ENGINEERING LABORATORY TEST REPORT

TITLE OF REPORT: Analysis of THC as a Rich Burn Formaldehyde Surrogate PROJECT NO.: 22208

MATRIX ONE DOCUMENT NO(S): TR-4854 PRODUCT LINE: VHP GSI

OWNER: Dennis Spaulding

APPLICABLE DOCUMENTS: X-3281, EPA-454/R-00-037 (EPA – CSU testing); CAE Report #10845

TEST PURPOSE: Develop pre-cat and post-cat CH2O – THC correlations for rich burn, natural gas engines, using a test st ructure that mimics the EPA-454/R00-037 testing done by the Environment al Protection Agency (EPA) at Colorado State University (CSU). Also, extend the test matrix to provide sufficient statistical power to likely identify and quantity factors that could affect this correlati on and to ensure a robust solution.

ABSTRACT: The test was run using Clean Air Engineering’s (CAE) FTIR instrumentation as well as Waukes ha’s in lab emissions bench. The test block that mimics the EPA-CS U test produced a pre-cat THC correlation that was superior to th e EPA-CSU CO lean burn correlation. When the extended matrix was cons idered the pre-cat correlation developed was not as good. A more complicated pre-cat correlation was developed from the ex tended matrix that was superior to both the simple “block 1” correlation and the EPA-CSU CO lean burn correlation. Post-cat data were also taken. Post-cat formaldehyde does not appear to require a surrogate correlati on as virtually complete CH2O destruction occurred regardless of pre-cat CH2O over a broad range of operating conditions.

KEY WORDS: Rich burn, THC, CH2O, formaldehy de, surrogate, correlationTR-4854 Analysis of THC as a Rich Burn Formaldehyde Surrogate Page 2 of 17

INTRODUCTION Regulatory changes are being considered relati ve to formaldehyde emissions from IC engines (RICE NESHAP rule, reference 74FR9698, Docket ID No. EPA-HQ-OAR-2008- 0708). CO measurement is expected to co ntinue to be allowed as a surrogate for actual formaldehyde (CH2O) measurement on lean burn engines. For rich burn, spark ignited engines it is expected that actual formaldehyde measurement will continue to be required unless a suitable surrogate correlation can be demonstrated. A recent post-review of test X-3281 indica ted a strong likelihood th at a pre-catalyst THC-CH2O correlation exists, but those dat a were not taken with the intent of developing a correlation. Also, those data did not include any post-catalyst measurements. However, the data and analysis were good enough to be submitted to the EPA to show the potential surrogate relationship and allow time for a proper data collection and review to be conducted.

OBJECTIVES Develop pre-cat and post-cat CH2O – THC corre lations for rich burn engines, using a test structure that mimi cs the EPA-454/R-00-037 testing done by the Environmental Protection Agency (EPA) at Colo rado State University (CSU) in 2000. Also, extend the test matrix to provide suffici ent statistical power to likely identify and quantity factors that could affect this correlation and to ensure a robust solution.

CONCLUSIONS The simple correlation developed using the 16 point matrix that mimics the EPA-CSU test (block 1) between pre- cat THC and pre-cat CH2O for rich burn engines is better than the lean burn CO - CH2O correlation fr om the EPA-CSU test. The EPA-CSU test in 2000 is believed to have been used by the EPA to determine the surrogate correlation between CO and CH2O for lean burn engines. When both data blocks are included in the analysi s, the simple correlation fit is not as good as the block 1 only fit and slightly worse than the fit believed to have been used for the lean burn correlation. However, the sl ope is about the same so, while adding more data makes the correlation more “noisy”, the “strength” of the corr elation remains. While this simple correlation applied to a broad data set or application base may be a slightly “noisier” correlation than is expected from block 1 alone, block 1 was based on a test design that mimicked what is thought to have been used for the lean burn CO correlation so it will likely be no different than what is seen in practice with the lean burn correlation. Also, an equation was developed to “clean up” this correl ation considerably using engine parameter measur ements that are readily avai lable through Waukesha’s ESM control ESP interface. There does not appear to be a good correlati on between post-cat CH2O and either precat CH2O or post-cat THC. However, that does not appear to be necessary as virtually complete CH2O reduction occurs regardless of the fairly broad conditions varied in this test.

RECOMMENDATIONS It is recommended that the simple THC corre lations created and shown in Figure 14 be submitted to the EPA as the rich burn engi ne out formaldehyde surrogate that isTR-4854 Analysis of THC as a Rich Burn Formaldehyde Surrogate Page 3 of 17 equivalent to the already accepted lean bur n CO surrogate. Th e better - but more complicated - correlation also developed in this report can be used if necessary. It is also recommended that, since a proper ly functioning NSCR catalyst can be expected to provide virtually complete formaldehyde destruct ion, no post-cat correlation is needed with THC or any other surrogate.

