Emission prediction and optimization of methanol/diesel dual-fuel engines based on ITransformer-BiGRU and NSGA-III - Mingzhang Pan; Xinxin Cao; Changcheng Fu; Shengyou Liao; Xiaorong Zhou; Wei Guan (Energy and AI)
To reduce engine pollutant emissions, an emission modeling and optimization scheme based on a hybrid artificial intelligence scheme is proposed in this study to reduce pollutant emissions of methanol/diesel dual-fuel engines under low load. Firstly, a data cleaning method based on isolated forest and correlation analysis is designed to improve the stability of the system. Secondly, a hybrid emission prediction model based on improved Transformer (ITransformer) and Bidirectional Gated Recurrent Unit (BiGRU) is built to obtain an accurate mathematical model between control parameters and emissions. Finally, based on the obtained mathematical model, the 3rd Non-dominated Sorting Genetic Algorithm (NGSA-III) is used to adjust and optimize the control parameters. Using engine bench test data to evaluate the proposed hybrid emission prediction model, the R2 of CO, HC, and NOx prediction is 0.9969, 0.9973, and 0.9982, respectively, which is higher than the accuracy of the seven existing modeling methods. Compared with the unoptimized MESR46, the CO, HC, and NOx emissions of the optimized scheme are reduced by at least 45.17 %, 15.30 %, and 17.32 % respectively, which can significantly reduce the CO, HC, and NOx emissions, and comparison and analysis with the most advanced optimization technologies show a competitive optimization effect.