Abstract
This study investigates the potential of oxygenated additive (ethanol) on adulterated diesel fuel on the performance and exhaust emission characteristics of a single cylinder diesel engine. Based on the engine experimental data, two artificial intelligence (AI) models, viz., artificial neural network (ANN) and adaptive-neuro fuzzy inference system (ANFIS), have been modeled for predicting brake thermal efficiency (Bth), brake specific energy consumption (BSEC), oxides of nitrogen (NOx), unburnt hydrocarbon (UBHC) and carbon monoxide (CO) with engine load (%), kerosene (vol %), and ethanol (vol %) as input parameters. Both the proposed AI models have the capacity for predicting input–output paradigms of diesel–kerosene–ethanol (diesosenol) blends with high accuracy. A (3–9–5) topology with Levenberg–Marquardt feed forward back propagation (trainlm) learning algorithm has been observed to be the ideal model for ANN. The comparative study of the two AI models demonstrated that ANFIS predicted results have higher accuracy than the ANN with a maximum RANFIS/RANN value of 1.000534.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of Thermal Science and Engineering Applications
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
