The Scope of Integrating Artificial Intelligence on the Evaporation Traits of Additive Blended Fuels
DOI:
https://doi.org/10.63746/njtd.v22i3.3026Keywords:
Ignition Delay, Artifical Intelligence, Machine Learning, evaporationAbstract
Nanoparticles and oxygenated additives significantly enhance fuel properties when blended with diesel or biodiesel, leading to improved engine performance and reduced harmful emissions. These additives influence ignition delay, a crucial factor in combustion. This study critically reviews the hot plate evaporation technique to evaluate the ignition delay of various blended fuels meticulously calibrated in diverse proportions and incorporated with cutting-edge additives, including nanoparticles (viz., CNT, CeO2, Al2O3), oxy-additives (viz., DEE, Ethylene glycol, Ethanol), and water emulsions to enhance the combustion, emission and performance characteristics of diesel engines. This fascinating research area has captured researchers' keen interest, marking it as an extensively explored scientific domain. This paper also explores the potential of integrating AI tools, such as artificial neural networks (ANN) and various algorithms, with the above technique to forecast combustion behaviour, enhance efficiency, and refine the emission characteristics of advanced fuels as a future scope of study.
References
Abdelaal, M. M., Rabee, B. A., & Hegab, A. H. (2013). Effect of adding oxygen to the intake air on a dual-fuel engine performance, emissions, and knock tendency. Energy, 61, 612–620. https://doi.org/10.1016/j.energy.2013.09.022
Abu-Zaid, M. (2003). An experimental study of the evaporation characteristics of emulsified liquid droplets. Heat and Mass Transfer, -1(1), pp.1–1.
Aghbashlo, M., Peng, W., Tabatabaei, M., Kalogirou, S. A., Soltanian, S., Hosseinzadeh-Bandbafha, H., Mahian, O., & Lam, S. S. (2021). Machine learning technology in biodiesel research: A review. Progress in Energy and Combustion Science, 85, 100904.
Ahmed, S., Lockwood, K., Miller, J. H., Huq, N., Luecke, J., Labbe, N., & Foust, T. (2023). Understanding fundamental effects of biofuel structure on ignition and physical fuel properties. Fuel, 358, 129999.
Ahmad, A., Yadav, A. K., Singh, A., Singh, D. K., & A?bulut, Ü. (2023). A hybrid RSM-GA-PSO approach on optimization of process intensification of linseed biodiesel synthesis using an ultrasonic reactor: Enhancing biodiesel properties and engine characteristics with ternary fuel blends. Energy, 288, 129077.
Al-Shanableh, F., Evcil, A., & Sava?, M. A. (2016). Prediction of Cold Flow Properties of Biodiesel Fuel Using Artificial Neural Network. Procedia Computer Science, 102, 273–280.
Awogbemi, O., & Kallon, D. V. V. (2023). Application of machine learning technologies in biodiesel production process—A review. Frontiers in Energy Research, 11.
Basha, J.S. and Anand, R. (2010b). 'Performance & emission characteristics of a DICI engine using carbon nanotubes blended diesel', International Journal of Applied Engineering Research, 2010, pp. 697-708.
Basha, J.S. and Anand, R.B. (2010). Effects of nanoparticle-blended water biodiesel emulsion fuel on working characteristics of a diesel engine. International Journal of Global Warming, 2(4), p.330.
Basha, J. S., & Anand, R. B. (2011). An experimental investigation in a diesel engine using carbon nanotubes blended Water–diesel emulsion fuel. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 225(3), 279–288.
Basha, J.S. and Anand, R.B. (2012). Effects of nanoparticle additive in the water-diesel emulsion fuel on a diesel engine's performance, emission and combustion characteristics. International Journal of Vehicle Design, 59(2/3), p.164.
Sadhik Basha, J., & Anand, R. B. (2013). The influence of nano additive blended biodiesel fuels on the working characteristics of a diesel engine. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 35(3), 257–264.
