Artificial intelligence techniques for the vibration, noise, and emission characteristics of a hydrogen-enriched diesel engine

References

• Akar, M. A. 2016. Performance and emission characteristics of compression ignition engine operating with false flax biodiesel and butanol blends. Advances in Mechanical Engineering 8 (2):1687814016632677. SAGE Publications Sage UK: London, England. doi:10.1177/1687814016632677.
   Web of Science ®Google Scholar
• Avşar, E. 2017. Dimensionality reduction for predicting CO conversion in water gas shift reaction over Pt-Based catalysts using support vector regression models. International Journal of Hydrogen Energy 42 (36):23326–33. doi:10.1016/j.ijhydene.2016.12.091.
   Web of Science ®Google Scholar
• Boser, B. E., I. M. Guyon, and V. N. Vapnik. 1992. A training algorithm for optimal margin classifiers. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory, 144–52. COLT ’92. New York, NY, USA, ACM. doi:10.1145/130385.130401.
   Google Scholar
• Çalık, A. 2018. Determination of vibration characteristics of a compression ignition engine operated by hydrogen enriched diesel and biodiesel fuels. Fuel 230 (October):355–58. doi:10.1016/j.fuel.2018.05.053.
   Google Scholar
• Cay, Y. 2013. Prediction of a gasoline engine performance with artificial neural network. Fuel 111:324–31. Elsevier Ltd doi:10.1016/j.fuel.2012.12.040.
   Web of Science ®Google Scholar
• Çay, Y., I. Korkmaz, A. Çiçek, and F. Kara. 2013. Prediction of engine performance and exhaust emissions for gasoline and methanol using artificial neural network. Energy 50 (1):177–86. Elsevier Ltd. doi:10.1016/j.energy.2012.10.052.
   Google Scholar
• Çelebi, K., E. Uludamar, and M. Özcanlı. 2017. Evaluation of fuel consumption and vibration characteristic of a compression ignition engine fuelled with high viscosity biodiesel and hydrogen addition. International Journal of Hydrogen Energy 42 (36):23379–88. doi:10.1016/j.ijhydene.2017.02.066.
   Web of Science ®Google Scholar
• Chiatti, G., O. Chiavola, and F. Palmieri. 2017. Vibration and acoustic characteristics of a city-car engine fueled with biodiesel blends. Applied Energy 185:664–70. Elsevier Ltd doi:10.1016/j.apenergy.2016.10.119.
   Web of Science ®Google Scholar
• Dehkordi, S. H. H. F., M. Almassi, A. M. Borghei, and B. Beheshti. 2013. Simulation of small diesel engine vibration using artificial neural network. International Journal of Agriculture and CropSciences 5 (18):2084-90.
   Google Scholar
• Dharma, S., M. H. Hassan, H. C. Ong, A. H. Sebayang, A. S. Silitonga, F. Kusumo, and J. Milano. 2017. Experimental study and prediction of the performance and exhaust emissions of Mixed Jatropha Curcas-Ceiba Pentandra biodiesel blends in diesel engine using artificial neural networks. Journal of Cleaner Production 164:618–33. Elsevier Ltd doi:10.1016/j.jclepro.2017.06.065.
   Web of Science ®Google Scholar
• Drucker, H., C. J. C. Burges, L. Kaufman, A. J. Smola, and V. Vapnik. 1997. Support vector regression machines. In Advances in neural information processing systems, ed. M. C. Mozer, M. I. Jordan, and T. Petsche, Vol. 9, 155–61. Cambridge, MA: MIT Press. http://papers.nips.cc/paper/1238-support-vector-regression-machines.pdf.
   Google Scholar
• Du, Y., Y. Xiumin, L. Liu, L. Runzeng, X. Zuo, and Y. Sun. 2017. Effect of addition of hydrogen and exhaust gas recirculation on characteristics of hydrogen gasoline engine. International Journal of Hydrogen Energy 42 (12):8288–98. Elsevier Ltd. doi:10.1016/j.ijhydene.2017.02.197.
   Web of Science ®Google Scholar
• Emang, D., M. Shitan, A. N. A. Ghani, and K. M. Noor. 2010. Forecasting with univariate time series models: A case of export demand for Peninsular Malaysia’s moulding and chipboard. Journal of Sustainable Development 3 (3):157. doi:10.5539/jsd.v3n3p157.
