Mitigating concentration instability in industrial tomato processing: A CFD-informed digital twin framework for evaporator control

Ensuring stable product concentration in industrial evaporation systems remains challenging due to complex flow behaviour and the limited observability of key internal phenomena using conventional instrumentation. This work introduces a generalisable Computational Fluid Dynamics (CFD)-informed methodology for diagnosing process inefficiencies and developing control-oriented surrogate models. The approach links otherwise unobservable heat and mass transfer dynamics to measurable process variables and production targets, with particular attention to non-Newtonian product behaviour. The simulation methodology was first demonstrated and validated on an industrial-scale tomato evaporation system. The simulations were used to reconstruct internal transport dynamics and quantify the effect of non-Newtonian product rheology on recirculation and concentration development. The actual recirculation flow rate was found to be approximately 17–25% higher than the nominal design value, explaining why the evaporator configuration was unable to reach the target product concentration. These results reveal limitations of traditional constant-viscosity design assumptions and demonstrate the diagnostic usefulness of CFD for addressing performance deviations. Based on a dedicated simulation campaign, a computationally efficient surrogate model was developed to provide rapid predictions based on process measurements and production targets, allowing to estimate total temperature increase and water evaporated with a low-cost multivariate regression model. The model can support hybrid feedback-feedforward control by enabling predictive adjustment of operating conditions and early detection of performance degradation. The proposed methodology supports a transition from reactive operation to predictive, model-based management of industrial evaporation processes, with potential reductions in material and energy losses caused by process deviations.