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Olowojebutu2020_Article_SCR-FilterModelOrderReduction2.pdf (5.87 MB)

SCR-filter model order reduction (2): proper orthogonal decomposition and artificial neural network

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journal contribution
posted on 2020-09-28, 12:59 authored by Seun Olowojebutu, Thomas SteffenThomas Steffen, P Bush
© 2020, The Author(s). Catalysed diesel particulate filters (DPF) have been described as multifunctional reactor systems. Integration of selective catalytic reduction (SCR) functionality in the DPF creates an SCR-in-DPF system that achieves nitrous oxides (NOx) treatment along with particulate matter (PM) collection. The physical and chemical aspects of the integrated SCR-filter complicate system modelling. The goal of this work is to develop low-complexity model of the SCR-filter system which retains high fidelity. A high-fidelity model of the SCR-coated filter has been developed and validated. The performance of the model was described in a previous paper. Model complexity reduction is attempted in this paper. The objective is to achieve simulation times that can support the deployment of the model for online system control in an engine control unit. Two approaches were taken for the SCR-coated filter model order reduction (MOR): a “grey-box” approach via proper orthogonal decomposition (POD) and a “black box” approach via artificial neural network (ANN) function approximation. The POD method is shown to deliver a significant MOR while maintaining a high degree of fidelity but with less than 5% improvement in simulation time. The ANN method delivers a substantial MOR with reduction of three orders of magnitude in simulation time. The accuracy of the ANN model is satisfactory with good generalisation to new test data but noticeably inferior to the POD method.

Funding

Engineering and Physical Sciences Research Council (EPSRC)

Eminox Ltd

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

Emission Control Science and Technology

Volume

6

Issue

4

Pages

410 - 430

Publisher

Springer

Version

  • VoR (Version of Record)

Rights holder

© The authors

Publisher statement

This is an Open Access Article. It is published by Springer under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

Acceptance date

2020-08-01

Publication date

2020-08-27

Copyright date

2020

ISSN

2199-3629

eISSN

2199-3637

Language

  • en

Depositor

Dr Thomas Steffen Deposit date: 22 September 2020

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