Exploring the specific heat capacity of water-based hybrid nanofluids for solar energy applications: A comparative evaluation of modern ensemble machine learning techniques

Said, Zafar; Sharma, Prabhakar; Elavarasan, Rajvikram Madurai; Tiwari, Arun Kumar; Rathod, Manish K.

doi:10.1016/j.est.2022.105230

Cited by 58 publications

(24 citation statements)

References 68 publications

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“…Kurtosis and skewness were performed to understand where the most information was lying (distribution) and evaluated the outliers in the experimental data. While skewness is a degree of asymmetry observed in a probability distribution that deviates from the symmetrical normal distribution in data, Kurtosis refers to the degree of presence of outliers in the data distribution [ 37 ]. In this study, all datasets except node-shoot, had symmetrical skewness and flatter kurtosis (pytokurtic).…”

Section: Methodsmentioning

confidence: 99%

Prediction of In vitro organogenesis of Bacopa monnieri using artificial neural networks and regression models

Viswanathan¹,

Gosukonda²,

Sherman³

et al. 2022

Heliyon

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Section: Methodsmentioning

confidence: 99%

Prediction of In vitro organogenesis of Bacopa monnieri using artificial neural networks and regression models

Viswanathan¹,

Gosukonda²,

Sherman³

et al. 2022

Heliyon

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“…Each forecast value for each study site means (i) 6 hours for WWTP El-Salitre and GPS; and (ii) 12 hours for the San Fernando WWTP. Subsequently, the absolute percentage error value (Absolute Percentage Error -APE) (Bowerman et al 2005;Kim et al 2022;Said et al 2022) (used as a performance indicator to evaluate and compare the seven different approaches) is calculated for each wavelength and every 30 time-steps. Therefore, based on the MAPE values (average APE value), the performance of each forecast methodology (lowest MAPE value) is established.…”

Section: Methodsmentioning

confidence: 99%

A Hybrid Approach to Forecasting Water Quality in Urban Drainage Systems

Plazas-Nossa

Torres

2022

Preprint

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The pollutant monitoring in urban sewer systems is currently done by captors based on UV-Vis spectrometry. These have the purpose of determining the dynamics of water quality indicator concentrations. Analyzing time series and UV-Vis absorbance spectra is necessary to develop forecast methodologies as supporting tools for online and real-time control support. This work presents a hybrid approach based on seven methodologies combined with Principal Component Analysis (PCA) for UV-Vis absorbance time series forecasting, such as: Principal Component Analysis combined with Discrete Fourier Transform (PCA/DFT), PCA combined with Chebyshev polynomials, PCA combined with Legendre polynomials (PCA/L-Poly), PCA combined with Feed-forward Artificial Neural Networks (PCA/ANN), PCA combined with Polynomial (PCA/PolyReg), PCA combined with SVM (PCA/SVM) and Clustering process combined with Markov chains (kmMC). Three UV-Vis absorbances time series collected online were used at different experimental sites in Colombia, two in Bogotá and one in Medellin. The Mean Absolute Percentage Error (MAPE) values were obtained between 0% and 57% for all the study sites. Results show that it is impossible to determine the best forecasting methodology among the proposed ones because they would complement each other for different forecasting time steps and spectra ranges according to the target water quality.

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“…The proposed models can help to reduce the time and resources needed for experiments. Also, the models in the present study can be used in a variety of applications such as heat sinks [85][86][87] , heat pipes 88 , microchannels 89,90 , heat exchangers [91][92][93] , enclosures 94,95 , solar energy 9,96,97 and automotive industry [98][99][100][101] .…”

Section: Combinatorial (Combi) Algorithmmentioning

confidence: 99%

“…Choi 2 proposed the idea of dispersing various nano-sized particles, including metal 3 , metal oxide 4 , 5 , and carbon-based nanomaterials 6 , for the first time in 1995 to improve the properties of the base fluid. Following the successful implementation of this concept, researchers conducted numerical 7 , analytical 8 , and experimental 9 , 10 studies in various fields using nanofluids.…”

Section: Introductionmentioning

confidence: 99%

Examining rheological behavior of CeO2-GO-SA/10W40 ternary hybrid nanofluid based on experiments and COMBI/ANN/RSM modeling

Sepehrnia

Maleki

Karimi

et al. 2022

Sci Rep

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In this study, the rheological behavior and dynamic viscosity of 10W40 engine oil in the presence of ternary-hybrid nanomaterials of cerium oxide (CeO2), graphene oxide (GO), and silica aerogel (SA) were investigated experimentally. Nanofluid viscosity was measured over a volume fraction range of VF = 0.25–1.5%, a temperature range of T = 5–55 °C, and a shear rate range of SR = 40–1000 rpm. The preparation of ternary-hybrid nanofluids involved a two-step process, and the nanomaterials were dispersed in SAE 10W40 using a magnetic stirrer and ultrasonic device. In addition, CeO2, GO, and SA nanoadditives underwent X-ray diffraction-based structural analysis. The non-Newtonian (pseudoplastic) behavior of ternary-hybrid nanofluid at all temperatures and volume fractions is revealed by analyzing shear stress, dynamic viscosity, and power-law model coefficients. However, the nanofluids tend to Newtonian behavior at low temperatures. For instance, dynamic viscosity declines with increasing shear rate between 4.51% (at 5 °C) and 41.59% (at 55 °C) for the 1.5 vol% nanofluid. The experimental results demonstrated that the viscosity of ternary-hybrid nanofluid declines with increasing temperature and decreasing volume fraction. For instance, assuming a constant SR of 100 rpm and a temperature increase from 5 to 55 °C, the dynamic viscosity increases by at least 95.05% (base fluid) and no more than 95.82% (1.5 vol% nanofluid). Furthermore, by increasing the volume fraction from 0 to 1.5%, the dynamic viscosity increases by a minimum of 14.74% (at 5 °C) and a maximum of 35.94% (at 55 °C). Moreover, different methods (COMBI algorithm, GMDH-type ANN, and RSM) were used to develop models for the nanofluid's dynamic viscosity, and their accuracy and complexity were compared. The COMBI algorithm with R2 = 0.9995 had the highest accuracy among the developed models. Additionally, RSM and COMBI were able to generate predictive models with the least complexity.

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Exploring the specific heat capacity of water-based hybrid nanofluids for solar energy applications: A comparative evaluation of modern ensemble machine learning techniques

Cited by 58 publications

References 68 publications

Prediction of In vitro organogenesis of Bacopa monnieri using artificial neural networks and regression models

Prediction of In vitro organogenesis of Bacopa monnieri using artificial neural networks and regression models

A Hybrid Approach to Forecasting Water Quality in Urban Drainage Systems

Examining rheological behavior of CeO2-GO-SA/10W40 ternary hybrid nanofluid based on experiments and COMBI/ANN/RSM modeling

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