Application of response surface methodology and artificial neural network methods in modelling and optimization of biosorption process

A Witek-Krowiak, K Chojnacka, D Podstawczyk… - Bioresource …, 2014 - Elsevier
Bioresource technology, 2014Elsevier
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 …
Abstract
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.
Elsevier
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