Real time optimization for quality control of batch thermal sterilization of prepackaged foods

In this contribution, we present a distributed decision-making architecture for control to optimally command thermal sterilization, despite process uncertainty or unexpected process disturbances. The control structure combines in a synchronous way modeling and simulation environments with efficient system identification and dynamic optimization tools and methods. Process simulation provides a complete dynamic description of the current status of the operation, including the evolution of temperature and pressure in the retort unit as well as temporal and spatial distribution of temperature and quality or safety parameters within the product. Such virtual representation will be regularly confronted with plant measurements to quantify the degree of discrepancy (uncertainty) between real plant and models and react accordingly when such discrepancy becomes unacceptable by re-estimating plant parameters, either during the cycle or from batch to batch. The virtual plant will be also accessed by the regulatory system as well as the dynamic optimization module. In the first instance to estimate unmeasured states related with the product status (e.g. temperature in the product or lethality) under feed-back control. In the second, to continuously recompute optimal cycle profiles so to respond to unexpected disturbances or deviations from the prescribed safety constraints while maximizing quality attributes. Experimental evidence of the complete control system performance will be given on the operation of a pilot plant prototype.

► A controller is proposed to guide sterilization cycle under temperature deviations. ► The controller integrates dynamic models with optimal control algorithms. ► Sterilization cycles are computed to maximize product quality for a given lethality. ► We give experimental evidence of controller performance under realistic scenarios.