An Intelligent Carbon-Based Prediction of Wastewater Treatment Plants Using Machine Learning Algorithms

Hilal, Anwer Mustafa; Althobaiti, Maha M.; Eisa, Taiseer Abdalla Elfadil; Alabdan, Rana; Hamza, Manar Ahmed; Motwakel, Abdelwahed; Duhayyim, Mesfer Al; Negm, Noha

doi:10.1155/2022/8448489

Cited by 17 publications

(7 citation statements)

References 29 publications

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“…The system can predict the COD index to support the relevant decision-making of the sewage treatment plant. Hilal et al [17] used the BKNN-ELM model combining KNN and extreme learning machine (ELM) to predict the SS index, and the prediction accuracy reached 93.56%. Liu et al [18] used the least squares support vector machine (LS-SVM) to build a prediction model, which was validated in the COD prediction of an anaerobic wastewater treatment system.…”

Section: Related Workmentioning

confidence: 99%

Prediction Model of Wastewater Pollutant Indicators Based on Combined Normalized Codec

Chen¹,

Zhang²,

Kong³

et al. 2022

Preprint

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Effective prediction of wastewater treatment is beneficial for precise control of wastewater treatment processes. The nonlinearity of pollutant indicators such as COD and TP makes the model difficult to fit and has low prediction accuracy. The classical deep learning methods have been shown to perform nonlinear modeling. However, there are enormous numerical differences between multi-dimensional data in the prediction problem of wastewater treatment, such as COD above 3000 mg/L and TP around 30 mg/L. It will make current normalization methods challenging to handle effectively, leading to the training failing to converge and the gradient disappears or exploding. This paper proposes a multi-factor prediction model based on deep learning. The model consists of a combined normalization layer and a codec. The combined normalization layer combines the advantages of three normalization calculation methods: z-score, Interval, and Max, which can realize the adaptive processing of multi-factor data, fully retain the characteristics of the data, and finally cooperate with the codec to learn the data characteristics and output the prediction results. Experiments show that the proposed model can overcome data differences and complex nonlinearity in predicting industrial wastewater pollutant indicators and achieve better prediction accuracy than classical models.

show abstract

Section: Related Workmentioning

confidence: 99%

Prediction Model of Wastewater Pollutant Indicators Based on Combined Normalized Codec

Chen¹,

Zhang²,

Kong³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The system can predict the COD index to support the relevant decisionmaking of the sewage treatment plant. Hilal et al [17] used the model combining KNN and extreme learning machine (ELM) to predict the SS index, and the prediction accuracy reached 93.56%. Liu et al [18] used the least squares support vector machine (LS-SVM) to build a prediction model, which was validated in the COD prediction of an anaerobic wastewater treatment system.…”

Section: Related Workmentioning

confidence: 99%

Prediction Model of Wastewater Pollutant Indicators Based on Combined Normalized Codec

Zhang

Kong

et al. 2022

Mathematics

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Effective prediction of wastewater treatment is beneficial for precise control of wastewater treatment processes. The nonlinearity of pollutant indicators such as chemical oxygen demand (COD) and total phosphorus (TP) makes the model difficult to fit and has low prediction accuracy. The classical deep learning methods have been shown to perform nonlinear modeling. However, there are enormous numerical differences between multi-dimensional data in the prediction problem of wastewater treatment, such as COD above 3000 mg/L and TP around 30 mg/L. It will make current normalization methods challenging to handle effectively, leading to the training failing to converge and the gradient disappearing or exploding. This paper proposes a multi-factor prediction model based on deep learning. The model consists of a combined normalization layer and a codec. The combined normalization layer combines the advantages of three normalization calculation methods: z-score, Interval, and Max, which can realize the adaptive processing of multi-factor data, fully retain the characteristics of the data, and finally cooperate with the codec to learn the data characteristics and output the prediction results. Experiments show that the proposed model can overcome data differences and complex nonlinearity in predicting industrial wastewater pollutant indicators and achieve better prediction accuracy than classical models.

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“…The incorporation of NF into RO systems substantially enhances the efficiency of the desalination process by actively reducing the presence of organics, pollutants, and ionic strength, and by softening the water. This combined approach also leads to a notable decrease in the overall expenditure tied to desalination [9,10]. The appeal of NF extends beyond its capacity to complement RO.…”

Section: Introductionmentioning

confidence: 99%

Integrated Modeling of Hybrid Nanofiltration/Reverse Osmosis Desalination Plant Using Deep Learning-Based Crow Search Optimization Algorithm

Abba,

Usman,

Abdulazeez

et al. 2023

Water

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The need for reliable, state-of-the-art environmental investigations and pioneering approaches to address pressing ecological dilemmas and to nurture the sustainable development goals (SDGs) cannot be overstated. With the power to revolutionize desalination processes, artificial intelligence (AI) models hold the potential to address global water scarcity challenges and contribute to a more sustainable and resilient future. The realm of desalination has exhibited a mounting inclination toward modeling the efficacy of the hybrid nanofiltration/reverse osmosis (NF–RO) process. In this research, the performance of NF–RO based on permeate conductivity was developed using deep learning long short-term memory (LSTM) integrated with an optimized metaheuristic crow search algorithm (CSA) (LSTM-CSA). Before model development, an uncertainty Monte Carlo simulation was adopted to evaluate the uncertainty attributed to the prediction. The results based on several performance statistical criteria (root mean square error (RMSE) and mean absolute error (MAE)) demonstrated the reliability of both LSTM (RMSE = 0.1971, MAE = 0.2022) and the LSTM-CSA (RMSE = 0.1890, MAE = 0.1420), with the latter achieving the highest accuracy. The accuracy was also evaluated using new 2D graphical visualization, including a cumulative distribution function (CDF) and fan plot to justify the other evaluation indicators such as standard deviation and determination coefficients. The outcomes proved that AI could optimize energy usage, identify energy-saving opportunities, and suggest more sustainable operating strategies. Additionally, AI can aid in developing advanced brine treatment techniques, facilitating the extraction of valuable resources from the brine, thus minimizing waste and maximizing resource utilization.

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An Intelligent Carbon-Based Prediction of Wastewater Treatment Plants Using Machine Learning Algorithms

Cited by 17 publications

References 29 publications

Prediction Model of Wastewater Pollutant Indicators Based on Combined Normalized Codec

Prediction Model of Wastewater Pollutant Indicators Based on Combined Normalized Codec

Prediction Model of Wastewater Pollutant Indicators Based on Combined Normalized Codec

Integrated Modeling of Hybrid Nanofiltration/Reverse Osmosis Desalination Plant Using Deep Learning-Based Crow Search Optimization Algorithm

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