Prediction of lead (Pb) adsorption on attapulgite clay using the feasibility of data intelligence models

Bhagat, Suraj Kumar; Paramasivan, Mariapparaj; Al-Mukhtar, Mustafa; Tiyasha, Tiyasha; Pyrgaki, Konstantina; Tung, Tran Minh; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher

doi:10.1007/s11356-021-12836-7

Cited by 20 publications

(3 citation statements)

References 114 publications

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“…The redundant and irrelevant predictors significantly deteriorate the performances of regression models and causes overfitting problem in the prediction models. Therefore, extracting a smaller subset of predictors with most relevant predictors might be useful since it saves time in data collection and computation [76], [77].…”

Section: Discussionmentioning

confidence: 99%

Proposition of New Ensemble Data-Intelligence Models for Surface Water Quality Prediction

et al. 2021

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An accurate prediction of water quality (WQ) related parameters is considered as pivotal decisive tool in sustainable water resources management. In this study, five different ensemble machine learning (ML) models including Quantile regression forest (QRF), Random Forest (RF), radial support vector machine (SVM), Stochastic Gradient Boosting (GBM) and Gradient Boosting Machines (GBM_H2O) were developed to predict the monthly biochemical oxygen demand (BOD) values of the Euphrates River, Iraq. For this aim, monthly average data of water temperature (T), Turbidity, pH, Electrical Conductivity (EC), Alkalinity (Alk), Calcium (Ca), chemical oxygen demand (COD), Sulfate (SO4), total dissolved solids (TDS), total suspended solids (TSS), and BOD measured for ten years period were used in this study. The performances of these standalone models were compared with integrative models developed by coupling the applied ML models with two different feature extraction algorithms i.e., Genetic Algorithm (GA) and Principal Components Analysis (PCA). The reliability of the applied models was evaluated based on the statistical performance criteria of determination coefficient (R 2 ), root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe model efficiency coefficient (NSE), Willmott index (d), and percent bias (PBIAS). Results showed that among the developed models, QRF model attained the superior performance. The performance of the evaluated models presented in this study proved that the developed integrative PCA-QRF model presented much better performance compared with the standalone ones and with those integrated with GA. The statistical criteria of R 2 , RMSE, MAE, NSE, d, and PBIAS of PCA-QRF were 0.94, 0.12, 0.05, 0.93, 0.98, and 0.3, respectively.

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Section: Discussionmentioning

confidence: 99%

Proposition of New Ensemble Data-Intelligence Models for Surface Water Quality Prediction

et al. 2021

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“…Pyrgaki et al, used raw and heat-treated attapulgite clay for Pb adsorption from aqueous solutions [ 15 ]. Bhagat et al, predicted a theoretical Pb adsorption process on attapulgite clay using the feasibility of data intelligence models [ 16 ]. Xu et al, applied an experimental density functional theory (DFT) calculation to study the adsorption selectivity of amino-modified attapulgite for Pb and Cu from solution [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Making Pb Adsorption-Saturated Attapulgite with Excellent Photocatalysis Properties through a Vulcanization Reaction and Its Application for MB Wastewater Degradation

Zhang¹,

Chen

Cheng³

et al. 2022

IJERPH

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Attapulgite (AT) is a clay mineral with rich reserves in China, and it has good adsorption activity for Pb-containing wastewater. However, as a hazardous waste, the treatment of Pb adsorption-saturated attapulgite was quite difficult. In this work, through a simple vulcanization reaction, the waste Pb adsorption-saturated attapulgite (AT@Pb) was transformed into composite materials (AT@PbS) with good photocatalytic performance. After comprehensive material characterization (including XRD, TEM, XPS, and UV-Vis), the photocatalytic degradation performance and mechanism of AT@PbS for methylene blue (MB) were investigated. The results revealed that AT@PbS was a composite material of attapulgite nanorods (500–600 nm) and nanosquare PbS particles (80–100 nm). Additionally, AT@PbS displayed good visible light absorption, improved photo-electric properties, excellent photodegradation performance for MB, and recycling stability. Moreover, the energy band range of AT@PbS was about −0.043 V to 1.367 V. The photo-generated holes and their derived hydroxyl radicals were the main active species for MB degradation. This work not only provides a new approach to construct the composite photocatalyst, but also demonstrates the possibility of the comprehensive utilization of heavy metal adsorbents for wastewater degradation.

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“…These technologies include chemical precipitation, ion exchange, membrane filtration, carbon adsorption and coprecipitation/adsorption [15][16][17]. However, these techniques have inherent limitations in practice (such as complicated treatment process, high cost and energy requirement) or pose danger of secondary pollution [18,19].…”

Section: Introductionmentioning

confidence: 99%

Evidence of lead ions on palygorskite surface after adsorptive process: kinetic and isotherms studies

et al. 2022

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Our previous work reported that palygorskite has potential for application as metal cations adsorbent due to its chemical and mineralogical properties. In this work, kinetic study and adsorption isotherms were performed in order to evaluate Pb (II) ions adsorption rate, maximum capacity and type of adsorption by using palygorskite as adsorbent. Adsorption tests were performed in batch, using pH of 5, 2 g of palygorskite, 40 mL of synthetic effluent solution and stirring for 1 hour. Kinetic experiments were performed using 34 mg L -1 of a lead synthetic effluent at same mass and pH conditions. Furthermore, after adsorption studies, the sample was characterized by X-ray fluorecence (XRF), Fourier Transform Infrared Spectroscopy (FTIR) and Scanning Electron Microscopy (SEM) coupled with Energy-dispersive X-ray spectroscopy (EDS) analysis, in order to verify and understand the interaction of lead ions in palygorskite. The results demonstrated that palygorskite presented an equilibrium time of 10 min with 99.14% of removal, following pseudosecond order kinetic. The maximum adsorption capacity was 21.65 mg g -1 and Gibbs' adsorption-free energy was -21.39 KJ mol -1 , with Langmuir model being the most suitable for adjustment of the data. Thus, its presence was confirmed by characterization techniques, indicating that the metal is distributed homogeneously on palygorskite surface, proving its efficiency as lead ions adsorbent.

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Prediction of lead (Pb) adsorption on attapulgite clay using the feasibility of data intelligence models

Cited by 20 publications

References 114 publications

Proposition of New Ensemble Data-Intelligence Models for Surface Water Quality Prediction

Proposition of New Ensemble Data-Intelligence Models for Surface Water Quality Prediction

Making Pb Adsorption-Saturated Attapulgite with Excellent Photocatalysis Properties through a Vulcanization Reaction and Its Application for MB Wastewater Degradation

Evidence of lead ions on palygorskite surface after adsorptive process: kinetic and isotherms studies

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