期刊文献+

CO_2提纯塔控制参数的优化

Optimization for Control Parameters of Carbon Dioxide Purifying Column
下载PDF
导出
摘要 研究了CO2提纯塔的影响因素对塔釜CO2出口浓度的影响。针对常规遗传算法的不足,对算法进行了有效的改进。运用改进的遗传算法,结合CO2提纯塔模型,对提纯塔的可控参数进行优化,得到了不同CO2进料浓度下的最优控制参数。实际生产检验表明,优化方法可行,优化结果有效。 The relation between the concentration of COz at the bottom exit of purifying column and its influence factors was researched. Genetic algorithm was improved because of the disadvantage of simple genetic algorithm. The control parameters of purifying column were optimized by the improved genetic algorithm through combining the model of purifying column of CO2. Aiming at the kinds of feed-in concentration of CO2, the best control parameters were obtained. The test indicated that optimization method was feasible and the optimization results were available in actual industry production.
出处 《化工技术与开发》 CAS 2005年第5期34-36,共3页 Technology & Development of Chemical Industry
基金 浙江省高校青年教师资助项目
关键词 二氧化碳 提纯塔 控制参数 遗传算法 优化 carbon dioxide purifying column control parameters genetic algorithm optimization
  • 相关文献

参考文献5

  • 1郑启富,李玉如,谢艳.基于RBFN-PLSR方法的CO_2提纯塔模型[J].化工技术与开发,2005,34(4):36-38. 被引量:3
  • 2Lothar M.Schmitt.Theory of genetic algorithms.Theoretical ComputerScience,2001,259:1-61.
  • 3M.A.Grishina,E.V.Bartashevich,V.A.Potemkin,et al.Genetic algorithm for predictingstructures and properties of molecular aggregates in organic substances[J].Journal ofStructural Chemistry,2002,43(6):1040-1044.
  • 4D.Pissoort,H.Rogier,F.Olyslager,et al.Optimization of a microstrip antenna with agenetic algorithm for use as a ground penetrating radar[J].Journal of ElectromagneticWaves and Applications,2003,17(8):1197-1216.
  • 5王小平 曹立明.遗传算法[M].西安:西安交通大学出版社,2002..

二级参考文献11

  • 1Hummels D M,Ahemed W,Musavi M T.Adaptive Detection of small Sinusodial Signals in Non-gaussian Noise using an RBF Neural Network[J].IEEE Trans on Neural Networks,1995,6(1): 214-219.
  • 2Liao Y,Fang SC,Nuttle HL.Relaxed conditions for radial-basis function networks to be universal approximators[J].Neural Netw,2003,16(7): 1019-1028.
  • 3Moody,Darken C.Fast Learning in Networks of Locally-tuned Processing Units[J].Neural Computation,1989,1(1): 281-294.
  • 4Chen S,Cowan C F N,Grant P M.Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks[J].IEEE Trans on Neural Networks,1991,2(2): 302-309.
  • 5Chen S,Billings S A,Grant P M.Recursive Hybrid Algorithm for Nonlinear Identification Using Radial Basis Function Networks[J].International Journal of Control,1995,55(5): 1051-1070.
  • 6Steve A.Billings,X.Hong.Dual-orthogonal radial basis function networks for nonlinear time series prediction[J].Neural Networks,1998,11: 479-493.
  • 7B.Walezak,D.L.Massart.The Radial Basis Functions-Partial Least Squares approach as a flexible non-linear regression technique[J].Analytic Chemica Acta.,1996,331: 177-185.
  • 8P.Geladi and B.R.Kowalski.Partial Least Square Regression(PLS)[J].A Tutorial,Analytic Chemica Acta.,1986,185:1-17.
  • 9王旭东,邵惠鹤.RBF神经元网络在非线性系统建模中的应用[J].控制理论与应用,1997,14(1):59-66. 被引量:68
  • 10赵伟祥,陈德钊,胡上序.势RBF网络及其在水质诊断中的应用[J].浙江大学学报(自然科学版),1999,33(2):147-151. 被引量:3

共引文献108

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部