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ATP-derived products are typically used as early indicators of fish quality loss during storage. In this work, we explore different biochemical routes that are potentially relevant in contributing to nucleotide degradation in hake (Merluccius merluccius). A major motivation of this study is to get more insight on the biochemical degradation mechanisms of nucleotide catabolites in hake muscle at fish storage and transport conditions. This requires the identification of its relevant pathways. To that purpose, different degradation routes proposed in the literature are considered and a mathematical model for the degradation process is derived. First order kinetics are assumed for all the reactions and temperature dependence is taken into account through the Arrhenius equation. Unknown model parameters, namely activation energies and pre-exponential Arrhenius coefficients, are estimated via fitting to experimental data. From the estimation results, relevant routes are identified. The kinetic study is performed on sterile fish juice to avoid coupling with microbial degradation mechanisms or possible interferences of the food matrix that might hide biochemical interactions. The proposed scheme adequately describes biochemical changes in nucleotide catabolites under variable temperature profiles. It also reveals a pathway which at least seems relevant for nucleotide degradation in hake

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 techniques—i.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

Fish quality has a direct impact on market price and its accurate assessment and prediction are of main importance to set prices, increase competitiveness, resolve conflicts of interest and prevent food wastage due to conservative product shelf-life estimations. In this work we present a general methodology to derive predictive models of fish freshness under different storage conditions. The approach makes use of the theory of optimal experimental design, to maximize data information and in this way reduce the number of experiments. The resulting growth model for specific spoilage microorganisms in hake (Merluccius merluccius) is sufficiently informative to estimate quality sensory indexes under time-varying temperature profiles. In addition it incorporates quantitative information of the uncertainty induced by fish variability. The model has been employed to test the effect of factors such as fishing gear or evisceration, on fish spoilage and therefore fish quality. Results show no significant differences in terms of microbial growth between hake fished by long-line or bottom-set nets, within the implicit uncertainty of the model. Similar conclusions can be drawn for gutted and un-gutted hake along the experiment horizon. In addition, whenever there is the possibility to carry out the necessary experiments, this approach is sufficiently general to be used in other fish species and under different stress variables

Rigorous, physics based, modeling is at the core of computer aided food process engineering. Models often require the values of some, typically unknown, parameters (thermo-physical properties, kinetic constants, etc). Therefore, parameter estimation from experimental data is critical to achieve desired model predictive properties. Unfortunately, it must be admitted that often experiment design and modeling are fully separated tasks: experiments are not designed for the purpose of modeling and models are usually derived without paying especial attention to available experimental data or experimentation capabilities. When, at some point, the parameter estimation problem is put on the table, modelers use available experimental data to ``manually'' tune the unknown parameters. This results in inaccurate parameter estimates, usually experiment dependent, with the implications this has in model validation. This work takes a new look into the parameter estimation problem in food process modeling. First the common pitfalls in parameter estimation are described. Second we present the theoretical background and the numerical techniques to define a parameter estimation protocol to iteratively improve model predictive capabilities. This protocol includes: reduced order modeling, structural and practical identifiability analyses, data fitting with global optimization methods and optimal experimental design. And, to finish, we illustrate the performance of the proposed protocol with an example related to the thermal processing of packaged foods. The model was experimentally validated in the IIM-CSIC pilot plant

Reduction of fish freshness during the supply chain results into severe losses both from the economic (drastic reductions on market price) and environmental (fish rejection) points of view. In this context, prediction techniques based on mathematical models emerge as useful tools for fish quality assessment. In this work we propose a methodology to forecast fish freshness during storage. In this regard, we found a strong correlation between fish freshness indicators commonly used in the industry, such as the Quality Sensory Method (QSM), and the growth of Specific Spoilage Organisms (SSO). The main idea is to develop a reliable mathematical model which, on the one hand, describes the growth of SSO during storage and, on the other hand, relates the QSM to SSO concentration. Issues such as storage conditions, fishing practices or gutting procedures are taken into account. Such model allows us to predict shelf-life and quality losses. In order to provide the model with reliable predictive capabilities, advanced model calibration techniques are employed. In this regard, optimal experimental design (OED) is used to generate sufficient nonisothermal informative experiments that allow us to reduce the uncertainty in the predictions. Also, the Monte Carlo sampling technique is exploited to detect the parameter confidence intervals and to calculate fish-to-fish variability in the predictions (core predictions). The approach is illustrated using hake (Merluccius merluccius) caught either by bottom-set net or long-line under different storage temperature conditions. Furthermore, the effect of gutting is also studied. This methodology is flexible enough to consider other fish species or stress variables such as atmosphere composition

