swed. Univ. Agric. Sci. , Uppsala, Sweden. (In Swedish, English Summary.) 2
Hamelinck C.N., Suurs, R.A.A., and Faaij, A.P.C. 20 05. International bioenergy transport costs and energy balance. Biomass and Bioenergy 29(2): 114–13 4. 2 He, X., Lau, A.K., Sokhansanj, S., Xiaotao, C.J., B i, X.T., and Melin, S. 2011. Dry matter losses in combination with gaseous emissions during the stora ge of forest residues. Fuel 95:662–664. 1 Hess, J.R., Kenney, K.L., Park Ovard, L., Searcy, E .M., Wright, C.T. 2009 Uniform-format solid feedstock supply system: a commodity-scale design t o produce an infrastructure compatible bulk solid from lignocellulosic biomass. Idaho National Laboratory. 22
Hess, J.R., Kenney, K.L., Wright, C.T., Perlack, R. , Turhollow, A. 2009. Corn stover availability for biomass conversion: Situation analysis. Cellulose 1 6 (4): 599-619 6 Hudson, J. B., Mitchell, C. P., Gardner, D. and Sto rry, P. 1988. A comparative study on storage and drying of chips and chunkwood in the UK. In Proceed ings of the lEA/BE Conference Task Ill/Activity 6&7: Production, Storage and Utilizati on of Wood Fuels, Vol. H, ed Danielsson, B.O., Dept of Operational Efficiency, Swed. University of Agricultural Science, Garpenberg, Sweden. 4

Huhnke, R.L. 2006. “Round Bale Hay Storage.” BAE-17 16, Oklahoma Extension Service. 12 Jirjis, R. 2003. Storage of forest residues in bale s. Department of Bioenergy, Swedish University of Agricultural Sciences, Uppsala. p. 23. 2 Jirjis, R., and Nordén, B. 2002. Lagring av buntat skogsbränsle – små substansförluster, inget arbetsmiljöproblem [Stockpiling of composite residue logs (CRLs) – small biomass losses and no health problems]. Skogforsk Resultat 12:1–2. 1
Jirjis, R., Pari, L., Sissot, F. 2008. Storage of p oplar wood chips in Northern Italy. Proceedings of the 8th World Bioenergy Congress, 27-29 Maggio Jonk oping, Sweden: 85-89. 1 Monti, A., Fazio, S., Venturi, G. 2009. The discrep ancy between plot and field yields: Harvest and storage losses of switchgrass. Biomass and Bioenergy 33(5): 841-847. 1 Mooney, D.F., Larson, J.A., English, B.C., Tyler, D .D. 2012. Effect of dry matter loss on profitabilit y of outdoor storage of switchgrass. Biomass and Bioenergy 44: 33-41 20 Nilsson, T. 1987. Comparison of storages of chunkwo od and fuel chips. Report no. 192, Dept of For. Prod, Swed. Univ. Agric. Sci., Uppsala, Sweede n. (In Swedish, English summary.) 3 Nurmi J. 1999. The storage of logging residue for f uel. Biomass and Bioenergy, 17:41–7. 2 Pettersson, M., and Nordfjell, T. 2007. Fuel qualit y changes during seasonal storage of compacted logging residues and young trees. Biomass Bioenergy 31: 782–792. 2 Sanderson, M.A., Egg, R.P., Wiselogel, A.E. 1997. B iomass losses during harvest and storage of switchgrass. Biomass and Bioenergy 12(2): 107-114 4November 2014 G-26 Source No of
cases Shinners, K.J., B.N. Binversie, R.E. Muck, and P.J. Weimer. 2007. Comparison of Wet and Dry Corn Stover Harvest and Storage. Biomass & Bioenergy, 31 :211-221. 7 Thornqvist, T., (Storing of saplings of salix spp.) Report no. 152, Dept For. Prod., Swed. Univ. Agric. Sci., Uppsala, 1984. (In Swedish, English su mmary.) 2November 2014 G-27



