Sensitivity analysis of diesel particulate filters to geometric parameters during soot loading and its multi-objective optimization

You, Hui; Gao, Ran; Hu, Pengfei; Liang, Ke; Zhou, Xiaorong; Huang, Xiaodong; Pan, Mingzhang

doi:10.1016/j.psep.2021.12.040

Cited by 25 publications

(1 citation statement)

References 47 publications

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“…With the rapid development of numerical simulation technology, intelligence optimization technology − has been widely used in many aspects of engineering science, such as signal processing, automatic control, and machine design, and has shown a powerful ability to search optimally. − However, numerical studies on direct injection parameter optimization of common rail diesel engine are very limited due to the complexity in modeling the performance in relation to injection parameters. In previous studies, response surface methodology and NSGA-II were adopted to optimize the coupling effects between emissions and injection parameters in a diesel engine. Therefore, it is necessary to carry out relevant research; the emission characteristics of various injection parameters were researched by the engine bench test, and then a prediction model of soot, NO x emission, and injection parameters based on the support vector machine (SVM) was established, which was used as a fitness model of NSGA-II, and then the Pareto front of soot and NO x was determined in the sample space, which provided a theoretical basis to improve engine emission performance.…”

Section: Introductionmentioning

confidence: 99%

Multi-Optimization of Injection Parameters Affecting Emissions in a Diesel Engine Fueled with Fischer–Tropsch Fuel Based on NSGA-II

Shi

Liu

et al. 2023

ACS Omega

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In this paper, an electronically controlled diesel engine fueled with Fischer–Tropsch fuel was selected to optimize soot and NO x 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 x 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 x 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 x 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 x emission, and the R 2 values of soot and NO x 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.

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