Methods of Experimental Design in Modeling and Control Tasks Illustrated by Fuel and Energy Complex Facilities

Objective. To carry out experimental design for twoand three-factor cases using facilities of the fuel and energy complex as examples.

Methods. An orthogonal central composite design was applied to improve model accuracy and account for nonlinear relationships among parameters.

Results. The design parameters were calculated, planning matrices were constructed, and regression equations were obtained. A two-factor experiment was implemented, yielding adequate quadratic regression models for predicting flow rate and pressure in a liquid-transport system. A three-factor experiment was conducted based on a sectional model of primary oil refining. The factors affecting the criterion were: superheated steam flow rate, reflux ratio, and potential content of light fractions in the feed. MatLab and Python software packages were used. The adequacy of the obtained quadratic regression relationships to the original data was evaluated by Fisher’s F-test.

Conclusions. The problem of maximizing liquid flow in the transport system was solved. The study of air injection into a circulating viscous liquid can serve as a convenient model for two-phase flow investigation. The application of the statistical model to oil rectification units contributes to improved operational efficiency of the installations.

1. Rudin M.G., Somov V.E., Fomin A.S. Pocket handbook of the oil refiner. Moscow: TSNITTEneftkhim; 2004:336 p. (In Russ)

2. Tugashova L., Bazhenov R., etc. Approximation method for the potential composition of petroleum fractions. E3S Web of Conferences. 2024; 471. 03003.

3. Andrijic Z.U., Cvetnic M., Bolf N. Soft sensor models for a fractionation reformate plant using small and bootstrapped data sets. Brazilian Journal Of Chemical Engineering. 2018;35:745–756. DOI: 10.1590/01046632.20180352s20150727.

4. Tugashova L.G., Zatonsky A.V. Investigation of petroleum-product quality using models. Chemical Physics and Mesoscopy. 2019;21(4):551–564. (In Russ)

5. Herceg S., Andrijić Z.U., Bolf N. Support Vector Machine-based Soft Sensors in the Isomerisation Process Chemical and Biochemical Engineering Quarterly. 2021;34(4):243–255. DOI: 10.15255/CABEQ.2020.1825.

6. Gao Shuzhi, Li Xiaoyu, Zhang Yimin, Wang Jun. A soft-sensor model of VCM rectification concentration based on an improved WOA-RBFNN. Measurement Science and Technology. 2021;32(8). DOI: 10.1088/1361-6501/abf8ed.

7. Wei Anran, Ye Han, Guo Zhenlin, Xiong Jie SISSO-assisted prediction and design of mechanical properties of porous graphene with a uniform nanopore array. Nanoscale Adv. 2022; 4(5):1455-1463. DOI: 10.1039/d1na00457c.

8. Fujii K., Yamamoto T. Development of a Nonlinear Soft Sensor Using a GMDH Network for a Refinery Crude Distillation Tower. Electrical Engineering in Japan. 2014;188: 31-38. DOI: 10.1002/eej.22459.

9. Gubaidullin M.G., Krayneva O.V. Expert assessment of the potential impact of oil on the geological environment of the coastal zone of the southeastern Barents Sea. Environmental Protection in the Oil and Gas Complex. Moscow: All-Russian Scientific Research Institute for Organization, Management and Economics of the Oil and Gas Industry (VNIIOENG); 2014;(4):9-14. (In Russ)

10. Ayele B., Lu J., Chen Q. Optimization of aeration enhanced surfactant soil washing for remediation of dieselcontaminated soils using response surface methodology. PeerJ. 2020; 8. DOI: 10.7717/peerj.8578.

11. Aziz S.Q., Fakhri I.S. Optimization of aeration style and cycle time for treatment of oil refinery wastewater using powdered activated carbon and sequential batch reactor. ZANCO Journal of Pure and Applied Sciences. 2017; 29( 4):140-153.

12. Beklemishchev F.S., Selivanova V.A. Construction and investigation of a model using Box’s orthogonal central composite design in the Simintech environment. In: Initiative in Science as a New Strategy for the Development of the Knowledge System:Proc.of Sci. Works.Kazan:SitIvent LLC; 2019:248–257. (In Russ)

13. Aptikaeva A.A. Optimization of cultivation conditions of the alkalophilic bacterium strain Paenibacillus mannanolyticus – producer of viscous exopolysaccharide. Modern Problems of Science and Education. 2015;(3):593. (In Russ)

14. Valshin I.R. New methods of intensification of mass-transfer processes in oil desalting. Oil. Gas. Innovations. 2018; (5):24–29. (In Russ)

15. Sazonnikova N.A. Management of technological processes: electronic textbook. Samara: Samara University Publishing; 2017:192 p. (In Russ)

16. Zatonsky A.V., Tugashova L.G. Modeling of control objects in MatLab: textbook. St. Petersburg: Lan Publishing; 2022:144 p. (In Russ)

17. Abramov G.S., Sarychev A.V. Practical Flowmetering in OilIndustry: textbook. Moscow: JSC op. «VNIIOENG»; 2002: 460 p. (In Russ)

18. Dmitrievsky B.S., Zatonsky A.V., Tugashova L.G. Control problem of oil rectification process and its solution method. Bulletin of Tomsk Polytechnic University. Georesources Engineering. 2018; 329(2):136–145. (In Russ)