Analysis on Emissions and Performance of Ceramic Coated Diesel Engine Fueled with Novel Blends Using Artificial Intelligence. (1st July 2021)

• Record Type: Journal Article
• Authors: Kotteda, Tarun Kumar; Chekuri, Rama Bhadri Raju; Naga Raju, B.; Kantheti, Prasada Raju; Balakumar, S.
• Other Names: Chelladurai Samson Jerold Samuel Academic Editor.
• Is Part Of: Advances in materials science and engineering. Volume 2021(2021)
• Publication Date: 2021-07-01
• Subjects: Materials science -- Periodicals; Materials science; Periodicals; 620.11
• DOI: 10.1155/2021/7954488
• Languages: English
• ISSNs: 1687-8434

Abstract

Abstract : The exhaustive nature of petroleum products triggers the obstacles of scarcity, economic imbalance, and environmental depletion. It is difficult to avoid their usage all of a sudden and switch to clean electric prime movers. Under all these circumstances, the researchers may initiate their investigations on alternative fuels for preeminent solution. The present study covers the performance and emissions of a single cylinder, four-stroke, diesel engine fueled with Pongamia pinnata and Calophyllum inophyllum biodiesels added with n -butanol additive at various proportions. In this investigation, the piston has been coated with ceramic material with a thickness of 200 µ m topcoat. The blends have been tested at 1500 rpm speed and rated compression ratio of 17.5 : 1 at various operating loads. A comparative result analysis has been made on the engine parameters operated by diesel and showed that mechanical efficiency gradually increases with a percentile increment of n -butanol in the blend. Moreover, emissions such as CO, CO2, NOx, and opacity were found to be reduced for the samples having high amount of n -butanol, whereas HC emissions slightly increased. In addition, all the exhaust gases have been predicted by using second-order polynomial equations generated and artificial Intelligence technique, and the comparative analysis has been made. It has been identified that ANN showed an average accuracy of prediction superior than regression analysis.