Volume 5, Issue 2, April 2019, Page: 40-48
ANN Analysis of Injection Timing on Performance Characteristics of Compression Ignition Engines Running on the Blends of Tropical Almond Based Biodiesel
Samson Kolawole Fasogbon, Department of Mechanical Engineering, University of Ibadan, Ibadan, Nigeria
Olusegun Oladapo Laosebikan, Department of Mechanical Engineering, University of Ibadan, Ibadan, Nigeria
Chukwuemeka Uguba Owora, Department of Mechanical Engineering, University of Ibadan, Ibadan, Nigeria
Received: Mar. 20, 2019;       Accepted: Apr. 27, 2019;       Published: Jun. 20, 2019
DOI: 10.11648/j.ajme.20190502.16      View  42      Downloads  8
Abstract
In the present work, biodiesel prepared from Tropical almond oil (Terminalia Catappa) was used as fuel in C. I engine. Performance studies were conducted on a single cylinder four-stroke water-cooled compression ignition engine. Experiments were conducted for different percentage of blends of Tropical almond ester with diesel at different injection timings. Experimental investigations on the performance parameters from the engine were done. Artificial neural network (ANN) of back-propagation feed-forward Levenberg-Marquardt algorithm was used to predict the performance characteristics of the engine. An ANN model was developed for the performance parameters. To train the network, blend percentage, percentage load and injection timings were used as the input variables whereas engine performance parameters (brake thermal efficiency, exhaust gas temperature, and brake specific fuel consumption) were used as the output variables. The obtained experimental results were used to train the network structure. Results showed very good correlation between the ANN predicted values and the desired values for various engine performance values. Mean relative error values were less than 10 percent which by many standards is acceptable. The results show that ANN is an accurately reliable tool for the prediction of engine performance.
Keywords
Tropical Almond Ester, Injection Timing, Artificial Neural Network, Blend Percentage, Percentage Load, Brake Thermal Efficiency, Exhaust Temperature, Brake Specific Energy Consumption
To cite this article
Samson Kolawole Fasogbon, Olusegun Oladapo Laosebikan, Chukwuemeka Uguba Owora, ANN Analysis of Injection Timing on Performance Characteristics of Compression Ignition Engines Running on the Blends of Tropical Almond Based Biodiesel, American Journal of Modern Energy. Vol. 5, No. 2, 2019, pp. 40-48. doi: 10.11648/j.ajme.20190502.16
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reference
[1]
Deepak. A, Lokesh. K, Avinash. K. A. (2008). Performance evaluation of a vegetable oil fuelled compression ignition engine. Renew. Energy 33:1147-1156.
[2]
Baiju. B, Naik. M. K, Das. L. M (2009). Comparative evaluation of Compression ignition characteristics using methyl and ethyl esters of Karanja oil. Renew. Energy 34:1616-1621.
[3]
Surendra. R. K (2010). Plant oils as fuels for C.I engines - A technical review and life cycle analysis. Renew. Energy 35:1-13.
[4]
Mustafa Cranakci. 2007. Combustion characteristics of a turbocharged DI compression ignition engine fuelled with petroleum diesel fuels and biodiesel. Bioresource technology 98 (2007) 1167-1175.
[5]
Wai-Hong Leong, Jun-Wei Lim, Man-Kee Lam, Yoshimitsu Uemura, Yeek-Chia Ho. Thirdn generation biofuels: a nutritional perspective in enhancing microbial lipid production. Renewable and sustainable energy reviews 91(2018) 950-961.
[6]
Purushathaman. K. and Nagarajan. G. 2009. Performance, emission and combustion characteristics of a compression ignition engine operating on neat orange oil. Renewable energy 34 (2009) 242-245.
[7]
Patrick Akpan and Paul Ozor. 2014. An estimation of orange oil (Biodiesel) quantity from orange peel in Nigeria. NIIE 2014 conference proceedings.
[8]
Bawadi Abdullah, Syed Anuar Faua’ad Syed Muhammad, Zahra Shokravi, Shahrul Ismail, Khairul Anuar Kassim, Azmi Nik Mahmood, Md Maniruzzaman A. Aziz. Fourth generation biofuel: a review on risks and mitigation strategies. Renewable and sustainable energy reviews 107 (2019) 37-50.
[9]
Kifayat Ullah, Vinod Kumar Sharma, Mushtaq Ahmed, Pengmei Lv, Jurgen Krahl, Zhongming Wang, Sofia. The insight views of advanced technologies and its application in bio-origin fuel synthesis from lignocellulose biomasses waste, a review. Renewable and sustainable energy reviews (2017).
[10]
Ali Abdulkhani, Peyman Alizadeh, Sahab Hedjazi, Yahya Hamzeh. Potential of soya as a raw material for a whole crop biorefinery. Renewable and sustainable energy reviews (2016).
[11]
Prasanna Borse, Amol Sheth. Technological and commercial update for first-and second-generation ethanol production in India. Springer international publishing AG 2017. DOI 10.1007/978-3-319-50219-9_13.
