Thermal performance of Al2O3 Nanoparticle Enhanced Ionic Liquids (NEILs) for Concentrated Solar Power (CSP) applications

Paul, Titan C.; Morshed, A. K. M. M.; Fox, Elise B.; Khan, Jamil A.

doi:10.1016/j.ijheatmasstransfer.2015.01.071

Cited by 62 publications

(30 citation statements)

References 31 publications

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“…To apply the above methods and construct an accurate model, in the first stage, a set of experimental data points for the network training is introduced. A review of empirical studies has been performed, and the C p values of some NEILs with wide ranges of nanoparticle concentrations and temperatures were found [20,21,[77][78][79]. The total collected experimental dataset (571) was randomly split into two parts: training (75%) and testing (25%) subsets.…”

Section: Data Preparationmentioning

confidence: 99%

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Estimating the Heat Capacity of Non-Newtonian Ionanofluid Systems Using ANN, ANFIS, and SGB Tree Algorithms

et al. 2020

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This work investigated the capability of multilayer perceptron artificial neural network (MLP–ANN), stochastic gradient boosting (SGB) tree, radial basis function artificial neural network (RBF–ANN), and adaptive neuro-fuzzy inference system (ANFIS) models to determine the heat capacity (Cp) of ionanofluids in terms of the nanoparticle concentration (x) and the critical temperature (Tc), operational temperature (T), acentric factor (ω), and molecular weight (Mw) of pure ionic liquids (ILs). To this end, a comprehensive database of literature reviews was searched. The results of the SGB model were more satisfactory than the other models. Furthermore, an analysis was done to determine the outlying bad data points. It showed that most of the experimental data points were located in a reliable zone for the development of the model. The mean squared error and R2 were 0.00249 and 0.987, 0.0132 and 0.9434, 0.0320 and 0.8754, and 0.0201 and 0.9204 for the SGB, MLP–ANN, ANFIS, and RBF–ANN, respectively. According to this study, the ability of SGB for estimating the Cp of ionanofluids was shown to be greater than other models. By eliminating the need for conducting costly and time-consuming experiments, the SGB strategy showed its superiority compared with experimental measurements. Furthermore, the SGB displayed great generalizability because of the stochastic element. Therefore, it can be highly applicable to unseen conditions. Furthermore, it can help chemical engineers and chemists by providing a model with low parameters that yields satisfactory results for estimating the Cp of ionanofluids. Additionally, the sensitivity analysis showed that Cp is directly related to T, Mw, and Tc, and has an inverse relation with ω and x. Mw and Tc had the highest impact and ω had the lowest impact on Cp.

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Section: Data Preparationmentioning

confidence: 99%

“…Paul et al [20,21] carried out an experimental study and observed an increase in C p of up to 49% compared with the base ILs when Al 2 O 3 nanoparticles were added to pyrrolidinium-and imidazolium-based NEILs. Furthermore, silver nanofluid heat transfer through a tube with twisted tape inserts was tested by Waghole et al [22].…”

Section: Introductionmentioning

confidence: 99%

Estimating the Heat Capacity of Non-Newtonian Ionanofluid Systems Using ANN, ANFIS, and SGB Tree Algorithms

et al. 2020

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“…Auch ausgewählte Vertreter aus der noch wachsenden Gruppe ionischer Flüssigkeiten werden aufgrund ihres sehr geringen Dampfdrucks als Wärmeträgermedium bei höheren Temperaturen vorgeschlagen. Durch den Zusatz von Al 2 O 3 ‐Nanopartikeln kann sowohl die Wärmeleitfähigkeit als auch die spezifische Wärmekapazität verbessert werden .…”

Section: Speicherung Von Sensibler Wärmeunclassified

Thermische Energiespeicher – Trends, Entwicklungen und Herausforderungen

Scheffler

2019

Chemie Ingenieur Technik

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Die Verfügbarkeit leistungsfähiger thermischer Energiespeicher ist essentielle Voraussetzung für das Gelingen der Energiewende. Basierend auf dem Anteil am Gesamtenergieverbrauch stehen (1) kostengünstige, sichere und niederschwellig nutzbare Speicher für die Bereitstellung von Raumheizung und Brauchwasser im Fokus. Darüber hinaus wird (2) Hochtemperaturspeichern zur Nutzung in solarthermischen Kraftwerken und Reaktoren ein großes Potenzial zugesprochen. Aussichtsreich ist das innovative Zusammenspiel von Materialien, intelligenter Steuerung, Vernetzung über Systemgrenzen hinaus und die Kombination verschiedener Technologien.

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“…The forced convection heat transfer of ILBNFs under laminar and turbulent flow regimes was experimentally studied by Paul [40], with different volume concentrations For future work, due to the wide variety of molten salts and ionic liquids, more systematic experimental investigations are needed. The synthesis method has a great influence on stability and measured values, so it is very worthwhile for further study.…”

Section: Ionic-liquid-based Nanofluidmentioning

confidence: 99%

A review on molten-salt-based and ionic-liquid-based nanofluids for medium-to-high temperature heat transfer

Wang

et al. 2018

J Therm Anal Calorim

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Molten-salt-based nanofluids and ionic-liquid-based nanofluids are developed for thermal storage and heat transfer at relatively high temperatures, in the past few years. Preparation and stabilization techniques are briefly introduced firstly, and then, thermal properties, e.g., specific heat, thermal conductivity and viscosity, are summarized and discussed in detail. The properties are not only affected by the characteristics of nanomaterials and base fluids, but also affected by the synthesis method, such as the sonication intensity and duration. Some of the thermophysical property data are still incomplete, especially the thermal conductivity of molten-salt-based nanofluids, and properties of ionic-liquid-based nanofluids at high temperatures. While several literature works show that the Krieger-Dougherty model can well predict the viscosity, no general models for thermal conductivity and specific heat have been developed yet for both types of nanofluids. Keywords Nanofluid Á Molten salt Á Ionic liquid Á Medium-to-high temperature Á Thermal properties

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Thermal performance of Al2O3 Nanoparticle Enhanced Ionic Liquids (NEILs) for Concentrated Solar Power (CSP) applications

Cited by 62 publications

References 31 publications

Estimating the Heat Capacity of Non-Newtonian Ionanofluid Systems Using ANN, ANFIS, and SGB Tree Algorithms

Estimating the Heat Capacity of Non-Newtonian Ionanofluid Systems Using ANN, ANFIS, and SGB Tree Algorithms

Thermische Energiespeicher – Trends, Entwicklungen und Herausforderungen

A review on molten-salt-based and ionic-liquid-based nanofluids for medium-to-high temperature heat transfer

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