Title: Capturing Uncertainty in Biofuels for Transportation. Resolving Environmental Performance and Enabling Improved Use
Coordinator: Fausto Freire, ADAI; Luis Dias
Dates start/end: October 2010 / October 2013
Entity: FCT (MIT/SET/0014/2009)
Proponent Institutions: ADAI
Participating Institutions: INESC Coimbra, MIT
Synopsis: This project addresses the challenge of developing a methodology for the extended LCA of biofuels systems’ sustainability. The R&D work will provide:
1) a quantitative assessment of the nature and magnitude of quality and environmental impact uncertainty of feedstock’s for transportation biofuels;
2) an analytical characterization of the potential for uncertainty-aware planning models to appropriately manage uncertainty;
3) a framework to combine and make trade offs between these tools in the context of industry-specific case studies. The explicit incorporation of quality uncertainty in bio-derived feedstock’s will enhance the ability to quantify the environmental performance of these fuels.
The project will provide critical information for the biofuel industry in sourcing particular feedstock’s both domestically and internationally, optimizing fuel outputs by managing the quality uncertainty of feedstock’s, and assessing their environmental impact. A “well-to-tank” modeling of the biofuels will be adopted. The project focuses on systems producing a major crop traded on the global market, and explores the use of next generation biofuels and feedstock’s that could be sourced domestically, which could provide a business opportunity for Portuguese fuel companies. The project will:
•Characterize the uncertainty in selected bio-derived fuel feedstock sources, including their quality and environmental performance. Uncertainty in quality deals with determining statistical quality distributions for incoming feedstock’s. Towards environmental impact uncertainty, this work will look at several uncertainties including indirect land use changes.
•Assess the environmental value of biofuel alternatives based on the complementary use of MCDA and LCA.
•Develop decision-support tools considering uncertainty of the quality performance function (that maps feedstock characteristics to final fuel properties) into blending decisions using stochastic optimization, including as inputs residual sources and conventional feedstock’s.
Combine LCA and blending algorithms using MCDA towards novel engineering systems methodologies through case studies that compare fuels for transportation on metrics of environmental performance.