Research On Intelligent Optimal Control Method For Wastewater Treatment Process

Activated sludge method is the main biological wastewater treatment technology currently. In the process, quality and quantity of the influent wastewater are sharp fluctuant, and the microbial growth mechanism is complex. As a result, the process is multivariable, nonlinear, strong coupling, time lagging, and uncertaint, which makes the wastewater treatment process control difficult. In addition, in our country, the level of process control is primary for activated sludge wastewater treatment, the effluent water qualities can not be guaranteed and the energy consumption is very high, so it is very necessary to focus on the intelligent optimization control strategies.Based on Benchmark Simulation Model No.1 (BSM1) proposed by the IAWQ, the neuron self-adaptive PID controller is designed for dissolved oxygen control system, and then the neural network predictive optimal control method is presented for nitrogen removal process. Finally, the optimal control strategy based on particle swarm optimization with division of work strategy for wastewater treatment is proposed. Simulation results have demonstrated the feasibility and the validity of the different control strategies. The main content of this paper is as follows:1. BSM1 is described for the activated sludge wastewater treatment process in detail. The proposed BSM1 is established and verified in Matlab. The validity and reliability of the model provide important basis for performance estimation of control strategies.2. A neuron self-adaptive PID controller is proposed for dissolved oxygen control in wastewater treatment. The weights and gain coefficients of this controller are able to optimize online. The controller contains simple structure, good robustness and easy-adjusting parameters. Compared to traditional PID controller, the proposed self-adaptive PID controller has better control performance under the condition of fluctuation of influent water parameter, variation of control condition.3. Neural network predictive optimal control strategy is presented for nitrogen removal process in wastewater treatment. Through profound analysis of inter-relations between the main components, dissolved oxygen concentration at the end of aerobic zone and nitrate concentration at the end of anoxic zone are selected as control variables. The neural network predictive optimal control strategy is applied to reduce effluent ammonia nitrogen and total nitrogen concentration. 4. Considering heavy energy consumption and high operational costs in wastewater treatment process, optimal control based on particle swarm optimization with division of work strategy is provided. The effluent quality, the aeration and pumping energy consumption are all taken into consideration, and the set points of lower level neuron self-adaptive PID controllers are optimized dynamically so as to improve the water quality of effluent with minimal energy consumption.In this dissertation, the works are focused on the intelligent optimal control for wastewater treatment, neuron self-adaptive PID, neural network predictive optimal control and optimal control based on particle swarm optimization are discussed and validated by simulation. Research work provides a method to the similar control problems of complex process.