Prediction of effluent total nitrogen and energy consumption in wastewater treatment plants: Bayesian optimization machine learning methods

Ye, Gang; Wan, Jinquan; Deng, Zhicheng; Wang, Yan; Chen, Jian; Zhu, Bin; Ji, Shiming

doi:10.1016/j.biortech.2024.130361

Cited by 13 publications

(2 citation statements)

References 36 publications

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“…Pearson correlation coefficient is a linear correlation coefficient used to measure the linear relationship between two variables [ 18 , 19 ]. The Pearson correlation coefficient is calculated as follows: Where cov( X , Y ) is the covariance of the variable ( X , Y ), σX and σY are their respective standard deviations.…”

Section: Data Collection and Selection Of Input Variablesmentioning

confidence: 99%

Online soft measurement method for chemical oxygen demand based on CNN-BiLSTM-Attention algorithm

Liu,

Tian,

et al. 2024

PLoS ONE

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The measurement of chemical oxygen demand (COD) is very important in the process of sewage treatment. The value of COD reflects the effectiveness and trend of sewage treatment to a certain extent, but obtaining accurate data requires high cost and labor intensity. To1 solve this problem, this paper proposes an online soft measurement method for COD based on Convolutional Neural Network-Bidirectional Long Short-Term Memory Network-Attention Mechanism (CNN-BiLSTM-Attention) algorithm. Firstly, by analyzing the mechanism of the aerobic tank stage in the Anaerobic-Anoxic-Oxic (A2O) wastewater treatment process, the selection range of input variables was preliminarily determined, and the collected sample dataset was subjected to correlation analysis. Finally, pH, dissolved oxygen (DO), electrical conductivity (EC), and water temperature (T) were determined as input variables for soft measurement prediction of COD.Then, based on the feature extraction ability of CNN and the advantage that BiLSTM is able to capture the backward and forward dependencies in time series data, combined with the attention mechanism that can assign higher weights to the key data, a CNN-BiLSTM-Attention algorithm model was established to soft measure COD in the effluent from the aerobic zone of the A2O wastewater treatment process. At the same time, root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient of determination (R2) were utilized Three indicators were used to evaluate the model, and the results showed that the model can accurately predict the value of COD and has a high accuracy. At the same time, compared with models such as CNN-LSTM-Attention, CNN-BiLSTM, CNN-LSTM, LSTM, RNN, BP, SVM, XGBoost, and RF etc., the results showed that the CNN-BiLSTM Attention model performed the best, proving the superiority of the algorithm model.The Wilcoxon signed-rank test indicates significant differences between the CNN-BiLSTM-Attention model and other models.

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Section: Data Collection and Selection Of Input Variablesmentioning

confidence: 99%

Online soft measurement method for chemical oxygen demand based on CNN-BiLSTM-Attention algorithm

Liu,

Tian,

et al. 2024

PLoS ONE

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show abstract

“…For instance, monitoring parameters like total nitrogen in WWTPs, which quantifies the amount of organic matter, proteins, and amino acids in water, is crucial for understanding contamination levels and serves as an indicator to optimize various treatment processes. Additionally, measuring this variable extends beyond the use of a simple sensor, requiring focus, experience, and specific measurement methods for reliable results [13][14][15].…”

mentioning

confidence: 99%

A One-Class-Based Supervision System to Detect Unexpected Events in Wastewater Treatment Plants

Arcano-Bea,

Timiraos,

Díaz-Longueira

et al. 2024

Applied Sciences

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The increasing importance of water quality has led to optimizing the operation of Wastewater Treatment Plants. This implies the monitoring of many parameters that measure aspects such as solid suspension, conductivity, or chemical components, among others. This paper proposes the use of one-class algorithms to learn the normal behavior of a Wastewater Treatment Plants and detect situations in which the crucial parameters of Chemical Oxygen Demand, Ammonia, and Kjeldahl Nitrogen present unexpected deviations. The classifiers are tested using different deviations, achieving successful results. The final supervision systems are capable of detecting critical situation, contributing to decision-making and maintenance effectiveness.

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Improvement of effluent quality and operational cost reduction of a wastewater treatment plant using intelligent computational hybrid control schemes

Ateunkeng,

Boum,

Bitjoka

2024

Clean Techn Environ Policy

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Prediction of effluent total nitrogen and energy consumption in wastewater treatment plants: Bayesian optimization machine learning methods

Cited by 13 publications

References 36 publications

Online soft measurement method for chemical oxygen demand based on CNN-BiLSTM-Attention algorithm

Online soft measurement method for chemical oxygen demand based on CNN-BiLSTM-Attention algorithm

A One-Class-Based Supervision System to Detect Unexpected Events in Wastewater Treatment Plants

Improvement of effluent quality and operational cost reduction of a wastewater treatment plant using intelligent computational hybrid control schemes

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