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Need for Optimization Under Uncertainty: Designing Flow Exchanges in Eco-Industrial Parks

[+] Author Affiliations
Hamid Afshari, Qingjin Peng

University of Manitoba, Winnipeg, MB, Canada

Romain Farel

PS2E, Les Loges-en-Josas, France

Paper No. DETC2016-59974, pp. V004T05A046; 10 pages
  • ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 4: 21st Design for Manufacturing and the Life Cycle Conference; 10th International Conference on Micro- and Nanosystems
  • Charlotte, North Carolina, USA, August 21–24, 2016
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5014-5
  • Copyright © 2016 by ASME


Eco-Industrial Parks (EIPs) and Industrial Symbioses (IS) have provided several cost-effective and environmental friendly solutions for the economic growth of countries. The need for excessive materials, water and energy can be reduced by exchanging wastes, by-products and energy among different clusters of industries, which is the fundamental goal of establishing synergies among industries. Symbioses design looks for the best set of connections among industries to satisfy defined objectives. However, there are not enough data to support the design of a new EIP for some industries. The existing research contains multiple objective optimization methods, but lacks details in the real industrial world to consider comprehensive criteria in design of flow exchanges due to the large cost and long establishment time for those synergies. This paper presents a multi-objective model to decide the best network of industries for several exchanges among them. The model helps minimizing costs for multiple product exchanges while considering environmental impacts to be reduced. Moreover, this paper investigates uncertainties affecting synergies within EIPs by incorporating in a modeling process. The presented models are validated through optimizing symbioses in an EIP. The efficiency of single and multiple objective models is analyzed for effects of the selected uncertainties. Future research directions are also discussed.

Copyright © 2016 by ASME



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