(38a) Optimization Models and Algorithms for Water Supply Chain Network Design and Operations in Shale Gas Production

In this work, we address the optimal design and operations of water supply chain networks for shale gas production [1, 2]. We develop a mixed-integer linear fractional programming (MILFP) model, the objective of which is to maximize the economic efficiency of freshwater in this shale water supply chain [2]. This model simultaneously takes into account the design and operational decisions, including freshwater source selection, multiple transportation modes, and different water management options [3-7]. Water management options include underground disposal, commercial centralized wastewater treatment (CWT), and onsite treatment (filtration, lime softening, thermal distillation) [8]. To globally optimize the resulting MILFP problem efficiently, we present three tailored solution algorithms: a parametric approach [9], a reformulation-linearization method [10], and a novel Branch-and-Bound & Charnes-Cooper transformation method. The proposed models and algorithms are illustrated through two case studies 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.

References

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[2]        J. Gao and F. You, "Shale Gas Supply Chain Design and Operations towards Better Economic and Life Cycle Environmental Performance: MINLP Model and Global Optimization Algorithm," ACS Sustainable Chemistry & Engineering, 2015, 10.1021/acssuschemeng.5b00122.

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[9]        Z. Zhong and F. You, "Globally convergent exact and inexact parametric algorithms for solving large-scale mixed-integer fractional programs and applications in process systems engineering," Computers & Chemical Engineering, vol. 61, pp. 90-101, 2014.

[10]      D. J. Yue, G. Guillen-Gosalbez, and F. Q. You, "Global Optimization of Large-Scale Mixed-Integer Linear Fractional Programming Problems: A Reformulation-Linearization Method and Process Scheduling Applications," AIChE Journal, vol. 59, pp. 4255-4272, 2013.