[12]
Daniela L. Aguilar, Rose, Rose M. Rodriguez-Jasso, Elisa Zanuso, Anely A. Lara-Flores, Cristobal N. Aguilar, Arturo Sanchez, Hector A. Ruiz. Operational strategies for enzymatic hydrolysis in a biorefinery. Springer international publishing AG 2018. https://doi.org/10.1007/978-3-319-67678-4_10.
[13]
Oladapo Martins Adeniyi, Ulugbek Azimov, Alexery Burluka. Algae biofuel: current status and future applications. Renewable and sustainable energy reviews 90 (2018) 316-335.
[14]
Poonam Singh Nigam, Anoop Singh. Production of liquid biofuels from renewable resources. Progress in energy and combustion science 37 (2011) 52-68.
[15]
Man Lee Lam, Keat Teong Lee. Microalgae biofuels: a critical review of issues, problems and the way forward. Biotechnology advances 30 (2012) 673-690.
[16]
Francesco Cherubini. The biorefinery concept: using biomass instead of oil for producing energy and chemical. Energy conversion and management 51 (2010) 1412-1421.
[17]
S. N. Naik, Vaibhav V. Goud, Prasant K. Rout, Ajay K. Dalai. Production of first and second generation biofuels: a comprehensive review. Renewable and sustainable energy reviews 14 (2010) 578-597.
[18]
Roman A. Voloshin, Margarita V. Rodionova, Sergey K. Zharmukhamedov, T. Nejat Veziroglu, Suleyman I. Allakhverdiev. Review: biofuel production from plant and algae biomass. International journal of hydrogen energy (2016) 1-17.
[19]
Cenk. S, Kadir. U, Mustafa. C (2008). Influence of injection timing on the exhaust emissions of a dual fuel CI engine. Renewable. Energy 33:1314- 1323.
[20]
Nwafor O. M. I (2007). Effect of advanced injection timing on theemission characteristics of a diesel engine running on natural gas. Renew. Energy 32:2361-2368.
[21]
B. Ashok, K. Nanthagopal, D. Arumuga Perumal, J. M. Babu, Anmol Tiwari, Akhil Sharma. An investigation on CRDi engine characteristic using renewable orange-peel oil. Energy conversion and management 180 (2019) 1026-1038.
[22]
B. Ashok, K. Nanthagopal, B. Saravanan, P. Somasundaram, C. Jegadheesan, Bhaskar Chaturvedi, Shivam Sharma, Gaurang Patni. A novel study on the effect lemon peel oil as a fuel in CRDI engine at various injection strategies. Energy conversion and management 172 (2018) 517-528.
[23]
A. Naresh Kumar, Dr. K. Brahma Raju, Dr. P. Srinivas Kishore, K. Narayana. Some experimental studies on effect of exhaust-gas recirculation on performance and emission characteristics of a compression-ignition engine fuelled with diesel and lemon-peel oil blends. Materials today: proceedings 5(2018) 6138-6148.
[24]
B. Ashok, K. Nanthagopal, Bhaskar Chaturvedi, Shivam Sharma, R. Thundil. A comparative assessment on common rail direct injection (CRDI) engine characteristics using low viscous biofuel blends. Applied thermal engineering (2018).
[25]
Subrata Bhowmik, Rajsekhar Panua, Subrata Kumar Ghosh, Durbadal Debroy, Abhishek Paul. 2017. A comparative study of Artificial Intelligence based models to predict performance and emission characteristics of a single cylinder Diesel engine fueled with Diesosenol. Journal of thermal science and engineering application.
[26]
O_guz, H, Sarıtas. I, and Baydan. H. E., 2014. Prediction of diesel engine performance using biofuels with artificial neural network. Expert system application 37(9), pp. 6579–6586.
[27]
Rezaei. J, Shahbakhti. M, Bahri.B, and Aziz.A. A. Performance prediction of HCCI engines with oxygenated fuels using artificial neural networks. Applied. energy, 138, pp. 460–473.
[28]
Togun N. K and Baysec S. Prediction of torque and specific fuel consumption of a gasoline engine by using artificial neural Networks. Applied Energy 2010; 87: 349-355.
[29]
Abhishek Sharma, Praderpta Kumar Sahoo, R. K. Tripath, and Lekha Charan Meher. ANN based prediction of performance and emission characteristics of CI engine using polanga as a biodiesel. International journal of ambient energy.
[30]
Adnan Qadir, Mansoor Imam (2006). Utilisation of waste plastic bags inbituminous mix for improved performance of roads.Journal of Solid Waste Technology and Management, Vol.32, No.3.
[31]
Nasr. G.E, Badr. E. A, Joun. C, 2003. Backpropagation neural networks for modelling gasoline consumption, energy convers. Manage, 44, 893–905.
[32]
Sayin, C., Ertunc, H.M., Hosoz, M., Kilicaslan, I. Canakci, M. Performance and exhaust emissions of a gasoline engine using artificial neural network.Applied Thermal Engineering, Vol. 27,No.1, pp. 46-54, 2007.
[33]
Yusuf Kurtgoz, Mustafa Karagoz, Emrah Deniz. 2018. Biogas engine performance estimation using ANN. Engineering science and technology, an international journal.
Browse journals by subject