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These nonlinearities bring about difficulty in operators' PID tuning, too. Even skilled operators cannot evaluate how the PID parameters are agreeable, in many cases. Judging from the stability and speed of response, they evaluate how inadequate the PID parameters are instead. They compare degrees of inadequacy among other PID parameters and tune them gradually. Since stability and speed of response are not compatible with each other, the optimum values of the PID parameters are the balanced points between them.
Target values exist for the bed and furnace final temperatures. The purpose of the control is to achieve these by deducing from all available data the signal to the air controller. The control variables are the size of black liquor droplets and the air distribution control. , no further corrections are needed. Significant improvement has been achieved in process operation. For instance, the typical average reduction degree in the same plant as in Fig. 3 %. Fvzty Logic Control in Finnish Industry x Droplet size A Lower air control 35 Bed temperature + Furnace upper temperature Fig.
The membership functions are displayed in Fig. 9. 5 OO Error der. 5 1 Membership functions for 4 rules Fig. 9. The membership functions used in the rule base. Figure 10 shows the amplitude responses of the closed-loop system simulations of both conventional PID controller and the proposed PID controller with fuzzy derivative gain adaptation. The conventional PID control system has a small amplitude at low and high frequencies and an amplitude peak at the natural frequency. The fuzzy derivative gain adjusting controller has a small 40 H .