Prediction of removal efficiency of Lanaset Red G on walnut husk using artificial neural network model.
An artificial neural network (ANN) model was used to predict removal efficiency of Lanaset Red (LR) G on walnut husk (WH). This adsorbent was characterized by FTIR-ATR. Effects of particle size, adsorbent dose, initial pH value, dye concentration, and contact time were investigated to optimize sorption process. Operating variables were used as the inputs to the constructed neural network to predict the dye uptake at any time as an output. Commonly used pseudo second-order model was fitted to the experimental data to compare with ANN model. According to error analyses and determination of coefficients, ANN was the more appropriate model to describe this sorption process. Results of ANN indicated that pH was the most efficient parameter (43%), followed by initial dye concentration (40%) for sorption of LR G on WH.
Department of Biology, Faculty of Art and Science, University of Gaziantep, 27310 Gaziantep, Turkey. celekli.a@gmail.com
Abuzer
A
Çelekli
Sevil Sungur
SS
Birecikligil
Faruk
F
Geyik
Hüseyin
H
Bozkurt
D000327:Adsorption; D001391:Azo Compounds / Q000737:chemistry / Q000302:isolation & purification*; D004396:Coloring Agents / Q000737:chemistry / Q000302:isolation & purification*; D056831:Coordination Complexes / Q000737:chemistry / Q000302:isolation & purification*; D006863:Hydrogen-Ion Concentration; D031324:Juglans / Q000737:chemistry*; D007700:Kinetics; D008956:Models, Chemical; D016571:Neural Networks, Computer; D010316:Particle Size; D012044:Regression Analysis; D017550:Spectroscopy, Fourier Transform Infrared; D013997:Time Factors; D014866:Waste Products / Q000032:analysis*
D001391:Azo Compounds; D004396:Coloring Agents; D056831:Coordination Complexes; D014866:Waste Products