Principles of creating a digital twin prototype for the process of alkylation of benzene with propylene based on a neural network
https://doi.org/10.32362/2410-6593-2023-18-5-482-497
Abstract
Objectives. To identify the principles of creating digital twins of an operating technological unit along the example of the process of liquid-phase alkylation of benzene with propylene, and to establish the sequence of stages of formation of a digital twin, which can be applied to optimize oil and gas chemical production.
Methods. The chemical and technological system consisting of reactor, mixer, heat exchangers, separator, rectification columns, and pump is considered as a complex high-level system. Data was acquired in order to describe the functioning of the isopropylbenzene production unit. The main parameters of the process were calculated by simulation modeling using UniSim® Design software. A neural network model was developed and trained. The influence of various factors of the reaction process of alkylation, separation of reaction products, and evaluation of economic factors providing market interest of the industrial process was also considered. The adequacy of calculations was determined by statistics methods. A microcontroller prototype of the process was created.
Results. A predictive neural network model and its creation algorithm for the process of benzene alkylation was developed. This model can be loaded into a microcontroller to allow for real-time determination of the economic efficiency of plant operation and automated optimization depending on the following factors: composition of incoming raw materials; the technological mode of the plant; the temperature mode of the process; and the pressure in the reactor.
Conclusions. The model of a complex chemicotechnological system of cumene production, created and calibrated on the basis of long-term industrial data and the results of calculations of the output parameters, enables the parameters of the technological process of alkylation to be calculated (yield of reaction products, energy costs, conditional profit at the output of finished products). During the development of a hardware-software prototype, adapted to the operation of the real plant, the principles and stages of creating a digital twin of the operating systems of chemical technology industries were identified and formulated.
About the Authors
K. G. KichatovRussian Federation
Konstantin G. Kichatov - Cand. Sci. (Chem.), Associate Professor, Department of Petrochemistry and Chemical Technology. Scopus Author ID 54917537800.
1, Kosmonavtov ul., Ufa, 450064
Competing Interests:
The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this document
T. R. Prosochkina
Russian Federation
Tatyana R. Prosochkina - Dr. Sci. (Chem.), Professor, Head of the Department of Petrochemistry and Chemical Technology. Scopus Author ID 6508101276
1, Kosmonavtov ul., Ufa, 450064
Competing Interests:
The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this document
I. S. Vorobyova
Russian Federation
Irina S. Vorobyova - Master Student, Department of Petrochemistry and Chemical Technology.
1, Kosmonavtov ul., Ufa, 450064
Competing Interests:
The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this document
References
1. Popov N.A. Business process optimization in the digitalization era of production. Strategic Decisions and Risk Management. 2019;10(1):28–35. https://doi.org/10.17747/2618-947X-2019-1-28-35
2. Geng Z., Zhang Y., Li C., Han Y., Cui Y., Yu B. Energy optimization and prediction modeling of petrochemical industries: An improved convolutional neural network based on cross-feature. Energy. 2020;194(4)116851. https://doi.org/10.1016/j.energy.2019.116851
