Michael Ratteree Page 1 August 25, 1999 August 25, 1999 The following is a summary from ENVIRON International Corporation (ENVIRON) summarizing the results of their review of Energy Pro USA’s Enterprise Optimization Model. (EOM) 1.0 EXECUTIVE SUMMARY ENVIRON was retained by Energy Pro USA (EP) to conduct an evaluation of EP’s Enterprise Optimization Model (EOM) for estimating expected energy and related cost savings in high-energy consumption (>$25M/year) manufacturing operations. This evaluation is intended to provide information to potential investors in EP. EP develops models to predict the energy consumption (or generation) based on process variables, such as material throughputs, number of operating days, or weather conditions. These predictions are compared with actual energy consumption after Energy Savings Initiatives (ESI) have been implemented. EP then bills its customers for a share of the savings, typically one-half the amount saved. ENVIRON evaluated the of the parameters or variables that were used to develop the models, the ESIs that were implemented, and the statistics of the models themselves, It should be noted that EP hired the accounting firm Schmersahl, Treloar & Co, PC (STC) to evaluate the invoicing process. ENVIRONS’s review of the above areas found no significant errors or problems with EP’s model development or implementation. The concept of the modeling process is sound, and EP’s sophisticated customers have relied on the models for payment of the shared savings. The models generally have R’ values close to 1.0, indicating that they explain the vast majority of the variations in the energy usage. While some deviations from EP’s formal were found, none of those deviations appears to be significant. In summary, ENVIRON believes that EP’s EOMs are appropriate for estimation of the actual energy savings achieved and for generating invoices to EP’s customers.

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Michael Ratteree Page 2 August 25, 1999 August 25, 1999 2.0 INTRODUCTION ENVIRON International Corporation (ENVIRON) was retained by Energy Pro USA (EP) to conduct an evaluation of EP’s EOM for estimating expected energy and related cost savings in high-energy consumption (>$25M/year) manufacturing operations. The purpose of this report is to provide an independent, third-party review of the EOM for potential EP investors, and may not be relied upon by any other person or entity without ENVIRON’s express written permission. The conclusions presented in this report represent ENVIRON’s professional judgment based on the information available to us during the course of this assignment and on conditions that existed at the time of the assessment. No independent verification of the information provided to ENVIRON was made. While ENVIRON has no reason to doubt the accuracy of the information provided, this report is accurate and complete only to the extent that information provided to ENVIRON was itself accurate and complete. ENVIRON’s evaluation of EP’s development and use of the EOM included the following tasks:

1) EOM Variable Analysis: ENVIRON reviewed the variables used in the EOMs to ensure they are readily measurable or known quantities (e.g., tons of fuel consumed, heating/cooling degree-days, operating days, shifts, etc.) and to ensure that they are appropriate for the operations conducted at the sites. ENVIRON only reviewed one full set of variables used to develop EOMs because these data were not available for the other models reviewed for this report. However, a list of the data currently being used to develop the models for the Bethlehem Lukens Plate Coatesville mill was available and was reviewed.

2) EP Billing Comparison: ENVIRON understands that an accounting firm Schmersahl, Treloar & Co., PC a (STC) has been retained by EP to determine whether the EOMs used to generate EP’s billing invoices were the same models agreed to by the customers. This work was originally in ENVIRON’s scope of work, but was deleted because STC had been hired to do an even more in-depth analysis. Therefore, no such analysis is presented in this report.

3) Auditing of Energy Savings Initiatives: ENVIRON performed more in-depth audits of three representative Energy Savings Initiatives from previous EP projects. The audits were conducted to evaluate the percentage energy savings and whether the savings are consistent with overall savings a projections.

4) Statistical Analysis: ENVIRON performed statistical analysis of the EOM developed for three separate data sets. This analysis included a review of the data sets actually used to derive the model. The analysis addressed the appropriateness of the fitted statistical model, the reproducibility of the model a (whether ENVIRON was able to reproduce the model results), and, where available, how well the model fit data that were not used to develop the models. No attempt was made to evaluate impact on accuracy of alternate methods of modeling or whether these would be justified considering the increased a complexity of the model.

