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Application of steady-state and dynamic modeling for the prediction of the BOD of an aerated lagoon at a pulp and paper mill [An article from: Chemical Engineering Journal]
Book Details
PublisherElsevier
ISBN / ASINB000RR4NTA
ISBN-13978B000RR4NT0
AvailabilityAvailable for download now
MarketplaceUnited States 🇺🇸
Description
This digital document is a journal article from Chemical Engineering Journal, published by Elsevier in 2004. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
Neural networks can provide effective predictive models for complex processes that are poorly described by first principle models, such as wastewater biological treatment systems. In this paper multilayer perceptron (MLP) and functional-link neural networks (FLN) are developed to predict inlet and outlet biochemical oxygen demand (BOD) of an aerated lagoon operated by International Paper of Brazil. In Part I, predictive models for both inlet and outlet BOD for the aerated lagoon were developed using linear multivariate regression techniques. For the current case study, MLP networks are the best choice for the prediction models. When only a relatively small number of samples is available, substantial improvement in inlet and outlet BOD prediction is shown for both FLN and MLP modeling using a reduced input variable set that was generated using partial least squares (PLS). Thus, this paper provides a novel approach for developing PLS-FLN model structures.
Description:
Neural networks can provide effective predictive models for complex processes that are poorly described by first principle models, such as wastewater biological treatment systems. In this paper multilayer perceptron (MLP) and functional-link neural networks (FLN) are developed to predict inlet and outlet biochemical oxygen demand (BOD) of an aerated lagoon operated by International Paper of Brazil. In Part I, predictive models for both inlet and outlet BOD for the aerated lagoon were developed using linear multivariate regression techniques. For the current case study, MLP networks are the best choice for the prediction models. When only a relatively small number of samples is available, substantial improvement in inlet and outlet BOD prediction is shown for both FLN and MLP modeling using a reduced input variable set that was generated using partial least squares (PLS). Thus, this paper provides a novel approach for developing PLS-FLN model structures.
