Hybrid Optimisation Model for the Synthesis of Centralised Utility System in Eco-Industrial Park

Kai Yuan Teo, Tze Yee Ng, Cze Min Wau, Jason T. S. Liew, Viknesh Andiappan, Denny K. S. Ng

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Eco-industrial park (EIP) is a community of manufacturing and service businesses collaborating to manage environmental and resource (e.g. water, material and energy) issues together to preserve the environment while bringing financial benefit to the individual party. One way to achieve this goal is to integrate the EIP network via a centralised utility system (CUS), which facilitates the material and energy exchange among participating companies (or individual actors, IAs) in EIP. This work presents a hybrid model that combines superstructure-based optimisation approach and insight-based, automated targeting approach to synthesise a sustainable CUS for an EIP with the consideration of variations. The superstructure encompasses tri-generation system, wastewater treatment system and a steam distribution network that consider the use of mechanical drive steam turbine (MDST). Meanwhile, automated targeting approach is adapted to provide valuable insight and detailed allocation of resources within the network. In this work, the synthesis of CUS is analysed based on the economic performance and the total potential environmental impact of the system. Since such objectives are frequently contradictory in nature, fuzzy optimisation approach is adapted in this work to synthesise a sustainable CUS which fulfils the two important objectives. In addition, multi-period optimisation approach is applied to address the multiple operational periods with the presence of variations in supply of raw materials, market price of raw materials, demand for desired end products, etc. To illustrate the proposed framework, a palm oil processing complex (POPC) case study is solved.
Original languageEnglish
Pages (from-to)33-57
Number of pages25
JournalProcess Integration and Optimization for Sustainability
Volume1
Issue number1
Early online date12 Apr 2017
DOIs
Publication statusPublished - May 2017

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