SCR-Filter Model Order Reduction (2): Proper Orthogonal Decomposition and Artificial Neural Network
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SCR-Filter Model Order Reduction (2): Proper Orthogonal Decomposition and Artificial Neural Network Seun Olowojebutu 1
&
Thomas Steffen 1 & Phillip Bush 2
Received: 14 May 2019 / Revised: 18 July 2020 / Accepted: 1 August 2020 # The Author(s) 2020
Abstract 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. Keywords NH3 SCR-in-DPF . Reducedorder modelling . Proper orthogonal decomposition . Machine learning . Neural networks function approximation . Diesel emission after-treatment
1 Introduction Diesel engines offer superior performance in fuel economy compared with gasoline engines [1], but the simultaneous control of soot/particulates (PM) and nitrogen oxides (NOx) is challenging. Integration of the selective catalytic reduction (SCR) catalyst within a diesel particulate filter (DPF) monolith is an emerging technology for simultaneous control of soot and NOx emissions. Modelling of the integrated SCR-filter1 unit is complicated. The interaction of physical and chemical considerations 1
SCR-coated filter also referred to as SCRF, SDPF, SCR-in-DPF
This is the second of two papers on the model order reduction of integrated SCR filter systems. * Seun Olowojebutu [email protected] 1
Loughborough University, Loughborough, UK
2
Eminox Ltd, Gainsborough, UK
within different phases of the monolith material over different timescales make the effort of developing adequate representation of the SCR-filter system complicated. The application of the system model in real-time online model-based controls further motivates the need to develop simple but adequate representative models. A model of the SCR-filter system has been
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