Modeling and multi-objective optimization of NOx conversion efficiency and NH3 slip for a diesel engine

Abstract

The objective of the study is to present the modeling and multi-objective optimization of NOx conversion efficiency and NH3 slip in the Selective Catalytic Reduction (SCR) catalytic converter for a diesel engine. A novel ensemble method based on a support vector machine (SVM) and genetic algorithm (GA) is proposed to establish the models for the prediction of upstream and downstream NOx emissions and NH3 slip. The data for modeling were collected from a steady-state diesel engine bench calibration test. After obtaining the two conflicting objective functions concerned in this study, the non-dominated sorting genetic algorithm (NSGA-II) was implemented to solve the multi-objective optimization problem of maximizing NOx conversion efficiency while minimizing NH3 slip under certain operating points. The optimized SVM models showed great accuracy for the estimation of actual outputs with the Root Mean Squared Error (RMSE) of upstream and downstream NOx emissions and NH3 slip being 44.01 × 10-6, 21.87 × 10-6 and 2.22 ×10-6, respectively. The multi-objective optimization and subsequent decisions for optimal performance have also been presented.