Prediction of total phosphorus removal in hybrid constructed wetlands: a machine learning approach for rice mill wastewater treatment DOI
Suresh Kumar, Naveen Chand,

Vikramaditya Sangwan

и другие.

Water Environment Research, Год журнала: 2025, Номер 97(5)

Опубликована: Май 1, 2025

Abstract Efficient prediction of pollutant concentrations in constructed wetlands is critical for optimizing treatment performance, yet existing methodologies often fail to account the influence meteorological conditions and flow rate variations real‐world scenarios. This study addresses this gap by developing predictive models Total Phosphorus (TP) removal hybrid (HCWs)—combining vertical subsurface free water surface flow—treating rice mill wastewater. Four modeling techniques: Multiple Linear Regression (MLR), Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forest (RF), were used predict best‐fit model TP based on specific conditions. The evaluates impact key parameters such as temperature (TEMP), hydraulic loading (HLR), initial chemical oxygen demand (CODin), total nitrogen (TNin), phosphorus (TPin), turbidity (TBin) efficiency. results revealed that SVM_rbf achieved highest accuracy with an R 2 value 0.735763, followed ANN (R : 0.73), RF 0.721298), MLR 0.689199). research highlights potential machine learning enhancing quantification reduction HCWs, offering a robust framework improving wastewater performance under varying environmental operational Practitioner Points Machine (SVM, ANN, RF, MLR) applied hybrid‐constructed treating demonstrated = 0.7358), outperforming prediction. analyzed temperature, rate, CODin, TNin, TPin, TBin integration ML wetland engineering principles enhances HCW design efficiency sustainable treatment. fills providing standardized experimental setup ML‐based prediction, reliability applicability.

Язык: Английский

Data-Driven Insights into Resin Screening for Targeted Per- and Polyfluoroalkyl Substances Removal Using Machine Learning DOI
Jing Zhang, Kaixing Fu, Shifa Zhong

и другие.

Environmental Science & Technology, Год журнала: 2025, Номер unknown

Опубликована: Фев. 11, 2025

In this study, we address the challenge of screening resins and optimizing operation conditions for removal 43 perfluoroalkyl polyfluoroalkyl substances (PFASs), spanning both long- short-chain fluorocarbon variants, across diverse water matrices, using machine learning (ML) models. We first develop ML models that can accurately predict efficiency PFASs based on resin properties, conditions, matrix. The model performance is validated by a test set our own experimental tests. key features from matrix influencing PFAS as well their interaction effects are comprehensively investigated. finally target long-chain (e.g., PFOS, PFOA) PFBS, GenX), developed to inversely screen determine optimal under specified Experimental tests demonstrated ML-guided approach achieves desired (RE) these PFASs, with RE values reaching 86.56% PFBS 83.73% GenX, outperforming many reported resins. This work underscores potential methodologies in operational optimization enabling efficient structurally varied compounds.

Язык: Английский

Процитировано

0
Prediction of total phosphorus removal in hybrid constructed wetlands: a machine learning approach for rice mill wastewater treatment DOI
Suresh Kumar, Naveen Chand,

Vikramaditya Sangwan

и другие.

Water Environment Research, Год журнала: 2025, Номер 97(5)

Опубликована: Май 1, 2025

Abstract Efficient prediction of pollutant concentrations in constructed wetlands is critical for optimizing treatment performance, yet existing methodologies often fail to account the influence meteorological conditions and flow rate variations real‐world scenarios. This study addresses this gap by developing predictive models Total Phosphorus (TP) removal hybrid (HCWs)—combining vertical subsurface free water surface flow—treating rice mill wastewater. Four modeling techniques: Multiple Linear Regression (MLR), Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forest (RF), were used predict best‐fit model TP based on specific conditions. The evaluates impact key parameters such as temperature (TEMP), hydraulic loading (HLR), initial chemical oxygen demand (CODin), total nitrogen (TNin), phosphorus (TPin), turbidity (TBin) efficiency. results revealed that SVM_rbf achieved highest accuracy with an R 2 value 0.735763, followed ANN (R : 0.73), RF 0.721298), MLR 0.689199). research highlights potential machine learning enhancing quantification reduction HCWs, offering a robust framework improving wastewater performance under varying environmental operational Practitioner Points Machine (SVM, ANN, RF, MLR) applied hybrid‐constructed treating demonstrated = 0.7358), outperforming prediction. analyzed temperature, rate, CODin, TNin, TPin, TBin integration ML wetland engineering principles enhances HCW design efficiency sustainable treatment. fills providing standardized experimental setup ML‐based prediction, reliability applicability.

Язык: Английский

Процитировано

0