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Customizing CO2 allocation using a new non-iterative method to reflect operational constraints in complex EU refineries

  • Victor Gordillo
  • Nicolas Rankovic
  • Amir F.N. Abdul-Manan
CARBON FOOTPRINTING
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Abstract

Purpose

Developing a robust method for CO2 allocation in oil refineries is an ongoing debate within the life cycle assessment (LCA) community. Several methodologies reported in the literature, mostly performing sequential and iterative calculations, tend to be biased toward diesel at the expense of gasoline, failing to properly consider the role played by hydrogen. This paper develops a new non-iterative refinery CO2 allocation method to explore the concept of customized allocation to overcome the inherent bias in standard methods.

Methods

The allocation methodology is based on a system of linear equations built around the material and energy balances of a refinery. After describing the process of building such system, it is shown that the carbon allocation values of all final products and intermediate streams are directly obtained by solving it. A numerical example of CO2 emission allocation to major refinery products is provided from an optimized refinery linear programming (LP) case for the European refining industry, based on literature projections for 2020.

Results and discussion

The paper presents the key emission sources in the European refinery sector, and by using a standard mass-based allocation technique, we show that the carbon intensities of refined petroleum products derived using the non-iterative method are consistent with other studies. We confirm the findings that the standard allocation typically used in attributional refinery LCA tends to reward diesel to the detriment of gasoline. We attempted reconciling this by applying a reallocation factor to customize the CO2 allocation to represent the “real” economic purposes of process units reflecting the constraints European refineries face today. This moderated the octane production effects given the important role the reformer plays in hydrogen co-production, where the emission burden of highly knock-resistant reformate is redistributed to hydrogen and carried through to diesel.

Conclusions

By customizing the allocation of CO2, we demonstrated that the differences between a consequential and an attributional approach in refinery LCA can partly be reconciled. We now run into the risk of increasing the subjectivity of the attributional method by using “judgment calls” to decide on the choice of weightage to be applied. We invite the wider LCA practitioners to further investigate the use of this new non-iterative method for allocating CO2 and explore the concept of reallocation factors as means to customize emission allocation.

Keywords

Attributional CO2 allocation Fuel GHG Joint production Linear programming Non-iterative methodology Oil refinery 

