Etude de la croissance de la Saccharomyces cerevisiae. Contrôle et observation d'un bioprocédé semi-continu

Le travail présenté dans ce rapport a pour objet l'amélioration de la productivité d'un procédé de fabrication de la levure Saccharomyces cerevisiae et en particulier la synthèse d'observateurs en vue de leur utilisation dans une loi de commande. La première partie de notre étude concerne les propriétés du modèle. On s'intéresse également à la synthèse d'un observateur Grand Gain et à un observateur de type Kalman étendu. Des simulations montrent le comportement dynamique des observateurs proposés

Genetic selection for reduced somatic cell counts in sheep milk: A review

Mastitis is an in\ufb02ammation of the udder, mainly caused by bacteria, and leads to economic loss, due to discarded milk, reduced milk production, reduced milk quality and increased health costs in both dairy sheep and cattle. Selecting for increased genetic resistance to mastitis can be done directly or indirectly, with the indirect selection corresponding to a prediction of the bacteriological status of the udder based on traits related to the infection. The most frequently used indirect method is currently milk somatic cell count (SCC) or somatic cell score (SCS). This review reports the state of the art relating to the genetic basis of mastitis resistance in sheep and explores the opportunities to use SCC as selection criterion in a breeding programme to improve resistance to mastitis in sheep, discussing the actual situation and prospects for improvement. It has been stressed, in particular, that although it is unlikely that selection for mastitis resistance by the farmers on their own will be successful, there is good prospect for genetic improvement if reliable pedigree and performance recording is implemented across \ufb02ocks, combined with breeding value estimation. To achieve this, a strong and well-structured organization to implement and support the programme is essential

Probabilistic Decision Trees using SVM for Multi-class Classification

In the automotive repairing backdrop, retrieving from previously solved incident the database features that could support and speed up the diagnostic is of great usefulness. This decision helping process should give a fixed number of the more relevant diagnostic classified in a likelihood sense. It is a probabilistic multi-class classification problem. This paper describes an original classification technique, the Probabilistic Decision Tree (PDT) producing a posteriori probabilities in a multi-class context. It is based on a Binary Decision Tree (BDT) with Probabilistic Support Vector Machine classifier (PSVM). At each node of the tree, a bi-class SVM along with a sigmoid function are trained to give a probabilistic classification output. For each branch, the outputs of all the nodes composing the branch are combined to lead to a complete evaluation of the probability when reaching the final leaf (representing the class associated to the branch). To illustrate the effectiveness of PDTs, they are tested on benchmark datasets and results are compared with other existing approaches.This research has been sponsored by PSA

On non-invertibilities for structural analysis

International audienceThis article deals with structural analysis, which is a simple but efficient method in the field of Fault Detection and Isolation (FDI), to determine systems properties, such as observability, fault detectability or diagnosability. Moreover, it allows to determine subsets of the model equations which may or may not yield fault indicators, namely residuals. Because some residuals are obtained by inverting parts of the model, the notion of constraint invertibility is used to assess the possibility of building a residual. Invertibilities are often considered a posteriori, after that the structural analysis has been performed, in order to keep the computable residuals. Taking into account these invertibility constraints in all steps of the structural method would allow, firstly, to provide directly computable residuals, and secondly, to reduce the complexity of structural analysis algorithms. Two types of non-invertibilities may be distinguished: those which are defined according to the nature of the functions, and those which are due to the structure of the model. Two algorithms are proposed for determining the latter ones. Integration of the two kinds of invertibilities from the first step of the structural analysis is the objective of this paper

Structural Analysis for FDI: a modified, invertibility-based canonical decomposition

International audienceStructural analysis is a simple but efficient method in the field of Fault Detection and Isolation (FDI), to determine system properties such as observability, fault detectability or diagnosability. Our method is based on the study of a bipartite graph derived from the behavioral model of the system, which represents the links between the variables of the system. A graph-decomposition tool, known as the Dulmage-Mendelsohn decomposition (also named canonical decomposition) is used in order to determine the monitorable and observable subsystems. Additionally, a structural analysis can be performed with the objective of designing fault indicators, i.e. residuals, which are used for FDI. Invertibility constraints of the relations of the behavioral model, are used to determine the calculability of the residuals. These invertibility constraints are not considered to derive the canonical decomposition. Consequently, some structurally monitorable subsystems may correspond to non-realizable (non-computable) residuals. In this paper, we propose a new canonical decomposition algorithm which takes into account the invertibility constraints: our modified decomposition redefines the monitorable and observable part of the system, so that each of these parts do not contain elements which would turn out to be unusable, from a calculability standpoint

Advanced monitoring and control of anaerobic treatment plants: software sensors and controllers for an anerobic digestor

A mass balanced based model representing the dynamical behaviour of anaerobic digester has served as a basis for the design of software sensors for the concentration of inorganic carbon, alkalinity and volatile fatty acids. The predictions of the sensors are close to the actual off-line measurements. The model has also been used to design a model-based adaptive linearizing controller and a fuzzy controller whose objective is to regulate the ratio of the intermediate alkalinity over the total alkalinity below some desired value (0.3) under which the process is assumed to remain in stable conditions and avoid VFA accumulation. Both controllers were calibrated via extensive numerical simulations and implemented. The controllers proved successful in maintaining the ratio of TA over PA below 0.3, even in presence of large variations of the organic load

Etude de la croissance de la Saccharomyces cerevisiae : controle et observation d'un bioprocede semi-continu

Programme 5 - Automatique, productique, traitement du signal et des donneesAvailable at INIST (FR), Document Supply Service, under shelf-number : 14802 E, issue : a.1995 n.2696 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueSIGLEFRFranc