With the continuous development of cities, the challenges faced in the sewage treatment process are increasing, such as significant fluctuations in incoming water quality, insufficient operational stability, high energy consumption, and difficulties in meeting discharge standards. Traditional analysis methods that rely solely on a single data source often struggle to fully reveal the inherent laws of process operation, resulting in limited optimization effectiveness. Therefore, this article proposes a wastewater treatment process optimization method based on multi-source data fusion. This method integrates data from various sources and structures, covering sensor monitoring indicators, equipment operating parameters, and external environmental meteorological information. In terms of data fusion strategy, we have studied the fusion of feature level and decision level methods to construct a process parameter optimization model. The experimental results show that compared with previous analysis methods based on single data, the new method improves the COD removal rate of effluent by 12.3%, reduces energy consumption by 15.8%, and significantly enhances the system’s anti-interference ability. This study provides a feasible new path for the intelligent upgrade of sewage treatment processes.
Fang et al. (Thu,) studied this question.
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