Abstract
The aim of this study was to develop an empirical model that provides accurate predictions of the biochemical oxygen demand of the output stream from the aerated lagoon at International Paper of Brazil, one of the major pulp and paper plants in Brazil. Predictive models were calculated from functional link neural networks (FLNNs), multiple linear regression, principal components regression, and partial least-squares regression (PLSR). Improvement in FLNN modeling capability was observed when the data were preprocessed using the PLSR technique. PLSR also proved to be a powerful linear regression technique for this problem, which presents operational data limitations.
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Oliveira-Esquerre, K.P., da Costa, A.C., Bruns, R.E., Mori, M. (2003). Simulation of Aerated Lagoon Using Artificial Neural Networks and Multivariate Regression Techniques. In: Davison, B.H., Lee, J.W., Finkelstein, M., McMillan, J.D. (eds) Biotechnology for Fuels and Chemicals. Applied Biochemistry and Biotechnology. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-4612-0057-4_36
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DOI: https://doi.org/10.1007/978-1-4612-0057-4_36
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