基于神经网络的加氢反应器温度优化控制

The Neural Network Optimization Control Strategy For Catalytic Hydrogen

根据加氢反应器的特点 ,提出了一种加氢反应器出口温度神经元网络优化控制方法。给出了作为模型预估器的神经网络GA—BP算法流程及GA算法实现 ,提出了最优控制指标选择原则及控制指标表达式。经计算机对四床层一段加氢裂化装置进行仿真研究表明 ,该控制方法具有良好的跟踪性能及抗干扰能力。

According to the characteristic of the pipeline cracking furnace, a new neural network optimization control strategy of hydrogen reactor's output temperature is put forward. The GA-BP learning algorithm of neural network, the GA learning algorithm, the rule of optimum control including their features were introduced. The computer simulation prove that the anti-jamming capacity and the track performance of the strategy is all right