TEST EQUIPMENT L7044GSI installed in cell 17, ESM Cal.: C21H000_NEW_XFR_CURVE.dat Maxim Catalytic Converter, model 3WC 674- 20, installed in the test cell.
WED Horiba emissions bench M odel#: PN-255951-1 Serial#: 0001 MKS FTIR owned and operated by Clean Air E ngineering (See CAE report for details)

RESULTS
Test Overview The base test was designed to mimic a test c ontracted by the EPA that was run at CSU in 2000 and believed used to establish the current lean burn CH2O – CO correlation. That base test is as shown in Table 1. Table 1 - Base EPA Test

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Run A:Speed B:Torque C:A/F D:Timing E:ICW F:JWT rpm % lambda Offset °F °F 1 1200 100 0.9985 0 130 180 2 1200 70 0.9985 0 130 180 3 1000 70 0.9985 0 130 180 4 1000 100 0.9985 0 130 180 5 1200 100 0.993 0 130 180 6 1200 100 1.004 0 130 180 7 1200 70 1.004 0 130 180 8 1000 100 0.993 0 130 180 9 1200 100 0.9985 0 120 180 10 1200 100 0.9985 0 140 180 11 1200 100 0.9985 0 130 170 12 1200 100 0.9985 0 130 190 13 1200 100 0.9985 -5 130 180 14 1200 100 0.9985 -2.5 130 180 15 1200 100 0.9985 0 130 180 16 1200 100 0.9985 0 130 180 This block 1 test should be sufficient to demonstrate the expected CH2O – THC correlation, however, there are issues with t he base design particularly relative to the factor power (see highlighted section of At tachment A). Theref ore, the test was augmented with another block to provide acceptable factor power for a full quadratic model. The additional block 2 incl uded the points shown in Table 2.TR-4854 Analysis of THC as a Rich Burn Formaldehyde Surrogate Page 4 of 17 Table 2 - Test Augmentation for better statistical power

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Run A:Speed B:Torque C:A/F D:Timing E:ICW F:JWT rpm % lambda Offset °F °F 17 1000 85 0.9985 -2.5 130 180 18 1200 70 1.004 -5 120 190 19 1200 100 0.993 -5 120 190 20 1000 70 0.993 -2.5 120 170 21 1100 100 0.993 -5 140 170 22 1000 100 0.993 -5 140 190 23 1200 70 1.004 -5 140 170 24 1200 100 1.004 -5 120 170 25 1000 100 1.004 -5 140 170 26 1200 70 0.993 -5 120 170 27 1000 85 1.004 0 120 190 28 1000 70 1.004 -5 140 190 29 1000 70 1.004 0 120 170 30 1000 100 1.004 -5 120 190 31 1000 100 0.993 -5 120 170 32 1000 70 0.993 -5 140 170 33 1200 85 1.004 -5 140 190 34 1000 85 1.004 0 140 170 35 1000 70 0.993 0 120 190 36 1100 70 0.993 -5 140 190 37 1000 85 1.004 -5 120 170 38 1100 70 1.004 0 140 190 39 1000 100 0.9985 0 120 170 40 1000 70 0.993 -5 120 190 41 1200 85 0.993 0 120 170 42 1100 100 1.004 0 140 190 43 1200 100 0.993 0 140 180 44 1000 70 1.004 0 140 190 45 1200 70 0.993 -2.5 140 190 46 1100 70 0.993 0 140 170 As shown in Attachment B this provides a much better test that has adequate power to identify factor effects of moderate magnitude. When Clean Air Engineering (CAE) arrived they did not bring any sample probes; they intended to just tap into the si de of the exhaust stack. This has been a poor practice in Waukesha experience and is not compliant with EPA sampling requirements. So WED gathered probes that could be used from lab stock and these were installed. The CAE FTIR instrumentation was set up follo wing their usual protocol of collecting 10 spectrum samples, then adding these samples to provide one “valid” sample. The spectrum samples are taken once per second, so “valid” samples can be posted once every ten seconds.

