References
- Akkaya, E. 2016. ANFIS based prediction model for biomass heating value using proximate analysis components. Fuel 180:687–93. doi:https://doi.org/10.1016/j.fuel.2016.04.112.
- Algayyim, S. J. M., and A. P. Wandel. 2020. Performance and emission levels of butanol, acetone-butanol-ethanol, butanol-acetone/diesel blends in a diesel engine. Biofuels 1–11. doi:https://doi.org/10.1080/17597269.2020.1759178.
- Babu, D., V. Thangarasu, and A. Ramanathan. 2020. Artificial neural network approach on forecasting diesel engine characteristics fuelled with waste frying oil biodiesel. Applied Energy 263:114612. doi:https://doi.org/10.1016/j.apenergy.2020.114612.
- Chandra, R., and M. Zhang. 2012. Cooperative coevolution of Elman recurrent neural networks for chaotic time series prediction. Neurocomputing 86:116–23. doi:https://doi.org/10.1016/j.neucom.2012.01.014.
- Chang, Y.-C., W.-J. Lee, S.-L. Lin, and L.-C. Wang. 2013. Green energy: Water-containing acetone–butanol–ethanol diesel blends fueled in diesel engines. Applied Energy 109:182–91. doi:https://doi.org/10.1016/j.apenergy.2013.03.086.
- Chang, Y.-C., W.-J. Lee, T. S. Wu, C.-Y. Wu, and S.-J. Chen. 2014. Use of water containing acetone–butanol–ethanol for NOx-PM (nitrogen oxide-particulate matter) trade-off in the diesel engine fueled with biodiesel. Energy 64:678–87. doi:https://doi.org/10.1016/j.energy.2013.10.077.
- Chen, S., Y. Ren, D. Friedrich, Z. Yu, and J. Yu. 2020. Sensitivity analysis to reduce duplicated features in ANN training for district heat demand prediction. Energy and AI 2:100028. doi:https://doi.org/10.1016/j.egyai.2020.100028.
- El-Seesy, A. I., M. Nour, A. M. A. Attia, Z. He, and H. Hassan. 2020. Investigation the effect of adding graphene oxide into diesel/higher alcohols blends on a diesel engine performance. International Journal of Green Energy 17 (3):233–53. doi:https://doi.org/10.1080/15435075.2020.1722132.
- Jin, Chao, Zhenlong Geng, Xin Liu, Jeffrey D. Ampah, Jing Ji, Gang Wang, Kun Niu, Nan Hu, and Haifeng Liu. 2021. Effects of water content on the solubility between Isopropanol-Butanol-Ethanol (IBE) and diesel fuel under various ambient temperatures. Fuel 286:119492. https://doi.org/10.1016/j.fuel.2020.119492
- Jin, Chao, Zhenlong Geng, Xiyuan Zhang, Mengxing Ma, Jing Ji, Gang Wang, Chunfeng Guan, and Haifeng Liu. 2019. Study on the Solubility between Diesel and Acetone–Butanol–Ethanol with or without Water. Energy & Fuels 34 (2):1166–76
- Jin, D., R. Ocone, K. Jiao, and J. Xuan. 2020. Energy and AI. Energy and AI 1:100002. doi:https://doi.org/10.1016/j.egyai.2020.100002.
- Karaca, Y. 2016. Case study on artificial neural networks and applications. Applied Mathematical Sciences 10 (45):2225–37. doi:https://doi.org/10.12988/ams.2016.65174.
- Koskela, T., M. Lehtokangas, J. Saarinen, and K. Kaski, editors. Time series prediction with multilayer perceptron, FIR and Elman neural networks. Proceedings of the World Congress on Neural Networks; 1996: Citeseer.
- Liu, H., J. Ma, F. Dong, Y. Yang, X. Liu, G. Ma, et al. 2018. Experimental investigation of the effects of diesel fuel properties on combustion and emissions on a multi-cylinder heavy-duty diesel engine. Energy Conversion and Management 171:1787–800. San Diego, USA. doi:https://doi.org/10.1016/j.enconman.2018.06.089.
- Mostafaei, M. 2018a. Prediction of biodiesel fuel properties from its fatty acids composition using ANFIS approach. Fuel 229:227–34. doi:https://doi.org/10.1016/j.fuel.2018.04.148.
- Mostafaei, M. 2018b. ANFIS models for prediction of biodiesel fuels cetane number using desirability function. Fuel 216:665–72. doi:https://doi.org/10.1016/j.fuel.2017.12.025.
- Gölcü, Mustafa, Yakup Sekmen, Perihan Erduranlı, and M. Sahir Salman. 2005. Artificial neural-network based modeling of variable valve-timing in a spark-ignition engine. Applied Energy 81 (2):187-97. https://doi.org/10.1016/j.apenergy.2004.07.008.
- Tekin, M., and S. Saridemir. 2019. Prediction of engine performance and exhaust emissions with different proportions of ethanol–gasoline blends using artificial neural networks. International Journal of Ambient Energy 40 (5):470–76. doi:https://doi.org/10.1080/01430750.2017.1410225.
- Uslu, S., and M. B. Celik. 2018. 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 21 (6):1194–201. doi:https://doi.org/10.1016/j.jestch.2018.08.017.
- Van Der Merwe, A., H. Cheng, J. Görgens, and J. Knoetze. 2013. Comparison of energy efficiency and economics of process designs for biobutanol production from sugarcane molasses. Fuel 105:451–58. doi:https://doi.org/10.1016/j.fuel.2012.06.058.
- Yu, W., and F. Zhao. 2019. Predictive study of ultra-low emissions from dual-fuel engine using artificial neural networks combined with genetic algorithm. International Journal of Green Energy 16 (12):938–46. doi:https://doi.org/10.1080/15435075.2019.1650048.
- Zhang, Y., S. Xu, S. Zhong, X.-S. Bai, H. Wang, and M. Yao. 2020. Large eddy simulation of spray combustion using flamelet generated manifolds combined with artificial neural networks. Energy and AI 2:100021. doi:https://doi.org/10.1016/j.egyai.2020.100021.
- Zheng, Z., L. Yue, H. Liu, Y. Zhu, X. Zhong, and M. Yao. 2015. Effect of two-stage injection on combustion and emissions under high EGR rate on a diesel engine by fueling blends of diesel/gasoline, diesel/n-butanol, diesel/gasoline/n-butanol and pure diesel. Energy Conversion and Management 90:1–11. doi:https://doi.org/10.1016/j.enconman.2014.11.011.