Modeling the magnetic cooling efficiency of spinel ferrite magnetocaloric compounds for magnetic refrigeration application using hybrid intelligent computational methods

Alqahtani, Abdullah; Saliu, Saibu; Owolabi, Taoreed O.; Aldhafferi, Nahier; Almurayh, Abdullah; Oyeneyin, Oluwatoba Emmanuel

doi:10.1016/j.mtcomm.2022.104310

Cited by 5 publications

(3 citation statements)

References 35 publications

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“…Unlike the backpropagation (BP) technique in conventional neural networks, with its attendant shortfalls, the ELM has the ability to avoid the need for numerous iterations and local minima problems [41]. It has, therefore, gained widespread usage in classification and regression tasks owing to its superior generalization capabilities, fast learning speed and unresponsiveness to parameters pre-defined by the user [31,[42][43][44][45][46]. The ELM algorithm works by randomly generating the input weights and biases of the hidden layer.…”

Section: Extreme Learning Machine Formulationmentioning

confidence: 99%

Modeling Temperature-Dependent Thermoelectric Performance of Magnesium-Based Compounds for Energy Conversion Efficiency Enhancement Using Intelligent Computational Methods

Ibn Shamsah

2024

Inorganics

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Eco-friendly magnesium-based thermoelectric materials have recently attracted significant attention in green refrigeration technology and wasted heat recovery applications due to their cost effectiveness, non-toxicity, and earth abundance. The energy conversion efficiency of these thermoelectric materials is controlled by a dimensionless thermoelectric figure of merit (TFM), which depends on thermal and electrical conductivity. The independent tuning of the electrical and thermal properties of these materials for TFM enhancement is challenging. The improvement in the TFM of magnesium thermoelectric materials through scattering and structural engineering is experimentally challenging, especially if multiple elements are to be incorporated at different concentrations and at different doping sites. This work models the TFM of magnesium-based thermoelectric materials with the aid of single-hidden-layer extreme learning machine (ELM) and hybrid genetic-algorithm-based support vector regression (GSVR) algorithms using operating absolute temperature, elemental ionic radii, and elemental concentration as descriptors. The developed TFM-G-GSVR model (with a Gaussian mapping function) outperforms the TFM-S-ELM model (with a sine activation function) using magnesium-based thermoelectric testing samples with improvements of 17.06%, 72%, and 73.03% based on correlation coefficient (CC), root mean square error (RMSE), and mean absolute error (MAE) assessment metrics, respectively. The developed TFM-P-GSVR (with a polynomial mapping function) also outperforms TFM-S-ELM during the testing stage, with improvements of 14.59%, 55.31%, and 62.86% using CC, RMSE, and MAE assessment metrics, respectively. Also, the developed TFM-G-ELM model (with a sigmoid activation function) shows superiority over the TFM-S-ELM model with improvements of 14.69%, 79.52%, and 83.82% for CC, RMSE, and MAE assessment yardsticks, respectively. The dependence of some selected magnesium-based thermoelectric materials on temperature and dopant concentration on TFM was investigated using the developed model, and the predicted patterns align excellently with the reported values. This unique performance demonstrated that the developed intelligent models can strengthen room-temperature magnesium-based thermoelectric materials for industrial and technological applications in addressing the global energy crisis.

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Section: Extreme Learning Machine Formulationmentioning

confidence: 99%

Modeling Temperature-Dependent Thermoelectric Performance of Magnesium-Based Compounds for Energy Conversion Efficiency Enhancement Using Intelligent Computational Methods

Ibn Shamsah

2024

Inorganics

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“…The activation functions that demonstrated better performance for relative cooling power prediction are the sine and sigmoid functions. The mathematical expression of the sigmoid function is presented in Equation ( 8), while the transformation of Equation ( 2) after sigmoid function inclusion is presented in Equation (9).…”

Section: Formulation Of Single Hidden Layer Extreme Learning Machinementioning

confidence: 99%

“…However, the requirement for sustainable magnetic refrigeration technology for hydrogen liquefaction and other applications, aside from the possession of a large magnetocaloric effect, is a huge relative cooling power (RCP). The RCP of a magnetic refrigerant measures and controls the amount of heat transferable between cold and hot reservoirs [9]. The experimental determination of RCP is laborious and costly, especially in applications in which high value of RCP is desired.…”

Section: Introductionmentioning

confidence: 99%

Single Hidden Layer Intelligent Approach to Modeling Relative Cooling Power of Rare-Earth-Transition-Metal-Based Refrigerants for Sustainable Magnetic Refrigeration Application

Alqahtani

2024

Sustainability

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Solid-state magnetocaloric-based magnetic refrigeration offers green and sustainable refrigeration with improved efficiency, compactness and environmental friendliness compared with commercialized gas compression refrigeration systems. Relative cooling power (RCP) plays a significant role in the candidature of any magnetic material refrigerants in this application, while the tunable physical and magnetic properties of rare-earth-transition-metal-based materials strengthen the potential of these materials to be used in a cooling system. This work develops single hidden layer (SIL) extreme learning machine intelligent models for predicting the RCP of rare-earth-transition-metal-based magnetocaloric compounds using elemental constituent ionic radii (IR) and maximum magnetic entropy change (EC) descriptors. The developed model based on the sine (SN) activation function with ionic radii (IR) descriptors (SN-SIL-IR) shows superior performance over the sigmoid (SG) activation function-based model, represented as SG-SIL-IR, with performance improvements of 71.86% and 69.55% determined using the mean absolute error (MAE) and root mean square error (RMSE), respectively, upon testing rare-earth-transition-metal-based magnetocaloric compounds. The developed SN-SIL-IR further outperforms the SN-SIL-EC and SG-SIL-EC models which employed maximum magnetic entropy change (EC) descriptors with improvements of 45.74% and 24.79%, respectively, on the basis of MAE performance assessment parameters. Estimates of the developed model agree well with the measured values. The dependence of the RCP on an applied magnetic field for various classes of rare-earth-transition-metal-based magnetocaloric compounds is established using a developed SN-SIL-IR model. The improved precision of the developed SN-SIL-IR model, coupled with ease of its descriptors, will strengthen and facilitate the comprehensive exploration of rare-earth-transition-metal-based magnetocaloric compounds for their practical implementation as magnetic refrigerants for promoting a sustainable system of refrigeration that is known to be efficient and environmentally friendly.

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Modeling the magnetocaloric effect of spinel ferrites for magnetic refrigeration technology using extreme learning machine and genetically hybridized support vector regression computational methods

Oke,

Aldhafferi,

Saliu

et al. 2023

Cogent Engineering

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Modeling the magnetic cooling efficiency of spinel ferrite magnetocaloric compounds for magnetic refrigeration application using hybrid intelligent computational methods

Cited by 5 publications

References 35 publications

Modeling Temperature-Dependent Thermoelectric Performance of Magnesium-Based Compounds for Energy Conversion Efficiency Enhancement Using Intelligent Computational Methods

Modeling Temperature-Dependent Thermoelectric Performance of Magnesium-Based Compounds for Energy Conversion Efficiency Enhancement Using Intelligent Computational Methods

Single Hidden Layer Intelligent Approach to Modeling Relative Cooling Power of Rare-Earth-Transition-Metal-Based Refrigerants for Sustainable Magnetic Refrigeration Application

Modeling the magnetocaloric effect of spinel ferrites for magnetic refrigeration technology using extreme learning machine and genetically hybridized support vector regression computational methods

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