Application of response surface methodology and artificial neural network methods in modelling and optimization of biosorption process.
A review on the application of response surface methodology (RSM) and artificial neural networks (ANN) in biosorption modelling and optimization is presented. The theoretical background of the discussed methods with the application procedure is explained. The paper describes most frequently used experimental designs, concerning their limitations and typical applications. The paper also presents ways to determine the accuracy and the significance of model fitting for both methodologies described herein. Furthermore, recent references on biosorption modelling and optimization with the use of RSM and the ANN approach are shown. Special attention was paid to the selection of factors and responses, as well as to statistical analysis of the modelling results.
Division of Chemical Engineering, Department of Chemistry, Wrocław University of Technology, Norwida 4/6, 50-373 Wrocław, Poland.
Anna
A
Witek-Krowiak
Institute of Inorganic Technology and Mineral Fertilizers, Department of Chemistry, Wrocław University of Technology, Smoluchowskiego 25, 50-372 Wrocław, Poland. Electronic address: katarzyna.chojnacka@pwr.wroc.pl.
Katarzyna
K
Chojnacka
Division of Chemical Engineering, Department of Chemistry, Wrocław University of Technology, Norwida 4/6, 50-373 Wrocław, Poland.
Daria
D
Podstawczyk
Division of Chemical Engineering, Department of Chemistry, Wrocław University of Technology, Norwida 4/6, 50-373 Wrocław, Poland.
Anna
A
Dawiec
Division of Chemical Engineering, Department of Chemistry, Wrocław University of Technology, Norwida 4/6, 50-373 Wrocław, Poland.
Karol
K
Bubała
D000327:Adsorption; D018533:Biomass; D001709:Biotechnology / Q000379:methods*; D008962:Models, Theoretical; D016571:Neural Networks, Computer
Artificial neural networks; Biosorption; Design of experiments; Optimization; Response surface methodology