摘要：The objective of the study is to present the modeling and multi-objective optimization of NO x conversion efficiency and NH 3 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 NO x emissions and NH 3 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 NO x conversion efficiency while minimizing NH 3 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 NO x emissions and NH 3 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.
关键词：NO x conversion efficiency; NH 3 slip; genetic algorithm; support vector machine; prediction model; multi-objective optimization
