Improving the resilience of energy flow exchanges in eco-industrial parks: Optimization under uncertainty

被引：19

作者：

Afshari H. ^{[1
]}

Farel R. ^{[2
]}

Peng Q. ^{[1
]}

机构：

[1] Department of Mechanical Engineering, University of Manitoba, EITC, 75A Chancellors Circle, Winnipeg, R3T 5V6, MB

[2] PS2E Research and Education Institute, Les Loges-en-Josas

来源：

Peng, Qingjin (Qingjin.Peng@Umanitoba.ca) | 1600年 / American Society of Mechanical Engineers (ASME), United States卷 / 03期

基金：

加拿大自然科学与工程研究理事会;

关键词：

Industrial plants;

D O I：

10.1115/1.4035729

中图分类号：

学科分类号：

摘要：

Eco-Industrial parks (EIPs) and industrial symbioses (IS) provide cost-effective and environmental friendly solutions for industries. They bring benefits from industrial plants to industrial parks and neighborhood areas. The exchange of materials, water, and energy is the goal of IS to reduce wastes, by-products, and energy consumption among a cluster of industries. However, although the IS design looks for the best set of flow exchanges among industries at a network level, the lack of access to accurate data challenges the optimal design of a new EIP. IS solutions face uncertainties. Considering the huge cost and long establishment time of IS, the existing studies cannot provide a robust model to investigate effects of uncertainty on the optimal symbioses design. This paper introduces a framework to investigate uncertainties in the EIP design. A multi-objective model is proposed to decide the optimal network of symbiotic exchanges among firms. The model minimizes the costs of multiple product exchanges and environmental impacts of flow exchanges. Moreover, this paper integrates the analysis of uncertainties effects on synergies into the modeling process. The presented models are depicted through optimizing energy synergies of an industrial zone in France. The efficiency of single and multiple objective models is analyzed for the effects of the identified uncertainties. In addition, the presented deterministic and robust models are compared to investigate how the uncertainties affect the performance and configuration of an optimal network. It is believed that the models could improve an EIP's resilience under uncertainties. Copyright © 2017 by ASME.

引用

共 67 条

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