A multi-objective optimization strategy of steam power system to achieve standard emission and optimal economic by NSGA-II

被引:35
|
作者
Xiao, Wu [1 ]
Cheng, Andi [1 ]
Li, Shuai [1 ]
Jiang, Xiaobin [1 ]
Ruan, Xuehua [1 ]
He, Gaohong [1 ]
机构
[1] Dalian Univ Technol, Dalian Engn Res Ctr High Effect Gas Separat, State Key Lab Fine Chem, Dalian 116024, Liaoning, Peoples R China
基金
中国国家自然科学基金;
关键词
Multi-objective optimization; Steam power system; Desulfurization; Denitrification; NSGA-II; HEAT-EXCHANGER NETWORK; CHEMICAL-PROCESS INDUSTRIES; HARDWARE COMPOSITES; TURBINE NETWORKS; CONCEPTUAL TOOL; UTILITY SYSTEM; DESIGN; MODEL; OPERATION;
D O I
10.1016/j.energy.2021.120953
中图分类号
O414.1 [热力学];
学科分类号
摘要
In this work, the models of desulfurization and denitrification are added to solve the problem of SO2 and NOX standardized discharge. The design and optimization strategy of steam power system (SPS) considering contaminant emissions reduction technology is proposed to achieve the trade-off between economic and environmental goals. Detailed superstructure networks of desulfurization based on wet limestone flue gas desulfurization and denitrification based on selective catalytic reduction were established and embedded in the SPS model. Then, based on this combined superstructure model, a mathematical formulation of multiple objective mixed integer nonlinear programming describing the SPS coupled with desulfurization and denitrification was established. The steam flow rate, outlet enthalpy, the consumption of the turbine power of the direct drive equipment and the electricity generated by the turbine, the flow rate and efficiency of desulfurization and denitrification are chosen as the optimization variables. The operating conditions and equipment parameters of the global system are optimized. Finally, the second-generation non-dominated sorting genetic algorithm (NSGA-II) was applied to obtain the Pareto optimization curve, exploring trade-offs between economic and environmental goals. Two case studies are used to assess the applicability and performance of the optimization formulation and solution algorithm. (c) 2021 Published by Elsevier Ltd.
引用
收藏
页数:14
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