IDEAS home Printed from https://ideas.repec.org/r/eee/energy/v50y2013icp177-186.html
   My bibliography  Save this item

Prediction of engine performance and exhaust emissions for gasoline and methanol using artificial neural network

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Deng, Banglin & Fu, Jianqin & Zhang, Daming & Yang, Jing & Feng, Renhua & Liu, Jingping & Li, Ke & Liu, Xiaoqiang, 2013. "The heat release analysis of bio-butanol/gasoline blends on a high speed SI (spark ignition) engine," Energy, Elsevier, vol. 60(C), pages 230-241.
  2. Chen, Zhanming & Zhang, Tiancong & Wang, Xiaochen & Chen, Hao & Geng, Limin & Zhang, Teng, 2021. "A comparative study of combustion performance and emissions of dual-fuel engines fueled with natural gas/methanol and natural gas/gasoline," Energy, Elsevier, vol. 237(C).
  3. Bahri, Bahram & Shahbakhti, Mahdi & Aziz, Azhar Abdul, 2017. "Real-time modeling of ringing in HCCI engines using artificial neural networks," Energy, Elsevier, vol. 125(C), pages 509-518.
  4. Muhammad Usman & Haris Hussain & Fahid Riaz & Muneeb Irshad & Rehmat Bashir & Muhammad Haris Shah & Adeel Ahmad Zafar & Usman Bashir & M. A. Kalam & M. A. Mujtaba & Manzoore Elahi M. Soudagar, 2021. "Artificial Neural Network Led Optimization of Oxyhydrogen Hybridized Diesel Operated Engine," Sustainability, MDPI, vol. 13(16), pages 1-24, August.
  5. Zhang, Bo & Ji, Changwei & Wang, Shuofeng & Liu, Xiaolong, 2014. "Combustion and emissions characteristics of a spark-ignition engine fueled with hydrogen–methanol blends under lean and various loads conditions," Energy, Elsevier, vol. 74(C), pages 829-835.
  6. Taghavifar, Hadi & Khalilarya, Shahram & Jafarmadar, Samad, 2014. "Diesel engine spray characteristics prediction with hybridized artificial neural network optimized by genetic algorithm," Energy, Elsevier, vol. 71(C), pages 656-664.
  7. Ekinci, Şerafettin & Çarman, Kazım & Kahramanlı, Humar, 2015. "Investigation and modeling of the tractive performance of radial tires using off-road vehicles," Energy, Elsevier, vol. 93(P2), pages 1953-1963.
  8. Rahimi molkdaragh, R. & Jafarmadar, S. & Khalilaria, Sh & Soukht Saraee, H., 2018. "Prediction of the performance and exhaust emissions of a compression ignition engine using a wavelet neural network with a stochastic gradient algorithm," Energy, Elsevier, vol. 142(C), pages 1128-1138.
  9. Sun, Ping & Zhang, Jufang & Dong, Wei & Li, Decheng & Yu, Xiumin, 2023. "Prediction of oxygen-enriched combustion and emission performance on a spark ignition engine using artificial neural networks," Applied Energy, Elsevier, vol. 348(C).
  10. Najafi, Bahman & Akbarian, Eivaz & Lashkarpour, S. Mehdi & Aghbashlo, Mortaza & Ghaziaskar, Hassan S. & Tabatabaei, Meisam, 2019. "Modeling of a dual fueled diesel engine operated by a novel fuel containing glycerol triacetate additive and biodiesel using artificial neural network tuned by genetic algorithm to reduce engine emiss," Energy, Elsevier, vol. 168(C), pages 1128-1137.
  11. Subrata Bhowmik & Rajsekhar Panua & Subrata K Ghosh & Abhishek Paul & Durbadal Debroy, 2018. "Prediction of performance and exhaust emissions of diesel engine fuelled with adulterated diesel: An artificial neural network assisted fuzzy-based topology optimization," Energy & Environment, , vol. 29(8), pages 1413-1437, December.
  12. Domínguez-Sáez, Aida & Rattá, Giuseppe A. & Barrios, Carmen C., 2018. "Prediction of exhaust emission in transient conditions of a diesel engine fueled with animal fat using Artificial Neural Network and Symbolic Regression," Energy, Elsevier, vol. 149(C), pages 675-683.