To accomplish the above tasks, ENVIRON reviewed electronic files and paper documents and the ENVIRON project manager visited the Bethlehem Lukens Plate Coatesville MIH to get a first hand view of the model development process.

Michael Ratteree Page 3 August 25, 1999 August 25, 1999 3.0 BACKGROUND EP works with industrial companies to help reduce energy usage and associated costs. In return for EP’s services, EP’s clients pay them a percentage of their cost savings over a period of years. To quantify the amount of the energy savings (and to calculate EP’s bill to the client), EP creates a model of the energy usage at the facility based on facility- specific data. This is called the Enterprise Optimization Model (EOM) and is based on the statistical analysis of as many as 150 variables collected over approximately three years using professional engineering and operational judgement. After EP creates a EOM for a facility that is agreed to by the client company, EP develops Energy Savings Initiatives (ESIs) to reduce energy use at the facility. Typical ESIs include installation of energy-efficient equipment, changes to production methods, and changes to production schedules. After implementation of ESIs, the EOM is used to estimate the quantity of energy that would have been used at the facility without implementation of the ESIs. The actual energy use is then subtracted from the predicted energy use to calculate the energy savings. EOM development begins with the preparation of input/output block diagrams and process flowsheets describing operations at the facility. EP personnel then collect information regarding the facility’s individual unit processes. Examples of unit processes include furnaces, mills, grinders, heat exchangers, boilers, and waste treatment systems. After the facility’s unit processes are evaluated, EP has a process to systematically identify all of the special or assignable causes of variability in energy consumption (e.g., energy use will generally increase when production rate increases). Three types of independent variables (i.e., production values, weather, and time) are identified during this process. After the initial set of potential independent variables has been selected, EP collects historical data for the variables, ideally for a number of days equal to the number of potential variables multiplied by ten (e.g., 100 independent variables would require approximately 1,000 days of data). Energy use data is also collected for this time period. This set of data is separated into two portions, one portion used for the derivation of the mathematical model (the correlation data) and another portion to be used for verification of the accuracy of the data (the validation data; preferably data for the most recent two to three months prior to implementation of the ESIs). After data for a comprehensive set of potential independent variables have been collected, an energy usage model is developed using the statistical technique of multiple linear regression. While non-linear regressions could be used, in the models reviewed by ENVIRON, linear regressions have produced satisfactory results, consistent with the normal drivers for energy use, which have linear relationships to energy use. According to EP, even at the initial model development stage, some variables may be eliminated from consideration as model variables if they would be directly affected by energy saving initiatives. For example, with the number of furnaces in operation is correlated with energy usage, it might be a good model variable. However, if EP believes it may want to make a change to the number of furnaces in operation to reduce energy use, that variable would not be appropriate for use in the model, as it would not give a true picture of the energy savings resulting from that improvement.

Michael Ratteree Page 4 August 25, 1999 August 25, 1999 Using a computer, a regression analysis is performed using the correlation data for the dependent energy use variable versus the selected independent variables, to obtain a regression model that describes the energy use in terms of the selected independent variables, regression coefficients, and constants. According to EP, variables not essential to the model are eliminated using a statistical t-test; if the ratio of a variable’s coefficient divided by its standard error is less than tabulated values in a t-test, the variable is not considered significant and is dropped from the model. In all cases, the accuracy of the regression model is checked by testing its ability to predict the energy use for the historical data set. As described by HP, it calculates the accuracy of the model by computing the percent deviation between the actual and predicted energy use on a daily, monthly, and yearly basis. If the monthly deviation is more than ± 5 %, or if the yearly deviation is more than +1 %, the model is rejected. Additional statistical parameters such as the R2 term are also used to evaluate the effectiveness of the model. If the model is rejected, one or more additional independent variables for the unit process are identified and added to the model and the new model is retested. This refinement of the model is repeated until the model meets the predictive accuracy tests described above. Once the model is refined using the tests described above, in most cases EP states that the predictive ability of the regression model is further checked by testing its ability to predict energy use over an independent validation data set. Validation data consist set of data beyond that included in the building of the model. The ability of the model to make acceptably accurate predictions over such an independent data set increases confidence that the model captures the fundamental elements of the process it is intended to represent, and can thus be applied to accurately estimate energy savings resulting from process improvements. For most of this evaluation, ENVIRON focused on the following three EOMs:

• KOBE Energy project plant-wide electric EOM approval dated 31 July 1998.

