Multivariate Statistical Process Control and Case-Based Reasoning for situation assessment of Sequencing Batch Reactors

ABSRACT This thesis focuses on the monitoring, fault detection and diagnosis of Wastewater Treatment Plants (WWTP), which are important fields of research for a wide range of engineering disciplines. The main objective is to evaluate and apply a novel artificial intelligent methodology based on situation assessment for monitoring and diagnosis of Sequencing Batch Reactor (SBR) operation. To this end, Multivariate Statistical Process Control (MSPC) in combination with Case-Based Reasoning (CBR) methodology was developed, which was evaluated on three different SBR (pilot and lab-scales) plants and validated on BSM1 plant layout.

Abstracting qualitative information for process supervision

Supervisory systems evolution makes the obtaining of significant information from processes more important in the way that the supervision systems' particular tasks are simplified. So, having signal treatment tools capable of obtaining elaborate information from the process data is important. In this paper, a tool that obtains qualitative data about the trends and oscillation of signals is presented. An application of this tool is presented as well. In this case, the tool, implemented in a computer-aided control systems design (CACSD) environment, is used in order to give to an expert system for fault detection in a laboratory plant

Aplicació en línia d'un sistema variable de control multivariable en un Reactor ..

L’objecte del present treball és la realització d’una aplicació que permeti portar a terme el control estadístic multivariable en línia d’una planta SBR. Aquesta eina ha de permetre realitzar un anàlisi estadístic multivariable complet del lot en procés, de l’últim lot finalitzat i de la resta de lots processats a la planta. L’aplicació s’ha de realitzar en l’entorn LabVIEW. L’elecció d’aquest programa ve condicionada per l’actualització del mòdul de monitorització de la planta que s’està desenvolupant en aquest mateix entorn

SQualTrack: A Tool for Robust Fault Detection

One of the techniques used to detect faults in dynamic systems is analytical redundancy. An important difficulty in applying this technique to real systems is dealing with the uncertainties associated with the system itself and with the measurements. In this paper, this uncertainty is taken into account by the use of intervals for the parameters of the model and for the measurements. The method that is proposed in this paper checks the consistency between the system's behavior, obtained from the measurements, and the model's behavior; if they are inconsistent, then there is a fault. The problem of detecting faults is stated as a quantified real constraint satisfaction problem, which can be solved using the modal interval analysis (MIA). MIA is used because it provides powerful tools to extend the calculations over real functions to intervals. To improve the results of the detection of the faults, the simultaneous use of several sliding time windows is proposed. The result of implementing this method is semiqualitative tracking (SQualTrack), a fault-detection tool that is robust in the sense that it does not generate false alarms, i.e., if there are false alarms, they indicate either that the interval model does not represent the system adequately or that the interval measurements do not represent the true values of the variables adequately. SQualTrack is currently being used to detect faults in real processes. Some of these applications using real data have been developed within the European project advanced decision support system for chemical/petrochemical manufacturing processes and are also described in this paper

I-SS: integrated supervision systems approach based on interactive components

Process supervision is the activity focused on monitoring the process operation in order to deduce conditions to maintain the normality including when faults are present Depending on the number/distribution/heterogeneity of variables, behaviour situations, sub-processes, etc. from processes, human operators and engineers do not easily manipulate the information. This leads to the necessity of automation of supervision activities. Nevertheless, the difficulty to deal with the information complicates the design and development of software applications. We present an approach called "integrated supervision systems". It proposes multiple supervisors coordination to supervise multiple sub-processes whose interactions permit one to supervise the global process

Interacting software-agents to support a kind of expert supervision systems

Expert supervision systems are software applications specially designed to automate process monitoring. The goal is to reduce the dependency on human operators to assure the correct operation of a process including faulty situations. Construction of this kind of application involves an important task of design and development in order to represent and to manipulate process data and behaviour at different degrees of abstraction for interfacing with data acquisition systems connected to the process. This is an open problem that becomes more complex with the number of variables, parameters and relations to account for the complexity of the process. Multiple specialised modules tuned to solve simpler tasks that operate under a co-ordination provide a solution. A modular architecture based on concepts of software agents, taking advantage of the integration of diverse knowledge-based techniques, is proposed for this purpose. The components (software agents, communication mechanisms and perception/action mechanisms) are based on ICa (Intelligent Control architecture), software middleware supporting the build-up of applications with software agent features

