Application of Artificial Neural Networks in Predicting Internal Combustion Engine Performance and Emission Characteristics: A Review of Key Methodologies and Findings
(1) Hamada Mohasab Mail (South Valley University, Egypt)
(2) Mostafa Abouelsoud Mail (South Valley University, Egypt)
(3) Ahmed N. Shmroukh Mail (1) Department of Mechanical Engineering, Faculty of Engineering, South Valley University, Qena 83523, Egypt. 2) Faculty of Industry and Energy Technology, New Cairo Technological University, Cairo 11835, Egypt)
(4) * Nouby Ghazaly Mail (1) Department of Mechanical Engineering, Faculty of Engineering, South Valley University, Qena 83523, Egypt. 2) Technical College, Imam Ja’afar Al-Sadiq University, Baghdad, Iraq, Iraq)
*corresponding author

Abstract


The global need for fuel-efficient coupled with minimizing the environmental impacts of ICEs. This review paper highlights how different ANN methodologies such as backpropagation, recurrent neural networks (RNNs), and long short-term memory (LSTM) networks have been applied to optimize engine calibration, improve fuel efficiency, and minimize emissions across a wide range of fuel blends, including hydrogen-gasoline and ethanol-gasoline mixtures. The research focuses on the application of ANN models to predict performance indicators such as brake thermal efficiency, brake-specific fuel consumption, and emissions, reducing reliance on costly and time-consuming experimental tests. The methodology involved a systematic review of peer-reviewed studies published between 2010 and 2024. Studies were selected based on criteria such as relevance to ICE performance and emission control, use of ANN methodologies, and the availability of experimental or simulation data for validation. involves the use of advanced ANN architectures, including backpropagation, RNNs, and LSTM networks, to establish nonlinear relationships between input parameters such as engine speed, load, and fuel type, and output performance indicators. Findings show that comparison between real model and developed program enhanced from ANN model make a difference prediction capability for engine performance enhanced by at least 10 to 15 % of the traditional modeling. techniques, provide better calibration method of ICEs for better fuel consumption. efficiency and reduced emissions. This present study seeks to establish itself in matters that have not been explored in other papers or researches as follows. integration of Hybrid ANN models, which are better than conventional methods in two major trends, one of which is the improvement of the predictive accuracy and the other is the achievement of increased computational efficiency. It is found that the ANN methodologies presents a strong armory in improving the performance of ICE coupled with lowering of emissions with the possibilities of additions for further enhancements of the technology through the incorporation of other machines use of learning techniques in the future studies.


Keywords


Artificial Neural Networks; Internal Combustion Engines; Emission Control; Engine Performance; Fuel Efficiency; Non-linear Modeling; Sustainable Engine Design

   

DOI

https://doi.org/10.31763/ijrcs.v4i4.1584 		      

Article metrics

10.31763/ijrcs.v4i4.1584 Abstract views : 109 | PDF views : 22

   

