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Bioenergy supply chain optimization - decision making under uncertainty

University of British Columbia

In an age of dwindling fossil fuels, increased air pollution, and toxic groundwater, it is time we embrace renewable energy sources and commit to global green initiatives. In principle, biomass could be used to manufacture all the fuels and chemicals currently being manufactured from fossil fuels. Unlike fossil fuels, which take millions of years to reach a usable form, biomass is an energy source that can close the loop on many of our recycling and hazardous waste problems. The goal of this research is to develop flexible and easy to use mathematical frameworks suitable for the design and planning of biomass supply chains. This thesis deals with the development of discrete-continuous decision support methodology and algorithms for solving complex optimization problems frequently encountered in the procurement of biomass for bioenergy production. Uncertainty and randomness is predominant, although often ignored, throughout the biomass supply chain. Uncertainty in the biomass supply chain may be classified as upstream (supply) uncertainty, internal (process) uncertainty, and downstream (demand) uncertainty. This thesis endeavors to incorporate uncertainty in the modeling of biomass supply chains. For this purpose, stochastic modeling and scenario analysis methodologies are utilized. The main contributions of this thesis are: (i) the development of a novel stochastic optimization methodology, called quantile-based scenario analysis (QSA); and (ii) the development of optimization algorithms, namely constrained cluster analysis (CCA) and min-min min-max optimization algorithm (MMROA), for the collection of bales across multiple adjoining fields. These methodologies are applied to three distinct biomass procurement case studies. Results show that QSA achieves more favorable solutions than those obtained using existing stochastic or deterministic approaches. In addition, QSA is found to be computationally more efficient. In a case study involving the collection of forest harvest residues for several competing power plants, QSA achieved an average cost reduction of 11%. In a case study involving the collection of sawmill residues, QSA obtained a 6% gain in performance by accounting for uncertainty in the model parameters. In a case study involving the collection of bales, an 8.7% reduction in the total travel distance was obtained by the MMROA.

Text

2018-01-10

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/64338

Chemical and Biological Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/