Multimodal Learning-Assisted Identification of Effluent Water Quality and Toxicity in Wastewater Treatment Plants

Effluent of wastewater treatment plants (WWTPs) poses significant ecological risks due to potential biological toxicity, demanding effective monitoring and assessment of water quality and toxicity. However, the complexity of the wastewater treatment processes, coupled with numerous control parameters and influencing factors, makes intelligent assessment and monitoring challenging. Traditional data mining and machine learning (ML) approaches often overlook partial multimodal factors such as applied treatment process technologies, leading to suboptimal predictions. To address this, this study proposed a multimodal learning (MML)-based framework for predicting effluent water quality and toxicity in WWTPs. Representation fusion and decision fusion strategies were adopted to optimize the MML models' performance. Additionally, a novel 3C-Encoding strategy was proposed to tackle the challenges of encoding process information. The results demonstrated that MML models achieved significant improvements over traditional methods (<i>R</i>² increase ranging from 0.057 to 0.234). The final MML models exhibited remarkable performance with an average <i>R</i>² of 0.874 for water quality and an <i>R</i>² of 0.904 for toxicity. This study provides a novel MML-based approach to leveraging diverse data modalities, substantially enhancing prediction accuracy and informed decision-making. It has the potential to facilitate advancements in intelligent process control and optimization while promoting environmental sustainability in WWTPs.