Cite

How to cite item
   

Full Text

Download

References


[1] A. A. Hairuddin, T. Yusaf, and A. P. Wandel, “A review of hydrogen and natural gas addition in diesel HCCI engines,” Renewable and Sustainable Energy Reviews, vol. 32, pp. 739-761, 2014, https://doi.org/10.1016/j.rser.2014.01.018.
[2] P. V. Rameez and M. Mohamed Ibrahim, “A comprehensive review on the utilization of hydrogen in low temperature combustion strategies: Combustion, performance and emission attributes,” Journal of the Energy Institute, vol. 113, p. 101511, 2024, https://doi.org/10.1016/j.joei.2023.101511.
[3] M. El-Adawy, M. A. Nemitallah, and A. Abdelhafez, “Towards sustainable hydrogen and ammonia internal combustion engines: Challenges and opportunities,” Fuel, vol. 364, p. 131090, 2024, https://doi.org/10.1016/j.fuel.2024.131090.
[4] M. Aghbashlo et al., “Machine learning technology in biodiesel research: A review,” Progress in Energy and Combustion Science, vol. 85, p. 100904, 2021, https://doi.org/10.1016/j.pecs.2021.100904.
[5] J. G. B. Churchill, V. B. Borugadda, and A. K. Dalai, “A review on the production and application of tall oil with a focus on sustainable fuels,” Renewable and Sustainable Energy Reviews, vol. 191, p. 114098, 2024, https://doi.org/10.1016/j.rser.2023.114098.
[6] J. Benajes, A. García, J. Monsalve-Serrano, and M. Guzmán-Mendoza, “A review on low carbon fuels for road vehicles: The good, the bad and the energy potential for the transport sector,” Fuel, vol. 361, p. 130647, 2024, https://doi.org/10.1016/j.fuel.2023.130647.
[7] Q. Wang et al., “The present situation, challenges, and prospects of the application of ammonia–coal co-firing technology in power plant boilers,” Journal of the Energy Institute, vol. 113, p. 101531, 2024, https://doi.org/10.1016/j.joei.2024.101531.
[8] E. M. A. Mokheimer, M. R. Shakeel, A. Harale, S. Paglieri, and R. Ben Mansour, “Fuel reforming processes for hydrogen production,” Fuel, vol. 359, p. 130427, 2024, https://doi.org/10.1016/j.fuel.2023.130427.
[9] M. S. Hofny, N. M. Ghazaly, A. N. Shmroukh, M. Abouelsoud, “Comparative Study of ANN and SVM Model Network Performance for Predicting Brake Power in SI Engines Using E15 Fuel,” International Journal of Robotics and Control Systems, vol. 4, no. 3, pp. 979-999, 2024, https://doi.org/10.31763/ijrcs.v4i3.1429.
[10] M. Gad and N. M. Ghazaly, “Evaluation of Parameters Affecting the Performance of the Spark Ignition Engine,” SVU-International Journal of Engineering Sciences and Applications, vol. 4, no. 2, pp. 216-224, 2023, https://doi.org/10.21608/svusrc.2023.197849.1113.
[11] I. Veza et al., “Review of artificial neural networks for gasoline, diesel and homogeneous charge compression ignition engine: Review of ANN for gasoline, diesel and HCCI engine,” Alexandria Engineering Journal, vol. 61, no. 11, pp. 8363-8391, 2022, https://doi.org/10.1016/j.aej.2022.01.072.
[12] S. K. Mahla, T. Goyal, D. Goyal, H. Sharma, A. Dhir, and G. Goga, “Optimization of engine operating variables on performance and emissions characteristics of biogas fuelled CI engine by the design of experiments: Taguchi approach,” Environmental Progress & Sustainable Energy, vol. 41, no. 2, p. e13736, 2022, https://doi.org/10.1002/ep.13736.
[13] A. C. T. Malaquias, N. A. D. Netto, R. B. R. da Costa, A. F. Teixeira, S. A. P. Costa, and J. G. C. Baêta, “An evaluation of combustion aspects with different compression ratios, fuel types and injection systems in a single-cylinder research engine,” Journal of the Brazilian Society of Mechanical Sciences and Engineering, vol. 42, 2020, https://doi.org/10.1007/s40430-020-02575-0.
[14] T. G. Leone et al., “The Effect of Compression Ratio, Fuel Octane Rating, and Ethanol Content on Spark-Ignition Engine Efficiency,” Environmental Science & Technology, vol. 49, no. 18, pp. 10778–10789, 2015, https://doi.org/10.1021/acs.est.5b01420.
[15] T. G. Leone, E. D. Olin, J. E. Anderson, H. H. Jung, M. H. Shelby, and R. A. Stein, “Effects of Fuel Octane Rating and Ethanol Content on Knock, Fuel Economy, and CO2 for a Turbocharged DI Engine,” SAE International Journal of Fuels and Lubricants, vol. 7, no. 1, pp. 9-28, 2014, https://doi.org/10.4271/2014-01-1228.
[16] M. A. Hashim, M. H. Nasef, A. E. Kabeel, and N. M. Ghazaly, “Combustion fault detection technique of spark ignition engine based on wavelet packet transform and artificial neural network,” Alexandria Engineering Journal, vol. 59, no. 5, pp. 3687–3697, 2020, https://doi.org/10.1016/j.aej.2020.06.023.
[17] M. S. Hofny, N. M. Ghazaly, A. N. Shmroukh, and M. Abouelsoud, “Predicting SI Engine Performance Using Deep Learning with CNNs on Time-Series Data,” Journal of Robotics and Control (JRC), vol. 5, no. 5, pp. 1457–1469, 2024, https://doi.org/10.18196/jrc.v5i5.22558.
[18] M. Baratta and N. Rapetto, “Mixture formation analysis in a direct-injection NG SI engine under different injection timings,” Fuel, vol. 159, pp. 675-688, 2015, https://doi.org/10.1016/j.fuel.2015.07.027.
[19] K. Zeng et al., “Combustion characteristics of a direct-injection natural gas engine under various fuel injection timings,” Applied Thermal Engineering, vol. 26, no. 8-9, pp. 806-813, 2006, https://doi.org/10.1016/j.applthermaleng.2005.10.011.
[20] L. Chen, X. Zhang, R. Zhang, J. Li, J. Pan, H. Wei, “Effect of hydrogen direct injection on natural gas/hydrogen engine performance under high compression-ratio conditions,” International Journal of Hydrogen Energy, vol. 47, no. 77, pp. 33082-33093, 2022, https://doi.org/10.1016/j.ijhydene.2022.07.176.
[21] B. Zhang, S. Wu, D. Wang, S. Yang, F. Jiang, and C. Li, “A review of surface quality control technology for robotic abrasive belt grinding of aero-engine blades,” Measurement, vol. 220, p. 113381, 2023, https://doi.org/10.1016/j.measurement.2023.113381.
[22] S. Salam, T. Choudhary, A. Pugazhendhi, T. N. Verma, and A. Sharma, “A review on recent progress in computational and empirical studies of compression ignition internal combustion engine,” Fuel, vol. 279, p. 118469, 2020, https://doi.org/10.1016/j.fuel.2020.118469.
[23] I. Veza, M. Spraggon, I. M. R. Fattah, and M. Idris, “Response surface methodology (RSM) for optimizing engine performance and emissions fueled with biofuel: Review of RSM for sustainability energy transition,” Results in Engineering, vol. 18, p. 101213, 2023, https://doi.org/10.1016/j.rineng.2023.101213.
[24] G. Leoncini, R. Mothier, B. Michel, and M. Clausse, “A review on challenges concerning thermal management system design for medium duty electric vehicles,” Applied Thermal Engineering, vol. 236, p. 121464, 2024, https://doi.org/10.1016/j.applthermaleng.2023.121464.
[25] A. Roy and S. Pramanik, “A review of the hydrogen fuel path to emission reduction in the surface transport industry,” International Journal of Hydrogen Energy, vol. 49, pp. 792–821, 2024, https://doi.org/10.1016/j.ijhydene.2023.07.010.
[26] Y. Lv, X. Yang, Y. Li, J. Liu, and S. Li, “Fault detection and diagnosis of marine diesel engines: A systematic review,” Ocean Engineering, vol. 294, p. 116798, 2024, https://doi.org/10.1016/j.oceaneng.2024.116798.
[27] Y. Yang, T. Nikolaidis, S. Jafari, and P. Pilidis, “Gas turbine engine transient performance and heat transfer effect modelling: A comprehensive review, research challenges, and exploring the future,” Applied Thermal Engineering, vol. 236, p. 121523, 2024, https://doi.org/10.1016/j.applthermaleng.2023.121523.
[28] M. Aydın, S. Uslu, and M. Bahattin Çelik, “Performance and emission prediction of a compression ignition engine fueled with biodiesel-diesel blends: A combined application of ANN and RSM based optimization,” Fuel, vol. 269, p. 117472, 2020, https://doi.org/10.1016/j.fuel.2020.117472.
[29] S. Uslu and M. B. Celik, “Prediction of engine emissions and performance with artificial neural networks in a single cylinder diesel engine using diethyl ether,” Engineering Science and Technology, an International Journal, vol. 21, no. 6, pp. 1194-1201, 2018, https://doi.org/10.1016/j.jestch.2018.08.017.
