Development of Artificial Neural Network (ANN) Model for Estimation of Vapor Liquid Equilibrium (VLE) Data

Abstract Lime is a significant material in many industrial processes, including steelmaking by blast furnace. Lime production through rotary kilns is a standard method in industries, yet it has depreciation, high energy consumption, and environmental pollution. A model of the lime production process can help to not only increase our knowledge and awareness but also can help reduce its disadvantages. This paper presents a black-box model by Artificial Neural Network (ANN) for the lime production process considering pre-heater, rotary kiln, and cooler parameters. To this end, actual data are collected from Zobahan Isfahan Steel Company, Iran, which consists of 746 data obtained in a duration of one year. The proposed model considers 23 input variables, predicting the amount of produced lime as an output variable. The ANN parameters such as number of hidden layers, number of neurons in each layer, activation functions, and training algorithm are optimized. Then, the sensitivity of the optimum model to the input variables is investigated. Top-three input variables are selected on the basis of one-group sensitivity analysis and their interactions are studied. Finally, an ANN model is developed considering the top-three most effective input variables. The mean square error of the proposed models with 23 and 3 inputs are equal to 0.000693 and 0.004061, respectively, which shows a high prediction capability of the two proposed models.

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Experimental study and application of an Artificial Neural Network (ANN) model on pulsed spray cooling heat transfer on a vertical surface

Experimental Thermal and Fluid Science ◽

10.1016/j.expthermflusci.2021.110347 ◽

2021 ◽

pp. 110347

Author(s):

P. Liu ◽

R. Kandasamy ◽

T.N. Wong

Keyword(s):

Neural Network ◽

Heat Transfer ◽

Experimental Study ◽

Artificial Neural Network ◽

Spray Cooling ◽

Vertical Surface ◽

Ann Model ◽

Artificial Neural ◽

Artificial Neural Network Ann

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Prediction of AC conductivity for organic semiconductors based on artificial neural network ANN model

Materials Research Express ◽

10.1088/2053-1591/ab250a ◽

2019 ◽

Vol 6 (8) ◽

pp. 085107 ◽

Cited By ~ 1

Author(s):

R A Mohamed

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Organic Semiconductors ◽

Ac Conductivity ◽

Ann Model ◽

Artificial Neural ◽

Artificial Neural Network Ann

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Modeling the concentration of carbonyl of ethylene propylene diene monomer during the thermal aging using artificial neural network

Algerian Journal of Renewable Energy and Sustainable Development ◽

10.46657/ajresd.2021.3.1.5 ◽

2021 ◽

Vol 03 (01) ◽

pp. 45-52

Author(s):

Hadjira Maouz ◽

◽

Asma Adda ◽

Salah Hanini ◽

◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Thermal Aging ◽

Training Data ◽

Ethylene Propylene Diene Monomer ◽

Ann Model ◽

Ethylene Propylene ◽

Artificial Neural ◽

Input Variables ◽

Artificial Neural Network Ann

The concentration of carbonyl is one of the most important properties contributing to the detection of the thermal aging of polymer ethylene propylene diene monomer (EPDM). In this publication, an artificial neural network (ANN) model was developed to predict concentration of carbenyl during the thermal aging of EPDM using a database consisting of seven input variables. The best fitting training data was obtained with the architecture of (7 inputs neurons, 10 hidden neurons and 1 output neuron). A Levenberg Marquardt learning (LM) algorithm, hyperbolic tangent transfer function were used at the hidden and output layer respectively. The optimal ANN was obtained with a high correlation coefficient R= 0.995 and a very low root mean square error RMSE = 0.0148 mol/l during the generalization phase. The comparison between the experimental and calculated results show that the ANN model is able of predicted the concentration of carbonyl during the thermal aging of ethylene propylene diene monomer

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