Artificial Neural Networks-Based Ignition Delay Time Prediction for Natural Gas Blends

Energy production from natural gas has considerably increased worldwide. The composition of natural gas is diverse, and each blend has a different reactivity. Understanding the reactivities of these fuels enables the designer to develop fuel-source independent combustors. Also, future engines such as scramjet and homogeneous charge compression ignition (HCCI) will rely heavily on the reactivity of the fuel. Ignition delay time (IDT) is a direct measure of a fuel's reactivity. In the current study, an artificial neural network (ANN) based model is developed to predict the IDT of different natural gas blends. The model has 13 inputs and three hidden layers and is trained using a back-propagation approach. The developed model is superior compared to a multiple linear regression approach and is validated with shock tube experiments. Furthermore, the model is used to predict the IDT of six different liquified natural gas blends (LNG), and the predicted results match the experimental data accurately. Additionally, the IDTs of four different commercial natural gas blends are predicted using the ANN model, showcasing the application of the tool in a real-world scenario.

Automated summary

Energy production from natural gas has increased globally. Understanding the reactivity of different gas blends is important for the development of fuel-source independent combustors and future engines. An artificial neural network-based model is developed to predict the ignition delay time of natural gas blends, which outperforms multiple linear regression and accurately predicts experimental data.