{"title":"Mg-Fe - (CO3)层双氢氧化物吸附去除亚砷的模拟与分析及其在实际地下水中的应用","authors":"M. Yadav, A. Gupta, P. Ghosal, A. Mukherjee","doi":"10.1080/10934529.2019.1646604","DOIUrl":null,"url":null,"abstract":"Abstract The kinetic, isotherm and thermodynamic modeling of the adsorption of arsenite by layered double hydroxide have been performed to analyze the feasibility, efficacy and mechanism of the system. The fast uptake was observed during the initial phase of the process, which reached equilibrium at 240 min following Elovich model. The diffusion kinetic model exhibited that the rate-limiting step of adsorption was controlled by film diffusion as well as intraparticle diffusion. The isotherm modeling revealed the applicability of the Freundlich equation with the Kf values as 8.19–13.99 (mg g−1)(L mg−1)1/n at 283–323 K showing the increasing trend of adsorption capacity, which was further confirmed by the positive value of ΔH0 (9.49 kJ mol−1) demonstrating the endothermic nature of the adsorption process. The spontaneous nature of the adsorption reaction was established by the negative values of ΔG0. Application of the calcined Mg–Fe–LDH adsorbent for the removal of arsenic from real arsenic contaminated groundwater was also successfully performed. The effect of process parameters of the adsorption system was modeled by an artificial neural network (ANN) for adsorption capacity and removal efficiency. The optimized model exhibited high R2, F-value and low values of error functions, establishing the significant applicability of the ANN model.","PeriodicalId":15733,"journal":{"name":"Journal of Environmental Science and Health, Part A","volume":"8 1","pages":"1318 - 1336"},"PeriodicalIF":0.0000,"publicationDate":"2019-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Modeling and analysis of adsorptive removal of arsenite by Mg–Fe–(CO3) layer double hydroxide with its application in real-life groundwater\",\"authors\":\"M. Yadav, A. Gupta, P. Ghosal, A. Mukherjee\",\"doi\":\"10.1080/10934529.2019.1646604\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract The kinetic, isotherm and thermodynamic modeling of the adsorption of arsenite by layered double hydroxide have been performed to analyze the feasibility, efficacy and mechanism of the system. The fast uptake was observed during the initial phase of the process, which reached equilibrium at 240 min following Elovich model. The diffusion kinetic model exhibited that the rate-limiting step of adsorption was controlled by film diffusion as well as intraparticle diffusion. The isotherm modeling revealed the applicability of the Freundlich equation with the Kf values as 8.19–13.99 (mg g−1)(L mg−1)1/n at 283–323 K showing the increasing trend of adsorption capacity, which was further confirmed by the positive value of ΔH0 (9.49 kJ mol−1) demonstrating the endothermic nature of the adsorption process. The spontaneous nature of the adsorption reaction was established by the negative values of ΔG0. Application of the calcined Mg–Fe–LDH adsorbent for the removal of arsenic from real arsenic contaminated groundwater was also successfully performed. The effect of process parameters of the adsorption system was modeled by an artificial neural network (ANN) for adsorption capacity and removal efficiency. The optimized model exhibited high R2, F-value and low values of error functions, establishing the significant applicability of the ANN model.\",\"PeriodicalId\":15733,\"journal\":{\"name\":\"Journal of Environmental Science and Health, Part A\",\"volume\":\"8 1\",\"pages\":\"1318 - 1336\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-08-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Environmental Science and Health, Part A\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/10934529.2019.1646604\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Environmental Science and Health, Part A","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/10934529.2019.1646604","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Modeling and analysis of adsorptive removal of arsenite by Mg–Fe–(CO3) layer double hydroxide with its application in real-life groundwater
Abstract The kinetic, isotherm and thermodynamic modeling of the adsorption of arsenite by layered double hydroxide have been performed to analyze the feasibility, efficacy and mechanism of the system. The fast uptake was observed during the initial phase of the process, which reached equilibrium at 240 min following Elovich model. The diffusion kinetic model exhibited that the rate-limiting step of adsorption was controlled by film diffusion as well as intraparticle diffusion. The isotherm modeling revealed the applicability of the Freundlich equation with the Kf values as 8.19–13.99 (mg g−1)(L mg−1)1/n at 283–323 K showing the increasing trend of adsorption capacity, which was further confirmed by the positive value of ΔH0 (9.49 kJ mol−1) demonstrating the endothermic nature of the adsorption process. The spontaneous nature of the adsorption reaction was established by the negative values of ΔG0. Application of the calcined Mg–Fe–LDH adsorbent for the removal of arsenic from real arsenic contaminated groundwater was also successfully performed. The effect of process parameters of the adsorption system was modeled by an artificial neural network (ANN) for adsorption capacity and removal efficiency. The optimized model exhibited high R2, F-value and low values of error functions, establishing the significant applicability of the ANN model.