## 7. Addendum II. Literature Review of Dry Matter Losses from Woody Biogenic Material Storage

7.1. Introduction
This addendum expands upon the literature review pr esented above in Section 4 on storage dry matter losses for woody biogenic material. Woody bi omass contains roughly 50% (by weight) water when harvested. The water content of biomass can fluctuate between harvest and processing or use for bioenergy as a result of drying or reabs orption of water from precipitation. Dry matter loss (DML) is of interest because dry matter exclud es the weight of water in the biomass and water does not contain biogenic carbon. The biogenic carb on is part of the dry weight fraction of the biomass (e.g., woody biomass dry matter contains ap proximately 50% carbon). For mechanical losses due to feedstock handling, there is no chemi cal change in the feedstock so it is reasonable to conclude that the proportion of carbon in material lost due to mechanical processes is the same as the proportion of carbon in the bulk feedstock. For chemical losses due to degradation of dry matter in storage, the DML can be assumed to be reasonably proportional to the weight percentage of carbon loss from the feedstock (e.g., a 1% loss in dry matter can be assumed to represent a 1% loss in feedstock carbon). 7,8



### 7.2. Storage Losses
Several factors, including storage method and time and related parameters such as material moisture content, temperature and particle size inf luence DML from woody biomass (He et al., 2012). According to Forest Products Laboratory (FPL ) (FPL, 2010), most decay can progress rapidly at temperatures that favor growth of plant life in general. For the most part, decay is relatively slow at temperatures below 10 o C (50°F) and above 35 o C (95°F). Decay essentially ceases when the temperature drops as low as 2 o C (35°F) or rises as high as 38 o C (100°F).
Significant decay can occur only when the moisture content of wood is above the fiber saturation point (average 30% moisture). Only when previously dried wood is contacted by water in the form of rain or condensation or is in contact with wet g round will the fiber saturation point be reached. By itself, the water vapor in humid air will not we t wood sufficiently to support significant decay, but it will permit development of some mold fungi ( FPL, 2010). Springer (1979) explained that

7 One Italian study (Barontini et al., 2014) of popl ar chips from stem wood and crowns representative o f logging residue found that the proportion of carbon in the feedstock before and after 6 months of stor age was similar although the overall dry matter of wood decreased. For crowns, the fraction of carbon in t he dry matter varied from 47.7 to 46.9% before and aft er storage, respectively. For stem wood, the fracti on of carbon in the dry matter varied from 47.2 to 47. 8% before and after storage, respectively.
8 In addition to observing a loss of dry matter over 1 year of storage, Nurmi (1999) found only a sligh t variation in the carbon fraction in the dry matter of comminuted Norway spruce logging residue feedsto ck (i.e., an increase of the carbon fraction in the dr y matter from 50% to 51% before and after storage, respectively).November 2014 G-28

wood dried below its fiber saturation point (20% to 24% moisture) is not subject to bacterial and fungal attack.
Anheller (2009) noted that DML becomes higher when woody biomass is stored for longer periods and in larger piles. The shape of the piles influen ces the heat development during storage of biomass. To minimize biomass losses, Anheller (2009 ) recommended keeping the biomass as dry as possible before storage, keeping as large a particl e size as possible, preferably under roof, uncommuted and uncompacted, as long as possible bef ore combustion. Anheller (2009) suggested that the most advantageous way to store wood fuels is as uncomminuted fuels, such as whole tree logs, reducing the size of fuel wood to chips as cl ose as possible to the time of use, to shorten the time of storage as chips and decrease degradation l osses associated with microbial activity. Similarly, Wilkerson et al. (2008) cited studies re commending that comminuted material be used as quickly as possible after grinding to minimize DML.

