Abstract 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.