Characterization of Fuel Properties of WCOME/AONP Biodiesel Using Taguchi Technique

Authors

  • Varun K R  Research Scholar, Department of Mechanical Engineering, UVCE, Bengaluru, Karnataka, India
  • Dr. G Harish  Department of Mechanical Engineering, UVCE, Bengaluru, Karnataka, India

DOI:

https://doi.org/10.32628/IJSRST22927

Keywords:

Biodiesel, WCOME, Flash and Fire Point, Taguchi

Abstract

The fuels which are derived from the biological process such as anaerobic digestion from the agricultural wastes are called bio fuels. These fuels are better than the fuels which are produced from geological processes which are involved in the formation of fossil fuels such as coal and petroleum. The fuels can also be extracted from the plants and industrial wastes which are renewable in nature. The biomass can also be used as biofuel which gives a good result in testing of engine performance. The biomass is obtained in three forms like solid, liquid and gaseous. Biodiesel, as a fuel, can be used in vehicles directly, but due to emission effects, it is mixed with the diesel which reduces the level of carbon-dioxide and NOx. In European countries it can be seen the use of biodiesel which are produced from fats and oils using the transesterification process. The aim of the present research work is to compare different Biodiesel blends from different percentages of waste cooking oil as a suitable fuel replacement for Diesel engines. Engine performance based on the blends of Diesel and Biodiesel was recorded and tabulated.

References

  1. Y. Yang, S. Chevallier, J. Wiart, and I. Bloch, "Subject specific time-frequency selection for multi-class motorimagery-based bcis using few laplacian eeg channels,"Biomedical Signal Processing and Control, vol. 38, pp.302 - 311, 2017.
  2. S. Sakhavi, C. Guan, and S. Yan, "Learning temporal information for brain-computer interface using convolutional neural networks," IEEE Trans. Neural Netw. Learn. Syst., vol. 29, no. 11, pp. 5619—5629,2018.
  3. J. Feng, E. Yin, J. Jin, R. Saab, 1. Daly, X. Wang,D. Hu, and A. Cichocki, "Towards correlation-based time window selection method for motor imagery bcis," Neural Networks, vol. 102, pp. 87—95, 2018.
  4. S. Kumar and A. Sharma, "A new parameter tuning approach for enhanced motor imagery EEG signal classification,"Medical & Biological Engineering & Computing, IEEE Access vol. 56, no. 10,pp. 1861—1874, 2018.
  5. Thanh, N.; Imaii, H.; Amin, K.; Lee, G.-B.; Chee, PL.; Saeid, N. Classification of Multi-Class BCI Data by Common Spatial Pattern and Fuzzy System. IEEE Access vol. 5, no.43,pp.143-157,2018.
  6. S. U. Amin, M. Alsulaiman, G. Muhammad, M. A. Bencherif and M. S. Hossain, "Multilevel Weighted Feature Fusion Using Convolutional Neural Networks for EEG Motor Imagery Classification, " in IEEE Access, vol no. 7, issue no. 4, pp. 189401-8950, 2019.
  7. Y. Li, X.-R. Zhang, B. Zhang, M.-Y. Lei, W.-G. cui, and Y.-Z. channel projection mixed-scale convolutional neural network for motor imagery EEG decoding, " IEEE Trans. Neural Syst. Rehabil. Eng., vol no.27, issue no. 6, pp no .1170— 1180, 2019.
  8. X. Zhao, H. Zhang, G. Thu, F. You, S. Kuang, and L. sun, "A multibranch 3D convolutional neural network for EEG-based motor imagery classification," IEEE Trans. Neural Syst. Rehabil. Eng., vol no. 27, issue no. 10, pp no. 21642177, 2019.
  9. Z. Gao, X. Cui, W. Wan, and Z. Gu, "Recognition of emotional states using multiscale information analysis of high frequency EEG oscillations," Entropy, vol. 21, no. 6, pg. 609,2019.
  10. N. Robinson, S. Lee and C. Guan, "EEG Representation in Deep Convolutional Neural Networks for Classification of Motor Imagery," IEEE Man and Cybernetics (SMC), vol no. Il,issue no.3, pp no. 13221326, 2019.
  11. J. Li, Z. L. Yu, Z. GU, M. Tan, Y. Wang, and Y. Li, "Spatial—temporal discriminative restricted Boltzmann machine for event-related potential detection and analysis," IEEE Trans. Neural syst. Rehabil. Eng., vol. 27,110. 2, pp. 139-151, 2019.
  12. A. M. Azab, L. Mihaylova, K. Keng Ang, and M. Arvaneh, "Weighted transfer learning for improving motor imagery-based brain—computer interface," IEEE Trans. Neural Syst. Rehabil. Eng., vol. 27, no. 7,pp. 1352-1359, 2019.
  13. J. Luo, Z. Feng, and N. Lu, "Spatio-temporal discrepancy feature for classification of motor Imageries," Biomedical Signal Processing and Control, vol. 47, pp. 137- 144, 2019.
  14. M. K. 1. Molla, A. A. Shim, M. R. Islam and T. Tanaka, "Discriminative Feature Selection-Based Motor Imagery Classification Using EEG Signal," in IEEE Access, vol.no 8,issue no.2 pp no.98255-98265, 2020.
  15. X. Tang and X. Zhang, "Conditional adversarial domain adaptation neural network for motor imagery EEG decoding," Entropy, vol. 22, no. l, p. 96,2020.
  16. D. Achanccaray and M. Hayashibe, "Decoding Hand Motor Imagery Tasks Within the Same Limb From EEG Signals Using Deep Learning," in, vol no. 2, issue no. 4, pp no. 692-699,2020.
  17. J. -S. Bang, M. -H. Lee, S. Fazii, C. Guan and S. - W. Lee, "Spatio-Spectral Feature Representation for Motor Imagery Classification Using Convolutional Neural Networks," in IEEE Transactions,vol no.5, issue no.8,pp no. 12-18,2020.
  18. X. Deng, B. Zhang, N.3 Yu, K. Liu and K. sun, "Advanced TSGL-EEGNet for Motor Imagery EEG Based Brain-Computer Interfaces," in IEEE Access, vol no. 9, issue no. Il, pp no. 25118-25130, 2021.
  19. P. Gaur, H. Gupta, A. Chowdhury, K. McCreadie, R. B. Pachori and H. Wang, "A Sliding Window Common Spatial Pattern for Enhancing Motor Imagery Classification in EEG- BCI," in IEEE, vol no.70, issue no. 19,pp. 1-9, 2021.
  20. Xiaolin Hong, QingqingZheng ,Luyan Liu ,Peiyin Chen, Kai Ma , Thongke Gao, Yefeng Zheng, " Dynamic Joint Domain Adaptation Network for Motor Imagery Classification " IEEE Transactions on Neural Systems and Rehabilitation Engineering. , vol no. 29, issue no.8 , pp no.556 - 565, 2021.

International Journal of Scientific Research in Science and Technology

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Published

2022-04-30

Issue

Volume 9, Issue 2, March-April-2022

Section

Research Articles

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How to Cite

[1]
Varun K R, Dr. G Harish "Characterization of Fuel Properties of WCOME/AONP Biodiesel Using Taguchi Technique" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 9, Issue 2, pp.344-350, March-April-2022. Available at doi : https://doi.org/10.32628/IJSRST22927