Ensemble hybrid machine learning to simulate dye/divalent salt fractionation using a loose nanofiltration membrane

Baig, Nadeem; Abba, S. I.; Usman, Jamilu; Benaafi, Mohammed; Aljundi, Isam H.

doi:10.1039/d3va00124e

Cited by 10 publications

(3 citation statements)

References 70 publications

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“…This could be attributed to the TFN‐0.8 membrane's superior hydrophilic and smooth surface, which would encourage molecular water flow on the membrane surface and lessen the concentration polarization occurrence. In addition, there are further mechanisms for salt rejection, such as adsorption and sieving 56,57 . The sieving process, which limits the amount of salt that percolates through the membrane and enhances salt retention, is encouraged by a decrease in the mean pore size of the membrane caused by an increase in CA‐FSM‐16 concentration.…”

Section: Resultsmentioning

confidence: 99%

Improving dye removal ability and fouling resistance of thin‐film nanocomposite nanofiltration membranes using a new citric acid functionalized FSM‐16 mesoporous

Mohammadifar,

Rahimi,

Zinadini

2024

J of Applied Polymer Sci

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The development of superior thin‐film nanocomposite (TFN) membranes with good separation performance and anti‐fouling capacity is required to address the larger‐scale freshwater need. In this study, a new citric acid‐functionalized FSM‐16 (CA‐FSM‐16) mesoporous was successfully synthesized and characterized. Subsequently, it was introduced on the surface of the sodium hydroxide/cyanuric chloride‐activated PES membrane to fabricate the CA‐FSM‐16 TFN membrane. It was investigated how various CA‐FSM‐16 loadings affected the overall physicochemical characteristics and performance of the membrane. The SEM analysis confirmed the TFN structure of the fabricated membranes. All manufactured membranes displayed improved hydrophilicity and flux. For the pristine membrane, the salt (Na2SO4) rejection was measured 73.20 ± 2.10%, whereas for the TFN‐0.08 membrane, it was measured 91.40 ± 1.80%. The dye rejection experiment in cross‐flow filtration showed that the TFN‐0.08 membrane had the maximum licorice retention among the membranes (99.10 ± 2.00%). In addition, the CA‐FSM‐16 TFN membranes demonstrated superior antifouling performance, especially the optimum membrane (TFN‐0.08), which showed a high flux recovery ratio (FRR) of 97.10 ± 1.90% and a low irreversible fouling ratio (DRir) of 2.90%. The introduction of CA‐FSM‐16 on the surface of the PES membrane via the surface modification method was a potentially useful technology to fabricate a high‐performing TFN membrane for water treatment applications.

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Section: Resultsmentioning

confidence: 99%

Improving dye removal ability and fouling resistance of thin‐film nanocomposite nanofiltration membranes using a new citric acid functionalized FSM‐16 mesoporous

Mohammadifar,

Rahimi,

Zinadini

2024

J of Applied Polymer Sci

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“…Reliability analysis has been employed in both scientific and engineering contexts to decipher the relationships and resilience of separate input variables towards desired results. For instance, some researchers, such as [40,41], have gauged stability through both linear and nonlinear feature sensitivity analyses.…”

Section: Performance Validation and Data Processingmentioning

confidence: 99%

Drinking Water Resources Suitability Assessment Based on Pollution Index of Groundwater Using Improved Explainable Artificial Intelligence

