Water quality soft-sensor prediction in anaerobic process using deep neural network optimized by Tree-structured Parzen Estimator

Highlights

● Hybrid deep-learning model is proposed for water quality prediction.

● Tree-structured Parzen Estimator is employed to optimize the neural network.

● Developed model performs well in accuracy and uncertainty.

● Usage of the proposed model can reduce carbon emission and energy consumption.

Abstract

Anaerobic process is regarded as a green and sustainable process due to low carbon emission and minimal energy consumption in wastewater treatment plants (WWTPs). However, some water quality metrics are not measurable in real time, thus influencing the judgment of the operators and may increase energy consumption and carbon emission. One of the solutions is using a soft-sensor prediction technique. This article introduces a water quality soft-sensor prediction method based on Bidirectional Gated Recurrent Unit (BiGRU) combined with Gaussian Progress Regression (GPR) optimized by Tree-structured Parzen Estimator (TPE). TPE automatically optimizes the hyperparameters of BiGRU, and BiGRU is trained to obtain the point prediction with GPR for the interval prediction. Then, a case study applying this prediction method for an actual anaerobic process (2500 m³/d) is carried out. Results show that TPE effectively optimizes the hyperparameters of BiGRU. For point prediction of COD_eff and biogas yield, R² values of BiGRU, which are 0.973 and 0.939, respectively, are increased by 1.03%–7.61% and 1.28%–10.33%, compared with those of other models, and the valid prediction interval can be obtained. Besides, the proposed model is assessed as a reliable model for anaerobic process through the probability prediction and reliable evaluation. It is expected to provide high accuracy and reliable water quality prediction to offer basis for operators in WWTPs to control the reactor and minimize carbon emission and energy consumption.

Graphical abstract

Keywords

Water quality prediction / Soft-sensor / Anaerobic process / Tree-structured Parzen Estimator

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Acknowledgements

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

CRediT Author Contribution Statement

Junlang Li: Algorithm design, Writing – Original draft. Zhenguo Chen: Code debugging, Writing – Review & Editing, Investigation. Xiaoyong Li: Algorithm optimization, Writing – Review & Editing. Xiaohui Yi: Conceptualization. Yingzhong Zhao: Experimental data visualization. Xinzhong He: Dataset resources. Zehua Huang: Formal analysis. Mohamed A. Hassaan: Writing – Review & Editing. Ahmed El Nemr: Writing – Review & Editing. Mingzhi Huang: Project administration, Funding acquisition.

Data Accessibility Statement

Data not available due to commercial restrictions.

Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://doi.org/10.1007/s11783-023-1667-3 and is accessible for authorized users.

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