A novel method overcomeing overfitting of artificial neural network for accurate prediction: Application on thermophysical property of natural gas

[1]  A. Al-Rashed,et al.  Effects on thermophysical properties of carbon based nanofluids: Experimental data, modelling using regression, ANFIS and ANN , 2018, International Journal of Heat and Mass Transfer.

[2]  Heather J Kulik,et al.  Predicting electronic structure properties of transition metal complexes with neural networks† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc01247k , 2017, Chemical science.

[3]  Marc J. Assael,et al.  Dynamic Viscosity Measurements of Three Natural Gas Mixtures—Comparison against Prediction Models , 2007 .

[4]  D. Tůma,et al.  Accurate experimental (p, ρ, T) data of natural gas mixtures for the assessment of reference equations of state when dealing with hydrogen-enriched natural gas , 2018, International Journal of Hydrogen Energy.

[5]  Daniel C Elton,et al.  Applying machine learning techniques to predict the properties of energetic materials , 2018, Scientific Reports.

[6]  Joe W. Magee,et al.  Isochoric (p, ρ,T) measurements for five natural gas mixtures fromT=(225 to 350) K at pressures to 35 MPa , 1997 .

[7]  M. He,et al.  A novel waste heat recovery system combing steam Rankine cycle and organic Rankine cycle for marine engine , 2020 .

[8]  Fernando F. Czubinski,et al.  Viscosity measurements of (CH4 + C3H8 + CO2) mixtures at temperatures between (203 and 420) K and pressures between (3 and 31) MPa , 2018, Fuel.

[9]  M. Goodarzi,et al.  Entropy generation of graphene–platinum hybrid nanofluid flow through a wavy cylindrical microchannel solar receiver by using neural networks , 2021, Journal of Thermal Analysis and Calorimetry.

[10]  Feng Yun,et al.  Research on optimum joint operation with various hydraulic systems basing on AB line and C line of Uzbekistan section in trans-asian natural gas pipeline , 2019 .

[11]  Mark O. McLinden,et al.  p−ρ−T Behavior of Four Lean Synthetic Natural-Gas-Like Mixtures from 250 K to 450 K with Pressures to 37 MPa , 2011 .

[12]  Nitish Srivastava,et al.  Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.

[13]  A. Ebrahimi-Moghadam,et al.  Precise calculation of natural gas sound speed using neural networks: An application in flow meter calibration , 2018, Flow Measurement and Instrumentation.

[14]  M. He,et al.  Improving the viscosity and density of n-butanol as alternative to gasoline by blending with dimethyl carbonate , 2021 .

[15]  Zheng Li,et al.  Density measurements on binary mixtures (nitrogen + carbon dioxide and argon + carbon dioxide) at temperatures from (298.15 to 423.15) K with pressures from (11 to 31) MPa using a single-sinker densimeter , 2015 .

[16]  A. E. Elmohlawy,et al.  Thermal performance analysis of a concentrated solar power system (CSP) integrated with natural gas combined cycle (NGCC) power plant , 2019, Case Studies in Thermal Engineering.

[17]  P. Duan,et al.  Combustion performance of domestic gas cookers with swirling strip-port and normal round-port on various natural gas compositions , 2019, Case Studies in Thermal Engineering.

[18]  Zhe Wang,et al.  Analysis on feasibility of a novel cryogenic heat exchange network with liquid nitrogen regeneration process for onboard liquefied natural gas reliquefaction , 2020 .

[19]  Fernando F. Czubinski,et al.  Viscosity of (CH4 + C3H8 + CO2 + N2) mixtures at temperatures between (243 and 423) K and pressures between (1 and 28) MPa: Experiment and theory , 2019, Fuel.

[20]  Seyed Amin Bagherzadeh,et al.  Synthesized CuFe2O4/SiO2 nanocomposites added to water/EG: Evaluation of the thermophysical properties beside sensitivity analysis & EANN , 2018, International Journal of Heat and Mass Transfer.

[21]  K. Hall,et al.  (p, Vm, T) and phase equilibrium measurements for a natural gas-like mixture using an automated isochoric apparatus , 2006 .

[22]  D. Toghraie,et al.  Applying Artificial Neural Networks (ANNs) for prediction of the thermal characteristics of engine oil –based nanofluids containing tungsten oxide -MWCNTs , 2021, Case Studies in Thermal Engineering.

[23]  R. Span,et al.  Accurate (p, ρ, T, x) Measurements of Hydrogen-Enriched Natural-Gas Mixtures at T = (273.15, 283.15, and 293.15) K with Pressures up to 8 MPa , 2014 .

[24]  H. Oktay,et al.  An Artificial Neural Network Model to Predict the Thermal Properties of Concrete Using Different Neurons and Activation Functions , 2019, Advances in Materials Science and Engineering.

