Use of artificial neural networks and genetic algorithms for prediction of sorption of an azo-metal complex dye onto lentil straw

• Published in: Bioresource technology Vol. 129; pp. 396 - 401
• Main Authors: Çelekli, Abuzer, Bozkurt, Hüseyin, Geyik, Faruk
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.02.2013; Elsevier
• Subjects: Absorption; Agronomy. Soil science and plant productions; Algorithms; Artificial neural network (ANN); Azo Compounds; Azo Compounds - chemistry; Azo Compounds - isolation & purification; Biodegradation, Environmental; Biological and medical sciences; chemistry; Coloring Agents; Coloring Agents - chemistry; Coloring Agents - isolation & purification; Computer Simulation; dyes; equations; Fundamental and applied biological sciences. Psychology; gene expression; Gene expression programming (GEP); General agronomy. Plant production; isolation & purification; Lanaset Red G; Lens Plant; Lens Plant - chemistry; Lentil straw; lentils; Metals; Metals - chemistry; Metals - isolation & purification; Models, Chemical; Nerve Net; neural networks; particle size; Plant Components, Aerial; Plant Components, Aerial - chemistry; prediction; Sorption; straw; Use of agricultural and forest wastes. Biomass use, bioconversion; Water; Water - chemistry; Water Pollutants, Chemical; Water Pollutants, Chemical - chemistry; Water Pollutants, Chemical - isolation & purification; Sorption; Lanaset Red G; Gene expression programming (GEP); Lentil straw; Artificial neural network (ANN); Dyes; Prediction; Metal complex; Programming; Lens culinaris; Neural network; Gene expression; Straw; Grain legume; Lentil; Leguminosae; Vegetable crop; Genetic algorithm; Dicotyledones; Angiospermae; Spermatophyta; Artificial intelligence
• ISSN: 0960-8524; 1873-2976; 1873-2976
• DOI: 10.1016/j.biortech.2012.11.085

[Display omitted] ► Predictive modeling of sorption of Lanaset Red (LR)G on lentil straw was studied. ► Artificial neural network (ANN) was found to be excellent model in representing the sorption kinetics data. ► The sorption at various operating factors in a single equation was described by gene expression programming (GEP). Artificial neural network (ANN), pseudo second-order kinetic, and gene expression programming (GEP) models were constructed to predict removal efficiency of Lanaset Red G (LR G) using lentil straw (LS) based on 1152 experimental sets. The sorption process was dependent on adsorbent particle size, pH, initial dye concentration, and contact time. These variables were used as input to construct a neural network for prediction of dye uptake as output. ANN was an excellent model because of the lowest error and the highest coefficient values. ANN indicated that initial dye concentration had the strongest effect on dye uptake, followed by pH. The GEP model successfully described the sorption kinetic process as function of adsorbent particle size, pH, initial dye concentration, and contact time in a single equation. Low cost adsorbent, LS, had a great potential to remove LR G as an eco-friendly process, which was well described by GEP and ANN.