Fuzzy modeling and particle swarm optimization of Al2O3/SiO2 nanofluid

Salameh, Tareq; Kumar, Polamarasetty P.; Sayed, Enas Taha; Abdelkareem, Mohammad Ali; Rezk, Hegazy; Olabi, Abdul Ghani

doi:10.1016/j.ijft.2021.100084

Cited by 45 publications

(11 citation statements)

References 46 publications

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“…The present study examines the most recent research on the use of machine learning methods in nanofluid heat transfer. Over the past decade, there has been a surge in research papers on the use of machine learning methods. − In the past, a variety of machine learning approaches have been employed to model and forecast the properties of nanofluids. ,, With the advent of newer machine learning methods, the ever-increasing computational power is the catalyst for the increasing popularity of machine learning techniques. , As a result, the current study attempts to compile a comprehensive report including the most recent developments in this subject for easy reference. Because these strategies are constantly changing, examining and portraying just the most recent work is critical.…”

Section: Methodsmentioning

confidence: 99%

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Sharma¹,

Said

Kumar³

et al. 2022

Energy Fuels

222

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Nanofluids have gained significant popularity in the field of sustainable and renewable energy systems. The heat transfer capacity of the working fluid has a huge impact on the efficiency of the renewable energy system. The addition of a small amount of high thermal conductivity solid nanoparticles to a base fluid improves heat transfer. Even though a large amount of research data is available in the literature, some results are contradictory. Many influencing factors, as well as nonlinearity and refutations, make nanofluid research highly challenging and obstruct its potentially valuable uses. On the other hand, data-driven machine learning techniques would be very useful in nanofluid research for forecasting thermophysical features and heat transfer rate, identifying the most influential factors, and assessing the efficiencies of different renewable energy systems. The primary aim of this review study is to look at the features and applications of different machine learning techniques employed in the nanofluid-based renewable energy system, as well as to reveal new developments in machine learning research. A variety of modern machine learning algorithms for nanofluid-based heat transfer studies in renewable and sustainable energy systems are examined, along with their advantages and disadvantages. Artificial neural networks-based model prediction using contemporary commercial software is simple to develop and the most popular. The prognostic capacity may be further improved by combining a marine predator algorithm, genetic algorithm, swarm intelligence optimization, and other intelligent optimization approaches. In addition to the well-known neural networks and fuzzy- and gene-based machine learning techniques, newer ensemble machine learning techniques such as Boosted regression techniques, K-means, K-nearest neighbor (KNN), CatBoost, and XGBoost are gaining popularity due to their improved architectures and adaptabilities to diverse data types. The regularly used neural networks and fuzzy-based algorithms are mostly black-box methods, with the user having little or no understanding of how they function. This is the reason for concern, and ethical artificial intelligence is required.

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Section: Methodsmentioning

confidence: 99%

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Sharma¹,

Said

Kumar³

et al. 2022

Energy Fuels

222

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“…It combines the learning and logical reasoning abilities of ANNs and fuzzy logic. ANFIS provides higher prognostic capabilities and is a preferable alternative to conventional neural networks for complicated nonlinear issues 41 . A typical fuzzy inference system (FIS) has five steps, starting with the introduction of inputs to aid in the fuzzification of fuzzy sets based on linguistic rule activation.…”

Section: Methodsmentioning

confidence: 99%

“…ANFIS provides higher prognostic capabilities and is a preferable alternative to conventional neural networks for complicated nonlinear issues. 41 A typical fuzzy inference system (FIS) has five steps, starting with the introduction of inputs to aid in the fuzzification of fuzzy sets based on linguistic rule activation. Specialists design certain rules, which are subsequently generated from test results.…”

Section: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

“…24,25 AI approaches can train from patterns; they are highly reliable in the notion that they can accommodate noisy data; they can handle nonlinear problems; and, once learned, they can do generalization and estimate at high speeds. 26,27 Several studies have reported the efficient use of ML approaches such as ANN, [28][29][30] random forest regression, 31,32 Bayesian regularization network, 33 Multivariate adaptive regression splines, 34 genetic algorithm, 35,36 response surface methodology, 37-39 and particle swarm optimization [40][41][42] for mapping and predicting the critical properties of energy-related areas. Among these, the literature survey reveals extensive use of ANNs for model prediction in the domain of nanofluid characterization.…”

Section: Introductionmentioning

confidence: 99%

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Comparative evaluation of AI ‐based intelligent GEP and ANFIS models in prediction of thermophysical properties of Fe ₃ O ₄ ‐coated MWCNT hybrid nanofluids for potential application in energy systems

Sharma¹,

Said

Memon

et al. 2022

Intl J of Energy Research

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“…Fuzzy-logic-based modeling is presented to characterize the interactive effects of the complex system variables. Fuzzy logic is an extension of traditional Boolean logic 14 that compensates for the theoretical model deficiency and improves the prediction accuracy. The fuzzy model considers unmodeled elements and their combined effects and utilizes the membership degree to determine the influence of the rotation speed, absorbent pH, and H 2 O 2 concentration on the flow patterns and reaction of the HiGee-AOP attenuation process.…”

Section: ■ Introductionmentioning

confidence: 99%

Fuzzy-Logic-Based Modeling and Control for HiGee-AOP Nitric Oxide Attenuation with a Complex Gas–Liquid Mass-Transfer-Reaction Process

Sun

Liu

et al. 2022

Ind. Eng. Chem. Res.

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The complex flows under high gravity in intricate advanced oxidation reactions expose a high-gravity advanced oxidation process (HiGee-AOP) modeling and control to uncertainty. Existing methods based on conservation equations cannot describe such a process with sufficient accuracy because of the mismatch between the assumptions and actual system behavior. Here, we propose a fuzzy-logic-based modeling and control scheme for a HiGee-AOP for nitric oxide (NO) attenuation. A hybrid fuzzy model is presented based on the theoretical model and a collection of if−then rules describing the difference between the theoretical model and the real system. Subsequently, the model serves as the internal model, associated with the fuzzy control rules illustrating the recommended actions under various conditions to construct the adaptive fuzzy controller. The results show that the modelsimulated values are in good agreement with the experimental data, with a deviation of −0.81 ± 15.02%, and that the controller has good tracking performance.

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Fuzzy modeling and particle swarm optimization of Al2O3/SiO2 nanofluid

Cited by 45 publications

References 46 publications

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Comparative evaluation of AI ‐based intelligent GEP and ANFIS models in prediction of thermophysical properties of Fe ₃ O ₄ ‐coated MWCNT hybrid nanofluids for potential application in energy systems

Fuzzy-Logic-Based Modeling and Control for HiGee-AOP Nitric Oxide Attenuation with a Complex Gas–Liquid Mass-Transfer-Reaction Process

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Fuzzy modeling and particle swarm optimization of Al2O3/SiO2 nanofluid

Cited by 45 publications

References 46 publications

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Comparative evaluation of AI ‐based intelligent GEP and ANFIS models in prediction of thermophysical properties of Fe 3 O 4 ‐coated MWCNT hybrid nanofluids for potential application in energy systems

Fuzzy-Logic-Based Modeling and Control for HiGee-AOP Nitric Oxide Attenuation with a Complex Gas–Liquid Mass-Transfer-Reaction Process

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Comparative evaluation of AI ‐based intelligent GEP and ANFIS models in prediction of thermophysical properties of Fe ₃ O ₄ ‐coated MWCNT hybrid nanofluids for potential application in energy systems