Online soft measurement for wastewater treatment system based on hybrid deep learning

Highlights

● A hybrid model is proposed to overcome limitations of single model with time series.

● CNN and bidirectional NLSTM are combined to solve complex nonlinear monitoring issue.

● Attention mechanism is suitably introduced to hybrid model for better convergence.

● TPE is used to find the optimal parameter combination faster rather than manual.

Abstract

The existing automated wastewater treatment control systems encounter challenges such as the utilization of specialized testing instruments, equipment repair complications, high operational costs, substantial operational errors, and low detection accuracy. An effective soft measure model offers a viable approach for real-time monitoring and the development of automated control in the wastewater treatment process. Consequently, a novel hybrid deep learning CNN-BNLSTM-Attention (CBNLSMA) model, which incorporates convolutional neural networks (CNN), bidirectional nested long and short-term memory neural networks (BNLSTM), attention mechanisms (AM), and Tree-structure Parzen Estimators (TPE), has been developed for monitoring effluent water quality during the wastewater treatment process. The CBNLSMA model is divided into four stages: the CNN module for feature extraction and data filtering to expedite operations; the BNLSTM module for temporal data’s temporal information extraction; the AM module for model weight reassignment; and the TPE optimization algorithm for the CBNLSMA model’s hyperparameter search optimization. In comparison with other models (TPE-CNN-BNLSTM, TPE-BNLSTM-AM, TPE-CNN-AM, PSO-CBNLSTMA), the CBNLSMA model reduced the RMSE for effluent COD prediction by 25.4%, decreased the MAPE by 32.9%, and enhanced the R² by 14.9%. For the effluent SS prediction, the CBNLSMA model reduced the RMSE by 26.4%, the MAPE by 21.0%, and improved the R² by 35.7% compared to other models. The simulation results demonstrate that the proposed CBNLSMA model holds significant potential for real-time effluent quality monitoring, indicating its high potential for automated control in wastewater treatment processes.

Graphical abstract

Keywords

Prediction model / Soft measurement / CNN-BNLSTM-AM model / TPE optimization algorithm

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Acknowledgements

This work was funded by the National Natural Science Foundation of China (Nos. 41977300 and 41907297), the Science and Technology Program of Guangzhou (No. 202002020055), and the Fujian Provincial Natural Science Foundation (No. 2020I1001).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://doi.org/10.1007/s11783-024-1780-y and is accessible for authorized users.

Data Accessibility Statement

Data not available due to legal restrictions.

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