神经预测和模糊推理在催化重整优化控制中的应用

THE APPLICATION OF NEURAL PREDICTION AND FUZZY REASONING IN THE OPTIMIZATION CONTROL OF CATALYTIC REFORM PROCESS

  • 摘要: 过程控制的对象有些是复杂非线性系统,其控制依赖于人的智能经验,它们是仅靠常规控制策略难以完成的.本文采用神经学习机制和模糊推理集成的思想来实现这类控制器,而我们提出的一种隐节点校正学习算法保证了这一思想的实现,以此思想为基础的优化控制系统已试运行在广东茂名石化公司二重整装置上,初步的记录数据表明这一方案是可行的.

     

    Abstract: Some process plants are complex and nonlinear,whose control depends on man's intelligent experience.And this control can hardly be performed by traditional control strategies.In this paper,we use the idea of integration of fuzzy reasoning and neural learning to realize a controller for this kind of plants,and propose a hidden node value regulation (HNR) algorithm for neural network learning to assure this integration.An optimization control system based on this schema has been in operation for controlling the second catalytic reform process of maoming.petroleum and chemical industry corporation in Guangdong province.Operation data have shown its feasibility.

     

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