Artificial intelligence, regression model, and cost estimation for removal of chlorothalonil pesticide by activated carbon prepared from casuarina charcoal

Chlorothalonil is a pesticide that can contaminate water bodies, detriment aquatic organisms, and cause cancers of the forestomach and kidney. In this study, a powdered activated carbon prepared from casuarina wood was used for the adsorption of chlorothalonil from aqueous solutions. Based on Scanni...

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Main Authors: Mohamed Gar Alalm, Mahmoud Nasr
Format: Article
Language:English
Published: BMC 2018-05-01
Series:Sustainable Environment Research
Online Access:http://www.sciencedirect.com/science/article/pii/S2468203917303047
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spelling doaj-a152beeef89e4197bf8c79495a7c0d6c2020-11-25T01:14:20ZengBMCSustainable Environment Research2468-20392018-05-01283101110Artificial intelligence, regression model, and cost estimation for removal of chlorothalonil pesticide by activated carbon prepared from casuarina charcoalMohamed Gar Alalm0Mahmoud Nasr1Department of Public Works Engineering, Mansoura University, Mansoura 35516, Egypt; Corresponding author.Sanitary Engineering Department, Alexandria University, Alexandria 21544, EgyptChlorothalonil is a pesticide that can contaminate water bodies, detriment aquatic organisms, and cause cancers of the forestomach and kidney. In this study, a powdered activated carbon prepared from casuarina wood was used for the adsorption of chlorothalonil from aqueous solutions. Based on Scanning Electron microscopy and Fourier Transform Infrared Spectroscopy analyses, the adsorbent material comprised pores and multiple functional groups that favored the entrapment of chlorothalonil onto its surface. At initial chlorothalonil concentration of 480 mg L−1, the equilibrium uptake capacity was 187 mg g−1 at pH: 7, adsorbent dosage: 0.5 g L−1, contact time: 40 min, and room temperature (25 ± 4 °C). The kinetic and isotherm studies indicated that the rate constant of pseudo-second-order model (k2) was 0.003 g mg−1 min−1, and the monolayer adsorption capacity was 192 mg g−1. Results from a quadratic model demonstrated that the plot of adsorption capacity versus pH, chlorothalonil concentration, adsorbent dosage, and contact time caused quadratic-concave, linear-up, flat, and quadratic-linear concave up curves, respectively. An artificial neural network with a structure of 4–5–1 was able to predict the adsorption capacity (R2: 0.982), and the sensitivity analysis using connection weights showed that pH was the most influential factor. An economic estimation using amortization and operating costs revealed that an adsorption unit subjected to 100 m3 d−1 containing chlorothalonil concentration of 250 ± 50 mg L−1 could cost 1.18 $ m−3. Keywords: Activated carbon, Artificial neural network, Chlorothalonil pesticide, Cost estimation, Kinetics and isothermshttp://www.sciencedirect.com/science/article/pii/S2468203917303047
collection DOAJ
language English
format Article
sources DOAJ
author Mohamed Gar Alalm
Mahmoud Nasr
spellingShingle Mohamed Gar Alalm
Mahmoud Nasr
Artificial intelligence, regression model, and cost estimation for removal of chlorothalonil pesticide by activated carbon prepared from casuarina charcoal
Sustainable Environment Research
author_facet Mohamed Gar Alalm
Mahmoud Nasr
author_sort Mohamed Gar Alalm
title Artificial intelligence, regression model, and cost estimation for removal of chlorothalonil pesticide by activated carbon prepared from casuarina charcoal
title_short Artificial intelligence, regression model, and cost estimation for removal of chlorothalonil pesticide by activated carbon prepared from casuarina charcoal
title_full Artificial intelligence, regression model, and cost estimation for removal of chlorothalonil pesticide by activated carbon prepared from casuarina charcoal
title_fullStr Artificial intelligence, regression model, and cost estimation for removal of chlorothalonil pesticide by activated carbon prepared from casuarina charcoal
title_full_unstemmed Artificial intelligence, regression model, and cost estimation for removal of chlorothalonil pesticide by activated carbon prepared from casuarina charcoal
title_sort artificial intelligence, regression model, and cost estimation for removal of chlorothalonil pesticide by activated carbon prepared from casuarina charcoal
publisher BMC
series Sustainable Environment Research
issn 2468-2039
publishDate 2018-05-01
description Chlorothalonil is a pesticide that can contaminate water bodies, detriment aquatic organisms, and cause cancers of the forestomach and kidney. In this study, a powdered activated carbon prepared from casuarina wood was used for the adsorption of chlorothalonil from aqueous solutions. Based on Scanning Electron microscopy and Fourier Transform Infrared Spectroscopy analyses, the adsorbent material comprised pores and multiple functional groups that favored the entrapment of chlorothalonil onto its surface. At initial chlorothalonil concentration of 480 mg L−1, the equilibrium uptake capacity was 187 mg g−1 at pH: 7, adsorbent dosage: 0.5 g L−1, contact time: 40 min, and room temperature (25 ± 4 °C). The kinetic and isotherm studies indicated that the rate constant of pseudo-second-order model (k2) was 0.003 g mg−1 min−1, and the monolayer adsorption capacity was 192 mg g−1. Results from a quadratic model demonstrated that the plot of adsorption capacity versus pH, chlorothalonil concentration, adsorbent dosage, and contact time caused quadratic-concave, linear-up, flat, and quadratic-linear concave up curves, respectively. An artificial neural network with a structure of 4–5–1 was able to predict the adsorption capacity (R2: 0.982), and the sensitivity analysis using connection weights showed that pH was the most influential factor. An economic estimation using amortization and operating costs revealed that an adsorption unit subjected to 100 m3 d−1 containing chlorothalonil concentration of 250 ± 50 mg L−1 could cost 1.18 $ m−3. Keywords: Activated carbon, Artificial neural network, Chlorothalonil pesticide, Cost estimation, Kinetics and isotherms
url http://www.sciencedirect.com/science/article/pii/S2468203917303047
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