This work presents the idea of predictive time–temperature integrator sensors (pTTIs) as a means to recover and to predict safety and quality evolution in thermal processing. Predictive TTIs combine a rigorous and computationally efficient model of the process with standard TTIs. The calibration of the pTTIs requires several steps, including the definition of the model, the appropriate means for its simulation and the use of adequate parameter estimation and optimal experimental design techniques to recover food and package thermo-physical properties. Results show how a limited number of experiments and standard TTIs are required to calibrate predictive sensors. Possible uses include the prediction of microbial lethality and quality attributes, recovery of processing conditions considering eventual perturbations or optimal process design. Calibration and uses are illustrated here with examples related to the tunnel pasteurization of highly viscous liquid foods.

Food processes are coupled systems that involve heat, mass and momentum transfer together with kinetic processes related to quality and safety. This work is devoted to illustrate how model-based techniques o er the possibility to rationally optimise processes even in real time. The contribution is mainly based on our group experience and illustrates concepts with several examples such as the refrigeration of fruits, the deep-fat frying of potato chips, the freeze-drying of dairy products and the thermal processing of packaged foods

In this paper, an efficient computational tool to predict the evolution of the key variables in the fruit refrigeration process is presented. This computational model is intended to be used in real time quality control. The tool is based on detailed mathematical models proposed in the literature which accounts for the main phenomena occurring during refrigeration. Since integration using standard numerical schemes leads to a computational cost unaffordable in real time environments, a reduced order model (ROM; able to combine a high accuracy with a low computational cost) is derived. In the derivation of this model, the role of the presence of quite disparate time scales is considered. In this respect, two alternative ROMs are considered: a general model (aiming at describing the evolution at arbitrary time scales) and a model assuming a strong coupling between the state variables (as expected in the long term behavior of the system).

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.

Background Systems biology allows the analysis of biological systems behavior under different conditions through in silico experimentation. The possibility of perturbing biological systems in different manners calls for the design of perturbations to achieve particular goals. Examples would include, the design of a chemical stimulation to maximize the amplitude of a given cellular signal or to achieve a desired pattern in pattern formation systems, etc. Such design problems can be mathematically formulated as dynamic optimization problems which are particularly challenging when the system is described by partial differential equations. This work addresses the numerical solution of such dynamic optimization problems for spatially distributed biological systems. The usual nonlinear and large scale nature of the mathematical models related to this class of systems and the presence of constraints on the optimization problems, impose a number of difficulties, such as the presence of suboptimal solutions, which call for robust and efficient numerical techniques. Results Here, the use of a control vector parameterization approach combined with efficient and robust hybrid global optimization methods and a reduced order model methodology is proposed. The capabilities of this strategy are illustrated considering the solution of a two challenging problems: bacterial chemotaxis and the FitzHugh-Nagumo model. Conclusions In the process of chemotaxis the objective was to efficiently compute the time-varying optimal concentration of chemotractant in one of the spatial boundaries in order to achieve predefined cell distribution profiles. Results are in agreement with those previously published in the literature. The FitzHugh-Nagumo problem is also efficiently solved and it illustrates very well how dynamic optimization may be used to force a system to evolve from an undesired to a desired pattern with a reduced number of actuators. The presented methodology can be used for the effi

In silico experimentation has opened new ways to analyze biological systems behavior under different conditions. The incorporation of an outer optimization loop may help to find the right operation conditions to achieve specific goals (maximization of a given product concentration, minimization of process energy/time, etc.). Mathematically, this is stated as a dynamic optimization problem being particularly challenging when the system is described by nonlinear sets of partial differential equations as well as when constraints are considered. These issues impose a number of difficulties, such as the presence of suboptimal solutions, which call for robust and efficient numerical techniques. In this work, the control vector parametrization approach is combined with reduced order methods and suitable hybrid global optimization methods to overcome such difficulties. The capabilities of this strategy are illustrated considering the solution of two challenging problems: bacterial chemotaxis and the FitzHugh-Nagumo model. The presented methodology can be used for the efficient dynamic optimization of generic distributed biological systems

In this work a robust nonlinear model predictive controller for nonlinear convection–diffusion-reaction systems is presented. The controller makes use of a collection of reduced order approximations of the plant (models) reconstructed on-line by projection methods on proper orthogonal decomposition (POD) basis functions. The model selection and model update step is based on a sufficient condition that determines the maximum allowable process-model mismatch to guarantee stable control performance despite process uncertainty and disturbances. Proofs on the existence of a sequence of feasible approximations and control stability are given. Since plant approximations are built on-line based on actual measurements, the proposed controller can be interpreted as a multi-model nonlinear predictive control (MMPC). The performance of the MMPC strategy is illustrated by simulation experiments on a problem that involves reactant concentration control of a tubular reactor with recycle.