7.2.1. Forest Biomass Storage Methods There are a variety of forest biomass storage metho ds depending on the source and intended use for the material. Forest biomass may be stored as w hole logs, in wood chip piles, as logging residue prior to chipping (piled or in bales), as bark, or as wood pellets. Whole logs may be processed into forest products such as lumber or veneer or chipped for use in pulp, wood product, or bioenergy production. Bark is often stored separately from wo od chips, especially at mills with different uses for bark (e.g., as fuel) and chips (e.g., forest pr oduct raw material).
Whole Logs Wood can be too wet for decay as well as too dry. I f the wood is water-soaked, the supply of air to the interior of the wood may not be adequate to sup port development of typical decay fungi. For this reason, logs stored in a pond or under a suita ble system of water sprays are not subject to decay by typical wood-decay fungi. For logs, rapid conversion into lumber or storage in water or under a water spray is used to avoid fungal damage. (FPL, 2010)
FPL (2004) explained that, today, softwood logs dec ked in a log yard are typically protected by water sprinkling during warm weather. Sprinkling pr ovides an effective method of reducing checking, sapwood stain, and decay. Sprinkling will not protect against insect attack, although it tends to be more effective than dry land storage. T o be effective, the ends of logs and exposed wood must be kept wet continuously during the entire sto rage period. Sprinkling reduces available oxygen, thereby deterring sapwood staining and deca y. Pond storage (e.g., in mill ponds, lakes, rivers, and saltwater estuaries), although once com mon practice, is seldom used in the continental U.S. today (FPL, 2004).
Sprinkling of logs has been practiced for decades. In 1959, Wright et al. of the USDA Forest Service Station (Portland, Oregon) discussed sprinkling to prevent decay in decked Western hemlock logs. Wright et al. (1959) noted that wood containing les s than 20% moisture (dry-weight basis) does not decay, and for decay to progress fairly rapidly , a moisture content of 30% or more is required. However, oxygen is also necessary for the growth of wood-rotting fungi, and decay will not developNovember 2014 G-29

when complete saturation with water eliminates air from the wood. Based on experimental research, Wright et al. (1959) concluded that:

• Both green and old western hemlock logs can be stor ed in un-sprinkled decks for 1 year without appreciable additional decay;

• Green logs can be decked for as long as 2 years wit hout serious additional loss, but old logs containing decay when decked suffer considerable lo ss; and

• Constant sprinkling with water during the warmer mo nths of the year, to keep ends and faces of logs wet at all times, effectively inhibit s the development of additional decay in old logs. Chip or Bark Piles Outside chip storage at pulp mills has been practic ed since the 1950’s in order to maintain large inventories without the use of bins or silos. In Sm ook (2002), it is recognized that losses of 1% wood substance per month are typical of outside sto rage due to a combination of respiration, chemical reactions, and micro-organism activity. Sm ook (2002) reports that wood losses and degradation during outside chip storage can be mini mized with effective chip pile management such as ground barriers (e.g., concrete, asphalt), fines screening, avoiding contamination of sound wood with decayed wood, and minimizing storage time

. While bark may be a contaminant in the pulping process, bark is less problematic for chips destined for use as boiler fuel (Smook, 2002).
Mill residues may be stored in piles (indoors or ou tdoors) or silos. Mills and other wood-using facilities keep chip or sawdust piles on-site or at nearby facilities when supply is low. Woody biomass is reduced in size in the forest and then t ransported for storage, or it is transported, reduced in size at the mill, and then stored. The r esulting material, usually chips, is stored outside in large piles and under cover in large silos or bins. Chips stored in bins are typically used within several hours or days while silos are used for long er-term storage needs. Silos and bins protect against contamination while at the same time allowi ng for uniform feeding and metering of the material. While storing, comminuted biomass makes h andling and transport relatively easy. If not managed carefully, the biomass will succumb to DML and in some cases self-ignition. High temperatures and acetic acid odor are signs that a chip pile is in danger of DML and self-igniting. Additionally, chip piles with excessive mold and fu ngi growth can lead to health risks for humans (Hubbard et al., 2007).
Dry matter loss of forest biomass, which i ncludes the degradation of lignin, cellulose, and hemicellulose, occurs when woody biomass, in any fo rm, is not used immediately after harvesting and has to be stored within a facility. The degree to which DML occurs depends largely on the material’s moisture content. Woody biomass having h igh moisture content is more susceptible to colonization by fungi and mold and at a faster rate . These microorganisms, via metabolic activity, generate heat, which in turn accelerates oxidation, moisture adsorption, hydrolysis, pyrolysis, and other chemical processes resulting in DML (Hubbard et al., 2007). Fungal activity is also responsible for the fermentation of the fractionated holocellul ose. However, when this happens in the presence of aerobic bacteria and oxygen, they can change the ethanol produced by natural degradation processes into acetic acid or vinegar. The degradat ion of biomass through hydrolysis, fermentation,November 2014 G-30

