Computer-aided HAZOP of batch processes

The modern batch chemical processing plants have a tendency of increasing technological complexity and flexibility which make it difficult to control the occurrence of accidents. Social and legal pressures have increased the demands for verifying the safety of chemical plants during their design and operation. Complete identification and accurate assessment of the hazard potential in the early design stages is therefore very important so that preventative or protective measures can be integrated into future design without adversely affecting processing and control complexity or capital and operational costs. Hazard and Operability Study (HAZOP) is a method of systematically identifying every conceivable process deviation, its abnormal causes and adverse hazardous consequences in the chemical plants. [Continues.

Towards semantics-driven modelling and simulation of context-aware manufacturing systems

Systems modelling and simulation are two important facets for thoroughly and effectively analysing manufacturing processes. The ever-growing complexity of the latter, the increasing amount of knowledge, and the use of Semantic Web techniques adhering meaning to data have led researchers to explore and combine together methodologies by exploiting their best features with the purpose of supporting manufacturing system's modelling and simulation applications. In the past two decades, the use of ontologies has proven to be highly effective for context modelling and knowledge management. Nevertheless, they are not meant for any kind of model simulations. The latter, instead, can be achieved by using a well-known workflow-oriented mathematical modelling language such as Petri Net (PN), which brings in modelling and analytical features suitable for creating a digital copy of an industrial system (also known as "digital twin"). The theoretical framework presented in this dissertation aims to exploit W3C standards, such as Semantic Web Rule Language (SWRL) and Web Ontology Language (OWL), to transform each piece of knowledge regarding a manufacturing system into Petri Net modelling primitives. In so doing, it supports the semantics-driven instantiation, analysis and simulation of what we call semantically-enriched PN-based manufacturing system digital twins. The approach proposed by this exploratory research is therefore based on the exploitation of the best features introduced by state-of-the-art developments in W3C standards for Linked Data, such as OWL and SWRL, together with a multipurpose graphical and mathematical modelling tool known as Petri Net. The former is used for gathering, classifying and properly storing industrial data and therefore enhances our PN-based digital copy of an industrial system with advanced reasoning features. This makes both the system modelling and analysis phases more effective and, above all, paves the way towards a completely new field, where semantically-enriched PN-based manufacturing system digital twins represent one of the drivers of the digital transformation already in place in all companies facing the industrial revolution. As a result, it has been possible to outline a list of indications that will help future efforts in the application of complex digital twin support oriented solutions, which in turn is based on semantically-enriched manufacturing information systems. Through the application cases, five key topics have been tackled, namely: (i) semantic enrichment of industrial data using the most recent ontological models in order to enhance its value and enable new uses; (ii) context-awareness, or context-adaptiveness, aiming to enable the system to capture and use information about the context of operations; (iii) reusability, which is a core concept through which we want to emphasize the importance of reusing existing assets in some form within the industrial modelling process, such as industrial process knowledge, process data, system modelling primitives, and the like; (iv) the ultimate goal of semantic Interoperability, which can be accomplished by adding data about the metadata, linking each data element to a controlled, shared vocabulary; finally, (v) the impact on modelling and simulation applications, which shows how we could automate the translation process of industrial knowledge into a digital manufacturing system and empower it with quantitative and qualitative analytical technics

Applications of Bayesian networks and Petri nets in safety, reliability, and risk assessments: A review

YesSystem safety, reliability and risk analysis are important tasks that are performed throughout the system lifecycle to ensure the dependability of safety-critical systems. Probabilistic risk assessment (PRA) approaches are comprehensive, structured and logical methods widely used for this purpose. PRA approaches include, but not limited to, Fault Tree Analysis (FTA), Failure Mode and Effects Analysis (FMEA), and Event Tree Analysis (ETA). Growing complexity of modern systems and their capability of behaving dynamically make it challenging for classical PRA techniques to analyse such systems accurately. For a comprehensive and accurate analysis of complex systems, different characteristics such as functional dependencies among components, temporal behaviour of systems, multiple failure modes/states for components/systems, and uncertainty in system behaviour and failure data are needed to be considered. Unfortunately, classical approaches are not capable of accounting for these aspects. Bayesian networks (BNs) have gained popularity in risk assessment applications due to their flexible structure and capability of incorporating most of the above mentioned aspects during analysis. Furthermore, BNs have the ability to perform diagnostic analysis. Petri Nets are another formal graphical and mathematical tool capable of modelling and analysing dynamic behaviour of systems. They are also increasingly used for system safety, reliability and risk evaluation. This paper presents a review of the applications of Bayesian networks and Petri nets in system safety, reliability and risk assessments. The review highlights the potential usefulness of the BN and PN based approaches over other classical approaches, and relative strengths and weaknesses in different practical application scenarios.This work was funded by the DEIS H2020 project (Grant Agreement 732242)