Expert supervision based on cases

The paper focuses on taking advantage of large amounts of data that are systematically stored in plants (by means of SCADA systems), but not exploited enough in order to achieve supervisory goals (fault detection, diagnosis and reconfiguration). The methodology of case base reasoning (CBR) is proposed to perform supervisory tasks in industrial processes by re-using the stored data. The goal is to take advantage of experiences, registered in a suitable structure as cam, avoiding the tedious task of knowledge acquisition and representation needed by other reasoning techniques as expert systems. An outlook of CBR terminology and basic concepts are presented. The adaptation of CBR in performing expert supervisory tasks, taking into account the particularities and difficulties derived from dynamic systems, is discussed. A special interest is focused in proposing a general case definition suitable for supervisory tasks. Finally, this structure and the whole methodology is tested in a application example for monitoring a real drier chamber

Diagnosi basada en models tenint en compte els retards en xarxes de comunicació

Els mètodes de detecció, diagnosi i aïllament de fallades (Fault Detection and Isolation - FDI) basats en la redundància analítica (és a dir, la comparació del comportament actual del procés amb l’esperat, obtingut mitjançant un model matemàtic del mateix), són àmpliament utilitzats per al diagnòstic de sistemes quan el model matemàtic està disponible. S’ha implementat un algoritme per implementar aquesta redundància analítica a partir del model de la plana conegut com a Anàlisi Estructural

A hybrid approach of knowledge-based reasoning for structural assessment

La idea básica de detección de defectos basada en vibraciones en Monitorización de la Salud Estructural (SHM), es que el defecto altera las propiedades de rigidez, masa o disipación de energía de un sistema, el cual, altera la respuesta dinámica del mismo. Dentro del contexto de reconocimiento de patrones, esta tesis presenta una metodología híbrida de razonamiento para evaluar los defectos en las estructuras, combinando el uso de un modelo de la estructura y/o experimentos previos con el esquema de razonamiento basado en el conocimiento para evaluar si el defecto está presente, su gravedad y su localización. La metodología involucra algunos elementos relacionados con análisis de vibraciones, matemáticas (wavelets, control de procesos estadístico), análisis y procesamiento de señales y/o patrones (razonamiento basado en casos, redes auto-organizativas), estructuras inteligentes y detección de defectos. Las técnicas son validadas numérica y experimentalmente considerando corrosión, pérdida de masa, acumulación de masa e impactos. Las estructuras usadas durante este trabajo son: una estructura tipo cercha voladiza, una viga de aluminio, dos secciones de tubería y una parte del ala de un avión comercial.

Representació qualitativa asíncrona de senyals per a la supervisió de sistemes dinàmics

L'objectiu general d'aquest treball és trobar i mostrar una eina que permeti obtenir una representació dels senyals procedents de sistemes dinàmics adequada a les necessitats dels sistemes de Supervisió Experta de processos. Aquest objectiu general es pot subdividir en diverses parts, que són tractades en els diferents capítols que composen el treball i que es poden resumir en els següents punts: En primer lloc, cal conèixer les necessitats dels sistemes de Supervisió: La gran quantitat de dades que provenen dels processos fa necessari el tractament d'aquestes dades per obtenir-ne d'altres, més elaborades, amb un nivell més elevat de representació. La utilització de raonament qualitatiu, pròpia dels éssers humans, comporta la necessitat de representar simbòlicament els senyals, de traduir les dades numèriques en símbols. La Supervisió de sistemes dinàmics comporta que el temps sigui una variable fonamental, la asincronia dels esdeveniments significatius per a la Supervisió fa que les representacions més adequades i útils dels senyals siguin asíncrones. Finalment,l'ús dels coneixements experimentals en la Supervisió dels processos comporta que les representacions més naturals siguin les més útils. Aquestes necessitats fan de la representació dels senyals mitjançant episodis l'eina amb més possibilitats per assolir els objectius que es volen assolir. Per això, es presenta un formalisme que permet descriure i incloure-hi la formalització i les diferents aproximacions a aquest tipus de representació ja existents i, al mateix temps, augmentar-ne la significació a través de característiques dels senyals que no es tenen en compte en les aproximacions ja existents. El següent pas és aprofitar el nou formalisme per obtenir una nova representació amb un grau més gran de significació, cosa que s'aconsegueix representant explícitament les discontinuïtats i els períodes estacionaris o d'estabilitat, molt significatius en Supervisió de processos. Un problema sempre present en el tractament de senyals és el soroll que els afecta. Per aquest motiu es presenta un mètode que permet filtrar el soroll de manera que les representacions resultants quedin afectades el mínim possible per aquest tractament. Finalment, es presenta l'aplicació en línia de les eines descrites. La representació en línia dels senyals comporta el tractament de la incertesa inherent al coneixement parcial del senyal (un episodi no pot ser determinat i caracteritzat completament fins que no s'acaba). L'obtenció de resultats amb determinats graus de certesa és perfectament coherent amb la seva utilització posterior mitjançant Sistemes Experts o altres eines de la IA. Totes les aportacions del treball vénen acompanyades d'exemples i/o aplicacions que permeten observar-ne la utilitat i les limitacions.

