Access the full text.
Sign up today, get DeepDyve free for 14 days.
R. Maurya, M. Saxena (2018)
Characterization of ringing intensity in a hydrogen-fueled HCCI engineInternational Journal of Hydrogen Energy, 43
Y. Zweiri, L. Seneviratne (2007)
Diesel Engine Indicated Torque Estimation Based on Artificial Neural Networks2007 IEEE/ACS International Conference on Computer Systems and Applications
K. Goudarzi, A. Moosaei, M. Gharaati (2015)
Applying artificial neural networks (ANN) to the estimation of thermal contact conductance in the exhaust valve of internal combustion engineApplied Thermal Engineering, 87
N. Akkouche, K. Loubar, F. Nepveu, M. Kadi, M. Tazerout (2020)
Micro-combined heat and power using dual fuel engine and biogas from discontinuous anaerobic digestionEnergy Conversion and Management, 205
Y. Çay (2013)
Prediction of a gasoline engine performance with artificial neural networkFuel, 111
E. Arcaklioğlu, İsmet Çelikten (2005)
A diesel engine's performance and exhaust emissionsApplied Energy, 80
N. Hariharan, V. Senthil, M. Krishnamoorthi, S. Karthic (2020)
Application of artificial neural network and response surface methodology for predicting and optimizing dual-fuel CI engine characteristics using hydrogen and bio fuel with water injectionFuel, 270
(2019)
Simulta - neous reduction of NOx and smoke emissions with low viscous
O. Basurko, Z. Uriondo (2015)
Condition-Based Maintenance for medium speed diesel engines used in vessels in operationApplied Thermal Engineering, 80
V. Manieniyan, G. Vinodhini, R. Senthilkumar, S. Sivaprakasam (2016)
Wear element analysis using neural networks of a DI diesel engine using biodiesel with exhaust gas recirculationEnergy, 114
S. Arumugam, G. Sriram, P. Subramanian (2012)
Application of Artificial Intelligence to Predict the Performance and Exhaust Emissions of Diesel Engine using Rapeseed Oil Methyl EsterProcedia Engineering, 38
S. Bhowmik, A. Paul, R. Panua, S. Ghosh, Durbadal Debroy (2018)
Performance-exhaust emission prediction of diesosenol fueled diesel engine: An ANN coupled MORSM based optimizationEnergy
(Alonso Raposo M, Ciuffo B, Ardente F et al (2019) The future of road transport—implications of automated, connected, low-carbon and shared mobility)
Alonso Raposo M, Ciuffo B, Ardente F et al (2019) The future of road transport—implications of automated, connected, low-carbon and shared mobilityAlonso Raposo M, Ciuffo B, Ardente F et al (2019) The future of road transport—implications of automated, connected, low-carbon and shared mobility, Alonso Raposo M, Ciuffo B, Ardente F et al (2019) The future of road transport—implications of automated, connected, low-carbon and shared mobility
P. Wong, L. Tam, K. Li, C. Vong (2010)
Engine idle-speed system modelling and control optimization using artificial intelligenceProceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 224
Shahaboddin Shamshirband, M. Tabatabaei, M. Aghbashlo, Por Yee, D. Petković (2016)
Support vector machine-based exergetic modelling of a DI diesel engine running on biodiesel–diesel blends containing expanded polystyreneApplied Thermal Engineering, 94
A. Anarghya, N. Rao, N. Nayak, Aditi Tirpude, D. Harshith, B. Samarth (2017)
Optimized ANN-GA and experimental analysis of the performance and combustion characteristics of HCCI engineApplied Thermal Engineering, 132
Murat Kapusuz, H. Ozcan, J. Yamin (2015)
Research of performance on a spark ignition engine fueled by alcohol–gasoline blends using artificial neural networksApplied Thermal Engineering, 91
Shrivastava 1 3 engine using nanoparticles additive
H. Taghavifar, H. Taghavifar, A. Mardani, A. Mohebbi, S. Khalilarya, S. Jafarmadar (2015)
On the modeling of convective heat transfer coefficient of hydrogen fueled diesel engine as affected by combustion parameters using a coupled numerical-artificial neural network approachInternational Journal of Hydrogen Energy, 40
C. Esonye, O. Onukwuli, A. Ofoefule, Ekechi Ogah (2019)
Multi-input multi-output (MIMO) ANN and Nelder-Mead’s simplex based modeling of engine performance and combustion emission characteristics of biodiesel-diesel blend in CI diesel engineApplied Thermal Engineering
