Study Of Energy-saving Optimization Strategy For Wastewater Treatment Process Base Onartificial Immune Algorithm

The problem of water shortage and water pollution of our country is growing badly, whichmakes it necessary to improve the treatment ability and efficiency of wastewater treatmentplants. It is of practical significance to its energy-saving and emission-reduction capability byresearching energy-reducing optimization strategy to wastewater treatment process, whileensuring the wastewater effluent quality.The paper regards the activated sludge wastewater treatment benchmark simulated model(BSM1) built on Matlab platform as simulation object. By modeling the optimization goal tomeet the effluent standards with minimal aeration and pumping energy consumption, dynamicoptimal strategy of the reference values of PI controllers has been researched, and artificialimmune algorithm has been chosen to solve the problem with its global searching ability.After having a deep knowledge about aritificial immune algorithm, improved method hasbeen presented by using the thought of sensitivity to guide antibodies’ updating, and bylocally searching around the current optimal extremum. The typical test functions’simulationsprove the improved immune algorithm can convergence fast while achieving satisfying globaloptimum accuracy, which makes it possible to solve the problem of futher or online real-timeoptimal control strategies in wastewater treatment area in the near future.According to the aim of energy-saving optimization, the paper has calculated the optimalsetting values of dissolved oxygen concentration of5th tank and nitrate nitrogenconcentration of2nd tank for each day by immune algorithm. This strategy proves its effecton energy-saving by comparing with constant control strategy. Then, considering influentdata is the main interference factor to the treatment effect, this paper presents a self-adaptivepartition optimization strategy by dividing control periods based on ordered sampleclustering method first, then calculating the optimal setting values of PI controllers on eachtime period. This strategy decreases the operation energy consumption in a further way.