Transformer based Multi-output Regression Learning for Wastewater Treatment

Wastewater treatment is an important link to solve the pollution of water resources and realize the recycling of water resources. Predicting effluent water quality by influent water quality is an important part of the actual production process. The existing forecasting methods have the problem of low accuracy and efficiency, and cannot be used well in the actual production process. To tackle the above issues, we propose a novel method to use the transformer model to fit the wastewater treatment process which improves the prediction of effluent water quality. Experiments on various datasets validate the effectiveness of our proposed method. In the worst case, the sample prediction accuracy rate of our model achieves as high as 96.8%. At the same time, our model training with thousands of real production data, the training time is not more than two minutes. As far as we know, we are the first to use the transformer model in wastewater treatment tasks and achieve fast and accurate predictions. In the future, we will apply our model in actual production to achieve accurate predictions in dynamic data update scenarios.