ANN Based Prediction of Engine Performance and Exhaust Emission Responses of a CI Engine Powered By Ternary Blends

Year 2020, Volume: 4 Issue: 3, 180 - 184, 30.09.2020

Mustafa Karagöz

https://doi.org/10.30939/ijastech..771789

Cited By: 8

Abstract

In this study, experimental data was gathered from a single cylinder diesel engine fuelled with pyrolytic oil, neat diesel and butanol fuel blends. The experiments were conducted at varying engine loads from 0.25 kW to 1 kW with the interval of 0.25 kW. The engine performance and emission data obtained were predicted using an artificial neural network (ANN) algorithm. Assessed responses are CO, NOx, BSFC, and BTE. The results were discussed in terms of R2, MBE, and RMSE metrics. The performance and emission responses were predicted with a good R2 value of 0.986, 0.963, 0.991, and 0967 for BTE, BSFC, NOx, and CO, respectively, and all MBE value is very close to zero and smaller than 1.14. In the conclusion, the present paper showed that the ternary form of n-butanol-pyrolytic fuel and diesel fuel can be used in a CI engine with no modification on the vehicular system and the emission and performance responses of ternary fuels can be accurately predicted using an artificial neural network.

Keywords

Artificial neural network,, engine,, n-butanol,, waste tire pyrolysis oil,

References

• Adam, A., Ramlan, N, A., Jaharudin, N, F., Hamzah, H., Othman, M, F., Mrwan, A., A., G., 2017. Analysis of combustion characteristics, engine performance and exhaust emissions of diesel engine fueled with upgraded waste source fuel. international journal of hydrogen energy. 42, 17993-18004.
• Ağbulut, Ü., Sarıdemir, S., Karagöz, M., 2020a. Experimental investigation of fusel oil (isoamyl alcohol) and diesel blends in a CI engine. Fuel. 267, 117042.
• Ağbulut, Ü., Ayyıldız, M., Sarıdemir, S., 2020b. Prediction of performance, combustion and emission characteristics for a CI engine at varying injection pressures. Energy. 197, 117257.
• Ağbulut, Ü., Karagöz, M., Sarıdemir, S., Öztürk, A., 2020c. Impact of various metal-oxide based nanoparticles and biodiesel blends on the combustion, performance, emission, vibration and noise characteristics of a CI engine. Fuel. 270, 117521.
• Ağbulut, Ü., Sarıdemir, S., Albayrak, S. 2019. Experimental investigation of combustion, performance and emission characteristics of a diesel engine fuelled with diesel–biodiesel–alcohol blends. Journal of the Brazilian Society of Mechanical Sciences and Engineering. 41(9), 389.
• Doğan, O., Çelik M. B., Özdalyan, B., 2012. The effect of tire derived fuel/diesel fuel blends utilization on diesel engine performance and emissions. Fuel. 95, 340–346.
• Doğan, O., 2011. The influence of n-butanol/diesel fuel blends utilization on a small diesel engine performance and emissions. Fuel. 90, 2467–2472.
• Frigo, S., Seggiani, M., Puccini, M., Vitolo, S., 2014. Liquid fuel production from waste tyre pyrolysis and its utilization in a Diesel engine. Fuel. 116,399–408.
• Joshi, M., P., Thipse, S., S., 2019. Combustion analysis of a compression-ignition engine fuelled with an algae biofuel blend and diethyl ether as an additive by using an artificial neural network. Biofuels,1-10.
• Karagöz, M., Ağbulut, Ü., Sarıdemir, S., 2020. Waste to energy: Production of waste tire pyrolysis oil and comprehensive analysis of its usability in diesel engines, Fuel, 275; 117844.
• Karthickeyan, V., Balamurugan, P., Rohith, G., Senthil, R., 2017. Developing of ANN model for prediction of performance and emission characteristics of VCR engine with orange oil biodiesel blends. J Braz Soc Mech Sci Eng. 39(7), 2877-88.
• Kurtgoz, Y., Karagoz, M., Deniz, E., 2017. Biogas engine performance estimation using ANN. Engineering Science and Technology, an International Journal. 20, 1563–1570.
• Madane, P., Panua, R., 2019. Investigation of performance of jatropha oil on diesel engine using artificial neural network model. Int J Comput Intell IoT. 2 (2).
• Murugan, S., Ramaswamy, M. C., Nagarajan, G. 2008. The use of tyre pyrolysis oil in diesel engines. Waste management. 28 (12), 2743-2749.
• Noor, C., W., M., Mamat, R., Najafi, G., Bakar, A., A., Samo, K., 2019. Determination of bio-diesel engine combustion pressure using neural network-based model. J Eng Sci Technol. 14(2), 909-21.
• Rao, K., P., Babu, T., V., Anuradha, G., Rao, B., A., 2017. IDI diesel engine performance and exhaust emission analysis using biodiesel with an artificial neural network (ANN). Egypt J Petrol. 26(3), 593-600.
• Yusri, I., M., Mamat, R., Akasyah, M., K., Jamlos, M., F., 2019. Yusop, A., F., Evaluation of engine combustion and exhaust emissions characteristics using diesel/butanol blended fuel. Applied Thermal Engineering. 156, 209–219.