A Digital Twin of Intensive Aquabiotechnological Production Based on a Closed Ecosystem Modeling & Simulation

  • Mikhail Zhabitskii 
  • Yuriy Andrienko,
  • Vladimir Malyshev,
  • Svetlana Chuykova,
  • Aleksey Zhosanov
  • a,b National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), 31, Kashirskoe shosse, Moscow, 115409, Russia (Moscow Engineering Physics Institute)
  • c,d,e  Second Institution, Full Address, City, Postcode, Country JSC “Paninskoe”, Panino 1, Medvenskiy district, Kursk region, 307032, Russia
Cite as
Zhabitskii M., Andrienko Y., Malyshev V., Chuykova S., Zhosanov A. (2021). A Digital Twin of Intensive Aquabiotechnological Production Based on a Closed Ecosystem Modeling & Simulation. Proceedings of the 33rd European Modeling & Simulation Symposium (EMSS 2021), pp. 247-252. DOI: https://doi.org/10.46354/i3m.2021.emss.034


Currently, intensive fish farming using closed water circulation technology is becoming one of the breakthrough technologies in the aquaculture production. Digital transformation for such production is necessary for the effective management of ultra-high-density aquaculture farms. This transformation is based on the digital twin of aqua-biotechnological farms. The authors performed digital modeling & simulation of the biotechnological component of intensive aqua farm. The main equations of the model are presented in the article. Models of lungfish and trout ecosystems are considered. The models were tested as part of the digital twin for the real aqua farm. The qualitative coincidence of the results of modeling & simulation with the behavior of the ecosystem is obtained. The model has not yet achieved sufficient accuracy for commercial use. The reasons for the insufficient accuracy of the simulation are discussed. Some variants of the simulation model’s development for simple closed aquatic ecosystems with a large biological load are considered. It is concluded that it is necessary to integrate a digital twin based on a simulation model of a biosystem with the technology of the Industrial Internet of Things to achieve the necessary accuracy of describing a complex engineering and biotechnological system.


  1. Carius, L., Pohlodek, J., Morabito, B., Franz, A., Mangold, M., Findeisen, R., Kienle, A. (2018) Model-based State Estimation Based on Hybrid Cybernetic Models IFAC-PapersOnLine, 51 (18), pp. 197-2023
  2. Fore, M., Frank, K., Norton, T., Svendsen, E., Alfredsen, J.A., Dempster, T., Eguiraun, H., Watson, W., Stahl, A., Sunde, L.M., Schellewald, C., Skoien, K.R., Alver, M.O., Berckmans, D. (2018) Precision fish farming: A new framework to improve production in aquaculture Biosystems Engineering, 173, pp. 176-193
  3. Ismail, R., Shafinah, K., Latif, K. A (2020) Proposed Model of Fishpond Water Quality Measurement and Monitoring System based on Internet of Things (IoT) IOP Conference Series: Earth and Environmental Science, 494 (1), paper № 12016
  4. Lagasco, F., Collu, M., Mariotti, A., Safier, E., Arena, F., Atack, T., Brizzi, G., Tett, P., Santoro, A., Bourdier, S., Salcedo Fernandez, F., Muggiasca, S., Larrea, I. (2019) New engineering approach for the development and demonstration of a multi-purpose platform for the blue growth economy. Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE, 6
  5. Mahalik, N.G.P.C., Kim, K. (2014) Retrofitting high-tech systems in land-based aquaculture to improve production efficiency: An Automated expert system architecture IETE Technical Review (Institution of Electronics and Telecommunication Engineers, India), 31 (2), pp. 153- 161.
  6. Ogorodnikov, P.I., Perunov, V.B., Chirkova, V.Yu. (2012) The influence of human factor on financial sustainability of agricultural production. Economy of Region, (2), pp. 232-239
  7. Safin, M.A., Gerasimov, E.I. (2019) Automated process control system for closed water supply installations for fish cultivation. E3S Web of Conferences, 124, paper № 05023
  8. Tarkhov, D.A., Malykhina, G.F. (2019) Neural network modeling methods for creating digital twins of real objects Journal of Physics: Conference Series, 1236 (1), paper № 012056
  9. Shufen, H., Wei, C., Shuiyin, X. A (2020) LabView-based Smart Aquaculture system. Journal of Physics: Conference Series, 1550 (4), paper № 042037
  10. Xiao, J., Zhang, Y. (2020) Marine factory farming techniques and equipment. IOP Conference Series: Earth and Environmental Science, 615 (1), paper № 012013
  11. Xu, L.-J., Wang, N., Feng, Y., Bao, D.-N., Jorshin, K. (2014) Design of aquaculture system based on wireless monitoring and its testing. International Journal of Interactive Mobile Technologies, 10 (5), pp. 68-73
  12. Zhabitskii, M G, Andryenko, Y A, Malyshev, V N, Chuykova, S V and Zhosanov A A (2021) Digital transformation model based on the digital twin concept for intensive aquaculture production using closed water circulation technology IOP Conf. Series: Earth and Environmental Science 723, paper № 032064 doi:10.1088/1755-1315/723/3/032064