[30] U. Ahmed, F. Ali, I. Jennions, “Signal processing of acoustic data for condition monitoring of an aircraft ignition system,” Machines, vol. 10, no. 9, p. 822, 2022, https://doi.org/10.3390/machines10090822.
[31] K. P. Rao, T. V. Babu, G. Anuradha, and B. V. A. Rao, “IDI diesel engine performance and exhaust emission analysis using biodiesel with an artificial neural network (ANN),” Egyptian Journal of Petroleum, vol. 26, no. 3, pp. 593-600, 2017, https://doi.org/10.1016/j.ejpe.2016.08.006.
[32] G. W. Malaczynski, M. Mueller, J. Pfeiffer, D. Cabush, and K. Hoyer, “Replacing Volumetric Efficiency Calibration Look-up Tables with Artificial Neural Network-based Algorithm for Variable Valve Actuation,” SAE Technical Paper, 2010, https://doi.org/10.4271/2010-01-0158.
[33] B. Ghobadian, H. Rahimi, A. M. Nikbakht, G. Najafi, and T. F. Yusaf, “Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network,” Renewable Energy, vol. 34, no. 4, pp. 976-982, 2009, https://doi.org/10.1016/j.renene.2008.08.008.
[34] L. Ma, Y. Han, K. Sun, J. Lu, and J. Ding, “Optimization of acidified oil esterification catalyzed by sulfonated cation exchange resin using response surface methodology,” Energy Conversion and Management, vol. 98, pp. 46-53, 2015, https://doi.org/10.1016/j.enconman.2015.03.092.
[35] A. N. Şener, M. E. Günay, A. Leba, and R. Yıldırım, “Statistical review of dry reforming of methane literature using decision tree and artificial neural network analysis,” Catalysis Today, vol. 299, pp. 289–302, 2018, https://doi.org/10.1016/j.cattod.2017.05.012.
[36] S. A. Naghibi, E. Salehi, M. Khajavian, V. Vatanpour, and M. Sillanpää, “Multivariate data-based optimization of membrane adsorption process for wastewater treatment: Multi-layer perceptron adaptive neural network versus adaptive neural fuzzy inference system,” Chemosphere, vol. 267, p. 129268, 2021, https://doi.org/10.1016/j.chemosphere.2020.129268.
[37] S. Fathi and H. Sadeghi, “Improvement of the five-hole probe calibration using artificial neural networks,” Flow Measurement and Instrumentation, vol. 86, p. 102189, 2022, https://doi.org/10.1016/j.flowmeasinst.2022.102189.
[38] S. Delfani, M. Esmaeili, and M. Karami, “Application of artificial neural network for performance prediction of a nanofluid-based direct absorption solar collector,” Sustainable Energy Technologies and Assessments, vol. 36, p. 100559, 2019, https://doi.org/10.1016/j.seta.2019.100559.
[39] A. H. A. Al-Waeli, K. Sopian, J. H. Yousif, H. A. Kazem, J. Boland, and M. T. Chaichan, “Artificial neural network modeling and analysis of photovoltaic/thermal system based on the experimental study,” Energy Conversion and Management, vol. 186, pp. 368-379, 2019, https://doi.org/10.1016/j.enconman.2019.02.066.
[40] T. R. Kiran and S. P. S. Rajput, “An effectiveness model for an indirect evaporative cooling (IEC) system: Comparison of artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS) and fuzzy inference system (FIS) approach,” Applied Soft Computing, vol. 11, no. 4, pp. 3525–3533, 2011, https://doi.org/10.1016/j.asoc.2011.01.025.
[41] S. M. Opalic et al., “ANN modelling of CO2 refrigerant cooling system COP in a smart warehouse,” Journal of Cleaner Production, vol. 260, p. 120887, 2020, https://doi.org/10.1016/j.jclepro.2020.120887.
[42] R. Selvaraj, I. G. Moorthy, R. V. Kumar, and V. Sivasubramanian, “Microwave mediated production of FAME from waste cooking oil: Modelling and optimization of process parameters by RSM and ANN approach,” Fuel, vol. 237, pp. 40–49, 2019, https://doi.org/10.1016/j.fuel.2018.09.147.
[43] A. Karaci, A. Caglar, B. Aydinli, and S. Pekol, “The pyrolysis process verification of hydrogen rich gas (H–rG) production by artificial neural network (ANN),” International Journal of Hydrogen Energy, vol. 41, no. 8, pp. 4570-4578, 2016, https://doi.org/10.1016/j.ijhydene.2016.01.094.
[44] S. Gupta, P. Patel, and P. Mondal, “Biofuels production from pine needles via pyrolysis: Process parameters modeling and optimization through combined RSM and ANN based approach,” Fuel, vol. 310, p. 122230, 2022, https://doi.org/10.1016/j.fuel.2021.122230.
[45] H. Vardhan, S. Sasamal, and K. Mohanty, “Fermentation process optimisation based on ANN and RSM for xylitol production from areca nut husk followed by xylitol crystal characterisation,” Process Biochemistry, vol. 122, pp. 146-159, 2022, https://doi.org/10.1016/j.procbio.2022.10.005.
[46] N. Sun et al., “An intelligent plate fin-and-tube heat exchanger design system through integration of CFD, NSGA-II, ANN and TOPSIS,” Expert Systems with Applications, vol. 233, p. 120926, 2023, https://doi.org/10.1016/j.eswa.2023.120926.
[47] A. Thapa, S. Nishad, D. Biswas, and S. Roy, “A comprehensive review on artificial intelligence assisted technologies in food industry,” Food Bioscience, vol. 56, p. 103231, 2023, https://doi.org/10.1016/j.fbio.2023.103231.
[48] D. Wang, D. Tan, and L. Liu, “Particle swarm optimization algorithm: an overview,” Soft Computing, vol. 22, no. 2, pp. 387-408, 2018, https://doi.org/10.1007/s00500-016-2474-6.
[49] O. F. Aje, O. Friday Aje, and A. A. Josephat, “The particle swarm optimization (PSO) algorithm application-A review,” Global Journal of Engineering and Technology Advances, vol. 2020, no. 03, pp. 1–6, 2020, https://doi.org/10.30574/gjeta.2020.3.3.0033.
[50] Y. Zhang, S. Wang, and G. Ji, “A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications,” Mathematical Problems in Engineering, vol. 2015, no. 1, pp. 1-38, 2015, https://doi.org/10.1155/2015/931256.
[51] A. A. Jiménez, F. P. García Márquez, V. B. Moraleda, and C. Q. Gómez Muñoz, “Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis,” Renewable Energy, vol. 132, pp. 1034-1048, 2019, https://doi.org/10.1016/j.renene.2018.08.050.
[52] A. Kumar, M. Zunaid, K. A. Subramanian, and H. Lim, “Recent Advances in Manufacturing and Thermal Engineering,” Lecture Notes in Mechanical Engineering, 2023, https://doi.org/10.1007/978-981-19-8517-1.
[53] R. Yang et al., “An Artificial Neural Network Model to Predict Efficiency and Emissions of a Gasoline Engine,” Processes, vol. 10, no. 2, p. 204, 2022, https://doi.org/10.3390/pr10020204.
[54] B. Ji et al., "A Survey of Computational Intelligence for 6G: Key Technologies, Applications and Trends," IEEE Transactions on Industrial Informatics, vol. 17, no. 10, pp. 7145-7154, 2021, https://doi.org/10.1109/TII.2021.3052531.
[55] O. I. Abiodun et al., "Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition," IEEE Access, vol. 7, pp. 158820-158846, 2019, https://doi.org/10.1109/ACCESS.2019.2945545.
[56] M. A. Shahin, M. B. Jaksa, and H. R. Maier, “Recent Advances and Future Challenges for Artificial Neural Systems in Geotechnical Engineering Applications,” Advances in Artificial Neural Systems, vol. 2009, no. 1, pp. 1-9, 2009, https://doi.org/10.1155/2009/308239.
[57] O. Alghushairy, R. Alsini, T. Soule, and X. Ma, “A Review of Local Outlier Factor Algorithms for Outlier Detection in Big Data Streams,” Big Data and Cognitive Computing, vol. 5, no. 1, p. 1, 2020, https://doi.org/10.3390/bdcc5010001.
[58] L. Guzzella, C. Onder, “Introduction to modeling and control of internal combustion engine systems,” Springer Science & Business Media, 2009, https://books.google.co.id/books?id=BsN9-v0esd8C&printsec=frontcover&redir_esc=y#v=onepage&q&f=false.
[59] Z. Yang et al., “A review on guided-ultrasonic-wave-based structural health monitoring: From fundamental theory to machine learning techniques,” Ultrasonics, vol. 133, p. 107014, 2023, https://doi.org/10.1016/j.ultras.2023.107014.
[60] Y. F. C. Chan et al., "Artificial Neural Network for Predicting a Turbocharged Engine Performance: An Empirical Study," 2022 International Conference on Edge Computing and Applications (ICECAA), pp. 1066-1075, 2022, https://doi.org/10.1109/ICECAA55415.2022.9936387.
[61] S. M. More, J. Kakati, and U. K. Saha, “Evaluating the Operating Parameters of a Compression Ignition Engine Fueled With Waste Cooking Oil—Diesel Blends Using Artificial Neural Network and Ensemble Methods,” Journal of Engineering for Gas Turbines and Power, vol. 145, no. 7, p. 071010, 2023, https://doi.org/10.1115/1.4062411.
