Article, 2023

Artificial neural networks in predicting of the gas molecular diffusion coefficient

Chemical Engineering Research and Design, ISSN 1744-3563, 0263-8762, Volume 200, Pages 407-418, 10.1016/j.cherd.2023.10.035

Contributors

Wang, Xiuqing [1] Daryapour, Mahboobeh [2] Shahrabadi, Abbas 0000-0003-4250-8526 (Corresponding author) [3] Pirasteh, Saied 0000-0002-3177-037X [1] Razavirad, Fatemeh [4]

Affiliations

  1. [1] Shaoxing University
    2. [NORA names: China; Asia, East];
  2. [2] Islamic Azad University Mahshahr
    3. [NORA names: Iran; Asia, Middle East];
  3. [3] National Iranian Oil Company (Iran)
    4. [NORA names: Iran; Asia, Middle East];
  4. [4] Aarhus University
    5. [NORA names: AU Aarhus University; University; Denmark; Europe, EU; Nordic; OECD]

Abstract

The diffusion coefficient is one of the most important parameters for designing two-phase operations between liquid and gas phases in refineries and petrochemical industries, as well as for the gas injection process in oil fields to enhance oil production. Accurate knowledge of this parameter is essential for the prediction of the dissolution rate of the gas phase into the liquid phase. Ideally, this parameter should be obtained experimentally. Given that setting up laboratory equipment and conducting experiments can be costly and time-consuming, mathematical modeling is used as an alternative. In some cases, this data is not either available or reliable, which poses a challenge for designs. Hence, empirically derived correlations are used to predict molecular diffusion coefficients. However, the success of empirical models depends mainly on the range of data at which they were originally developed. Empirical models are not comprehensive for applying to the other data. Recent studies demonstrated that the alternative approach to modeling complex processes and identifying the effective parameters is the artificial neural network (ANN), a suitable prediction method. This study presents a new model developed to predict the molecular diffusion coefficient of methane in crude oil. The model is developed using 172 data points collected from recent literature. Out of the total laboratory data, 90% (155 data points) were used for training the desired neural network, while 10% (17 data points) were reserved for testing and evaluating the performance of the network. The multi-layer perceptron (MLP) neural network architecture with back-propagation (BP) training algorithm was used successfully for the prediction of diffusion coefficients of methane in crude oil. The developed model is compared with the empirical data, which shows the developed model predicts the methane molecular diffusion coefficient in crude oil with an average absolute error of 4.18%.

Keywords

MLP, absolute error, accurate knowledge, algorithm, alternative, alternative approach, approach, architecture, artificial neural network, average absolute error, back propagation, cases, coefficient, complex process, correlation, crude oil, data, data points, design, diffusion, diffusion coefficient, diffusion coefficient of methane, dissolution, dissolution rate, effective parameters, empirical data, empirical model, enhance oil production, equipment, error, experiments, field, gas, gas injection process, gas phase, industry, injection process, knowledge, laboratory equipment, liquid phase, literature, mathematical model, methane, method, model, model complex processes, molecular diffusion coefficient, multi-layer perceptron (MLP, network, network architecture, neural network, neural network architecture, oil, oil field, oil production, operation, parameters, performance, petrochemical, petrochemical industry, phase, point, prediction, prediction method, process, production, range, range of data, rate, refinery, study, success, test, time-consuming, training, training algorithm, two-phase operation

Funders

  • National Natural Science Foundation of China

Data Provider: Digital Science

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