<p>The NOx conversion efficiency of a combined Selective Catalytic Reduction and</p>
<p>Ammonia Slip Catalyst (SCR-ASC) in a Diesel Aftertreatment (AT) system degrades with</p>
<p>time. A novel model-informed data-driven On-Board Diagnostic (OBD) binary classification</p>
<p>strategy is proposed in this paper to distinguish an End of Useful Life (EUL) SCR-ASC catalyst</p>
<p>from Degreened (DG) ones. An optimized supervised machine learning model was used for the</p>
<p>classification with a calibrated single-cell 3-state Continuous Stirred Tank Reactor (CSTR)</p>
<p>observer used for state estimation. The method resulted in 87.5% classification accuracy when</p>
<p>tested on 8 day-files from 4 trucks (2 day-files per truck; 1 DG and 1 EUL) operating in realworld on-road conditions.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/22695871 |
| Date | 27 April 2023 |
| Creators | Atharva Tandale (15351352) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Atharva_Masters_Thesis_Final_pdf/22695871 |
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