Полиминеральная тыловая зона околожильного метасоматического ореола в мезотермальном месторождении золота Зун-Холба (Восточный Саян)

Estimating the parameters of a dynamical system based on measurements is an important task in industrial and scientific practice. Since a model's quality is directly linked to its parameter values, obtaining globally rather than locally optimal values is especially important in this context. In practice, however, local methods, are used almost exclusively. This is mainly due to the high computational cost of global dynamic parameter estimation, which limits its application to relatively small problems comprising no more than a few equations and parameters. In addition, there is still a lack of software packages that allow global parameter estimation in dynamical systems without expert knowledge. Therefore, we propose an efficient computational method for obtaining globally optimal parameter estimates of dynamical systems using well-established, user-friendly software packages. The method is based on the so-called incremental identification procedure, in combination with deterministic global optimization tools for nonlinear programs

Probabilistic Observers for a Class of Uncertain Biological Processes

http://www3.interscience.wiley.com/cgi-bin/jabout/5510/ProductInformation.htmlIn this paper, probabilistic observers are considered for a class of continuous biological processes described by mass-balance-based models. It is assumed that the probability density functions (PDFs) of the uncertain parameters and inputs of the model, as well as the PDFs of the missing initial conditions are known. Then, the PDFs of the unmeasured state variables are obtained, at any time, by considering the image of these initial PDFs by the flow of the dynamic model (differential system). In comparison to classical open-loop asymptotic and interval observers, the method provides information on the confidence level of the estimates rather than simple upper and lower bounds. Moreover, unlike Kalman filters, probabilistic observers are not restricted to Gaussian distributions for the uncertain parameters. The design and application of a probabilistic observer to an industrial wastewater treatment plant is presented. Finally, a number of practical considerations is discussed in connection to both implementation and utilization issues

NCO Tracking for Singular Control Problems Using Neighboring Extremals

A powerful approach for dynamic optimization in the presence of uncertainty is to incorporate measurements into the optimization framework so as to track the necessary conditions of optimality (NCO), the so-called NCO- tracking approach. For nonsingular control problems, this can be done by tracking active constraints along boundary arcs, and using neighboring- extremal (NE) control along interior arcs to force the first-order variation of the NCO to zero. In this paper, an extension of NE control to singular control problems is proposed. The idea is to design NE controllers from successive time differentiations of the first-order variation of the NCO. Based on these results, a NCO-tracking controller that is easily tractable from a real-time optimization perspective is proposed, whose application guarantees that the first-order variation of the NCO converges to zero exponentially. The performance of this NCO-tracking controller is illustrated via the case study of a steered car, a 5th-order two-input dynamical system

Reduction of the ASM1 Model for Optimal Control of Small-Size Activated Sludge Treatment Plants

L'adoption par l'Union Européenne de normes de rejets plus contraignantes implique une meilleure gestion des stations d'épuration. L'utilisation de modèles de simulation dynamique dans des schémas de commande en boucle fermée constitue une alternative intéressante pour répondre à ce problème. Sur la base du modèle ASM 1, un modèle réduit est ici élaboré pour le procédé à boues activées en aération séquentielle, en vue de la commande optimale du système d'aération. Les simplifications considérées sont de deux types : (i) les dynamiques lentes du système sont identifiées au moyen d'une méthode d'homotopie, puis éliminées du modèle ; (ii) des simplifications plus heuristiques consistant à prendre en compte un composé organique unique et à éliminer la concentration des composés organiques azotés sont ensuite appliquées. Elles conduisent à un modèle simplifié de 5 variables. L'application d'une procédure d'identification paramétrique permet alors de démontrer que le comportement dynamique du modèle simplifié est en bonne adéquation avec celui du modèle ASM 1 sur un horizon de prédiction de plusieurs heures, même lorsque les concentrations de l'influent ne sont pas connues. Il est également vérifié que le modèle proposé est observable et structurellement identifiable, sous des conditions d'aérobiose et d'anoxie, à partir des mesures en ligne des concentrations en oxygène dissous, ammoniaque et nitrate. Le modèle simplifié développé présente ainsi toutes les propriétés requises pour une future utilisation au sein de schémas de commande en boucle fermée, en vue de la commande optimale des petites stations d'épuration à boues activées

