As an example representative of many operations in the food industry let us consider the thermal processing of solid foods. Once a model describing the temporal and spatial evolution of temperature in the solid is available, it is possible to predict through simulation, the evolution of process parameters related with operation efficiency, product safety and quality. Among them, we might cite energy inventory, process time, microbiological lethality, nutrient contents, color, texture, etc. In addition, simulation can be combined with efficient optimizers and control tools to determine and to implement safe operation policies that optimize energy consumption, process time and quality.

However, as in this example, the class of materials to be processed and the batch or semi-batch mode of operation of many food processes makes of simulation a very costly computational activity. Accurate models for processes such as thermal processing, drying, extrusion or bio-reactions, for instance, involve complex sets of coupled nonlinear partial differential equations that account for the temporal and spatial distribution of their representative variables (fluid velocity profiles, temperatures and/or concentrations). In turn, the solution of these models is usually carried out through finite differences or finite element schemes leading to very large dimensional sets of ordinary differential equations.

In this conference, account is given of some work done in the aim of reducing the computational cost of simulating distributed process systems by producing reduced, yet accurate, models still based on sound first principles. Model reduction is carried out by projecting the original model on a low dimensional subspace which retains the most relevant features of the process. This subspace can be obtained from real data or direct numerical simulation by use of the so-called proper orthogonal decomposition (POD) technique. Despite non-linearity or spatial domain irregularity, the resulting reduced-model consists of a very small set of ordinary differential and algebraic equations which can be solved very efficiently. As a consequence, such a reduced description becomes appropriate for on-line simulation (and thus prediction), real time optimization identification and control applications. Different examples related with thermal processing and bioreactors will illustrate the technique as well as its impact on fast development of optimal control strategies, selection of sensors and actuators, and advanced robust control.

BS degree in Industrial Chemistry and PhD in Chemical Engineering (1993), both from the University of Santiago de Compostela. After a one year post-doctoral period at Carnegie Mellon University, Dr. A. Alonso joined the Department of Chemical Engineering at the University of Vigo. From 1995 to 1998 as Assistant Professor and since then to present as Associate Professor. Currently, he is also engaged as a Tenured Researcher in the area of Food Process Engineering at the CSIC (Spanish Council for Scientific Research). He has held visiting appointments at Carnegie Mellon and Princeton University. Prof. Alonso is the author of more than 40 refereed publications in food and bio-process engineering, and during the last years has participated in several national and EU funded research projects and programmes in that area. His research interests include dynamic analysis and control of distributed (convection-diffusion) nonlinear systems, modeling and optimal control of biochemical networks and process design and control integration of food and biotechnology related processes.

The Department of Chemical Engineering at the University of Vigo has a faculty of about 35, including full, associate and assistant Professors. It offers courses at both the undergraduate and doctoral level in Chemical Engineering related disciplines. Research areas include: Corrosion and Material science, Phase Equilibrium, Process Engineering (Design, Optimization and Control) and Biotechnology. Research activity is highly multidisciplinary and pays special attention to the surrounding chemical and agro-food industrial sector.