and oxidation that results in the production of ace tic acid lowers the average pH of the moisture in the wood chips. This decrease in pH can lead to rap id, premature decay of the piled material. For example, in a chip and bark pile during six months of storage, the initial pH dropped from nearneutral to neutral (5 to 7) to an average of 4 (Sla ven et al., 2011).
As noted above, biomass moisture can lead to an inc rease in temperature in piled biomass, which in turn leads to DML. As shown in Figure G-4, Wihersaa ri (2005) used temperature to illustrate the pattern of DML for dried and fresh (green) feedstoc k. Wihersaari (2005) and others explain how pile temperature relates to DML as follows:

• Dry matter that is intentionally dried through a me chanical process and then stored undergoes an initial increase in temperature, which in turn increases microbial activity within the stored pile and acts as a catalyst to ma terial degradation. However, this increase in temperature is short-lived since after approxima tely one week, microbial colonies begin to die out due to their residence in an inhospitabl e, low-moisture environment. The death of these microbes in turn reduces temperature within t he pile and provides a decline in the rate of decay of the stored material (Wihersaari 20 05).

• Stored piles of fresh forest matter generally do no t reach internal temperatures as high as piled dry forest materials, but they maintain a hig her temperature (above 40 o C [104 o F]) much longer than dried forest materials. As Figure G-4 shows, fresh forest matter tends to maintain a temperature above 40 o C (104 o F) for longer than 27 weeks. While fresh forest materials stored outdoors will lose less material d ue to mechanical losses such as wind, due to their weight, storing forest materials without d rying them first creates problems. Storing fresh forest materials in large piles increases mic robial activity by providing a hospitable environment for growth, which leads to rapid materi al decay. These fresh forest material piles, with extended periods of high internal tempe ratures, are also more susceptible to spontaneous combustion (Wihersaari, 2005; Slaven et al., 2011; Hubbard et al., 2007).November 2014 G-31

Figure G-4. Simplified Principle of Degradation Behav ior (Wihersaari, 2005) In his study, Wihersaari (2005) found that when the average chip size in a pile of dried forest matter was 30 millimeters (mm), the temperature ros e to 40 to 50 o C (104 to 122 o F); but when the average chip size was 70 mm, the temperature did no t rise above 30 o C (86 o F). Wihersaari (2005) explained that the biological activity producing he at takes place on the surface of the chips; the smaller the chip size, the larger the surface area per volume and consequentially the higher the biological activity leading to higher temperatures. Hubbard et al. (2007) explained that the small particle size gained by chipping restricts air flow and prevents heat dissipation, while chipping releases the soluble contents of plant cells provid ing microbes with nutrients.
Anheller (2009) also explained that heat developmen t can occur in in chip piles if the moisture content is high enough for microbial growth, usuall y >20% for wood fuel. In fresh wood chips, respiration heat from the living cells contributes to initial heat development and the chipping process releases soluble sugar from the wood, which together with heat, moisture and oxygen can create a favorable environment for microbes. By chi pping the material, the area where the microbes can attack increases. In a pile of chipped material , the air movement is also more limited because of the smaller material, which prevents heat dissipati on and causes heat accumulation and thereby increases degradation losses. In large piles, the m aterial can additionally become more compact because of weight, which further amplifies the abov ementioned factors (Anheller 2009).
Similarly, Janze (2011) concluded that particle siz e within a biomass pile effects moisture absorption, heat build-up, heat dissipation and DML . Piles containing a large amount of fines absorb greater amounts of water, generally heat up faster due to greater microbial action, and restrict air movement through the pile, thereby limiting heat di ssipation; all of this leading to increased DML and possibly spontaneous combustion. Conversely, pi les consisting of large wood chunks heat upNovember 2014 G-32

