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October 18, 2019, Capabilities

Refinery Integration Analysis

Capability Title Refinery Integration Analysis
Laboratories Argonne National Laboratory (ANL), Idaho National Laboratory (INL), National Renewable Energy Laboratory (NREL), Pacific Northwest National Laboratory (PNNL)
Capability experts Troy Hawkins (ANL), Yuan Jiang (PNNL), M.M. Ramirez-Corredores (INL), Avantika Singh (NREL)
Description The refinery integration analysis quantifies the economic values of bio-blendstocks to petroleum refiners and their environmental impacts. The primary software used for profit analysis is Aspen PIMS, a widely used planning software for refiners to optimize their operations and maximize profitability. With known fuel properties, the economic values of new blendstocks can be evaluated by blending optimization and/or full refinery linear programming models of various representative plant configurations and crude slates in different US PADDs. The profit analysis can be integrated with petroleum refinery life cycle inventory model (PRELIM) to analyze the environmental impacts of bio-blendstocks.
Limitations
  • Uncertainties in blending fuel properties – evaluate impacts with sensitivity analysis
  • Limitations in publicly available data for characterizing PADD and refinery-specific operations – using transparent datasets and clearly stated assumptions
Unique aspects Allows petroleum refineries to estimate the potential benefits that a new blend stock may offer
Availability The analysis team is available for consultation with industry and academic partners.
Citations/references

Jiang, Yuan; Phillips, Steven D; Singh, Avantika; Jones, Susanne B; Gaspar, Daniel J; Economic values of low-vapor-pressure gasoline-range bio-blendstocks: property estimation, and blending optimization, to be submitted to Fuel.

Sun, Pingping; Young, Ben; Elgowainy, Amgad; Lu, Zifeng; Wang, Michael; Morelli, Ben; Hawkins, Troy. ‘Criteria Air Pollutant and Greenhouse Gas Emissions from U.S. Refineries Allocated to Refinery Products.’ Environmental Science & Technology, 53(11), 6556-6569, 2019.