An artificial intelligence-based strategy for multi-objective optimization of diesel engine fueled with ammonia-diesel-hydrogen blended fuel

Zhiqing Zhang, Jingyi Hu, Yuguo Wang, Mingzhang Pan, Kai Lu, Yanshuai Ye and Zibin Yin

Energy, 2025, vol. 318, issue C

Abstract: Ammonia-diesel blended fuel is a promising alternative fuel combination. However, ammonia has issues such as low laminar flame velocity and weak reaction activity. As an efficient and low-carbon fuel, hydrogen enhances the thermal efficiency of the combustion process when introduced. This study establishes a three-dimensional simulation model to explore the effects of hydrogen energy substitution rate, fuel injection timing, and intake pressure on the combustion and emission characteristics of the blended fuel. The results indicate that the hydrogen energy substitution rate can significantly improve fuel combustion efficiency. Although nitrogen oxide (NOx) emissions increase, unburned ammonia emissions are reduced due to the hydrogen addition. In addition, an extreme learning machine (ELM) is used to predict the output variables. Subsequently, the multi-objective particle swarm optimization (MOPSO) algorithm is coupled with ELM to solve the dual objective problem. The research results show that the lowest unburned ammonia and NOx emissions generated are 213.467 ppm and 597.43 ppm, corresponding to a hydrogen energy substitution rate of 8 %, fuel injection timing of −9.215 °CA, and intake pressure of 0.228 MPa. The paper provides a theoretical basis for further optimizing the combustion process of internal combustion engines and reducing emissions.

Keywords: Ammonia-diesel blended fuel; Hydrogen; Inject strategy; Artificial intelligence algorithm; Multi-objective optimization (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:318:y:2025:i:c:s0360544225003433

DOI: 10.1016/j.energy.2025.134701

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