[25]  Q. Bach,et al.  Develop optimal network topology of artificial neural network (AONN) to predict the hybrid nanofluids thermal conductivity according to the empirical data of Al2O3 – Cu nanoparticles dispersed in ethylene glycol , 2020 .

[26]  H. Alsulami,et al.  Influence of base fluid, temperature, and concentration on the thermophysical properties of hybrid nanofluids of alumina–ferrofluid: experimental data, modeling through enhanced ANN, ANFIS, and curve fitting , 2020, Journal of Thermal Analysis and Calorimetry.

[27]  E. May,et al.  Density measurements of methane + propane mixtures at temperatures between (256 and 422) K and pressures from (24 to 35) MPa , 2016 .

[28]  Ravinder Kumar,et al.  Comparing various machine learning approaches in modeling the dynamic viscosity of CuO/water nanofluid , 2019, Journal of Thermal Analysis and Calorimetry.

[29]  R. Hernández-Gómez,et al.  Accurate Experimental (p, ρ, T) Data for the Introduction of Hydrogen into the Natural Gas Grid: Thermodynamic Characterization of the Nitrogen–Hydrogen Binary System from 240 to 350 K and Pressures up to 20 MPa , 2017 .

[30]  Kenneth R. Hall,et al.  p–ρ–T Behavior of Three Lean Synthetic Natural Gas Mixtures Using a Magnetic Suspension Densimeter and Isochoric Apparatus from (250 to 450) K with Pressures up to 150 MPa: Part II , 2011 .

[31]  M. He,et al.  General Model Based on Artificial Neural Networks for Estimating the Viscosities of Oxygenated Fuels , 2019, ACS omega.

[32]  D. Campo,et al.  Characterization of a biomethane-like synthetic gas mixture through accurate density measurements from (240 to 350) K and pressures up to 14 MPa , 2017 .

[33]  Masoud Afrand,et al.  Appraising influence of COOH-MWCNTs on thermal conductivity of antifreeze using curve fitting and neural network , 2019, Physica A: Statistical Mechanics and its Applications.

[34]  K. Hall,et al.  Accurate p–ρ–T Data for a Synthetic Residual Natural Gas Mixture (0.95 CH4 + 0.04 C2H6 + 0.01 C3H8) at Temperatures between (135 and 500) K at Pressures to 200 MPa , 2016 .

[35]  K. Hall,et al.  Accurate density measurements for a 91% methane natural gas-like mixture , 2007 .

[36]  Seyed Amin Bagherzadeh,et al.  Develop 24 dissimilar ANNs by suitable architectures & training algorithms via sensitivity analysis to better statistical presentation: Measure MSEs between targets & ANN for Fe–CuO/Eg–Water nanofluid , 2019, Physica A: Statistical Mechanics and its Applications.

[37]  Seyed Amin Bagherzadeh,et al.  A novel sensitivity analysis model of EANN for F-MWCNTs–Fe3O4/EG nanofluid thermal conductivity: Outputs predicted analytically instead of numerically to more accuracy and less costs , 2019, Physica A: Statistical Mechanics and its Applications.

[38]  Marc J. Assael,et al.  Viscosity of Natural-Gas Mixtures: Measurements and Prediction , 2001 .

[39]  Thomas M. Cover,et al.  Estimation by the nearest neighbor rule , 1968, IEEE Trans. Inf. Theory.

[40]  R. Span,et al.  Development of a special single-sinker densimeter for cryogenic liquid mixtures and first results for a liquefied natural gas (LNG) , 2016 .

[41]  S. Wongwises,et al.  Modeling of thermal conductivity of ZnO-EG using experimental data and ANN methods , 2015 .

[42]  Shahaboddin Shamshirband,et al.  Performance investigation of micro- and nano-sized particle erosion in a 90° elbow using an ANFIS model , 2015 .

[43]  Zhedong Zheng,et al.  CamStyle: A Novel Data Augmentation Method for Person Re-Identification , 2019, IEEE Transactions on Image Processing.

[44]  R. Span,et al.  Density measurements of liquefied natural gas (LNG) over the temperature range from (105 to 135) K at pressures up to 8.9 MPa , 2017 .

[45]  Wei Zhang,et al.  Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation , 2018, J. Intell. Manuf..

[46]  Praveen Linga,et al.  Review of natural gas hydrates as an energy resource: Prospects and challenges ☆ , 2016 .

[47]  Xuewen Cao,et al.  Supersonic refrigeration performances of nozzles and phase transition characteristics of wet natural gas considering shock wave effects , 2021 .

[48]  Kenneth R. Hall,et al.  Burnett and pycnometric (p,Vm,T) measurements for natural gas mixtures , 1997 .

[49]  M. El‐Halwagi,et al.  Viscosity Measurements and Data Correlation for Two Synthetic Natural Gas Mixtures , 2010 .

[50]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.