D. Babu, Vinoth Thangarasu, A. Ramanathan (2020)
Artificial neural network approach on forecasting diesel engine characteristics fuelled with waste frying oil biodieselApplied Energy, 263
R. molkdaragh, S. Jafarmadar, Shahram Khalilaria, H. Saraee (2017)
Prediction of the performance and exhaust emissions of a compression ignition engine using a wavelet neural network with a stochastic gradient algorithmEnergy, 142
V. Karthickeyan, S. Thiyagarajan, V. Geo, B. Ashok, K. Nanthagopal, O. Chyuan, R. Vignesh (2019)
Simultaneous reduction of NOx and smoke emissions with low viscous biofuel in low heat rejection engine using selective catalytic reduction techniqueFuel
H. Saraee, H. Taghavifar, S. Jafarmadar (2017)
Experimental and numerical consideration of the effect of CeO2 nanoparticles on diesel engine performance and exhaust emission with the aid of artificial neural networkApplied Thermal Engineering, 113
S. Uslu, M. Çelik (2020)
Performance and Exhaust Emission Prediction of a SI Engine Fueled with I-amyl Alcohol-Gasoline Blends: An ANN Coupled RSM Based OptimizationFuel
T. Yusaf, B. Yousif, M. El-awad (2011)
Crude palm oil fuel for diesel-engines: Experimental and ANN simulation approachesFuel and Energy Abstracts
Abid Haleem, M. Javaid, I. Khan (2019)
Current status and applications of Artificial Intelligence (AI) in medical field: An overviewCurrent Medicine Research and Practice
J. Rezaei, M. Shahbakhti, B. Bahri, A. Aziz (2015)
Performance prediction of HCCI engines with oxygenated fuels using artificial neural networksApplied Energy, 138
Lukáš Falát, Lucia Pancikova (2015)
Quantitative Modelling in Economics with Advanced Artificial Neural NetworksProcedia. Economics and finance, 34
J. Martínez-Morales, Héctor Quej-Cosgaya, José Lagunas-Jiménez, E. Palacios-Hernandez, J. Morales‐Saldaña (2019)
Design optimization of multilayer perceptron neural network by ant colony optimization applied to engine emissions dataScience China Technological Sciences, 62
H. Hazar, H. Gul (2016)
Modeling analysis of chrome carbide (Cr3C2) coating on parts of combustion chamber of a SI engineEnergy, 115
M. Etghani, M. Shojaeefard, A. Khalkhali, M. Akbari (2013)
A hybrid method of modified NSGA-II and TOPSIS to optimize performance and emissions of a diesel engine using biodieselApplied Thermal Engineering, 59
H. Taghavifar, H. Taghavifar, A. Mardani, A. Mohebbi, S. Khalilarya, S. Jafarmadar (2016)
Appraisal of artificial neural networks to the emission analysis and prediction of CO2, soot, and NOx of n-heptane fueled engineJournal of Cleaner Production, 112
Erinç Uludamar, Erdi Tosun, G. Tüccar, Safak Yildizhan, Ahmet Çalık, Sefa Yildirim, H. Serin, Mustafa Ozcanli (2017)
Evaluation of vibration characteristics of a hydroxyl (HHO) gas generator installed diesel engine fuelled with different diesel–biodiesel blendsInternational Journal of Hydrogen Energy, 42
Charudatta Kshirsagar, Ramanathan Anand (2017)
Artificial neural network applied forecast on a parametric study of Calophyllum inophyllum methyl ester-diesel engine out responsesApplied Energy, 189
Ibham Veza, M. Said, Z. Latiff (2019)
Progress of acetone-butanol-ethanol (ABE) as biofuel in gasoline and diesel engine: A reviewFuel Processing Technology
O. Agwu, J. Akpabio, S. Alabi, A. Dosunmu (2018)
Artificial intelligence techniques and their applications in drilling fluid engineering: A reviewJournal of Petroleum Science and Engineering
R. Mehra, Hao Duan, Sijie Luo, A. Rao, Fanhua Ma (2018)
Experimental and artificial neural network (ANN) study of hydrogen enriched compressed natural gas (HCNG) engine under various ignition timings and excess air ratiosApplied Energy
C. Sayın, H. Ertunc, M. Hosoz, İ. Kılıçaslan, M. Çanakçı (2007)
Performance and exhaust emissions of a gasoline engine using artificial neural networkApplied Thermal Engineering, 27
Subhasish Subhasish, Ashmita Ghosh, A. Das, R. Banerjee (2015)
Development and validation of a GEP model to predict the performance and exhaust emission parameters of a CRDI assisted single cylinder diesel engine coupled with EGRApplied Energy, 140