• Sparrows Point Energy project plant-wide electrical generation EOM Revision #1, 18 July 1998.

• Gulf States Steel Energy Project plant-wide natural gas HOM dated 4 May 1999.

Michael Ratteree Page 5 August 25, 1999 August 25, 1999 4.0 EOM VARIABLE ANALYSIS ENVIRON reviewed the variables used to develop one EOM to ensure they are readily measurable or known quantities (e.g., tons of fuel consumed, heating/cooling degree-days, operating days, shifts, etc.) and to ensure that they are appropriate for the operations conducted at the sites. Typically, EP considers over 100 different variables when it develops a model, but usually less than 20 are actually used in the model, because some of the variables are redundant while others have no significant impact on the model. ENVIRON was only able to review the full set of variables for the Bethlehem Lukens Plate Coatesville mill, because fill data sets for the other sites were not available. However, detailed process flow diagrams and a list of the variables currently being used to develop the models for Coatesville were available and were reviewed. ENVIRON’s review of the variables for this section of the report focused on a engineering first principles (non-statistical) evaluation of the variables, using the following criteria:

• Are the variables measurable or known quantities? • Are the variables expected to be correlated with or directly affect energy consumption? • Are there potential variables that were not considered, that are expected to be significantly correlated

with energy consumption or production? • Do the coefficients make sense from an engineering perspective?

ENVIRON did not find problems with the variables chosen, nor were missing variables identified that would have a significant impact on energy consumption or production. While there are a few missing variables that could theoretically affect energy consumption, such as flux usage, ENVIRON does not believe that they would materially affect the model, since the flux usage is a small fraction of the mass going through a steel mill. Some variables, for example the number of down days, could be subject to more error because manual record keeping is required. However, since the models improve significantly when such variables are used, and because they can be checked by reviewing process throughputs, ENVIRON does not see a problem using such data. Of course another way to evaluate the variable selection process is to look at the models themselves and the accuracy with which they predict the energy consumption. The models evaluated by ENVIRON fit the data quite well, with errors that are significantly smaller than the expected energy savings. 5.0 EP BILLING COMPARISON ENVIRON understands that an accounting firm Schmersahl, Treloar & Co., PC (STC) has been retained by EP to determine whether the EOMs used to generate EP’s billing invoices were the same models agreed to by the customers. This work was originally in ENVIRON’s scope of work, but was deleted because STC had been hired to do an even more in-depth analysis. Therefore, no such analysis is presented in this report.

Michael Ratteree Page 6 August 25, 1999 August 25, 1999 6.0 AUDITING OF ENERGY SAVINGS INITIATIVES ENVIRON reviewed a status report from Frank Greco of EP dated July 8, 1999, along with other documents that were not clearly identified, that described the status of several energy savings initiatives (ESI). There are a number of ESIs discussed in the report, including:

• Reduction of fuel oil use in pilot burners at the Permwood boilers. These pilots are run continuously to stabilize the blast furnace gas fired burners.

• Reduction of leaks of fluids such as steam, compressed air, nitrogen, and argon resulting in dramatic energy losses that are not apparent when viewing a single leak. EP has an aggressive program to detect and repair leaks, which has resulted in substantial savings, frequently at low cost.

• Boiler and furnace efficiency improvements are another very cost-effective means of reducing energy consumption. Both oxygen and carbon monoxide trim systems have been installed, which can reduce energy losses associated with hot flue gases.

