文献类型：期刊文献

中文题名：A novel 3-layer mixed cultural evolutionary optimization framework for optimal operation of syngas production in a Texaco coal-water slurry gasifier

英文题名：为德士古水煤浆气化炉合成气生产优化操作设计的一种创新三层混合文化进化算法框架(英文)

作者：曹萃文[1];张亚坤[1];于腾[1];顾幸生[1];辛忠[2];李杰[3]

机构：[1]Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology;[2]State Key Laboratory of Chemical Engineering, East China University of Science and Technology;[3]State Key Laboratory of Multiphase Complex Systems, Institute of Processing Engineering, Chinese Academy of Sciences

年份：2015

卷号：23

期号：9

起止页码：1484

中文期刊名：Chinese Journal of Chemical Engineering

外文期刊名：中国化学工程学报（英文版）

收录：CSTPCD;;Scopus;CSCD:【CSCD2015_2016】；

基金：Supported by the National Natural Science Foundation of China(61174040,U1162110,21206174);Shanghai Commission of Nature Science(12ZR1408100)

语种：中文

中文关键词：3-Layer mixed cultural evolutionary framework Optimal operation Syngas production Coal-water slurry gasifier

外文关键词：德士古水煤浆气化;算法框架;优化运行;文化进化;优化生产;混合算法;合成气;粒子群优化算法

摘要：Optimizing operational parameters for syngas production of Texaco coal-water slurry gasifier studied in this paper is a complicated nonlinear constrained problem concerning 3 BP(Error Back Propagation) neural networks. To solve this model, a new 3-layer cultural evolving algorithm framework which has a population space, a medium space and a belief space is firstly conceived. Standard differential evolution algorithm(DE), genetic algorithm(GA), and particle swarm optimization algorithm(PSO) are embedded in this framework to build 3-layer mixed cultural DE/GA/PSO(3LM-CDE, 3LM-CGA, and 3LM-CPSO) algorithms. The accuracy and efficiency of the proposed hybrid algorithms are firstly tested in 20 benchmark nonlinear constrained functions. Then, the operational optimization model for syngas production in a Texaco coal-water slurry gasifier of a real-world chemical plant is solved effectively. The simulation results are encouraging that the 3-layer cultural algorithm evolving framework suggests ways in which the performance of DE, GA, PSO and other population-based evolutionary algorithms(EAs) can be improved,and the optimal operational parameters based on 3LM-CDE algorithm of the syngas production in the Texaco coalwater slurry gasifier shows outstanding computing results than actual industry use and other algorithms.
Optimizing operational parameters for syngas production of Texaco coal-water slurry gasifier studied in this paper is a complicated nonlinear constrained problem concerning 3 BP（Error Back Propagation） neural networks. To solve this model, a new 3-layer cultural evolving algorithm framework which has a population space, a medium space and a belief space is firstly conceived. Standard differential evolution algorithm（DE）, genetic algorithm（GA）, and particle swarm optimization algorithm（PSO） are embedded in this framework to build 3-layer mixed cultural DE/GA/PSO（3LM-CDE, 3LM-CGA, and 3LM-CPSO） algorithms. The accuracy and efficiency of the proposed hybrid algorithms are firstly tested in 20 benchmark nonlinear constrained functions. Then, the operational optimization model for syngas production in a Texaco coal-water slurry gasifier of a real-world chemical plant is solved effectively. The simulation results are encouraging that the 3-layer cultural algorithm evolving framework suggests ways in which the performance of DE, GA, PSO and other population-based evolutionary algorithms（EAs） can be improved,and the optimal operational parameters based on 3LM-CDE algorithm of the syngas production in the Texaco coalwater slurry gasifier shows outstanding computing results than actual industry use and other algorithms.