Shi Jinhong, Liu Kun, Xu Ming, Ji Zhiyong
College of Vehicle Engineering, Taiyuan University of Technology, Taiyuan, Shanxi 030024, China.
ACS Omega. 2023 May 31;8(23):20293-20302. doi: 10.1021/acsomega.2c07465. eCollection 2023 Jun 13.
In this paper, an electronically controlled diesel engine fueled with Fischer-Tropsch fuel was selected to optimize soot and NO emissions. First, the effects of injection parameters on exhaust performance and combustion properties were studied on an engine test bench and then a prediction model based on a support vector machine (SVM) was established according to the test results. On this basis, a decision analysis of soot and NO solutions assigned with different weights was performed based on the TOPSIS analysis method. It turned out that the "trade-off" relation between soot and NO emission was improved effectively. As a matter of fact, the Pareto front selected by this method showed a significant decline compared with the original operating points, in which soot declined by 3.7-7.1% and NO declined by 1.2-2.6%. Finally, the experiments were used to confirm the validity of the results, which indicated that the Pareto front corresponded well with the test value. The maximum relative error between the soot Pareto front and the measured value is 8% while it is 5% for NO emission, and the values of soot and NO under various conditions are more than 0.9. This instance proved that research on diesel engine emission optimization based on the SVM and NSGA-II is feasible and valid.
本文选用一台燃用费托合成燃料的电控柴油机，以优化碳烟和氮氧化物排放。首先，在发动机试验台上研究了喷射参数对排气性能和燃烧特性的影响，然后根据试验结果建立了基于支持向量机（SVM）的预测模型。在此基础上，基于理想解法（TOPSIS）分析方法，对赋予不同权重的碳烟和氮氧化物解决方案进行了决策分析。结果表明，碳烟和氮氧化物排放之间的“权衡”关系得到了有效改善。事实上，该方法选择的帕累托前沿与原始运行点相比有显著下降，其中碳烟下降了3.7%-7.1%，氮氧化物下降了1.2%-2.6%。最后，通过实验验证了结果的有效性，结果表明帕累托前沿与试验值吻合良好。碳烟帕累托前沿与测量值之间的最大相对误差为8%，氮氧化物排放的最大相对误差为5%，各种工况下碳烟和氮氧化物的相关系数均大于0.9。该实例证明了基于支持向量机和非支配排序遗传算法（NSGA-II）的柴油机排放优化研究是可行且有效的。
