Optimizing silt density index prediction in water treatment systems using pressure-based gradient boosting hybridized with Salp Swarm Algorithm

Journal of Water Process Engineering, Journal Year: 2024, Volume and Issue: 68, P. 106479 - 106479

Published: Nov. 6, 2024

Language: Английский

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Enhancing BOD5 Forecasting Accuracy with the ANN-Enhanced Runge Kutta Model

Journal of environmental chemical engineering, Journal Year: 2025, Volume and Issue: unknown, P. 115430 - 115430

Published: Jan. 1, 2025

Language: Английский

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Optimized ensemble deep learning approach for accurate breast cancer diagnosis using transfer learning and grey wolf optimization

Evolving Systems, Journal Year: 2025, Volume and Issue: 16(2)

Published: April 29, 2025

Language: Английский

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Evaluating Machine Learning-Based Soft Sensors for Effluent Quality Prediction in Wastewater Treatment Under Variable Weather Conditions

Sensors, Journal Year: 2025, Volume and Issue: 25(6), P. 1692 - 1692

Published: March 8, 2025

Maintaining effluent quality in wastewater treatment plants (WWTPs) comes with significant challenges under variable weather conditions, where sudden changes flow rate and increased pollutant loads can affect performance. Traditional physical sensors became both expensive susceptible to failure extreme conditions. In this study, we evaluate the performance of soft based on artificial intelligence (AI) predict components underlying calculation index (EQI). We thus focus our study three ML models: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) Transformer. Using Benchmark Simulation Model no. 2 (BSM2) as WWTP, were able obtain datasets for training models their dry scenarios, rainy episodes, storm events. To improve classification networks according type weather, developed a Random Forest (RF)-based meta-classifier. The results indicate that conditions Transformer network achieved best performance, while rain episodes scenarios GRU was capture variations highest accuracy. LSTM performed normally stable but struggled rapid fluctuations. These support decision integrate AI-based predictive WWTPs, highlighting top performances recurrent feed-forward (Transformer) obtaining predictions different

Language: Английский

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Water quality prediction and carbon reduction mechanisms in wastewater treatment in Northwest cities using Random Forest Regression model

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Dec. 28, 2024

With the accelerated urbanization and economic development in Northwest China, efficiency of urban wastewater treatment importance water quality management have become increasingly significant. This work aims to explore carbon reduction mechanisms China alleviate resource pressure. By utilizing online monitoring data from pilot systems, it conducts an in-depth analysis impacts different processes on parameters. pays particular attention their impact key indicators such as Chemical Oxygen Demand (COD), NH4+-N, Total Phosphorus (TP), Nitrogen (TN), application predictive models. The first establishes a Random Forest Regression (RFR) model. RFR algorithm integrates Bagging ensemble learning random subspace theory construct multiple decision trees aggregate predictions, thereby enhancing model's prediction accuracy stability. Using bootstrap sampling, model generates training subsets original randomly selects variables regression trees. Its performance predicting various is then evaluated. results show that exhibits excellent performance, achieving high levels stability for all indicators. For example, R2 COD 0.99954, while values TP, TN predictions reach 0.99989. Compared five other models, demonstrates best across indicator predictions. provides critical support optimizing technologies developing policies. These findings also offer essential theoretical empirical insights future improvement decision-making.

Language: Английский

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Biological oxygen demand prediction using artificial neural network and random forest models enhanced by the neural architecture search algorithm

Modeling Earth Systems and Environment, Journal Year: 2024, Volume and Issue: 11(1)

Published: Dec. 19, 2024

Language: Английский

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Enhancing sewage flow prediction using an integrated improved SSA-CNN-Transformer-BiLSTM model

AIMS Mathematics, Journal Year: 2024, Volume and Issue: 9(10), P. 26916 - 26950

Published: Jan. 1, 2024

<p>Accurate prediction of sewage flow is crucial for optimizing treatment processes, cutting down energy consumption, and reducing pollution incidents. Current models, including traditional statistical models machine learning have limited performance when handling nonlinear high-noise data. Although deep excel in time series prediction, they still face challenges such as computational complexity, overfitting, poor practical applications. Accordingly, this study proposed a combined model based on an improved sparrow search algorithm (SSA), convolutional neural network (CNN), transformer, bidirectional long short-term memory (BiLSTM) prediction. Specifically, the CNN part was responsible extracting local features from series, Transformer captured global dependencies using attention mechanism, BiLSTM performed temporal processing features. The SSA optimized model's hyperparameters to improve accuracy generalization capability. validated dataset actual plant. Experimental results showed that introduced mechanism significantly enhanced ability handle data, effectively hyperparameter selection, improving training efficiency. After introducing SSA, CNN, modules, $ {R^{\text{2}}} increased by 0.18744, RMSE (root mean square error) decreased 114.93, MAE (mean absolute 86.67. difference between predicted peak/trough monitored within 3.6% appearance 2.5 minutes away time. By employing multi-model fusion approach, achieved efficient accurate highlighting potential application prospects field treatment.</p>

