1. A U.S. Department of Energy Office of Science Laboratory Operated by The University of Chicago Argonne National Laboratory Office of Science U.S. Department of Energy Methodology and Results of the Energy Performance Indicator for Cement Manufacturing Presented to the Portland Cement Association Manufacturing Technical Committee September 27th, 2004 - Chicago Illinois By Gale A. Boyd, PhD

2. Pioneering Science and Technology Office of Science U.S. Department of Energy Estimating the “Energy Efficiency Gap” Engineering models may represent best practice, while statistical models are typically based on average practice. -Measures of energy intensity based on average practice are of limited use in managing energy use or for corporate goal setting. -A more useful measure represents where a company or plant lies within a distribution of performance. “Is performance close (or far) from the industry best practice?” We modify the typical statistical approach to develop Industrial Energy Performance Indicators (EPI) to measure “best practice” and the “efficiency gap” for ENERGY STAR -Method: Stochastic frontier regression analysis -Data: Plant level data on energy and production

3. Pioneering Science and Technology Office of Science U.S. Department of Energy The EPI Statistically Identifies “Best Practice” “Best” practice is defined for a specific application, with observable economic and structural differences accounted for -Variation in observed practice can exist for a number of reasons; - Economic decisions - Energy prices - Utilization rates - Structural differences - Production processes - Materials choice -Those differences are not part of the “efficiency gap” Statistical models are well suited to account for these differences -Statistical models are commonly based on aggregate data -No explicit treatment of “best” and “average” practice.

4. Pioneering Science and Technology Office of Science U.S. Department of Energy Stochastic Frontier is a Modified Regression Linear regression computes the “typical” performance given exogenous effects -Explains the data by finding the best fit line which “goes through the middle” of the data -Any deviation is “statistical noise” which is assumed to be normally distributed, i.e. can be positive or negative. The frontier computes the best-performing given those same effects -Explains the data by finding the best fit line which “envelopes the frontier” of the data -Some deviation is “noise”, but deviations may also be inefficiency which is assumed to follow a one-sided distribution. Estimating the statistical distribution allows us to compute a normalized percentile score

5. Pioneering Science and Technology Office of Science U.S. Department of Energy Plant Level Data as a Key Component Analysis uses confidential plant level data from two sources -Center for Economic Studies (CES), U.S. Bureau of the Census -Data provided to ANL by PCA Data from CES includes the non-public, plant-level data which is the basis of the government statistics on manufacturing. Title 13 of the U.S. Code protects this data, - CES allows researchers with Special Sworn Status to access these confidential micro-data at a Research Data Center (RDC). - Confidentiality prevents the disclosure of any information that would allow for the identification of a specific plant or firm’s activities.

6. Pioneering Science and Technology Office of Science U.S. Department of Energy Scope of the Current EPI Analysis Uses 1992 and 1997 Census of Manufacturing (CM) Data -Those years have the most detail on production. -Some plants were dropped for missing data Manufacturing Energy Consumption Survey (MECS) provides plant energy mix. -All other plants were excluded from the analysis. -Since MECS is a statistical sample, this is a “good” representation of the industry. PCA Plant Information Summary Report provides -Kiln capacity and utilization. -Cross check the MECS fuel and CM production PCA Labor and Energy Survey data was provided to ANL under a non-disclosure agreement -This data is being used to check data used in the current EPI -Additional analysis will be done as warranted

7. Pioneering Science and Technology Office of Science U.S. Department of Energy Basic Inputs to the Cement EPI Uses Plant Total Primary Energy (TPE) -Total BTU’s of coal, waste derived fuel, etc -kWh of electricity purchased from the grid is converted to BTUs at power plant thermal efficiency (average 10,236 BTU/kWh) Includes these Factors -Total Capacity of all Kilns in the plant -Number of Kilns (average capacity) -Capacity utilization – percentage -Production Mix - ASTM 1, 2 or 5 - ASTM 3 - ASTM 4 - Masonry - Other Cement - Clinker Shipped as a Separate Product

8. Pioneering Science and Technology Office of Science U.S. Department of Energy Basic Steps in Frontier Statistical Analysis Specify the systematic relationship (linear) between variables like production, plant size, output mix, etc. Run linear regression on plant level dataset -Data includes 137 observations -About 69 plants for two years Adjust the parameters using Maximum Likelihood methods to improve the fit based on frontier assumption that inefficiency has an exponential distribution -Shift the linear regression intercept so that all data are on one side of the line -Adjust linear regression to represent simultaneous best fit of slopes, error (noise) variance σ 2, and Exponential distribution parameter θ

9. Pioneering Science and Technology Office of Science U.S. Department of Energy The Statistical Frontier Model for Cement Stochastic frontier regression separates energy intensity into -Systematic effects, -Statistical (random) error -Inefficiency -E is energy use, Y includes outputs, X is the vector of systematic economic variables, Z is the vector of systematic external factors, β is the vector of parameters to be estimated, -v is the typical random error term -u is distributed according to some one-sided error distribution, for example exponential

10. Pioneering Science and Technology Office of Science U.S. Department of Energy Parameter Estimates for the Current EPI

11. Pioneering Science and Technology Office of Science U.S. Department of Energy Input Section of Cement EPI Spreadsheet

12. Pioneering Science and Technology Office of Science U.S. Department of Energy Output Section of Cement EPI Spreadsheet

13. Pioneering Science and Technology Office of Science U.S. Department of Energy The “Average” Cement Plant Has a kiln capacity of around 800,000 TPY Consumed 3.5 trillion BTUs of Primary Energy -2.7 trillion BTUs of Fuel -0.8 trillion BTUs of Electricity -5.1 million BTU TPE per ton of clinker Shipped $50 million in products …but the average doesn’t tell the whole story…

14. Pioneering Science and Technology Office of Science U.S. Department of Energy Some Stylized Results Include Lower plant utilization incurs an implicit “penalty” in energy use “Size” matters -Larger plants are more efficient -Larger kilns are more efficient Cement types other than ASTM I, II, or V involve amounts of additional energy

15. Pioneering Science and Technology Office of Science U.S. Department of Energy A Large Kiln is “Better” than Several Small Ones

16. Pioneering Science and Technology Office of Science U.S. Department of Energy Some Cement Types have Higher Energy Requirements

17. Pioneering Science and Technology Office of Science U.S. Department of Energy Larger Kilns - Lower Observed Best Practice

18. Pioneering Science and Technology Office of Science U.S. Department of Energy Lower Utilization - Higher Observed Best Practice

19. Pioneering Science and Technology Office of Science U.S. Department of Energy Analysis for the Cement EPI is Ongoing Some additional adjustments using engineering principles are under development -Low Alkali cement may require kiln by-pass -Other emissions control also require by-pass Use of PCA data will enhance the analysis because of (hopefully) better accounting of -By-product based fuels -Clinker production Final version will be publicly available from EPA and PCA