• Incandescent lighting has been replaced with metal halide lighting. This not only reduces electrical energy consumption, but it also reduces the labor involved in replacing incandescent bulbs, which typically have a much shorter life than metal halide bulbs. In some cases, training plant personnel to shut off lighting in unused areas is a very cost-effective means of reducing energy.

• Construction of preheaters for ladles has also been started, and in one case the projected savings are $110,000 with only $7,000 in capital cost to achieve that savings.

• Material substitutions are also considered. In one case, compressed air was substituted for nitrogen. While the volume of gas would not be different, the cost of compressed air is substantially lower than for nitrogen.

• Several projects to use more coke oven gas instead of natural gas have been implemented. This allows coke oven gas to be usefully burned instead of being vented, reducing purchased fuel costs.

ENVIRON’s review of these projects indicates that EP is proposing and implementing a broad range of projects with very attractive payout periods. In addition, EP gets the benefit of a;. show-up factor.” Simply put, energy consumption in a facility tends to decrease when EP arrives on-site because of the questions the EP staff asks, and because the focus that it puts on energy use causes facility staff to reduce energy consumption before ESIs are implemented, and at very little additional cost to EP. ENVIRON identified two additional areas of savings from which EP may be able to profit. They are:

Michael Ratteree Page 7 August 25, 1999 August 25, 1999

• Air Pollution Emissions Reductions: Many energy reductions can also result in environmental benefits, particularly air pollution emissions reductions. In many jurisdictions, air emissions reductions can be directly associated with cost reductions. For instance, large facilities are normally assessed fees based on their emissions, which are frequently driven by fuel use. If fuel use and therefore emissions are reduced, emission fees are reduced. If included as part of the contract, EP could then bill the client for a share of the saved emissions fees.

• Electrical Demand Management: In many areas, the price of electricity is a function of the time of day, with prices significantly higher during peak usage periods, such as summer afternoons. Projects to manage peak demand or to shift usage to off peak times could also result in cost savings, even if total energy use was not reduced. However, the large steel mills that are HP’s current clients tend to run 24 hours per day, 7 days per week. This type of operation does not present significant opportunities to save money by changing schedules. In addition, the EOM would have to be significantly more complex to show the savings from this type of initiative, which may not be practical.

7.0 STATISTICAL ANALYSIS ENVIRON conducted a detailed evaluation of the statistical basis of three EOMs. The details and results of this analysis are as follows: Data Copies of EOM agreements for three projects were received and used as the basis for comparison in our analysis:

i. KOBE Energy Project plant-wide electric EOM approval dated 31 July 1998.

ii. Sparrows Point Energy Project plant-wide electrical generation EOM Revision #1, 18 July 1998.

iii. Gulf States Steel Energy Project plant-wide natural gas EOM dated 4 May 1999.

Spreadsheet files containing the data used to develop the above EOMs were provided by EP. Methods Data from the spreadsheet files provided by EP were imported into S-PLUS statistical programming package and used to develop a linear least squares multiple regression model for each of the three EOM’s using the appropriate S-PLUS function (lm). Fitted model coefficients from S-PLUS were then compared with the documentation provided by EP. In addition, the Rsquare, regression F-statistic, and coefficient values output by S-PLUS were compared with values reported in the regression output included in EP’s spreadsheet files.

Michael Ratteree Page 8 August 25, 1999 August 25, 1999 A series of standard diagnostic analyses were performed on each regression model. These included:

• Examination of the frequency distributions of each variable to identify the nature of departures from normality and unusual features that might impact the regression analysis.

• Examination of plots of dependent vs. independent variables to identify unusual/noteworthy features in the data set

• Examination of R-square and F-statistics and of plots of observed vs. fitted values to evaluate how well the model fits the data. A good model will have an R-square value close to one indicating close correlation between observed and fitted values and an Fstatistic associated with a high probability of significance (i.e., a low “p” value) indicating that the model explains an overwhelming proportion of the variability in the dependent variable.

• Calculation of Cook’s distances for each data point to identify “high leverage” values (i.e., observations with an unusually strong effect on the regression fit), which should be investigated further.

