Experimental investigation of synthesized Al2O3 Ionanofluid's energy storage properties: Model-prediction using gene expression programming

[1]  Z. Lai,et al.  Quinuclidinium-piperidinium based Dual Hydroxide Anion Exchange Membranes as Highly Conductive and Stable Electrolyte Materials for Alkaline Fuel Cell Applications , 2022, Electrochimica Acta.

[2]  X. Nguyen,et al.  Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System , 2022, Energy & Fuels.

[3]  A. Minea,et al.  Improved thermophysical properties of Graphene Ionanofluid as heat transfer fluids for thermal applications , 2022, Journal of Ionic Liquids.

[4]  M. H. Doranehgard,et al.  Using machine learning in photovoltaics to create smarter and cleaner energy generation systems: A comprehensive review , 2022, Journal of Cleaner Production.

[5]  M. H. Doranehgard,et al.  Price inflation effects on a solar-geothermal system for combined production of hydrogen, power, freshwater and heat , 2022, International Journal of Hydrogen Energy.

[6]  M. H. Doranehgard,et al.  Dynamic multi-objective optimization applied to a solar-geothermal multi-generation system for hydrogen production, desalination, and energy storage , 2022, International Journal of Hydrogen Energy.

[7]  M. H. Doranehgard,et al.  Thermo-electro-environmental analysis of a photovoltaic solar panel using machine learning and real-time data for smart and sustainable energy generation , 2022, Journal of Cleaner Production.

[8]  F. Rezaei,et al.  Modeling thermal conductivity of nanofluids using advanced correlative approaches: Group method of data handling and gene expression programming , 2022, International Communications in Heat and Mass Transfer.

[9]  Mohammad Khalid,et al.  Comparative evaluation of AI‐based intelligent GEP and ANFIS models in prediction of thermophysical properties of Fe3O4‐coated MWCNT hybrid nanofluids for potential application in energy systems , 2022 .

[10]  M. Nematzadeh,et al.  An evolutionary approach for formulation of ultimate shear strength of steel fiber-reinforced concrete beams using gene expression programming , 2021, Structures.

[11]  L. Abualigah,et al.  Estimating the density of hybrid nanofluids for thermal energy application: Application of non-parametric and evolutionary polynomial regression data-intelligent techniques , 2021, Measurement.

[12]  A. Shahsavar,et al.  Experimental evaluation and development of predictive models for rheological behavior of aqueous Fe3O4 ferrofluid in the presence of an external magnetic field by introducing a novel grid optimization based-Kernel ridge regression supported by sensitivity analysis , 2021 .

[13]  Yasmin Murad,et al.  Flexural strength prediction for concrete beams reinforced with FRP bars using gene expression programming , 2021 .

[14]  J. Khan,et al.  Nanoparticles size effect on thermophysical properties of ionic liquids based nanofluids , 2021, Journal of Molecular Liquids.

[15]  Mohamed El Amine Ben Seghier,et al.  Predicting solubility of nitrous oxide in ionic liquids using machine learning techniques and gene expression programming , 2021, Journal of the Taiwan Institute of Chemical Engineers.

[16]  R. Saidur,et al.  State-of-the-art ionic liquid & ionanofluids incorporated with advanced nanomaterials for solar energy applications , 2021 .

[17]  M. Dzida,et al.  Effect of ultrasonication time on microstructure, thermal conductivity, and viscosity of ionanofluids with originally ultra-long multi-walled carbon nanotubes , 2021, Ultrasonics sonochemistry.

[18]  Z. Said,et al.  Recent advances on the fundamental physical phenomena behind stability, dynamic motion, thermophysical properties, heat transport, applications, and challenges of nanofluids , 2021, Physics Reports.

[19]  R. Mahamud,et al.  A Critical Review on the Development of Ionic Liquids-Based Nanofluids as Heat Transfer Fluids for Solar Thermal Energy , 2021, Processes.

[20]  I. Ahmadianfar,et al.  On the specific heat capacity estimation of metal oxide-based nanofluid for energy perspective – A comprehensive assessment of data analysis techniques , 2021 .

[21]  S. M. Sohel Murshed,et al.  Ionic Liquids-Based Nanocolloids—A Review of Progress and Prospects in Convective Heat Transfer Applications , 2021, Nanomaterials.

[22]  A. Minea,et al.  Ionanofluids with [C2mim][CH3SO3] ionic liquid and alumina nanoparticles: An experimental study on viscosity, specific heat and electrical conductivity , 2021 .

[23]  B. Salam,et al.  A review on nanofluid: preparation, stability, thermophysical properties, heat transfer characteristics and application , 2020, SN Applied Sciences.

[24]  A. Minea,et al.  Viscosity and isobaric specific heat capacity of alumina nanoparticle enhanced ionic liquids: An experimental approach , 2020 .

[25]  A. Minea,et al.  Experimental evaluation of electrical conductivity of ionanofluids based on water–[C2mim][CH3SO3] ionic liquids mixtures and alumina nanoparticles , 2020, Journal of Thermal Analysis and Calorimetry.

[26]  Sandip K. Singh,et al.  Ionic liquids synthesis and applications: An overview , 2020 .

