Dynamic multiobjective optimization of large-scale industrial production systems: An emerging strategy

被引:12
|
作者
Benali, M.
Hammache, A.
Aube, F.
Dipama, J.
Cantave, R.
机构
[1] CANMET Energy Technol Ctr, Nat Resources Canada, Varennes, PQ J3X 1S6, Canada
[2] Ecole Polytech, Inst Nucl Engn, Montreal, PQ H3C 1A7, Canada
关键词
multiobjective optimization; industrial complex processes; exergoeconomic analysis; thermoeconomic analysis; evolutionary algorithms; memetic algorithms; CGAM problem;
D O I
10.1002/er.1329
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
The large industrial production systems face several conflicting challenges of energy efficiency improvements, lower consumption of raw materials per unit of production, extending the product durability, minimizing the environmental impacts, and cautious use of natural resources. In this context it is well obvious that the optimization problem is multiobjective in nature. The paper emphasizes critical review of optimization methods of large-scale industrial production systems and presentation of a novel systematic multiobjective and multiscale optimization methodology based mainly on a combined local optimality search with global optima determination, and advanced system decomposition and constraint handling. (C) Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2007.
引用
收藏
页码:1202 / 1225
页数:24
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