The first task was to determine how many sa mples were required to get a consistent average. However, while attempting this, se veral issues were encountered. The first issue was very high catalyst outlet temperatur es particularly at low engine loads. This was determined to be caused by having the wr ong spark plugs installed in the engine. The cylinder heads that were used on the engine fo r this test were ju st transferred fromTR-4854 Analysis of THC as a Rich Burn Formaldehyde Surrogate Page 5 of 17 another engine that was configured as an LT . The spark plugs had not been checked as part of this transfer since it was rus hed from a time stand point. The 69919D spark plugs were removed and correct 60999Z plugs gapped to 0.011” were installed. There was no trouble for the remainder of the test ing. The second issue encountered was a high %O2 reading from the CAE instrum entation compared to the Waukesha Horiba instrumentation. There was a rigorous in vestigation to determine the source of a perceived leak. Finally, the lab’s Testo wa s plugged into the exhaust of CAE’s FTIR and it registered little O2 proving that C AE’s instrument was not reading correctly. Since there was good agreement on NOx, CO, CO2 and THC between WED and CAE instruments it was determined that the FTIR results were OK and only the O2 values from CAE’s instruments had to be discarded. The WED O2 readings were used for testing. Since O2 is a good indicator of a line leak the Testo was left in the FTIR exhaust and checked with each data collect to ensure the reading was less than 0.5% O2. After these issues were cleared up the testing went well until about noon on Friday (point 43 in run order). Then, apparently, a valve was bumped in the CAE trailer causing a reduced sample flow resulting in very long stabilization times, longer than the expected total sampling time. So, points 43- 46 were re-run to get good data after a flow valve adjustment. Sample Time Determination To determine the sampling time required, r un #1 was monitored for 1⁄2 hour pre-cat and 1⁄2 hour post cat. The stripchart results of these runs are shown in Figure 1 and Figure 2.

Pre Cat Stripchart

-20 -15 -10 -5 0 5 10

1 8 15 22 29 36 43 50 57 64 71 78 85 92 99 106 113 120 127 134 141 148 155 162 169 176 183 190 197

Sample NumberAcetaldehyde, Acrolein, Formaldehyde (ppm)

0 500 1000 1500 2000 2500 3000 3500 4000 4500CO, NO (ppm)

Acetaldehyde 191C Acrolein 191c Formaldehyde 191C CO ppm 191C (1of2) NO 191C Figure 1 - Run 1 Pre-Cat StripchartTR-4854 Analysis of THC as a Rich Burn Formaldehyde Surrogate Page 6 of 17

Post Cat Stripchart

-20 -15 -10 -5 0 5 10

1 8 15 22 29 36 43 50 57 64 71 78 85 92 99 106 113 120 127 134 141 148 155 162 169 176 183 190 197 204 211

Sample NumberAcetaldehyde, Acrolein, Formaldehyde (ppm)

0 50 100 150 200 250 300 350 400 450CO, NO (ppm)

Acetaldehyde 191C Acrolein 191c Formaldehyde 191C CO ppm 191C (1of2) NO 191C Figure 2 - Run 1 Pos t-Cat Stripchart
The data from these stripcharts were then averaged in a building average manner, where initially the first two samples were averaged, and then the first three, and then the first four, and so on until the la st value became the grand aver age of the whole data set. The results of this are shown in Figure 3 and Figure 4. This analysis follows the Law of Large Numbers where, after a certain numbe r of samples form an average, adding more samples does not significantly affect the average. As c an be seen from these figures about 60 samples were required to get a stable average. So, given the CAE sampling routine described above, this would require sampling points for 10 minutes to obtain a stable, representative value. Afte r each 10 minute sample charts similar to those shown in Figure 1 and Figure 2 were created and reviewed to ensure the sample was stable. Charts similar to those show n in Figure 3 and Figure 4 were also created and reviewed to ensure enough data had been taken to obtain a stable average. These results are not included in th is report but are available in the “Processed CAE Data Files.zip” in Matrix under this TR.TR-4854 Analysis of THC as a Rich Burn Formaldehyde Surrogate Page 7 of 17

Pre Cat Building Average

-6 -4 -2 0 2 4 6 8

1 8 15 22 29 36 43 50 57 64 71 78 85 92 99 106 113 120 127 134 141 148 155 162 169 176 183 190 197 204

Samples AveragedAcetaldehyde, Acrolein, Formaldehyde (ppm)

0 500 1000 1500 2000 2500 3000 3500 4000 4500CO, NO (ppm)

Acetaldehyde 191C Acrolein 191c Formaldehyde 191C CO ppm 191C (1of2) NO 191C Figure 3 - Run 1 Pre-Cat Building Average

Post Cat Building Average

-7 -6 -5 -4 -3 -2 -1 0 1

1 8 15 22 29 36 43 50 57 64 71 78 85 92 99 106 113 120 127 134 141 148 155 162 169 176 183 190 197 204 211

Samples AveragedAcetaldehyde, Acrolein, Formaldehyde (ppm)

0 50 100 150 200 250CO, NO (ppm)

Acetaldehyde 191C Acrolein 191c Formaldehyde 191C CO ppm 191C (1of2) NO 191C Figure 4 - Run 1 Post-Cat Building AverageTR-4854 Analysis of THC as a Rich Burn Formaldehyde Surrogate Page 8 of 17

Data Collection Monitoring Since this test was essentially a “one shot ” deal where, if next week after further analysis a point needed to be re-run it would be impossible, extra steps were taken to ensure the data were good. For each data co llect the CAE FTIR va lue was subtracted from the WED emissions bench value for pr e-cat and post-cat for CO, NOx, CO2, and THC. The summary results of these are shown in Figure 5 through Figure 12. One thing to note is that, since there were two different technologies used to measure these results, the values were not necessarily ex pected to match exactly. The values were just used for trend review, making sure that t he last value was not substantially different than the “mass” of values accumulated before. The minimal outliers (shown as “*” in the boxplot portion) are evidence that the dat a are consistent and the discrepancies are either random or a constant “block” offset.