Basha, J. (2015). - Applications of Functionalized Carbon-Based Nanomaterials (pp. 598–613).crc.
Sun, X., Abdelrahman, A., Alexandrovich, T. P., Opulencia, M. J. C., Khan, A., & Algarni, M. (2022). Modelling and optimization of vegetable oil biodiesel production with heterogeneous nano catalytic process: Multi-layer perceptron, decision regression tree, and K-Nearest Neighbor methods. Environmental Technology & Innovation, 27, 102794.
Bukkarapu, K. R., & Krishnasamy, A. (2024). Investigations on the applicability of machine learning algorithms to optimize biodiesel composition for improved engine fuel properties. International Journal of Engine Research.
Cho, P., Law, C.K. and Mizomoto, M. (1991). Effect of Pressure on the Micro-explosion of Water/Oil Emulsion Droplets Over a Hot Plate. Journal of Heat Transfer, 113(1), pp.272–274.
Choi, Y., & Chen, J.-Y. (2005). Fast prediction of start-of-combustion in HCCI with combined artificial neural networks and ignition delay model. Proceedings of the Combustion Institute, 30(2), 2711–2718.
Das, S.K., Choi, S.U., Yu, W. and T. Pradeep (2007). Nanofluids. John Wiley & Sons.
Garg, A., & Jain, S. (2020). Process parameter optimization of biodiesel production from algal oil by response surface methodology and artificial neural networks. Fuel, 277, 118254.
Giwa, S. O., Adama, K. O., Mukaila, M. O., & Adekomaya, S. O. (2015). Prediction of selected biodiesel fuel properties using artificial neural network. Frontiers in Energy, 9(4), 433–445.
Pope, D.N. (2002). Effects of gravity and ambient pressure on liquid fuel droplet evaporation. International Journal of Heat and Mass Transfer, 46(2), pp.283–296.
Hossain, S., Sultana, N., Irfan, M., Sk Manirul Haque, Nawaf Nasr and Razzak, A. (2022). Artificial intelligence?based super learner approach for prediction and optimization of biodiesel synthesis—A case of waste utilization. 46(14), pp.20519–20534.
Sadhik Basha, J. (2014). An Experimental Analysis of a Diesel Engine Using Alumina Nanoparticles Blended Diesel Fuel.
J. Sadhik Basha (2016). Impact of Nanoadditive Blended Biodiesel Fuels in Diesel Engines. Green chemistry and sustainable technology, pp.325–339.
J. Sadhik Basha (2018). Impact of Carbon Nanotubes and Diethyl ether as additives with biodiesel emulsion fuels in a diesel engine – An experimental investigation. Journal of the Energy Institute, 91(2), pp.289–303.
J. Sadhik Basha and Anand. R (2010). Effects of alumina nanoparticles blended jatropha biodiesel fuel on working characteristics of a diesel engine. ResearchGate, [online] 53(2), pp.53 –62.
J. Sadhik Basha and Anand. R (2010). Effects of alumina nanoparticles blended jatropha biodiesel fuel on working characteristics of a diesel engine. Journal of Industrial Engg. & Tech., 53(2), pp.53 –62.
Basha, J. Sadhik, and R.B. Anand (2020), editors. Recent Technologies for Enhancing Performance and Reducing Emissions in Diesel Engines. IGI Global,
J. Sadhik Basha, Said, A., Mubarak, M., Al-Fazari, M., Sulaiman, A., Khalfan, M. and Balushi, A. (2019). The Influence of Hexanol and Methyl Acetate as Oxygenated Additives with Diesel Fuel in a Diesel Engine Generator. International Journal of I C Engines and Gas Turbines, 5(1), pp.1–9.
Javed, T., Ahmed, A., Lovisotto, L., Issayev, G., Badra, J., Sarathy, S. M., & Farooq, A. (2017). Ignition studies of two low-octane gasolines. Combustion and Flame, 185, 152–159.