   Google Scholar
• Gürgen, S., B. Ünver, and İ. Altın. 2018. Prediction of cyclic variability in a diesel engine fueled with N-Butanol and diesel fuel blends using artificial neural network. Renewable Energy 117 (March):538–44. doi:10.1016/j.renene.2017.10.101.
   Google Scholar
• Gurlek, C., and M. Sahin. 2018. Estimation of the global solar radiation with the artificial neural networks for the City of Sivas. European Mechanical Science 2 (2):46–51. doi:10.26701/ems.359681.
   Google Scholar
• Hasan, M. M., and M. M. Rahman. 2017. Performance and emission characteristics of biodiesel–diesel blend and environmental and economic impacts of biodiesel production: A review. Renewable and Sustainable Energy Reviews 74 (March):938–48. Elsevier Ltd. doi:10.1016/j.rser.2017.03.045.
   Google Scholar
• Ismail, M., H. Harun, N. Kiat, C. W. Queck, and S. Gan. 2012. Artificial neural networks modelling of engine-out responses for a light-duty diesel engine fuelled with biodiesel blends. Applied Energy 92:769–77. Elsevier Ltd doi:10.1016/j.apenergy.2011.08.027.
   Web of Science ®Google Scholar
• Javed, S., Y. V. V. Satyanarayana Murthy, R. U. Baig, and T. Nagarjuna Rao. 2016. Vibration analysis of a diesel engine using biodiesel fuel blended with nano particles by dual fueling of hydrogen. Journal of Natural Gas Science and Engineering 33 (July):217–30. Elsevier. doi:10.1016/J.JNGSE.2016.05.026.
   Google Scholar
• Javed, S. Y., V. V. Satyanarayana Murthy, R. U. Baig, and D. Prasada Rao. 2015. Development of ANN model for prediction of performance and emission characteristics of hydrogen dual fueled diesel engine with jatropha methyl ester biodiesel blends. Journal of Natural Gas Science and Engineering 26:549–57. Elsevier B.V doi:10.1016/j.jngse.2015.06.041.
   Web of Science ®Google Scholar
• Kshirsagar, C. M., and R. Anand. 2017. Artificial neural network applied forecast on a parametric study of calophyllum inophyllum methyl ester-diesel engine out responses. Applied Energy 189:555–67. doi:10.1016/j.apenergy.2016.12.045.
   Web of Science ®Google Scholar
• Kurtgoz, Y., M. Karagoz, and E. Deniz. 2018. Biogas engine performance estimation using ANN. Engineering Science and Technology, an International Journal 20 (6):1563–70. Karabuk University. doi:10.1016/j.jestch.2017.12.010.
   Google Scholar
• Li, Q., Q. Meng, J. Cai, H. Yoshino, and A. Mochida. 2009. Applying support vector machine to predict hourly cooling load in the building. Applied Energy 86 (10):2249–56. doi:10.1016/j.apenergy.2008.11.035.
   Web of Science ®Google Scholar
• Manieniyan, V., and S. Sivaprakasam. 2013. Artificial neural network based modelling for vibration characteristics of DI diesel engine using bio-diesel. International Journal of Advanced Research in Computer Science and Software Engineering 3 (8):634–38.
   Google Scholar
• Mohammad, H. S., and R. Ahmadi. 2017. Performance and emissions characteristics in the combustion of Co-fuel diesel-hydrogen in a heavy duty engine. Applied Energy 205 (May):911–25. Elsevier. doi:10.1016/j.apenergy.2017.08.044.
   Google Scholar
• Moosavian, M. G., G. Najafi, B. Ghobadian, R. Mamat, M. M. Noor, and A. Moosavian. 2015. Support vector machine to predict diesel engine performance and emission parameters fueled with nano-particles additive to diesel fuel. IOP Conference Series: Materials Science and Engineering 100 (1):12069. http://stacks.iop.org/1757-899X/100/i=1/a=012069.
   Google Scholar
• Najafi, G., B. Ghobadian, A. Moosavian, T. Yusaf, R. Mamat, M. Kettner, and W. H. Azmi. 2016. SVM and ANFIS for prediction of performance and exhaust emissions of a SI engine with gasoline–ethanol blended fuels. Applied Thermal Engineering 95:186–203. doi:10.1016/j.applthermaleng.2015.11.009.
