Council of Industrial Boiler Owners (CIBO)

Suggested Method for Determination of Fuel Variability Factor for

Hydrogen Chloride (HCl) Emission Standard for Solid Fuel Boilers for the Boiler MACT Rule

This memo supplements the discussion of the CIBO and OMB on December 18, 2012, regarding the

proposed reconsidered Boiler MACT rule. CIBO provided a series of tables and explained their relevance

during the meeting. This memo memorializes the explanation.

Summary

.

CIBO analyzed the data and inventory and EPA data analysis for HCl emissions for solid fuel boilers. HCl

controls are among the highest cost controls required under the Boiler MACT rule. The standard as

proposed in the reconsideration rule

‐‐ 0.022 lb/mmBtu HCl

‐‐ severely limits boiler owners’ options for

controlling HCl. CIBO’s analysis of EPA’s data shows that a logical analysis of the data lead to an

emission limit in the range that would permit more than one control option for sources, reducing the

compliance burden on sources by $1 Billion, while still greatly reducing HCl emissions.

Specific conclusions.

Utilization of all legitimate fuel quality data for the best performers and a three sigma based fuel

variability factor would result in a higher HCl emission limit for existing sources and allow a major

reduction in compliance capital cost (~$1 Billion) with very little impact on HCl emission reductions. The

cost savings are directly associated with existing jobs that would be put at risk with the 0.022 lb/mmBtu

HCl limit.

Use of a higher HCl limit (0.04

‐ 0.05 lb/mmBtu) would allow increased utilization of lower cost DSI for

HCl control while still achieving a high level of HCl reductions.

If regulated users can obtain lower Cl coal at competitive costs, the increased application of DSI with a

slightly higher HCl limit will further reduce cost impacts.

EPA analysis of Chlorine data set for top performing solid fuel units.

In its analysis of the data set for determining an appropriate fuel variability factor (FVF), EPA attempted

to identify and remove “outliers” to ensure the analysis was based on representative data. Outliers,

generally described as data points that are not representative of the underlying data set, are typically

due to one of two factors: Either the data does not exhibit a statistical normal distribution, or due to

error in the measurements. Nowhere in the record does EPA claim that US coal reserves fail to exhibit

normal distribution, so CIBO infers that EPA must interpret outliers as being the result of measurement

error.

The identification of outliers is a fundamentally subjective process, which is best informed by an

understanding of both the underlying properties being measured and the accuracy of the measurement

techniques being employed. Given that EPA does not possess the same detailed understanding of the

sampling protocols, analysis techniques, and statistical accuracy as the owner/operators of the topperformers, the burden is on the agency to use a robust and widely accepted protocol for identifying

outliers prior to calculating the FVF used to set the standard. EPA’s method to identify any data point

outside of three standard deviations from the mean is a not a valid method to identify outliers.

CIBO’s rationale and suggested methods for determination of the fuel variability factor (FVF).

In comments submitted February 21, 2011 by CIBO on the December 23, 2011 Proposed

Reconsideration, CIBO requested that fuel variability be considered in the setting of the emission

standard for mercury (Hg) and hydrogen chloride (HCl) applicable to solid fuel boilers:

EPA has the discretion and should include a fuel variability factor in the MACT floor analysis

for the solid fuel subcategory HCl and Hg limits so fuel pollutant content variability among the

top performers is adequately considered. This approach would make achievability of these

standards more viable, while reflecting the real world operational and fuel variability that boilers

experience. (Reference page 11 of CIBO comments).

The December 23, 2011 proposal included an HCl emission standard for solid fuel boilers of 0.022

lb/mmBtu.

The MACT Floor memorandum (“Revised MACT Floor Analysis (November 2011) for the Industrial,

Commercial, and Institutional Boilers and Process Heater National Emission Standards for Hazardous Air

Pollutants

– Major Source”) includes a discussion of EPA’s method for calculating FVFs but then states

that it was unnecessary to incorporate additional fuel variability through use of a FVF beyond the UPL

calculation derived from stack test data from the top performers.