[62] L. T. Le, H. Nguyen, J. Dou, J. Zhou, “A comparative study of PSO-ANN, GA-ANN, ICA-ANN, and ABC-ANN in estimating the heating load of buildings’ energy efficiency for smart city planning,” Applied Sciences, vol. 9, no. 13, p. 2630, 2019, https://doi.org/10.3390/app9132630.
[63] M. Krishnamoorthi and R. Malayalamurthi, “Engine characteristics analysis of chaulmoogra oil blends and corrosion analysis of injector nozzle using scanning electron microscopy/energy dispersive spectroscopy,” Energy, vol. 165, pp. 1292-1319, 2018, https://doi.org/10.1016/j.energy.2018.10.112.
[64] A. Kurani, P. Doshi, A. Vakharia, M. Shah, “A comprehensive comparative study of artificial neural network (ANN) and support vector machines (SVM) on stock forecasting,” Annals of Data Science, vol. 10, no. 1, pp. 183-208, 2023, https://doi.org/10.1007/s40745-021-00344-x.
[65] I. M. Yusri, A. P. P. Abdul Majeed, R. Mamat, M. F. Ghazali, O. I. Awad, and W. H. Azmi, “A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel,” Renewable and Sustainable Energy Reviews, vol. 90, pp. 665-686, 2018, https://doi.org/10.1016/j.rser.2018.03.095.
[66] P. Sinčák, P. Hartono, M. Virčíková, J. Vaščák, and R. Jakša, “Emergent Trends in Robotics and Intelligent Systems,” Advances in Intelligent Systems and Computing, 2015, https://doi.org/10.1007/978-3-319-10783-7.
[67] H. R. Lewis, “Ideas That Created the Future: Classic Papers of Computer Science,” The MIT Press, 2021, https://doi.org/10.7551/mitpress/12274.001.0001.
[68] B. Liu, L. Wang and M. Liu, "Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems," IEEE Robotics and Automation Letters, vol. 4, no. 4, pp. 4555-4562, 2019, https://doi.org/10.1109/LRA.2019.2931179.
[69] U. Kamath, K. L. Graham, and W. Emara, “Transformers for Machine Learning,” Chapman and Hall/CRC, 2022, https://doi.org/10.1201/9781003170082.
[70] Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang and P. S. Yu, "A Comprehensive Survey on Graph Neural Networks," IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 1, pp. 4-24, 2021, https://doi.org/10.1109/TNNLS.2020.2978386.
[71] G. D. Franco and M. Santurro, “Machine learning, artificial neural networks and social research,” Quality & Quantity, vol. 55, no. 3, pp. 1007–1025, 2021, https://doi.org/10.1007/s11135-020-01037-y.
[72] J. C. Hoskins and D. M. Himmelblau, “Artificial Neural Network Models Of Knowledge Representation In Chemical Engineering,” Computers & Chemical Engineering, vol. 12, no. 9-10, pp. 881-890, 1988, https://doi.org/10.1016/0098-1354(88)87015-7.
[73] H. Soukht Saraee, H. Taghavifar, and S. Jafarmadar, “Experimental and numerical consideration of the effect of CeO2 nanoparticles on diesel engine performance and exhaust emission with the aid of artificial neural network,” Applied Thermal Engineering, vol. 113, pp. 663-672, 2017, https://doi.org/10.1016/j.applthermaleng.2016.11.044.
[74] S. Agatonovic-Kustrin and R. Beresford, “Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research,” Journal of Pharmaceutical and Biomedical Analysis, vol. 22, no. 5, pp. 717-727, 2000, https://doi.org/10.1016/S0731-7085(99)00272-1.
[75] K. Suzuki, “Artificial Neural Networks - Methodological Advances and Biomedical Applications,” IntechOpen, 2011, https://doi.org/10.5772/644.
[76] N. Kasabov, “Foundations of neural networks, fuzzy systems, and knowledge engineering,” MIT Press, 1996, https://doi.org/10.7551/mitpress/3071.001.0001.
[77] D. Y. Wang, D. L. Lin, Y. Z. Fan, “An Adaptive Fuzzy Neural Network Controller and its Application,” IFAC Proceedings Volumes, vol. 32, no. 2, pp. 5261-5264, 1999, https://doi.org/10.1016/S1474-6670(17)56895-0.
[78] R. M. Borisyuk and A. B. Kirillov, “Biological Cybemetics Bifurcation analysis of a neural network model,” Biological Cybernetics, 1992, https://doi.org/10.1007/BF00203668.
[79] F. Feng, W. Na, J. Jin, J. Zhang, W. Zhang and Q. -J. Zhang, "Artificial Neural Networks for Microwave Computer-Aided Design: The State of the Art," IEEE Transactions on Microwave Theory and Techniques, vol. 70, no. 11, pp. 4597-4619, 2022, https://doi.org/10.1109/TMTT.2022.3197751.
[80] I. N. Silva, D. H. Spatti, R. A. Flauzino, L. Liboni, S. Alves, “Artificial neural networks: a practical course,” Springer, 2016, https://library.wur.nl/WebQuery/titel/2197406.
[81] J. Yao, X. Wang, S. Hu, and W. Fu, “Adaline neural network-based adaptive inverse control for an electro-hydraulic servo system,” JVC/Journal of Vibration and Control, vol. 17, no. 13, pp. 2007-2014, 2011, https://doi.org/10.1177/1077546310395972.
[82] M. H. Sazlı, “A brief review of feed-forward neural networks,” Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, vol. 50, no. 1, pp. 11–17, 2006, https://doi.org/10.1501/commua1-2_0000000026.
[83] T. K. Gupta and K. Raza, “Optimization of ANN Architecture: A Review on Nature-Inspired Techniques,” Machine Learning in Bio-Signal Analysis and Diagnostic Imaging, pp. 159-182, 2019, https://doi.org/10.1016/B978-0-12-816086-2.00007-2.
[84] V. K. Ojha, A. Abraham, and V. Snášel, “Metaheuristic design of feedforward neural networks: A review of two decades of research,” Engineering Applications of Artificial Intelligence, vol. 60, pp. 97-116, 2017, https://doi.org/10.1016/j.engappai.2017.01.013.
[85] E. Amaldi, E. Mayorazl, and D. D. Werra, “A review of combinatorial problems arising in feedforward neural network design,” Discrete Applied Mathematics, vol. 52, no. 2, pp. 111-138, 1994, https://doi.org/10.1016/0166-218X(92)00184-N.
[86] L. Luo, “Architectures of neuronal circuits,” Science, vol. 373, no. 6559, p. 7285, 2021, https://doi.org/10.1126/science.abg7285.
[87] Z. C. Lipton, J. Berkowitz, and C. Elkan, “A Critical Review of Recurrent Neural Networks for Sequence Learning,” Machine Learning, 2015, https://doi.org/10.48550/arXiv.1506.00019.
[88] S. B. Unadkat, M. M. Ciocuiu, L. R. Medsker, “Recurrent Neural Networks: Design and Applications,” CRC Press, 2001, https://books.google.co.id/books/about/Recurrent_Neural_Networks.html?id=dM6pngEACAAJ&redir_esc=y.
[89] A. Merabet, S. Kanukollu, A. Al-Durra, and E. F. El-Saadany, “Adaptive recurrent neural network for uncertainties estimation in feedback control system,” Journal of Automation and Intelligence, vol. 2, no. 3, pp. 119–129, 2023, https://doi.org/10.1016/j.jai.2023.07.001.
[90] A. Marvuglia, A. Messineo, “Using Recurrent Artificial Neural Networks to Forecast Household Electricity Consumption,” Energy Procedia, vol. 14, pp. 45-55, 2012, https://doi.org/10.1016/j.egypro.2011.12.895.
[91] Q. Wang et al., “Real-time prediction of wave-induced hull girder loads for a large container ship based on the recurrent neural network model and error correction strategy,” International Journal of Naval Architecture and Ocean Engineering, vol. 16, p. 100587, 2024, https://doi.org/10.1016/j.ijnaoe.2024.100587.
[92] A. Goulas, F. Damicelli, and C. C. Hilgetag, “Bio-instantiated recurrent neural networks: Integrating neurobiology-based network topology in artificial networks,” Neural Networks, vol. 142, pp. 608–618, 2021, https://doi.org/10.1016/j.neunet.2021.07.011.
[93] E. Egrioglu, U. Yolcu, C. H. Aladag, and E. Bas, “Recurrent Multiplicative Neuron Model Artificial Neural Network for Non-linear Time Series Forecasting,” Procedia - Social and Behavioral Sciences, vol. 109, pp. 1094-1100, 2014, https://doi.org/10.1016/j.sbspro.2013.12.593.
[94] J. L. Bedford and I. M. Hanson, “A recurrent neural network for rapid detection of delivery errors during real-time portal dosimetry,” Physics and Imaging in Radiation Oncology, vol. 22, pp. 36-43, 2022, https://doi.org/10.1016/j.phro.2022.03.004.
[95] S. Sharma, S. Sharma, and A. Athaiya, “Activation Functions in Neural Networks,” International Journal of Engineering Applied Sciences and Technology, vol. 4, no. 12, pp. 310-316, 2020, https://doi.org/10.33564/IJEAST.2020.v04i12.054.
