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Nonlinear dynamic partial least squares modeling of a full-scale biological wastewater treatment plant

Paper ID Volume ID Publish Year Pages File Format Full-Text
36058 45119 2006 8 PDF Available
Title
Nonlinear dynamic partial least squares modeling of a full-scale biological wastewater treatment plant
Abstract

Partial least squares (PLS) has been extensively used in process monitoring and modeling to deal with many, noisy, and collinear variables. However, the conventional linear PLS approach may be not effective due to the fundamental inability of linear regression techniques to account for nonlinearity and dynamics in most chemical and biological processes. A hybrid approach, by combining a nonlinear PLS approach with a dynamic modeling method, is potentially very efficient for obtaining more accurate prediction of nonlinear process dynamics. In this study, neural network PLS (NNPLS) were combined with finite impulse response (FIR) and auto-regressive with exogenous (ARX) inputs to model a full-scale biological wastewater treatment plant. It is shown that NNPLS with ARX inputs is capable of modeling the dynamics of the nonlinear wastewater treatment plant and much improved prediction performance is achieved over the conventional linear PLS model.

Keywords
Multivariate statistical process control; Neural network; Partial least squares (PLS); Dynamic system; Nonlinear system; Wastewater treatment plant
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Nonlinear dynamic partial least squares modeling of a full-scale biological wastewater treatment plant
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Publisher
Database: Elsevier - ScienceDirect
Journal: Process Biochemistry - Volume 41, Issue 9, September 2006, Pages 2050–2057
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us