The objective of this work is to improve the combustion management in W18V50DF dual fuel engine by determining for a desired power, the optimal values of the parameters of pressures and subsequently to map them in real time based on a power set point. The interest was mainly focused in pressure parameters, other being considered as constant. Two methods have been used, namely mathematical modeling and learning by neural networks. The results show that, in the beginning mathematical modeling result helps to monitor the ongoing process and with longer learning period the result with neural network become better and significant due to the adaptation to the reality. Furthermore, the neural network method improves significantly in the long term the rationalization of fuel consumption in such a system in order to significantly reduce the carbon dioxide emission rate. Finally, work has proved that for an immediate result mathematical model can be used but without robustness on the control process, this is obtained by a neural network. But this approach requires a good data base and long learning time.

这项工作的目标是通过确定所需功率、压力参数的最佳值，然后根据功率设定点实时绘制它们，从而改进 W18V50DF 双燃料发动机的燃烧管理。兴趣主要集中在压力参数上，其他被认为是恒定的。已经使用了两种方法，即数学建模和神经网络学习。结果表明，最初数学建模的结果有助于监控正在进行的过程，随着学习时间的延长，神经网络的结果由于对现实的适应而变得更好和显着。此外，从长远来看，神经网络方法显着改善了此类系统中燃料消耗的合理化，从而显着降低了二氧化碳排放率。最后，工作证明，对于直接结果数学模型可以使用，但对控制过程没有鲁棒性，这是通过神经网络获得的。但是这种方法需要良好的数据库和较长的学习时间。