The quality of ice-cream is at a large extent determined by the smooth texture and creamy mouth feel which are closely related to the crystal size and product viscosity. Model based computer aided process engineering may enable the design of optimal operation conditions so as to maximize ice-cream quality while minimizing, for example, energy consumption. In this regard, the development of predictive mathematical models describing the ice-cream crystallization process is object of intensive research. However, mathematical modelling is a time consuming task that involves several steps from the definition of the questions to be addressed to the development of a final model with satisfactory predictive capabilities. In this concern, model identification, plays a crucial role in model development since it involves structure characterisation, identifiability analysis, parameter estimation and experimental design. The objective of this work is to perform a complete study of the model identification for the ice-cream crystallization process. A simple yet accurate mathematical model relating the process inputs and outputs was derived, based on the method of moments. The AMIGO toolbox (Advanced Model Identification using global optimization) was used to iteratively identify model unknowns from experimental data obtained in the pilot plant located at CEMAGREF and to design new experiments to improve model predictive capabilities

Freeze-drying is a dehydration process often employed in high added-value food and biochemical goods.This technique facilitates the later re-hydration of the resulting product while it helps to maintain its organoleptic and nutritional properties. In order to preserve quality, as well as to reduce the costs of a high demanding energy and time consuming process, the proper handling of the product temperature during the operation course becomes critical. Direct – invasive – measurement methods are potentially dangerous to product integrity what calls for new non-invasive strategies as the one proposed in this work. Our approach combines the mathematical modelling of the leading physical phenomena involved in freeze-drying together with experimental data obtained from pressure rise disturbances in the drying chamber. The data collected from pressure essays are employed to complete the partial differential equation system describing heat transfer mechanism. Numerical issues usually related to solving moving boundary problems, as it is the freeze-drying case, are overcome by applying coordinate transformations which lead to a fixed reference frame. On this new framework, order reduction methods can be used as a tool not only to simplify the system analysis and control but to minimize the computational effort as well, especially when 2D or 3D spatial domains with complex geometries are involved

In this contribution, we present a distributed decision-making architecture to optimally command thermal processing operation, despite process uncertainty or sudden process disturbances. The network combines and links in a synchronous way modelling and simulation environments with efficient dynamic optimization and system identification tools and methods. The simulation environment takes the place of a virtual plant providing a complete dynamic representation of the system 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 under feed-back control. In the second, to continuously re-compute optimal cycle profiles so to respond to unexpected disturbances or deviations from the prescribed safety constraints while maximizing quality attributes. Experimental evidences of the resulting complete system performance will be given on a pilot plant scenario

This chapter contains sections titled: Introduction, Model Reduction of DPS: Theoretical Background, Model Reduction in Identification of Bioprocesses, Model Reduction in Control Applications, Conclusions

This work presents a methodology which exploits the underlying biochemical structure of bioprocesses to estimate concentrations in aerobic fermenters from oxygen measurements. Although a number of estimators have been proposed over the years in the literature, the methodology proposed in this work is able to operate in transient conditions while does not require the knowledge of the growth kinetics. In addition, it can be also applied to fermenters where the spatial distribution of the concentrations is relevant. In this case, we propose a systematic approach to optimally locate the sensors based on the use of reduced order models. This method allows the reconstruction of the oxygen concentrations from a limited number of sensors. Finally, the methodology proposed will be illustrated on a horizontal tubular reactor for the production of gluconicacid by free-growth of Aspergillus niger.