This standard is very stringent for coal fired boilers to meet and CIBO believes it does not represent

emission levels for the top performers when fuel variability is properly considered. CIBO has estimated

capital costs of compliance with the proposed standard at $3.1 Billion. This cost is driven by sources

being forced to install very expensive scrubbers to control HCl to these levels. In many cases, scrubbers

will not be practical due to space constraints. A less expensive technology is duct sorbent injection (DSI)

and would be the likely choice for many sources if they have any hope of compliance with a stringent

HCl emissions standard. CIBO re

‐ analyzed compliance assuming DSI deployed on an existing boiler

equipped with an electrostatic precipitator (ESP) can achieve a 50 percent reduction of HCl and DSI

deployed on a boiler equipped with a fabric filter (FF) can achieve an 80 percent reduction of HCl. CIBO

also analyzed a scenario where a source would first reduce the chlorine in its fuel supply to 0.12 percent

and then deploy the technology to meet the standard. Units that could not meet the standard using the

DSI alternative were assumed to resort to the more expensive scrubber option ($8 Million instead of $2

Million for a 250 mmBtu/hr capacity boiler). The results of this analysis are shown in the table below.

CIBO then conducted a sensitivity analysis to determine cost savings associated with various higher

emission standards. Those results are shown as well in the table below and they reveal that a nearly $1

Billion capital cost savings is possible with a standard of 0.04 and another $1 Billion savings with a

standard of 0.06 lb/mmBtu.More detailed explanation of the CIBO/URS capital cost estimates for HCl control for various

assumptions in the above table.

 Data on units and emissions was obtained from the most recent EPA database. Existing unit

emission rates were used for boilers where provided. Emission rates were assigned to other

boilers based on database identified boiler and emission control configuration using the

emission rates in the database as a guide.

 Various assumed HCl limits are in the left column. Latest EPA HCl limit for existing solid fuel

fired boilers was 0.022 lb/mmBtu. Costs for higher levels were calculated for 0.04, 0.05, and

0.06 lb/mmBtu limits for comparison purposes.

 First cost column (HCl Upgrade Cost) was factored from the base case assumed wet scrubber

installation capital cost for a 250 mmBtu/hr coal fired boiler ($8 Million) scaling cost with 0.6

power factor for each boiler based on design heat input (note that the scrubber cost is also

considered representative of a spray dryer absorber or circulating dry scrubber for this analysis).

Where existing acid gas control was not shown to meet the limit, a lower scrubber upgrade cost

($4 Million for the 250 mmBtu/hr reference boiler) was applied, using the same scaling factor

approach. As shown, there are modest decreases in capital cost for the incremental increase in

emission limits.

 An attempt was made to determine the potential capital cost requirements for those emission

rates including consideration of potential application of Dry Sorbent Injection (DSI) upstream of

a new or existing baghouse or electrostatic precipitator (ESP). The belief is that lower cost DSI

could be installed for boilers that do not require a very high percent reduction in HCl. For this

analysis it was assumed that the capital cost for adding DSI to the base case 250 mmBtu/hr

boiler would be $2 Million, and that cost was also factored with boiler design heat input. For

applicability of using DSI, it was further assumed that 50% HCl control could be achieved with

use of downstream ESPs and up to 80% HCl reduction could be achieved with use of a

downstream baghouse. These were basic assumptions, knowing that different sorbents and

unit design/operation would impact control effectiveness. Results for this estimate approach

are in the middle column (Alternate HCl Upgrade). As indicated, increasing the HCl limit for

existing boilers to 0.04

‐ 0.05 lb/mmBtu range would likely result in a capital cost saving of

approximately $1 Billion.

 One additional step was taken to determine sensitivity of the costs to coal chlorine content. The

far right cost column (Alternate HCl Upgrade Cost with Coal Chloride Cap of 0.12%) uses the 

### *Assume sorbent injection if have ESP and need 50% control or have FF and need 80%

control.

**0.095 lb/MMBtu HCl HCl Limi t HCl Upgrade Cos t A lternate* HCl Upgrade

Cos t A lternate HCl Upgrade Cost with

Coal Chloride Cap of 0.12%**

0.022 $3.1B $2.8B $2.2B

0.04 $2.9B $1.9B $1.9B

0.05 $2.8B $1.8B $748M

0.06 $2.7B $912M $704Msame methodology as the middle column (include DSI installation and costs) but limit the coal

chlorine content to 0.12% in coal, which is near the average level for coal in the database and is

a typical Cl level for eastern bituminous coal. Obviously every facility would not be able to

obtain coal with that Cl level or lower, but this approach shows potential savings if that could be

done. As shown, there is a further slight reduction in costs that becomes more significant at

higher HCl limits.