Jeong, I., Lee, K.-H. and Kim, J. (2008). Characteristics of auto-ignition and micro-explosion behaviour of a single droplet of water-in-fuel. Journal of Mechanical Science and Technology, 22(1), pp.148–156
Kadota, T. and Yamasaki, H. (2002). 'Recent advances in the combustion of water fuel emulsion', Progress in Energy and Combustion Science, 28(5), pp. 385-404.
Khan, M., Ullah, Z., Mašek, O., Raza Naqvi, S., & Nouman Aslam Khan, M. (2022). Artificial neural networks for the prediction of biochar yield: A comparative study of metaheuristic algorithms. Bioresource Technology, 355, 127215.
Kumar, S. (2019). Comparison of linear regression and artificial neural network technique for prediction of a soybean biodiesel yield. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 42(12), 1425–1435.
Lobo, A., Bharath, L., Kumar, T. D., & Ramesha, D. K. (2021). Experimental investigation of performance, emission and combustion characteristics of compression ignition engine using waste cooking oil derived biodiesel with ethanol.
Miron, L., Chiriac, R., Brabec, M., & B?descu, V. (2021). Ignition delay and its influence on the performance of a Diesel engine operating with different Diesel–biodiesel fuels. Energy Reports, 7, 5483–5494.
Mohaddes, D., Boettcher, P., & Ihme, M. (2021). Hot surface ignition of a wall-impinging fuel spray: Modeling and analysis using large-eddy simulation. Combustion and Flame, 228, 443–456.
O?uz, H., Sar?tas, I. and Baydan, H.E. (2010). Prediction of diesel engine performance using biofuels with artificial neural network. Expert Systems with Applications, 37(9), pp.6579–6586.
Oo, C. W., Nakao, S., Shioji, M., Dugos, N. P., Roces, S. A., Reksowardojo, I., & Dung, N. N. (2015). Ignition and combustion characteristics of various biodiesel fuels (BDFs). Fuel, 158, 279–287.
Qiao, Y. M., & Chandra, S. (1996). Boiling of droplets on a hot surface in low gravity. International Journal of Heat and Mass Transfer, 39(7), 1379–1393.
Ramachandran, E., Krishnaiah, R., Venkatesan, E. P., Parida, S., Reddy Dwarshala, S. K., Khan, S. A., Asif, M., & Linul, E. (2023). Prediction of RCCI combustion fueled with CNG and algal biodiesel to sustain efficient diesel engines using machine learning techniques. Case Studies in Thermal Engineering, 51, 103630.
Rao, P. M., Dhoria, S. H., Patro, S. G. K., Gopidesi, R. K., Alkahtani, M. Q., Islam, S., Vijaya, M., Jayanthi, J. L., Khan, M. A., Razak, A., Kumar, R., Rizal, A., & Ammarullah, M. I. (2023). Artificial intelligence based modelling and hybrid optimization of linseed oil biodiesel with graphene nanoparticles to stringent biomedical safety and environmental standards. Case Studies in Thermal Engineering, 51, 103554.
Rocabruno-Valdés, C. I., Ramírez-Verduzco, L. F., & Hernández, J. A. (2015). Artificial neural network models to predict density, dynamic viscosity, and cetane number of biodiesel. Fuel, 147, 9–17.
Sadhik Basha, J. (2015). Preparation of Water-Biodiesel Emulsion Fuels with CNT & amp; Alumina Nano-Additives and their Impact on the Diesel Engine Operation. SAE Technical Paper Series.
Sadhik Basha, J. (2016). Impact of Carbon Nanotubes and Di-Ethyl Ether as additives with biodiesel emulsion fuels in a diesel engine – An experimental investigation. Journal of the Energy Institute, 91(2), 289–303.
Sadhik Basha, J. and Anand, R.B. (2011). Role of nano additive blended biodiesel emulsion fuel on the working characteristics of a diesel engine. Journal of Renewable and Sustainable Energy, 3(2), p.023106.
