Optimizing the model-prediction of date palm fronds-derived producer gas and third generation biodiesel powered dual-fuel engine by employing Bayesian-optimized Boosted Regression Trees for enhanced prognostics

Abstract

The global issue concerning the dual problem of economic energy shortage as well as greenhouse gas emission needs urgent attention. Due to its abundance and carbon neutrality, waste biomass is a good energy conversion feedstock. This research suggests a unique method for using date palm frond waste biomass-derived producer gas and algal biodiesel as sustainable diesel engine fuels. The Bayesian-optimized neural networks and Bayesian-optimized Boosted Regression Trees were employed to optimize engine combustion and emission performance. This was further enhanced with the application of k-cross fold to prevent the model from overfitting during training. The working of dual-fuel engines is a complex phenomenon owing to the employment of two different types of fuel one is liquid (biodiesel + diesel) while the other one is gaseous (Producer gas). The present endeavor was undertaken to handle this with the application of advanced data learning methods. The engine was tested at different compression ratios, engine loads, and fuel injection pressures for brake thermal efficiency, diesel fuel substitution, and emission. Both the Bayesian-optimized neural networks and Bayesian-optimized Boosted Regression Trees could predict the engine output with good prediction efficiency with comparable performance, however, the Bayesian-optimized Boosted Regression Trees-based models were more robust with a coefficient of determination value in the range of 0.9617–0.9999 and mean squared errors as low as 0.001. The mean absolute percentage error was very low in the range of 0.0316–5.231. The Theil’s U2 values were in the range of 0.0029–0.0126.