Process Resilience Analysis Framework for Design and Operations

Process plants are complex socio-technical systems that degrade gradually and change with advancing technology. This research deals with exploring and answering questions related to the uncertainties involved in the process systems, and their complexity. It aims to systematically integrate resilience in process design and operations through three different phases of prediction, survival, and recovery using a novel framework called Process Resilience Analysis Framework (PRAF). The analysis relies on simulation, data-driven models and optimization approach employing the resilience metrics developed in this research. In particular, an integrated method incorporating aspects of process operations, equipment maintenance, and process safety is developed for the following three phases: •Prediction: to find the feasible operating region under changing conditions using Bayesian approach, global sensitivity analysis, and robust simulation methods, •Survival: to determine optimal operations and maintenance strategies using simulation, Bayesian regression analysis, and optimization, and •Recovery: to develop a strategy for emergency barriers in abnormal situations using dynamic simulation, Bayesian analysis, and optimization. Examples of a batch reactor, and cooling tower operations process unit are used to illustrate the application of PRAF. The results demonstrate that PRAF is successful in capturing the interactions between the process operability characteristics, maintenance, and safety policy. The prediction phase analysis leads to good dynamic response and stability of operations. The survival phase helps in the reduction of unplanned shutdown and downtime. The recovery phase results in in reduced severity of consequences, and response time and overall enhanced recovery. Overall, PRAF achieves flexibility, controllability and reliability of the system, supports more informed decision-making and profitable process systems

Quantitative Risk Analysis using Real-time Data and Change-point Analysis for Data-informed Risk Prediction

Incidents in highly hazardous process industries (HHPI) are a major concern for various stakeholders due to the impact on human lives, environment, and potentially huge financial losses. Because process activities, location and products are unique, risk analysis techniques applied in the HHPI has evolved over the years. Unfortunately, some limitations of the various quantitative risk analysis (QRA) method currently employed means alternative or more improved methods are required. This research has obtained one such method called Big Data QRA Method. This method relies entirely on big data techniques and real-time process data to identify the point at which process risk is imminent and provide the extent of contribution of other components interacting up to the time index of the risk. Unlike the existing QRA methods which are static and based on unvalidated assumptions and data from single case studies, the big data method is dynamic and can be applied to most process systems. This alternative method is my original contribution to science and the practice of risk analysis The detailed procedure which has been provided in Chapter 9 of this thesis applies multiple change-point analysis and other big data techniques like, (a) time series analysis, (b) data exploration and compression techniques, (c) decision tree modelling, (d) linear regression modelling. Since the distributional properties of process data can change over time, the big data approach was found to be more appropriate. Considering the unique conditions, activities and the process systems use within the HHPI, the dust fire and explosion incidents at the Imperial Sugar Factory and the New England Wood Pellet LLC both of which occurred in the USA were found to be suitable case histories to use as a guide for evaluation of data in this research. Data analysis was performed using open source software packages in R Studio. Based on the investigation, multiple-change-point analysis packages strucchange and changepoint were found to be successful at detecting early signs of deteriorating conditions of component in process equipment and the main process risk. One such process component is a bearing which was suspected as the source of ignition which led to the dust fire and explosion at the Imperial Sugar Factory. As a result, this this research applies the big data QRA method procedure to bearing vibration data to predict early deterioration of bearings and final period when the bearing’s performance begins the final phase of deterioration to failure. Model-based identification of these periods provides an indication of whether the conditions of a mechanical part in process equipment at a particular moment represent an unacceptable risk. The procedure starts with selection of process operation data based on the findings of an incident investigation report on the case history of a known process incident. As the defining components of risk, both the frequency and consequences associated with the risk were obtained from the incident investigation reports. Acceptance criteria for the risk can be applied to the periods between the risks detected by the two change-point packages. The method was validated with two case study datasets to demonstrate its applicability as procedure for QRA. The procedure was then tested with two other case study datasets as examples of its application as a QRA method. The insight obtained from the validation and the applied examples led to the conclusion that big data techniques can be applied to real-time process data for risk assessment in the HHPI

