Study On The Optimization Of Energy Consumption In Biochemical Treatment In A Certain Sewage Plant

With the increasingly serious energy crisis, energy optimization is the necessary way to reduce costs and increase competitiveness in the future. This paper studies on the optimization transformation of the main energy-consuming equipment, namely pumps and draught fans, through a comprehensive investigation of a biological wastewater treatment plant’s production processes, process route, energy equipment distribution and main energy unit. It is designed a frequency conversion transform and optimization-combination improvement programs for pumps. And it is established a dissolved oxygen-aeration fuzzy control model, with the dissolved oxygen concentration as a controlled variable and aeration as control variables, to achieve the control of high-speed centrifugal fans and effective energy saving. Finally, by use of an efficient particle swarm algorithm, it is to optimize the fuzzy control model.The main content of this research includes:(1) It is based on a comprehensive survey of the entire sewage treatment plant layout, biochemical process, energy equipment distribution and major energy distribution unit to complete an accurate energy map, a biological wastewater treatment model of the sewage plant and identification on input and output of each subsystem.(2) It is designed a frequency conversion transform and optimization combination improvement programs for pumps.(3) Through on-line monitoring of dissolved oxygen solubility, it is established a dissolved oxygen-aeration fuzzy control model, with the dissolved oxygen concentration as a controlled variable and aeration as control variables. Simulate the model’s validity and science.(3) Based on the smart thought,"teach him to fish, rather than give him fish", a new PSO named as the Velocity improvement-Particle Swarm Optimization (V-PSO) was developed in this paper. It optimizes speed iterative strategy. Experimental results show its accuracy and stability are superior to the traditional PSO algorithm, which has been successfully applied to fuzzy control model quantization factor optimization.