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

Abstract

A 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.

Performance Evaluation of Benzyl Alcohol Oxidation with tert-Butyl Hydroperoxide to Benzaldehyde Using the Response Surface Methodology, Artificial Neural Network, and Adaptive Neuro-Fuzzy Inference System Model

“…Hence, this experiment was designed using the central composite experimental design (CCD). In accordance with CCD, the minimum number of experiments required to effectively study the responses of any given input variables is given by the design equation ( N ) of eq , N = 2 k + 2 k + No where k is the number of input factors and No is the center point.…”

Removing Heavy Metals: Cutting-Edge Strategies and Advancements in Biosorption Technology

“…Biosorption is often integrated with other water treatment technologies, such as membrane filtration and precipitation, to achieve a more comprehensive and efficient pollutant removal. For example, biosorption is used to treat wastewater from the textile industry, where it helps to remove various pollutants, including dyes and pigments [ 25 , 26 , 27 , 28 ]. The versatility of biosorbents makes biosorption a valuable tool for addressing the complex nature of pollutants generated in textile processes.…”