NO_x Conversion Performance Prediction of Low-Temperature Ethanol-SCR Using Machine Learning Models

DOI

10.2991/978-94-6239-668-5_47How to use a DOI?

Keywords

NOx reduction; SCR; Optimization; Machine learning

Abstract

Mitigating global warming and reducing harmful air pollution, the effective management of NO_x emissions via Selective Catalytic Reduction (SCR) systems is a major priority. For this reason, This study presents a comprehensive investigation of the NO_x reduction performance of Sb-doped Ce/TiO₂–Cordierite catalysts within an ethanol-selective catalytic reduction (EtOH-SCR) system, modeled through machine learning techniques. The experimental phase involved the synthesis of Ce/TiO₂–Cordierite catalysts doped with different molar ratios of antimony (Sb) using the wash-coating method. Ce and Sb acted as active components, TiO₂ served as the secondary support, and cordierite functioned as the primary carrier material. Performance evaluations under real engine exhaust conditions revealed that Sb incorporation remarkably improved low-temperature catalytic activity. The maximum NO_x conversion efficiency reached 93.24% at 270 ℃, 40,000 h⁻¹ space velocity, and 5 kW engine load for the 1.33SCT catalyst. To further predict and analyze system behavior under varying temperature and load conditions, SVM and Ensemble of Trees models were developed. The proposed framework integrates experimental insights with data-driven modeling to enhance the understanding and optimization of low-temperature SCR systems for cleaner engine applications.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Himmet Özarslan
AU  - İhsan Uluocak
PY  - 2026
DA  - 2026/05/14
TI  - NOx Conversion Performance Prediction of Low-Temperature Ethanol-SCR Using Machine Learning Models
BT  - Proceedings of the International Conference on Current Problems in Engineering and Applied Sciences (ICCPEAS 2025)
PB  - Atlantis Press
SP  - 457
EP  - 467
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-668-5_47
DO  - 10.2991/978-94-6239-668-5_47
ID  - Özarslan2026
ER  -