• Examination of normal probability plots to identify departures from normality in the residuals. Such departures would indicate a violation of the statistical assumptions underlying the regression procedure and could contribute to inappropriate interpretation of model predictions.

• Examination of plots of residuals against the fitted values to identify heteroscedasticity in the residuals (i.e., the distribution of the residuals should be independent of the fitted value, otherwise the model is not appropriate for the data).

• Examination of autocorrelations in the residuals to evaluate if serial correlations in the data are significant. Significant serial correlations would violate the assumptions underlying the regression model and could produce misleading results.

• Computation of jackknifed residuals (i.e., cross-validation) to estimate the level of performance one would expect to get when applying the model to an independent data sample.

• Examination of statistical significance of each model coefficient. Generally speaking, variables with insignificant coefficients should not be included in the model.

Michael Ratteree Page 9 August 25, 1999 August 25, 1999 Results (KOBE Electric Usage) We were able to reproduce the KOBE Energy Project plant-wide electric EOM approved 31 July 1998 from the data provided. Regression coefficients computed by the S-PLUS im function are identical to the stated values to within at least three decimal places. The R-square and Fstatistic are also the same as in the spreadsheets provided by EP. As shown by the normal quantile-quantile plot in Figure 7-1, residuals from the model are normally distributed suggesting the model formulation is appropriate for these data (normally distributed data appear as a straight line on such a plot). A plot of the residuals against the fitted values (Figure 7-2) does not show obvious heteroscedasticity (i.e., variations in the distribution of residuals with the fitted values). Autocorrelations in the residuals (Figure 7-3) are minimal. No individual observations showed unusually high leverage (Figure 7-4). Jackknifed (studentized) residuals are nearly identical to the standardized residuals as shown below. Standardized Residuals Minimum. 1st Quarter Median Mean 3rd Quarter Maximum -2.834 -0.6366 0.03658 -0.0007386 0.6812 2.99 Studentized Residuals Minimum. 1st Quarter Median Mean 3rd Quarter Maximum -2.858 -0.6362 0.03654 -0.0009343 0.6808 3.018 Normal quantile-quantile plots for the model variables show that, while the dependent variable (MKWH) appears to be normally distributed, some of the independent variables have a bimodal distribution with a peak at zero as shown for two of the variables in Figure 7-5. This suggests that it may be possible to obtain a better fitting model that explicitly accounts for this bimodal behavior. One approach which might be used is to develop a hybrid model consisting of a decision tree which first isolates the cases with zero values and then fit a separate regression model to these special cases. However, no attempt was made in our analysis to evaluate the impact on accuracy of alternate methods of modeling or whether improvements in accuracy would be justified when the potentially increased complexity of the alternatives is taken into consideration. Data Validation ENVIRON’s review of the KOBE Electric model found a discrepancy from the EP general procedure of reserving the most recent historical data to validate the model. EP’s creation of the model incorporated all available historical data for the plant. The most recent historical data were not set aside for use in validating the model, and thus an opportunity to test its predictability is no longer available. According to EP, KOBE requested the modified procedure to maximize the amount of data used to generate the models. Although testing of the model, using an independent validation data set, is the established procedure set up by EP and was not followed, this does not necessarily mean that the model is flawed from a statistical point of view. Users of the model must recognize, however, that the model is only known to be valid for conditions similar to those represented by the data set which served as the basis for its development.

