Research On Energy-saving Optimization Control Strategy For Aeration System In Wastewater Treatment Process

Aeration system is the critical part of aerobic biological wastewater treatment process such as activated sludge and biological contact oxidation, and it is the main unit of energy consumption. Because the dissolved oxygen process is nonlinear and time-varying and it is difficult to be modeled accurately, there are some common problems that the control accuracy of dissolved oxygen concentration is not high and the set value is relatively large as well as the energy consumption in aeration system which uses the conventional control strategy. Aimed at the characteristics and problems, the research on energy-saving optimization control strategy for aeration system is conducted.First of all, the control strategy of aeration system is summarized and classified, and the related energy-saving principle is analyzed in detail. Used the material balance equation of chemical reaction with variation of flow rate and concentration of influent of the wastewater treatment plant, the mechanism model of dissolved oxygen process is established. In view of the long lag time and long response time of dissolved oxygen online measuring instrument, the predictive model of dissolved oxygen feedback values is also established by study on the dynamic characteristics of dissolved oxygen online measuring instrument.Secondly, for the intense fluctuation of ON-OFF control and other problems that the effluent cannot be always met the requirement, and the blower is at the maximum load basically, and the energy consumption is high etc., the optimization is carried out by reducing the motor speed and adjusting the ON-OFF control action threshold gradually. The fuzzy ON-OFF control is proposed after the optimizing experience is summarized. By simulation, when used fuzzy ON-OFF control, the start and stop frequency is reduced, and the effluent can be always met the requirement, and1.01%of energy consumption is saved.Then, the fuzzy adaptive PID control strategy is researched in that the control effect of PID controller whose parameter is fixed cannot be always optimal and then leads to problems that the set value is relatively large and the energy consumption is very high. After used fuzzy adaptive PID control, the control accuracy and steady performance are improved. The set value is optimized by ammonia feedforward strategy, and thus the energy consumption index is reduced by28.3percent. Finally, the generalized predictive control of dissolved oxygen is researched, used the online identification of fading memory recursive least squares, for it is few that research on control based on model of dissolved oxygen. Although the energy consumption index of generalized predictive control is a little less than that of fuzzy adaptive PID control, the generalized predictive control is energy saving in the long run. In research, it is proved that the softening factor a and prediction step p is different from or even contrary to the empirical value. Consequently, the empirical value of parameter should be corrected and extended to the maximum range when generalized predictive control is used to a large time-varying system or the control model is not accurate. This is an improvement on parameter tuning of generalized predictive control.