  13. Zhen, Xudong & Wang, Yang, 2015. "An overview of methanol as an internal combustion engine fuel," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 477-493.
  14. Ağbulut, Ümit & Gürel, Ali Etem & Sarıdemir, Suat, 2021. "Experimental investigation and prediction of performance and emission responses of a CI engine fuelled with different metal-oxide based nanoparticles–diesel blends using different machine learning alg," Energy, Elsevier, vol. 215(PA).
  15. Zhou, Daming & Gao, Fei & Breaz, Elena & Ravey, Alexandre & Miraoui, Abdellatif, 2017. "Degradation prediction of PEM fuel cell using a moving window based hybrid prognostic approach," Energy, Elsevier, vol. 138(C), pages 1175-1186.
  16. Babu, D. & Thangarasu, Vinoth & Ramanathan, Anand, 2020. "Artificial neural network approach on forecasting diesel engine characteristics fuelled with waste frying oil biodiesel," Applied Energy, Elsevier, vol. 263(C).
  17. Yurdusevimli Metin, Ece & Aygün, Hakan, 2019. "Energy and power aspects of an experimental target drone engine at non-linear controller loads," Energy, Elsevier, vol. 185(C), pages 981-993.
  18. Mehra, Roopesh Kumar & Duan, Hao & Luo, Sijie & Rao, Anas & Ma, Fanhua, 2018. "Experimental and artificial neural network (ANN) study of hydrogen enriched compressed natural gas (HCNG) engine under various ignition timings and excess air ratios," Applied Energy, Elsevier, vol. 228(C), pages 736-754.
  19. Najafi, Gholamhassan & Ghobadian, Barat & Yusaf, Talal & Safieddin Ardebili, Seyed Mohammad & Mamat, Rizalman, 2015. "Optimization of performance and exhaust emission parameters of a SI (spark ignition) engine with gasoline–ethanol blended fuels using response surface methodology," Energy, Elsevier, vol. 90(P2), pages 1815-1829.
  20. García, Antonio & Monsalve-Serrano, Javier & Villalta, David & Lago Sari, Rafael & Gordillo Zavaleta, Victor & Gaillard, Patrick, 2019. "Potential of e-Fischer Tropsch diesel and oxymethyl-ether (OMEx) as fuels for the dual-mode dual-fuel concept," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
  21. Balki, Mustafa Kemal & Sayin, Cenk, 2014. "The effect of compression ratio on the performance, emissions and combustion of an SI (spark ignition) engine fueled with pure ethanol, methanol and unleaded gasoline," Energy, Elsevier, vol. 71(C), pages 194-201.
  22. Yusri, I.M. & Abdul Majeed, A.P.P. & Mamat, R. & Ghazali, M.F. & Awad, Omar I. & Azmi, W.H., 2018. "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, Elsevier, vol. 90(C), pages 665-686.
  23. Wang, Xin & Ge, Yunshan & Liu, Linlin & Peng, Zihang & Hao, Lijun & Yin, Hang & Ding, Yan & Wang, Junfang, 2015. "Evaluation on toxic reduction and fuel economy of a gasoline direct injection- (GDI-) powered passenger car fueled with methanol–gasoline blends with various substitution ratios," Applied Energy, Elsevier, vol. 157(C), pages 134-143.
  24. Hu, Jibin & Wu, Wei & Yuan, Shihua & Jing, Chongbo, 2013. "Fuel combustion under asymmetric piston motion: Tested results," Energy, Elsevier, vol. 55(C), pages 209-215.
  25. Ganesan, P. & Rajakarunakaran, S. & Thirugnanasambandam, M. & Devaraj, D., 2015. "Artificial neural network model to predict the diesel electric generator performance and exhaust emissions," Energy, Elsevier, vol. 83(C), pages 115-124.
  26. Gharehghani, Ayat & Abbasi, Hamid Reza & Alizadeh, Pouria, 2021. "Application of machine learning tools for constrained multi-objective optimization of an HCCI engine," Energy, Elsevier, vol. 233(C).
  27. Tian, Junjian & Liu, Yu & Bi, Haobo & Li, Fengyu & Bao, Lin & Han, Kai & Zhou, Wenliang & Ni, Zhanshi & Lin, Qizhao, 2022. "Experimental study on the spray characteristics of octanol diesel and prediction of spray tip penetration by ANN model," Energy, Elsevier, vol. 239(PA).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.