Combination of artificial neural network and genetic algorithm method for modeling of methylene blue adsorption onto wood sawdust from water samples. (March 2016)

• Record Type: Journal Article
• Authors: Khajeh, Mostafa; Sarafraz-Yazdi, Ali; Natavan, Zahra Bameri
• Is Part Of: Toxicology and industrial health. Volume 32:Number 3(2016)
• Page Start: 437
• Page End: 446
• Publication Date: 2016-03
• Subjects: Methylene blue -- sawdust -- solid-phase extraction -- artificial neural network -- genetic algorithm; Toxicology, Experimental -- Periodicals; Industrial toxicology -- Periodicals; 615.9
• DOI: 10.1177/0748233713502842
• Languages: English
• ISSNs: 0748-2337

Abstract

The aim of this research was to develop a low price and environmentally friendly adsorbent with abundant of source to remove methylene blue (MB) from water samples. Sawdust solid-phase extraction coupled with high-performance liquid chromatography was used for the extraction and determination of MB. In this study, an experimental data-based artificial neural network model is constructed to describe the performance of sawdust solid-phase extraction method for various operating conditions. The pH, time, amount of sawdust, and temperature were the input variables, while the percentage of extraction of MB was the output. The optimum operating condition was then determined by genetic algorithm method. The optimized conditions were obtained as follows: 11.5, 22.0 min, 0.3 g, and 26.0°C for pH of the solution, extraction time, amount of adsorbent, and temperature, respectively. Under these optimum conditions, the detection limit and relative standard deviation were 0.067 μg L −1 and <2.4%, respectively. The Langmuir and Freundlich adsorption models were applied to describe the isotherm constant and for the removal and determination of MB from water samples.