Ohenoja, M., Koistinen, A., Hultgren, M., Remes, A., Kortelainen, J., Kaartinen, J., Peltoniemi, M., & Ruusunen, M. (2023). Continuous adaptation of a digital twin model for a pilot flotation plant. In Minerals Engineering (Vol. 198, p. 108081). Elsevier BV. https://doi.org/10.1016/j.mineng.2023.108081
Continuous adaptation of a digital twin model for a pilot flotation plant
|Author:||Ohenoja, Markku1; Koistinen, Antti1; Hultgren, Matias2;|
1Control Engineering Research Group, Environmental and Chemical Engineering, University of Oulu, P.O.Box 4300, FI-90014 Oulu, Finland
2Metso Outotec Finland Oy, P.O.Box 1000, FI-02231 Espoo, Finland
3Oulu Mining School, University of Oulu, P.O.Box 3000, FI-90014 Oulu, Finland
|Online Access:||PDF Full Text (PDF, 5.8 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe20230907121232
|Publish Date:|| 2023-09-07
Model-based methods have a key role in achieving the technical, economical, and environmental performance improvements of the mineral processing systems. However, unmodeled process phenomena and disturbances leading to unreliable modeling results, may prevent the efficient online utilization of these methods at the operational level after the deployment of the model. This study demonstrates the feasibility of online adaptation of a dynamic, mechanistic process models in mineral beneficiation application at a pilot environment. At first, a digital twin of the grinding and flotation stages of a pilot-scale plant was developed. In the experimental campaign, a change from copper-zinc-pyrite ore to a mixture of pyrite-rich and non-sulfide gangue-rich material was carried out. Thus, during experiments, a notable change in the flotation performance was observed, which could not be replicated by a constant-parameter digital twin model. The proposed parameter adaptation framework, encompassing stochastic optimization in moving time windows, was found to be suitable for finding new optimal model parameters during the changing experimental conditions by using the elemental grades in different flotation stages. In addition, simulation studies are presented to highlight the challenges of digital twin parameter adaptation in mineral processing applications, where often only a sparse and disturbance influenced data from the key process variables are available. The adaptive digital twin allows applying the dynamic, mechanistic process models efficiently for predictive simulations in operational decisions leading to more sustainable and resource-efficient minerals processing.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
222 Other engineering and technologies
This research work was carried out as a part of Business Finland Co- innovation joint action ‘Autonomous Processes Facilitated by Artificial Sensing Intelligence (APASSI)’.
The data that has been used is confidential.
© 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).