MILFP model and algorithms for network design and long-term planning of water management system for shale gas production

Jiyao Gao, Fengqi You*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations

Abstract

This paper addresses the optimal design and operations of water supply chain networks for shale gas production. We develop a mixed-integer linear fractional programming (MILFP) model with the objective to maximize profit per unit freshwater consumption, such that both economic performance and water-use efficiency are optimized. The model simultaneously accounts for the design and operational decisions for freshwater source selection, multiple transportation modes, and water management options. Water management options include underground injection, commercial centralized wastewater treatment (CWT), and different onsite treatment technologies. To globally optimize the resulting MILFP problem efficiently, we present three tailored solution algorithms: a parametric approach, a reformulation-linearization method, and a novel Branch-and-Bound & Charnes-Cooper transformation method. The proposed models and algorithms are illustrated through one case study based on Marcellus shale play, in which onsite treatment shows its superiority in improving freshwater conservancy, maintaining a stable water flow, and reducing transportation burden.

Original languageEnglish (US)
Title of host publicationChemical Engineering Transactions
PublisherItalian Association of Chemical Engineering - AIDIC
Pages1423-1428
Number of pages6
Volume43
ISBN (Electronic)9788895608341
DOIs
StatePublished - Jan 1 2015

ASJC Scopus subject areas

  • Chemical Engineering(all)

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    Gao, J., & You, F. (2015). MILFP model and algorithms for network design and long-term planning of water management system for shale gas production. In Chemical Engineering Transactions (Vol. 43, pp. 1423-1428). Italian Association of Chemical Engineering - AIDIC. https://doi.org/10.3303/CET1543238