| The energy reserves in China show the structural characteristics of "rich coal,less oil and gas shortage".Methanol is a very basic chemical product,which can be obtained by distillation of coal chemical industry.Methanol distillation tower is the key equipment of methanol distillation,but the distillation control of methanol distillation tower is a complex control process with multi variable,nonlinear,strong coupling and time-varying characteristics.However,the traditional PLC control is still used in the distillation control of methanol distillation tower at present.The temperature and pressure on the top of the tower top of the methanol distillation tower are coupled,and there are some shortcomings such as slow control speed,large overshoot and poor robustness.In view of the shortcomings of the traditional PLC control method in the distillation process of methanol distillation tower at present,an intelligent control method based on FPGA for temperature and pressure decoupling of RBF neural network had be proposed.Had used the reasonable feedforward compensation decoupling method,selected RBF neural network as the decoupling algorithm.Combining FPGA control hardware.FPGA selected the cyclone Ⅱ series ep2c35f672c7 chip.Through the simulation comparison between MATLAB and traditional PLC control.The adjustment had reduced by 7.9%,the steady-state error had reduced by 2%,and the rise time of BP neural network decoupling control based on FPGA is reduced by 0.4 seconds and the adjustment time had reduced by 2.6 seconds.It is proved that the decoupling control of RBF neural network based on FPGA has some improvement on the control of distillation process of methanol distillation tower at present,which improves the control efficiency and the quality of refined methanol products,and then improves the economic benefits of the enterprise.The decoupling control of RBF neural network based on FPGA can not only be applied to the distillation process control of methanol distillation tower,but also can be used for reference in other fields.Figure 27;Table 3;Reference 57... |