In this work a new approach to the control of simulated moving bed (SMB) chromatographic separation processes is presented. This approach is based on the combination of the wave theory and Multi-Model Predictive Control (MMPC). The wave theory provides the theoretical framework in which the control law is formulated whereas receding-horizon MPC is used for determining the appropriate controller parameters. As SMB plants are distributed parameter systems (DPS) with nonlinearity arising from the expression of the adsorption isotherms, classical numerical methods for the solution of DPS are computationally demanding for MPC purposes. Reduced-order models are therefore derived using the proper orthogonal decomposition (POD) technique. To ensure stability, the POD model is updated on-line, resulting in MMPC

This work presents the theoretical design and the experimental validation of a real time optimisation logic for distributed parameter systems. This logic consists of a hierarchy of two layers. The upper layer is responsible of obtaining the optimal control profile by a suitable combination of the control vector parameterisation approach with a hybrid global-local optimiser and the use of reduced models for distributed parameter systems. The lower layer corresponds to a PID controller designed in the framework of Internal Model Control to keep tracking capabilities at short time scales. This logic was applied to the real time optimisation of the thermal processing of packaged foods in batch retorts at the pilot plant available at the IIM-CSIC. The objective was to maximise food product quality while satisfying safety constraints. The proposed scheme was able to optimally operate the system under standard plant perturbations and under a pressure drop

In many cases, it is difficult to derive a precise mathematical model, based on first principles, for a given process. Besides, the computation of the solution of models obtained through this methodology may require a large computational effort making them useless for real time tasks like control or optimization. Neurofuzzy modelling, which permits an easy way to derive successful models, is a good alternative which can be employed to overcome such limitations. In this paper, together with the neurofuzzy modelling, several strategies based on non-linear predictive control are presented. The low computational cost associated with neurofuzzy models and controllers makes them suitable candidates to be implemented into industrial Programmable Logic Controllers (PLC). Both the model and controllers are validated and implemented in a pilot plant for the thermal sterilization of solid canned food in steam retorts and based on the results, a comparison between the different predictive control strategies is presented

A control technique based on Reinforcement Learning is proposed for controlling thermal sterilization of canned food. Without using an a-priori mathematical model of the process, the proposed Model-Free Learning Controller (MFLC) aims to follow a temperature profile during two stages of the process: first heating by manipulating the saturated steam valve and then cooling by opening the water valve) by learning. From the defined state-action space, the MFLC agent learns the environment interacting with the process batch to batch and then using a tabular state-action mapping. The results show the advantages of the proposed technique for this kind of processes

The thermal processing of packaged foods in batch retorts is a standard opera¬tion in the food industry where it is of vital importance to reduce the activity of the harmful micro-organisms to a given level and to stabilize the bioproduct for sub¬sequent storage. However, as part of the thermal processing, organoleptic properties of the food product are also deteriorated. Thus being pertinent to find the operation conditions that may satisfy both safety and quality criteria. This work presents the theoretical development and experimental validation of the real time dynamic optimization of the process with the objective of guaranteeing the maximization of the surface retention of nutrients while satisfying the micro¬biological requirements under typical perturbed process operation

In the paper, a class of bioprocesses with fully unknown kinetics is considered. Indirect adaptive control algorithm where the process kinetics is presented as unknown time-varying parameter is derived. A general procedure for optimal tuning of kinetic estimator design parameters is proposed by stability analysis of the control scheme. Theoretical results are verified by simulations of the control scheme of continuous fermentation of gluconic acid production by Aspergillus niger. In the conclusion, some other applications of proposed procedure are discussed

Reaction–Diffusion (RD) mechanisms can describe many biological phenomena such as neuron firing in the brain, the heartbeat, cellular organization activities or even biological disorders such as fibrillation. The FitzHugh–Nagumo (FHN) model is a particular case of RD systems. It is able to capture the key features of many biological processes and since it is relatively simple it has been widely employed during recent years. Some examples of its predictive capabilities include the representation of the normal behavior of some physiological phenomena, related to a travelling plane wave, as well as biological disorders associated with spiral or irregular fronts. The objective of this work is to design a control law that is able to stabilize complex behaviors (travelling plane wave) in biological systems using the FHN model as a case study. Since, in biological systems there usually exists a lack of detailed information on the system structure, our control law will be designed to be robust, i.e., it must be able to reach the predefined reference regardless the presence of structural uncertainties. To this purpose, we will extend some classical results on the finite-dimensional robust control theory to RD systems by means of order reduction techniques, in particular the Proper Orthogonal Decomposition method

The dissipative nature of spatially distributed process systems is exploited to develop efficient exponential state observers based on a low-dimensional dynamic representation of the original set of partial differential equations. The suggested approach combines standard observer design techniques for reactors, where the reaction rates are unknown with efficient model reduction methodologies based on projection of the original concentration and temperature fields on low-dimensional subspaces capturing the slow dynamics of the process. The global exponential stability of the resulting observer is derived combining classical Lyapunov analysis with a transformation that allows us to obtain a diffusion system from a diffusion-convection system. In addition, aspects related to the location of sensors and their influence on the ability to reconstruct the necessary fields to feed the observer will also be considered

