Estimation of Permeability from Porosity, Specific Surface Area, and Irreducible Water Saturation using an Artificial Neural Network

In the present research, artificial neural network (ANN) modelling was utilized to determine the relative importance of effective variables to achieve optimum specific surface areas of a synthesized catalyst.

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Development of artificial neural network models for predicting water saturation and fluid distribution

Journal of Petroleum Science and Engineering ◽

10.1016/j.petrol.2009.06.017 ◽

2009 ◽

Vol 68 (3-4) ◽

pp. 197-208 ◽

Cited By ~ 36

Author(s):

Nabil Al-Bulushi ◽

Peter R. King ◽

Martin J. Blunt ◽

Martin Kraaijveld

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Water Saturation ◽

Network Models ◽

Fluid Distribution ◽

Neural Network Models ◽

Artificial Neural ◽

Artificial Neural Network Models

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PREDICTION OF THE SIZE OF NANOPARTICLES AND MICROSPORE SURFACE AREA USING ARTIFICIAL NEURAL NETWORK

Genetics & Applications ◽

10.31383/ga.vol1iss1pp65-70 ◽

2018 ◽

Vol 1 (1) ◽

pp. 65

Author(s):

Dženana Sarajlić ◽

Layla Abdel-Ilah ◽

Adnan Fojnica ◽

Ahmed Osmanović

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Transfer Function ◽

Surface Area ◽

Network Architecture ◽

Neural Network Architecture ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Hidden Layer ◽

Experimental Values

This paper presents development of Artificial Neural Network (ANN) for prediction of the size of nanoparticles (NP) and microspore surface area (MSA). Developed neural network architecture has the following three inputs: the concentration of the biodegradable polymer in the organic phase, surfactant concentration in the aqueous phase and the homogenizing pressure. Two-layer feedforward network with a sigmoid transfer function in the hidden layer and a linear transfer function in the output layer is trained, using Levenberg-Marquardt training algorithm. For training of this network, as well as for subsequent validation, 36 samples were used. From 36 samples which were used for subsequent validation in this ANN, 80,5% of them had highest accuracy while 19,5% of output data had insignificant differences comparing to experimental values.

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Breakthrough Pressure Prediction Based on Neural Network Model

Geofluids ◽

10.1155/2021/6154468 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Shuren Hao ◽

Jixiang Cao ◽

Hua Zhang ◽

Yulian Liu ◽

Haian Liang ◽

...

Keyword(s):

Neural Network ◽

Carbon Dioxide ◽

Specific Surface ◽

Surface Area ◽

Network Model ◽

Specific Surface Area ◽

Neural Network Model ◽

Throat Radius ◽

Cap Rock ◽

Breakthrough Pressure

The increasing carbon dioxide content is identified as the main cause of global warming. Capturing carbon dioxide in the atmosphere and transporting it to deep salt layer for storage have been proven and practiced in many aspects, which considered to be an effective way to reduce the content of carbon dioxide in the atmosphere. The sealing property of cap rocks is one of the key factors to determine whether CO2 can be effectively stored for a long time. In view of the disadvantages of tedious and time-consuming laboratory test methods for breakthrough pressure of cap rock, this paper explores the relationship between breakthrough pressure and other parameters such as porosity, permeability, density, specific surface area, maximum throat radius, and total organic carbon. The results show that the rock breakthrough pressure is closely related to the maximum throat radius and permeability determined by the mercury injection method, followed by the porosity and specific surface area, and less related to the density, depth, and TOC content of the rock itself. Then, with the selected parameters, a neural network model is established to predict the breakthrough pressure of cap rock, which can achieve good prediction results.

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Artificial Neural Network Estimation of Thermal Insulation Value of Children’s School Wear in Kuwait Classroom

Advances in Artificial Neural Systems ◽

10.1155/2015/421215 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Khaled Al-Rashidi ◽

Radhi Alazmi ◽

Mubarak Alazmi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Surface Area ◽

Body Surface Area ◽

Thermal Insulation ◽

Body Surface ◽

The Body ◽

Artificial Neural ◽

Thermal Insulation Value ◽

Insulation Value

Artificial neural network (ANN) was utilized to predict the thermal insulation values of children’s school wear in Kuwait. The input thermal insulation data of the different children’s school wear used in Kuwait classrooms were obtained from study using thermal manikins. The lowest mean squared error (MSE) value for the validation data was 1.5 × 10−5 using one hidden layer of six neurons and one output layer. The R2 values for the training, validation, and testing data were almost equal to 1. The values from ANN prediction were compared with McCullough’s equation and the standard tables’ methods. Results suggested that the ANN is able to give more accurate prediction of the clothing thermal insulation values than the regression equation and the standard tables methods. The effect of the different input variables on the thermal insulation value was examined using Garson algorithm and sensitivity analysis and it was found that the cloths weight, the body surface area nude (BSA0), and body surface area covered by one layer of clothing (BSAC1) have the highest effect on the thermal insulation value with about 29%, 27%, and 23%, respectively.

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