Analysis on Emissions and Performance of Ceramic Coated Diesel Engine Fueled with Novel Blends Using Artificial Intelligence
| Autor: | S. Balakumar, Tarun Kumar Kotteda, Rama Bhadri Raju Chekuri, Prasada Raju Kantheti, B. Naga Raju |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Materials science
Article Subject 020209 energy 02 engineering and technology Diesel engine Cylinder (engine) law.invention Diesel fuel Piston Petroleum product 020401 chemical engineering law 0202 electrical engineering electronic engineering information engineering General Materials Science 0204 chemical engineering Materials of engineering and construction. Mechanics of materials NOx biology business.industry Pongamia General Engineering biology.organism_classification Compression ratio TA401-492 Artificial intelligence business |
| Zdroj: | Advances in Materials Science and Engineering, Vol 2021 (2021) |
| ISSN: | 1687-8442 1687-8434 |
| Popis: | 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 µm 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. |
| Databáze: | OpenAIRE |
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