AI-based Diagnostics for Fault Detection and Isolation in Process Equipment Service

Recent industry requires efficient fault discovering and isolation solutions in process equipment service. This problem is a real-world problem of typically ill-defined systems, hard to model, with large-scale solution spaces. Design of precise models is impractical, too expensive, or often non-existent. Support service of equipment requires generating models that can analyze the equipment data, interpreting the past behavior and predicting the future one. These problems pose a challenge to traditional modeling techniques and represent a great opportunity for the application of AI-based methodologies, which enable us to deal with imprecise, uncertain data and incomplete domain knowledge typically encountered in real-world applications. In this paper the state of the art, theoretical background of conventional and AI-based techniques in support of service tasks and illustration of some applications to process equipment service on bio-ethanol production process are shortly described

Development of monitoring and control systems for biotechnological processes

The field of biotechnology represents an important research area that has gained increasing success in recent times. Characterized by the involvement of biological organisms in manufacturing processes, its areas of application are broad and include the pharmaceuticals, agri-food, energy, and even waste treatment. The implication of living microorganisms represents the common element in all bioprocesses. Cell cultivations is undoubtedly the key step that requires maintaining environmental conditions in precise and defined ranges, having a significant impact on the process yield and thus on the desired product quality. The apparatus in which this process occurs is the bioreactor. Unfortunately, monitoring and controlling these processes can be a challenging task because of the complexity of the cell growth phenomenon and the limited number of variables can be monitored in real-time. The thesis presented here focuses on the monitoring and control of biotechnological processes, more specifically in the production of bioethanol by fermentation of sugars using yeasts. The study conducted addresses several issues related to the monitoring and control of the bioreactor, in which the fermentation takes place. First, the topic concerning the lack of proper sensors capable of providing online measurements of key variables (biomass, substrate, product) is investigated. For this purpose, nonlinear estimation techniques are analyzed to reconstruct unmeasurable states. In particular, the geometric observer approach is applied to select the best estimation structure and then a comparison with the extended Kalman filter is reported. Both estimators proposed demonstrate good estimation capabilities as input model parameters vary. Guaranteeing the achievement of the desired ethanol composition is the main goal of bioreactor control. To this end, different control strategies, evaluated for three different scenarios, are analzyed. The results show that the MIMO system, together with an estimator for ethanol composition, ensure the compliance with product quality. After analyzing these difficulties through numeric simulations, this research work shifts to testing a specific biotechnological process such as manufacturing bioethanol from brewery’s spent grain (BSG) as renewable waste biomass. Both acid pre-treatment, which is necessary to release sugars, and fermentation are optimized. Results show that a glucose yield of 18.12 per 100 g of dried biomass is obtained when the pre-treatment step is performed under optimized conditions (0.37 M H2SO4, 10% S-L ratio). Regarding the fermentation, T=25°C, pH=4.5, and inoculum volume equal to 12.25% v/v are selected as the best condition, at which an ethanol yield of 82.67% evaluated with respect to theoretical one is obtained. As a final step, the use of Raman spectroscopy combined with chemometric techniques such as Partial Least Square (PLS) analysis is evaluated to develop an online sensor for fermentation process monitoring. The results show that the biomass type involved significantly affects the acquired spectra, making them noisy and difficult to interpret. This represents a nontrivial limitation of the applied methodology, for which more experimental data and more robust statistical techniques could be helpful

Grapes and Wine

Grape and Wine is a collective book composed of 18 chapters that address different issues related to the technological and biotechnological management of vineyards and winemaking. It focuses on recent advances, hot topics and recurrent problems in the wine industry and aims to be helpful for the wine sector. Topics covered include pest control, pesticide management, the use of innovative technologies and biotechnologies such as non-thermal processes, gene editing and use of non-Saccharomyces, the management of instabilities such as protein haze and off-flavors such as light struck or TCAs, the use of big data technologies, and many other key concepts that make this book a powerful reference in grape and wine production. The chapters have been written by experts from universities and research centers of 9 countries, thus representing knowledge, research and know-how of many regions worldwide

Contribution au diagnostic de pannes pour\ud les systèmes différentiellement plats

Cette thèse s’intéresse au diagnostic de pannes dans les systèmes différentiellement plats, ceci constituant une large classe de systèmes non linéaires. La propriété de platitude différentielle est caractérisée par des relations qui permettent d’exprimer les états d’un système et ses entrées en fonction de ses sorties plates et de leurs dérivées. Ces relations qui sont à la base de la commande plate sont aussi utiles pour la réalisation du diagnostic de pannes. Ainsi sont introduites les notions de minimalité pour les sorties plates, de platitude stricte et de degré additionnel de redondance. Ceci conduit à la proposition d’une méthode globale de détection de pannes basée sur la platitude. Partant alors de la constatation que les systèmes différentiellement plats de complexité élevée sont souvent constituer de sous systèmes eux mêmes différentiellement plats, l’approche de détection de pannes précédente peut être démultipliée au sein de cette structure de façon à en identifier les sous systèmes défaillants. On s’intéresse alors au cas courant de la platitude différentielle implicite et on montre dans le cadre d’une application aéronautique comment les réseaux de neurones permettent de constituer une solution numérique au problème de détection de pannes. La disponibilité en temps réel de dérivées successives des sorties étant essentielle pour la mise en oeuvre de ces méthodes, on étudie alors les performances d’un filtre dérivateur alors que le système est lui-même soumis à une commande plate, ceci conduira a modifié légèrement une telle loi de commande afin d’effectuer l’effet des erreurs d’estimation. On s’intéresse finalement à la détection des pannes dans les systèmes chaotiques différentiellement plats. On montre sur plusieurs exemples comment la propriété de platitude peut être mise à profit pour détecter et identifier des variations paramétriques au sein d’un tel type de système chaotique. Des résultats de simulation sont présentés. Finalement des thèmes de recherche complémentaires à cette approche sont relevés. --------------------------------------------------------------------- This thesis is devoted to the diagnostic of faults in differentially flat systems, where\ud differentially flat systems constitute a rather large class of non linear systems. The flatness\ud property is characterized by relations allowing to express states and input as functions of the\ud outputs and their derivatives up to a finite order. These relations are the basis for the synthesis\ud of flat control laws and are, is it displayed here, useful to perform an efficient diagnostic of\ud additional redundancy degree. Then a global fault detection method based on the flatness\ud property is proposed. It is shown that many differentially flat subsystems so that the proposed\ud fault detection method can be applied within the corresponding structure allowing then the\ud identification of faulty subsystems. Then the frequent case of implicitly differentially flat\ud systems is considered and it is shown through an aeronautical application that neural networks\ud can provide a numerical solution approach to this fault detection problem. Since with this\ud approach the one line availability of successive derivatives of the outputs is imperative, the\ud performance of a derivative filter is studied. To eliminate the effect of the resulting estimation\ud errors, some improvements are introduced to the current flat control law. In the last section of\ud the report the diagnostic of differentially flat chaotic systems is considered. In different cases it is shown how the differential flatness property can be used to detect and identify variations of the parameters of the chaotic system. Simulation results are displayed. Finally some complementary fields of research are pointed out\u

Aerospace Medicine and Biology: Cumulative index, 1979

This publication is a cumulative index to the abstracts contained in the Supplements 190 through 201 of 'Aerospace Medicine and Biology: A Continuing Bibliography.' It includes three indexes-subject, personal author, and corporate source