Correspondence/Rebuttal pubs.acs.org/est
Comment on “Analysis of Energy Use and CO2 Emissions in the U.S. Refining Sector, With Projections for 2025”
H
irshfeld and Kolb1 argue for rejecting an emissions model that is based on public data and its publicly reproducible results2 in favor of their model and its results. The failure to allow their model’s data to be verified and its results to be reproduced by independent reviewers forestalls that approach. Meanwhile, their results contradict their argument and reveal flaws in their model. The authors estimate average energy and CO2 emission intensities of refining denser, higher sulfur crude oils using a linear programming (LP) model that constructs process-byprocess energy allocations. Their text implies that this “LP” model uses public data, but the model is proprietary, and they do not report process-level energy inputs and other data needed to verify its inputs and reproduce its results. This problem with proprietary refinery models is noted by previous work2−4 and partially remedied by a public domain model I reported.2 Remarkably, the authors argue for rejecting this public domain (PD) model without describing its methods or citing any critique that applies to them. I analyzed public data from operating plants for processing relationships supported by engineering knowledge, and distinguished effects of crude quality on processing, energy use, and emissions from those of refinery product slates, utilization rates, and fuels burned quantitatively using partial least-squares regression analyses.2 This “PD” model does not assume a sudden total shift to heavy oil or exclude process-specific data as the authors imply. It estimates average emissions from differences in crude feeds actually processed, and relates process-specific data to refinerylevel energy use in operating plants.2 That is a strength, given the authors’ caution that the LP model may assume ideal conditions and understate energy used in actual operation (Supporting Information of ref 1). LP results contradict the authors’ critique of the PD model. This is masked by an erroneous comparison that excludes PD results for crude feeds similar to those they analyze, selects PD results for much denser, higher sulfur oils, and assigns the wrong feed volume to those results. Correcting these errors, instead of the order of magnitude difference they report, LP emission results are within 3−10% of PD results for similar crude feeds (Figure 1A). However, LP results diverge from observed data as crude density increases. For example, LP energy intensity results are within ≈2% of observed data for similar-density crude in scenario S1, but underestimate observations by ≈16% in denser crude scenario S4 (Figure 1B). This widening discrepancy is important because densities and sulfur levels of low-quality oils2 exceed those the authors model by several times their S1−S4 range. Reasons for this discrepancy might include undisclosed modeling assumptions. LP results represent a refining strategy that increases coking but not hydrocracking as crude quality worsens, and would require West Coast refiners that now run average crude feeds in the S3−S4 density range to add catalytic © 2012 American Chemical Society
Figure 1. Corrections to Hirshfeld and Kolb1 Figure 1. LP (numbered white diamonds): linear programming results for scenarios S2−S4 (S1−S4) from ref 1, Table 3. PD: public domain results from ref 2, Supporting Information Table S8. Error: erroneous values given as PD results from ref 1, Supporting Information Table SI-13. Black circles: observed data from ref 2, Supporting Information Table S1.
cracking capacity while idling existing hydrocracking and hydrogen production (ref 1, Supporting Information Table SI-10; ref 2, Supporting Information Table S1). This carbon rejection strategy may use less energy than hydrogen addition at the deep conversion step in processing, but it yields a lower volume of liquid intermediates with higher sulfur and olefinic content, and more petroleum coke.4−6 Energy and emission costs of these conversion yield problems could exceed idealized process design predictions in real-world operation, and would grow as crude quality declines. Underestimation of those costs could explain why LP energy, emission, and processing results diverge from observations as crude quality worsens. This possibility (and others) cannot be verified because LP model inputs and supporting data are not disclosed. Put simply, “secrecy almost always impedes scientific progress” and “frequently permits hazards to develop that could be eliminated if information were publicly available.”7 We can learn more by confronting data secrecy and comparing analysis methods based on openly reproducible science. Published: June 21, 2012 7921
dx.doi.org/10.1021/es301915z | Environ. Sci. Technol. 2012, 46, 7921−7922
Environmental Science & Technology
Correspondence/Rebuttal
Greg Karras*
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Communities for a Better Environment (CBE), 1904 Franklin Street, Suite 600, Oakland, California, 94612, United States
AUTHOR INFORMATION
Corresponding Author
*Phone: (510) 302-0430 ext 19; e-mail:
[email protected]. Notes
The authors declare no competing financial interest.
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REFERENCES
(1) Hirshfeld, D. S.; Kolb, J. A. Analysis of energy use and CO2 emissions in the U.S. refining sector, with projections for 2025. Environ. Sci. Technol. 2012, 46, 3697−3704, DOI: 10.1021/es204411c. (2) Karras, G. Combustion emissions from refining lower quality oil: What is the global warming potential? Environ. Sci. Technol. 2010, 44, 9584−9589, DOI: 10.1021/es1019965. (3) Wang, M.; Lee, H.; Molburg, J. Allocation of energy use in petroleum refineries to petroleum products, implications for life-cycle energy use and emission inventory of petroleum transportation fuels. Int. J. Life Cycle Assess. 2004, 9 (1), 34−44. (4) Brandt, A. R. Variability and uncertainty in life cycle assessment models for greenhouse gas emissions from Canadian oil sands production. Environ. Sci. Technol. 2011, 46, 1253−1261, DOI: 10.1021/es202312p. (5) Robinson, P. R.; Dolbear, G. E. Commercial hydrotreating and hydrocracking. In Hydroprocessing of Heavy Oils and Residua; Ancheyta, J., Speight, J. G., Eds; Chemical Industries; CRC Press, Taylor & Francis Group: Boca Raton, FL, 2007: Vol. 117, pp 281−311. (6) Brederson, L.; Quiceno-Gonzalez, R.; Riera-Palou, X.; Harrison, A. Factors driving refinery CO2 intensity, with allocation into products. Int. J. Life Cycle Assess. 2010, 15, 817−826, DOI: 10.1007/s11367-0100204-3. (7) Edsall, J. T. Scientific freedom and responsibility: Report of the AAAS Committee on Scientific Freedom and Responsibility. Science 1975, 188 (4189), 687−693.
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