Modeling of Magnetic Refrigeration Device by Using Artificial Neural Networks Approach

2021 ◽  
Vol 10 (4) ◽  
pp. 68-76
Author(s):  
Younes Chiba ◽  
Yacine Marif ◽  
Noureddine Henini ◽  
Abdelhalim Tlemcani
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Room Temperature ◽  
Magnetic Refrigeration ◽  
Coefficient Of Performance ◽  
Refrigeration Cycle ◽  
Multiple Linear Regressions ◽  
Artificial Neural ◽  
Experimental Data Collection ◽  
Linear Regressions

The aim of this work is to use multi-layered perceptron artificial neural networks and multiple linear regressions models to predict the efficiency of the magnetic refrigeration cycle device operating near room temperature. For this purpose, the experimental data collection was used in order to predict coefficient of performance and temperature span for active magnetic refrigeration device. In addition, the operating parameters of active magnetic refrigerator cycle are used for solid magnetocaloric material under application 1.5 T magnetic fields. The obtained results including temperature span and coefficient of performance are presented and discussed.

scholarly journals The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models over particulate matter (PM10) variability during haze and non-haze episodes: A decade case study

2019 ◽  
Vol 15 (2) ◽  
pp. 164-172 ◽  
Author(s):  
Ku Mohd Kalkausar Ku Yusof ◽  
Azman Azid ◽  
Muhamad Shirwan Abdullah Sani ◽  
Mohd Saiful Samsudin ◽  
Siti Noor Syuhada Muhammad Amin ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Particulate Matter ◽  
Meteorological Factors ◽  
Model Development ◽  
Eastern Region ◽  
Multiple Linear Regressions ◽  
Artificial Neural ◽  
Linear Regressions

The comprehensives of particulate matter studies are needed in predicting future haze occurrences in Malaysia. This paper presents the application of Artificial Neural Networks (ANN) and Multiple Linear Regressions (MLR) coupled with sensitivity analysis (SA) in order to recognize the pollutant relationship status over particulate matter (PM10) in eastern region. Eight monitoring studies were used, involving 14 input parameters as independent variables including meteorological factors. In order to investigate the efficiency of ANN and MLR performance, two different weather circumstances were selected; haze and non-haze. The performance evaluation was characterized into two steps. Firstly, two models were developed based on ANN and MLR which denoted as full model, with all parameters (14 variables) were used as the input. SA was used as additional feature to rank the most contributed parameter to PM10 variations in both situations. Next, the model development was evaluated based on selected model, where only significant variables were selected as input. Three mathematical indices were introduced (R2, RMSE and SSE) to compare on both techniques. From the findings, ANN performed better in full and selected model, with both models were completely showed a significant result during hazy and non-hazy. On top of that, UVb and carbon monoxide were both variables that mutually predicted by ANN and MLR during hazy and non-hazy days, respectively. The precise predictions were required in helping any related agency to emphasize on pollutant that essentially contributed to PM10 variations, especially during haze period.

The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models for predicting SO2 concentration

Urban Climate ◽  
2021 ◽  
Vol 37 ◽  
pp. 100837
Author(s):  
Seyedeh Reyhaneh Shams ◽  
Ali Jahani ◽  
Saba Kalantary ◽  
Mazaher Moeinaddini ◽  
Nematollah Khorasani
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Multiple Linear Regressions ◽  
Artificial Neural ◽  
Linear Regressions ◽  
So2 Concentration

Performance Characteristics of a Magnetic Ericsson Refrigeration Cycle Using La(Fe0.88Si0.12)13H1 or Gd as the Working Substance

2013 ◽  
Vol 631-632 ◽  
pp. 322-325 ◽  
Author(s):  
Jun Yi Wang ◽  
Gildas Diguet ◽  
Guo Xing Lin ◽  
Jin Can Chen
Keyword(s):  
Numerical Calculation ◽  
Room Temperature ◽  
Magnetic Refrigeration ◽  
Performance Characteristics ◽  
Coefficient Of Performance ◽  
Refrigeration Cycle ◽  
Regenerative Heat ◽  
Heat Quantity ◽  
Room Temperature Magnetic Refrigeration

Based on the experimental characteristics of iso-field entropy varying with temperature for the room-temperature magnetic refrigeration material La(Fe0.88Si0.12)13H1 or Gd, the regenerative Ericsson refrigeration cycle using La(Fe0.88Si0.12)13H1 or Gd as the working substance is established and their thermodynamic performances are evaluated and analyzed. By means of numerical calculation, the influence of non-perfect regeneration on the main thermodynamic performances of the cycle is revealed and discussed. Furthermore, the coefficient of performance (COP), non-perfect regenerative heat quantity, and net cooling quantity of the Ericsson refrigeration cycle using La(Fe0.88Si0.12)13H1 or Gd as the working substance are compared. The results obtained show that it is beneficial to the cooling quantity of the cycles using La(Fe0.88Si0.12)13H1 or Gd as the working substance to operate in the region of Tcold >T0 and, at the condition of a same temperature span, the cooling quantity for La(Fe0.88Si0.12)13H1 is larger than that for Gd.

Application of artificial neural networks (ANNs) and linear regressions (LR) to predict the deflection of concrete deep beams

2013 ◽  
Vol 11 (3) ◽  
pp. 237-252 ◽  
Author(s):  
Mohammad Mohammadhassani ◽  
Hossein Nezamabadi-pour ◽  
Mohd Zamin Jumaat ◽  
Mohammed Jameel ◽  
Arul M.S. Arumugam
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Beams ◽  
Artificial Neural ◽  
Linear Regressions
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