Abba,

Yassin,

Mubarak

et al. 2023

Sustainability

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The global significance of fluoride and nitrate contamination in coastal areas cannot be overstated, as these contaminants pose critical environmental and public health challenges across the world. Water quality is an essential component in sustaining environmental health. This integrated study aimed to assess indexical and spatial water quality, potential contamination sources, and health risks associated with groundwater resources in Al-Hassa, Saudi Arabia. Groundwater samples were tested using standard methods. The physiochemical results indicated overall groundwater pollution. This study addresses the critical issue of drinking water resource suitability assessment by introducing an innovative approach based on the pollution index of groundwater (PIG). Focusing on the eastern region of Saudi Arabia, where water resource management is of paramount importance, we employed advanced machine learning (ML) models to forecast groundwater suitability using several combinations (C1 = EC + Na + Mg + Cl, C2 = TDS + TA + HCO3 + K + Ca, and C3 = SO4 + pH + NO3 + F + Turb). Six ML models, including random forest (RF), decision trees (DT), XgBoost, CatBoost, linear regression, and support vector machines (SVM), were utilized to predict groundwater quality. These models, based on several performance criteria (MAPE, MAE, MSE, and DC), offer valuable insights into the complex relationships governing groundwater pollution with an accuracy of more than 90%. To enhance the transparency and interpretability of the ML models, we incorporated the local interpretable model-agnostic explanation method, SHapley Additive exPlanations (SHAP). SHAP allows us to interpret the prediction-making process of otherwise opaque black-box models. We believe that the integration of ML models and SHAP-based explainability offers a promising avenue for sustainable water resource management in Saudi Arabia and can serve as a model for addressing similar challenges worldwide. By bridging the gap between complex data-driven predictions and actionable insights, this study contributes to the advancement of environmental stewardship and water security in the region.

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“…The need for precise wind speed predicting is paramount for different business and management sectors. In order to advance generalization ability, input data has to be pre-processed by splitting the input so that the estimating approach is planned for each subclass and the standard deviation function, mean, variance, slope calculated for the model (Baig et al, 2023;Yassin et al, 2024). This technique of pre-processing will redefine the way of indecision in the data set (Santhosh, 2020).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Wind Speed Estimation for Renewable Energy Optimization in Urban Environments: A Case Study in Kano State, Nigeria

A. M.,

J. M.,

J. K.

et al. 2024

Advanced Journal of Science, Technology and Engineering

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Climate change always had a massive effect on worldwide cities. which can only be decreased through considering renewable energy sources (wind energy, solar energy). However, the need to focus on wind energy prediction will be the best solution to the world electricity petition. Wind power (WP) estimating techniques have been used for diverse literature studies for many decades. The hardest way to improve WP is its nature of differences that make it a tough undertaking to forecast. In line with the outdated ways of predicting wind speed (WS), employing machine learning methods (ML) has become an essential tool for studying such a problem. The methodology used for this study focuses on sanitizing efficient models to precisely predict WP regimens. Two ML models were employed “Gaussian Process Regression (GPR), and Feed Forward Neural Network (FFNN)” for WS estimation. The experimental methods were used to focus the WS prediction. The prophecy models were trained using a 24-hour’ time-series data driven from Kano state Region, one of the biggest cities in Nigeria. Thus, investigating the (ML) forecast performance was done in terms of coefficient of determination (R²), linear correlation coefficient (R), Mean Square Error (MSE), and Root Mean square error (RMSE). Were. The predicted result shows that the FFNN produces superior outcomes compared to GPR. With R²= 1, R = 1, MSE = 6.62E-20, and RMSE = 2.57E-10

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Ensemble hybrid machine learning to simulate dye/divalent salt fractionation using a loose nanofiltration membrane

Abstract: The escalating quantity of wastewater from multiple sources has raised concerns about both water reuse and environmental preservation.

Cited by 10 publications

References 70 publications

Improving dye removal ability and fouling resistance of thin‐film nanocomposite nanofiltration membranes using a new citric acid functionalized FSM‐16 mesoporous

Improving dye removal ability and fouling resistance of thin‐film nanocomposite nanofiltration membranes using a new citric acid functionalized FSM‐16 mesoporous

Drinking Water Resources Suitability Assessment Based on Pollution Index of Groundwater Using Improved Explainable Artificial Intelligence

Machine Learning-Based Wind Speed Estimation for Renewable Energy Optimization in Urban Environments: A Case Study in Kano State, Nigeria

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