400 200 -0 -200

Median

Mean

210 200 190 180 170 160 150 1st Q uartile 130.03 Median 177.49 3rd Q uartile 235.95 Maximum 425.13

164.80 213.13

154.33 201.66

67.49 102.49 A -Squared 0.41 P-V alue 0.332

Mean 188.97 StDev 81.37 V ariance 6621.77 Skewness 0.654292 Kurtosis 0.364089 N 46

Minimum 44.57 A nderson-Darling Normality Test

95% C onfidence Interv al for Mean

95% C onfidence Interv al for Median

95% C onfidence Interv al for StDev 95% Confidence Intervals Summary for WED-CAE CO (ppm dry) Sample Label = Pre Cat

Figure 5 - WED-CAE Pre-Cat CO SummaryTR-4854 Analysis of THC as a Rich Burn Formaldehyde Surrogate Page 9 of 17

0 -60 -120 -180 -240 -300

Median

Mean

-150 -160 -170 -180 -190 1st Q uartile -204.16 Median -163.27 3rd Q uartile -134.29 Maximum -72.22

-186.24 -156.49

-181.75 -151.70

40.99 62.54 A -Squared 0.38 P-V alue 0.383

Mean -171.36 StDev 49.51 V ariance 2451.05 Skewness -0.411791 Kurtosis 0.022860 N 45

Minimum -288.41 A nderson-Darling Normality Test

95% C onfidence Interv al for Mean

95% C onfidence Interv al for Median

95% C onfidence Interv al for StDev 95% Confidence Intervals Summary for WED-CAE NOx (ppm dry) Sample Label = Pre Cat

Figure 6 - WED-CAE Pre-Cat NOx Summary

1.5 1.4 1.3 1.2 1.1

Median

Mean

1.40 1.38 1.36 1.34 1.32 1.30 1st Q uartile 1.2822 Median 1.3296 3rd Q uartile 1.3991 Maximum 1.5241

1.3126 1.3674

1.3028 1.3891

0.0765 0.1162 A -Squared 0.33 P-V alue 0.506

Mean 1.3400 StDev 0.0922 V ariance 0.0085 Skewness 0.034271 Kurtosis -0.808170 N 46

Minimum 1.1620 A nderson-Darling Normality Test

95% C onfidence Interv al for Mean

95% C onfidence Interv al for Median

95% C onfidence Interv al for StDev 95% Confidence Intervals Summary for WED-CAE CO2 (% dry) Sample Label = Pre Cat

Figure 7 - WED-CAE Pre-Cat CO2 SummaryTR-4854 Analysis of THC as a Rich Burn Formaldehyde Surrogate Page 10 of 17

90 60 30 0 -30

Median

Mean

72 70 68 66 64 62 60 1st Q uartile 57.581 Median 66.041 3rd Q uartile 78.851 Maximum 91.463

64.852 71.589

60.493 71.714

9.409 14.287 A -Squared 1.37 P-V alue < 0.005

Mean 68.221 StDev 11.343 V ariance 128.671 Skewness 0.39071 Kurtosis -1.23408 N 46

Minimum 54.264 A nderson-Darling Normality Test

95% C onfidence Interv al for Mean

95% C onfidence Interv al for Median

95% C onfidence Interv al for StDev 95% Confidence Intervals Summary for WED-CAE THC (ppm wet) Sample Label = Pre Cat

Figure 8 - WED-CAE Pre-Cat THC Summary

400 200 -0 -200

Median

Mean

-10 -20 -30 -40 -50 -60 1st Q uartile -50.301 Median -18.930 3rd Q uartile -8.412 Maximum 52.756

-58.379 -21.131

-26.319 -13.853

52.018 78.990 A -Squared 4.03 P-V alue < 0.005

Mean -39.755 StDev 62.715 V ariance 3933.122 Skewness -2.26909 Kurtosis 6.87708 N 46

Minimum -306.634 A nderson-Darling Normality Test

95% C onfidence Interv al for Mean

95% C onfidence Interv al for Median

95% C onfidence Interv al for StDev 95% Confidence Intervals Summary for WED-CAE CO (ppm dry) Sample Label = Post Cat

Figure 9 - WED-CAE Post-Cat CO SummaryTR-4854 Analysis of THC as a Rich Burn Formaldehyde Surrogate Page 11 of 17