more slowly, permit better air flow due to the larg e voids, dissipate the heat faster due to better ai r circulation, have lower rates of microbial action a nd lower DML.
Janze (2011) recommended that for long storage peri ods that fuel be stored in fairly large particles and only re-processed to the final size shortly bef ore usage in the boiler. Janze (2011) further recommended that radically different types of mater ial such as clean wood chips, short rotation coppice and forest debris should not be stored in t he same pile, as they decompose at different rates, which can lead to spontaneous combustion. Su ch materials should be stored separately and blended just prior to transport to the boiler (Janz e, 2011). Logging Residue Prior to Chipping Logging residues may be left in small piles at the feedstock production site, chipped after some time, or compacted into bundles. Chipping is the me thod most commonly used in North America (Afzal et al., 2010). Hubbard et al. (2007) explain ed that there are several advantages to storing unconsolidated woody biomass immediately after harv est. When stored unconsolidated in mounded piles of moderate size, leaves and needles can fall, reducing the material’s ash content. Moreover, when woody biomass is stored in smaller p iles, drying occurs (i.e., moisture escapes through leaves and other open wood surfaces). This process lowers the moisture content and increases the heating value. Drying occurs when bio mass is stored in windrows as well, but it is not as efficient as small piles because foliage is not allowed to drop. Additionally, when stored on the harvesting site, vital nutrients are released back into the soil. The major disadvantages to storing woody biomass immediately after harvest on-site are (1) the need for detailed inventory tracking, (2) the cost of forgoing reforestation until the pi les of biomass have been removed, and (3) the cost and time-sensitivity of having a contractor return to the site to collect, pre-process, and transport the material to the wood-using facility (Hubbard et al., 2007).
Woody biomass can be bundled and stored under cover to gain advantages (ease of handling and transport) that come along with storing chipped mat erial. At the same time this approach protects the material from the disadvantages that come along with chipped material: DML, moisture retention, heat generation, and health hazards (Hub bard et al., 2007). Logging Residue Chips Wilkerson et al. (2008) noted that whole tree chips containing a large proportion of leaves and bark have a propensity to self-heat and can possibly cau se chip pile fires. Wet chip pile heating can be mitigated by keeping pile heights under 9 meters (m ) (30 ft) and/or limiting the amount of time the chips are piled to less than 10 days. Wilkerson et al. (2008) suggested that whole tree chips for energy that must be stored for more than 10 days ma y need some form of treatment to avoid degradation or spontaneous combustion. Drying to le ss than 20% moisture is the only viable option for safe long term storage of whole wood chips, but drying results in added expense, and dried fuel would need to be protected from rain with shelter o r covering.
Earlier, Springer (1979) also explained that whole- tree chips deteriorate more rapidly than clean, debarked chips and present a greater hazard for spo ntaneous ignition when stored in outdoor piles. Springer (1979) noted that whole-tree chips can be stored for only a short period of time to prevent ignition, that frequently rotating storage piles in creases handling costs, and drying preventsNovember 2014 G-33

deterioration and heating if the dried chips are st ored under cover. According to Springer (1979), the costs of drying can be recovered if the chips a re burned for fuel. Springer also referenced studies conducted in Norway in the last 1970’s that concluded that bark and foliage increase the rate of chip deterioration, with chip weight loss d uring storage following the order: clean debarked chips < whole-tree chips < bark < foliage (Springer , 1979).

Wood Pellets Wood used to form woody biomass pellets is dried pr ior to the pelletizing process. Drying temperatures from 100 to 400°C (212 to 750 o F) are used to reduce moisture content to less than 10% (Yazdanpanah et al., 2014). The average moistur e content of commercially produced pellets (in Sweden) is about 10% to 12% (Lehtikangas, 2001) . The Pellet Fuels Institute (PFI) standards for pellets produced in the U.S. range from less than 8 % to 10% moisture (PFI, 2011).
Wood pellets have flow characteristics and are well suited for storage in silos (Janze, 2011). Pellets must be kept dry during storage (Janze, 2011; NEBTW G, 2012).