Language: Английский

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Enhancing sewage flow prediction using an integrated improved SSA-CNN-Transformer-BiLSTM model

AIMS Mathematics, Journal Year: 2024, Volume and Issue: 9(10), P. 26916 - 26950

Published: Jan. 1, 2024

<p>Accurate prediction of sewage flow is crucial for optimizing treatment processes, cutting down energy consumption, and reducing pollution incidents. Current models, including traditional statistical models machine learning have limited performance when handling nonlinear high-noise data. Although deep excel in time series prediction, they still face challenges such as computational complexity, overfitting, poor practical applications. Accordingly, this study proposed a combined model based on an improved sparrow search algorithm (SSA), convolutional neural network (CNN), transformer, bidirectional long short-term memory (BiLSTM) prediction. Specifically, the CNN part was responsible extracting local features from series, Transformer captured global dependencies using attention mechanism, BiLSTM performed temporal processing features. The SSA optimized model's hyperparameters to improve accuracy generalization capability. validated dataset actual plant. Experimental results showed that introduced mechanism significantly enhanced ability handle data, effectively hyperparameter selection, improving training efficiency. After introducing SSA, CNN, modules, $ {R^{\text{2}}} increased by 0.18744, RMSE (root mean square error) decreased 114.93, MAE (mean absolute 86.67. difference between predicted peak/trough monitored within 3.6% appearance 2.5 minutes away time. By employing multi-model fusion approach, achieved efficient accurate highlighting potential application prospects field treatment.</p>

Language: Английский

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Predicting COD and TN in A2O+AO Process Considering Influent and Reactor Variability: A Dynamic Ensemble Model Approach

Water, Journal Year: 2024, Volume and Issue: 16(22), P. 3212 - 3212

Published: Nov. 8, 2024

The prediction of the chemical oxygen demand (COD) and total nitrogen (TN) in integrated anaerobic–anoxic–oxic (A2O) anoxic–oxic (AO) processes (i.e., A2O+AO process) was achieved using a dynamic ensemble model that reflects dynamics wastewater treatment plants (WWTPs). This effectively captures variability influent characteristics fluctuations within each reactor process. By employing time-lag approach based on hydraulic retention time (HRT), artificial intelligence (AI) selects suitable input pH, temperature, dissolved solid (TDS), NH3-N, NO3-N) output (COD TN) data pairs for training, minimizing error between predicted observed values. Data collected over two years from actual process were utilized. adopted machine learning-based XGBoost COD TN predictions. outperformed static model, with mean absolute percentage (MAPE) ranging 9.5% to 15.2%, compared model’s range 11.4% 16.9%. For TN, errors ranged 9.4% 15.5%, while showed lower specific reactors, particularly anoxic oxic stages due their stable characteristics. These results indicate is predicting water quality WWTPs, especially as may increase external environmental factors future.

Language: Английский

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Comparative Analysis of Machine Learning Models and Explainable Artificial Intelligence for Predicting Wastewater Treatment Plant Variables

Advances in Environmental and Engineering Research, Journal Year: 2024, Volume and Issue: 05(04), P. 1 - 23

Published: Oct. 17, 2024

Increasing urban wastewater and rigorous discharge regulations pose significant challenges for treatment plants (WWTP) to meet regulatory compliance while minimizing operational costs. This study explores the application of several machine learning (ML) models specifically, Artificial Neural Networks (ANN), Gradient Boosting Machines (GBM), Random Forests (RF), eXtreme (XGBoost), hybrid RF-GBM in predicting important WWTP variables such as Biochemical Oxygen Demand (BOD), Total Suspended Solids (TSS), Ammonia (NH₃), Phosphorus (P). Several feature selection (FS) methods were employed identify most influential variables. To enhance ML models’ interpretability understand impact on prediction, two widely used explainable artificial intelligence (XAI) methods-Local Interpretable Model-Agnostic Explanations (LIME) SHapley Additive exPlanations (SHAP) investigated study. Results derived from FS XAI compared explore their reliability. The model performance results revealed that ANN, GBM, XGBoost, have great potential variable prediction with low error rates strong correlation coefficients R² value 1 training set 0.98 test set. also common each model’s prediction. is a novel attempt get an overview both LIME SHAP explanations

Language: Английский

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