Mathematical models, in particular, physics-based models, are essential tools to food product and process design, optimization and control. The success of mathematical models relies on their predictive capabilities. However, describing physical, chemical and biological changes in food processing requires the values of some, typically unknown, parameters. Therefore, parameter estimation from experimental data is critical to achieving desired model predictive properties. This work takes a new look into the parameter estimation (or identification) problem in food process modeling. First, we examine common pitfalls such as lack of identifiability and multimodality. Second, we present the theoretical background of a parameter identification protocol intended to deal with those challenges. And, to finish, we illustrate the performance of the proposed protocol with an example related to the thermal processing of packaged 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 feedback 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.

Increasing oil temperature and heating duration in deep-fat frying of potato chips can improve textural quality but worsen the chemical safety of acrylamide formation. Optimal design of this complex process is formulated as a non-linear constrained optimization problem where the objective is to compute the oil temperature profile that guarantees the desired final moisture content while minimizing final acrylamide content subject to operating constraints and the process dynamics. The process dynamics uses a multicomponent and multiphase transport model in the potato as a porous medium taken from literature. Results show that five different heating zones offer a good compromise between process duration (shorter the better) and safety in terms of lower acrylamide formation. A short, high temperature zone at the beginning with a progressive decrease in zone temperatures was found to be the optimal design. The multi-zone optimal operating conditions show significant advantages over nominal constant temperature processes, opening new avenues for optimization.

Food processes, bio-processes and bio-systems are coupled systems that may involve heat, mass and momentum transfer together with kinetic processes. This work illustrates, with a number of examples, how model-based techniquesi.e. simulation, optimization and control-offer the possibility to improve our knowledge about the system at hand and facilitate process design and optimisation even in real time. The contribution is mainly based on the authors experience and illustrates concepts with several examples such as biofilm formation, gluconic acid production, deep-fat frying of potato chips and the thermal processing of packaged foods.

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