A Multi-objective Optimal Control Method for Urban Sewage Treatment Process Based on Adaptive Selection Strategy
An adaptive selection, multi-objective optimization technology, applied in the direction of adaptive control, water/sewage multi-stage treatment, water/sludge/sewage treatment, etc. To ensure stable and efficient operation, improve optimal control performance, and ensure the effect of effluent quality
Active Publication Date: 2022-03-15
BEIJING UNIV OF TECH
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Problems solved by technology
In the sewage treatment process, due to the time-varying characteristics of the sewage treatment process, there is no clear optimization target model for reference, and most of the current target models are static, which is not suitable for the sewage treatment system with strong time-varying characteristics, so , how to establish a dynamic model of the sewage treatment process at different times is of great significance
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[0074] The present invention obtains a multi-objective optimization control method for urban sewage treatment process based on an adaptive selection strategy, extracts the dynamic characteristics of the sewage treatment process through a comprehensive optimization framework, and establishes an aeration energy consumption model and pumping energy consumption model. model and effluent water quality model, the multi-objective particle swarm optimization algorithm based on adaptive selection strategy simultaneously optimizes the model to obtain the optimal set value, and realizes the dissolved oxygen S O and nitrate nitrogen S NO Concentration tracking control solves the problem that it is difficult to achieve dynamic optimal control in the sewage treatment process, improves the optimal control performance of the sewage treatment process, saves investment and operating costs while ensuring the quality of the effluent, and ensures the stable and efficient operation of the sewage tre...
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A multi-objective optimal control method for urban sewage treatment process based on self-adaptive selection strategy belongs to both the field of control and the field of water treatment. First, a comprehensive optimization model was designed based on radial basis function to extract the complex and time-varying characteristics of the sewage treatment process, and a data-driven model for effluent quality, aeration energy consumption, and pumping energy consumption was established; secondly, for According to the characteristics of the data-driven model, a multi-objective particle swarm optimization algorithm based on adaptive selection strategy is proposed to obtain the optimal variable setting value of the data-driven model; after that, the PID controller is used to control the dissolved oxygen S O and nitrate nitrogen S NO Finally, the optimal 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 based on radial basis kernel functions to extract the dynamic characteristics of the sewage treatment process, establishes a dynamic energy consumption and water quality model, and utilizes an adaptive selection strategy based The multi-objective particle swarm algorithm is used to optimize the energy consumption and effluent water quality model, so as to obtain the optimal variable setting value, and realize the dissolved oxygen S through the obtained optimized setting value. 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. Back...
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IPC IPC(8): G05B13/04C02F9/14
CPCG05B13/042C02F3/30C02F2001/007
Inventor 韩红桂阿音嘎张璐乔俊飞
Owner BEIJING UNIV OF TECH