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Prediction of total phosphorus in wastewater treatment plant effluent based on deep learning
Excessive phosphorus in water is one of the key factors causing eutrophication in water bodies. Therefore, total phosphorus is an important water quality control parameter for sewage treatment. Traditional total phosphorus testing methods can not realize real-time monitoring of effluent total phosphorus, which is not conducive to the intelligent development of treatment process. This paper used back propagation neural network(BPNN), convolutional neural network(CNN), long short-term memory recurrent neural network(LSTM), and Informer to establish a prediction model for total phosphorus in sewage treatment plant effluent. The analysis showed that the R2 of the BPNN model was 0.459 7, and the prediction results of the model were poorly stationary. The evaluation indicators of the CNN model were poor, and it was not suitable for the prediction of total phosphorus in the sewage treatment plant effluent. The mean square error(MSE), root mean square error(RMSE), mean absolute error(MAE), and R2 of the LSTM model were 0.008 2, 0.090 5, 0.068 4 and 0.606 8 respectively, and the model prediction accuracy was high. Compared with the LSTM model, the MSE, RMSE, and MAE of the Informer model were reduced by 21.95%, 11.60%, and 28.65%, respectively, and the R2 was increased by 19.94%, which had obvious prediction advantages. The Informer model had high prediction accuracy and strong universality, with good stability in prediction results. The Informer model could effectively predict the total phosphorus in wastewater treatment plant effluent, which was of great significance for improving real-time intelligence level, optimizing treatment process, improving phosphorus removal efficiency, reducing energy consumption, and achieving carbon neutrality in wastewater treatment plants.
Prediction of total phosphorus in wastewater treatment plant effluent based on deep learning
Excessive phosphorus in water is one of the key factors causing eutrophication in water bodies. Therefore, total phosphorus is an important water quality control parameter for sewage treatment. Traditional total phosphorus testing methods can not realize real-time monitoring of effluent total phosphorus, which is not conducive to the intelligent development of treatment process. This paper used back propagation neural network(BPNN), convolutional neural network(CNN), long short-term memory recurrent neural network(LSTM), and Informer to establish a prediction model for total phosphorus in sewage treatment plant effluent. The analysis showed that the R2 of the BPNN model was 0.459 7, and the prediction results of the model were poorly stationary. The evaluation indicators of the CNN model were poor, and it was not suitable for the prediction of total phosphorus in the sewage treatment plant effluent. The mean square error(MSE), root mean square error(RMSE), mean absolute error(MAE), and R2 of the LSTM model were 0.008 2, 0.090 5, 0.068 4 and 0.606 8 respectively, and the model prediction accuracy was high. Compared with the LSTM model, the MSE, RMSE, and MAE of the Informer model were reduced by 21.95%, 11.60%, and 28.65%, respectively, and the R2 was increased by 19.94%, which had obvious prediction advantages. The Informer model had high prediction accuracy and strong universality, with good stability in prediction results. The Informer model could effectively predict the total phosphorus in wastewater treatment plant effluent, which was of great significance for improving real-time intelligence level, optimizing treatment process, improving phosphorus removal efficiency, reducing energy consumption, and achieving carbon neutrality in wastewater treatment plants.
Prediction of total phosphorus in wastewater treatment plant effluent based on deep learning
AN Yuning (author) / ZHU Sifu (author) / LIU Jing (author) / DU Liwei (author) / LIU Changqing (author)
2024
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Prediction of effluent concentration in a wastewater treatment plant using machine learning models
| Online Contents | 2015
| American Chemical Society | 2024