Michael Ratteree Page 10 August 25, 1999 The statistical portion of the model has been analyzed as to how well it fits the data. AN R2 of 0.853 indicates a good fit of the model to the data: the model explains 85 percent of the variability in the data. The coefficients have relatively small standard errors indicating their values are highly reliable. As with statistical models, users of the model should be aware of the limitations imposed by the range of the historical data and the expected range of errors associated with the statistical analysis (see Extrapolation discussion of the Limitations section.). Sparrows Point Electric Generation We were able to reproduce the Sparrows Point Energy Project plant-wide electrical generation EOM approved 18 July 1998 from the data provided. Regression coefficients for the no intercept model computed by the S-PLUS 1 m function are identical to the stated values to within at least three decimal places. The R-square and F-statistic are also the same as in the spreadsheets provided by EP. As shown by the normal quantile-quantile plot in Figure 7-6, residuals from the model are normally distributed except for an apparent departure from normality at the upper end of the distribution (normaRy distributed data would appear to fall along a single straight line on such a plot). A brief review of the data via scatter plots of each variable against the residuals and side by side box plots for observations corresponding to residuals above and below the breakpoint did not indicate obvious shifts in the model process parameters corresponding to this break in the distribution of residuals. It is possible that some other parameter not included in the model is responsible for this feature of the data. A plot of the residuals against the fitted values (Figure 7-7) does not show obvious heteroscedasticity. Autocorrelations in the residuals (Figure 7-8) are somewhat greater than is the case for the KOBE Electric EOM. However, including the previous days’ electric generation as one of the predictors in the model, improved the model R-square very slightly from 0.9975 to 0.9980, and reduced the standard error of the residuals slightly from 99.25 to 85.01 MWH/day. A plot of Cook’s distances (Figure 7-9) indicates that three observations stand out as having a much greater influence on the model fit than the remaining observations. While this is a common characteristic found in many data sets, ENVIRON recommends that these observations and their effect on the model be investigated further. Jackknifed (studentized) residuals are nearly identical to the following standardized residuals. Standardized Residuals Minimum. 1st Quarter Median Mean 3rd Quarter Maximum -3.856 -0.6649 -0.03082 -0.001442 0.6195 3.742 Studentized Residuals Minimum. 1st Quarter Median Mean 3rd Quarter Maximum -3.902 -0.6646 -0.03079 -0.001308 0.6192 3.783

Michael Ratteree Page 11 August 25, 1999 Normal quantile-quantile plots for the model variables show that, while the dependent variable appears to be normally distributed, some of the independent variables have a bimodal distribution with a peak at zero as was the case for the KOBE Electric. (EOM) This suggests that it may be possible to obtain a better fitting model by using a decision tree approach to isolate the cases with zero values as described above. No attempt was made to evaluate impact on accuracy of alternate methods of modeling or whether these would be justified considering the associated increase in complexity of the model. Gulf States Steel Natural Gas We were able to reproduce the Gulf States Steel Energy Project natural gas EOM -model approved 4 May 1999 from the data provided. Regression coefficients for the no intercept model computed by the S-PLUS im function are identical to the stated values to within at least two decimal places. As shown by the normal quantile-quantile plot in Figure 7-10, residuals from the model are approximately normally distributed with the exception of slightly “heavy” tails (i.e., extremely large and small values are slightly more likely to occur than would be the case for normally distributed residuals). A plot of the residuals against the fitted values (Figure 7-11) does not show obvious heteroscedasticity. Autocorrelations in the residuals (Figure 7-12) are minimal with an autocorrelation of less than 0.3 at a one-day lag. A plot of Cook’s distances (Figure 7-13) indicates that three observations stand out as having a much greater influence on the model fit than the remaining observations. While this is a common characteristic found in many data sets, ENVIRON recommends that these observations and their effect on the model be investigated further. Jackknifed (studentized) residuals are nearly identical to the standardized residuals as shown below. Standardized Residuals Minimum. 1st Quarter Median Mean 3rd Quarter Maximum -3.853 -0.6228 -0.03114 0.006901 0.5866 4.388 Studentized Residuals Minimum. 1st Quarter Median Mean 3rd Quarter Maximum -3.921 -0.6223 -0.0311 0.007107 0.5861 4.492 Normal quantile-quantile plots for the model variables show that, while the dependent variable appears to be normally distributed; some of the independent variables have a bimodal distribution with a peak at zero, as was the case for the KOBE Electric and Sparrows Electric EOMs. This suggests that it may be possible to obtain a better fitting model by a decision tree approach to isolate the cases with zero values as described above. No attempt was made to evaluate impact on accuracy of alternate methods of modeling or whether these would be justified considering the associated increase in complexity of the model.

