Construction of a model for measurement control over liquefied petroleum gas components based on a multilayer perceptron

Authors

DOI:

https://doi.org/10.15587/1729-4061.2025.340608

Keywords:

liquefied petroleum gas, multilayer perceptron, mean absolute error, coefficient of determination

Abstract

This study's object is the process of measuring control over liquefied petroleum gas components using a multilayer perceptron. The problem considered is insufficient efficiency of existing methods for measuring control over liquefied petroleum gas components. It can be partially solved by remote measurement of components of liquefied petroleum gas and processing of the received data and, accordingly, control by a neural network. However, another issue arises, associated with the complexity of using neural networks in combination with peripheral devices, in particular, means, sensors, gauges, etc., and the need for significant computing power.

This paper reports a model for measuring control over liquefied petroleum gas components, which takes into account its physical characteristics, using a multilayer perceptron, which provides communication with gas measurement devices. The mechanism for achieving these results involves training the model based on performance indicators derived from input data, taking into account the formed features. High generalization ability and efficiency are illustrated by the coefficient of determination, which is 0,845. High accuracy is illustrated by the low overall average value of the mean absolute error, which is 1,1%. That was made possible by the distinctive features of the proposed solution, namely the optimized architecture of the model in accordance with the object of study and its input features. These features are the areas of the light streaks, their logarithmic ratios, temperature, the sum and difference of densities of the components of liquefied petroleum gas.

The results can be applied practically to problems involving liquefied gas composition analysis, especially at gas filling stations, oil and gas processing plants, gas storage facilities, and similar sites

