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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/26786
Title: Use of artificial intelligence for optimizing biosorption of textile wastewater using agricultural waste
Authors: K A.
Kumar A.
Agarwal S.
Garg M.C.
Joshi, Himanshu
Published in: Environmental Technology (United Kingdom)
Abstract: Most of the dyes are toxic and non-biodegradable in textile industry wastewaters. Therefore, removal of textile dye using agriculture waste becomes crucial for the environment. This can be accomplished by the biosorption process which is the passive uptake of pollutants by agricultural waste. In this study, Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used to obtain optimum conditions for Methylene Blue (MB) removal using sugarcane bagasse and peanut hulls as low-cost agricultural waste. The experimental design was carried out to study the effect of temperature, pH, biosorbent amount and dye concentration. The maximum MB dye removal considering the effect of total dissolved solids from aqueous solutions of 74.49% and 67.99% by sugarcane bagasse and peanut hulls, respectively. The models specify that they could predict biosorption with high accuracy having R 2-value above 0.9. Statistical studies for RSM, ANFIS and ANN models were compared. Further, the models were optimized for maximum dye removal was at 1.21 g of biosorbent, pH 5.24, 31.24 mg/L MB concentration, 22.29°C of dye solution using sugarcane bagasse and at 1.37 g of biosorbent, pH 5.77, 36.7 mg/L MB concentration, 26.8°C of dye solution using peanut hulls. Additionally, Fourier Transform Infra-Red (FTIR) spectral analysis was also carried out to confirm the biosorption. © 2021 Informa UK Limited, trading as Taylor & Francis Group.
Citation: Environmental Technology (United Kingdom)
URI: https://doi.org/10.1080/09593330.2021.1961874
http://repository.iitr.ac.in/handle/123456789/26786
Issue Date: 2021
Publisher: Taylor and Francis Ltd.
Keywords: Artificial neural network
biosorption
peanut hulls
response surface methodology
sugarcane bagasse
ISSN: 9593330
Author Scopus IDs: 57219607160
57221973593
55446546200
56222136700
7103239839
Author Affiliations: K, A., Amity Institute of Environmental Sciences, Amity University, Noida, India
Kumar, A., Department of Hydrology, Indian Institute of Technology Roorkee, Roorkee, India
Agarwal, S., Department of Electronics and Communication Engineering, MNNIT Allahabad, Prayagraj, India
Garg, M.C., Amity Institute of Environmental Sciences, Amity University, Noida, India
Joshi, H., Department of Hydrology, Indian Institute of Technology Roorkee, Roorkee, India
Corresponding Author: Garg, M.C.; Amity Institute of Environmental Sciences, Sector 125, India; email: manoj28280@gmail.com
Appears in Collections:Journal Publications [HY]

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