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费托合成反应是天然气制油(GTL)或煤制油(CTL)生产技术的核心。在费托合成反应器换热系统中汽包压力的控制具有大惯性、大滞后、强非线性等特点,参数固定不变的传统PID控制器难以进行精确的压力控制。为了解决这一问题,设计了费托合成反应器换热系统汽包压力BP神经网络自整定PID控制器。在MATLAB中实现了BP神经网络自整定PID程序的编写,并通过MATLAB与组态王(KINGVIEW)的动态数据交换(DDE),方便的实现了上位机与控制系统中其它硬件的通信。在此基础上进行了具有大惯性、大滞后汽包压力控制的实验,实验结果表明,在汽包压力的定值控制和阶跃变化控制中BP神经网络自整定PID控制器的静态偏差维持在-0.03 MPa~+0.01 MPa,最大误差为1.5%,并且对系统的非线性变化具有一定的适应性,其控制效果明显优于参数固定的传统PID控制器。
Fischer-Tropsch synthesis is at the core of GTL or CTL production technologies. In the Fischer-Tropsch synthesis reactor heat transfer system, steam drum pressure control has the characteristics of large inertia, large hysteresis and strong non-linearity. The traditional PID controller with fixed parameters is difficult to control with accurate pressure. In order to solve this problem, the self-tuning PID controller of steam-pressure BP neural network was designed for Fischer-Tropsch reactor heat exchanger system. In MATLAB, the BP neural network self-tuning PID program is written, and the communication between the host computer and other hardware in the control system is conveniently realized through the dynamic data exchange (DDE) between MATLAB and KingView. On the basis of this, experiments on the control of steam drum pressure with large inertia and large hysteresis were carried out. The experimental results show that the static deviation of BP neural network self-tuning PID controller is maintained at the constant value of steam drum pressure and step change control -0.03 MPa ~ +0.01 MPa, the maximum error is 1.5%, and has some adaptability to the nonlinear variation of the system. The control effect is obviously better than the traditional PID controller with fixed parameters.