7.2.1. Storage Time and Challenges in Tracking Biomass Inv entory Janze (2011) explained that the amount of woody bio mass storage required depends upon:

• Minimum fuel storage; how much storage the plant’s financiers, regulators, clients and/or insurers require as a minimum to ensure continuous operation;

• Known fuel delivery interruptions; the length of pe riods when fuel delivery can be anticipated to be interrupted, say for long weekend s, or when supplier’s mills are shutdown;

• Reliability of fuel delivery; there must be enough fuel on hand to cover likely transportation delays;

• Contingency supplies, to cover periods when forests are inaccessible for fire season, the wet season or during spring break-up;

• Fiber supply contractual requirements; the ability to continue to stockpile fuel when the power plant is shut down for annual maintenance; an d

• Often the space available dictates the size and sha pe of the storage pile and the maximum amount of fuel that can be stored.
According to Janze (2011), typically, biomass-fired power plants [in the European Union] will stockpile a minimum of 20 to 30 days of fuel, but m any will store for 60 days or more (i.e., 1 to 2 months storage). Wilkerson et al. (2008) noted that some areas of the U.S. can harvest woody biomass year around, and long term storage is not n ecessary. However, in other areas a 1 to 6 month supply of biomass may be required.
Hubbard et al. (2007) reported that c hip-pile storage is most common type of storage in the southeastern U.S. and recommended that owners short en the storage time of chipped material to minimize the risk of microbial decomposition which will in turn decrease DML. Hubbard et al. (2007) indicated that the ideal storage period typi cally varies from 2 to 6 weeks (0.5 to 1.5 months) as determined by each facilities wood supply situat ion.November 2014 G-34

Smook (2002) indicated that, because chip deteriora tion is largely a function of storage time, the most effective way to minimize losses is to minimiz e storage time (e.g., use first-in, first-out practices). Referring to pulp wood chips, Smook (20 02) noted that optimum chip handling depends on use for the chip. For example, 2-month storage o f chips used for sulfite pulping reduces extractives problematic in the sulfite pulping proc ess. However, if maximum recovery of extractives-based pulping byproducts (e.g., tall oi l, turpentine) is desired then fresh chips should bypass storage in order to maximize byproduct yield

. Janze (2011) explained some of the challenges with tracking biomass pile inventory, noting that problems stem from the variability in biomass physi cal properties, including different species, moisture contents, and bulk densities, and varying amounts of compaction and dry fiber loss. Additionally, using multiple inventory tracking mea sures, including “green” or “bone dry” mass, solid wood or bulk densities, and solid wood volume s or bulk volumes, lead to inventory errors when converting from one measure to another and bac k again (Janze 2011).



### 7.3. References
Afzal, M.T., A.H. Bedane, S. Sokhansanj and W. Mahm ood. 2010. "Storage of Comminuted and Uncomminuted Forest Biomass and Its Effect on Fuel Quality." BioResources 5(1):55-69. Anheller,	M.	2009. Biomass	Losses	During	Short-Term	Storage	of	Bark	an d	Recovered	Wood .	Swedish University	of	Agricultural	Sciences. Barontini,	M.,	A.	Scarfone,	R.	Spinelli,	F.	Gallucc i,	E.	Santangelo,	A.	Acampora,	R.	Jirjis,	V.	Civita rese and	L.	Pari.	2014.	Storage	Dynamics	and	Fuel	Qualit y	of	Poplar	Chips. Biomass	and	Bioenergy 62:17-25. Forest	Products	Laboratory	(FPL).	2010 .	Wood	handbook—Wood	as	an	engineering	material

