Feasibility of ANFIS-PSO and ANFIS-GA Models in Predicting Thermophysical Properties of Al2O3-MWCNT/Oil Hybrid Nanofluid

Alarifi, Ibrahim M.; Nguyen, Hoang M.; Bakhtiyari, Ali Naderi; Asadi, Amin

doi:10.3390/ma12213628

Cited by 98 publications

(48 citation statements)

References 65 publications

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“…On the other hand, Wang et al [10] employed a fuzzy multicriteria decision-making model (MCDM) for raw material supplier selection in the plastic industry. Likewise, Lin et al [11] applied fuzzy collaborative intelligence approach for fall detection in four existing smart technology applications and a methodology for obtaining technological mean roughness (Ra) for the EDM process, Alarifi et al [42] employed genetic algorithms and particle swarm optimization to determine the parameters of an ANFIS model to predict the thermo-physical properties of Al 2 O 3 -MWCNT/thermal oil hybrid nanofluid and an analysis of the PSO implementation in designing parameters of manufacturing processes as well as a benchmark with other optimization techniques can be found in the review study of Sibalija [43]. On the other hand, Alajmi et al [44] used an ANFIS-QPSO to predict the surface roughness of the dry and cryogenic turning process of AISI 304 stainless steel.…”

Section: State Of the Artmentioning

confidence: 99%

A Proposal of an Adaptive Neuro-Fuzzy Inference System for Modeling Experimental Data in Manufacturing Engineering

Pérez

2020

Mathematics

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In Manufacturing Engineering there is a need to be able to model the behavior of technological variables versus input parameters in order to predict their behavior in advance, so that it is possible to determine the levels of variation that lead to optimal values of the response variables to be obtained. In recent years, it has been a common practice to rely on regression techniques to carry out the above-mentioned task. However, such models are sometimes not accurate enough to predict the behavior of these response variables, especially when they have significant non-linearities. In this present study a comparative analysis between the precision of different techniques based on conventional regression and soft computing is initially carried out. Specifically, regression techniques, based on the response surface model, as well as the use of artificial neural networks and fuzzy inference systems along with adaptive neuro-fuzzy inference systems will be employed to predict the behavior of the aforementioned technological variables. It will be shown that when there are difficulties in predicting the response parameters by using regression models, soft computing models are highly effective, being much more efficient than conventional regression models. In addition, a new method is proposed in this study that consists of using an iterative process to obtain a fuzzy inference system from a design of experiments and then using an adaptive neuro-fuzzy inference system for tuning the constants of the membership functions. As will be shown, with this method it is possible to obtain improved results in the validation metrics. The means of selecting the membership functions to develop this model from the design of experiments is discussed in this present study in order to obtain an initial solution, which will be then tuned by using an adaptive neuro-fuzzy inference system, to predict the behavior of the response variables. Moreover, the obtained results will also be compared.

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Section: State Of the Artmentioning

confidence: 99%

A Proposal of an Adaptive Neuro-Fuzzy Inference System for Modeling Experimental Data in Manufacturing Engineering

Pérez

2020

Mathematics

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“…Using only a single hidden layer, the MLP-ANN showed an R 2 value equal to 0.99974. Alarifi et al [47] measured the thermal conductivity behaviour of Al 2 O 3 -MWCNT/thermal oil hybrid nanofluids; in predicting the behaviour of the nanofluids, the ANFIS was optimised with genetic algorithms (GA) and particle swarm optimisation. The study concluded that while both models could predict thermal conductivity in the hybrid to a significant degree of accuracy; the ANFIS optimised with PSO had a better predictive performance compared to the ANFIS model optimised by GA.…”

Section: Introductionmentioning

confidence: 99%

An intelligent approach to predicting the effect of nanoparticle mixture ratio, concentration and temperature on thermal conductivity of hybrid nanofluids