Model-based objects recognition in industrial environments for autonomous vehicles control

Behavior-based navigation of autonomous vehicles requires the recognition of the navigable areas and the potential obstacles. In this paper we describe a model-based objects recognition system which is part of an image interpretation system intended to assist the navigation of autonomous vehicles that operate in industrial environments. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using a rule-based cooperative expert system

Embedding objects into Matlab/Simulink for process supervision

This paper introduces how artificial intelligence technologies can be integrated into a known computer aided control system design (CACSD) framework, Matlab/Simulink, using an object oriented approach. The aim is to build a framework to aid supervisory systems analysis, design and implementation. The idea is to take advantage of an existing CACSD framework, Matlab/Simulink, so that engineers can proceed: first to design a control system, and then to design a straightforward supervisory system of the control system in the same framework. Thus, expert systems and qualitative reasoning tools are incorporated into this popular CACSD framework to develop a computer aided supervisory system design (CASSD) framework. Object-variables an introduced into Matlab/Simulink for sharing information between tools

Predictive motion control of a mirosot mobile robot

This paper discusses predictive motion control of a MiRoSoT robot. The dynamic model of the robot is deduced by taking into account the whole process - robot, vision, control and transmission systems. Based on the obtained dynamic model, an integrated predictive control algorithm is proposed to position precisely with either stationary or moving obstacle avoidance. This objective is achieved automatically by introducing distant constraints into the open-loop optimization of control inputs. Simulation results demonstrate the feasibility of such control strategy for the deduced dynamic model

Development of a decision support system for the integrated control of membrane bioreactors

The work presented in this PhD thesis includes various partial studies aimed at developing a decision support system for membrane bioreactor integrated control. The decision support systems (DSS) have as a main goal to facilitate the operation of complex processes due to the multiple variables that are processed. For this reason, the research used has focused on aspects related to nutrient removal, and on the development of indicators or sensors capable of facilitating, automating and controlling the filtration process in an integrated way with the biological processes that taking place. Work has also been done on the design, development, implementation and validation of tools based on the knowledge made available by the automatic control and the supervision of the MBRs

Operation and control of SBR processes for enhanced biological nutrient removal from wastewater

In the last decades, the awareness of environmental issues has increased in society considerably. There is an increasing need to improve the effluent quality of domestic wastewater treatment processes. This thesis describes the application of the Sequencing Batch Reactor (SBR) technology for Biological Nutrient Removal (BNR) from the wastewater. In particular, the work presented evolves from the nitrogen removal to the biological nutrient removal (i.e. nitrogen plus phosphorous removal) with special attention to the operational strategy design, the identification of possible reactor cycle controls or the influent composition related to the process efficiency. In such sense, also the use of ethanol as an external carbon (when low influent Carbon:Phosphorus (C:P) or Carbon:Nitrogen (C:N) ratios are presented) are studied as an alternative to maintain the BNR efficiency.