In this work, we present a library of software units for the simulation, optimisation and control of thermal processes in the food industry. The Ecosimpro© environment has been selected as the programming tool. The reason is twofold: On the one hand it allows working with hybrid systems as it is the case of food processing plants and, on the other hand it is equipped with a graphical user interface (GUI) which allows users to handle models in an intuitive way (by means of icons). Although this work is focused on processes of the canned food industry, new units dealing with other processes can be added without modifying the existing. Finally, the models have been validated in a pilot plant installed at the IIIM-CSIC.

En este trabajo se presenta una librería de unidades de operación en EcosimPro para la simulación, optimización y control de procesos térmicos en la industria alimentaria. Las plantas de procesamiento de alimentos son buenos ejemplos de sistemas híbridos donde las dinámicas continuas no lineales están acopladas con eventos discretos. El entorno EcosimPro permite trabajar con dichos sistemas de forma eficiente e incorpora una interfaz gráfica de usuario (EcoDiagram) que facilita el manejo de modelos matemáticos complejos a los usuarios no expertos. Para la programación de dichos modelos en el entorno EcosimPro se ha seguido el paradigma de la programación orientada a objetos (POO) que incluye características como la reutilización, herencia, abstracción o encapsulamiento. La librería de componentes se puede utilizar, por ejemplo, para analizar el efecto de tecnologías alternativas en la producción o para diseñar nuevas políticas de operación en el caso de condiciones de suministro fluctuantes. Aunque este trabajo se centra en procesos de la industria conservera, se pueden añadir otras unidades para la simulación de procesos como la pasteurización o secado sin necesidad de modificar los componentes ya existentes. Los modelos han sido validados utilizando una planta piloto instalada en el IIM-CSIC aunque pueden ser aplicados a otras con diferentes especificaciones. Finalmente, algunas de las ventajas de disponer de esta librería de componentes se ilustran en una serie de ejemplos de uso.

There are many distributed processes in the chemical industry as it is the case of tubular reactors in which the parameters or the structure of the reaction terms are only a rough approximation of reality. In order to efficiently control this kind of systems, it is important to take into account this lack of detailed information (robustness). In this work, we make use of the classical theory on the robust nonlinear control for finite dimensional systems and extend it to distributed process systems by taking advantage of the special nature of dissipative systems. In this way, theoretical issues related to the nonlinearity of the diffusion terms and inhomogeneous boundary conditions are handled by means of the Kirchhoff and state transformations, respectively. In addition, and for practical reasons, the problem of controller saturation is considered. The different aspects of the methodology will be illustrated through a number of computational experiments concerning non-isothermal tubular reactors with convection and/or diffusion terms

In this article, we develop a systematic approach for efficient field reconstruction in distributed process systems from a limited number of measurements. The approach generalizes previous methods for sensor placement so as to be able to handle field reconstruction problems in arbitrary spatial domains where complex nonlinear phenomena take place. Pattern formation in fluid dynamics or diffusion-reaction systems are examples exhibiting complex nonlinear distributed behaviors, especially when taking place in arbitrary 2D or 3D domains. Our approach exploits the dissipative nature of the diffusion-convection process and the underlying algebraic structure of the finite element method to efficiently construct field representations in terms of globally defined basis functions and to optimally select the placement of sensors. The results will be illustrated on a fluid dynamic process: the Rayleigh−Bénard problem

Many phenomena such as neuron firing in the brain, the travelling waves which produce the heartbeat, arrythmia and fibrillation in the heart, catalytic reactions or cellular organization activities, among others, can be described by a unifying paradigm based on a class of nonlinear reaction–diffusion mechanisms. The FitzHugh–Nagumo (FHN) model is a simplified version of such class which is known to capture most of the qualitative dynamic features found in the spatiotemporal signals. In this paper, we take advantage of the dissipative nature of diffusion–reaction systems and results in finite dimensional nonlinear control theory to develop a class of nonlinear feedback controllers which is able to ensure stabilization of moving fronts for the FHN system, despite structural or parametric uncertainty. In the context of heart or neuron activity, this class of control laws is expected to prevent cardiac or neurological disorders connected with spatiotemporal wave disruptions. In the same way, biochemical or cellular organization related with certain functional aspects of life could also be influenced or controlled by the same feedback logic. The stability and robustness properties of the controller will be proved theoretically and illustrated on simulation experiments