[96] Y. Wang, Y. Li, Y. Song, and X. Rong, “The influence of the activation function in a convolution neural network model of facial expression recognition,” Applied Sciences, vol. 10, no. 5, p. 1897, 2020, https://doi.org/10.3390/app10051897.
[97] P. Sibi, S. A. Jones, and P. Siddarth, “Analysis Of Different Activation Functions Using Back Propagation Neural Networks,” Journal of Theoretical and Applied Information Technology, vol. 47, no. 3, pp. 1264-1268, 2013, https://www.jatit.org/volumes/Vol47No3/61Vol47No3.pdf.
[98] A. Apicella, F. Donnarumma, F. Isgrò, and R. Prevete, “A survey on modern trainable activation functions,” Neural Networks, vol. 138, pp. 14-32, 2021, https://doi.org/10.1016/j.neunet.2021.01.026.
[99] S. Hayou, A. Doucet, and J. Rousseau, “On the Impact of the Activation Function on Deep Neural Networks Training,” Proceedings of the 36th International Conference on Machine Learning, pp. 2672-2680, 2019, https://proceedings.mlr.press/v97/hayou19a.html.
[100] Ö. F. Ertuğrul, “A novel type of activation function in artificial neural networks: Trained activation function,” Neural Networks, vol. 99, pp. 148-157, 2018, https://doi.org/10.1016/j.neunet.2018.01.007.
[101] J. Ilonen, J.-K. Kamarainen, and J. Lampinen, “Differential Evolution Training Algorithm for Feed-Forward Neural Networks,” Neural Processing Letters, vol. 17, pp. 93-105, 2003, https://doi.org/10.1023/A:1022995128597.
[102] F. -L. Fan, J. Xiong, M. Li and G. Wang, "On Interpretability of Artificial Neural Networks: A Survey," IEEE Transactions on Radiation and Plasma Medical Sciences, vol. 5, no. 6, pp. 741-760, 2021, https://doi.org/10.1109/TRPMS.2021.3066428.
[103] M. Chen, U. Challita, W. Saad, C. Yin and M. Debbah, "Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial," IEEE Communications Surveys & Tutorials, vol. 21, no. 4, pp. 3039-3071, 2019, https://doi.org/10.1109/COMST.2019.2926625.
[104] K. K. Jasińska, S. Nematova, H. Brice, and X. Yang, “Neural Activation during Phonological Processing in Primary-School Children with Limited Reading Experience: Insights from Rural Côte d’Ivoire,” Mind, Brain, and Education, vol. 18, no. 3, pp. 307-322, 2024, https://doi.org/10.1111/mbe.12411.
[105] M. Kamil and M. M. Rahman, “Performance prediction of spark-ignition engine running on gasoline-hydrogen and methane-hydrogen blends,” Applied Energy, vol. 158, pp. 556–567, 2015, https://doi.org/10.1016/j.apenergy.2015.08.041.
[106] Z. Liang, F. Xie, K. Lai, H. Chen, J. Du, and X. Li, “Study of single and split injection strategies on combustion and emissions of hydrogen DISI engine,” International Journal of Hydrogen Energy, vol. 49, pp. 1087-1099, 2024, https://doi.org/10.1016/j.ijhydene.2023.10.060.
[107] R. Novella, A. García, J. Gomez-Soriano, and Á. Fogué-Robles, “Numerical assessment of water injection for improved thermal efficiency and emissions control in a medium-duty hydrogen engine for transportation applications,” Fuel, vol. 359, p. 130455, 2024, https://doi.org/10.1016/j.fuel.2023.130455.
[108] M. H. Dinesh and G. N. Kumar, “Experimental investigation of variable compression ratio and ignition timing effects on performance, combustion, and Nox emission of an ammonia/hydrogen-fuelled Si engine,” International Journal of Hydrogen Energy, vol. 48, no. 90, pp. 35139-35152, 2023, https://doi.org/10.1016/j.ijhydene.2023.05.219.
[109] S. Wang, J. Li, and A. Fan, “Effects of channel height on the combustion efficiency and thermal performances of a micro Swiss-roll combustor,” Applied Thermal Engineering, vol. 238, p. 121956, 2024, https://doi.org/10.1016/j.applthermaleng.2023.121956.
[110] N. Gültekin, H. E. Gülcan, and M. Ciniviz, “Investigation of the effects of hydrogen energy ratio and valve lift amount on performance and emissions in a hydrogen-diesel dual-fuel compression ignition engine,” International Journal of Hydrogen Energy, vol. 49, pp. 352-366, 2024, https://doi.org/10.1016/j.ijhydene.2023.07.294.
[111] M. M. El-Kassaby, Y. A. Eldrainy, M. E. Khidr, and K. I. Khidr, “Effect of hydroxy (HHO) gas addition on gasoline engine performance and emissions,” Alexandria Engineering Journal, vol. 55, no. 1, pp. 243-251, 2016, https://doi.org/10.1016/j.aej.2015.10.016.
[112] S. J. Navale, R. R. Kulkarni, and S. S. Thipse, “An experimental study on performance, emission and combustion parameters of hydrogen fueled spark ignition engine with the timed manifold injection system,” International Journal of Hydrogen Energy, vol. 42, no. 12, pp. 8299-8309, 2017, https://doi.org/10.1016/j.ijhydene.2017.01.059.
[113] C. Ji, S. Wang, and B. Zhang, “Performance of a hybrid hydrogen-gasoline engine under various operating conditions,” Applied Energy, vol. 97, pp. 584–589, 2012, https://doi.org/10.1016/j.apenergy.2011.11.056.
[114] D. Sáinz, P. M. Diéguez, C. Sopena, J. C. Urroz, and L. M. Gandía, “Conversion of a commercial gasoline vehicle to run bi-fuel (hydrogen-gasoline),” International Journal of Hydrogen Energy, vol. 37, no. 2, pp. 1781–1789, 2012, https://doi.org/10.1016/j.ijhydene.2011.10.046.
[115] K. V. Shivaprasad, S. Raviteja, P. Chitragar, and G. N. Kumar, “Experimental Investigation of the Effect of Hydrogen Addition on Combustion Performance and Emissions Characteristics of a Spark Ignition High Speed Gasoline Engine,” Procedia Technology, vol. 14, pp. 141-148, 2014, https://doi.org/10.1016/j.protcy.2014.08.019.
[116] S. Wang and C. Ji, “Cyclic variation in a hydrogen-enriched spark-ignition gasoline engine under various operating conditions,” International Journal of Hydrogen Energy, vol. 37, no. 1, pp. 1112–1119, 2012, https://doi.org/10.1016/j.ijhydene.2011.02.079.
[117] X. Duan et al., “Performance analysis and comparison of the spark ignition engine fuelled with industrial by-product hydrogen and gasoline,” Journal of Cleaner Production, vol. 424, p. 138899, 2023, https://doi.org/10.1016/j.jclepro.2023.138899.
[118] C. P. García, S. Orjuela Abril, and J. P. León, “Analysis of performance, emissions, and lubrication in a spark-ignition engine fueled with hydrogen gas mixtures,” Heliyon, vol. 8, no. 11, p. e11353, 2022, https://doi.org/10.1016/j.heliyon.2022.e11353.
[119] R. Wang et al., “Experimental investigation of the influence of pre-chamber structural parameters on combustion and emission characteristics of a high compression ratio SI engine,” Thermal Science and Engineering Progress, vol. 47, p. 102318, 2024, https://doi.org/10.1016/j.tsep.2023.102318.
[120] C. Ji, S. Wang, and B. Zhang, “Combustion and emissions characteristics of a hybrid hydrogen-gasoline engine under various loads and lean conditions,” International Journal of Hydrogen Energy, vol. 35, no. 11, pp. 5714-5722, 2010, https://doi.org/10.1016/j.ijhydene.2010.03.033.
[121] S. Sarıkoç, “Effect of H2 addition to methanol-gasoline blend on an SI engine at various lambda values and engine loads: A case of performance, combustion, and emission characteristics,” Fuel, vol. 297, p. 120732, 2021, https://doi.org/10.1016/j.fuel.2021.120732.
[122] H. Yun, Z. Bu, Z. Yang, L. Wang, and B. Zhang, “Optimization of fuel injection timing and ignition timing of hydrogen fueled SI engine based on DOE-MPGA,” International Journal of Hydrogen Energy, vol. 48, no. 25, pp. 9462–9473, 2023, https://doi.org/10.1016/j.ijhydene.2022.12.068.
[123] F. Zhao, B. Sun, S. Yuan, L. Bao, H. Wei, and Q. Luo, “Experimental and modeling investigations to improve the performance of the near-zero NOx emissions direct-injection hydrogen engine by injection optimization,” International Journal of Hydrogen Energy, vol. 49, pp. 713-724, 2024, https://doi.org/10.1016/j.ijhydene.2023.09.039.
[124] C. Park et al., “Effect of fuel injection timing and injection pressure on performance in a hydrogen direct injection engine,” International Journal of Hydrogen Energy, vol. 47, no. 50, pp. 21552–21564, 2022, https://doi.org/10.1016/j.ijhydene.2022.04.274.
[125] Y. Qiang et al., “Study on the effect of variable valve timing and spark timing on the performance of the hydrogen-fueled engine with passive pre-chamber ignition under partial load conditions,” Energy Conversion and Management, vol. 302, p. 118104, 2024, https://doi.org/10.1016/j.enconman.2024.118104.