3. Cozmiuc D., Petrisor I. Industrie 4.0 by Siemens. J. Cases Inf. Technol. 2018;20(2):30–48. https://doi.org/10.4018/JCIT.2018040103
4. Ardito L., Petruzzelli A.M., Panniello U., Garavelli A.C. Towards Industry 4.0: Mapping digital technologies for supply chain management-marketing integration. Bus. Process Manag. J. 2019;25(2):323–346. https://doi.org/10.1108/BPMJ-04-2017-0088
5. Rindfleisch A., O’Hern M., Sachdev V. The Digital Revolution, 3D Printing, and Innovation as Data. J. Product Innov. Manag. 2017;34(5):681–690. https://doi.org/10.1111/jpim.12402
6. D’Ippolito B., Messeni Petruzzelli A., Panniello U. Archetypes of incumbents’ strategic responses to digital innovation. J. Intellectual Capital. 2019;20(5):662–679. https://doi.org/10.1108/JIC-04-2019-0065
7. Theorin A., Bengtsson K., Provost J., Lieder M., Johnsson C., Lundholm T., et al. An event-driven manufacturing information system architecture for Industry 4.0. Int. J. Prod. Res. 2017;55(5):1297–1311. https://doi.org/10.1080/00207543.2016.1201604
8. Broekhuizen T.L.J., Broekhuis M., Gijsenberg M.J., Wieringa J.E. Introduction to the special issue – Digital business models: A multi-disciplinary and multi-stakeholder perspective. J. Bus. Res. 2021;122:847–852. https://doi.org/10.1016/j.jbusres.2020.04.014
9. Appio F.P., Frattini F., Petruzzelli A.M., Neirotti P. Digital Transformation and Innovation Management: A Synthesis of Existing Research and an Agenda for Future Studies. J. Product Innov. Manag. 2021;38(1):4–20. https://doi.org/10.1111/jpim.12562
10. Kholopov V.A., Antonov S.V., Kurnasov E.V., Kashirskaya E.N. Digital Twins in Manufacturing. Russ. Engin. Res. 2019;39(12):1014–1020. https://doi.org/10.3103/s1068798X19120104
11. Agrawal A., Gans J., Goldfarb A. Prediction Machines: The Simple Economics of Artificial Intelligence. Boston, Massachusetts: Harvard Business Review Press; 2018. 272 p.
12. Ceipek R., Hautz J., Petruzzelli A.M., De Massis A., Matzler K. A motivation and ability perspective on engagement in emerging digital technologies: The case of Internet of Things solutions. Long Range Plann. 2021;54(5):101991. https://doi.org/10.1016/j.lrp.2020.101991
13. Eggers J.P., Kaul A. Motivation and Ability? A Behavioral Perspective on the Pursuit of Radical Invention in Multi-Technology Incumbents. Acad. Manage. J. (AMJ). 2018;61(1):67–93. https://doi.org/10.5465/amj.2015.1123
14. Libert B., Beck M., Wind Y. (Jerry). 7 Questions to Ask before Your Next Digital Transformation. Harvard Bus. Rev. 2016;12(7):11–13. URL: https://hbr.org/2016/07/7questions-to-ask-before-your-next-digital-transformation. Accessed January 5, 2022.
15. Correani A., De Massis A., Frattini F., Petruzzelli A.M., Natalicchio A. Implementing a Digital Strategy: Learning from the Experience of Three Digital Transformation Projects. Calif. Manag. Rev. 2020;62(4):37–56. https://doi.org/10.1177/0008125620934864
16. Grieves M., Vickers J. Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems. In: Kahlen J., Flumerfelt S., Alves A. (Eds.). Transdisciplinary Perspectives on Complex Systems. Cham.: Springer; 2017. P. 85–113. https://doi.org/10.1007/978-3-31938756-7_4
17. Negri E., Fumagalli L., Macchi M. A Review of the Roles of Digital Twin in CPS-based Production Systems. Procedia Manuf. 2017;11:939–948. https://doi.org/10.1016/j.promfg.2017.07.198
18. Zhou X., Eibeck A., Lim M.Q., Krdzavac N.B., Kraft M. An agent composition framework for the J-Park Simulator – A knowledge graph for the process industry. Comput. Chem. Eng. 2019;130(2):106577. https://doi.org/10.1016/j.compchemeng.2019.106577