 Notes :

o All of the above costs are only project level capital cost estimates that do not account

for particularly difficult retrofit situations.

o The costs also do not include additional ongoing O&M costs associated with the

controls; those costs would be significant, but that is an additional complication that is

more difficult to determine.

o The estimates also assume that all units would continue firing coal and commit to some

means to control HCl emissions to meet the emission limit. Obviously some units will

cease operation, some will convert to natural gas or other fuel firing, and some boilers

will be replaced with gas fired boilers or CHP systems. Those options all entail some

level of cost that will be decided on a unit by unit basis and more stringent HCl limits

would drive increased use of alternative fuel approaches.

EPA’s methodology to determine the FVF for the solid fuel HCl standard.

CIBO offers the following description and recommendations.

EPA’s methodology :

1. EPA located fuel chlorine data for 13 out of the 46 top performers.

2. EPA determined the chlorine input during the stack test for each of the 13 units. This

resulted in 1512 data points.

3. Then, EPA determined a ratio of the chlorine in each fuel sample other than during the stack

test and divided it by the stack test fuel chlorine concentration to determine a ratio for each

sample.

4. Next, EPA determined the mean plus three standard deviations for the data for each unit

and excluded all samples that were above three standard deviations as an outlier. 37

samples from one unit (Eastman Boiler 30) were identified as outliers and removed from the

data set.

5. EPA then set the FVF as the mean of all of the ratios above 1. See the chart below:CIBO does not believe this method properly accounts for the variability in the data set. First, outliers are

not defined as any data outside of the mean plus three standard deviations. Outliers are defined as data

that “stands apart” from the rest of the data. The chart below shows all the data from the one source

(Eastman) which had outliers as defined by EPA. There are no outliers in this dataset. 0.000 1.000 2.000 3.000 4.000 5.000 6.000 7.000 

# Plot of Solid Fuel Sub

‐ Category HCl Top Performers

Chlorine Data

(Ratio = Sample/Stack Test Sample)

With EPA Outliers Removed

Ratio EPA Method:

FVF = 1.91 Excludes

37 data

points

below 6.5Secondly, as can be seen in the table below, all of the 13 units, with the exception of the Eastman unit,

had very few data points. These typically are from a one month period of time around the stack test as

required by EPA in the Information Collection Request. The Eastman unit, on the other hand, had data

from a five year period of time and depicts the real

‐ world variability of coal as the market ebbs and

flows over time. To use a FVF set at the mean (note the mean is driven by the Eastman boiler since it

represents 90 percent of the data set) would result in chlorine feed rates above the standard 50 percent

of the time.

CIBO suggests one of two methods. Either, EPA could use the maximum ratio (6.53) or the mean plus

three standard deviations (99 th percentile) which, as shown in the chart below, would equal 3.7. 0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00

1/26/2006 1/26/2007 1/26/2008 1/26/2009 1/26/2010 

# Eastman

Boiler 30

HCl Ratios (Sample/Stack Test)

Ratio

Count

with

Outliers Max with

Outliers Count

without

Outliers Max

without

Outliers

ARAnthonyForestProductsSN

‐ 12 9 2.34 9 2.58

EastmanBoiler 30 1359 6.53 1322 3.84

GAGPMadisonPly800 Wood Waste Boiler 12 0.95 12 1.11

IAArchersDanielsMidlandDesMoinesAsea Boiler #1 12 1.03 12 1.06

IARoquetteAmericaCirculating Fluidized Bed Boiler (121) 11 2.03 11 2.75

INAlcoaWarrickUnit #3 21 1.19 21 1.76

MSWeyerhaeuserBruceAA

‐ 002 No. 2 Boiler 15 1.41 15 1.59

NDCargillWestFargoFoster Wheeler Boiler (EU43) 9 1.00 9 1.11

SCCogenSouthB001

‐ Main Boiler 9 1.12 9 1.16

SCMarlboroPaperHogged Fuel Boiler 12 0.92 12 1.26

VASmurfitStoneWestptPB08 25 1.04 25 1.75

VAUniversityofVirginia7103

‐ 1

‐ 01R 9 0.91 9 0.97

WINewPageBironB24 9 2.14 9 2.310.000 1.000 2.000 3.000 4.000 5.000 6.000 7.000 

# Plot of Solid Fuel Sub

‐ Category HCl Top Performers

Chlorine Data

(Ratio = Sample/Stack Test Sample)

Ratio Recommended

FVF = Mean + 3

Standard

Deviations = 3.7 No outliers indentified