Towards smart sterilization: developing a digital shadow for the canning industry
DOI:
https://doi.org/10.17979/ja-cea.2026.47.13820Keywords:
Model calibration, Predictive simulation, Digitization, Digital twin, Autoclave, Model upkeepAbstract
Sterilization of packaged foods is a critical operation in the canning industry, as product quality and food safety depend on it. Mathematical modeling and optimization have already been applied to these systems to improve their performance. However, existing approaches address the problem in isolation, focusing on transfer phenomena, microbial inactivation kinetics, or offline optimization of temperature profiles.
This work proposes a digital shadowing solution for the sterilization of canned fish in steam autoclaves. The solution integrates a real-time, updated predictive model, based on the physical laws of water-steam equilibrium thermal processes, capable of describing and predicting the main dynamic phenomena during operation. An efficient computational implementation allows for real-time simulation of the model. It also incorporates a disturbance estimator based on online dynamic optimization, which keeps the model synchronized with the process.
References
Acerbi, L., 2018. Variational Bayesian Monte Carlo. Advances in Neural Information Processing Systems 31, 8222–8232.
Acerbi, L., Ma, W. J., 2017. Practical Bayesian optimization for model fitting with Bayesian adaptive direct search. Advances in Neural Information Processing Systems 30, 1834–1844.
Alonso, A. A., Arias-Méndez, A., Balsa-Canto, E., García, M. R., Molina, J. I., Vilas, C., Villafín, M., 2013. Real time optimization for quality control of batch thermal sterilization of prepackaged foods. Food Control 32 (2), 392–403. DOI: https://doi.org/10.1016/j.foodcont.2013.01.002
Alonso, A. A., Banga, J. R., Perez-Martin, R., 1997. A complete Dynamic model for the thermal processing of bioproducts in batch units and its application to controller design. Chemical Eng. Science 52 (8), 1307–1322. DOI: https://doi.org/10.1016/S0009-2509(96)00484-8
Andersson, J. A. E., Gillis, J., Horn, G., Rawlings, J. B., Diehl, M., 2019. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11 (1), 1–36. DOI: https://doi.org/10.1007/s12532-018-0139-4
Deubert, D., Klingel, L., Selig, A., 2024. Online simulation at machine level: a systematic review. Int. Journal of Advanced Manufacturing Technology. DOI: https://doi.org/10.1007/s00170-024-13065-1
Erdogdu, F., Balaban, M. O., 2003. Complex method for nonlinear constrained multi-criteria (multi-objective function) optimization of thermal processing. Journal of Food Process Engineering 26 (4), 357–375. DOI: https://doi.org/10.1111/j.1745-4530.2003.tb00607.x
Galan, A., De Prada, C., Gutierrez, G., Sarabia, D., Gonzalez, R., 2021. Realtime reconciled simulation as decision support tool for process operation. Journal of Process Control 100, 41–64. DOI: https://doi.org/10.1016/j.jprocont.2021.02.003
Grieves, M., Vickers, J., 2016. Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In: Transdisciplinary perspectives on complex systems. Springer, pp. 85–113.
Hindmarsh, A. C., Brown, P. N., Grant, K. E., Lee, S. L., Serban, R., Shumaker, D. E., Woodward, C. S., 2005. Sundials: Suite of nonlinear and differential/algebraic equation solvers. ACM Transactions on Mathematical Software (TOMS) 31 (3), 363–396. DOI: https://doi.org/10.1145/1089014.1089020
InfluxData, 2025. Why time series matters. Tech. rep. URL: https://www.influxdata.com
Leibman, M., Edgar, T., Lasdon, L., 1992. Efficient data reconciliation and estimation for dynamic processes using nonlinear programming techniques. Computers & Chemical Engineering 16 (10), 963–986. DOI: https://doi.org/10.1016/0098-1354(92)80030-D
Novoa, M., Pitarch, J. L., Antelo, L. T., Vilas, C., 2024. Development and calibration of a mathematical model for the predictive control of the solid food sterilization process. Jornadas de Automática. DOI: https://doi.org/10.17979/ja-cea.2024.45.10915
Pina, L., Botto, M. A., 2006. Simultaneous state and input estimation of hybrid systems with unknown inputs. Automatica 42 (5), 755–762. DOI: https://doi.org/10.1016/j.automatica.2005.12.014
Pitarch, J. L., Vilas, C., de Prada, C., Palacín, C., Alonso, A. A., 2021. Optimal operation of thermal processing of canned tuna under product variability. Journal of Food Engineering 304, 110594. DOI: https://doi.org/10.1016/j.jfoodeng.2021.110594
Simpson, R., Abakarov, A., 2011. Optimization of food thermal processing: Sterilization stage and plant production scheduling. In: Aguilera, J. M.,
Simpson, R., Welti-Chanes, J., Bermudez-Aguirre, D., Barbosa-Canovas, G. (Eds.), Food Engineering Interfaces. Springer New York, pp. 261–284.
Wallscheid, O., Ngoumtsa, E. F. B., 2020. Investigation of disturbance observers for model predictive current control in electric drives. IEEE Transactions on Power Electronics 35 (12), 13563–13572. DOI: https://doi.org/10.1109/TPEL.2020.2992784
Downloads
Published
Issue
Section
License
Copyright (c) 2026 José Luís Pitarch, Carlos Vilas
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
