Data-Knowledge-Driven Inductive Learning Method for Modeling Wastewater Treatment Processes

In wastewater treatment processes (WWTPs), data and knowledge are employed to build an effective model for monitoring its operation. Unfortunately, they are difficult to be fused due to their heterogeneity, which struggles to provide a united and reliable solution. To solve this issue, a data-knowledge-driven inductive learning (DKIL) method is introduced to WWTPs. First, a fuzzy-based expression strategy is introduced to describe the operational status of WWTPs. This strategy captures the available data, constraint knowledge and semantic knowledge for the modeling process. Second, a heterogeneous assimilation mechanism is designed to integrate data and knowledge. This mechanism supports their interaction to form a unified scheme through fusion operations. Third, a collaborative optimization algorithm is developed to extract the operational features of WWTPs. This algorithm updates the parameters using both error information and semantic knowledge, which enhances the modeling performance. In the experiment, the results have verified that DKIL can efficiently model WWTPs.