HAZOP: Our Primary Guide in the Land of Process Risks: How can we improve it and do more with its results?

PresentationAll risk management starts in determining what can happen. Reliable predictive analysis is key. So, we perform process hazard analysis, which should result in scenario identification and definition. Apart from material/substance properties, thereby, process conditions and possible deviations and mishaps form inputs. Over the years HAZOP has been the most important tool to identify potential process risks by systematically considering deviations in observables, by determining possible causes and consequences, and, if necessary, suggesting improvements. Drawbacks of HAZOP are known; it is effort-intensive while the results are used only once. The exercise must be repeated at several stages of process build-up, and when the process is operational, it must be re-conducted periodically. There have been many past attempts to semi- automate the HazOp procedure to ease the effort of conducting it, but lately new promising developments have been realized enabling also the use of the results for facilitating operational fault diagnosis. This paper will review the directions in which improved automation of HazOp is progressing and how the results, besides for risk analysis and design of preventive and protective measures, also can be used during operations for early warning of upcoming abnormal process situations

Automated Control Flaw Generation Procedure: Cheakamus Dam Case Study

This thesis deals with the problem of aging hydropower infrastructure systems and system components, a problem that is very common across Canada. Flaws of common risk analysis methods are noted, and the need for new risk analysis approaches is identified. System dynamics simulation method is introduced as an implementation mechanism for the System Theoretic Process Analysis (STPA). STPA and its adaptation to complex hydropower systems are explained thoroughly. Fuzzy logic is used to model operator’s decision making. The main objectives of the research include the development of an automated generic approach that implements STPA and fuzzy logic for the investigation and identification of potentially hazardous actions and hazardous system states. The developed methodology is illustrated using a case study based on the BC Hydro’s Cheakamus Dam, British Columbia, Canada

An analytic framework to assess organizational resilience

Background: Resilience Engineering is a paradigm for safety management that focuses on coping with complexity to achieve success, even considering several conflicting goals. Modern socio-technical systems have to be resilient to comply with the variability of everyday activities, the tight-coupled and underspecified nature of work and the nonlinear interactions among agents. At organizational level, resilience can be described as a combination of four cornerstones: monitoring, responding, learning and anticipating. Methods: Starting from these four categories, this paper aims at defining a semi-quantitative analytic framework to measure organizational resilience in complex socio-technical systems, combining the Resilience Analysis Grid (RAG) and the Analytic Hierarchy Process (AHP). Results: This paper presents an approach for defining resilience abilities of an organization, creating a structured domain-dependent framework to define a resilience profile at different levels of abstraction, to identify weaknesses and strengths of the system and thus potential actions to increase system’s adaptive capacity. An illustrative example in an anaesthesia department clarifies the outcomes of the approach. Conclusions: The outcome of the RAG, i.e. a weighted set of probing questions, can be used in different domains, as a support tool in a wider Safety-II oriented managerial action to bring safety management into the core business of the organization

A case-based reasoning approach to improve risk identification in construction projects

Risk management is an important process to enhance the understanding of the project so as to support decision making. Despite well established existing methods, the application of risk management in practice is frequently poor. The reasons for this are investigated as accuracy, complexity, time and cost involved and lack of knowledge sharing. Appropriate risk identification is fundamental for successful risk management. Well known risk identification methods require expert knowledge, hence risk identification depends on the involvement and the sophistication of experts. Subjective judgment and intuition usually from par1t of experts’ decision, and sharing and transferring this knowledge is restricted by the availability of experts. Further, psychological research has showed that people have limitations in coping with complex reasoning. In order to reduce subjectivity and enhance knowledge sharing, artificial intelligence techniques can be utilised. An intelligent system accumulates retrievable knowledge and reasoning in an impartial way so that a commonly acceptable solution can be achieved. Case-based reasoning enables learning from experience, which matches the manner that human experts catch and process information and knowledge in relation to project risks. A case-based risk identification model is developed to facilitate human experts making final decisions. This approach exploits the advantage of knowledge sharing, increasing confidence and efficiency in investment decisions, and enhancing communication among the project participants

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Proceedings of the Second Joint Technology Workshop on Neural Networks and Fuzzy Logic, volume 2

Documented here are papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by NASA and the University of Texas, Houston. Topics addressed included adaptive systems, learning algorithms, network architectures, vision, robotics, neurobiological connections, speech recognition and synthesis, fuzzy set theory and application, control and dynamics processing, space applications, fuzzy logic and neural network computers, approximate reasoning, and multiobject decision making