19. Kockmann N. Digital methods and tools for chemical equipment and plants. React. Chem. Eng. 2019;4(9):1522–1529. https://doi.org/10.1039/C9RE00017H
20. Perno M., Hvam L., Haug A. Implementation of digital twins in the process industry: A systematic literature review of enablers and barriers. Comput. Ind. 2022;134:103558. https://doi.org/10.1016/j.compind.2021.103558
21. Hsu Y., Chiu J.M., Liu J.S. Digital Twins for Industry 4.0 and Beyond. In: 2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE; 2019. P. 526–530. https://doi.org/10.1109/IEEM44572.2019.8978614
22. Lu Y., Liu C., Wang K.I.K., Huang H., Xu X. Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues. Robot. Comput. Integr. Manuf. 2020;61:101837. https://doi.org/10.1016/j.rcim.2019.101837
23. Durão L.F.C.S., Haag S., Anderl R., Schützer K., Zancul E. Digital Twin Requirements in the Context of Industry 4.0. In: Chiabert P., Boura A., Noë F., Ríos J. (Eds.). Product Lifecycle Management to Support Industry 4.0. PLM 2018. IFIP Advances in Information and Communication Technology. Cham.: Springer; 2018. V. 540. P. 204–214. https://doi.org/10.1007/978-3-030-01614-2_19
24. Kuehner K.J., Scheer R., Strassburger S. Digital Twin: Finding Common Ground – A Meta-Review. Procedia CIRP. 2021;104(11):1227–1232. https://doi.org/10.1016/j.procir.2021.11.206
25. Adamenko D., Kunnen S., Pluhnau R., Loibl A., Nagarajah A. Review and comparison of the methods of designing the Digital Twin. Procedia CIRP. 2020;91:27–32. https://doi.org/10.1016/j.procir.2020.02.146
26. Zweber J.V., Kolonay R.M., Kobryn P., Tuegel E.J. Digital Thread and Twin for Systems Engineering: Requirements to Design. In: 55th AIAA Aerospace Sciences Meeting. Reston, Virginia: American Institute of Aeronautics and Astronautics; 2017. https://doi.org/10.2514/6.2017-0875
27. Schleich B., Anwer N., Mathieu L., Wartzack S. Shaping the digital twin for design and production engineering. CIRP Annals. 2017;66(1):141–144. https://doi.org/10.1016/j.cirp.2017.04.040
28. Zhou G., Zhang C., Li Z., Ding K., Wang C. Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing. Int. J. Prod. Res. 2020;58(4):1034–1051. https://doi.org/10.1080/00207543.2019.1607978
29. Melesse T.Y., Di Pasquale V., Riemma S. Digital Twin Models in Industrial Operations: A Systematic Literature Review. Procedia Manuf. 2020;42:267–272. https://doi.org/10.1016/j.promfg.2020.02.084
30. Aghayarzadeh M., Alizadeh R., Shafiei S. Simulation and Optimization of Styrene Monomer Production Using Neural Network. Iranian Journal of Chemical Engineering (IJChE). 2014;11(1-Serial Number 1, January):30–41. https://dorl.net/dor/20.1001.1.17355397.2014.11.1.3.2
31. Alizadeh M., Sadrameli S.M. Modeling of Thermal Cracking Furnaces Via Exergy Analysis Using Hybrid Artificial Neural Network–Genetic Algorithm. J. Heat Transfer. 20161;138(4):042801. https://doi.org/10.1115/1.4032171
32. Xin S., Yingya W., Huajian P., Jinsen G., Xinguing L. Prediction of Coke Yield of FCC Unit Using Different Artificial Neural Network Models. China Petroleum Processing and Petrochemical Technology. 2016;18(3):102–109. URL: http://www.chinarefining.com/EN/Y2016/V18/I3/102
33. Meyers R.A. Handbook of Petrochemicals Production Processes. 1st ed. New York, Chicago, San Francisco, Athens, London, Madrid, Mexico City, Milan, New Delhi, Singapore, Sydney, Toronto: McGraw-Hill Education; 2005. 744 p.
34. Ananieva E.A., Egorova E.V., Larin L.B. Current status and future tends of combined process producing acetone and phenol. 1. The market review and modern state phenol preparation processes. Fine Chem. Technol. 2007;2(2):27–43 (in Russ.).
