RegCNN: A Deep Multi-output Regression Method for Wastewater Treatment

Wastewater treatment is a pivotal approach dealing with water pollution problem. During the actual production process of wastewater treatment, predicting input water's output quality acts as a critical step. Existing methods rely mainly on biotechnology and demand a long time cost, which cannot be applied in practice. Therefore, we propose a novel approach to address this problem, which utilizes the recent advance in deep neural networks, and achieves both high accuracy and low running cost simultaneously. Only less than 3% test samples have prediction errors that exceed limits even in the worst case, and only less than 40 seconds are needed when training our model on a real-world dataset of thousands of samples. To our best knowledge, we are the first to apply convolutional neural network to predict output water quality indicators accurately and rapidly. Furthermore, our proposed model supports dynamic update to cope with the variations of wastewater treatment plant configurations.