References

  1. Abdul-Manan AF (2015) Uncertainty and differences in GHG emissions between electric and conventional gasoline vehicles with implications for transport policy making. Energy Policy 87:1–7CrossRefGoogle Scholar
  2. Abdul-Manan AF (2017) Lifecycle GHG emissions of palm biodiesel: unintended market effects negate direct benefits of the Malaysian economic transformation plan (ETP). Energy Policy 104:56–65CrossRefGoogle Scholar
  3. Abdul-Manan AF, Arfaj A, Babiker H (2017) Oil refining in a CO2 constrained world: effects of carbon pricing on refineries globally. Energy 121:264–275CrossRefGoogle Scholar
  4. ANL (2013) Greenhouse gases, regulated emissions and energy use in transportation model. Argonne National Laboratory, ArgonneGoogle Scholar
  5. Bessou C, Chase LD, Henson IE, Abdul-Manan AF, Mila-i-Canals L, Agus F, Chin M (2014) Pilot application of PalmGHG, the RSPO greenhouse gas calculator for oil palm products. J Clean Prod 73:136–145CrossRefGoogle Scholar
  6. Bouvart F, Saint-Antonin V, Gruson J-F (2013) “Well-to-tank” carbon impact of fossil fuels. French Environment and Energy Management Agency (ADEME)Google Scholar
  7. Bredeson L, Quiceno-Gonzalez R, Riera-Palou X, Harrison A (2010) Factors driving refinery CO2 intensity, with allocation into products. Int J Life Cycle Assess 15:817–826CrossRefGoogle Scholar
  8. CARB (2009) Proposed regulation to implement the low carbon fuel standard. Volume I. Staff Report: Initial Statement of Reasons. California Air Resources BoardGoogle Scholar
  9. Cherubini F, Jungmeier G (2010) LCA of a biorefinery concept producing bioethanol, bioenergy, and chemicals from switchgrass. Int J Life Cycle Assess 15(1):53–66CrossRefGoogle Scholar
  10. CONCAWE (2013) Oil refining in the EU in 2020, with perspectives to 2030. CONCAWE, BrusselsGoogle Scholar
  11. CONCAWE (2017) Estimating the marginal CO2 intensities of EU refinery products. CONCAWE, BrusselsGoogle Scholar
  12. Dale BE, Kim S (2014) Can the predictions of consequential life cycle assessment be tested in the real world? Comment on “Using attributional life cycle assessment to estimate climate-change mitigation…”. J Ind Ecol 18(3):466–467Google Scholar
  13. Daystar J, Venditti R, Kelley SS (2017) Dynamic greenhouse gas accounting for cellulosic biofuels: implications of time based methodology decisions. Int J Life Cycle Assess 22:812–826CrossRefGoogle Scholar
  14. EC. (2009) Directive 2009/28/EC of the European parliament and of the council of 23 April 2009 on the promotion of the use of energy from renewable sources and amending and subsequently repealing directives 2001/77/EC and 2003/30/EC. Off J Eur Union:16–62Google Scholar
  15. El-Houjeiri HM, Vafi K, Duffy J, McNally S, Brandt AR (2015) Oil production greenhouse gas emissions estimator OPGEE v1.1 draft EGoogle Scholar
  16. EPA (2010) Part II Environmental Protection Agency. 40 CFR Part 80 Regulation of fuels and fuel additives: changes to Renewable Fuels Standard Program; Final Rule. Washington: National Archives and Records AdministrationGoogle Scholar
  17. European Commission (2015) Reference document on best available techniques for mineral oil and gas refineries. Seville, Integrated Pollution Prevention and Control (IPPC)Google Scholar
  18. European Union (2016) BioGrace. (The Intelligent Energy Europe Programme) Retrieved from http://www.biograce.net/
  19. Furuholt E (1995) Life cycle assessment of gasoline and diesel. Resour Conserv Recycl 14:251–263CrossRefGoogle Scholar
  20. Han J, Forman GS, Elgowainy A, Cai H, Wang M, DiVita VB (2015) A comparative assessment of resource efficiency in petroleum refining. Fuel 157:292–298CrossRefGoogle Scholar
  21. Hertwich E (2014) Understanding the climate mitigation benefits of product systems: comment on “using attributional life cycle assessment to estimate climate-change mitigation”. J Ind Ecol 18(3):464–465CrossRefGoogle Scholar
  22. ICCT (2014) Upstream emissions of fossil fuel feedstocks for transport fuels consumed in the European Union. The International Council on Clean Transportation, Washington DCGoogle Scholar
  23. JEC (2014a) Well-to-Wheels analysis of future automotive fuels and powertrains in the European conext, version 4.a. Joint Research Centre-EUCAR-CONCAWE collaborationGoogle Scholar
  24. JEC (2014b) Well-to-tank appendix 2—version 4a. Joint Research Centre-EUCAR-CONCAWE collaboration. Ispra, European CommissionGoogle Scholar
  25. JRC-IEA (2010) International reference life cycle data system (ILCD) handbook—general guide for life cycle assessment—detailed guidance. Publications Office of the European Union, Luxembourg Retrieved from http://lct.jrc.ec.europa.eu/ Google Scholar
  26. Luo L, van der Voet E, Huppes G, Udo de Haes HA (2009) Allocation issues in LCA methodology: a case study of corn stover-based fuel ethanol. Int J Life Cycle Assess 14(6):529–539CrossRefGoogle Scholar
  27. Plevin R, Delucchi M, Creutzig F (2013) Using attributional life cycle assessment to estimate climate-change mitigation benefits misleads policy makers. J Ind Ecol 18(1):73–83CrossRefGoogle Scholar
  28. Schmidt JH (2008) System delimitation in agricultural consequential LCA. Int J Life Cycle Assess 13(4):350–364CrossRefGoogle Scholar
  29. Searchinger T, Heimlich R, Houghton R, Dong F, Elobeid A, Fabiosa J, Yu T-H (2008) Use of U.S. croplands for biofuels increases greenhouse gases through emissions from land-use change. Sci 319(5867):1238–1240CrossRefGoogle Scholar
  30. thinkstep (2012) GaBi 6 Professional database Retrieved from http://www.gabi-software.com/support/gabi/gabi-6-lci-documentation/
  31. Wang M, Lee H, Molburg J (2004) Allocation of energy use in petroleum refineries to petroleum products. Int J Life Cycle Assess 9(1):34–44CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Strategic Transport Analysis TeamAramco Fuel Research Center, AOC BV ParisRueil-MalmaisonFrance
  2. 2.Strategic Transport Analysis Team, Fuel Technology R&D, Research & Development Center (R&D)DhahranSaudi Arabia

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