Michael Ratteree Page 12 August 25, 1999 FIGURES: Figure 7-1: Normal Quantile-Quantile plot for residuals of KOBE Electric EOM. Figure 7-2: Residuals vs. fitted values: KOBE Electric EOM. Figure 7-3: Autocorrelations in residuals for the KOBE Electric EOM. Figure 7-4: Cook’s distances for the KOBE Electric EOM. Figure 7-5: Histograms for two independent variables in the KOBE Electric EOM. Figure 7-6: Normal Quantile-Quantile plot for residuals of Sparrows Point Electric EOM. Figure 7-7: Residuals vs. fitted values: Sparrows Point Electric EOM. Figure 7-8: Autocorrelations in residuals for the Sparrows Point Electric EOM. Figure 7-9: Cook’s distances for the Sparrows Point Electric EOM. Figure 7-10: Normal Quantile-Quantile plot for residuals of Gulf States Steel natural gas EOM. Figure 7-11: Residuals vs. fitted values: Gulf States Steel natural gas EOM. Figure 7-12: Autocorrelations in residuals for the Gulf States Steel natural gas EOM. Figure 7-13: Cook’s distances for the Gulf States Steel natural gas EOM.

Michael Ratteree Page 13 August 25, 1999 Summary and Conclusions from the Statistical Analysis Summary of Findings We were able to reproduce the KOBE Electric, Sparrows Point Electric, and Gulf States Steel natural gas EOMs using data in the spreadsheet files provided by EP. Our analyses of these models indicate that the EOMs are appropriate statistical representations of the data from which they are based. However, it may be possible to obtain slightly better fitting models by using a decision tree approach to isolate cases with zero values for some of the dependent variables that have a local maximum at x=0 in their probability density functions. Limitations Although the models are sometimes validated with additional historical data, a caveat must be understood by anyone relying on the model. The statistical part of the model, used as a predictor, is only valid for the domain of the independent variables used in creating the model. The model is analyzed only within the domain of the variables and should not extrapolate past the limits of the data. As an example, if the independent variable, Boiler I Operation Days (in a month), domain (or range) was between 0 days and 18 days for the model building, then for prediction purposes the model is valid only during months where the Boiler I Operation Days are between 0 and 18 days. Outside of this range the model has not been analyzed or validated. It should be noted a significant process change may cause the independent variables to go beyond the range of the historical data, and EP has established a procedure for formal review of EOMs with the customer once significant process change occurs. If it is concluded during this review that the model is no longer accurate, EP will revise the model taking the process change into account. Both EP and the customer must then agree to the revised model. 8.0 OVERALL CONCLUSIONS ENVIRON’S review of the above areas found no significant errors or problems with EP’s model development or implementation. The concept of the modeling process is sound, and the implementation of the models is such that EPs customers have relied on them to be invoiced for the shared savings. The models generally have R2 values close to 1.0, indicating that they explain the vast majority of the variations in the energy usage. While some deviations from EP’s formal process were found, none of those deviations appears to be significant. In summary, ENVIRON believes that EP’s EOMs are appropriate for estimation of the actual energy savings achieved and for generating invoices to EP’s customers.

Michael Ratteree Page 14 August 25, 1999

EOM REPORT: PLANT-WIDE NATURAL GAS Historical Period: January 2,1997 to March 31, 1998 (excluding 33 days due to missing or bad data) (421 production days)

Mean Absolute Variation, Daily: 7.90% Mean Absolute Variation, Monthly: 1.16%

Total Actual Natural Gas Usage: 7,547,297 MMBTU Mean daily Natural Gas Usage: 17,927 MMBTU

Correction Coefficient: 0.873

Source of Data Actual Natural Gas Usage

Sum of Pearl Total, Palm Total and Oakwood Total, minus Sales to Praxair, minus Nat Gas to the Tuyers, minus Nat Gas to preheat scrap in the BOP vessels -daily values obtained from USS/KOBE’s FU&S Monthly Distribution Report