0 -60 -120 -180 -240 -300

Median

Mean

-40 -50 -60 -70 -80 1st Q uartile -78.249 Median -60.686 3rd Q uartile -32.043 Maximum 3.239

-75.728 -48.669

-70.480 -40.262

37.788 57.383 A -Squared 1.40 P-V alue < 0.005

Mean -62.198 StDev 45.559 V ariance 2075.636 Skewness -1.40078 Kurtosis 2.62145 N 46

Minimum -209.804 A nderson-Darling Normality Test

95% C onfidence Interv al for Mean

95% C onfidence Interv al for Median

95% C onfidence Interv al for StDev 95% Confidence Intervals Summary for WED-CAE NOx (ppm dry) Sample Label = Post Cat

Figure 10 - WED-CAE Post-Cat NOx Summary

1.5 1.4 1.3 1.2 1.1

Median

Mean

1.15 1.14 1.13 1.12 1.11 1.10 1.09 1st Q uartile 1.0805 Median 1.1217 3rd Q uartile 1.1710 Maximum 1.3027

1.1104 1.1511

1.0972 1.1436

0.0568 0.0862 A -Squared 0.72 P-V alue 0.056

Mean 1.1308 StDev 0.0685 V ariance 0.0047 Skewness 0.721362 Kurtosis -0.063509 N 46

Minimum 1.0256 A nderson-Darling Normality Test

95% C onfidence Interv al for Mean

95% C onfidence Interv al for Median

95% C onfidence Interv al for StDev 95% Confidence Intervals Summary for WED-CAE CO2 (% dry) Sample Label = Post Cat

Figure 11 - WED-CAE Post-Cat CO2 SummaryTR-4854 Analysis of THC as a Rich Burn Formaldehyde Surrogate Page 12 of 17

90 60 30 0 -30

Median

Mean

12 10 8 6 4 2 0 1st Q uartile -4.4963 Median 6.8673 3rd Q uartile 15.4516 Maximum 29.0124

2.6066 9.8286

1.1817 12.0171

10.0857 15.3154 A -Squared 0.22 P-V alue 0.811

Mean 6.2176 StDev 12.1597 V ariance 147.8582 Skewness -0.296224 Kurtosis 0.025357 N 46

Minimum -28.0177 A nderson-Darling Normality Test

95% C onfidence Interv al for Mean

95% C onfidence Interv al for Median

95% C onfidence Interv al for StDev 95% Confidence Intervals Summary for WED-CAE THC (ppm wet) Sample Label = Post Cat

Figure 12 - WED-CAE Post-Cat CO SummaryTR-4854 Analysis of THC as a Rich Burn Formaldehyde Surrogate Page 13 of 17 Brake Specific Fuel Consumpt ion (BSFC) was also monitored relative to previous development data with the summary shown in Figure 13. The expect ation was that the measured BSFC value should be within ±3% of the expected valu e from previous development data. There were a few points that exceeded t hat, but those were typically at lower loads. Algorithms based on prev ious data historically have not predicted performance values at lower loads as well as at higher loads so no corrective action was taken. This review provides evi dence that the engine was in generally good “health” as most engine irregularitie s have a fuel consumption impact.

0.04 0.03 0.02 0.01 0.00 -0.01

Median

Mean

0.014 0.012 0.010 0.008 0.006 1st Q uartile -0.001605 Median 0.009819 3rd Q uartile 0.021971 Maximum 0.039645

0.008219 0.013782

0.006708 0.014625

0.011730 0.015710 A -Squared 0.80 P-V alue 0.037

Mean 0.011001 StDev 0.013430 V ariance 0.000180 Skewness 0.097719 Kurtosis -0.928529 N 92

Minimum -0.015398 A nderson-Darling Normality Test

95% C onfidence Interv al for Mean

95% C onfidence Interv al for Median

95% C onfidence Interv al for StDev 95% Confidence Intervals

Summary for BSFC Error (% / 100)

Figure 13 – Test BSFC Error Relative to Previous Development Data
What can be concluded from this analysis is that the WED emissions bench and CAE FTIR were in good agreement and that the engine was runni ng properly throughout the test.

Pre-Catalyst Formaldehyde Correlation Development As stated before, the test was run in 2 blocks. The first bl ock mimicking a test that the EPA contracted with CSU for a lean burn engine during HAP testing at CSU in 2000. The second block providing higher statistical power for identifying fa ctors that may affect this correlation. Using only the data from the first block the simple correlation shown in Figure 14 was developed. There are two correlations ; one using the WED measured THC and the other using the CAE measured THC. Both are very similar in slope and fit indicating that either could be used with equ al effectiveness. Also, at this stage, both are a better R 2 fit than was observed in X- 3281 for the lean burn CO-CH2O fit from either the WEDCAE data (R 2 =0.6047) or the CSU-EPA data (R 2 =0.633).TR-4854 Analysis of THC as a Rich Burn Formaldehyde Surrogate Page 14 of 17 Based on this, it can be concluded that the simple correlation developed here between pre-cat THC and pre-cat CH2O for rich burn engi nes is better, in an “apples to apples” data set comparison, than what is believed to have been used by the EPA to determine a correlation between CO and CH2O for lean burn engines.