S. Sarıdemir, A. Gürel, Ü. Ağbulut, Faruk Bakan (2020)
Investigating the role of fuel injection pressure change on performance characteristics of a DI-CI engine fuelled with methyl esterFuel
A. Baldwin (2009)
OperationBMJ : British Medical Journal, 339
Samet Gürgen, Bedir Ünver, İsmail Altın (2018)
Prediction of cyclic variability in a diesel engine fueled with n-butanol and diesel fuel blends using artificial neural networkRenewable Energy, 117
J. Mohammadhassani, A. Dadvand, S. Khalilarya, M. Solimanpur (2015)
Prediction and reduction of diesel engine emissions using a combined ANN-ACO methodAppl. Soft Comput., 34
M. Kiani, B. Ghobadian, T. Tavakoli, A. Nikbakht, G. Najafi (2010)
Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol- gasoline blendsEnergy, 35
N. Shrivastava, Z. Khan (2018)
Application of Soft Computing in the Field of Internal Combustion Engines: A ReviewArchives of Computational Methods in Engineering, 25
K. Muralidharan, D. Vasudevan (2014)
Applications of artificial neural networks in prediction of performance, emission and combustion characteristics of variable compression ratio engine fuelled with waste cooking oil biodieselJournal of the Brazilian Society of Mechanical Sciences and Engineering, 37
H. Ismail, H. Ng, Cheen Queck, S. Gan (2012)
Artificial neural networks modelling of engine-out responses for a light-duty diesel engine fuelled with biodiesel blendsApplied Energy, 92
S. Haykin (1994)
Neural Networks: A Comprehensive Foundation
M. Deb, P. Majumder, A. Majumder, Subhasish Subhasish, R. Banerjee (2016)
Application of artificial intelligence (AI) in characterization of the performance–emission profile of a single cylinder CI engine operating with hydrogen in dual fuel mode: An ANN approach with fuzzy-logic based topology optimizationInternational Journal of Hydrogen Energy, 41
A. Parlak, Y. İslamoğlu, H. Yaşar, Aysun Egrisogut (2006)
Application of artificial neural network to predict specific fuel consumption and exhaust temperature for a Diesel engineApplied Thermal Engineering, 26
M. Bietresato, A. Calcante, F. Mazzetto (2015)
A neural network approach for indirectly estimating farm tractors engine performancesFuel, 143
M. Aydın, S. Uslu, M. Çelik (2020)
Performance and emission prediction of a compression ignition engine fueled with biodiesel-diesel blends: A combined application of ANN and RSM based optimizationFuel, 269
B. Bahri, M. Shahbakhti, K. Kannan, A. Aziz (2016)
Identification of ringing operation for low temperature combustion enginesApplied Energy, 171
A. Durán, M. Lapuerta, J. Rodríguez-Fernández (2005)
Neural networks estimation of diesel particulate matter composition from transesterified waste oils blendsFuel, 84
M. Aghbashlo, Shahaboddin Shamshirband, M. Tabatabaei, Por Yee, Y. Larimi (2016)
The use of ELM-WT (extreme learning machine with wavelet transform algorithm) to predict exergetic performance of a DI diesel engine running on diesel/biodiesel blends containing polymer wasteEnergy, 94
M. Taghavi, A. Gharehghani, F. Nejad, M. Mirsalim (2019)
Developing a model to predict the start of combustion in HCCI engine using ANN-GA approachEnergy Conversion and Management
J. Syed, R. Baig, S. Algarni, Y. Murthy, M. Masood, Mohammed Inamurrahman (2017)
Artificial Neural Network modeling of a hydrogen dual fueled diesel engine characteristics: An experiment approachInternational Journal of Hydrogen Energy, 42
N. Shubber, J. Sheppard, M. Alradhawi, Y. Ali (2020)
The impacts of the novel SARS-CoV-2 outbreak on surgical oncology - A letter to the editor on “The socio-economic implications of the coronavirus and COVID-19 pandemic: A review”International Journal of Surgery (London, England), 79
M. Ilangkumaran, G. Sakthivel, G. Nagarajan (2016)
Artificial neural network approach to predict the engine performance of fish oil biodiesel with diethyl ether using back propagation algorithmInternational Journal of Ambient Energy, 37
G. Dwivedi, M. Sharma (2014)
Impact of cold flow properties of biodiesel on engine performanceRenewable & Sustainable Energy Reviews, 31
