Biotechnology and Bioprocess Engineering:BBE

The Korean Society for Biotechnology and Bioengineering (한국생물공학회)
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Predictive Modeling of Competitive Biosorption Equilibrium Data

  • Chu K.H. (Department of Chemical and Process Engineering, University of Canterbury) ;
  • Kim E.Y. (Department of Chemical Engineering, University of Seoul)
  • Published : 2006.01.01
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Abstract

This paper compares regression and neural network modeling approaches to predict competitive biosorption equilibrium data. The regression approach is based on the fitting of modified Langmuir-type isotherm models to experimental data. Neural networks, on the other hand, are non-parametric statistical estimators capable of identifying patterns in data and correlations between input and output. Our results show that the neural network approach outperforms traditional regression-based modeling in correlating and predicting the simultaneous uptake of copper and cadmium by a microbial biosorbent. The neural network is capable of accurately predicting unseen data when provided with limited amounts of data for training. Because neural networks are purely data-driven models, they are more suitable for obtaining accurate predictions than for probing the physical nature of the biosorption process.

Keywords

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