Data-driven machine learning model of a Selective Catalytic Reduction on Filter (SCRF) in a heavy-duty diesel engine: A comparison of Artificial Neural Network with Tree-based algorithms

Abstract

The Selective Catalytic Reduction on Filter (SCRF) system is yet to be deployed in current heavy-duty diesel engine aftertreatment system. Due to the thermal, space and cost benefits of the SCRF, it could become a useful component of the after-treatment system of a heavy-duty diesel engine, as regulators continue to demand an even cleaner environment. Ammonia cross-sensitivity of NOx sensors at the post-SCRF location poses challenges in measuring NOx emission accurately at this location and in turn affects the NOx conversion efficiency calculations. Developing a model that could replace the NOx sensor helps to mitigate the ammonia cross-sensitivity challenge as well as provides a medium to measure post-SCRF NOx concentration and NOx conversion efficiency while saving the cost on NOx sensors. This work focuses on a data-driven approach to developing a model for predicting NOx conversion efficiency across the SCRF using Artificial Neural Network, Bootstrap Forest, and Boosted Tree methods. Further, the three modeling techniques were also compared for accuracy and computation cost.