Alonso Maria, Ciuffo Biagio, Alves Patricia, Ardente Fulvio, Aurambout Philippe, Baldini Gianmarco, Baranzelli Claudia, Blagoeva Darina, Bobba Silvia, Braun Robert, Cassio Giulia, Chawdhry Pravir, Christidis Panayotis, Christodoulou Aris, Corrado Sara, Duboz Amandine, Duch Nestor, Felici Sofia, Fernandez Enrique, F. Jaime, Fulli Gianluca, Galassi Cristina, Georgakaki Aliki, Gkoumas Konstantinos, Grosso Monica, Gomez Jonatan, Hajdu Marton, Iglesias Maria, Julea Maria, Krause Jette, Kriston Akos, Lavalle Carlo, L. Laura, R. Alexandre, M. Michail, M. Antonios, Marmier Alain, Marques Fabio, M. Bertin, Mattas Konstantinos, M. Fabrice, Menzel Gerhard, M. Fabrizio, Mondello Silvia, Moretto Pietro, Mortara Barbara, Navajas Elena, Paffumi Elena, Pasimeni Francesco, Pavel Claudiu, Pekar Ferenc, Pisoni Enrico, R. Ioan, Sala Serenella, Saveyn Bert, Scholz Harald, Serra Natalia, Tamba Marie, Thiel Christian, Trentadue Germana, Tecchio Paolo, Tsakalidis Anastasios, Uihlein Andreas, V. Mitchell, Vandecasteele Ine (2019)
The future of road transport : implications of automated, connected, low-carbon and shared mobilityEUR (Luxembourg. Online)
Sakir Tasdemir, I. Saritas, M. Ciniviz, N. Allahverdi (2011)
Artificial neural network and fuzzy expert system comparison for prediction of performance and emission parameters on a gasoline engineExpert Syst. Appl., 38
B. Bahri, A. Aziz, M. Shahbakhti, M. Said (2013)
Understanding and detecting misfire in an HCCI engine fuelled with ethanolApplied Energy, 108
H. Taghavifar, H. Taghavifar, A. Mardani, A. Mohebbi, S. Khalilarya (2015)
A numerical investigation on the wall heat flux in a DI diesel engine fueled with n-heptane using a coupled CFD and ANN approachFuel, 140
(Zweiri YH, Seneviratne LD (2007) Diesel engine indicated torque estimation based on artificial neural networks, pp 791–798)
Zweiri YH, Seneviratne LD (2007) Diesel engine indicated torque estimation based on artificial neural networks, pp 791–798Zweiri YH, Seneviratne LD (2007) Diesel engine indicated torque estimation based on artificial neural networks, pp 791–798, Zweiri YH, Seneviratne LD (2007) Diesel engine indicated torque estimation based on artificial neural networks, pp 791–798
P. Verma, M. Sharma, G. Dwivedi (2016)
Impact of alcohol on biodiesel production and propertiesRenewable & Sustainable Energy Reviews, 56
L. Vandepaer, J. Cloutier, B. Amor (2017)
Environmental impacts of Lithium Metal Polymer and Lithium-ion stationary batteriesRenewable & Sustainable Energy Reviews, 78
Y. Çay, A. Çiçek, F. Kara, S. Sağiroğlu (2012)
Prediction of engine performance for an alternative fuel using artificial neural networkApplied Thermal Engineering, 37
G. Kalghatgi (2014)
WITHDRAWN: Developments in internal combustion engines and implications for combustion science and future transport fuels, 35
P. Danaiah, P. Kumar, Y. Rao (2015)
Performance and emission prediction of a tert butyl alcohol gasoline blended spark-ignition engine using artificial neural networksInternational Journal of Ambient Energy, 36
M. Çanakçı, A. Ozsezen, E. Arcaklioğlu, A. Erdil (2009)
Prediction of performance and exhaust emissions of a diesel engine fueled with biodiesel produced from waste frying palm oilExpert Syst. Appl., 36
S. Sarıdemir, Ü. Ağbulut (2019)
Combustion, performance, vibration and noise characteristics of cottonseed methyl ester–diesel blends fuelled engineBiofuels, 13
N. Shrivastava, S. Varma, M. Pandey (2013)
Experimental investigation of diesel engine using EGR and fuelled with Karanja oil methyl esterInternational Journal of Sustainable Engineering, 6
Kamyar Nikzadfar, A. Shamekhi (2014)
Investigating the relative contribution of operational parameters on performance and emissions of a common-rail diesel engine using neural networkFuel, 125
M. Traver, R. Atkinson, C. Atkinson (1999)
Neural Network-Based Diesel Engine Emissions Prediction Using In-Cylinder Combustion PressureSAE transactions, 108
Efraín Quiroz-Pérez, C. Gutiérrez‐Antonio, R. Vázquez-Román (2019)
