Study on Hydrocarbon Fuel Ignition Characterization Based on Optimized BP Neural Network

Zhihan Chen, Lulin Wei, Hongan Ma, Yang Liu (), Meng Yue and Junrui Shi
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Zhihan Chen: School of Computer Science and Engineering, Shenyang Jianzhu University, Shenyang 110168, China
Lulin Wei: School of Computer Science and Engineering, Shenyang Jianzhu University, Shenyang 110168, China
Hongan Ma: Liaoning Key Lab of Advanced Test Technology for Aerospace Propulsion System, School of Aeroengine, Shenyang Aerospace University, Shenyang 110136, China
Yang Liu: College of Petroleum Engineering, Liaoning Petrochemical University, Fushun 113001, China
Meng Yue: Green and Low-Carbon Smart Heating and Cooling Technology Characteristic Laboratory, Shandong Huayu University of Technology, Dezhou 253034, China
Junrui Shi: School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255049, China

Energies, 2024, vol. 17, issue 9, 1-16

Abstract: The investigation of the ignition delay of hydrocarbon fuel is highly valuable for enhancing combustion efficiency, optimizing fuel thermal efficiency, and mitigating pollutant emissions. This paper has developed a BP-MRPSO neural network model for studying hydrocarbon fuel ignition and clarified the novelty of this model compared to the traditional BP and ANN models from the literature. The model integrates the particle swarm optimization (PSO) algorithm with MapReduce-based parallel processing technology. This integration improves the prediction accuracy and processing efficiency of the model. Compared to the traditional BP model, the BP-MRPSO model can increase the average correlation coefficient, from 0.9745 to 0.9896. The R 2 value for predicting fire characteristics using this model can exceed 90%. Meanwhile, when the two hidden layers of both the BP and BP-MRPSO models consist of 9 and 8 neurons, respectively, the accuracy of the BP-MRPSO model is increased by 38.89% compared to the BP model. This proved that the new BP-MRPSO model has the capacity to handle large datasets while achieving great precision and efficiency. The findings could provide a new perspective for examining the properties of fuel ignition, which is expected to contribute to the development and assessment of aviation fuel ignition characteristics in the future.

Keywords: ignition delay time; BP-MRPSO algorithm; BP neural network; equivalence ratio; hydrocarbon fuel (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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