Interpretable prediction, classification and regulation of water quality: A case study of Poyang Lake, China

The Science of The Total Environment, Год журнала: 2024, Номер 951, С. 175407 - 175407

Опубликована: Авг. 9, 2024

Язык: Английский

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Applying Machine Learning Approach to Design Operational Control Strategies for a Wastewater Treatment Plant in Typical Scenarios

Water, Год журнала: 2025, Номер 17(3), С. 310 - 310

Опубликована: Янв. 23, 2025

When confronted with different influent conditions, WWTPs often lack targeted and effective operational control strategies. For the three typical scenarios of low C/N, water temperature high temperature, 441 carbon source dosage DO concentration coordination strategies were designed under premise ensuring effluent quality meets standard. The purpose was to provide clear guidance for efficient operation in scenarios. To determine optimal strategy, prediction model based on LSTM GRU constructed testing. results showed that: (1) LSTM-GRU is better than SVR RF predicting COD TN; (2) In C/N scenario, should be controlled between 0.23 t/h 0.26 t/h, ranging from 2.0 mg/L 2.6 mg/L; (3) 0.25 0.27 2.8 (4) 0.20 2.5 mg/L.

Язык: Английский

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Advanced Temporal Deep Learning Framework for Enhanced Predictive Modeling in Industrial Treatment Systems

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104158 - 104158

Опубликована: Янв. 1, 2025

Язык: Английский

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Early Warning and Management of Excessive Discharge of Water Pollutants in Municipal Wastewater Treatment Plants Based on Fluctuation Coefficients

Environmental Research, Год журнала: 2025, Номер unknown, С. 121127 - 121127

Опубликована: Фев. 1, 2025

Язык: Английский

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A probabilistic deep learning approach to enhance the prediction of wastewater treatment plant effluent quality under shocking load events

Water Research X, Год журнала: 2024, Номер 26, С. 100291 - 100291

Опубликована: Дек. 3, 2024

Sudden shocking load events featuring significant increases in inflow quantities or concentrations of wastewater treatment plants (WWTPs), are a major threat to the attainment treated effluents discharge quality standards. To aid real-time decision-making for stable WWTP operations, this study developed probabilistic deep learning model that comprises encoder-decoder long short-term memory (LSTM) networks with added capacity producing probability predictions, enhance robustness effluent prediction under such events. The LSTM (P-ED-LSTM) was tested an actual WWTP, where bihourly total nitrogen performed and compared classical models, including LSTM, gated recurrent unit (GRU) Transformer. It found events, P-ED-LSTM could achieve 49.7% improvement accuracy predictions concentration GRU, A higher quantile data from output, indicated value more approximate real quality. also exhibited predictive power next multiple time steps scenarios. captured approximately 90% over-limit discharges up 6 hours ahead, significantly outperforming other models. Therefore, model, its robust adaptability fluctuations, has potential broader applications across WWTPs different processes, as well providing strategies system regulation emergency conditions.

Язык: Английский

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Optimizing Wastewater Treatment Plant Operational Efficiency Through Integrating Machine Learning Predictive Models and Advanced Control Strategies

Process Safety and Environmental Protection, Год журнала: 2024, Номер 188, С. 995 - 1008

Опубликована: Май 31, 2024

Язык: Английский

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Elucidating and forecasting the organochlorine pesticides in suspended particulate matter by a two-stage decomposition based interpretable deep learning approach

Water Research, Год журнала: 2024, Номер 266, С. 122315 - 122315

Опубликована: Авг. 23, 2024

Язык: Английский

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Leveraging ionic information for machine learning-enhanced source identification in integrated wastewater treatment plant

Journal of Water Process Engineering, Год журнала: 2025, Номер 74, С. 107784 - 107784

Опубликована: Апрель 23, 2025

Язык: Английский

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Physical aeration mechanism of SDBS wastewater in a tank aerated by a microporous tube system

Chemical Engineering Journal, Год журнала: 2025, Номер 516, С. 164048 - 164048

Опубликована: Май 26, 2025

Язык: Английский

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Enhancing membrane fouling control in wastewater treatment processes through artificial intelligence modeling: research progress and future perspectives

Euro-Mediterranean Journal for Environmental Integration, Год журнала: 2024, Номер unknown

Опубликована: Окт. 3, 2024

Abstract Membrane filtration processes have demonstrated remarkable effectiveness in wastewater treatment, achieving high contaminant removal and producing high-quality effluent suitable for safe reuse. technologies play a primary role combating water scarcity pollution challenges. However, the need more effective strategies to mitigate membrane fouling remains critical concern. Artificial intelligence (AI) modeling offers promising solution by enabling accurate predictions of fouling, thus supporting advanced mitigation strategies. This review examines recent progress application AI models, with particular focus on artificial neural networks (ANNs), simulating treatment processes. It highlights substantial potential ANNs, particularly widely studied multi-layer perceptron (MLP) other emerging configurations, accurately predict thereby enhancing process optimization efforts. The discusses both benefits current limitations AI-based strategies, analyzing studies offer valuable insights designing ANNs capable providing predictions. Specifically, it provides guidance selecting appropriate model architectures, input/output variables, activation functions, training algorithms. Finally, this connect research findings practical applications full-scale plants. Key steps crucial address challenge been identified, emphasizing revolutionize control drive paradigm shift toward efficient sustainable membrane-based treatment.

Язык: Английский

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