CONTROL PROBLEM FOR A VACUUM TECHNOLOGICAL COMPLEX
Authors
DOI:
https://doi.org/10.31489/2024No4/71-78Keywords:
Model, Control problem, Vacuum block, Technological process, Oil fractionAbstract
Based on a comprehensive study of technological processes occurring in the vacuum block for an installation of the ELOU-AVT type, the features for the complex technological complex under consideration as a control object were analyzed. In this regard, a physically based mathematical formulation for the optimal control problem of the block under study has been developed, taking into account restrictive conditions on control and input parameters. Taking into account the compiled mathematical models for the quantitative and qualitative characteristics of the process under consideration and the algorithm for their gradient adaptation, to numerically solve the problem of optimizing the functioning for this block, the classical Lagrange method multipliers is used, which allows the transition from the problem of a conditional extremum to the problem of finding the unconditional extremum for the constructed Lagrange function. This method, as well as the proposed algorithm and control principles, were applied for the first time to the vacuum block of the primary oil refining installation of the ELOU-AVT type under study. In wide range conditions of changes in input disturbing factors in quantity and quality, as well as insufficient operational quality information on the selected petroleum products, the proposed method and principles of development algorithm for controlling the process under study allows for prompt preliminary local regulation modes correction and the selection of new optimal modes for adaptive control as a whole. This circumstance leads to an increase in the economic production efficiency and the achievement of the greatest stability in the quality for the resulting target products.
References
Guseinov I.A., Melikov E.A., Khanbutaeva N.A., Efendiev I.R. (2012) Models and algorithms for a multilevel control system of primary oil refinery installations. J. Comput. Syst. Sci. Int., 51, 138–146. DOI: 10.1134/S1064230711060098. DOI: https://doi.org/10.1134/S1064230711060098
Guseinov I.A., Kurbanov Z.G., Melikov E.A., Efendiev A.I., Efendiev I.R. (2014) Nonstationary Multistage Process Control in the Petrochemical Industry. J. Comput. Syst. Sci. Int., 53, 556-564. DOI: 10.1134/S1064230714030095. DOI: https://doi.org/10.1134/S1064230714030095
Melikov E.A., Magerramova T.M., Safarova A.A. (2023) Logical-Linguistic Model for Reactor Cleaning from Impurities. Proceeding of the 15th Intern. Conf. on Applications of Fuzzy Systems, Soft Computing and Artificial Intelligence Tools – ICAFS-2022. ICAFS 2022. Lecture Notes in Networks and Systems, 610. Springer, Cham. DOI:10.1007/978-3-031-25252-5_44. DOI: https://doi.org/10.1007/978-3-031-25252-5_44
Wang L.X. (1998) Stable and optimal fuzzy control of linear systems. IEEE Transactions on Fuzzy Systems, 6, 137-143. Available at: https://hdl.handle.net/1783.1/25922. DOI: https://doi.org/10.1109/91.660813
Morari M., Arkun Y., Stephanopoulos G. (1980) Studies in the synthesis of control structures for chemical processes. Analysis of the Optimizing Control Structures, 220-232. DOI: 10.1002/aic.690260205. DOI: https://doi.org/10.1002/aic.690260205
Giwa A. (2012) Decoupling PID Control of a Reactive Packed Distillation Column. International Journal of Engineering Research & Technology, 1, 10-12. Available at: https://www.researchgate.net/publication/236899250.
Gupta A. (2013) Control of distillation process using neuro-fuzzy technique. International Journal of Electrical, Electronics and Data Communication, 1, 16-20. Available at: http://ijeedc.iraj.in/paper_detail.php?paper_id=233.
Aliyeva K., Jafarov R. (2023) Safety control systems for ethylene production. Multidisciplinary Journal, Refereed & Reviewed Journal, 35 (04), 206-213. DOI: 10.36962/PAHTEI35122023-206. DOI: https://doi.org/10.36962/PAHTEI35122023-206
Žilka V., Halás M., Huba M. (2009) Nonlinear Controllers for a Fluid Tank System. Computer Aided Systems Theory - EUROCAST 2009. Lecture Notes in Computer Science, 5717, 618-625. Springer, Cham. DOI:10.1007/978-3-642-04772-5_80. DOI: https://doi.org/10.1007/978-3-642-04772-5_80
Khelassi A., Wilson J.A., Bendib R. (2004) Assessment of Interaction in Process Control Systems. Proceedings – Dynamical Systems and Applications, 463-471. Available at: https://www.researchgate.net/publication/228985993.
Attarakih M. (2013) Dynamic analysis and control of sieve tray gas absorption column using MATLAB and SIMULINK. Applied Soft Computing, 13 (2), 1152-1169. DOI: 10.1016/j.asoc.2012.10.011. DOI: https://doi.org/10.1016/j.asoc.2012.10.011
Solatian P., Abbasi S.H., Shabaninia F. (2012) Simulation Study of Flow Control Based On PID ANFIS Controller for Non-Linear Process Plants. American Journal of Intelligent Systems, 2(5), 104-110. DOI: 10.5923/j.ajis.20120205.04. DOI: https://doi.org/10.5923/j.ajis.20120205.04
Yakubov M.S., Xoshimov B.M. (2023) Operational control of the vacuum column of the unit of oil primary distillation. “Al-Farg’oniy Avlodlari” Electronic Scientific Journal, 1 (1), 27-34. DOI: 10.5281/zenodo.7698708.
H’ng S.X., Ng L.Y., Ng D.K.S., Andiappan V. (2024) Optimisation of Vacuum Distillation Units in Oil Refineries Using Surrogate Models. Process Integration and Optimization for Sustainability, 8 , 351–373. DOI: 10.1007/s41660-024-00395-6. DOI: https://doi.org/10.1007/s41660-024-00395-6
Kazakova V.N., Mikhaylova P.G. (2021) Development and research of control systems with fuzzy regulators for installation of primary petroleum refining. Scientific Journal “Advances in Chemistry and Chemical Engineering, 35, 22-26. Available at: https://cyberleninka.ru/article/n/razrabotka-i-issledovanie-sistem-upravleniya-s-nechetkimi-regulyatorami-dlya-ustanovki-pervichnoy-pererabotki-nefti/viewer
Downloads
Received
Revised
Accepted
Published online
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