. General	Technical	Report	FPL-GTR-190.	Madison,	WI: U.S.	Department	of	Agriculture,	Forest Service,	Forest	Products	Laboratory. Forest	Products	Laboratory	(FPL).	2004. Storage	of	softwood	Logs .	Madison,	WI:	U.S.	Department	of Agriculture,	Forest	Service,	Forest	Products	Labora tory.	February	2004. He,	X.,	A.K.	Lau,	S.	Sokhansanj,	C.J.	Lim,	X.T.	Bi and	S.	Melin.	2012.	Dry	Matter	Losses	in	Combinatio n with	Gaseous	Emissions	During	the	Storage	of	Forest Residues. Fuel

95:662-664. Hubbard,	W.;	L.	Biles;	C.	Mayfield;	S.	Ashton	(Eds. ).	2007. Sustainable	Forestry	for	Bioenergy	and	Biobased	Products:	Trainers	Curriculum	Notebook .	Athens,	GA:	Southern	Forest	Research Partnership,	Inc. Janze, P. 2011. Biomass	Storage	Pile	Basics. In	Biomass	Handling:	All	you	wanted	to	know	about biomass	handling	but	were	afraid	to	ask

. Lehtikangas,	P.	2001.	Quality	Properties	of	Pelleti sed	Sawdust,	Logging	Residues	and	Bark. Biomass and	Bioenergy

20(5):	351-360.
NEBTWG	2012. Wood	Pellets:	Quality	and	Safety	Assurance	for	Tran sportation	and	Delivery. Document	created	by	the	Northeast	Biomass	Thermal	W orking	Group	(NEBTWG),	with assistance	from	the	Biomass	Thermal	Energy	Council (BTEC)	and	the	Pellet	Fuels	Institute (PFI).	Available	at http://nebioheat.org/pdf/woodPellets_safetyTranspor tDelivery_web.pdfNovember	2014 G-35

Nurmi,	J.	1999.	The	Storage	of	Logging	Residue	for Fuel. Biomass	and	Bioenergy 17(1):41-47. Pellet	Fuels	Institute	(PFI).	2011. Pellet	Fuels	Institute	Standard	Specification	for Residential/Commercial	Densified	Fuel. Available	at http://pelletheat.org/wpcontent/uploads/2011/11/PFI-Standard-Specification- November-2011.pdf Slaven,	I.,	E.H.	and	D.Cassens.	2011. Properties	of	Wood	Waste	Stored	for	Energy	Producti on

. Department	of	Forestry	and	Natural	Resources	at	Pur due	University.	ID-421-W. Smook,	G.	2002. Handbook	for	Pulp	&	Paper	Technologists. 3rd	Edition.	Angus	Wilde	Publications Inc.,	Bellingham	WA. Springer,	Edward	L.	1979. Should	Whole-Tree	Chips	for	Fuel	Be	Dried	before	St orage? FPL-0241. Wihersaari,	M.	2005.	Evaluation	of	Greenhouse	Gas	E mission	Risks	from	Storage	of	Wood	Residue. Biomass	and	Bioenergy

28(5):444-453. Wilkerson,	E.	D.B.	Blackwelder,	R.D.	Perlack,	D.J. Muth,	and	J.R.	Hess.	2008. A	Preliminary	Assessment of	the	State	of	Harvest	and	Collection	Technology	f or	Forest	Residues .	Oak	Ridge	National Laboratory.	ORNL/TM-2007/195. Wright,	E.,	A.C.	Knauss,	R.M.	Lindgren.	1959.	Sprin kling	to	Prevent	decay	in	Decked	Western Hemlock	Logs.	U.S.	Department	of	Agriculture,	Fores t	Service,	Pacific	Northwest	Forest	and Range	Experiment	Station.	Portland,	OR.	No.	177. Yazdanpanah,	F.,	S.	Sokhansanj,	H.	Rezaei,	C.	J.	Li m,	A.	K.	Lau,	X.T.	Bi,	S.	Melin,	J.S.	Tumuluru,	and C.S. Kim.	2014. Measurement	of	Off-Gases	in	Wood	Pellet	Storage. Intech. http://cdn.intechopen.com/pdfs-wm/46210.pdf