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Process modeling of municipal solid waste compost ash for reactive red 198 dye adsorption from wastewater using data driven approaches

In the present study, reactive red 198 (RR198) dye removal from aqueous solutions by adsorption using municipal solid waste (MSW) compost ash was investigated in batch mode. SEM, XRF, XRD, and BET/BJH analyses were used to characterize MSW compost ash. CNHS and organic matter content analyses showed...

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Autores principales: Dehghani, Mohammad Hadi, Salari, Mehdi, Karri, Rama Rao, Hamidi, Farshad, Bahadori, Roghayeh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8172897/
https://www.ncbi.nlm.nih.gov/pubmed/34078966
http://dx.doi.org/10.1038/s41598-021-90914-z
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author Dehghani, Mohammad Hadi
Salari, Mehdi
Karri, Rama Rao
Hamidi, Farshad
Bahadori, Roghayeh
author_facet Dehghani, Mohammad Hadi
Salari, Mehdi
Karri, Rama Rao
Hamidi, Farshad
Bahadori, Roghayeh
author_sort Dehghani, Mohammad Hadi
collection PubMed
description In the present study, reactive red 198 (RR198) dye removal from aqueous solutions by adsorption using municipal solid waste (MSW) compost ash was investigated in batch mode. SEM, XRF, XRD, and BET/BJH analyses were used to characterize MSW compost ash. CNHS and organic matter content analyses showed a low percentage of carbon and organic matter to be incorporated in MSW compost ash. The design of adsorption experiments was performed by Box–Behnken design (BBD), and process variables were modeled and optimized using Box–Behnken design-response surface methodology (BBD-RSM) and genetic algorithm-artificial neural network (GA-ANN). BBD-RSM approach disclosed that a quadratic polynomial model fitted well to the experimental data (F-value = 94.596 and R(2) = 0.9436), and ANN suggested a three-layer model with test-R(2) = 0.9832, the structure of 4-8-1, and learning algorithm type of Levenberg–Marquardt backpropagation. The same optimization results were suggested by BBD-RSM and GA-ANN approaches so that the optimum conditions for RR198 absorption was observed at pH = 3, operating time = 80 min, RR198 = 20 mg L(−1) and MSW compost ash dosage = 2 g L(−1). The adsorption behavior was appropriately described by Freundlich isotherm, pseudo-second-order kinetic model. Further, the data were found to be better described with the nonlinear when compared to the linear form of these equations. Also, the thermodynamic study revealed the spontaneous and exothermic nature of the adsorption process. In relation to the reuse, a 12.1% reduction in the adsorption efficiency was seen after five successive cycles. The present study showed that MSW compost ash as an economical, reusable, and efficient adsorbent would be desirable for application in the adsorption process to dye wastewater treatment, and both BBD-RSM and GA-ANN approaches are highly potential methods in adsorption modeling and optimization study of the adsorption process. The present work also provides preliminary information, which is helpful for developing the adsorption process on an industrial scale.
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spelling pubmed-81728972021-06-04 Process modeling of municipal solid waste compost ash for reactive red 198 dye adsorption from wastewater using data driven approaches Dehghani, Mohammad Hadi Salari, Mehdi Karri, Rama Rao Hamidi, Farshad Bahadori, Roghayeh Sci Rep Article In the present study, reactive red 198 (RR198) dye removal from aqueous solutions by adsorption using municipal solid waste (MSW) compost ash was investigated in batch mode. SEM, XRF, XRD, and BET/BJH analyses were used to characterize MSW compost ash. CNHS and organic matter content analyses showed a low percentage of carbon and organic matter to be incorporated in MSW compost ash. The design of adsorption experiments was performed by Box–Behnken design (BBD), and process variables were modeled and optimized using Box–Behnken design-response surface methodology (BBD-RSM) and genetic algorithm-artificial neural network (GA-ANN). BBD-RSM approach disclosed that a quadratic polynomial model fitted well to the experimental data (F-value = 94.596 and R(2) = 0.9436), and ANN suggested a three-layer model with test-R(2) = 0.9832, the structure of 4-8-1, and learning algorithm type of Levenberg–Marquardt backpropagation. The same optimization results were suggested by BBD-RSM and GA-ANN approaches so that the optimum conditions for RR198 absorption was observed at pH = 3, operating time = 80 min, RR198 = 20 mg L(−1) and MSW compost ash dosage = 2 g L(−1). The adsorption behavior was appropriately described by Freundlich isotherm, pseudo-second-order kinetic model. Further, the data were found to be better described with the nonlinear when compared to the linear form of these equations. Also, the thermodynamic study revealed the spontaneous and exothermic nature of the adsorption process. In relation to the reuse, a 12.1% reduction in the adsorption efficiency was seen after five successive cycles. The present study showed that MSW compost ash as an economical, reusable, and efficient adsorbent would be desirable for application in the adsorption process to dye wastewater treatment, and both BBD-RSM and GA-ANN approaches are highly potential methods in adsorption modeling and optimization study of the adsorption process. The present work also provides preliminary information, which is helpful for developing the adsorption process on an industrial scale. Nature Publishing Group UK 2021-06-02 /pmc/articles/PMC8172897/ /pubmed/34078966 http://dx.doi.org/10.1038/s41598-021-90914-z Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Dehghani, Mohammad Hadi
Salari, Mehdi
Karri, Rama Rao
Hamidi, Farshad
Bahadori, Roghayeh
Process modeling of municipal solid waste compost ash for reactive red 198 dye adsorption from wastewater using data driven approaches
title Process modeling of municipal solid waste compost ash for reactive red 198 dye adsorption from wastewater using data driven approaches
title_full Process modeling of municipal solid waste compost ash for reactive red 198 dye adsorption from wastewater using data driven approaches
title_fullStr Process modeling of municipal solid waste compost ash for reactive red 198 dye adsorption from wastewater using data driven approaches
title_full_unstemmed Process modeling of municipal solid waste compost ash for reactive red 198 dye adsorption from wastewater using data driven approaches
title_short Process modeling of municipal solid waste compost ash for reactive red 198 dye adsorption from wastewater using data driven approaches
title_sort process modeling of municipal solid waste compost ash for reactive red 198 dye adsorption from wastewater using data driven approaches
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8172897/
https://www.ncbi.nlm.nih.gov/pubmed/34078966
http://dx.doi.org/10.1038/s41598-021-90914-z
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