EPA (Block 1) Pre-Cat Data

y = 0.016280x + 1.334714 R 2 = 0.715848 y = 0.02081x + 1.43140 R 2 = 0.68342

0 1 2 3 4 5 6 7 8 9

0 50 100 150 200 250 300 350 400 450 PreCat THC (ppmvw)PreCat CH2O (ppmvw)

CAE THC WED THC Linear (WED THC) Linear (CAE THC) Figure 14 - Simple Pre-Cat Correlation (Block 1 only)

When both blocks are included in the analysis, see Figure 15, the simple correlation fit (R 2 ) is not as good as the block 1 only fit and s lightly worse than the fit believed to have been used for the lean burn CO correlation. However, the slopes are still about the same as shown in Figure 14, indicating that, while adding mo re data makes the correlation more “noisy”, the “strength” of the correlati on remains. The lower R 2 implies more scatter in the data which is not entir ely unexpected as the complete test (both blocks) was designed to maximize the ability to identify significant effects. While, as discussed above, block 1 only data still hav e these effects but do not provide enough statistical power to likely be able to identify and quantify these effects.TR-4854 Analysis of THC as a Rich Burn Formaldehyde Surrogate Page 15 of 17

All (Block 1 & 2) Pre-Cat Data

y = 0.013025x + 2.063414 R

2 = 0.516614 y = 0.01545x + 2.38918 R

2 = 0.47788

0 1 2 3 4 5 6 7 8 9

0 50 100 150 200 250 300 350 400 450 PreCat THC (ppmvw)Pre Cat CH2O (ppmvw)

CAE THC WED THC Linear (WED THC) Linear (CAE THC) Figure 15 - Simple Pre-Cat Correlation (Both Blocks) To make use of the additional statistical po wer provided by both data blocks to identify the cause of this scatter, the residuals (i.e . measured - predicted) of each point for both WED and CAE THC were calculat ed. Two models were then fi t to these residuals with the potential terms being the fa ctors that made up the test ma trix (i.e. speed, torque, a/f ratio, ign. timing, intercooler and jacke t water temps.) and their interactions. Both the WED and CAE THC models agreed on the terms to include and both fits were approximately the same as shown in Tabl e 3 and Table 4. The only issue with both models is that they fail a “lack of fit” test. This is not fa tal to a model since all that means is that error in the model is more t han the replicated design poi nts. A test that has very reproducible responses, as the sa mple time determination procedure at the beginning of this test was intended to pr oduce, could easily encounter this. The R 2 s shown in Table 5 indicate that both models account for about 90% of the variation seen in Figure 15.

Table 3 - Models in Terms of Coded Factors CAE resid = WED resid =

0.075292603 0.065746552 0.097424296 * A 0.059891564 * A -0.18219422 * B -0.118481554 * B 0.656414148 * C 0.652349586 * C 0.177116838 * D 0.166338081 * D -0.098851252 * F -0.10540293 * F -0.242956505 * A * B -0.228602795 * A * BTR-4854 Analysis of THC as a Rich Burn Formaldehyde Surrogate Page 16 of 17
Table 4 - Models in Te rms of Actual Factors CAE resid = WED resid =

-132.3207951 -130.5387507 0.014741778 * Speed 0.013553074 * Speed 0.166021822 * Torque 0.159743279 * Torque

119.348027 * A/F 118.6090157 * A/F 0.070846735 * Timing 0.066535232 * Timing -0.009885125 * JWT -0.010540293 * JWT -0.000161971 * Speed * Torque -0.000152402 * Speed * Torque

Table 5 - Residual Model R 2 s CAE THC WED THC R-Squared 0.910 0.915 Adj R-Squared 0.896 0.901 Pred R-Squared 0.865 0.872

The conclusion drawn from this analysis is that the initial simple correlation, when applied to a broad data set or ap plication base, may be a sli ghtly “noisier” correlation than is expected from Figure 14 alone. Howe ver, since Figure 14 was based on a test design that mimicked what is thought to have been used to develop the lean burn CO correlation, it will likely be no different than w hat is seen in practice with the lean burn CO correlation. Also, as demonstrat ed with the equation dev eloped above, the correlation can be “cleaned up” considerably (t o be superior to the accepted lean burn CO correlation), by using conventional engin e parameter measurement s that are readily available through Waukesha’s ES M control ESP interface.

