A hybrid meta-heuristic approach for multi-objective optimization of heat exchanger networks considering costs and environmental impacts

被引:0
|
作者
Pavão L.V. [1 ]
Costa C.B.B. [1 ]
Jiménez L. [2 ]
Ravagnani M.A.S.S. [1 ]
机构
[1] Department of Chemical Engineering, State University of Maringá, Av. Colombo, 5790, Bloco D90, Maringá, PR
[2] Departament d'Enginyeria Química, Universitat Rovira i Virgili, Av. Països Catalans, 26, Tarragona
来源
Ravagnani, Mauro A. S. S. (massravagnani@uem.br) | 1600年 / Italian Association of Chemical Engineering - AIDIC卷 / 61期
关键词
Decision makers - Heat exchanger network - HEN synthesis - Hybrid Meta-heuristic - Life Cycle Assessment (LCA) - Meta-heuristic approach - Objective functions - Substantial reduction;
D O I
10.3303/CET1761228
中图分类号
学科分类号
摘要
Heat Exchanger Networks (HEN) are able to reduce the operating costs in an industrial plant. Cost-optimal HEN generally leads also to a substantial reduction in pollutant gases emissions. However, HEN synthesis approaches are based generally on the optimization of total annual costs (TAC) only. In that manner, this work aims to address environmental impacts (EI) more directly. The EI were converted into an objective function by using a Life Cycle Assessment (LCA) methodology. With two objective functions (EI and TAC), a multi-objective optimization (MOO) may be performed. A meta-heuristic approach is adapted to handle MOO in the formulation proposed. As expected, a conflictive behaviour was noticed. The approach proposed was able to yield Pareto fronts efficiently, providing good intermediate solutions that could be used by decision-makers in search of a trade-off HEN configuration which is able to present low costs and is also environmentally friendly. The results reported in the case study also demonstrate the method reliability with intermediate solutions that are much more environmentally friendly than the cost optimal configurations, presenting costs only marginally higher. Copyright © 2017, AIDIC Servizi S.r.l.
引用
收藏
页码:1381 / 1386
页数:5
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