Abstract

This study presents an experimental framework for hybrid grey-box–ARX identification and constrained model predictive control (MPC) in a 250 L jacketed batch milk pasteurizer. The plant was represented as a closed, stirred, energy-accumulating system rather than as a continuous high-temperature short-time process. The workflow combined manufacturer specifications, field measurements, a batch–jacket grey-box model, local autoregressive with exogenous input (ARX) identification in the approach and holding region, and controller evaluation under common sampling, reference, actuator, and exclusion conditions. In five paired experimental blocks, the nominal MPC–ARX reduced RMSE by 1.959 °C, IAE by 7801 °C s, specific energy consumption by 0.0081 kWh/kg, overshoot by 3.573 °C, actuator saturation by 44.46 percentage points, and thermal overexposure by 4717 °C s relative to a fixed-parameter PI controller with anti-windup. The optimization remained feasible at all evaluated instants, with a mean computation time of 6.4 ms for a 5 s control period. Supervised adaptive strategies provided diagnostic value but did not outperform the nominal MPC. The conclusions are restricted to batches near the nominal fill condition and the 65 °C/30 min milk recipe.

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