[126] F. S. D. Carvalho et al., “Experimental investigation of hydrogen-producer gas mixtures in an optically accessible SI engine,” International Journal of Hydrogen Energy, vol. 58, pp. 500-513, 2024, https://doi.org/10.1016/j.ijhydene.2024.01.165.
[127] C. Ji, X. Cong, S. Wang, L. Shi, T. Su, and D. Wang, “Performance of a hydrogen-blended gasoline direct injection engine under various second gasoline direct injection timings,” Energy Conversion and Management, vol. 171, pp. 1704-1711, 2018, https://doi.org/10.1016/j.enconman.2018.06.112.
[128] C. Ji, S. Wang, and B. Zhang, “Effect of spark timing on the performance of a hybrid hydrogen-gasoline engine at lean conditions,” International Journal of Hydrogen Energy, vol. 35, no. 5, pp. 2203-2212, 2010, https://doi.org/10.1016/j.ijhydene.2010.01.003.
[129] I. M. M. Elsemary, A. A. A. Attia, K. H. Elnagar, and M. S. Elsaleh, “Spark timing effect on performance of gasoline engine fueled with mixture of hydrogen–gasoline,” International Journal of Hydrogen Energy, vol. 42, no. 52, pp. 30813-30820, 2017, https://doi.org/10.1016/j.ijhydene.2017.10.125.
[130] G. Fan, Z. Zheng, and L. Li, “Effect of hydrogen injection coupled with high-energy ignition on the combustion stability of a lean-burn gasoline engine,” International Journal of Hydrogen Energy, vol. 49, pp. 602-620, 2024, https://doi.org/10.1016/j.ijhydene.2023.08.294.
[131] J. S. M, E. G. Varuvel, and L. J. M. M, “Quantitative assessment of small-bore gasoline direct injection engine for homogenous stoichiometric lean condition with hydrogen addition in dual fuel mode,” International Journal of Hydrogen Energy, vol. 57, pp. 1446-1456, 2024, https://doi.org/10.1016/j.ijhydene.2023.12.033.
[132] X. Zhen, Z. Tian, Y. Wang, D. Liu, and X. Li, “A model to determine the effects of low proportion of hydrogen and the flame kernel radius on combustion and emission performance of direct injection spark ignition engine,” Process Safety and Environmental Protection, vol. 147, pp. 1110-1124, 2021, https://doi.org/10.1016/j.psep.2021.01.040.
[133] Y. Karagöz, N. Yuca, T. Sandalci, and A. S. Dalkiliç, “Effect of hydrogen and oxygen addition as a mixture on emissions and performance characteristics of a gasoline engine,” International Journal of Hydrogen Energy, vol. 40, no. 28, pp. 8750-8760, 2015, https://doi.org/10.1016/j.ijhydene.2015.05.039.
[134] X. Yu et al., “Effects of hydrogen direct injection on combustion and emission characteristics of a hydrogen/Acetone-Butanol-Ethanol dual-fuel spark ignition engine under lean-burn conditions,” International Journal of Hydrogen Energy, vol. 45, no. 58, pp. 34193-34203, 2020, https://doi.org/10.1016/j.ijhydene.2020.09.080.
[135] O. H. Ghazal, “A comparative evaluation of the performance of different fuel induction techniques for blends hydrogen-methane SI engine,” International Journal of Hydrogen Energy, vol. 38, no. 16, pp. 6848-6856, 2013, https://doi.org/10.1016/j.ijhydene.2013.03.090.
[136] J. Matijošius, Y. Gutarevych, Y. Shuba, A. Rimkus, and O. Syrota, “Effect of the addition of hydrogen-containing (H2/O2) gas on indicated and effective parameters of a gasoline engine,” International Journal of Hydrogen Energy, vol. 56, pp. 66-74, 2024, https://doi.org/10.1016/j.ijhydene.2013.03.090.
[137] X. Yu, Y. Du, P. Sun, L. Liu, H. Wu, and X. Zuo, “Effects of hydrogen direct injection strategy on characteristics of lean-burn hydrogen–gasoline engines,” Fuel, vol. 208, pp. 602-611, 2017, https://doi.org/10.1016/j.ijhydene.2013.03.090.
[138] J. Zhang, P. Sun, X. Yu, and D. Li, “The effects of hydrogen peroxide-ethanol blend/gasoline on the combustion and emission performance of a combined injection spark ignition engine,” International Journal of Hydrogen Energy, vol. 450, pp. 66-74, 2024, https://doi.org/10.1016/j.ijhydene.2023.11.289.
[139] L. V. Amaral, A. C. Teixeira Malaquias, M. A. Fraga, R. B. Torres, R. C. O. Sebastião, and F. J. P. Pujatti, “Combustion and specific fuel consumption evaluation of a single-cylinder engine fueled with ethanol, gasoline, and a hydrogen-rich mixture,” Case Studies in Thermal Engineering, vol. 57, p. 104316, 2024, https://doi.org/10.1016/j.csite.2024.104316.
[140] S. T. P. Purayil, S. A. B. Al-Omari, and E. Elnajjar, “Experimental investigation of spark timing on extension of hydrogen knock limit and performance of a hydrogen-gasoline dual-fuel engine,” International Journal of Hydrogen Energy, vol. 49, pp. 910-922, 2024, https://doi.org/10.1016/j.ijhydene.2023.09.139.
[141] I. M. M. Elsemary, A. A. A. Attia, K. H. Elnagar, and A. A. M. Elaraqy, “Experimental investigation on performance of single cylinder spark ignition engine fueled with hydrogen-gasoline mixture,” Applied Thermal Engineering, vol. 106, pp. 850-854, 2016, https://doi.org/10.1016/j.applthermaleng.2016.05.177.
[142] S. T. P. Purayil, S. A. B. Al-Omari, and E. Elnajjar, “Effect of hydrogen blending on the combustion performance, emission, and cycle-to-cycle variation characteristics of a single-cylinder GDI spark ignition dual-fuel engine,” International Journal of Thermofluids, vol. 20, p. 100403, 2023, https://doi.org/10.1016/j.ijft.2023.100403.
[143] F. Y. Hagos, A. R. A. Aziz, and S. A. Sulaiman, “Effect of air-fuel ratio on the combustion characteristics of syngas (H2:CO) in direct-injection spark-ignition engine,” Energy Procedia, pp. 2567-2571, 2014, https://doi.org/10.1016/j.egypro.2014.12.047.
[144] X. Yu et al., “A comparative study on effects of homogeneous or stratified hydrogen on combustion and emissions of a gasoline/hydrogen SI engine,” International Journal of Hydrogen Energy, vol. 44, no. 47, pp. 25974–25984, 2019, https://doi.org/10.1016/j.ijhydene.2019.08.029.
[145] Z. Fu, Y. Li, W. Wu, Y. Li, and W. Gao, “Experimental study on the combustion and emission performance of the hydrogen direct injection engine,” International Journal of Hydrogen Energy, vol. 61, pp. 1047-1059, 2024, https://doi.org/10.1016/j.ijhydene.2024.02.276.
[146] J. Kim, K. M. Chun, S. Song, H. K. Baek, and S. W. Lee, “Hydrogen effects on the combustion stability, performance and emissions of a turbo gasoline direct injection engine in various air/fuel ratios,” Applied Energy, vol. 228, pp. 1353-1361, 2018, https://doi.org/10.1016/j.apenergy.2018.06.129.
[147] S. M. M. E. Ayad, C. R. P. Belchior, and J. R. Sodré, “Hydrogen addition to ethanol-fuelled engine in lean operation to improve fuel conversion efficiency and emissions,” International Journal of Hydrogen Energy, vol. 49, pp. 744–752, 2024, https://doi.org/10.1016/j.ijhydene.2023.09.048.
[148] C. Ji and S. Wang, “Combustion and emissions performance of a hybrid hydrogen-gasoline engine at idle and lean conditions,” International Journal of Hydrogen Energy, vol. 35, no. 1, pp. 346–355, 2010, https://doi.org/10.1016/j.ijhydene.2023.09.048.
[149] C. Ji and S. Wang, “Effect of hydrogen addition on combustion and emissions performance of a spark ignition gasoline engine at lean conditions,” International Journal of Hydrogen Energy, vol. 34, no. 18, pp. 7823–7834, 2009, https://doi.org/10.1016/j.ijhydene.2009.06.082.
[150] S. Farooq and D. V. Kumar, “Experimental investigation of gasoline ethanol methanol iso-stoichiometric blends on SI engine,” Materialstoday Proceedings, 2023, https://doi.org/10.1016/j.matpr.2023.06.138.
[151] A. Verma, N. S. Dugala, and S. Singh, “Experimental investigations on the performance of SI engine with Ethanol-Premium gasoline blends,” Materialstoday Proceedings, pp. 1224–1231, 2022, https://doi.org/10.1016/j.matpr.2021.08.255.
[152] H. Hosseini, A. Hajialimohammadi, I. Jafari Gavzan, and M. Ali Hajimousa, “Numerical and experimental investigation on the effect of using blended gasoline-ethanol fuel on the performance and the emissions of the bi-fuel Iranian national engine,” Fuel, vol. 337, p. 127252, 2023, https://doi.org/10.1016/j.fuel.2022.127252.
