Advances in Energy Research

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Adaptive Neuro-fuzzy-based modeling of exhaust emissions from dual-fuel engine using biodiesel and producer gas

  • Prabhakar Sharma (School of Engineering Science, Delhi Skill and Entrepreneurship University) ;
  • Avdhesh Kr Sharma (Mechanical Engineering Dept., D.C.R. University of Sci. & Technology)
  • Received : 2021.07.20
  • Accepted : 2022.08.28
  • Published : 2022.09.25
⟨ Previous Next ⟩

Abstract

The dual-fuel technology, which uses gaseous fuel as the main fuel and liquid as the pilot fuel, is an appealing technology for reducing the exhaust emissions. The current study proposes emission models based on ANFIS for a dual-fuel using producer gas (PG)-diesel engine. Emissions measurements were taken at different engine load levels and fuel injection timings. The proposed model predictions were examined using statistical methods. With R2 values in the range of 0.9903 to 0.9951, the established ANFIS model was found to be consistently robust in predicting emission characteristics. The mean absolute percentage deviate in range 1.9 to 4.6%, and mean squared error varies in range 0.0018 to 13.9%. The evaluation of the ANFIS model developed shows a reliable claim of intrinsic sensitivity, strength, and outstanding generalization. The presented meta-model can be used to simulate the engine's operation in order to create an efficient control tool.

Keywords

Acknowledgement

The authors acknowledge the contribution of staff in ICEGT laboratory, DCR University of Science and Technology, Murthal, HARYANA, INDIA.