Wole‐Osho

Okonkwo

Adun

et al. 2020

J Therm Anal Calorim

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Hybrid nanofluids are better heat transfer fluids than conventional nanofluids because of the combined properties of two or more nanoparticles. In this study, the thermal conductivity of Al 2 O 3-ZnO nanoparticles suspended in a base fluid of distilled water is investigated. The experiments were conducted for three mixture ratios (1:2, 1:1 and 2:1) of Al 2 O 3-ZnO nanofluid at five different volume concentrations of 0.33%, 0.67%, 1.0%, 1.33% and 1.67%. X-ray diffractometric analysis, X-ray fluorescence spectrometry and scanning electron microscopy were used to characterise the nanoparticles. The highest thermal conductivity enhancement achieved for Al 2 O 3-ZnO hybrid nanofluids with 1:2, 1:1 and 2:1 (Al 2 O 3 :ZnO) mixture ratios was 36%, 35% and 40%, respectively, at volume concentration 1.67%. The study observed the highest thermal conductivity for Al 2 O 3-ZnO nanofluid was achieved at a mixture ratio of 2:1. A "deeping" effect was observed at a mixture ratio of 1:1 representing the lowest value of thermal conductivity within the considered range. The study proposed and compared three models for obtaining the thermal conductivity of Al 2 O 3-ZnO nanofluids based on temperature, volume concentration and nanoparticle mixture ratio. A polynomial correlation model, the adaptive neuro-fuzzy inference system model and an artificial neural network model optimised with three different learning algorithms. The adaptive neuro-fuzzy inference system model was most accurate in forecasting the thermal conductivity of the Al 2 O 3-ZnO hybrid nanofluid with an R 2 value of 0.9946.

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“…It continuously extends its base of knowledge through the addition of new rules which makes the system upgrading easier and faster with no need for long shutdowns [19]. To achieve optimum MG energy operation and control, metaheuristic optimization algorithms such as the genetic algorithm (GA) and particle swarm optimization (PSO) are further proposed for training the ANFIS [20].…”

Section: Introductionmentioning

confidence: 99%

Power Management Strategy Based on Adaptive Neuro Fuzzy Inference System for AC Microgrid

et al. 2020

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Microgrids (MGs) have been widely implemented as they increase the efficiency and resiliency of electrical networks. However, the uncertain nature of renewable energy resources (RERs) integrated into the MGs usually results in different technical problems. System stability, the most challenging problem, can be achieved via a robust power management strategy (PMS) of the MG. This paper introduces a PMS based on adaptive neuro fuzzy inference system (ANFIS) for AC MG consisting of a diesel generator (DG), a double fed induction generator (DFIG) driven by a wind turbine (WT) and a solar photovoltaic (PV) panel. The proposed strategy aims to achieve MG power balance, decrease DG fossil fuel to minimum consumption, keep the MG voltage stability and finally tracking the maximum power point (MPP) of each RER. Metaheuristic optimization techniques; including genetic algorithm (GA) and particle swarm optimization (PSO), are employed to train the ANFIS to accomplish the desired objectives and fulfill the generation/consumption balance. The proposed AC MG with the PMS is simulated by the MATLAB/Simulink software in order to analyze the system performance under different climatic conditions. The simulation results under symmetrical and asymmetrical electrical faults validated the effectiveness of the proposed strategy. INDEX TERMS Microgrid, renewable energy resources, power management strategy, voltage stability, adaptive neuro fuzzy inference system, double fed induction generator, genetic algorithm, particle swarm optimization.. NOMENCLATURE efficiency coefficient of the wind turbine G solar irradiation PV generator current DFIG rotor leakage inductance DFIG stator leakage inductance diesel generator output active power grid side converter active power diesel generator input mechanical power PV generator active power DFIG rotor active power DFIG stator active power extracted wind power DFIG stator reactive power rotor radius DFIG stator resistance DFIG rotor resistance slip t time interval PV generator voltage wind speed rotor angular frequency synchronously rotating reference frame angular velocity slip angular frequency turbine rotational speed blade pitch angle tip-speed ratio air density This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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Feasibility of ANFIS-PSO and ANFIS-GA Models in Predicting Thermophysical Properties of Al2O3-MWCNT/Oil Hybrid Nanofluid

Cited by 98 publications

References 65 publications

A Proposal of an Adaptive Neuro-Fuzzy Inference System for Modeling Experimental Data in Manufacturing Engineering

A Proposal of an Adaptive Neuro-Fuzzy Inference System for Modeling Experimental Data in Manufacturing Engineering

An intelligent approach to predicting the effect of nanoparticle mixture ratio, concentration and temperature on thermal conductivity of hybrid nanofluids

Power Management Strategy Based on Adaptive Neuro Fuzzy Inference System for AC Microgrid

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