Neural Networks for Performance Prediction of Biogas-Powered Hybrid Engines

Artificial Neural Networks (ANNs) have revolutionized the evaluation of performance predictions across multiple disciplines, including internal combustion engines. However, concerning biogas-hybrid engine performance implementation, it remains a topic of contention or significance due to the interdependencies of fuel composition, combustion dynamics, and emissions characterizations. This undertaking will highlight the performance of an ANN-based predictive model implementing biogas-diesel fuel hybrid performance predictions in consideration of combustion characteristics, emissions, and power output stability. Contrasting other machine learning models such as Random Forest, I demonstrate that ANNs present better accuracies for predicting natural gas biodiesel. Additional exploration will invite contemplation for the implications of ANNs for improving gas turbine efficiencies, hybrid system architectures, and overall sustainable energy strategies, all of which will determine the development of the most-accurate dataset, translations, and application. This study will further analyze the evolving nature of ANNs which would include features like real-time data collection that might offer a more finely tuned optimization of the biogas hybrid routes. Ultimately, the nature and performance of the study will serve to facilitate a discourse around recent developments, challenges, and future paths for AI-enabled sustainable energy opportunities.