[153] I. Cesur, “Effects of water injection strategies on performance and exhaust emissions in an ethanol fueled gasoline engine,” Thermal Science and Engineering Progress, vol. 34, 2022, https://doi.org/10.1016/j.tsep.2022.101397.
[154] P. V. Inbanaathan et al., “Comprehensive study on using hydrogen-gasoline-ethanol blends as flexible fuels in an existing variable speed SI engine,” International Journal of Hydrogen Energy, vol. 48, no. 99, pp. 39531-39552, 2023, https://doi.org/10.1016/j.ijhydene.2023.03.107.
[155] A. Yakın and R. Behçet, “Effect of different types of fuels tested in a gasoline engine on engine performance and emissions,” International Journal of Hydrogen Energy, vol. 46, no. 66, pp. 33325–33338, 2021, https://doi.org/10.1016/j.ijhydene.2021.07.133.
[156] M. Mourad and K. Mahmoud, “Investigation into SI engine performance characteristics and emissions fuelled with ethanol/butanol-gasoline blends,” Renewable Energy, vol. 143, pp. 762-771, 2019, https://doi.org/10.1016/j.renene.2019.05.064.
[157] F. Yüksel and B. Yüksel, “The use of ethanol-gasoline blend as a fuel in an SI engine,” Renewable Energy, vol. 29, no. 7, pp. 1181-1191, 2004, https://doi.org/10.1016/j.renene.2003.11.012.
[158] M. Koç, Y. Sekmen, T. Topgül, and H. S. Yücesu, “The effects of ethanol-unleaded gasoline blends on engine performance and exhaust emissions in a spark-ignition engine,” Renewable Energy, vol. 34, no. 10, pp. 2101-2106, 2009, https://doi.org/10.1016/j.renene.2009.01.018.
[159] M. İ. İlhak, R. Doğan, S. O. Akansu, and N. Kahraman, “Experimental study on an SI engine fueled by gasoline, ethanol and acetylene at partial loads,” Fuel, vol. 261, p. 116148, 2020, https://doi.org/10.1016/j.fuel.2019.116148.
[160] S. Shetty and B. R. Shrinivasa Rao, “In-cylinder pressure based combustion analysis of cycle-by-cycle variations in a dual spark plug SI engine using ethanol-gasoline blends as a fuel,” Materialstoday Proceedings, pp. 780-786, 2022, https://doi.org/10.1016/j.matpr.2021.10.148.
[161] A. O. Hasan, H. Al-Rawashdeh, A. H. Al-Muhtaseb, A. Abu-jrai, R. Ahmad, and J. Zeaiter, “Impact of changing combustion chamber geometry on emissions, and combustion characteristics of a single cylinder SI (spark ignition) engine fueled with ethanol/gasoline blends,” Fuel, vol. 231, pp. 197–203, 2018, https://doi.org/10.1016/j.fuel.2018.05.045.
[162] A. V. N. S. Kiran, B. Ramanjaneyulu, and T. Hariprasad, “Experimental investigations on SI engine with piston coatings and gasoline blends,” Materialstoday Proceedings, 2023, https://doi.org/10.1016/j.matpr.2023.08.020.
[163] Z. Liu, P. Sun, Y. Du, X. Yu, W. Dong, and J. Zhou, “Improvement of combustion and emission by combined combustion of ethanol premix and gasoline direct injection in SI engine,” Fuel, vol. 292, p. 120403, 2021, https://doi.org/10.1016/j.fuel.2021.120403.
[164] Y. Zhuang, Y. Ma, Y. Qian, Q. Teng, and C. Wang, “Effects of ethanol injection strategies on mixture formation and combustion process in an ethanol direct injection (EDI) plus gasoline port injection (GPI) spark-ignition engine,” Fuel, vol. 268, p. 117346, 2020, https://doi.org/10.1016/j.fuel.2020.117346.
[165] Q. Tang, K. Ren, X. Xie, T. Chen, P. Jiang, and D. Zhang, “The effect of acetone-butanol-ethanol and gasoline blends on the knocking performance of spark-ignition engine,” Thermal Science and Engineering Progress, vol. 46, p. 102175, 2023, https://doi.org/10.1016/j.tsep.2023.102175.
[166] R. Kang, L. Zhou, J. Hua, D. Feng, H. Wei, and R. Chen, “Experimental investigation on combustion characteristics in dual-fuel dual-injection engine,” Energy Conversion and Management, vol. 181, pp. 15-25, 2019, https://doi.org/10.1016/j.enconman.2018.11.057.
[167] Y. Li, X. Duan, Y. Liu, J. Liu, G. Guo, and Y. Tang, “Experimental investigation the impacts of injection strategies coupled with gasoline/ethanol blend on combustion, performance and emissions characteristics of a GDI spark-ignition engine,” Fuel, vol. 256, p. 115910, 2019, https://doi.org/10.1016/j.fuel.2019.115910.
[168] N. Kim, S. Cho, and K. Min, “A study on the combustion and emission characteristics of an SI engine under full load conditions with ethanol port injection and gasoline direct injection,” Fuel, vol. 158, pp. 725-732, 2015, https://doi.org/10.1016/j.fuel.2015.06.025.
[169] Y. Kim, W. Il Kim, B. Min, J. Seo, and K. Lee, “Experimental investigation of combustion characteristics of ethanol–gasoline blended fuel in a T-GDI engine,” Applied Thermal Engineering, vol. 208, p. 118168, 2022, https://doi.org/10.1016/j.applthermaleng.2022.118168.
[170] D. Suresh and E. Porpatham, “Influence of high compression ratio and hydrogen addition on the performance and emissions of a lean burn spark ignition engine fueled by ethanol-gasoline,” International Journal of Hydrogen Energy, vol. 48, no. 38, pp. 14433-14448, 2023, https://doi.org/10.1016/j.ijhydene.2022.12.275.
[171] W.-D. Hsieh, R.-H. Chen, T.-L. Wu, and T.-H. Lin, “Engine performance and pollutant emission of an SI engine using ethanol-gasoline blended fuels,” Atmospheric Environment, vol. 36, no. 3, pp. 403, 410, 2002, https://doi.org/10.1016/S1352-2310(01)00508-8.
[172] Y. Huang, N. C. Surawski, Y. Zhuang, J. L. Zhou, G. Hong, “Dual injection: An effective and efficient technology to use renewable fuels in spark ignition engines,” Renewable and Sustainable Energy Reviews, vol. 143, p. 110921, 2021, https://doi.org/10.1016/j.rser.2021.110921.
[173] S. Phuangwongtrakul, W. Wechsatol, T. Sethaput, K. Suktang, and S. Wongwises, “Experimental study on sparking ignition engine performance for optimal mixing ratio of ethanol-gasoline blended fuels,” Applied Thermal Engineering, vol. 100, pp. 869–879, 2016, https://doi.org/10.1016/j.applthermaleng.2016.02.084.
[174] Y. Huang, G. Hong, and R. Huang, “Investigation to charge cooling effect and combustion characteristics of ethanol direct injection in a gasoline port injection engine,” Applied Energy, vol. 160, pp. 244–254, 2015, https://doi.org/10.1016/j.apenergy.2015.09.059.
[175] G. Najafi, B. Ghobadian, T. Tavakoli, D. R. Buttsworth, T. F. Yusaf, and M. Faizollahnejad, “Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network,” Applied Energy, vol. 86, no. 5, pp. 630–639, 2009, https://doi.org/10.1016/j.apenergy.2008.09.017.
[176] Z. Zhao et al., “Experimental study on combustion and emission of an SI engine with ethanol /gasoline combined injection and EGR,” Journal of Cleaner Production, vol. 331, p. 129903, 2022, https://doi.org/10.1016/j.jclepro.2021.129903.
[177] S. Liu, Z. Lin, H. Zhang, Q. Fan, N. Lei, and Z. Wang, “Experimental study on combustion and emission characteristics of ethanol-gasoline blends in a high compression ratio SI engine,” Energy, vol. 274, p. 127398, 2023, https://doi.org/10.1016/j.energy.2023.127398.
[178] Z. Guo, X. Yu, G. Li, Y. Sun, Z. Zhao, and D. Li, “Comparative study of different injection modes on combustion and particle emission of acetone-butanol-ethanol (ABE) and gasoline in a dual-injection SI engine,” Fuel, vol. 281, p. 118786, 2020, https://doi.org/10.1016/j.fuel.2020.118786.
[179] X. Yu et al., “Investigation of combustion and emissions of an SI engine with ethanol port injection and gasoline direct injection under lean burn conditions,” Energy, vol. 189, p. 116231, 2019, https://doi.org/10.1016/j.energy.2019.116231.
[180] H. Chen, J. He, and X. Zhong, “Engine combustion and emission fuelled with natural gas: A review,” Journal of the Energy Institute, vol. 92, no. 4, pp. 1123-1136, 2019, https://doi.org/10.1016/j.joei.2018.06.005.
[181] A. R. Tabar, A. A. Hamidi, and H. Ghadamian, “Experimental investigation of CNG and gasoline fuels combination on a 1.7 L bi-fuel turbocharged engine,” International Journal of Energy and Environmental Engineering, vol. 8, no. 1, pp. 37-45, 2017, https://doi.org/10.1007/s40095-016-0223-3.
[182] D. Yap, S. M. Peucheret, A. Megaritis, M. L. Wyszynski, and H. Xu, “Natural gas HCCI engine operation with exhaust gas fuel reforming,” International Journal of Hydrogen Energy, vol. 31, no. 5, pp. 587-595, 2006, https://doi.org/10.1016/j.ijhydene.2005.06.002.
