Elman and cascade neural networks with conjugate gradient Polak-Ribière restarts to predict diesel engine performance and emissions fueled by butanol as sustainable biofuel

Abstract

This study explores the possibility of utilizing green butanol, a promising type of biofuel, in diesel engines to measure its impact on engine performance and emissions. Experimental data were collected on several parameters, including brake-specific fuel consumption (BSFC), brake thermal efficiency (BTE), exhaust gas temperature, all of which are influenced by the biofuel type used, in this case, butanol. The study examined emissions such as carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NOx), and smoke opacity. Such emissions have the potential to improve with the use of different biofuels ratios, such as butanol. Advanced machine learning techniques, Elman and Cascade Neural Networks, were employed to predict the performance and emission characteristics of engines using butanol. The models were trained using a Conjugate Gradient Learning Function with Polak-Ribière Restarts to simulate the effects of butanol as biofuel, on diesel engines. Key findings revealed that when incorporating butanol into diesel fuel blends, potential improvements in BTE and fuel efficiency were observed. Notably, using butanol as a biofuel reduced exhaust gas temperatures and CO emissions, demonstrating the potential of this particular biofuel. Conversely, there were observed increases in HC emissions and smoke opacity, signifying the complexities of using biofuels such as butanol. Cascade neural network proved to be highly accurate in predicting engine performance parameters fueled with butanol as biofuel. Overall, the study offers valuable insights into the use of butanol as a biofuel, its potential benefits, and challenges, underscoring the importance of continuous research in sustainable biofuels such as butanol.