Post-Catalyst Formaldehyde Correlation Development Attempting the same correlation techniques used for the pre-cat correlation was not possible for the post-cat samples. Block 1 of the test only had one data point where post cat CH2O was above the CAE formaldehyde detection limit. Adding block 2 to the data set included a few more points that were above the CAE formaldehyde detection limit, but the vast majority of the data were still below this limit making any attempted correlation bogus. This is caused by the virtually complete destruction of CH2O regardless of its initial level or the THC le vel. The CAE report (Appendix C) shows that the mean, observed, post-cat CH2O level is under the 0.23 ppmvw detection limit with the maximum measured value, under any oper ating condition, less than 0.8 ppmvw (800 ppbvw). This is particularly signific ant considering the wide range of conditions that this test covered, maki ng a strong case that virtually complete CH2O reduction can be considered a given for most applications with a properly functioni ng NSCR catalyst. The term “most applications” is used because t here is a potentially significant variable that was missing from this study, which is fu el quality (i.e. WKI or LHV). It was not included in the study, because the factors were set to mimic the test done by the EPA at CSU which did not include fuel quality as a variable. In addition, Waukesha’s Engineering Laboratory has only lim ited capability to vary fuel gas composition in a realistic manner. The conclusion drawn from this analysis is that, while a correlation between post-cat CH2O and either pre-cat CH2O or post-cat THC can not be made, the correlation doesTR-4854 Analysis of THC as a Rich Burn Formaldehyde Surrogate Page 17 of 17 not appear to be necessary as virtually complete CH2O reduction occurs regardless of the fairly broad conditions evaluated in this test.

WED versus CAE THC Measurement Throughout this test all of the analyses were essentially doubled by using both the WED measured THC and CAE measured THC. While there were slight value differences between these two data sets, there was at no time a substantive difference in the conclusions that would be drawn from either method. So either could have been used and the same conclusions reached, lending furt her credibility to the broad applicability of the conclusions drawn here.

Summary

• In process checks were made to ensure data integrity. T hese validated engine performance and criteria emissions agr eement between the two measurement methods.

• The simple pre-catalyst correla tion CH2O(ppmvw) = 0.013025 * THC(ppmvw) + 2.063414, using the Waukesha measured THC is better, in an “apples to apples” data set comparison, than what is believed to have been used by the EPA to determine a correlation between CO and CH2O for lean burn engines.

• The simple pre-catalyst correlation c an be enhanced, for broader application by adding: -130.53875 + 0.013553 * Speed + 0.15974 * Torque + 118.60902 * A/F + 0.066535 * Timing - 0.010540 * JWT - 1.52402E-004 * Speed * Torque to reduce about 90% of the noise.

• No post catalyst CH2O correlation appears to be required, as virtually complete reduction occurs regardless of the fairly broad conditions evaluated in this test.

INDEX OF ATTACHMENTS
Attachment A - ORIGINAL BASE TEST EVALUATION
Attachment B - AUGMENTED TEST EVALUATION
Attachment C – FORMAL REPORT OF HAP TESTING AT WAUKESHA BY CAE (See Matrix for copy of Attachment C)Attachment A - ORIGINAL BASE TEST EVALUATION

6 Factors: A, B, C, D, E, F

Design Matrix Evaluation for Response Surface Linear Model

No aliases found for Linear Model

Degrees of Freedom for Evaluation Model 6 Residuals 9

Lack 0f Fit 7 Pure Error 2 Corr Total 15

Power at 5 % alpha level to detect signal/noise ratios of Term StdErr** VIF Ri-Squared 1 Std. Dev. 1.5 Std. Dev. 2 Std. Dev. A 0.36 1.26 0.2054 23.8 % 46.2 % 69.8 % B 0.36 1.26 0.2054 23.8 % 46.2 % 69.8 % C 0.58 1.33 0.2500 12.1 % 21.4 % 34.1 % D 0.49 1.05 0.0496 15.1 % 28.1 % 45.0 % E 0.71 1.00 0.0000 9.7 % 15.8 % 24.5 % F 0.71 1.00 0.0000 9.7 % 15.8 % 24.5 % **Basis Std. Dev. = 1.0

Measures Derived From the (X'X) -1 Matrix

Std Leverage Point Type 1 0.1031 ?? 2 0.4648 ?? 3 0.5258 ?? 4 0.4648 ?? 5 0.4364 ?? 6 0.4364 ?? 7 0.4648 ?? 8 0.4648 ?? 9 0.6031 ?? 10 0.6031 ?? 11 0.6031 ?? 12 0.6031 ?? 13 0.8041 ?? 14 0.2165 ??15 0.1031 ?? 16 0.1031 ?? Average = 0.4375

Maximum Prediction Variance (at a design point) = 0.804 Average Prediction Variance = 0.438