Prise de décision en gestion des actifs industriels en tenant compte des risques

Les entreprises modernes sont des organisations complexes par leur structure organisationnelle, opérationnelle et le type de gestion. Elles évoluent dans un environnement opérationnel et d’affaires complexe confronté à des incertitudes significatives liées à des facteurs naturels, techniques, technologiques, commerciaux, organisationnels, économiques, financiers, politiques, etc. affectant leur gestion et leurs opérations. L'environnement opérationnel et d’affaires complexe génère également de nouveaux types de risques relativement inconnus il y a quelques décennies (par exemple,la cyber sécurité). Un tel environnement crée aussi des conditions favorables à l'émergence d'événements extrêmes et rares susceptibles de perturber sérieusement la performance des entreprises à court et à long terme. Les pratiques et analyses actuelles négligent généralement de prendre en compte ces types de risques. Les intrants des experts techniques, des planificateurs stratégiques ou des gestionnaires pourraient s’avérer insuffisants ou trop circonscrits pour tenir compte adéquatement de la complexité dans un environnement complexe en constante évolution et à peine prévisible. Cette situation est généralement causée par un manque de connaissances concernant le type et l’envergure des incertitudes, la nature des interconnexions entre les facteurs d’influence, le niveau de complexité, ainsi que notre faible capacité à prédire les événements futurs. La mondialisation et la forte concurrence font partie de l'environnement opérationnel et d’affaires contemporain typique. La capacité des entreprises à créer et à mettre en oeuvre des concepts innovants est déterminante pour répondre aux exigences en matière de compétitivité et pour assurer leur fonctionnement durable et leur développement futur. Au cours des deux dernières décennies, la gestion des actifs (GDA) est devenue une approche répandue parmi les organisations à succès en tant que concept efficace permettant de générer de la valeur à partir des actifs et d'assurer la durabilité de l'entreprise et de ses opérations. La prise de décision est essentielle dans la GDA. Elle est influencée par différents facteurs (stratégiques, techniques/technologiques, économiques, organisationnels, réglementaires/juridiques, sécurité, marchés, concurrence, etc.). La prise de décision adéquate doit tenir compte de la complexité et des facteurs d’influence pertinents pour équilibrer les risques, les opportunités, la performance, les coûts et les bénéfices. Malgré les progrès récents afin de mieux comprendre les défis et développer de nouvelles approches de modélisation, les programmes de gestion d'actifs n'ont pas toujours réussi à éviter des pertes coûteuses ou même des faillites d'organisations causées par divers facteurs économiques ou non techniques discutés ci-dessus qui n’ont pas été compris ou pris en compte adéquatement dans le processus de prise de décision. La pratique montre également qu'une définition inadéquate des rôles et des responsabilités et le manque de communication contribuent également à l'inefficacité de la GDA et de son processus de prise de décision. Le but du présent travail de recherche est de développer une méthodologie de prise de décision en gestion des actifs en tenant compte de la complexité de l’environnement d’affaires et opérationnel. Dans la présente recherche, une méthodologie intégrale de prise de décision en GDA en tenant compte des risques (Risk-Informed Decision-Making – RIDM) en trois étapes a été développée. La GDA est considérée comme un système de systèmes complexes adaptatifs. La recherche a également développé la méthode de caractérisation et d'intégration des risques d'événements extrêmes et rares dans le processus décisionnel par l'application de la science de la complexité et de la théorie des valeurs extrêmes. La méthodologie est appliquée et validée avec succès dans le cas de trois industries : minière, nucléaire et une utilité électrique. Elle démontre le potentiel d'une application répandue dans diverses industries lors d’un développement futur. Modern companies are complex organizations as per their organizational, management and operational structure. They also operate in a complex business and operational environment facing significant uncertainties related to natural, technical, technological, market, organizational, economic, financial, political, etc. influential factors affecting their business, management and operations. The complex business and operational environment also generates new types of risks that were relatively unknown just a few decades ago (e.g. cyber security) and creates favorable conditions for the emerging of extreme and rare events that may seriously disrupt the short and long-term performance of enterprises. Current practices and analyses generally neglect taking into account those risks. Advice and input from technical experts, strategic planners or knowledgeable managers may be insufficient or too narrowly focused to adequately manage the complexity of the systems and structures in a constantly changing and barely predictable environment. It is generally due to a lack of knowledge regarding the type and range of uncertainties, the nature of interconnections, the level of complexity, as well as our low ability to predict future events. Globalization and strong competition are part of a typical contemporary operational and business environment. The ability of enterprises to create and implement innovative concepts is decisive to meet the demands regarding competitiveness, and to ensure their sustainable operations and further development. During the last two decades, Asset Management (AsM) has become prevalent approach among successful organizations as an effective concept allowing delivering value from assets and ensuring the sustainability of the business and its operations. The decision-making is essential in AsM. It is influenced by various factors (strategic, technical/technological, economic, organizational, regulatory/legal, safety, markets, competition, etc.). A sound decision-making ought to take into account relevant factors for balancing risks, opportunities, performance, costs, and benefits. Despite recent progress in better understanding challenges and developing new modeling approaches, asset management programs have not always been successful in avoiding costly losses or even bankruptcies of organizations caused by various economic or non-technical factors discussed above that have not been eitherunderstood or adequately considered and addressed in the decision-making process. Practice also shows that inadequate definition of roles and responsibilities and lack of communication also contribute to the inefficiency of the AsM and its decisionmaking process.The goal of this thesis is to develop an integral asset management decision-making methodology taking into account the complexity of the business and operational environment. A holistic three-step Risk-Informed Decision-Making (RIDM) methodology tailored for AsM considering it as a Complex Adaptive System of Systems (CASoS) has been developed in this research work. The research has also developed the method regarding the integration of risks of extreme and rare events into the RIDM through the application of the complexity science and the extreme value theory. The methodology is successfully applied and validated in the case of three industries: mining, nuclear and electrical utilities. It demonstrates its potential of a large application across various industries through a further development