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

Abstract 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 analys...

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Autores principales:	Mohammad Hadi Dehghani, Mehdi Salari, Rama Rao Karri, Farshad Hamidi, Roghayeh Bahadori
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f39d49a224c94b179fe604b01f1fb614
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spelling	oai:doaj.org-article:f39d49a224c94b179fe604b01f1fb6142021-12-02T17:51:13ZProcess modeling of municipal solid waste compost ash for reactive red 198 dye adsorption from wastewater using data driven approaches10.1038/s41598-021-90914-z2045-2322https://doaj.org/article/f39d49a224c94b179fe604b01f1fb6142021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90914-zhttps://doaj.org/toc/2045-2322Abstract 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 R2 = 0.9436), and ANN suggested a three-layer model with test-R2 = 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.Mohammad Hadi DehghaniMehdi SalariRama Rao KarriFarshad HamidiRoghayeh BahadoriNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-20 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mohammad Hadi Dehghani Mehdi Salari Rama Rao Karri Farshad Hamidi Roghayeh Bahadori Process modeling of municipal solid waste compost ash for reactive red 198 dye adsorption from wastewater using data driven approaches
description	Abstract 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 R2 = 0.9436), and ANN suggested a three-layer model with test-R2 = 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.
format	article
author	Mohammad Hadi Dehghani Mehdi Salari Rama Rao Karri Farshad Hamidi Roghayeh Bahadori
author_facet	Mohammad Hadi Dehghani Mehdi Salari Rama Rao Karri Farshad Hamidi Roghayeh Bahadori
author_sort	Mohammad Hadi Dehghani
title	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_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_sort	process modeling of municipal solid waste compost ash for reactive red 198 dye adsorption from wastewater using data driven approaches
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f39d49a224c94b179fe604b01f1fb614
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Process modeling of municipal solid waste compost ash for reactive red 198 dye adsorption from wastewater using data driven approaches

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