| Biofuels are a promising renewable transportation fuel that can improve energy security and rural economics. How to develop an efficient and effective biomass production and provision system is important for successful large-scale biofuels production. The overall objective of this dissertation is to develop multiple-scale supply chain optimization models and decision support tools to facilitate biomass production and provision. An interdisciplinary approach, Concurrent Science Engineering and Technology (ConSEnT), was applied to facilitate systems informatics and analysis for optimization of biomass feedstock provision. The ConSEnT approach for large-scale biomass supply chain management was developed through the integration of operations research, geographic information systems (GIS), processing modeling, techno-economic analysis, and cyberinfrastructure.;In this dissertation, three optimization modeling tools and a CyberGIS-enabled biomass feedstock provision decision support platform have been developed to facilitate large-scale biomass feedstock provision.;BioScope model, a strategic planning model, was developed to optimize long-term decisions, such as facility numbers, locations, capacities, and biomass distribution patterns, for a three-stage biomass-biofuel production system. The model was implemented to evaluate Illinois Miscanthus based biofuel supply chain system through minimizing annual Miscanthus-ethanol production costs at different scenarios. The results showed that biorefinery related costs are the most important factor, followed by biomass procurement, transportation, and centralized storage and preprocessing (CSP) related costs. Cropland usage rate, biomass demand, transportation mode, and facility capacity limit are the key factors affecting the production costs.;To better understand the development of biofuel production, Dynamic BioScope model, a multi-period strategic planning model, was developed to address how a biomass provision system would be best evolved to meet the increasing biofuels production demand over time. The model minimizes total production costs throughout the planning period by optimizing decisions including building and expansion timings, numbers, locations, and capacities of facilities and biomass distribution patterns within the system. The model was applied to evaluate the systems performance of Miscanthus-ethanol production in Illinois from 2012 to 2022. The results showed that Miscanthus-ethanol production costs will be reduced as the system evolved, mainly due to the achievement of the economies of scale through building larger biorefineries and better biomass supply chain infrastructure.;To better understand the interactions between strategic and tactical decisions, an integrated biomass supply chain optimization model was developed to minimize annual biomass-ethanol production costs by optimizing both strategic and tactical planning decisions simultaneously. The numbers, locations, and capacities of facilities as well as biomass and ethanol flow patterns are the key strategic decisions; while biomass production, delivery, and operating schedules as well as inventory monitoring are the key tactical decisions. The model comprises four modules including farm management, logistics planning, facility allocation, and ethanol distribution. The activities optimized by the model range from biomass harvesting, packing, in-field transportation, stacking, transportation, preprocessing, storage, to ethanol production and distribution. The model was implemented to study the Miscanthus-ethanol supply chain in Illinois. Among the biomass production activities, biomass baling and harvesting are the two most expensive operations. The biomass delivery schedules showed seasonal variations. Sensitivity analysis showed a 50% reduction in biomass yield would increase biofuels production costs by 11%.;A CyberGIS-enabled biomass supply chain decision support platform was developed to improve the accessibility and computing performance of the BioScope model. The platform includes four major components: BioScope optimization model, an interactive CyberGIS Gateway interface, GISolve middleware, and high-performance cyberinfrastructure (CI). The workflow and functions of each component were provided to illustrate the development and usage of the platform. Empowered by high performance CI, the platform improved the computing performance for both single and multiple job submissions. This implementation example could serve as a protocol for further integration development of cyberinfrastruture, operations research, and geospatial analysis. |
