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Abstract

In view of the large lag of industrial furnace temperature, serious nonlinear, high order model, etc., we proposed a temperature prediction and control model of a joint two-layer, in which the PFC was taken as the supervisory layer, and the fuzzy logic control, FLC for short, was taken as the control layer.

The model combines the advantages of PFC and FLC. The upper layer is to realize the advance regulation by using predictive functional control (PFC) to forecast the deviation of the system, and the lower layer is to realize fuel flow and air flow control by using the cascade fuzzy controller. The cascade fuzzy controller can handle multiple process control parameters, greatly reducing the number of fuzzy rules; and by using differential evolution algorithm (DE) to optimize the quantization factor and scaling factor of the membership function of fuzzy controller, which greatly improve the accuracy of prediction. After utilizing this model to process the control analyses of polystyrene industrial furnace temperature in a petrochemical enterprise in Zhanjiang, the result shows that the model has the advantages of fast tracking, accurate control, no overload and so on. The control effect is better than that of the fuzzy neural network control and the conventional PID control method.

Keywords

Predictive functional control fuzzy logic control differential evolution algorithm industrial furnace temperature control

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Temperature prediction model of two-layer cascade control industrial furnace based on PFC-FLC-DE

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