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Optimization of biomass logistics system using genetic algorithm and particle swarm optimization for biofuel production.

机译：利用遗传算法和粒子群算法优化生物质物流系统，以生产生物燃料。

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摘要

As time goes by, Renewable Energy keeps proving to be an important and potential replacement for fossil fuels. All the different types of Renewable Energy offer a relief to the environmental aftermaths of the prolonged reliance on fossil fuel energy. Bioenergy is one of the types of Renewable Energy that can help by minimizing the emissions of fossil fuels. The Energy Independence and Security Act mandates the use of 21 billion gallons of advanced biofuels including 16 billion gallons of cellulosic biofuels by the year 2022. Biomass and Biofuels can clearly become a significant aid to sustainably supply energy in the future. Nevertheless, the sustainable supply of energy has proven to be quite challenging. The logistic challenge of supplying biomass to biorefineries while being efficient and keeping costs low is one that demands to be tackled. The motivation for this thesis is to provide an approach to the biofuel supply chain challenges along with a cost-effective solution. In order to meet the mandate set by the government, corn ethanol production has significantly increased in the last few years. Conversely, the production of advanced biofuels, such as the ones obtained from biomass, are not meeting the target amount of biofuel production set in the mandate. This can be attributed to the significant economic and logistical challenges for regional planners and biofuel entrepreneurs in terms of feedstock supply assurance, supply chain development, biorefinery establishment, and setting up transport, storage and distribution infrastructure. With the high logistics operation cost, is it crucial that an optimal logistics system is designed in order to allow for the smooth transition from fossil fuels to biofuels. The thesis presents two different approaches to the optimization of a biomass-to-biofuel logistics system through the use of evolutionary algorithms that mimic nature, Genetic Algorithm and Particle Swarm Optimization. The performance of these two metaheuristic methodologies is compared in this work. In the past, these types of problems have been solved using mathematical methods such as Linear Programming and Mixed Integer Programming. Metaheuristic methods can provide near-optimal results significantly faster than mathematical optimization methods for complex problems such as this one. For that reason, this thesis presents two metaheuristic approaches for the optimization of a biomass-to-biofuel logistics system design considering multiple types of feedstock and demonstrates that metaheuristic optimization methods are suitable to solve combinatorial problems such as the one tackled in this research work.
机译：随着时间的流逝，可再生能源一直被证明是化石燃料的重要且潜在的替代品。所有不同类型的可再生能源为长期依赖化石燃料能源带来的环境后果提供了缓解。生物能源是可再生能源的一种，可以通过最大程度地减少化石燃料的排放来提供帮助。 《能源独立与安全法》规定，到2022年，必须使用210亿加仑的先进生物燃料，其中包括160亿加仑的纤维素生物燃料。生物质和生物燃料显然可以成为未来可持续供应能源的重要手段。然而，事实证明，可持续的能源供应具有很大的挑战性。在向生物炼油厂提供生物质的同时保持高效和保持低成本的后勤挑战是需要解决的一项挑战。本论文的动机是提供一种解决生物燃料供应链挑战的方法以及具有成本效益的解决方案。为了满足政府规定的任务，过去几年玉米乙醇的产量已大大增加。相反，高级生物燃料的生产，例如从生物质获得的生物燃料，未达到任务规定中设定的目标生物燃料生产量。这可以归因于区域计划者和生物燃料企业家在原料供应保证，供应链发展，生物精炼厂的建立以及建立运输，储存和分配基础设施方面的重大经济和后勤挑战。在高昂的物流运营成本下，至关重要的是设计一个最佳的物流系统，以实现从化石燃料到生物燃料的平稳过渡。通过模拟自然界的进化算法，遗传算法和粒子群算法，提出了两种不同的生物质制燃料物流系统优化方法。在这项工作中比较了这两种元启发式方法的性能。过去，这些类型的问题已使用数学方法（例如线性规划和混合整数规划）解决。对于诸如此类的复杂问题，元启发式方法可以比数学优化方法更快地提供接近最优的结果。因此，本文提出了两种考虑多种原料的生物启发式生物燃料物流系统设计优化的元启发式方法，并证明元启发式优化方法适合解决组合问题，例如本研究工作中要解决的问题。

著录项

  • 作者

    Martinez-Schabez, Ethel Regina.;

  • 作者单位

    The University of Texas at El Paso.;

  • 授予单位 The University of Texas at El Paso.;
  • 学科 Engineering Industrial.;Operations Research.;Sustainability.
  • 学位 M.S.
  • 年度 2013
  • 页码 105 p.
  • 总页数 105
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 语言学;
  • 关键词

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