(86a) Refining Energy Utilization and CO2 Emissions Modeling

The U.S. petroleum refining sector consumes large quantities of energy in order to convert crude oil into transportation fuels and other products. According to the Energy Information Administration, the sector consumed 31% of total U.S. industrial energy in 2006, resulting in significant CO₂ emissions.  Every petroleum refinery is configured differently, processing different quality crudes into varying product slates; thus, no two refineries are the same. Moreover, process specific energy use and CO₂ emissions are not reported to the U.S. DOE or the EPA, making it difficult to understand or forecast future process energy demands and emissions.  A methodology for estimating individual process energy requirements and emissions as a function of varying feedstocks, products and processing will help identify for policy makers and researchers the potential for improving energy efficiency and reducing emissions from the petroleum refining sector.  As a first step in examining the alternatives, it is necessary to establish refinery baseline energy and emissions profiles.  Lawrence Berkley National Laboratory has been working to establish such baselines under the EPA’s Climate Change Program.  In this paper, we present initial results of our analyses, which examine both conventional and non-conventional petroleum resources for the production of gasoline, jet and diesel fuels.   

This paper will report energy consumption and CO₂ emissions for the refining of four classes of crude oil:  low-sulfur light; medium-sulfur heavy, high-sulfur heavy, and high-sulfur very-heavy crude.  A bottom-up approach was used to estimate energy use and emissions using process-level data on the production and refining of these oils.  Refinery fuel, steam, cooling water and electricity consumption are estimated for each individual process in the refinery. Hydrogen production, on-site steam and power generation, and grid purchased electricity and natural gas are included and benchmarked to historic data. The model performs a “carbon balance” tracking all carbon inputs, internal consumption, and product outputs.  This approach not only allows the carbon footprint of the various crudes to be distinguished, but also provides valuable insights relative to the carbon and energy intensity of the various refining steps required to produce specification fuels.