In this work, the dissipative nature of spatially distributed bioprocess systems is exploited to develop efficient state observers based on a low dimensional dynamic representation of the original set of partial differential equations. The approach we suggest combines standard observer design techniques for bioreactors with efficient model reduction methodologies based on projection of the original concentration fields on low dimensional subspaces capturing the slow dynamics of the process. Aspects related with the location of sensors and their influence on the ability to reconstruct concentration fields will also be considered. Finally, the different aspects of the methodology, as well as the efficiency of the resulting observers will be illustrated on a case study of industrial interest, namely a tubular bioreactor producing gluconic acid by Aspergillus Niger

In this work we present the development of an efficient model-based real time dynamic optimization (DO) architecture for the control of distributed parameter systems (DPS). The approach takes advantage of the dissipative nature of this class of systems to obtain reduced order models (ROM) which are then used by the optimization modules to compute in real time the optimal operation policy. The DO module is based on the combination of the control vector parameterization (CVP) approach and a suitable NLP solver selected among several local and global possibilities

Food processing plants are good examples of hybrid systems where highly non-linear continuous dynamics are coupled with discrete events. We present a user friendly interface and a library of unit operations for food industry. This simulation environment use new methods in model building and is able to reconstruct plant operation at different levels of detail. The architecture allows the embedment of product transformations into different unit operations, and these into an economic model

Plant optimization and control usually rely on detailed first principle mathematical models and must be first calibrated and tested by extensive long-term tedious experiments. This contribution presents a number of tools to check system identifiability conditions that will allow us to select the appropriate process variables to do parameter estimation

In this contribution we present an overview of some recent works carried out in our group to develop systematic and efficient methods for model reduction and its application to simulation, dynamic optimization and robust control of complex distributed process systems. The numerical projection methods we developed exploit the underlying finite element structure of the numerical PDE system to efficiently evaluate and to integrate the spatial differential terms, and thus to systematically project the original PDE set into a low dimensional subspace. This results into a reduced order description which is able to capture the relevant dynamics of the original system. Details on computational aspects of the methodology as well as applications in the context of dynamic optimization and robust control will be discussed on a number of representative case studies involving nonlinear diffusion-reaction and fluid dynamic systems.

In this paper, we explore connections between the underlying physics of dissipative systems and nonlinear robust control. In particular, we concentrate on the problem of stabilizing stationary solutions of nonlinear dissipative systems with states distributed in space. Dissipative systems are equipped with an entropy function which we employ to relate dissipation with the Hamilton–Jacobi–Bellman equation. This relation allows us to establish formal links between the dynamic properties of dissipative systems, passivity and optimal stabilizing control, as it is understood in systems theory. Robustness issues in controller design, are also discussed in the context of front or pulse spatial pattern stabilization.

[ES] ROS (Robot Operating System) es un framework para el desarrollo de sistemas robóticos de código abierto ampliamente utilizado en la industria y la investigación. Los sistemas robóticos asistidos requieren una comunicación fluida y fiable entre ROS y el controlador externo, a fin de lograr un control y supervisión eficaz del mismo. En este artículo, se presenta el procedimiento necesario para establecer la comunicación a través de una red local entre un robot móvil que implementa ROS y un dispositivo remoto desde el que enviar y recibir información del robot. Además, se propone un ejemplo de HMI (Interfaz Hombre-Máquina) desarrollado en MATLAB, y que puede ser instalada en equipos Windows, Linux o MacOSX, para la teleoperación de un robot en un entorno industrial. La comunicación bidireccional en tiempo real, las capacidades de procesamiento de datos y su versatilidad la convierten en una herramienta completa para la gestión de datos robóticos en entornos industriales., [EN] ROS (Robot Operating System) is an open-source robotics development framework widely used in industry and research. Human assisted robotic systems require of robust and constant communication between ROS and the remote controller in order to achieve effective control and monitoring of the system. In this paper, we propose a methodology to connect a mobile ROS with a remote device, using a wireless local network. This device serves as link between the robot and the rest of the system. Additionally, an HMI (Human-Machine Interface) developed using MATLAB is presented. The HMI can be implemented in Windows, Linux or MacOSX computers and provides teleoperation capabilities for a robot in an industrial environment. Real-time bidirectional communication, data processing capabilities and intrinsic versatility make this HMI a robust tool for robotic data management in industrial applications.