Simulation and Off-line Optimization of an Acrylonitrile Fluidized-bed Reactor Based on Artificial N

• 作 者: 李伟 王效斗
• 出 处: Chinese Journal of Chemical Engineering
• 发表日期: 2002年02期
• 关键词: fluidized simplex searching terminated deviation delta propylene train preserve

A mathematical model is developed for an industrial acrylonitrile fluidized-bed reactor based on artificial neural networks. A new algorithm, which combines the characteristics of both genetic algorithm (GA) and generalized delta-rule (GDR) is used to train artificial neural network (ANN) in order to avoid search terminated at a local optimal solution. For searching the global optimum, a new algorithm called SM-GA, incorporating advantages of both simplex method (SM )and GA, is proposed and applied to optimize the operating conditions of an acrylonitrile fluidized-bed reactor in industry.

A mathematical model is developed for an industrial acrylonitrile fluidized-bed reactor based on artificial neural networks. A new algorithm, which combines the characteristics of both genetic algorithm (GA) and generalized delta-rule (GDR) is used to train artificial neural networks ANN) in order to avoid search terminated at a local optimal solution. For searching the global optimum, a new algorithm called SM-GA, incorporating advantages of both simplex method (SM) and GA, is proposed and applied to optimize the operating conditions of an acrylonitrile fluidized-bed reactor in industry.