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Incorporating agricultural waste-to-energy pathways into biomass product and process network through data-driven nonlinear adaptive robust optimization

机译:通过数据驱动的非线性自适应鲁棒优化,将农业废物转化为能源的途径纳入生物质产品和过程网络

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摘要

A biomass product and process network that displays how organic waste and other non-traditional biomass feedstocks may be converted into useful bioproducts and biofuels is a necessary addition to the field of biomass conversion and utilization. We develop a processing network of 216 technologies and 172 materials/compounds that contains conversion pathways of agricultural and organic waste biomass sources, such as food peels, animal manure, and grease. To examine the effectiveness and economic feasibility of these conversion pathways, the biomass product and process network is optimized for return on investment. The resulting problem is a data-driven two-stage adaptive robust mixed-integer nonlinear fractional program, which was effectively solved via a tailored optimization algorithm. The proposed approach is applied to two case studies in which traditional agricultural feedstocks are used alongside biological and agricultural waste feedstocks. The selected feedstocks were used to satisfy and, in some cases, even exceed demand for selected products. The optimal pathways have returns on investment of 26.1% and 6.2%, with utilized conversion technologies ranging from hydrocracking to microwave hydrodiffusion. In both cases, we find that profitable processing pathways are utilized at maximum capacities to increase return on investment. Specifically, in the case study where orange peel wastes are used to produce pectin, we find that this pathway is highly profitable at the given market price. The two cases that are run using the proposed model are then compared to additional cases to display differences that arise when uncertainty is not considered and the objective function of the model is changed. (C) 2019 Elsevier Ltd. All rights reserved.
机译:显示生物废物和其他非传统生物质原料如何转化为有用的生物产品和生物燃料的生物质产品和工艺网络是生物质转化和利用领域的必要补充。我们开发了由216种技术和172种材料/化合物组成的加工网络,其中包含农业和有机废弃物生物质资源(例如,果皮,动物粪便和油脂)的转化途径。为了检查这些转化途径的有效性和经济可行性,对生物质产品和工艺网络进行了优化,以实现投资回报。由此产生的问题是一个数据驱动的两阶段自适应鲁棒混合整数非线性分数程序,该程序已通过定制优化算法得到有效解决。拟议的方法适用于两个案例研究,其中传统农业原料与生物和农业废物原料一起使用。选定的原料用于满足某些情况下的需求,甚至超过其需求。最佳途径的投资回报率分别为26.1%和6.2%,并采用了从加氢裂化到微波加氢扩散的转化技术。在这两种情况下,我们都发现,以最大的产能利用了有利可图的加工途径来增加投资回报。具体来说,在使用桔皮废料生产果胶的案例研究中,我们发现,在给定的市场价格下,这种途径是非常有利的。然后将使用建议的模型运行的两种情况与其他情况进行比较,以显示在不考虑不确定性和更改模型的目标函数时出现的差异。 (C)2019 Elsevier Ltd.保留所有权利。

著录项

  • 来源
    《Energy》 |2019年第1期|556-571|共16页
  • 作者单位

    Cornell Univ, Robert Frederick Smith Sch Chem & Biomol Engn, Ithaca, NY 14853 USA|Cornell Univ, Sch Operat Res & Informat Engn, Ithaca, NY 14853 USA;

    Cornell Univ, Robert Frederick Smith Sch Chem & Biomol Engn, Ithaca, NY 14853 USA;

    Cornell Univ, Robert Frederick Smith Sch Chem & Biomol Engn, Ithaca, NY 14853 USA;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Adaptive robust optimization; Return on investment; Biomass conversion; Machine learning;

    机译:自适应鲁棒优化;投资回报率;生物质转化;机器学习;

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