Studies Of Predictive Function Control In Coagulant Dosing

The coagulant dosing process is one of the most important parts in water treatment, which not only affects the quality of the final effluent but also has a significant impact on the treatment efficiency of flowing treatment. Therefore, it is necessary to study precise control of coagulant dosing in coagulant dosing process. Due to the characteristic of coagulant dosing process, traditional dosing control cannot adapt to the sharp change of water quality, which with bad robustness. PFC takes rolling optimization which is based on prediction model. PFC with low requirements of model , greatly simplifies the online computation and has good tracking performance and robustness. PFC not only widely-used in complex industrial production, but also have achieved good results. So the predictive functional control applying in coagulant dosing process.According to the characteristic of coagulant dosing process with strong nonlinear, long time delay and measurable disturbance. Therefore, we proposed a kind of PFC mode—nonlinear regression prediction mode with measurable disturbance feedforward compensation. Take the traditional predictive functional control can only fit limited points in the time domain of prediction into account, so we propose a new method of predictive functional control-through weighted basis functions, which can improve the approach of trajectory and reference trajectory prediction. Our work mainly covers the following aspects:1. As traditional predictive functional control being unable to optimize the target function in the whole time domain of prediction, we proposed a predictive functional control adopting the weighted step basis function. According to the characteristics of reference trajectory, step basis function with the forecast weighted coefficient decreases when the step increase, which can improve the approach of trajectory and reference trajectory prediction , increase the overall optimization goal. The Simulation testing results show that time-varying radix predictive functional control is effective.2. According to the characteristic of coagulant dosing process with nonlinear measurable disturbance and the traditional predictive functional control has no capacity to well applied to nonlinear system, we proposed a kind of PFC mode—nonlinear regression prediction mode with measurable disturbance feedforward compensation .Using the taylor expansion method to approximate the nonlinear variable. The nonlinear regression mode has characteristic of simple model structure , easy to identify. The feedforward compensation of measurable disturbance improve the prediction accuracy of input values and strengthen the control capability of anti-model mismatch. The Simulation testing results show that the effectiveness of nonlinear regression prediction mode with measurable disturbance feedforward compensation.3. Due to the characteristic of coagulant dosing process and predictive functional control adopting the weighted step basis function, which is based on nonlinear regression prediction mode with measurable disturbance feedforward compensation. Paper proposed a design of coagulant dosing controller which is based on predictive functional control strategy. For the non-linearity and measurable rotation of control variable,double-loop control model which combines PFC and PID is also been proposed. The controller can make sure the adaptation of the water quality changes, and has good tracking performance. The testing results from a certain water plant showed that the predictive functional control strategy possessed nicer properties4. We use VB to design the monitoring system of coagulant dosing, present implementation of data acquisition, display and preservation, real-time curve drawing, historic review curves, alarm and control functions dosing and so on.The monitoring system of coagulant dosing has been in correspondence with control device through DDE. The run results in water plant show that the system can meet the demands of monitor.Finally, The author make a summary of full paper and have a outlook on task.