35. Larin L.B., Egorova E.V., Ananieva E.A. Current status and future tends of combined process for producing acetone and phenol. II. Intensification methods of cumene oxidation process. Fine Chem. Technol. 2008;3(3):53–60 (in Russ.).
36. Pathak A.S., Agarwal S., Gera V., Kaistha N. Design and Control of a Vapor-Phase Conventional Process and Reactive Distillation Process for Cumene Production. Ind. Eng. Chem. Res. 2011;50(6):3312–3326. https://doi.org/10.1021/ie100779k
37. Zhai J., Liu Y., Li L., Zhu Y., Zhong W., Sun L. Applications of dividing wall column technology to industrialscale cumene production. Chem. Eng. Res. Des. 2015;102:138–149. https://doi.org/10.1016/j.cherd.2015.06.020
38. Chudinova A., Salischeva A., Ivashkina E., Moizes O., Gavrikov A. Application of Cumene Technology Mathematical Model. Procedia Chem. 2015;15:326–334. https://doi.org/10.1016/j.proche.2015.10.052
39. Zarutskii S.A., Kichatov K.G., Nikitina A.P., Prosochkina T.P., Samoilov N.A. Simulation of the Process for Cumene Production by Alkylation of Benzene in Equilibrium Reactor. Pet. Chem. 2018;58(8):681–686. https://doi.org/10.1134/S0965544118080212
40. Mahmoudian F., Moghaddam A.H., Davachi S.M. Genetic‐based multi‐objective optimization of alkylation process by a hybrid model of statistical and artificial intelligence approaches. Can. J. Chem. Eng. 2022;100(1):90–102. https://doi.org/10.1002/cjce.24072
41. Sun X.Y., Xiang S.G. Product Distributions of Benzene Alkylation with Propylene Estimation Using Artificial Neural Network (ANN). Adv. Mat. Res. 2013;772:227–232. https://doi.org/10.4028/www.scientific.net/AMR.772.227
42. Tikhonenkov A.S., Peresypkin A.V., Toporskaya A.S., Suloeva E.S. Modeling of measuring systems based on programmable debugging circuits Arduino. In: 2017 XX IEEE International Conference on Soft Computing and Measurements (SCM). IEEE; 2017. P. 519–521. https://doi.org/10.1109/sCM.2017.7970636
43. Vorobyova I.S., Kichatov K.G., Prosochkina T.R. Neural network for determining the optimal parameters of the alkylation of benzene with propylene. Computer program registration certificate RU 2020612986, 03.06.2020. Application № 2020612093 dated February 26, 2020 (in Russ.).
44. Prokhorov A., Lysachev M. Digital Twin. Analysis, Trends, Global Experience. 1st ed. Borovkov A. (Ed.). Moscow: AliancePrint; 2020. 401 р. (in Russ.). URL: https://datafinder.ru/files/new4/digital_twin_book.pdf
45. Mandolla C., Petruzzelli A.M., Percoco G., Urbinati A. Building a digital twin for additive manufacturing through the exploitation of blockchain: A case analysis of the aircraft industry. Comput. Ind. 2019;109:134–152. https://doi.org/10.1016/j.compind.2019.04.011
Supplementary files
|
1. Dependence of the principal profit on the parameters of the technological process | |
Subject | ||
Type | Исследовательские инструменты | |
View
(480KB)
|
Indexing metadata ▾ |
- A predictive neural network model and its creation algorithm for the process of benzene alkylation was developed.
- This model can be loaded into a microcontroller to allow for real-time determination of the economic efficiency of plant operation and automated optimization depending on the following factors: composition of incoming raw materials; the technological mode of the plant; the temperature mode of the process; and the pressure in the reactor.
Review
For citations:
Kichatov K.G., Prosochkina T.R., Vorobyova I.S. Principles of creating a digital twin prototype for the process of alkylation of benzene with propylene based on a neural network. Fine Chemical Technologies. 2023;18(5):482-497. https://doi.org/10.32362/2410-6593-2023-18-5-482-497