Modelling of production processes for liquid biofuels through CFD: A review of conventional and intensified technologiesChemical Engineering and Processing - Process Intensification
Ü. Ağbulut, S. Sarıdemir (2018)
A general view to converting fossil fuels to cleaner energy source by adding nanoparticlesInternational Journal of Ambient Energy, 42
Necla Togun, S. Bayseç (2010)
Prediction of torque and specific fuel consumption of a gasoline engine by using artificial neural networksApplied Energy, 87
A. Gharehghani, H. Pourrahmani (2019)
Performance evaluation of diesel engines (PEDE) for a diesel-biodiesel fueled CI engine using nano-particles additiveEnergy Conversion and Management
Y. Choi, J.-Y. Chen (2005)
Fast prediction of start-of-combustion in HCCI with combined artificial neural networks and ignition delay model, 30
(Traver ML, Atkinson RJ, Atkinson CM (1999) Neural network-based diesel engine emissions prediction using in-cylinder combustion pressure. SAE Pap 1)
Traver ML, Atkinson RJ, Atkinson CM (1999) Neural network-based diesel engine emissions prediction using in-cylinder combustion pressure. SAE Pap 1Traver ML, Atkinson RJ, Atkinson CM (1999) Neural network-based diesel engine emissions prediction using in-cylinder combustion pressure. SAE Pap 1, Traver ML, Atkinson RJ, Atkinson CM (1999) Neural network-based diesel engine emissions prediction using in-cylinder combustion pressure. SAE Pap 1
B. Ghobadian, H. Rahimi, A. Nikbakht, G. Najafi, T. Yusaf (2009)
Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural networkRenewable Energy, 34
GT Kalghatgi (2015)
Developments in internal combustion engines and implications for combustion science and future transport fuelsProc Combust Inst, 35
Sefa Yildirim, Erdi Tosun, Ahmet Çalık, Ihsan Uluocak, E. Avşar (2018)
Artificial intelligence techniques for the vibration, noise, and emission characteristics of a hydrogen-enriched diesel engineEnergy Sources, Part A: Recovery, Utilization, and Environmental Effects, 41
Bin Huang, Z. Pan, Xiangyu Su, L. An (2018)
Recycling of lithium-ion batteries: Recent advances and perspectivesJournal of Power Sources
K. Celebi, Erinç Uludamar, Erdi Tosun, Safak Yildizhan, K. Aydın, Mustafa Ozcanli (2017)
Experimental and artificial neural network approach of noise and vibration characteristic of an unmodified diesel engine fuelled with conventional diesel, and biodiesel blends with natural gas additionFuel, 197
D. Karonis, E. Lois, F. Zannikos, A. Alexandridis, H. Sarimveis (2003)
A neural network approach for the correlation of exhaust emissions from a diesel engine with diesel fuel propertiesEnergy & Fuels, 17
Xudong Zhen, Wang Yang, Daming Liu (2020)
Bio-butanol as a new generation of clean alternative fuel for SI (spark ignition) and CI (compression ignition) enginesRenewable Energy, 147
Rajvikram Elavarasan, Rishi Pugazhendhi (2020)
Restructured society and environment: A review on potential technological strategies to control the COVID-19 pandemicThe Science of the Total Environment, 725
Abid Haleem, M. Javaid, Raju Vaishya (2020)
Effects of COVID-19 pandemic in daily lifeCurrent Medicine Research and Practice, 10
G. Sakthivel, M. Ilangkumaran, G. Nagarajan (2013)
Predicting the engine performance using ethyl ester of fish oil with the aid of artificial neural networkInternational Journal of Ambient Energy, 34
Subhasish Subhasish, R. Banerjee, P. Bose (2014)
Performance and exhaust emissions prediction of a CRDI assisted single cylinder diesel engine coupled with EGR using artificial neural networkApplied Energy, 119
Aida Domínguez-S, Giuseppe Ratt, C. Barrios (2018)
Prediction of exhaust emission in transient conditions of a diesel engine fueled with animal fat using Artificial Neural Network and Symbolic RegressionEnergy, 149
S. Javed, Y. Murthy, R. Baig, D. Rao (2015)
Development of ANN model for prediction of performance and emission characteristics of hydrogen dual fueled diesel engine with Jatropha Methyl Ester biodiesel blendsJournal of Natural Gas Science and Engineering, 26
T. Yusaf, D. Buttsworth, K. Saleh, B. Yousif (2010)