Nouvelle approche pour la gestion optimale de l aération des petites stations d épuration par boues activées

La gestion actuelle de l'aération pour les petites stations d'épuration par boues activées s'effectue au moyen de dispositifs simples d'asservissement au potentiel redox, à la concentration en oxygène dissous ou à la concentration en ammoniaque. Une alternative pour améliorer le fonctionnement du procédé tant économiquement qu'en terme de fiabilité, consiste à appliquer les techniques d'optimisation dynamique. Une formulation du problème d'optimisation du fonctionnement des petites stations d'épuration équipées d'aérateurs de surface est présentée. Une comparaison est faite entre le mode de gestion optimal développé et deux modes de conduite du procédé. Elle montre qu'il est possible d'envisager une réduction de 40% du coût de fonctionnement relatif à l'aérateur

STATE ESTIMATION FOR WASTEWATER TREATMENT PROCESSES

http://www.wiley-vch.de/publish/dt/books/bySubjectCH00/newTitles200611/0-471-49929-3/?sID=In this chapter, we provide the key ideas on how to build software sensors (also called observers) for wastewater treatment plants (WWTPs). We give an overview of the existing linear and nonlinear observers and discuss criteria that help to identify which observer is best suited with respect to the amount of information being available for the WWTP. Depending on the model reliability, the available measurements and the level of uncertainties associated to the influent concentrations, different class of observers can be considered. We distinguish between those that rely on a full model description (e.g., the extended Kalman filter), and those based on a mass-balance model wherein the biological kinetics are assimilated to unknown inputs (e.g., the asymptotic observer). Moreover, if bounds are known for the uncertainties, then interval observers can be designed. We discuss the principles of each class of observers and illustrate them through a number of examples

Scale-up of batch processes via decentralized control

The economic environment in the specialty chemicals industry requires short times to market and thus the ability to develop new products and processes very rapidly. This, in turn, calls for large scale-ups from laboratory to production. Due to scale-related differences in operating conditions, direct extrapolation of conditions obtained in the laboratory is often impossible, especially when terminal objectives must be met and path constraints respected. This paper proposes a decentralized control scheme for scaling-up the operation of batch and semi-batch processes. The targets to be reached are either taken directly from laboratory experiments or adjusted to account for production constraints. Some targets are reached on-line within a given run, while others are implemented on a run-to-run basis. The methodology is illustrated in simulation via the scale-up of a semi-batch reactor

Study of Tricyclic Cascade Networks using Dynamic Optimization

The mitogen-activated protein kinase (MAPK) cascades are ubiquitous in eukaryotic signal transduction, and these pathways are conserved in cells from yeast to mammals. They relay extracellular stimuli from the plasma membrane to targets in the cytoplasm and nucleus, initiating diverse responses involving cell growth, mitogenesis, differentiation and stress responses in mammalian cells. Detailed kinetics models of MAPK cascades have been constructed in recent years that are comprised of mixed sets of differential and algebraic equations (DAEs). Such models typically involve many parameters, such as the kinetic rate constants and the concentration ratios between various kinases and phosphatases, the values of which are not directly accessible in vivo and are subject to large uncertainty. Dynamic optimization has proved to be a very useful tool to help relate the model parameters to functions in MAPK networks. Large-scale, nonlinear DAE models can be handled within this framework, as well as a large variety of objective functions and constraints. In a recent work, the response of an interconvertible monocyclic cascade (phosphorylation- dephosphorylation cycle) has been studied. It was shown, using dynamic optimization, that values of the kinetic parameters that confer, at the same time, (i) a short response time, (ii) a large amplification capability, and (iii) a steep response profile to a graded input (ultrasensitivity) can be found. However, it was also found that, in a monocyclic cascade, these properties are not robust towards variations in the ratio between signaling enzyme and substrate kinase concentrations as well as the ratio between phosphatase and substrate kinase concentrations. In this presentation, we extend the analysis to the general case of multiple levels of cascades, with emphasis on a linear three-kinase model. The same response properties as in the monocyclic case are considered, and dynamic optimization is employed to identify parameter values that optimize these response properties. Special emphasis is placed on the robustness of the resulting tricyclic cascades in the face of variations in kinase and phosphatase concentration ratios. Comparisons with the monocyclic cascade case are also presented