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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 R2 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. [ABSTRACT FROM AUTHOR]

NOx is one of the main sources of pollutants for motor vehicles. Nowadays, many diesel vehicle manufacturers may use emission-cheating equipment to make the vehicles meet compliance standards during emission tests, but the emissions will exceed the standards during actual driving. In order to strengthen the supervision of diesel vehicles for emission monitoring, this article intends to establish a model that can predict the transient emission characteristics of heavy-duty diesel vehicles and provide a solution for remote online monitoring of diesel vehicles. This paper refers to the heavy-duty vehicle National VI emission regulations and uses vehicle-mounted portable emission testing equipment (PEMS) to conduct actual road emission tests on a certain country's VI heavy-duty diesel vehicles. Then, it proposes a new feature engineering processing method that uses gray correlation analysis and principal component analysis to eliminate invalid data and reduce the dimensionality of the aligned data, which facilitates the rapid convergence of the model during the training process. Then, a double-hidden-layer BP (Back propagation) neural network was established, and the improved gray wolf algorithm was used to optimize the threshold and weight of the neural network, and a heavy-duty diesel vehicle NOx emission prediction model was obtained. Through the training of the network, the root mean square error (RMSE) of the improved model on the test set between the predicted value and the true value is 1.9144 (mg/s), and the coefficient of determination (R2) is 0.87024. Compared with single-hidden-layer network and double-hidden-layer BP neural network models, the accuracy of the model has been improved. The model can well predict the actual road NOx emissions of heavy-duty diesel vehicles. [ABSTRACT FROM AUTHOR]