CNG-diesel engine performance and exhaust emission analysis with the aid of artificial neural networkApplied Energy, 87
Satishchandra Salam, T. Verma (2019)
Appending empirical modelling to numerical solution for behaviour characterisation of microalgae biodieselEnergy Conversion and Management
M. Krishnamoorthi, R. Malayalamurthi (2018)
Engine characteristics analysis of chaulmoogra oil blends and corrosion analysis of injector nozzle using scanning electron microscopy/energy dispersive spectroscopyEnergy
G. Kalghatgi (2019)
Development of Fuel/Engine Systems—The Way Forward to Sustainable TransportEngineering
P. Shanmugam, V. Sivakumar, A. Murugesan, M. Ilangkumaran (2011)
Performance and Exhaust Emissions of a Diesel Engine Using Hybrid Fuel with an Artificial Neural NetworkEnergy Sources, Part A: Recovery, Utilization, and Environmental Effects, 33
S. Channapattana, Abhay Pawar, Prashant Kamble (2017)
Optimisation of operating parameters of DI-CI engine fueled with second generation Bio-fuel and development of ANN based prediction modelApplied Energy, 187
M Nicola, Z Alsafi, C Sohrabi (2020)
10.1016/j.ijsu.2020.04.018The socio-economic implications of the coronavirus and COVID-19 pandemic: a review
Wanli Liu, Mostafa Shadloo, I. Tlili, A. Maleki, Q. Bach (2020)
The effect of alcohol–gasoline fuel blends on the engines’ performances and emissionsFuel, 276
Han Wang, Donovan Chaffart, L. Ricardez‐Sandoval (2019)
Modelling and optimization of a pilot-scale entrained-flow gasifier using artificial neural networksEnergy, 188
G. Kannan, K. Balasubramanian, R. Anand (2013)
Artificial neural network approach to study the effect of injection pressure and timing on diesel engine performance fueled with biodieselInternational Journal of Automotive Technology, 14
M. Redel-Macias, C. Hervás‐Martínez, Pedro Gutiérrez, S. Pinzi, A. Cubero-Atienza, M. Dorado (2018)
Computational models to predict noise emissions of a diesel engine fueled with saturated and monounsaturated fatty acid methyl estersEnergy, 144
A. Hemeida, S. Hassan, A. Mohamed, Salem Alkhalaf, Mountasser Mahmoud, T. Senjyu, A. Eldin (2020)
Nature-inspired algorithms for feed-forward neural network classifiers: A survey of one decade of researchAin Shams Engineering Journal, 11
D. Kumar, P. Kumar, M. Kumari (2013)
Prediction of Performance and Emissions of a Biodiesel Fueled Lanthanum Zirconate Coated Direct Injection Diesel Engine Using Artificial Neural NetworksProcedia Engineering, 64
P. Verma, M. Sharma, G. Dwivedi (2016)
Evaluation and enhancement of cold flow properties of palm oil and its biodieselEnergy Reports, 2
A. Uzun (2012)
A parametric study for specific fuel consumption of an intercooled diesel engine using a neural networkFuel, 93
S. Kalogirou (2001)
Artificial neural networks in renewable energy systems applications: a reviewRenewable & Sustainable Energy Reviews, 5
N. Shrivastava, Devanshu Shrivastava, V. Shrivastava (2018)
Experimental investigation of performance and emission characteristics of diesel engine using Jatropha biodiesel with alumina nanoparticlesInternational Journal of Green Energy, 15
Y. Çay, I. Korkmaz, A. Çiçek, F. Kara (2013)
Prediction of engine performance and exhaust emissions for gasoline and methanol using artificial neural networkEnergy, 50
S. Kartheeswaran, D. Durairaj (2017)
A data-parallelism approach for PSO-ANN based medical image reconstruction on a multi-core systemInformatics in Medicine Unlocked, 8
Subhasish Subhasish, A. Das, P. Bose, R. Banerjee (2014)
ANN metamodel assisted Particle Swarm Optimization of the performance-emission trade-off characteristics of a single cylinder CRDI engine under CNG dual-fuel operationJournal of Natural Gas Science and Engineering, 21
H. Oğuz, I. Saritas, Hakan Baydan (2010)
Prediction of diesel engine performance using biofuels with artificial neural networkExpert Syst. Appl., 37
Elnaz Siami-Irdemoosa, S. Dindarloo (2015)
Prediction of fuel consumption of mining dump trucks: A neural networks approachApplied Energy, 151
Anping Huang, Xinjiang Zhang, Runmiao Li, Yu Chi (2017)
Memristor Neural Network Design
GR Kannan, KR Balasubramanian, R Anand (2013)