[27]  Divya P. Soman,et al.  Impact of viscosity of nanofluid and ionic liquid on heat transfer , 2019, Journal of Molecular Liquids.

[28]  A. Minea,et al.  Experimental study on thermophysical properties of alumina nanoparticle enhanced ionic liquids , 2019, Journal of Molecular Liquids.

[29]  Xue-Hong Wu,et al.  Variations of thermophysical properties and heat transfer performance of nanoparticle-enhanced ionic liquids , 2019, Royal Society Open Science.

[30]  Mohsen Ahmadi,et al.  Presentation of new thermal conductivity expression for $$\hbox {Al}_2\hbox {O}_3$$Al2O3–water and $$\hbox {CuO}$$CuO–water nanofluids using gene expression programming (GEP) , 2019 .

[31]  G. Pazuki,et al.  A novel Nanodiamond based IoNanofluid: Experimental and mathematical study of thermal properties , 2018, Journal of Molecular Liquids.

[32]  A. Minea,et al.  A review on development of ionic liquid based nanofluids and their heat transfer behavior , 2018, Renewable and Sustainable Energy Reviews.

[33]  J. Arias-Pardilla,et al.  Antiwear performance of ionic liquid+graphene dispersions with anomalous viscosity-temperature behavior , 2018, Tribology International.

[34]  Christa Boer,et al.  Correlation Coefficients: Appropriate Use and Interpretation , 2018, Anesthesia and analgesia.

[35]  W. Jamshed,et al.  Mathematical model for thermal and entropy analysis of thermal solar collectors by using Maxwell nanofluids with slip conditions, thermal radiation and variable thermal conductivity , 2018 .

[36]  J. Khan,et al.  Enhanced thermophysical properties of NEILs as heat transfer fluids for solar thermal applications , 2017 .

[37]  A. Coronas,et al.  Ru-Imidazolium Halide IoNanofluids: Synthesis, Structural, Morphological and Thermophysical Properties , 2016 .

[38]  B. Moghtaderi,et al.  Influence of Controlled Aggregation on Thermal Conductivity of Nanofluids , 2016 .

[39]  H. Md. Azamathulla,et al.  GEP to predict characteristics of a hydraulic jump over a rough bed , 2016 .

[40]  P. Simões,et al.  Transport and thermal properties of quaternary phosphonium ionic liquids and IoNanofluids , 2013 .

[41]  A. Visser,et al.  Thermophysical Properties of Nanoparticle-Enhanced Ionic Liquids (NEILs) Heat-Transfer Fluids , 2013 .

[42]  G. J. Kabo,et al.  Physicochemical Properties of Imidazolium-Based Ionic Nanofluids: Density, Heat Capacity, and Enthalpy of Formation , 2013 .

[43]  S. M. Sohel Murshed,et al.  Thermal Conductivity of [C4mim][(CF3SO2)2N] and [C2mim][EtSO4] and Their IoNanofluids with Carbon Nanotubes: Experiment and Theory , 2013 .

[44]  Zhengguo Zhang,et al.  Surfactant-free ionic liquid-based nanofluids with remarkable thermal conductivity enhancement at very low loading of graphene , 2012, Nanoscale Research Letters.

[45]  Amir Hossein Gandomi,et al.  A new predictive model for compressive strength of HPC using gene expression programming , 2012, Adv. Eng. Softw..

[46]  A. Visser,et al.  THE POTENTIAL OF NANOPARTICLE ENHANCED IONIC LIQUIDS (NEILS) AS ADVANCED HEAT TRANSFER FLUIDS , 2011 .

[47]  H. Metselaar,et al.  A review of nanofluid stability properties and characterization in stationary conditions , 2011 .

[48]  A. Ribeiro,et al.  Thermal Properties of Ionic Liquids and Ionanofluids , 2011 .

[49]  O. Tillement,et al.  Structure and rheology of SiO2 nanoparticle suspensions under very high shear rates. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[50]  Xianju Wang,et al.  Influence of pH on the Stability Characteristics of Nanofluids , 2009, 2009 Symposium on Photonics and Optoelectronics.

[51]  Shuo Yang,et al.  Investigation of pH and SDBS on enhancement of thermal conductivity in nanofluids , 2009 .

[52]  Zhu Dongsheng,et al.  Dispersion behavior and thermal conductivity characteristics of Al2O3–H2O nanofluids , 2009 .

[53]  T. Tsuda,et al.  Electrochemistry of Room-Temperature Ionic Liquids and Melts , 2009 .

[54]  K. Leong,et al.  Investigations of thermal conductivity and viscosity of nanofluids , 2008 .

[55]  Gang Chen,et al.  Enhanced thermal conductivity and viscosity of copper nanoparticles in ethylene glycol nanofluid , 2008 .

[56]  R. Prasher,et al.  Brownian-motion-based convective-conductive model for the effective thermal conductivity of nanofluids , 2006 .

[57]  Cândida Ferreira Gene Expression Programming in Problem Solving , 2002 .

[58]  G. Batchelor The effect of Brownian motion on the bulk stress in a suspension of spherical particles , 1977, Journal of Fluid Mechanics.

[59]  Thomas J. Dougherty,et al.  A Mechanism for Non‐Newtonian Flow in Suspensions of Rigid Spheres , 1959 .