Research on Wastewater Treatment Monitoring Algorithms Based on Deep Convolutional Neural Networks

Abstract

Around the problems of data loss, noise, and different temporal and spatial scale features of urban wastewater treatment process data, a method of monitoring and predicting wastewater treatment process data based on deep convolutional neural networks is proposed in the paper. Firstly, to address the problem that urban wastewater treatment process data has multiple spatial and temporal scale characteristics, which makes it difficult for the data to be used effectively, a spatial and temporal data fusion model based on fuzzy neural network (FNN) is proposed. Fuzzy neural networks have strong generalization ability and robustness. Secondly, to enable accurate and real-time monitoring of the content of the monitored components in the effluent of the urban wastewater treatment process, an intelligent prediction model based on SDF-FNN is established for the effluent. Finally, in order to verify the effectiveness of this intelligent prediction model, the model is tested using data collected from actual municipal wastewater treatment plants. The experimental results show that the wastewater treatment intelligent monitoring model is effective and can predict the content of the effluent monitoring index with high accuracy.