   Web of Science ®Google Scholar
• Najafi, G., B. Ghobadian, T. Tavakoli, D. R. Buttsworth, T. F. Yusaf, and M. Faizollahnejad. 2009. Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network. Applied Energy 86 (5):630–39. Elsevier. doi:10.1016/J.APENERGY.2008.09.017.
   Web of Science ®Google Scholar
• Ozcanli, M., M. A. Akar, A. Calik, and H. Serin. 2017. Using HHO (Hydroxy) and hydrogen enriched castor oil biodiesel in compression ignition engine. International Journal of Hydrogen Energy 42 (36):23366–72. doi:10.1016/j.ijhydene.2017.01.091.
   Web of Science ®Google Scholar
• Patel, C., A. K. Agarwal, N. Tiwari, S. Lee, C. S. Lee, and S. Park. 2016. Combustion, noise, vibrations and spray characterization for Karanja biodiesel fuelled engine. Applied Thermal Engineering 106:506–17. Elsevier Ltd doi:10.1016/j.applthermaleng.2016.06.025.
   Web of Science ®Google Scholar
• Prasada Rao, K., T. Victor Babu, G. Anuradha, and B. V. Appa Rao. 2017. IDI diesel engine performance and exhaust emission analysis using biodiesel with an Artificial Neural Network (ANN). Egyptian Journal of Petroleum 26 (3):593–600. Egyptian Petroleum Research Institute. doi:10.1016/j.ejpe.2016.08.006.
   Google Scholar
• Rai, A. N., S. Kumar, P. Srinivasa Pai, B. R. Shrinivasa Rao, N. Satheesh Kumar, P. P. Srinivasa, and R. B. R. Shrinivasa. 2012. Fuzzy logic based prediction of performance and emission parameters of a LPG-diesel dual fuel engine. Procedia Engineering 38:280–92. doi:10.1016/j.proeng.2012.06.036.
   Google Scholar
• Rezaei, J., M. Shahbakhti, B. Bahri, and A. A. Aziz. 2015. Performance prediction of HCCI engines with oxygenated fuels using artificial neural networks. Applied Energy 138:460–73. Elsevier Ltd doi:10.1016/j.apenergy.2014.10.088.
   Web of Science ®Google Scholar
• Ruhul, A. M., M. A. Kalam, H. H. Masjuki, S. A. Shahir, A. Alabdulkarem, Y. H. Teoh, H. G. How, and S. S. Reham. 2017. Evaluating combustion, performance and emission characteristics of Millettia Pinnata and Croton Megalocarpus biodiesel blends in a diesel engine. Energy 141:2362–76. Elsevier Ltd doi:10.1016/j.energy.2017.11.096.
   Web of Science ®Google Scholar
• Satsangi, D. P., and N. Tiwari. 2018. Experimental investigation on combustion, noise, vibrations, performance and emissions characteristics of Diesel/n-Butanol blends driven genset engine. Fuel 221 (December 2017):44–60. Elsevier. doi:10.1016/j.fuel.2018.02.060.
   Google Scholar
• Taghizadeh-Alisaraei, A., and A. Rezaei-Asl. 2016. The effect of added ethanol to diesel fuel on performance, vibration, combustion and knocking of a CI engine. Fuel. doi:10.1016/j.fuel.2016.08.041.
   PubMedGoogle Scholar
• Taghizadeh-Alisaraei, A., B. Ghobadian, T. Tavakoli-Hashjin, and S. S. Mohtasebi. 2012. Vibration analysis of a diesel engine using biodiesel and petrodiesel fuel blends. Fuel 102 (2012):414–22. Elsevier Ltd. doi:10.1016/j.fuel.2012.06.109.
   Google Scholar
• Thakur, A. K., K. K. S. Mer, and A. Kaviti. 2017 January. An artificial neural network approach to predict the performance and exhaust emissions of a gasoline engine using ethanol–gasoline blended fuels. Biofuels 1–15. doi:10.1080/17597269.2016.1271630.
   Google Scholar
• Tosun, E., T. Ozgur, C. Ozgur, M. Ozcanli, H. Serin, and K. Aydin. 2017. Comparative analysis of various modelling techniques for emission prediction of diesel engine fueled by diesel fuel with nanoparticle additives. European Mechanical Science 1 (1):15–23. doi:10.26701/ems.320490.
