An optimal control method for sewage treatment process based on dynamic multi-objective particle swarm optimization algorithm

Abstract

The invention proposes an optimization control method for the sewage treatment process based on a dynamic multi-objective particle swarm algorithm, which simultaneously meets the requirements of the effluent quality reaching the standard and reducing energy consumption during the sewage treatment process. First, a comprehensive optimization framework is used to extract the complex and time-varying characteristics of the sewage treatment process, and a data-driven model of effluent water quality, aeration energy consumption, and pumping energy consumption is established; secondly, according to the characteristics of the dynamic data-driven model, a A dynamic multi-objective particle swarm optimization algorithm was used to solve the multi-objective optimization problem; then, the multivariable PID controller O and nitrate nitrogen S NO Finally, the dynamic optimization control method is applied to the actual sewage treatment process. The experimental results show that the method can reduce energy consumption while ensuring the quality of the effluent, which is beneficial to the optimal control performance of the sewage treatment process.

Description

technical field [0001] Based on the dynamic characteristics of the sewage treatment biochemical reaction process, the present invention designs a comprehensive optimization framework for extracting the dynamic characteristics of the sewage treatment process, thereby establishing a dynamic energy consumption and water quality model, using a dynamic multi-objective particle swarm algorithm The optimization control method realizes the simultaneous optimization of three dynamic models in the sewage treatment process, and realizes the dissolved oxygen S O and nitrate nitrogen S NO Concentration tracking control; this optimal control method saves investment and operating costs while ensuring the quality of effluent water, and ensures the stable and efficient operation of sewage treatment plants. It belongs to both the field of control and the field of water treatment. Background technique [0002] With the development of my country's economy and society and the improvement of peo...

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Problems solved by technology

[0004] The principle of traditional multi-objective optimization control methods mostly adopts the conversion method, and the multi-objective problem is transformed into a single-objective problem through the weight coefficient method. However, since the sewage treatment process itself is a conflicting multi-objective problem, it is highly nonlinear and time-varying. and uncertainty, therefore, the optimal set value obtained by this method has the disadvantage of low precision

In recent years, there have also been researches based on intelligent optimal control methods at home and abroad, which can solve the problem of low precision of optimal set values ​​obtained by traditional multi-objective optimal control methods.

However, it still cannot adapt to the dynamic sewage treatment process. First of all, the above intelligent optimization control method does not have a unified multi-objective function expression in different sewage treatment processes, which makes the optimal set value obtained by this method and the actual sewage treatment plant. Mismatch phenomenon, its multi-objective function expression has time-varying characteristics; secondly, the above intelligent optimization control methods are mostly static optimization and steady state optimization, it is difficult to make dynamic real-time adjustment according to the change of influent water quality and quantity, and no dynamic optimization is adopted method, it is impossible to obtain an accurate time-varying dynamic optimization setting value

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