References

  1. Aghbashlo, M., Peng, W. and Tabatabaei, M. (2021), "Machine learning technology in biodiesel research: A review", Progress Energ. Combustion Sci., 85, 100904. https://doi.org/10.1016/j.pecs.2021.100904.
  2. Chen, W.H., Wang, J.S., Chang, M.H., Mutuku, J.K. and Hoang, A.T. (2021), "Efficiency improvement of a vertical-axis wind turbine using a deflector optimized by Taguchi approach with modified additive method", Energ. Convers. Manage., 245, 114609. https://doi.org/https://doi.org/10.1016/j.enconman.2021.114609.
  3. Dirik, M. (2022), "Prediction of NOx emissions from gas turbines of a combined cycle power plant using an ANFIS model optimized by GA", Fuel, 21, 124037. https://doi.org/10.1016/J.FUEL.2022.124037.
  4. Fuess, L.T., Cruz, R., Zaiat, M. and Nascimento, C.A.O. (2021), "Diversifying the portfolio of sugarcane biorefineries: Anaerobic digestion as the core process for enhanced resource recovery", Renew. Sust. Energ. Rev., 147, 111246. https://doi.org/10.1016/J.RSER.2021.111246.
  5. Gobbato, P., Masi, M. and Benetti, M. (2015), "Performance analysis of a producer gas-fuelled heavy-duty SI engine at full-load operation", Energ. Procedia, 82, 149-155. https://doi.org/10.1016/j.egypro.2015.12.007.
  6. Kumar, A. and Sharma, A.K. (2017), "Design and development of gas carburettor for a gasifier-engine system", J. Inst. Engineers (India): Series C, 98(2), 83-89. https://doi.org/10.1007/s40032-016-0330-1.
  7. Milidonis, K., Blanco, M.J., Grigoriev, V., Panagiotou, C.F., Bonanos, A.M., Constantinou, M., Pye, J. and Asselineau, C.A. (2021), "Review of application of AI techniques to solar tower systems", Solar Energ., 224, 500-515. https://doi.org/10.1016/j.solener.2021.06.009.
  8. Miyamoto, M. and Takeuchi, K. (2019), "Climate agreement and technology diffusion: Impact of the Kyoto protocol on international patent applications for renewable energy technologies", Energ. Policy, 129, 1331-1338. https://doi.org/10.1016/J.ENPOL.2019.02.053.
  9. Ong, H.C., Tiong, Y.W., Goh, B.H.H., Gan, Y.Y., Mofijur, M., Rizwanul Fattah, I.M., Chong, C.T., Asraful Alame, M.D., Lee, H.V., Silitonga, A.S. and Mahlia, T.M.I. (2021), "Recent advances in biodiesel production from agricultural products and microalgae using ionic liquids: Opportunities and challenges", Energ. Convers. Manage., 228, 113647. https://doi.org/10.1016/J.ENCONMAN.2020.113647.
  10. Razavi, R., Sabaghmoghadam, A., Bemani, A., Baghban, A., Chau, K.W. and Salwana, E. (2019), "Application of ANFIS and LSSVM strategies for estimating thermal conductivity enhancement of metal and metal oxide based nanofluids", Eng. Appl. Comput. Fluid Mech., 13(1), 560-578. https://doi.org/10.1080/19942060.2019.1620130.
  11. Saengsuriwong, R., Onsree, T., Phromphithak, S. and Tippayawong, N. (2021), "Biocrude oil production via hydrothermal liquefaction of food waste in a simplified high-throughput reactor", Bioresour. Technol., 341, 125750. https://doi.org/10.1016/J.BIORTECH.2021.125750.
  12. Said, Z., Rahman, S., Sharma, P., Hachicha, A.A. and Issa, S. (2022), "Performance characterization of a solarpowered shell and tube heat exchanger utilizing MWCNTs/water-based nanofluids: An experimental, numerical, and artificial intelligence approach", Appl. Therm. Eng., 212, 118633. https://doi.org/10.1016/J.APPLTHERMALENG.2022.118633.
  13. Saravanakumar, L. and Prakash, R. (2020), "Validation of performance and emissions of a CI engine fueled with calophyllum inophyllum methyl esters using soft computing technique", Fuel, 266, 117070. https://doi.org/10.1016/J.FUEL.2020.117070.
  14. Sharma, A.K. (2011), "Experimental Investigations on a 20 KWe, solid biomass gasification system", Biomass Bioenerg., 35(1), 421-428. https://doi.org/10.1016/j.biombioe.2010.08.060.
  15. Sharma, P. (2021), "Prediction-optimization of the effects of di-tert butyl peroxide-biodiesel blends on engine performance and emissions using Multi-Objective Response Surface Methodology (MORSM)", J. Energ. Resour. Technol., 1-26. https://doi.org/10.1115/1.4052237.
  16. Sharma, P. and Sahoo, B.B. (2022a), "An ANFIS-RSM based modeling and multi-objective optimization of syngas powered dual-fuel engine", Int. J. Hydrogen Energ., https://doi.org/10.1016/j.ijhydene.2022.04.093.
  17. Sharma, P., Sahoo, B.B., Said, Z., Hadiyanto, H., Nguyen, X.P., Nizetic, S., Huang, Z., Hoang, A.T. and Li, C. (2022), "Application of machine learning and cox-cehnken design in optimizing engine characteristics operated with a dual-fuel mode of algal biodiesel and waste-derived biogas", Int. J. Hydrogen Energ., https://doi.org/10.1016/J.IJHYDENE.2022.04.152.
  18. Sharma, P. and Sahoo, B.B. (2022b), "Precise prediction of performance and emission of a Waste derived biogas-biodiesel powered dual-fuel engine using modern ensemble boosted regression tree: A critique to artificial neural network", Fuel, 321, 124131. https://doi.org/10.1016/j.fuel.2022.124131.
  19. Sharma, P., Said, Z., Memon,S., Elavarasan, R.M., Khalid, M., Nguyen, X.P., Arici, M., Hoang, A.T. and Nguyen, L.H. (2022), "Comparative evaluation of AI-based intelligent GEP and ANFIS models in prediction of thermophysical properties of Fe3O4-coated MWCNT hybrid nanofluids for potential application in energy systems", Int. J. Energ. Res., https://doi.org/10.1002/er.8010.
  20. Sharma, P. and Sharma, A.K. (2020), "Experimental evaluation of thermal and combustion performance of a DI diesel engine using waste cooking oil methyl ester and diesel fuel blends", In Smart Innovation, Systems and Technologies. https://doi.org/10.1007/978-981-15-2647-3_50.
  21. Simsek, S., Uslu, S. and Simsek, H. (2022), "Proportional impact prediction model of animal waste fat-derived biodiesel by ANN and RSM technique for diesel engine", Energy, 239, 122389. https://doi.org/10.1016/J.ENERGY.2021.122389.
  22. Singh, D., Sharma, D., Soni, S.L., Inda, C.S., Sharma, S., Sharma, P.K. and Jhalani, A. (2021), "A comprehensive review of physicochemical properties, production process, performance and emissions characteristics of 2nd generation biodiesel feedstock: Jatropha curcas", Fuel, 285, 119110. https://doi.org/10.1016/J.FUEL.2020.119110.
  23. Singh, N.K., Singh, Y., Sharma, A. and Rahim, E.A. (2020), "Prediction of performance and emission parameters of Kusum biodiesel based diesel engine using neuro-fuzzy techniques combined with genetic algorithm", Fuel, 280, 118629. https://doi.org/10.1016/J.FUEL.2020.118629.
  24. Taghavi, M., Gharehghani, A., Bakhtiari Nejad, F. and Mirsalim, M. (2019), "Developing a model to predict the start of combustion in HCCI engine using ANN-GA approach", Energ. Convers. Manage., 195, 57-69. https://doi.org/10.1016/j.enconman.2019.05.015.