Author Biographies

Bogdan Knysh, Vinnytsia National Technical University

PhD, Associate Professor

Department of General Physics

Yaroslav Kulyk, Vinnytsia National Technical University

PhD, Associate Professor

Department of Automation and Intelligent Information Technologies

References

  1. Bilynskyi, Y. Y., Knysh, B. P. (2017). Termooptychnyi metod i zasib vymiriuvalnoho kontroliu komponentiv skraplenoho naftovoho hazu. Vinnytsia: VNTU, 112. Available at: https://press.vntu.edu.ua/index.php/vntu/catalog/book/317
  2. ASTM D1835-20 (2022). Standard Specification for Liquefied Petroleum (LP) Gases. https://doi.org/10.1520/D1835-20
  3. Chernova, O., Kryvenko, G. (2023). Analysis of hazards during storage of liquefied hydrocarbon gases. Ecological Sciences, 2 (47), 112–116. https://doi.org/10.32846/2306-9716/2023.eco.2-47.18
  4. Zhou, M., Wang, S., Li, J., Wei, Z., Shui, L. (2025). A Wireless Sensor Network-Based Combustible Gas Detection System Using PSO-DBO-Optimized BP Neural Network. Sensors, 25 (10), 3151. https://doi.org/10.3390/s25103151
  5. Zhou, K., Liu, Y. (2021). Early-Stage Gas Identification Using Convolutional Long Short-Term Neural Network with Sensor Array Time Series Data. Sensors, 21 (14), 4826. https://doi.org/10.3390/s21144826
  6. Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T. et al. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv. https://doi.org/10.48550/arXiv.1704.04861
  7. Zhang, X., Zhou, X., Lin, M., Sun, J. (2018). ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2018.00716
  8. Jia, T., Guo, T., Wang, X., Zhao, D., Wang, C., Zhang, Z. et al. (2019). Mixed Natural Gas Online Recognition Device Based on a Neural Network Algorithm Implemented by an FPGA. Sensors, 19 (9), 2090. https://doi.org/10.3390/s19092090
  9. Badreldin, O. A., El Ela, M. A., El-Banbi, A. H. (2024). Development of Artificial Neural Network Model to Predict the Performance of the Fractionation Towers in Gas Processing Plant. Mediterranean Offshore Conference. https://doi.org/10.2118/223340-ms
  10. Renuka, G., Sai Manikanta, K., Uday Karthik, S., Ayyappa Naga Sekhara Reddy, K., Varma, B. V. S. (2023). Gas detection using 3D-CNN and autoencoder on hyperspectral images. IOSR Journal of Engineering (IOSRJEN), 14 (4), 40–50.
  11. Fan, H.-H., Wang, X.-L., Feng, J., Li, W.-Y. (2025). Comprehensive Quantitative Analysis of Coal-Based Liquids by Mask R-CNN-Assisted Two-Dimensional Gas Chromatography. Separations, 12 (2), 22. https://doi.org/10.3390/separations12020022
  12. Moreira de Lima, J. M., Meneghetti Ugulino de Araújo, F. (2019). Deep learning based inference system for real-time estimation of lpg contaminants’ molar fraction. Proceedings of the 25th International Congress of Mechanical Engineering. https://doi.org/10.26678/abcm.cobem2019.cob2019-0225
  13. Lamamra, K., Rechem, D. (2016). Artificial neural network modelling of a gas sensor for liquefied petroleum gas detection. 2016 8th International Conference on Modelling, Identification and Control (ICMIC), 163–168. https://doi.org/10.1109/icmic.2016.7804292
  14. Sadighi, S., Mohaddecy, S. R. S., Abbasi, A. (2018). Modeling and Optimizing a Vacuum Gas Oil Hydrocracking Plant using an Artificial Neural Network. International Journal of Technology, 9 (1), 99. https://doi.org/10.14716/ijtech.v9i1.44
  15. Müller, A., Ersoy, V., Menser, J., Endres, T., Schulz, C. (2025). Real-time analysis of flame chemiluminescence spectra for equivalence ratio and gas composition using neural network approaches. Applications in Energy and Combustion Science, 23, 100345. https://doi.org/10.1016/j.jaecs.2025.100345
  16. Sifakis, N., Sarantinoudis, N., Tsinarakis, G., Politis, C., Arampatzis, G. (2023). Soft Sensing of LPG Processes Using Deep Learning. Sensors, 23 (18), 7858. https://doi.org/10.3390/s23187858
  17. Bilynsky, Y. Y., Knysh, B. P., Ratushny, P. M., Wójcik, W., Grądz, Ż. M., Bainazarov, U. et al. (2017). Thermo-optical method and a means of measuring mass fraction control of liquefied petroleum gas components. Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2017, 10445, 104450Q. https://doi.org/10.1117/12.2280973
  18. Rožanec, J. M., Trajkova, E., Lu, J., Sarantinoudis, N., Arampatzis, G., Eirinakis, P. et al. (2021). Cyber-Physical LPG Debutanizer Distillation Columns: Machine-Learning-Based Soft Sensors for Product Quality Monitoring. Applied Sciences, 11 (24), 11790. https://doi.org/10.3390/app112411790
  19. Dawod, R. G., Dobre, C. (2022). ResNet interpretation methods applied to the classification of foliar diseases in sunflower. Journal of Agriculture and Food Research, 9, 100323. https://doi.org/10.1016/j.jafr.2022.100323
  20. Zhan, C., He, J., Pan, M., Luo, D. (2021). Component Analysis of Gas Mixture Based on One-Dimensional Convolutional Neural Network. Sensors, 21 (2), 347. https://doi.org/10.3390/s21020347
  21. Wang, Z., Yan, B., Wang, H. (2024). Application of Deep Learning in Predicting Particle Concentration of Gas–Solid Two-Phase Flow. Fluids, 9 (3), 59. https://doi.org/10.3390/fluids9030059
  22. Szoplik, J., Muchel, P. (2023). Using an artificial neural network model for natural gas compositions forecasting. Energy, 263, 126001. https://doi.org/10.1016/j.energy.2022.126001
Construction of a model for measurement control over liquefied petroleum gas components based on a multilayer perceptron

Downloads

Published

2025-10-31

How to Cite

Knysh, B., & Kulyk, Y. (2025). Construction of a model for measurement control over liquefied petroleum gas components based on a multilayer perceptron. Eastern-European Journal of Enterprise Technologies, 5(6 (137), 14–22. https://doi.org/10.15587/1729-4061.2025.340608

Issue

Section

Technology organic and inorganic substances