Optimization model of process parameters for waterworks based on CNN-ISSA-BiGRU

Abstract

After conducting an extensive investigation into the current situation of Chinese water supply plants, it was imperative to systematically optimize the critical operational parameters of various treatment units using a machine learning approach. This holistic optimization aimed to reduce operational costs and ensure the desired effluent quality. This work explored the impact of key process parameters on turbidity and CODMn through a pilot experiment, and a CNN-ISSA-BiGRU model was introduced for predicting the optimization of process parameters in water treatment plants. The CNN layer played a crucial role in preprocessing the original data and extracting short-term relationships within the dataset. The ISSA layer was responsible for optimizing the parameters of the BiGRU layer, and the BiGRU layer focused on the bi-directional prediction of data, fully extracting features between input variables. The application of the CNN-ISSA-BiGRU model to actual water plant operations led to a significant reduction in operation costs, up to 17.1%. Furthermore, a correlation analysis of pilot experiment data was conducted to determine the relative importance of each parameter on effluent quality. The results indicated that coagulant dosage had the greaexperiment impact on effluent turbidity, while disinfectant dosage had the utmost effect on CODMn. This comprehensive analysis provided valuable guidance for environmental regulators and operators, offering insights into the mechanisms that govern water treatment processes and aiding in the optimization of plant operations.