Sadhik Basha, J. and Anand, R.B. (2013). The influence of nano additive blended biodiesel fuels on the working characteristics of a diesel engine. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 35(3), pp.257–264.
Sadhik Basha, J. and Anand, R.B. (2014). Performance, emission and combustion characteristics of a diesel engine using Carbon Nanotubes blended Jatropha Methyl Ester Emulsions. Alexandria Engineering Journal, [online] 53(2), pp.259–273.
Seda ?ahin (2023). Comparison of machine learning algorithms for predicting diesel/biodiesel/iso-pentanol blend engine performance and emissions. Heliyon, 9(11), pp.e21365–e21365.
Sewsynker-Sukai, Y., Faloye, F., & Kana, E. B. G. (2016). Artificial neural networks: an efficient tool for modelling and optimization of biofuel production (a mini review). Biotechnology & Biotechnological Equipment, 31(2), 221–235.
Sharma, P. (2021). Artificial intelligence?based model prediction of biodiesel?fueled engine performance and emission characteristics: A comparative evaluation of gene expression programming and artificial neural network. Heat Transfer.
Sakane, Y., Kobashi, Y., Ogawa, H., Kanno, R., Yurui, Y., & Shibata, G. (2023). Arrhenius type empirical ignition delay equations based on the phenomenology of in-cylinder conditions for wide operating ranges in modern diesel engines. International Journal of Engine Research, 24(8), 3720–3733.
Treve, M., Patra, I., Prabu, P., Rama Sree, S., Keerthi Kumar, N., Methkal Abd Algani, Y., Kiran Bala, B., & Balaji, S. (2022). Performance evaluation of artificial neural networks in sustainable modelling biodiesel synthesis. Sustainable Energy Technologies and Assessments, 52, 102098.
Tuan Hoang, A., Nižeti?, S., Chyuan Ong, H., Tarelko, W., Viet Pham, V., Hieu Le, T., Quang Chau, M., & Phuong Nguyen, X. (2021). A review on the application of artificial neural network (ANN) for performance and emission characteristics of diesel engine fueled with biodiesel-based fuels. Sustainable Energy Technologies and Assessments, 47, 101416.
Tuan, N. V., Khoa, N. X., Lim, O., & Minh, D. Q. (2023). A Study to Predict Ignition Delay of an Engine Using Diesel and Biodiesel Fuel Based on the ANN and SVM Machine Learning Methods. ACS Omega, 8(11), 9995–10005.
Tyagi, H., Phelan, P.E., Prasher, R., Peck, R., Lee, T., Pacheco, J.R. and Arentzen, P. (2008). Increased Hotplate Ignition Probability for Nanoparticle-Laden Diesel Fuel. Nano Letters, 8(5), pp.1410–1416.
Wang, X., Dai, M., Wang, J., Xie, Y., Ren, G. and Jiang, G. (2019). Effect of ceria concentration on the evaporation characteristics of diesel fuel droplets. Fuel, 236, pp.1577–1585.
Wang, X., Dai, M., Xie, Y., Han, J., Ma, Y. and Chen, C. (2020). Experimental investigation of evaporation characteristics of biodiesel-diesel blend droplets with carbon nanotubes and nanoceria as nano additives. Applied Surface Science, 505, p.144186.
Zandie, M., Ng, H. K., Gan, S., Muhamad Said, M. F., & Cheng, X. (2022). Multi-input multi-output machine learning predictive model for engine performance and stability, emissions, combustion and ignition characteristics of diesel-biodiesel-gasoline blends. Energy, 262, 125425.
Zahid, A., Alathlawi, H. J., Mukhtar, Z., Islam, S., Hasan, M. A., Ali, S. K., Sher, M., Khan, M. S., Shahid, S., Patil, B. R., Al Ansari, M. S., Nawaz, Z., Qamar, M. A., & Shariq, M. (2023). Synthesis of Mn-Doped ZnO Nanoparticles and Their Application in the Transesterification of Castor Oil. Catalysts, 13(1), 105.
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