Process optimization and enhancement of pesticide adsorption by porous adsorbents by regression analysis and parametric modelling

Abstract In the present study, the adsorptive removal of organophosphate diazinon pesticide using porous pumice adsorbent was experimentally investigated in a batch system, modelled and optimized upon response surface methodology (RSM) and artificial neural network-genetic algorithm (ANN-GA), fitted...

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Autores principales:	Mohammad Hadi Dehghani, Amir Hessam Hassani, Rama Rao Karri, Bahareh Younesi, Mansoureh Shayeghi, Mehdi Salari, Ahmad Zarei, Mahmood Yousefi, Zoha Heidarinejad
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c9d7d6913d274c48953b6aa37b86e02b
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spelling	oai:doaj.org-article:c9d7d6913d274c48953b6aa37b86e02b2021-12-02T18:24:53ZProcess optimization and enhancement of pesticide adsorption by porous adsorbents by regression analysis and parametric modelling10.1038/s41598-021-91178-32045-2322https://doaj.org/article/c9d7d6913d274c48953b6aa37b86e02b2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91178-3https://doaj.org/toc/2045-2322Abstract In the present study, the adsorptive removal of organophosphate diazinon pesticide using porous pumice adsorbent was experimentally investigated in a batch system, modelled and optimized upon response surface methodology (RSM) and artificial neural network-genetic algorithm (ANN-GA), fitted to isotherm, kinetic and thermodynamic models. The quantification of adsorbent elements was determined using EDX. XRD analysis was utilized to study the crystalline properties of adsorbent. The FT-IR spectra were taken from adsorbent before and after adsorption to study the presence and changes in functional groups. The constituted composition of the adsorbent was determined by XRF. Also, the ionic strength and adsorbent reusability were explored. The influences of operational parameters like pH, initial pesticide concentration, adsorbent dosage and contact time were investigated systematically. ANN-GA and RSM techniques were used to identify the optimal process variables that result in the highest removal. Based on the RSM approach, the optimization conditions for maximum removal efficiency is obtained at pH = 3, adsorbent dosage = 4 g/L, contact time = 30 min, and initial pesticide concentration = 6.2 mg/L. To accurately identify the parameters of nonlinear isotherm and kinetic models, a hybrid evolutionary differential evolution optimization (DEO) is applied. Results indicated that the equilibrium adsorption data were best fitted with Langmuir and Temkin isotherms and kinetic data were well described by pseudo-first and second-order kinetic models. The thermodynamic parameters such as entropy, enthalpy and Gibbs energy were evaluated to study the effect of temperature on pesticide adsorption.Mohammad Hadi DehghaniAmir Hessam HassaniRama Rao KarriBahareh YounesiMansoureh ShayeghiMehdi SalariAhmad ZareiMahmood YousefiZoha HeidarinejadNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mohammad Hadi Dehghani Amir Hessam Hassani Rama Rao Karri Bahareh Younesi Mansoureh Shayeghi Mehdi Salari Ahmad Zarei Mahmood Yousefi Zoha Heidarinejad Process optimization and enhancement of pesticide adsorption by porous adsorbents by regression analysis and parametric modelling
description	Abstract In the present study, the adsorptive removal of organophosphate diazinon pesticide using porous pumice adsorbent was experimentally investigated in a batch system, modelled and optimized upon response surface methodology (RSM) and artificial neural network-genetic algorithm (ANN-GA), fitted to isotherm, kinetic and thermodynamic models. The quantification of adsorbent elements was determined using EDX. XRD analysis was utilized to study the crystalline properties of adsorbent. The FT-IR spectra were taken from adsorbent before and after adsorption to study the presence and changes in functional groups. The constituted composition of the adsorbent was determined by XRF. Also, the ionic strength and adsorbent reusability were explored. The influences of operational parameters like pH, initial pesticide concentration, adsorbent dosage and contact time were investigated systematically. ANN-GA and RSM techniques were used to identify the optimal process variables that result in the highest removal. Based on the RSM approach, the optimization conditions for maximum removal efficiency is obtained at pH = 3, adsorbent dosage = 4 g/L, contact time = 30 min, and initial pesticide concentration = 6.2 mg/L. To accurately identify the parameters of nonlinear isotherm and kinetic models, a hybrid evolutionary differential evolution optimization (DEO) is applied. Results indicated that the equilibrium adsorption data were best fitted with Langmuir and Temkin isotherms and kinetic data were well described by pseudo-first and second-order kinetic models. The thermodynamic parameters such as entropy, enthalpy and Gibbs energy were evaluated to study the effect of temperature on pesticide adsorption.
format	article
author	Mohammad Hadi Dehghani Amir Hessam Hassani Rama Rao Karri Bahareh Younesi Mansoureh Shayeghi Mehdi Salari Ahmad Zarei Mahmood Yousefi Zoha Heidarinejad
author_facet	Mohammad Hadi Dehghani Amir Hessam Hassani Rama Rao Karri Bahareh Younesi Mansoureh Shayeghi Mehdi Salari Ahmad Zarei Mahmood Yousefi Zoha Heidarinejad
author_sort	Mohammad Hadi Dehghani
title	Process optimization and enhancement of pesticide adsorption by porous adsorbents by regression analysis and parametric modelling
title_short	Process optimization and enhancement of pesticide adsorption by porous adsorbents by regression analysis and parametric modelling
title_full	Process optimization and enhancement of pesticide adsorption by porous adsorbents by regression analysis and parametric modelling
title_fullStr	Process optimization and enhancement of pesticide adsorption by porous adsorbents by regression analysis and parametric modelling
title_full_unstemmed	Process optimization and enhancement of pesticide adsorption by porous adsorbents by regression analysis and parametric modelling
title_sort	process optimization and enhancement of pesticide adsorption by porous adsorbents by regression analysis and parametric modelling
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c9d7d6913d274c48953b6aa37b86e02b
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Process optimization and enhancement of pesticide adsorption by porous adsorbents by regression analysis and parametric modelling

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