Simulation of aerated lagoon using artificial neural networks and multivariate regression techniques

被引：0
|
作者：
Karla Patricia Oliveira-Esquerre
Aline C. da Costa
Roy Edward Bruns
Milton Mori
机构：
[1] DPQ/FEQ/UNICAMP,
[2] IQ/UNICAMP,undefined
来源：
Applied Biochemistry and Biotechnology | 2003年 / 106卷 / 1-3期
关键词：
Biochemical oxygen demand; functional link neural networks; partial least squares; principal components regression; multiple linear regression;
D O I：
10.1385/ABAB:106:1-3:437
中图分类号：
学科分类号：
摘要：
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.
引用
收藏
页码：437 / 449
页数：12
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