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

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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 CODeff 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.

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Data Accessibility Statement Data not available due to commercial restrictions.

Abbreviations

WWTPs:

Wastewater Treatment Plants

SVM:

Support Vector Machine

RF:

Random Forest

GPR:

Gaussian Process Regression

ANN:

Artificial Neural Network

RNN:

Recurrent Neural Network

LSTM:

Long Short-Time Memory

GRU:

Gated Recurrent Unit

BiGRU:

Bidirectional Gated Recurrent Unit

TPE:

Tree-structured Parzen Estimator

RS:

Random Search

GS:

Grid Search

RBF:

Radial Basis Function

SE:

Square Exponential Covariance function

RQ:

Rational Quadratic

ALK:

Alkalinity

OLR:

Organic Loading Rate

HRT:

Hydraulic Retention Time

COD:

Chemical Oxygen Demand

MSE:

Mean Square Error

RMSE:

Root Mean Square Error

MAPE:

Mean Absolute Percentage Error

CP:

Coverage Percentage

MWP:

Mean Width Percentage

CRPS:

Continuous Ranked Probability Score

CDF:

Cumulative Distribution Function

PDF:

Probability Density Function

PIT:

Probability Integral Transform

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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).

Author information

Authors and Affiliations

  1. SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, School of Environment, South China Normal University, Guangzhou, 510006, China

    Junlang Li, Zhenguo Chen, Xiaoyong Li, Xiaohui Yi & Mingzhi Huang

  2. Fujian Environmental Protection Design Institute Co. Ltd, Fuzhou, 350000, China

    Yingzhong Zhao, Xinzhong He & Zehua Huang

  3. National Institute of Oceanography and Fisheries, NIOF, Alexandria, 21556, Egypt

    Mohamed A. Hassaan & Ahmed El Nemr

  4. SCNU Qingyuan Institute of Science and Technology Innovation Co., Ltd., Qingyuan, 511517, China

    Mingzhi Huang

Authors
  1. Junlang Li
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  2. Zhenguo Chen
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  3. Xiaoyong Li
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  4. Xiaohui Yi
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  5. Yingzhong Zhao
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  6. Xinzhong He
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  7. Zehua Huang
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  8. Mohamed A. Hassaan
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  9. Ahmed El Nemr
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  10. Mingzhi Huang
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Contributions

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.

Corresponding authors

Correspondence to Zhenguo Chen or Mingzhi Huang.

Additional information

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.

Special Issue—Artificial Intelligence/Machine Learning on Environmental Science & Engineering (Responsible Editors: Yongsheng Chen, Xiaonan Wang, Joe F. Bozeman III & Shouliang Yi)

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Li, J., Chen, Z., Li, X. et al. Water quality soft-sensor prediction in anaerobic process using deep neural network optimized by Tree-structured Parzen Estimator. Front. Environ. Sci. Eng. 17, 67 (2023). https://doi.org/10.1007/s11783-023-1667-3

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  • DOI: https://doi.org/10.1007/s11783-023-1667-3

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