Economic and system reliability optimization of heat exchanger networks using NSGA-II algorithm. (September 2017)

• Record Type: Journal Article
• Authors: Lv, Junfeng; Jiang, Xiaobin; He, Gaohong; Xiao, Wu; Li, Shuai; Sengupta, Debalina; El-Halwagi, Mahmoud M.
• Is Part Of: Applied thermal engineering. Volume 124(2017)
• Page Start: 716
• Page End: 724
• Publication Date: 2017-09
• Subjects: Economic -- System reliability -- Heat exchanger networks -- The non-dominated sorting genetic algorithm -- Multi-objective optimization; Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2017.05.154
• Languages: English
• ISSNs: 1359-4311

Abstract

Graphical abstract: Highlights: System reliability is incorporated as an objective function into the design of cost-effective HEN. NSGA-II is applied in multi-objective optimization of HEN. The results of three cases shows the tradeoff between economic and system reliability. Abstract: Optimization of heat exchanger network (HEN) has traditionally been driven by economic objectives. Notwithstanding the importance of minimizing the total annual cost (TAC) of HEN, it is also important to ensure reliability. In order to obtain economical HEN considering system reliability simultaneously, a multi-objective optimization formulation of economic and system reliability is proposed in the design of HEN. A stage-wise superstructure is used to obtain feasible HEN, and the system reliability based on the number of heat exchangers in maximum irrelevant sub-network of HEN and the TAC are calculated as two objective functions. Then the non-dominated sorting genetic algorithm (NSGA-II) is applied to solve the multi-objective optimization mixed integer nonlinear programming model. Three case studies from literatures are used to assess the applicability and performance of the optimization formulation and solution algorithm. The system reliability is enhanced with TAC closes to the reported minimum, which is more meaningful than those obtained using single-objective optimization. The optimal solution set can aid in the selection of a safe and cost-effective network configuration for industrial plants and the proposed approach can easily be applied to include other objectives (e.g., sustainability and safety).