Condition Number of Coefficient Matrix = 3.059

Scaled D-optimality Criterion = 3.222

Determinant of (X'X) -1 = 1.342E-5 Trace of (X'X) -1 = 2.166Attachment B - AUGMENTED TEST EVALUATION

6 Factors: A, B, C, D, E, F

Design Matrix Evaluation for Response Surface Quadratic Model

No aliases found for Quadratic Model

Degrees of Freedom for Evaluation Blocks 1 Model 27 Residuals 17

Lack 0f Fit 15 Pure Error 2 Corr Total 45

Power at 5 % alpha level to detect signal/noise ratios of Term StdErr** VIF Ri-Squared 1 Std. Dev. 1.5 Std. Dev. 2 Std. Dev. Block 1 0.49 Block 2 A 0.19 1.51 0.3376 68.8 % 95.6 % 99.8 % B 0.19 1.42 0.2952 69.8 % 96.0 % 99.9 % C 0.20 1.31 0.2352 64.5 % 93.7 % 99.6 % D 0.21 1.79 0.4426 61.1 % 91.9 % 99.4 % E 0.22 1.55 0.3559 55.8 % 88.4 % 98.8 % F 0.20 1.20 0.1694 65.3 % 94.1 % 99.7 % AB 0.19 1.19 0.1567 70.4 % 96.2 % 99.9 % AC 0.22 1.31 0.2351 57.2 % 89.4 % 99.0 % AD 0.19 1.29 0.2260 70.7 % 96.3 % 99.9 % AE 0.22 1.28 0.2175 56.5 % 88.9 % 98.9 % AF 0.22 1.20 0.1661 57.6 % 89.7 % 99.0 % BC 0.22 1.36 0.2664 55.5 % 88.2 % 98.7 % BD 0.19 1.27 0.2150 71.3 % 96.5 % 99.9 % BE 0.21 1.11 0.1019 62.3 % 92.6 % 99.5 % BF 0.21 1.14 0.1215 59.5 % 90.9 % 99.2 % CD 0.21 1.28 0.2157 62.7 % 92.8 % 99.5 % CE 0.21 1.18 0.1543 63.0 % 92.9 % 99.5 % CF 0.22 1.27 0.2110 58.3 % 90.1 % 99.1 % DE 0.20 1.20 0.1693 63.8 % 93.4 % 99.6 % DF 0.21 1.18 0.1522 63.2 % 93.1 % 99.6 % EF 0.20 1.15 0.1289 63.8 % 93.4 % 99.6 % A 2 0.66 1.96 0.4888 29.7 % 57.0 % 81.2 % B 2 0.54 1.50 0.3336 42.1 % 75.1 % 94.0 %C 2 0.51 2.51 0.6012 46.0 % 79.6 % 96.0 % D 2 0.68 1.67 0.4002 28.7 % 55.3 % 79.7 % E 2 0.70 4.92 0.7968 27.2 % 52.7 % 77.1 % F 2 0.60 3.76 0.7337 35.0 % 65.4 % 88.1 % **Basis Std. Dev. = 1.0

Measures Derived From the (X'X) -1 Matrix

Std Leverage Point Type 1 0.1302 ?? 2 0.4130 ?? 3 0.3706 ?? 4 0.3264 ?? 5 0.4064 ?? 6 0.5284 ?? 7 0.5130 ?? 8 0.6201 ?? 9 0.6944 ?? 10 0.6075 ?? 11 0.6234 ?? 12 0.6527 ?? 13 0.4282 ?? 14 0.4331 ?? 15 0.1302 ?? 16 0.1302 ?? 17 0.7545 PlaneCent 18 0.9093 Vertex 19 0.7681 Vertex 20 0.6503 CentEdge 21 0.7185 CentEdge 22 0.8775 Vertex 23 0.9256 Vertex 24 0.8040 Vertex 25 0.8210 Vertex 26 0.8110 Vertex 27 0.7154 CentEdge 28 0.6683 Vertex 29 0.7630 Vertex 30 0.7846 Vertex 31 0.6905 Vertex 32 0.7903 Vertex 33 0.8063 CentEdge 34 0.7000 CentEdge 35 0.7060 Vertex 36 0.6547 CentEdge 37 0.7760 CentEdge 38 0.4769 CentEdge 39 0.6743 CentEdge40 0.6812 Vertex 41 0.6603 CentEdge 42 0.6388 CentEdge 43 0.7041 CentEdge 44 0.5691 Vertex 45 0.7987 CentEdge 46 0.6943 CentEdge Average = 0.6304

Maximum Prediction Variance (at a design point) = 0.926 Average Prediction Variance = 0.630

Condition Number of Coefficient Matrix = 60.060

Scaled D-optimality Criterion = 2.624

Determinant of (X'X) -1 = 2.747E-35 Trace of (X'X) -1 = 4.339