Nanofluids thermal conductivity prediction applying a novel hybrid data-driven model validated using Monte Carlo-based sensitivity analysis

Naseri, Ali; Jamei, Mehdi; Ahmadianfar, Iman; Behbahani, Mohammad

doi:10.1007/s00366-020-01163-z

Cited by 18 publications

(9 citation statements)

References 112 publications

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“…Five different ensemble ML models were developed for this purpose. The selection of those models was owing to their massive implementation received and confirming their potential in hydrological, climatological and environmental researches [25]- [28]. The obtained modeling results were compared with several well-established literature on river WQ prediction of diverse region all around the world.…”

Section: The Significant Of the Selected Case Studymentioning

confidence: 96%

Proposition of New Ensemble Data-Intelligence Models for Surface Water Quality Prediction

et al. 2021

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An accurate prediction of water quality (WQ) related parameters is considered as pivotal decisive tool in sustainable water resources management. In this study, five different ensemble machine learning (ML) models including Quantile regression forest (QRF), Random Forest (RF), radial support vector machine (SVM), Stochastic Gradient Boosting (GBM) and Gradient Boosting Machines (GBM_H2O) were developed to predict the monthly biochemical oxygen demand (BOD) values of the Euphrates River, Iraq. For this aim, monthly average data of water temperature (T), Turbidity, pH, Electrical Conductivity (EC), Alkalinity (Alk), Calcium (Ca), chemical oxygen demand (COD), Sulfate (SO4), total dissolved solids (TDS), total suspended solids (TSS), and BOD measured for ten years period were used in this study. The performances of these standalone models were compared with integrative models developed by coupling the applied ML models with two different feature extraction algorithms i.e., Genetic Algorithm (GA) and Principal Components Analysis (PCA). The reliability of the applied models was evaluated based on the statistical performance criteria of determination coefficient (R 2 ), root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe model efficiency coefficient (NSE), Willmott index (d), and percent bias (PBIAS). Results showed that among the developed models, QRF model attained the superior performance. The performance of the evaluated models presented in this study proved that the developed integrative PCA-QRF model presented much better performance compared with the standalone ones and with those integrated with GA. The statistical criteria of R 2 , RMSE, MAE, NSE, d, and PBIAS of PCA-QRF were 0.94, 0.12, 0.05, 0.93, 0.98, and 0.3, respectively.

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Section: The Significant Of the Selected Case Studymentioning

confidence: 96%

Proposition of New Ensemble Data-Intelligence Models for Surface Water Quality Prediction

et al. 2021

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“…The training stage corresponds to the recorded data from 2007 to 2012, while the testing stage employed the data from 2013 to 2015. The accuracy and suitability of the models were assessed using the root mean square error (RMSE), the relative RMSE (RRMSE), and Pearson correlation coefficient (R) of the linear regression forced to the origin of the n pairs of observed and predicted GSR values, mean absolute error (MAE) [40][41][42][43][44][45].…”

Section: Developed Modelsmentioning

confidence: 99%

Data-Driven Models for Predicting Solar Radiation in Semi-Arid Regions

Jamei¹,

Bailek²,

Bouchouicha³

et al. 2023

Computers, Materials &Amp; Continua

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Solar energy represents one of the most important renewable energy sources contributing to the energy transition process. Considering that the observation of daily global solar radiation (GSR) is not affordable in some parts of the globe, there is an imperative need to develop alternative ways to predict it. Therefore, the main objective of this study is to evaluate the performance of different hybrid data-driven techniques in predicting daily GSR in semi-arid regions, such as the majority of Spanish territory. Here, four ensemble-based hybrid models were developed by hybridizing Additive Regression (AR) with Random Forest (RF), Locally Weighted Linear Regression (LWLR), Random Subspace (RS), and M5P. The base algorithms of the developed models are scarcely applied in previous studies to predict solar radiation. The testing phase outcomes demonstrated that the AR-RF models outperform all other hybrid models. The provided models were validated by statistical metrics, such as the correlation coefficient (R) and root mean square error (RMSE). The results proved that Scenario #6, utilizing extraterrestrial solar radiation, relative humidity, wind speed, and mean, maximum, and minimum ambient air temperatures as the model inputs, leads to the most accurate predictions among all scenarios (R = 0.968-0.988 and RMSE = 1.274-1.403 MJ/m 2 •d). Also, Scenario #3 stood in the next rank of accuracy for predicting the solar radiation in both validating stations. The AD-RF model was the best predictive, followed by AD-RS and AD-LWLR.

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“…We use the polynomial correlation model, ANFIS model, and ANN model algorithm to predict the TC of nanofluids. Naseri et al . took the volume fraction of nanoparticles, average particle size, thermal conductivity, the temperature, and TC of the substrate liquid as input parameters, using LSSVM-ISA to predict the TC.…”

Section: Predicting the Thermal Conductivity Of Nanofluids Using Mach...mentioning

confidence: 99%

“…We use the polynomial correlation model, ANFIS model, and ANN model algorithm to predict the TC of nanofluids. Naseri et al 163 took the volume fraction of nanoparticles, average particle size, thermal conductivity, the temperature, and TC of the substrate liquid as input parameters, using LSSVM-ISA to predict the TC. Jamei et al developed three soft computing technologies for the first time, 164 namely, genetic programming (GP), model tree (MT), and multiple linear regression (MLR) models and used them to accurately predict TC of the hybrid nanofluid.…”

Section: Hybrid Nanofluidsmentioning

confidence: 99%

A Comprehensive Review of Predicting the Thermophysical Properties of Nanofluids Using Machine Learning Methods

Wang

Chen

2022

Ind. Eng. Chem. Res.

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Nanofluids are often used as heat transfer fluids due to their good thermal and flow properties. Nanofluids are widely used in energy systems such as solar collectors, heat exchangers, and heat pipes. The thermophysical properties of nanofluids can significantly affect their performance in engineering systems. According to current research in this field, machine learning is a very attractive method to predict the thermophysical properties of nanofluids. We believe that, compared with experiments, machine learning methods are more efficient, rapid, accurate, and practical. The accuracy of the model is affected by factors such as the input variables, the data set selected during the modeling process, and the applied algorithm. This is a comprehensive review that describes the use of different machine learning methods to predict the thermophysical properties of nanofluids (conventional and hybrid nanofluids). This is very useful for researchers, scientists, and engineers in the field of nanofluids.

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Nanofluids thermal conductivity prediction applying a novel hybrid data-driven model validated using Monte Carlo-based sensitivity analysis

Cited by 18 publications

References 112 publications

Proposition of New Ensemble Data-Intelligence Models for Surface Water Quality Prediction

Proposition of New Ensemble Data-Intelligence Models for Surface Water Quality Prediction

Data-Driven Models for Predicting Solar Radiation in Semi-Arid Regions

A Comprehensive Review of Predicting the Thermophysical Properties of Nanofluids Using Machine Learning Methods

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