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Mass yield and operating time for a pork meat dehydration–impregnation–soaking (DIS) process were optimized using a coupled genetic algorithm/sequential quadratic programming method in order to obtain the optimal operating conditions: temperature and soaking solution concentrations. The DIS process was simulated by a neural network model. The non-linear optimization problem was constrained to ensure the main product characteristics: stability indicated by the water activity target and flavour characterized by the phenol gain target. The climatic conditions, the model validity region, the raw material costs and the operator working schedule were taken into account. Optimal solutions are discussed for three different batch configurations: single-stage processing under constant conditions, single-stage processing under varying temperature and two-stage processing under constant conditions. The most convenient operation resulted in a two-stage soaking process because of time, energy and cost savings, control convenience, product cooling anticipation and a reasonably high mass yield.

25 ref.; International audience; The drying of paddy rice may result in quality degradation, expressed as a head kernel yield, leading to significant commercial depreciation of the product. A mathematical model of the drying and of the quality degradation process was combined with a dynamic optimization algorithm to determine the drying conditions (air temperature and relative humidity as functions of time) that ensured the highest possible final product quality for a specified drying time and a specified final moisture content. The robustness of the optimal drying strategy with respect to the initial state of the product, to the model parameters and to the initialization of the optimization algorithm was verified. The compromise between the highest achievable final quality and the allowed total drying time was studied. The combination of simulation and optimization yielded a new insight in the rice drying process and in the quality preservation strategies.

International audience; Frying is one of the way for performing drying of food material that permits to obtain very short duration. However the control of such operation is difficult because of the immersion of product into hot oil bath that imply difficult or impossible sensors implementation. Such problems are encoutered for Dehydration Soaking Process (DISP). In the case of Batch drying using frying of food stuffs, an original approach is presented. An indirect measurement of moisture content is performed as a combination of easy to do oil bath temperature mesasurement and an estimator. Validation is provided and performance is well established. Because of the ability of moisture content real time measurements, a control strategy is studied and put into practice. An optimal predictive controller is proprosed, able to put into practice several kind of control objectives. Results are discussed and extension of the strategy is introduced

International audience; A predictive optimal control algorithm for a fruit refrigeration process is described. The future inlet air temperature profile is optimized on line, taking into account the most recent process measurements, and thus introducing feedback. The optimality criterion is directly based on economic costs. Technological and product quality constraints are included. The future control profile optimization requires long term predictions of process variables evolution, achieved using a physical model of the plant. The model is adjusted on line in order to account for unmeasured disturbances and poorly known product properties. Both the optimality criterion and the plant model are nonlinear. The approach is experimentally validated on a pilot scale refrigeration plant. Economic benefits from using an optimal, rather than a traditional constant temperature profile are discussed, revealing the importance of the process configuration. Robustness of the control algorithm with respect to modeling errors, measured and unmeasured disturbances, and temporary plant failures is demonstrated.; Un algorithme de contrôle optimal prédictif d'un procédé de réfrigération des fruits est décrit. La future courbe de température d'entrée de l'air est optimisée en ligne en tenant compte des mesures de process les plus récentes et en introduisant de ce fait un retour d'informations. Le critère d'optimalité est directement fondé sur les coûts économiques. Les contraintes de qualité du produit et les contraintes technologiques sont comprises. L'optimisation à venir de la courbe de contrôle requiert des prévisions à long terme de l'évolution des variables de process obtenue à l'aide d'un modèle physique de l'installation. Le modèle est ajusté en ligne afin de tenir compte des perturbations non mesurées et des propriétés de produit mal connues. Le critère d'optimalité et le modèle d'installation sont tous deux non-linéaires. L'approche est validée du point de vue expérimental sur une installation de réfrigération à l'échelle pilote. Les avantages économiques découlant de l'utilisation d'une courbe de température optimale plutôt que d'une courbe de température constante traditionnelle sont abordés, ce qui révèle l'importance de la configuration du process. La robustesse de l'algorithme de contrôle par rapport aux erreurs de modélisation, aux perturbations mesurées et non mesurées et les pannes temporaires de l'installation est démontrée.

Aim The aim of this study is to investigate the effect of a broad spectrum of culture conditions on the acidification activity and viability of Carnobacterium maltaromaticum CNCM I-3298, the main technological properties that determine the shelf-life of biological time-temperature integrator (TTI) labels. Methods and results Cells were cultivated at different temperatures (20-37°C) and pH (6-9·5) according to a modified central composite design and harvested at increasing times up to 10 h of stationary phase. Acidification activity and viability of freeze-thawed concentrates were assessed in medium mimicking the biological label. Acidification activity was influenced by all three culture conditions, but pH and harvest time were the most influential. Viability was not significantly affected by the tested range of culture conditions. Conclusions Carnobacterium maltaromaticum CNCM I-3298 must be cultivated at 20°C, pH 6 and harvested at the beginning of stationary phase to exhibit fastest acidification activities. However, if slower acidification activities are pursued, the recommended culture conditions are 30°C, pH 9·5 and a harvest time between 4-6 h of stationary phase. Significance and impact of the study Quantifying the impact of fermentation temperature, pH and harvest time has led to a predictive model for the production of biological TTI covering a broad range of shelf-lives.

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