Beijing University of Technology

Ying Hou

Junfei Qiao

Honggui Han

Hongyan Yang

Wenjing Li

Xing Bai

An optimal control method for wastewater treatment process (WWTP) based on a self-adjusting multi-task particle swarm optimization (SA-MTPSO) algorithm belongs to the field of WWTP. To balance the relationship between the effluent water quality (EQ) and energy consumption (EC) and achieve optimization online quickly, the invention establishes a data-based multi-task optimization model for WWTP to describe the relationship between the control variables and EQ, EC. Then, the SA-MTPSO algorithm is adopted to solve the optimal set-points of the nitrate nitrogen and dissolved oxygen concentration for WWTP. The PID controller is used to track the optimal set-points, so as to reduce EC while ensuring EQ, and realize the online optimal control of WWTP.

Optimal control method for wastewater treatment process based on self-adjusting multi-task particle swarm optimization

A computing system is designed for measuring the A/O effluent total phosphorus based on data-driven method. Several related variables are obtained by analyzing the relationship between effluent total phosphorus and other process variables. In addition, a hardware platform is designed and built to further analysis sample information of each variable. Finally, the computing system for measuring total phosphorus in effluent is developed by combining the hardware and software as provided in implementations herein.

Computing system for detecting total phosphorus in effluent using data driven A/O process

An intelligent method is designed for predicting the effluent ammonia-nitrogen concentration in the urban wastewater treatment process (WWTP). The technology of this invention is part of advanced manufacturing technology, belongs to both the field of control engineering and environment engineering. In order to improve the predicting efficiency, a recurrent self-organizing neural network, which can adjust the structure and parameters concurrently to train the parameters, is developed to design this intelligent method. This intelligent method can predict the effluent ammonia-nitrogen concentration with acceptable accuracy and solve the problem that the effluent ammonia-nitrogen concentration is difficult to be measured online. Moreover, the online information of effluent ammonia-nitrogen concentration, predicted by this intelligent method, can enhance the quality monitoring level and alleviate the current situation of wastewater to strengthen the whole management of WWTP.

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Beijing, China

Ying Hou