State-based modelling in hazard identification

The signed directed graph (SDG) is the most commonly used type of model for automated hazard identification in chemical plants. Although SDG models are efficient in simulating the plant, they have some weaknesses, which are discussed here in relation to typical process industry examples. Ways to tackle these problems are suggested, and the view is taken that a state-based formalism is needed, to take account of the discrete components in the system, their connection together, and their behaviour over time. A strong representation for operations and actions is also needed, to make the models appropriate for modelling batch processes. A research prototype for HAZOP studies on batch plants (CHECKOP) is also presented, as an illustration of the suggested approach to modelling

An automated system for batch hazard and operability studies

A widely used hazard identification technique within the process industry is HAZOP (hazard and operability study). To overcome the repetitive and time-consuming nature of the technique automated systems are being developed. This work considers batch processes, in which material undergoes processing in distinct stages within the plant equipment items according to a set of operating procedures, rather than each equipment item remaining in a “steady state”, as is normal for continuous plants. In batch plants deviations which can lead to hazards can arise both from deviations from operating procedures and process variable deviations. Therefore, the effect of operator actions needs to be considered. CHECKOP is an automated batch HAZOP identification system being developed as a joint project between HAZID Technologies Ltd and Loughborough University. CHECKOP uses a state-based approach to HAZOP analysis. CHECKOP takes a plant description and a set of operating instructions as input and produces a HAZOP report automatically. The overall system architecture and the details of the major components of the systems will be described. Examples of incorrect plant operation along with the resulting output generated by CHECKOP will be shown. The advantages and limitations of CHECKOP will be discussed

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.

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

A Reinforcement Learning-based Framework for Proactive Supply Chain Risk Identification

Over the past few decades, global supply chains (GSCs) have seen a significant increase with the widespread adoption of digital technologies and improved trade policies. GSCs are a network of organisations or individuals across the world involved in producing and delivering goods and services to customers. While this globalisation and the use of global technologies have increased the efficiency of supply chain operations, it has also exposed them to various additional uncertainties and risk types that can negatively impact their operations. Thus, for GSCs to function properly, such uncertainties must be managed. Hence, supply chain risk management is critical in the smooth operation of GSCs. The first task in supply chain risk management is risk identification, where risk managers identify the risk events that may negatively impact their operations for further analysis. It is crucial that risk identification is undertaken in a timely manner so that risk managers can be proactive in managing the possible impacts of the identified risks on their operations. This task can be done manually which is tedious and time-consuming, however, with the increased sophistication and capability of artificial intelligence (AI), there is a potential for AI algorithms to be used to enhance the efficacy and efficiency of this task. A review of the existing literature detailed in this thesis highlights that while AI has been widely employed in different disciplines, it has shortcomings which are specific to the area of risk identification in supply chains. In other words, the majority of the existing risk identification techniques in supply chain risk management are either reactive or predictive in their working nature. This means that such techniques either identify the risk events after they occur or predict future occurrences of the known risk events based on their past pattern of occurrences. However, as emphasised in this thesis, for the supply chain risk identification process to be effective and comprehensive, it has to be proactive in its working nature rather than reactive or predictive. By being proactive, the risk identification techniques aim to identify beforehand known or unknown events of risks that have the potential to occur and negatively impact an activity. The analysis obtained assists the risk manager to perform the steps of risk analysis and risk evaluation on the identified risks before developing plans to manage them. Existing literature on supply chain risk identification lacks techniques to achieve this aim. To address this gap in the literature, this thesis develops a framework, namely Reinforcement Learning-based Supply Chain Risk Identification, which assists risk managers in automatedly and accurately identifying the risk events that may have the potential to impact their operations and bring them to his/her attention for further follow up. The proposed framework adopts the science and engineering research approach and four different frameworks are developed that identify the risk events of interest to the risk manager, extract related news articles on these risk events and analyse them, before recommending the most important news articles to the risk manager for follow-up actions. The functionality and viability of these prototypes are validated by experiments and systematised by a supply chain case study to highlight their effectiveness