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Introducing machine learning model to response surface methodology for biosorption of methylene blue dye using Triticum aestivum biomass

تقديم نموذج التعلم الآلي لمنهجية سطح الاستجابة للامتصاص الحيوي لصبغة الميثيلين الزرقاء باستخدام الكتلة الحيوية Triticum aestivum
Authors: Sudesh Kumari; Anoop Verma; Pinki Sharma; Smriti Agarwal; Vishnu D. Rajput; Tatiana Minkina; Priyadarshani Rajput; +2 Authors

Introducing machine learning model to response surface methodology for biosorption of methylene blue dye using Triticum aestivum biomass

Abstract

AbstractA major environmental problem on a global scale is the contamination of water by dyes, particularly from industrial effluents. Consequently, wastewater treatment from various industrial wastes is crucial to restoring environmental quality. Dye is an important class of organic pollutants that are considered harmful to both people and aquatic habitats. The textile industry has become more interested in agricultural-based adsorbents, particularly in adsorption. The biosorption of Methylene blue (MB) dye from aqueous solutions by the wheat straw (T. aestivum) biomass was evaluated in this study. The biosorption process parameters were optimized using the response surface methodology (RSM) approach with a face-centred central composite design (FCCCD). Using a 10 mg/L concentration MB dye, 1.5 mg of biomass, an initial pH of 6, and a contact time of 60 min at 25 °C, the maximum MB dye removal percentages (96%) were obtained. Artificial neural network (ANN) modelling techniques are also employed to stimulate and validate the process, and their efficacy and ability to predict the reaction (removal efficiency) were assessed. The existence of functional groups, which are important binding sites involved in the process of MB biosorption, was demonstrated using Fourier Transform Infrared Spectroscopy (FTIR) spectra. Moreover, a scan electron microscope (SEM) revealed that fresh, shiny particles had been absorbed on the surface of the T. aestivum following the biosorption procedure. The bio-removal of MB from wastewater effluents has been demonstrated to be possible using T. aestivum biomass as a biosorbent. It is also a promising biosorbent that is economical, environmentally friendly, biodegradable, and cost-effective.

Keywords

Biomass (ecology), Pulp and paper industry, Science, Environmental engineering, Organic chemistry, Wastewater, Article, Central composite design, Environmental science, Catalysis, Chemical engineering, Engineering, Response surface methodology, Humans, Biomass, Photocatalysis, Coloring Agents, Biology, Triticum, FOS: Chemical engineering, Water Science and Technology, Chromatography, Methylene blue, Q, R, Adsorption of Water Contaminants, Industrial wastewater treatment, FOS: Environmental engineering, Fourier transform infrared spectroscopy, Hydrogen-Ion Concentration, Materials science, Agronomy, Methylene Blue, Nuclear chemistry, Kinetics, Chemistry, Effluent, Environmental Science, Physical Sciences, Biosorption, Medicine, Thermodynamics, Sorption, Adsorption, Water Pollutants, Chemical

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    27
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
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    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
27
Top 10%
Average
Top 10%
Green
hybrid