Towards a comprehensive optimization of engine efficiency and emissions by coupling artificial neural network (ANN) with genetic algorithm (GA)

In response to the stringent emission regulations, artificial neural network (ANN) coupled with genetic algorithm (GA) is employed to optimize a novel internal combustion engine strategy named direct dual fuel stratification (DDFS). An enhanced ANN model is introduced to improve the accuracy and stability of predictions. Compared to the conventional computational fluid dynamics (CFD)-GA optimization method, the ANN-GA method can identify a better solution to achieve higher fuel efficiency and lower nitrogen oxide (NOx) emissions with the lower computational time. This is attributed to the lower cost of ANN calculation, which allows ANN-GA to introduce larger population to seek optimal solutions. Through combining the required new training data with the previous ones, the original ANN model can be updated to adapt to a wider parameter range. Thus, ANN-GA can readily deal with the optimization problems with variable parameters and objectives. When more re-optimizations are required, ANN-GA can save the computational time over 75% than CFD-GA owing to the data-driven nature of ANN-GA by fully utilizing the available data. Overall, the ANN-GA method shows the superiority in accuracy, efficiency, expansibility, and flexibility for DDFS strategy optimization. It is promising to integrate ANN with optimization algorithm for further improvements of engine performance. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

The study proposes a new approach for optimizing internal combustion engine strategies using the ANN-GA method, which shows higher efficiency, lower computational time, and stronger adaptability compared to traditional methods.