Artificial neural network (ANN) modeling of thermal conductivity of supercritical ethane

Indonesia with abundant limestone raw materials, lightweight brick is the most important component in building construction, so it needs a light brick product that qualifies in thermal, mechanical and acoustic properties. In this paper raised the lightweight brick domains that qualify on the properties of thermal conductivity as building wall components.The advantage of low light density brick (500-650 kg/m3), more economical, suitable for high rise building can reduce the weight of 30-40% in compared to conventional brick (clay brick). To obtain AAC type lightweight brick product that qualifies for low thermal and density properties to the effect of Aluminum (Al) additive element variation using artificial neural network (ANN). The composition of the main elements of lightweight brick O (29-45 % wt), Si (25-35% wt) and Ca (20-40 % wt). Mixing ratio of the main element of light brick (Ca, O and Si) with Aluminum additive element (Al), is done by simulation method of artificial neural network (ANN), Al additive element as a porosity regulator is formed. The simulation of thermal conductivity to the influence of main element variation: Ca (22-32 % wt), Si (12-33 % wt). Simulation of thermal conductivity to effect of additive Al variation (1-7 % wt). Simulation of thermal conductivity to density variation (500-1200 kg/m3). The simulated results of four AAC brick samples showed the thermal conductivity (0.145-0.192 W/m.K) to the influence of qualified Aluminum additives (2.10-6.75 % wt). Additive Al the higher the lower density value (higher porosity) additive Al smaller than 2.10 % wt does not meet the requirements in the simulation.Thermal conductivity of AAC light brick sample (0.184 W/m.K) the influence of the main elements that qualify Ca (20.32-30.35 % wt) and Si (26.57 % wt). Simulation of artificial neural network (ANN) of light brick shows that maximum allowable Si content of 26.57 % wt, Ca content is in the range 20.32-30.35 % wt, and the minimum content of aluminum in brick is light at 2.10 % wt. ANN tests performed to predict the thermal conductivity of light brick samples obtained results of the average AAC light brick thermal conductivity of 0.151 W/m.K. The best performance with Artificial Neural Network (ANN) characteristics has a validation MSE of 0.002252.

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Artificial Neural Network (ANN) Modeling for Prediction of Pesticide Wastewater Degradation by FeGAC/H2O2 Process

E3S Web of Conferences ◽

10.1051/e3sconf/20186505004 ◽

2018 ◽

Vol 65 ◽

pp. 05004

Author(s):

Augustine Chioma Affam ◽

Malay Chaudhuri ◽

Chee Chung Wong ◽

Chee Swee Wong

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Close Agreement ◽

Transfer Functions ◽

Cod Removal ◽

Experimental Results ◽

Ann Model ◽

Ann Modeling ◽

Artificial Neural ◽

Artificial Neural Network Ann

The study examined artificial neural network (ANN) modeling for the prediction of chlorpyrifos, cypermethrin and chlorothalonil pesticides degradation by the FeGAC/H2O2 process. The operating condition was the optimum condition from a series of experiments. Under these conditions; FeGAC 5 g/L, H2O2 concentration 100 mg/L, pH 3 and 60 min reaction time, the COD removal obtained was 96.19%. The ANN model was developed using a three-layer multilayer perceptron (MLP) neural network to predict pesticide degradation in terms of COD removal. The configuration of the model with the smallest mean square error (MSE) of 0.000046 contained 5 inputs, 9 hidden and, 1 output neuron. The Levenberg–Marquardt backpropagation training algorithm was used for training the network, while tangent sigmoid and linear transfer functions were used at the hidden and output neurons, respectively. The predicted results were in close agreement with the experimental results with correlation coefficient (R2) of 0.9994 i.e. 99.94% showing a close agreement to the actual experimental results. The sensitivity analysis showed that FeGAC dose had the highest influence with relative importance of 25.33%. The results show how robust the ANN model could be in the prediction of the behavior of the FeGAC/H2O2 process.

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Artificial Neural Network (ANN) modeling of the pulsed heat load during ITER CS magnet operation

Cryogenics ◽

10.1016/j.cryogenics.2014.03.003 ◽

2014 ◽

Vol 63 ◽

pp. 231-240 ◽

Cited By ~ 8

Author(s):

L. Savoldi Richard ◽

R. Bonifetto ◽

S. Carli ◽

A. Froio ◽

A. Foussat ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Heat Load ◽

Ann Modeling ◽

Artificial Neural ◽

Artificial Neural Network Ann

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Development of thermal conductivity measuring device using single operational amplifier and its modeling by artificial neural network (ANN)

INTERNATIONAL JOURNAL OF PROCESSING AND POST HARVEST TECHNOLOGY ◽

10.15740/has/ijppht/8.2/83-88 ◽

2017 ◽

Vol 8 (2) ◽

pp. 83-88

Author(s):

SHIVMURTI SRIVASTAV

Keyword(s):

Neural Network ◽

Thermal Conductivity ◽

Artificial Neural Network ◽

Operational Amplifier ◽

Measuring Device ◽

Artificial Neural ◽

Artificial Neural Network Ann

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Artificial Neural Network (ANN) Modeling for the Energy Absorption of Hot‐Rolled Plates in Charpy Impact Tests

Multidiscipline Modeling in Materials and Structures ◽

10.1163/157361108783470423 ◽

2008 ◽

Vol 4 (1) ◽

pp. 37-46

Author(s):

W. Tan ◽

Z.Y. Liu ◽

D. Wu ◽

G.D. Wang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Energy Absorption ◽

Charpy Impact ◽

Ann Modeling ◽

Impact Tests ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Hot Rolled ◽

Rolled Plates

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Using artificial neural network to predict dry density of soil from thermal conductivity

Materials and Geoenvironment ◽

10.1515/rmzmag-2017-0012 ◽

2017 ◽

Vol 64 (3) ◽

pp. 169-180 ◽

Cited By ~ 3

Author(s):

Oluseun Adetola Sanuade ◽

Rasheed Babatunde Adesina ◽

Joel Olayide Amosun ◽

Akindeji Opeyemi Fajana ◽

Olayiwola Grace Olaseeni

Keyword(s):

Neural Network ◽

Thermal Conductivity ◽

Artificial Neural Network ◽

Dry Density ◽

Grid Pattern ◽

Southwestern Nigeria ◽

Ogun State ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Thermal Conductivities

Abstract Artificial neural network (ANN) was used to predict the dry density of soil from its thermal conductivity. The study area is a farmland located in Abeokuta, Ogun State, Southwestern Nigeria. Thirty points were sampled in a grid pattern, and the thermal conductivities were measured using KD-2 Pro thermal analyser. Samples were collected from 20 sample points to determine the dry density in the laboratory. MATLAB was used to perform the ANN analysis in order to predict the dry density of soil. The ANN was able to predict dry density with a root-mean-square error (RMSE) of 0.50 and a correlation coefficient (R2) of 0.80. The validation of our model between the actual and predicted dry densities shows R2 to be 0.99. This fit shows that the model can be applied to predict the dry density of soil in study areas where the thermal conductivities are known.

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