Boiler Blast Furnace Gas MMBTU Total MMBTU of Blast Furnace Gas delivered to the boilers = (daily values of WST WALL BFG MCF minus “WEST BLEEDER”) times daily BTU1CF of Blast Furnace Gas -all daily values obtained from USS/KOBE’s FU&S Monthly Distribution Report

Hrs25HzGen@Max = hours the 25Hz Generator was operated on demand to produce as much electricity as possible even if Natural Gas was required as reported on the USS/KOBE Utilities Daily Report

Oil to #4 Tuyere = gallons of #6 oil injected at #4 Blast Furnace, daily values obtained from USS/KOBE’s FU&S Monthly Distribution Report

Michael Ratteree Page 15 August 25, 1999 #3&4 BF SchedDelayHrs

= Scheduled Delay Hours for#3BIast Furnace from the “Iron ProdM-T-D Delay Report” plus Scheduled Delay Hours for#4 Blast Furnace from the Iron Prod.M-T-D Delay Report”

#3&4 BF UnschedDelayl-lrs = Unscheduled Delay Hours for #3BIast Furnace from the “Iron Prod.M-T-D Delay Report plus Unscheduled DelayHoursforl4Blast Furnace from the lron Prod.M-T-D Delay Report”

#3 Blast Furnace Down = 1 if #3 Blast Furnace Tons = 0; 0 otherwise

#4 Blast Furnace Down = 1 #4 Blast Furnace Tons = 0; = 0 otherwise

BF#4 Stove Problems = 1 up to and including March 12, 1997. During this period #4BF Stoves were frequently Out of Service and Hot Blast Temperature was low. =O after March 12, 1997

Post#4 BF reline = 1 after#4 Blast Furnace reline complete (afterAugust22, 1997); = 0 up to and including August 22, 1997

Total Billet + Bloom Caster Heats = # of Billet Caster plus Bloom Caster heats produced daily, obtained from the “Daily Report of Operations”

Rolling Mill Charged Tons = “R-R BLT CHARGE from monthly production report produced by Rolling/Bar Accounting

Rolling Mill Average Deseamer Temperature (Fahrenheit) from Bob Diamond, Primary Rolling Quality

#4 Seamless Charged Tons from #4 Seamless Hot Mill Production & Consumption Report

#4 Seamless Upshifts = number of shifts on which tons were produced from #4 Seamless Hot Mill Production & Consumption Report

#4 Seamless Preheat2 = 1 if the last shift of a down period of 2 or more shifts; = 0 otherwise from #4 Seamless Hot Mill Production & Consumption Report

12” Charged Tons from Bar Mill Production and Delay Statistics

12” Hold 1 if a single down shift (no tons produced) is immediately preceded by, and is immediately followed by, shifts during which tons are produced; = 0 otherwise from Bar Mill Production and Delay Statistics

12” Preheat2 = I if the last shift of a down period of 2 or more shifts; = 0 otherwise from Bar Mill Production and Delay Statistics

10” Charged Tons from Bar Mill Production and Delay Statistics

#3 Seamless Mill Hours from #3 Seamless Hot Mill Production & Consumption Report

#3 Seamless Rollout2 = 1 if a shift during which tons are produced (an “Upshift”) is immediately followed by at least two successive shifts during which none are produced (“Downshifts”); = 0 otherwise from #3 Seamless Hot Mill Production & Consumption Report

Michael Ratteree Page 16 August 25, 1999 #3 Seamless Active Day

= 0 during periods of at least three days when no tons are produced; = 1 otherwise. from #3 Seamless Hot Mill Production & Consumption Report

#3 Seamless Startup = 1 on the day immediately before Active Day increases from 0 to 1; = Don other days, from #3 Seamless Hot Mill Production & Consumption Report

Heating Degree Days = HOD calculated from the Daily High and LowTemperature readings reported by the Midwestern Climate Center for ELYRIA_3_E, OH

NOTE: All the production and climate data listed above are time-shifted to 3:00 PM to coincide with gas meter readings