10.1007/s40095-021-00458-1

Fuzzy-based prediction of compression ignition engine distinctiveness powered by novel graphene oxide nanosheet additive diesel–Aegle marmelos pyrolysis oil ternary opus

  • Baranitharan Paramasivam*1,
  • S. Kumanan1,
  • V. Kavimani2,
  • M. Varatharajulu1,
  1. Department of Production Engineering, National Institute of Technology, Tiruchirappalli, 620015, IN
  2. Centre for Material Science, Department of Mechanical Engineering, Karpagam Academy of Higher Education, Coimbatore, 641021, IN

Published in Issue 2022-01-08

How to Cite

Paramasivam, B., Kumanan, S., Kavimani, V., & Varatharajulu, M. (2022). Fuzzy-based prediction of compression ignition engine distinctiveness powered by novel graphene oxide nanosheet additive diesel–Aegle marmelos pyrolysis oil ternary opus. International Journal of Energy and Environmental Engineering, 13(2 (June 2022). https://doi.org/10.1007/s40095-021-00458-1
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Abstract

Abstract Nanosheet-based catalysts were used as additives in engine fuels to enhance the engine performance characteristics. In this research, graphene oxide nanosheets (GON) were used as a catalyst to Aegle marmelos (AM) bio-oil/diesel opus at 20 and 30 gm/l. The operating characteristics of a direct injection diesel engine powered by GON fuel blends were compared with A20 and diesel (B0). All test fuel blends (A20, A20G20 and A20G30) have been analyzed for the mutual effects of varying engine load (W) and compression ratio (CR) in test engine via experimental investigation and fuzzy prediction approach. With the augmentation of GON concentration in the blend, reduction in hydrocarbon (HC), carbon monoxide (CO), and soot emissions is observed along with increased oxides of nitrogen (NOx) and carbon dioxide (CO 2 ) emission. The engine performance was also enhanced with augmenting in GON addition with fuel blends. Engine parameters were precisely predicted by the fuzzy model (trapezoidal mf, Mamdani FIS and centroid-weighted average). The developed fuzzy model predicted the engine operating attributes with a greater coefficient of determination ( R 2  = 0.91) and correlation coefficients ( R  = 0.95). The fuzzy validation outcomes endorse the adaptability of the developed model with better accuracy and depict that AM bio-oil nanoadditive opus is a good alternative for diesel in transportation fleets.

Keywords

  • Fuzzy logic,
  • Aegle marmelos,
  • Bio-oil,
  • Graphene oxide nanosheets,
  • Engine analysis

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