Mathematical support for monitoring the status and control of operating modes of cryogenic storage systems

E. V. Larkin   ; E. S. Soldatov   ; A. V. Bogomolov

Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ, Matematika, mehanika, fizika, Tome 16 (2024) no. 1, pp. 23-31

Résumé

This article considers the development of mathematical support for monitoring and controlling operating modes of cryogenic storage systems which increases their safety and prevent losses during storage and transportation. A large number of stationary and transport cryogenic storage systems have strict requirements for safety and the speed of a system’s response to emerging pre-emergency situations which necessitate the automation of managing storage modes using artificial intelligence (AI). The structure of the two-layer neural network for monitoring the state and selecting the storage mode of cryogenic products is presented, including first-layer neurons associated with the monitoring system and second-layer neurons whose outputs are connected to the inputs of logical blocks for selecting the operating mode of the storage system. Options are proposed for processing neural network signals using linear filtering and using a filter based on order statistics – the use of which is advisable in conditions of impulse noise in data transmission channels from the sensors to the monitoring network. A mathematical description of the procedure which initiates the algorithms for correcting the state of the monitoring object is given. The scheme can be used for a wide range of stationary and transport storage systems, including those equipped with refrigeration for recondensing cryogenic vapors.

Détail

Keywords: storage of cryogenic products, monitoring of storage systems, control of operating modes, neural network, monitoring of thermodynamic processes, liquefied natural gas.
Mots-clés : constituent unit

Bibliographie

[1] Ismagilova V.S., Chekushina T.V., “Transportation of Pipeline and Liquefied Natural Gas: Comparative Analysis of Pros and Cons”, Earth Sciences and Subsoil Use, 46:1 (82) (2023), 61–71 | DOI

[2] Soldatov E.S., “Modeling Software and Monitoring of Processes in Reservoirs and Tanks for Long Storage of Cryogenic Products”, News of Tula State University. Technical Science, 2019, no. 10, 385–393 (in Russ.)

[3] E. Larkin, A. Bogomolov, A. Privalov, “Discrete Model of Mobile Robot Assemble Fault-Tolerance”, Lecture Notes in Computer Science, 11659, Springer, Cham, 2019, 204–215 | DOI

[4] Makarenko V.G., Podorozhnjyak A.A., Rudakov S.V., Bogomolov A.V., “An Inertial-Satellite Navigation Control System for Vehicles”, Probl. Upr., 2007, no. 1, 64–71 (in Russ.)

[5] Arkharov I.A., “On the Need for the Revival of Cryogenic Engineering in Russia”, Bulletin of the International Academy of Refrigeration, 2023, no. 1, 6–9 (in Russ.)

[6] E. Soldatov, A. Bogomolov, “Issues of Energy-Efficient Storage of Fuel in Multimodal Transport Units”, Smart Innovation, Systems and Technologies, 232 (2022), 393–402 | DOI

[7] Epikhin A.I., Fadeev M.I., Vasagan I.Yu., “Monitoring and Forecasting of Fuel Consumption by Vessels using Neural Networks”, Operation of maritime transport, 2023, no. 2(107), 104–107 | DOI

[8] Golosovsky M.S., Bogomolov A.V., Tobin D.S., “Algorithm for Setting up Fuzzy Logical Inference Systems based on Statistical Data”, Scientific and Technical Information. Series 2: Information Processes and Systems, 2023, no. 1, 1–9 | DOI

[9] G. Nowak, A. Rusin, “Using the Artificial Neural Network to Control the Steam Turbine Heating Process”, Applied Thermal Engineering, 108 (2016), 204–210 | DOI

[10] E. Larkin, A. Bogomolov, T. Akimenko, A. Privalov, “Digital Control of Continuous Production with Dry Friction at Actuators”, Smart Innovation, Systems and Technologies, 232 (2022), 427–436 | DOI

[11] S.W. Chen, T. Wang, N. Atanasov et al., “Large Scale Model Predictive Control with Neural Networks and Primal Active Sets”, Automatica, 135 (2022), 109947 | DOI

[12] Makarenko V.G., Bogomolov A.V., Rudakov S.V., Podorozhnyak A.A., “Technique of Construction of an Inertia-Satellite Navigational Control System of Vehicles with Neuron Network by Optimization of a Structure of Vector of Gaugings”, Mechatronics, automation, control, 2007, no. 1, 39–44 (in Russ.)

[13] R.S. Sutton, A.G. Barto, Reinforcement Learning, second edition: An Introduction, MIT Press, 2018, 552 pp.

[14] J. Krenek, K. Kuca, P. Blazek, O. Krejcar, D. Jun, “Application of Artificial Neural Networks in Condition Based Predictive Maintenance”, Recent Developments in Intelligent Information and Database Systems, Studies in Computational Intelligence book series, 642, 2016, 75–86 | DOI