[183] J. P. Bhasker and E. Porpatham, “Effects of compression ratio and hydrogen addition on lean combustion characteristics and emission formation in a Compressed Natural Gas fuelled spark ignition engine,” Fuel, vol. 208, pp. 260-270, 2017, https://doi.org/10.1016/j.fuel.2017.07.024.
[184] A. A. Yontar and Y. Doğu, “Effects of equivalence ratio and CNG addition on engine performance and emissions in a dual sequential ignition engine,” International Journal of Engine Research, vol. 21, no. 6, pp. 1067-1082, 2020, https://doi.org/10.1177/1468087419834190.
[185] S. Molina, S. Ruiz, J. Gomez-Soriano, and M. Olcina-Girona, “Impact of hydrogen substitution for stable lean operation on spark ignition engines fueled by compressed natural gas,” Results in Engineering, vol. 17, p. 100799, 2023, https://doi.org/10.1016/j.rineng.2022.100799.
[186] S. Pan, J. Wang, and Z. Huang, “Effects of hydrogen injection strategy on the hydrogen mixture distribution and combustion of a gasoline/hydrogen SI engine under lean burn condition,” International Journal of Hydrogen Energy, vol. 47, no. 57, pp. 24069–24079, 2022, https://doi.org/10.1016/j.ijhydene.2022.05.197.
[187] Q. Luo et al., “Experimental investigation of combustion characteristics and NOx emission of a turbocharged hydrogen internal combustion engine,” International Journal of Hydrogen Energy, vol. 44, no. 11, pp. 5573-5584, 2019, https://doi.org/10.1016/j.ijhydene.2018.08.184.
[188] C. Ji and S. Wang, “Effect of hydrogen addition on lean burn performance of a spark-ignited gasoline engine at 800 rpm and low loads,” Fuel, vol. 90, no. 3, pp. 1301-1304, 2011, https://doi.org/10.1016/j.fuel.2010.11.014.
[189] T. D’Andrea, P. F. Henshaw, and D. S. Ting, “The addition of hydrogen to a gasoline-fuelled SI engine,” International Journal of Hydrogen Energy, vol. 29, no. 14, pp. 1541-1552, 2004, https://doi.org/10.1016/j.ijhydene.2004.02.002.
[190] D. Nguyen, Y. Choi, C. Park, Y. Kim, and J. Lee, “Effect of supercharger system on power enhancement of hydrogen-fueled spark-ignition engine under low-load condition,” International Journal of Hydrogen Energy, vol. 46, no. 9, pp. 6928–6936, 2021, https://doi.org/10.1016/j.ijhydene.2020.11.144.
[191] J. Zhang et al., “Effects of hydrogen peroxide on the combustion and emissions of an ethanol/gasoline combined injection engine under different excess air ratios,” Fuel, vol. 359, p. 130521, 2024, https://doi.org/10.1016/j.fuel.2023.130521.
[192] Y. Karagöz, L. Yüksek, T. Sandalci, and A. S. Dalkiliç, “An experimental investigation on the performance characteristics of a hydroxygen enriched gasoline engine with water injection,” International Journal of Hydrogen Energy, vol. 40, no. 1, pp. 692–702, 2015, https://doi.org/10.1016/j.ijhydene.2014.11.013.
[193] X. Li, Y. Zhuang, Y. Wang, Z. Zhu, Y. Qian, and R. Zhai, “An experimental investigation on the lean-burn characteristics of a novel hydrogen fueled spark ignition engine: Hydrogen injection via a micro-hole on the spark plug,” International Journal of Hydrogen Energy, vol. 57, pp. 990–999, 2024, https://doi.org/10.1016/j.ijhydene.2024.01.008.
[194] S. Wang, C. Ji, B. Zhang, X. Cong, and X. Liu, “Effect of CO2 dilution on combustion and emissions characteristics of the hydrogen-enriched gasoline engine,” Energy, vol. 96, pp. 118–126, 2016, https://doi.org/10.1016/j.energy.2015.12.017.
[195] Y. Du, X. Yu, L. Liu, R. Li, X. Zuo, and Y. Sun, “Effect of addition of hydrogen and exhaust gas recirculation on characteristics of hydrogen gasoline engine,” International Journal of Hydrogen Energy, vol. 42, no. 12, pp. 8288–8298, 2017, https://doi.org/10.1016/j.ijhydene.2017.02.197.
[196] C. Park, Y. Kim, J. Oh, J. Choi, and Y. Choi, “Effect of fuel injection timing on performance and emissions with a dedicated direct injector in a hydrogen engine,” Energy Conversion and Management: X, vol. 18, p. 100379, 2023, https://doi.org/10.1016/j.ecmx.2023.100379.
[197] G. Kaya, “Experimental comparative study on combustion, performance and emissions characteristics of ethanol-gasoline blends in a two stroke uniflow gasoline engine,” Fuel, vol. 317, p. 120917, 2022, https://doi.org/10.1016/j.fuel.2021.120917.
[198] L. V. Amaral, N. D. S. A. Santos, V. R. Roso, R. de C. de O. Sebastião, and F. J. P. Pujatti, “Effects of gasoline composition on engine performance, exhaust gases and operational costs,” Renewable and Sustainable Energy Reviews, vol. 135, p. 110196, 2021, https://doi.org/10.1016/j.rser.2020.110196.
[199] X. Wu, R. Daniel, G. Tian, H. Xu, Z. Huang, and D. Richardson, “Dual-injection: The flexible, bi-fuel concept for spark-ignition engines fuelled with various gasoline and biofuel blends,” Applied Energy, vol. 88, no. 7, pp. 2305–2314, 2011, https://doi.org/10.1016/j.apenergy.2011.01.025.
[200] R. H. Chen, L. Bin Chiang, C. N. Chen, and T. H. Lin, “Cold-start emissions of an SI engine using ethanol-gasoline blended fuel,” Applied Thermal Engineering, vol. 31, no. 8–9, pp. 1463–1467, 2011, https://doi.org/10.1016/j.applthermaleng.2011.01.021.
[201] Y. Zhuang, G. Zhu, Z. Gong, C. Wang, and Y. Huang, “Experimental and numerical investigation of performance of an ethanol-gasoline dual-injection engine,” Energy, vol. 186, p. 115835, 2019, https://doi.org/10.1016/j.energy.2019.07.165.
[202] M. Al-Hasan, “Effect of ethanol-unleaded gasoline blends on engine performance and exhaust emission,” Energy Conversion and Management, vol. 44, no. 9, pp. 1547-1561, 2003, https://doi.org/10.1016/S0196-8904(02)00166-8.
[203] D. Turner, H. Xu, R. F. Cracknell, V. Natarajan, and X. Chen, “Combustion performance of bio-ethanol at various blend ratios in a gasoline direct injection engine,” Fuel, vol. 90, no. 5, pp. 1999–2006, 2011, https://doi.org/10.1016/j.fuel.2010.12.025.
[204] M. B. Celik, “Experimental determination of suitable ethanol-gasoline blend rate at high compression ratio for gasoline engine,” Applied Thermal Engineering, vol. 28, no. 5-6, pp. 396–404, 2008, https://doi.org/10.1016/j.applthermaleng.2007.10.028.
[205] Y. Huang, G. Hong, and R. Huang, “Effect of injection timing on mixture formation and combustion in an ethanol direct injection plus gasoline port injection (EDI+GPI) engine,” Energy, vol. 111, pp. 92–103, 2016, https://doi.org/10.1016/j.energy.2016.05.109.
[206] A. H. Kakaee and A. Paykani, “Research and development of natural-gas fueled engines in Iran,” Renewable and Sustainable Energy Reviews, vol. 26, pp. 805-821, 2013, https://doi.org/10.1016/j.rser.2013.05.048.
[207] M. A. Kalam, H. H. Masjuki, M. A. Amalina, H. Abdesselam, and T. M. I. Mahlia, “Co, hc and nox emissions characteristics of a retrofitted bi-fuel natural gas engine,” HKIE Transactions, vol. 12, no. 3, pp. 25–30, 2005, https://doi.org/10.1080/1023697X.2005.10668009.
[208] K. N. Duc, V. N. Duy, L. Hoang-Dinh, T. N. Viet, and T. Le-Anh, “Performance and emission characteristics of a port fuel injected, spark ignition engine fueled by compressed natural gas,” Sustainable Energy Technologies and Assessments, vol. 31, pp. 383-389, 2019, https://doi.org/10.1016/j.seta.2018.12.018.
[209] Z. Lv et al., “Comparison of CO2, NOx, and VOCs emissions between CNG and E10 fueled light-duty vehicles,” Science of the Total Environment, vol. 858, p. 159966, 2023, https://doi.org/10.1016/j.scitotenv.2022.159966.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Nouby Ghazaly

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

 

About the JournalJournal PoliciesAuthor Information

International Journal of Robotics and Control Systems
e-ISSN: 2775-2658
Website: https://pubs2.ascee.org/index.php/IJRCS
Email: ijrcs@ascee.org
Organized by: Association for Scientific Computing Electronics and Engineering (ASCEE), Peneliti Teknologi Teknik Indonesia, Department of Electrical Engineering, Universitas Ahmad Dahlan and Kuliah Teknik Elektro
Published by: Association for Scientific Computing Electronics and Engineering (ASCEE)
Office: Jalan Janti, Karangjambe 130B, Banguntapan, Bantul, Daerah Istimewa Yogyakarta, Indonesia