Artificial neural network approach to study the effect of injection pressure and timing on diesel engine performance fueled with biodieselInt J Automot Technol, 14
Seyyed Hosseini, Ahmad Taghizadeh-Alisaraei, B. Ghobadian, Ahmad Abbaszadeh-Mayvan (2020)
Artificial neural network modeling of performance, emission, and vibration of a CI engine using alumina nano-catalyst added to diesel-biodiesel blendsRenewable Energy, 149
M. Krishnamoorthi, R. Malayalamurthi, R. Sakthivel (2019)
Optimization of compression ignition engine fueled with diesel - chaulmoogra oil - diethyl ether blend with engine parameters and exhaust gas recirculationRenewable Energy
P. Shrivastava, Satishchandra Salam, T. Verma, O. Samuel (2020)
Experimental and empirical analysis of an IC engine operating with ternary blends of diesel, karanja and roselle biodieselFuel, 262
Saeed Lotfan, Reza Ghiasi, M. Fallah, M. Sadeghi (2016)
ANN-based modeling and reducing dual-fuel engine’s challenging emissions by multi-objective evolutionary algorithm NSGA-IIApplied Energy, 175
PK Wong, LM Tam, K Li, CM Vong (2010)
Engine idle-speed system modelling and control optimization using artificial intelligenceJ Automob Eng
G. Dwivedi, Siddharth Jain, M. Sharma (2011)
Impact analysis of biodiesel on engine performance—A reviewRenewable & Sustainable Energy Reviews, 15
G. Dwivedi, M. Sharma (2014)
Prospects of biodiesel from Pongamia in IndiaRenewable & Sustainable Energy Reviews, 32
Erdi Tosun, K. Aydın, S. Merola, A. Irimescu (2017)
Estimation of operational parameters for a direct injection turbocharged spark ignition engine by using regression analysis and artificial neural networkThermal Science, 21
V. Çelik, E. Arcaklioğlu (2005)
Performance maps of a diesel engineApplied Energy, 81
G. Najafi, B. Ghobadian, T. Tavakoli, D. Buttsworth, T. Yusaf, M. Faizollahnejad (2009)
Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural networkApplied Energy, 86
Mahsa Daraei, E. Thorin, A. Avelin, E. Dotzauer (2019)
Potential biofuel production in a fossil fuel free transportation system: A scenario for the County of Västmanland in SwedenEnergy Procedia
B. Bahri, M. Shahbakhti, A. Aziz (2017)
Real-time modeling of ringing in HCCI engines using artificial neural networksEnergy, 125
M. Çanakçı, A. Erdil, E. Arcaklioğlu (2006)
Performance and exhaust emissions of a biodiesel engineApplied Energy, 83
(Hutter M (2005) Universal artificial intelligence)
Hutter M (2005) Universal artificial intelligenceHutter M (2005) Universal artificial intelligence, Hutter M (2005) Universal artificial intelligence
M. Gölcü, Y. Sekmen, Perihan Erduranlı, M. Salman (2005)
Artificial neural-network based modeling of variable valve-timing in a spark-ignition engineApplied Energy, 81
(Meireles MR, Almeida PE, Simões MG (2003) A comprehensive review for industrial applicability of ANNs. IEEE Trans Ind Electron 50:585–601)
Meireles MR, Almeida PE, Simões MG (2003) A comprehensive review for industrial applicability of ANNs. IEEE Trans Ind Electron 50:585–601Meireles MR, Almeida PE, Simões MG (2003) A comprehensive review for industrial applicability of ANNs. IEEE Trans Ind Electron 50:585–601, Meireles MR, Almeida PE, Simões MG (2003) A comprehensive review for industrial applicability of ANNs. IEEE Trans Ind Electron 50:585–601
F. Şahin (2015)
Effects of engine parameters on ionization current and modeling of excess air coefficient by artificial neural networkApplied Thermal Engineering, 90
Sara Kaviani, I. Sohn (2020)
Influence of random topology in artificial neural networks: A surveyICT Express, 6
Yogesh Dwivedi, Laurie Hughes, Elvira Ismagilova, G. Aarts, C. Coombs, Tom Crick, Y. Duan, R. Dwivedi, J. Edwards, Aled Eirug, Vassilis Galanos, P. Ilavarasan, M. Janssen, Paul Jones, A. Kar, Hatice Kizgin, Bianca Kronemann, Banita Lal, B. Lucini, R. Medaglia, K. Meunier-FitzHugh, L. Meunier-FitzHugh, S. Misra, E. Mogaji, S. Sharma, Jang Singh, Vishnupriya Raghavan, R. Raman, N. Rana, Spyridon Samothrakis, Jak Spencer, K. Tamilmani, Annie Tubadji, P. Walton, Michael Williams (2019)
Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policyInternational Journal of Information Management
Marcus Hutter (2004)
Universal artificial intelligence
T. Zheng, Yu Zhang, Yongfu Li, Lichen Shi (2019)
Real-time combustion torque estimation and dynamic misfire fault diagnosis in gasoline engineMechanical Systems and Signal Processing
(2003)
A comprehensive review for industrial applicability of ANNs
K. Tsita, S. Kiartzis, Nikolaos Ntavos, P. Pilavachi (2020)
Next generation biofuels derived from thermal and chemical conversion of the Greek transport sectorThermal science and engineering, 17
N. Shrivastava (2016)
Experimental investigation of performance, emission, and noise parameters of water-emulsified Karanja biodiesel: a prospective Indian fuelJournal of the Brazilian Society of Mechanical Sciences and Engineering, 39
S. Kalogirou (2003)
Artificial intelligence for the modeling and control of combustion processes: a reviewProgress in Energy and Combustion Science, 29
L. Ferreira, F. Cunha, R. Oliveira, E. Filho (2019)
Estimation of reference evapotranspiration in Brazil with limited meteorological data using ANN and SVM – A new approachJournal of Hydrology
H. Yücesu, A. Sözen, Tolga Topgül, E. Arcaklioğlu (2007)
Comparative study of mathematical and experimental analysis of spark ignition engine performance used ethanol–gasoline blend fuelApplied Thermal Engineering, 27
R. Banerjee, M. Mikulski, A. Chakraborty, Subhasish Subhasish, P. Bose (2017)
ANN meta-model assisted MOPSO application in an EPA-Tier 4 constrained emission-performance trade-off calibration problem of a hydrogen-diesel-EGR dual fuel operationFuel, 208
Shivakumar, P. Pai, B. Rao (2011)
Artificial Neural Network based prediction of performance and emission characteristics of a variable compression ratio CI engine using WCO as a biodiesel at different injection timingsApplied Energy, 88
Syed Javed, R. Baig, Y. Murthy (2018)
Study on noise in a hydrogen dual-fuelled zinc-oxide nanoparticle blended biodiesel engine and the development of an artificial neural network modelEnergy
Yuhyeok Jo, K. Min, Dong-Wha Jung, M. Sunwoo, Manbae Han (2019)
Comparative study of the artificial neural network with three hyper-parameter optimization methods for the precise LP-EGR estimation using in-cylinder pressure in a turbocharged GDI engineApplied Thermal Engineering
The automotive industry is facing a crucial time. The transformation from internal combustion engines to new electrical technologies requires enormous investment, and hence the IC engines are likely to serve as a means of transportation for the coming decades. The search for sustainable green alternative fuel and operating parameter optimization is a current feasible solution and is a critical issue among the scientific community. Engine experiments are complicated, costly, and time-consuming, especially when the global economy is drastically down due to the COVID-19 pandemic and putting the limitation of social distancing. Industries are looking for proven computational solutions to address these issues. Recently, artificial neural network has been proven beneficial in several areas of engineering to reduce the time and experimentation cost. The IC engine is one of them. ANN has been used to predict and analyze different characteristics such as performance, combustion, and emissions of the IC engine to save time and energy. The complex nature of ANN may lead to computation time, energy, and space. Recent studies are centered on changing the network topology, deep learning, and design of ANN to get the highest performance. The present study summarizes the application of ANN to predict and optimize the complicated characteristics of various types of engines with different fuels. The study aims to investigate the network topologies adopted to design the model and thereafter statistical evaluation of the developed ANN models. A comparison of the ANN model with other prediction models is also presented.
Archives of Computational Methods in Engineering – Springer Journals
Published: Mar 1, 2022
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.