   Google Scholar
• Tsujimura, T., and Y. Suzuki. 2017. The utilization of hydrogen in hydrogen/diesel dual fuel engine. International Journal of Hydrogen Energy 42 (19):14019–29. Elsevier Ltd. doi:10.1016/j.ijhydene.2017.01.152.
   Web of Science ®Google Scholar
• Uludamar, E. 2018. Effect of hydroxy and hydrogen gas addition on diesel engine fuelled with microalgae biodiesel. International Journal of Hydrogen Energy 1–9. doi:10.1016/j.ijhydene.2018.01.075.
   Google Scholar
• Uludamar, E., Ş. Yıldızhan, K. Aydın, and M. Özcanlı. 2016. Vibration, noise and exhaust emissions analyses of an unmodified compression ignition engine fuelled with low sulphur diesel and biodiesel blends with hydrogen addition. International Journal of Hydrogen Energy 41 (26):11481–90. doi:10.1016/j.ijhydene.2016.03.179.
   Web of Science ®Google Scholar
• Uludamar, E., E. Tosun, G. Tüccar, Ş. Yıldızhan, A. Çalık, S. Yıldırım, H. Serin, and M. Özcanlı. 2017. Evaluation of vibration characteristics of a hydroxyl (HHO) gas generator installed diesel engine fuelled with different diesel–biodiesel blends. International Journal of Hydrogen Energy 42 (36):23352–60. doi:10.1016/j.ijhydene.2017.01.192.
   Web of Science ®Google Scholar
• Uludamar, E., E. Tosun, and K. Aydın. 2016. Experimental and regression analysis of noise and vibration of a compression ignition engine fuelled with various biodiesels. Fuel 177:326–33. doi:10.1016/j.fuel.2016.03.028.
   Web of Science ®Google Scholar
• Uyumaz, A. 2018. Combustion, performance and emission characteristics of a DI diesel engine fueled with mustard oil biodiesel fuel blends at different engine loads. Fuel 212 (August 2016):256–67. Elsevier. doi:10.1016/j.fuel.2017.09.005.
   Google Scholar
• Vapnik, V., S. E. Golowich, and A. J. Smola. 1997. Support vector method for function approximation, regression estimation and signal processing. In Advances in neural information processing systems, ed. M. C. Mozer, M. I. Jordan, and T. Petsche, Vol. 9, 281–87. Cambridge, MA: MIT Press. http://papers.nips.cc/paper/1187-support-vector-method-for-function-approximation-regression-estimation-and-signal-processing.pdf.
   Google Scholar
• Wolff, B., E. Lorenz, and O. Kramer. 2016. Statistical learning for short-term photovoltaic power predictions BT - computational sustainability. In Computational sustainability, ed. J. Lässig, K. Kersting, and K. Morik, 31–45. Cham: Springer International Publishing. doi:10.1007/978-3-319-31858-5_3.
   Google Scholar
• Wu, X., V. Kumar, J. R. Quinlan, J. Ghosh, Q. Yang, H. Motoda, G. J. McLachlan, et al. 2008. Top 10 algorithms in data mining. Knowledge and Information Systems 14 (1):1–37. doi:10.1007/s10115-007-0114-2.
   Web of Science ®Google Scholar
• Yildizhan, Ş., E. Uludamar, A. Çalık, G. Dede, and M. Özcanlı. 2017. Fuel properties, performance and emission characterization of Waste Cooking Oil (WCO) in a Variable Compression Ratio (VCR) diesel engine. European Mechanical Science 1 (2):56–62. doi:10.26701/ems.321789.
   Google Scholar
• Zareh, P., A. A. Zare, and B. Ghobadian. 2017. Comparative assessment of performance and emission characteristics of castor, coconut and waste cooking based biodiesel as fuel in a diesel engine. Energy 139:883–94. Elsevier Ltd doi:10.1016/j.energy.2017.08.040.
   Web of Science ®Google Scholar
• Zuo, Q., X. Zhu, Z. Liu, J. Zhang, W. Gang, and L. Yuelin. 2018. Prediction of the performance and emissions of a spark ignition engine fueled with Butanol-Gasoline blends based on support vector regression. Environmental Progress & Sustainable Energy Wiley-Blackwell. doi:10.1002/ep.13042.
   Web of Science ®Google Scholar