Integrated spatially explicit landscape and cellulosic biofuel supply chain optimization under biomass yield uncertainty

Eric G. O'Neill, Rafael A. Martinez-Feria, Bruno Basso, Christos T. Maravelias

Research output: Contribution to journalArticlepeer-review

Abstract

This paper proposes an integrated stochastic mixed-integer linear programming model for biofuel supply chain and landscape design optimization that considers the interactions between uncertainty in biomass yield, spatially explicit feedstock availability, supply chain configuration, operational decisions, and the system's environmental impact. By modeling crop establishment and fertilization as strategic decisions made before the realization of uncertainty, model solutions identify integrated supply chain configurations better suited to mitigate uncertain biomass yields. Importantly, the paper presents an approach for gathering and processing the large amount of necessary real-world data, including an accurate accounting of soil carbon sequestration. A case study located in Michigan, USA, demonstrating the capabilities of the integrated model with realistic data, is presented. Results at a variety of harvesting site resolutions and number of uncertainty scenarios, show that large-scale instances, at fine spatial resolutions, identify attractive environmental solutions and that solving the stochastic problem leads to an economic benefit.

Original languageEnglish (US)
Article number107724
JournalComputers and Chemical Engineering
Volume160
DOIs
StatePublished - Apr 2022

All Science Journal Classification (ASJC) codes

  • Chemical Engineering(all)
  • Computer Science Applications

Keywords

  • Biofuel
  • Landscape Design
  • Stochastic Optimization
  • Supply Chain

Fingerprint

Dive into the research topics of 'Integrated spatially explicit landscape and cellulosic biofuel supply chain optimization under biomass yield uncertainty'. Together they form a unique fingerprint.

Cite this