SAFE-FLOW : a systematic approach for safety analysis of clinical workflows

The increasing use of technology in delivering clinical services brings substantial benefits to the healthcare industry. At the same time, it introduces potential new complications to clinical workflows that generate new risks and hazards with the potential to affect patients’ safety. These workflows are safety critical and can have a damaging impact on all the involved parties if they fail.Due to the large number of processes included in the delivery of a clinical service, it can be difficult to determine the individuals or the processes that are responsible for adverse events. Using methodological approaches and automated tools to carry out an analysis of the workflow can help in determining the origins of potential adverse events and consequently help in avoiding preventable errors. There is a scarcity of studies addressing this problem; this was a partial motivation for this thesis.The main aim of the research is to demonstrate the potential value of computer science based dependability approaches to healthcare and in particular, the appropriateness and benefits of these dependability approaches to overall clinical workflows. A particular focus is to show that model-based safety analysis techniques can be usefully applied to such areas and then to evaluate this application.This thesis develops the SAFE-FLOW approach for safety analysis of clinical workflows in order to establish the relevance of such application. SAFE-FLOW detailed steps and guidelines for its application are explained. Then, SAFE-FLOW is applied to a case study and is systematically evaluated. The proposed evaluation design provides a generic evaluation strategy that can be used to evaluate the adoption of safety analysis methods in healthcare.It is concluded that safety of clinical workflows can be significantly improved by performing safety analysis on workflow models. The evaluation results show that SAFE-FLOW is feasible and it has the potential to provide various benefits; it provides a mechanism for a systematic identification of both adverse events and safeguards, which is helpful in terms of identifying the causes of possible adverse events before they happen and can assist in the design of workflows to avoid such occurrences. The clear definition of the workflow including its processes and tasks provides a valuable opportunity for formulation of safety improvement strategies

A new methodology for automated Petri Net generation: Method application

A new methodology for automated Petri Net generation: Method applicatio

Yap: Tool Support for Deriving Safety Controllers from Hazard Analysis and Risk Assessments

Safety controllers are system or software components responsible for handling risk in many machine applications. This tool paper describes a use case and a workflow for YAP, a research tool for risk modelling and discrete-event safety controller design. The goal of this use case is to derive a safety controller from hazard analysis and risk assessment, to define a design space for this controller, and to select a verified optimal controller instance from this design space. We represent this design space as a stochastic model and use YAP for risk modelling and generation of parts of this stochastic model. For the controller verification and selection step, we use a stochastic model checker. The approach is illustrated by an example of a collaborative robot operated in a manufacturing work cell

Dynamic Reliability Assessment of PEM Fuel Cell Systems

In this paper, a novel model for the dynamic reliability analysis of a polymer electrolyte membrane fuel cell system is developed to account for multi-state dynamics and ageing. The modelling approach involves the combination of physical and stochastic sub-models with shared variables. The physical model consists of deterministic calculations of the system state described by variables such as temperature, pressure, mass flow rates and voltage output. Additionally, estimated component degradation rates are also taken into account. The non-deterministic model is implemented with stochastic Petri nets which model the failures of the balance of plant components within the fuel cell system. Using this approach, the effects of the operating conditions on the reliability of the system were investigated. Monte Carlo simulations of the process highlighted a clear influence of both purging and load cycles on the longevity of the fuel cell system

Dynamic risk assessment of process facilities using advanced probabilistic approaches

A process accident can escalate into a chain of accidents, given the degree of congestion and complex arrangement of process equipment and pipelines. To prevent a chain of accidents, (called the domino effect), detailed assessments of risk and appropriate safety measures are required. The present study investigates available techniques and develops an integrated method to analyze evolving process accident scenarios, including the domino effect. The work presented here comprises two main contributions: a) a predictive model for process accident analysis using imprecise and incomplete information, and b) a predictive model to assess the risk profile of domino effect occurrence. A brief description of each is presented below. In recent years the Bayesian network (BN) has been used to model accident causation and its evolution. Though widely used, conventional BN suffers from two major uncertainties, data and model uncertainties. The former deals with the used of evidence theory while the latter uses canonical probabilistic models. High interdependencies of chemical infrastructure makes it prone to the domino effect. This demands an advanced approach to monitor and manage the risk posed by the domino effect is much needed. Given the dynamic nature of the domino effect, the monitoring and modelling methods need to be continuous time-dependent. A Generalized Stochastic Petrinet (GSPN) framework was chosen to model the domino effect. It enables modelling of an accident propagation pattern as the domino effect. It also enables probability analysis to estimate risk profile, which is of vital importance to design effective safety measures

Computer-aided applications in process plant safety

Process plants that produce chemical products through pre-designed processes are fundamental in the Chemical Engineering industry. The safety of hazardous processing plants is of paramount importance as an accident could cause major damage to property and/or injury to people. HAZID is a computer system that helps designers and operators of process plants to identify potential design and operation problems given a process plant design. However, there are issues that need to be addressed before such a system will be accepted for common use. This research project considers how to improve the usability and acceptability of such a system by developing tools to test the developed models in order for the users to gain confidence in HAZID s output as HAZID is a model based system with a library of equipment models. The research also investigates the development of computer-aided safety applications and how they can be integrated together to extend HAZID to support different kinds of safety-related reasoning tasks. Three computer-aided tools and one reasoning system have been developed from this project. The first is called Model Test Bed, which is to test the correctness of models that have been built. The second is called Safe Isolation Tool, which is to define isolation boundary and identify potential hazards for isolation work. The third is an Instrument Checker, which lists all the instruments and their connections with process items in a process plant for the engineers to consider whether the instrument and its loop provide safeguards to the equipment during the hazard identification procedure. The fourth is a cause-effect analysis system that can automatically generate cause-effect tables for the control engineers to consider the safety design of the control of a plant as the table shows process events and corresponding process responses designed by the control engineer. The thesis provides a full description of the above four tools and how they are integrated into the HAZID system to perform control safety analysis and hazard identification in process plants

Modelling polymer electrolyte membrane fuel cells for dynamic reliability assessment

Tackling climate change is arguably the biggest challenge humanity faces in the 21st century. Rising average global temperatures threaten to destabilize the fragile ecosystem of the Earth and bring unprecedented changes to human lives if nothing is done to prevent it. This phenomenon is caused by the anthropogenic greenhouse effect due to the increasing atmospheric concentrations of carbon dioxide (CO2). One way to avert the disaster is to drastically reduce the consumption of fossil fuels in all spheres of human activities, including transportation. To do this, research and development of electric vehicles (EVs) to make them more efficient, reliable and accessible is essential. [Continues.