AN INVESTIGATION INTO AN EXPERT SYSTEM FOR TELECOMMUNICATION NETWORK DESIGN

Many telephone companies, especially in Eastern-Europe and the 'third world', are developing new telephone networks. In such situations the network design engineer needs computer based tools that not only supplement his own knowledge but also help him to cope with situations where not all the information necessary for the design is available. Often traditional network design tools are somewhat removed from the practical world for which they were developed. They often ignore the significant uncertain and statistical nature of the input data. They use data taken from a fixed point in time to solve a time variable problem, and the cost formulae tend to be on an average per line or port rather than the specific case. Indeed, data is often not available or just plainly unreliable. The engineer has to rely on rules of thumb honed over many years of experience in designing networks and be able to cope with missing data. The complexity of telecommunication networks and the rarity of specialists in this area often makes the network design process very difficult for a company. It is therefore an important area for the application of expert systems. Designs resulting from the use of expert systems will have a measure of uncertainty in their solution and adequate account must be made of the risk involved in implementing its design recommendations. The thesis reviews the status of expert systems as used for telecommunication network design. It further shows that such an expert system needs to reduce a large network problem into its component parts, use different modules to solve them and then combine these results to create a total solution. It shows how the various sub-division problems are integrated to solve the general network design problem. This thesis further presents details of such an expert system and the databases necessary for network design: three new algorithms are invented for traffic analysis, node locations and network design and these produce results that have close correlation with designs taken from BT Consultancy archives. It was initially supposed that an efficient combination of existing techniques for dealing with uncertainty within expert systems would suffice for the basis of the new system. It soon became apparent, however, that to allow for the differing attributes of facts, rules and data and the varying degrees of importance or rank within each area, a new and radically different method would be needed. Having investigated the existing uncertainty problem it is believed that a new more rational method has been found. The work has involved the invention of the 'Uncertainty Window' technique and its testing on various aspects of network design, including demand forecast, network dimensioning, node and link system sizing, etc. using a selection of networks that have been designed by BT Consultancy staff. From the results of the analysis, modifications to the technique have been incorporated with the aim of optimising the heuristics and procedures, so that the structure gives an accurate solution as early as possible. The essence of the process is one of associating the uncertainty windows with their relevant rules, data and facts, which results in providing the network designer with an insight into the uncertainties that have helped produce the overall system design: it indicates which sources of uncertainty and which assumptions are were critical for further investigation to improve upon the confidence of the overall design. The windowing technique works by virtue of its ability to retain the composition of the uncertainty and its associated values, assumption, etc. and allows for better solutions to be attained.BRITISH TELECOMMUNICATIONS PL

Strategic Risk and Reliability Assessment in the Container Liner Shipping Industry Under High Uncertainties

The container liner shipping industry (CLSI) can be defined as one consisting of a fleet of vessels that provides a fixed service at regular intervals between ports of call. It is noteworthy that the CLSI is remarkably acting as an artery in making contributions to the growth of the global economy. However, in an era of unprecedented global changes, the CLSI faces a variety of internal and external risks. Moreover, the reliability and capability of liner shipping operators (LSOs) vary under different environmental conditions. Consequently, it is important for LSOs to ensure that the safety and reliability of their internal operations as well as external environments through proactive assessment of their reliability and capability are intact. The literature indicates that disruptive events have been assessed and investigated by many researchers and practitioners whilst the root causes arising from external risks have not yet been fully identified. The aim of this research was to develop integrated frameworks for assessing risk and reliability in the CLSI under high uncertainties. As a result, three interlocking levels of analysis have been highlighted in this research: 1) business environment-based risk (BEBR), 2) organisational reliability and capability (ORC) of LSOs, and 3) punctuality of containerships. To achieve the aim, firstly, this research employed a combination of different decision-making methods (i.e. Analytic Hierarchy Process (AHP), Fuzzy Set Theory (FST) and Evidential Reasoning (ER)) for the assessment of the BEBR. The research outcomes are providing LSOs with a powerful decision-making tool to assess the risk value of a country prior to investment and strategic decisions. In addition, LSOs are also able to regularly assess the overall level of existing BEBR in a host country prior to development of mitigation strategies that can help to minimise financial losses. Secondly, this research employs the Fuzzy Bayesian Belief Network (FBBN) method for evaluating the ORC of LSOs. By exploiting the proposed FBBN model, LSOs are able to conduct a self-evaluation of their ORC prior to the selection of a strategy for enhancing their competitive advantages in the CLSI. A significant concern in container liner shipping operations is the punctuality of containerships. Therefore, thirdly, this research concentrated on analysing and predicting the arrival punctuality of a liner vessel under dynamic environments by employing a combination of Fuzzy Rule-Base (FRB) and FBBN methods. Finally, a probabilistic model for analysing and predicting the departure punctuality of a liner vessel was generated. Accordingly, from the outcomes of this research LSOs are able to forecast their vessels’ arrival and departure punctuality and, further, tactical strategies can be implemented if a vessel is expected to be delayed. In addition, both arrival and departure punctuality models are capable of helping academic researchers and industrial practitioners to comprehend the influence of uncertain environments on the service punctuality. In order to demonstrate the practicability of the proposed methodologies and models, several real test cases were conducted by choosing the Malaysian maritime industry as a focus of study. The results obtained from these test cases have provided useful information for recommending preventive measures, improvement strategies and tactical solutions. The frameworks and models that have been proposed in this research for assessing risk and reliability of the CLSI will provide managerial insights for modelling and assessing complex systems dealing with both quantitative and qualitative criteria in a rational, reliable and transparent manner. In addition, these models have been developed in a generic sense so that they can be tailored for application in other industrial sectors

Bayesian participatory-based decision analysis : an evolutionary, adaptive formalism for integrated analysis of complex challenges to social-ecological system sustainability

Includes bibliographical references (pages. 379-400).This dissertation responds to the need for integration between researchers and decision-makers who are dealing with complex social-ecological system sustainability and decision-making challenges. To this end, we propose a new approach, called Bayesian Participatory-based Decision Analysis (BPDA), which makes use of graphical causal maps and Bayesian networks to facilitate integration at the appropriate scales and levels of descriptions. The BPDA approach is not a predictive approach, but rather, caters for a wide range of future scenarios in anticipation of the need to adapt to unforeseeable changes as they occur. We argue that the graphical causal models and Bayesian networks constitute an evolutionary, adaptive formalism for integrating research and decision-making for sustainable development. The approach was implemented in a number of different interdisciplinary case studies that were concerned with social-ecological system scale challenges and problems, culminating in a study where the approach was implemented with decision-makers in Government. This dissertation introduces the BPDA approach, and shows how the approach helps identify critical cross-scale and cross-sector linkages and sensitivities, and addresses critical requirements for understanding system resilience and adaptive capacity