Optimal Design and Simulation for the Intelligent Control of Sewage Treatment Based on Multi-Objective Particle Swarm Optimization

Abstract

With the continuous increase in emphasis on the environmental protection industry, sewage treatment plants have been built in many places, and these sewage treatment plants undoubtedly occupy an important position in protecting the local environment. The sewage treatment process is generally complicated and the treatment environment is difficult, which means that the treatment plant must have an excellent control system. At this stage, the sewage treatment systems in many cities have the issue of possessing backward technology and huge costs, which hinder the development of urban sewage treatment. In this paper, a new intelligent control method for sewage treatment is proposed, combined with the multi-objective particle swarm optimization (MOPSO) algorithm. The MOPSO algorithm is used to optimize the parameters and control rules of the controller globally, thereby improving the performance and work efficiency of the controller. Practice has shown that the intelligent control system combined with the MOPSO algorithm can make chemical oxygen demand (COD) in the sewage treatment quickly meet the expected requirements, and the control accuracy is also very accurate, which greatly improves the sewage treatment performance. Through our calculations, the new method improved the sewage treatment efficiency by 7.15%.

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“…It assumes that there exists a group of particles in the space, and these particles seek the optimal point in the whole space. During the flight process, the particles continuously adjust the flight position through information exchange to obtain the optimal position of each, and finally get the optimal solution of the whole particle swarm 23 . Its velocity and position update equations are shown as follows: $$$$ {v}_{id}^{k+1}={wv}_{id}^k+{c}_1{r}_1\left({g}_{best\kern0.3em id}^{\kern1.5em k}-{x}_{id}^k\right)+{c}_2{r}_2\left({p}_{best\kern0.3em id}^{\kern1.5em k}-{x}_{id}^k\right) $$$$ $$$$ {x}_{id}^{k+1}={x}_{id}^k+{v}_{id}^{k+1}, $$$$ …”

“…During the flight process, the particles continuously adjust the flight position through information exchange to obtain the optimal position of each, and finally get the optimal solution of the whole particle swarm. 23 Its velocity and position update equations are shown as follows:…”