Security risk assessment and protection in the chemical and process industry

This article describes a security risk assessment and protection methodology that was developed for use in the chemical- and process industry in Belgium. The approach of the method follows a risk-based approach that follows desing principles for chemical safety. That approach is beneficial for workers in the chemical industry because they recognize the steps in this model from familiar safety models .The model combines the rings-of-protection approach with generic security practices including: management and procedures, security technology (e.g. CCTV, fences, and access control), and human interactions (pro-active as well as re-active). The method is illustrated in a case-study where a practical protection plan was developed for an existing chemical company. This chapter demonstrates that the method is useful for similar chemical- and process industrial activities far beyond the Belgian borders, as well as for cross-industrial security protection. This chapter offers an insight into how the chemical sector protects itself on the one hand, and an insight into how security risk management can be practiced on the other hand

Fault detection, identification and accommodation techniques for unmanned airborne vehicles

Unmanned Airborne Vehicles (UAV) are assuming prominent roles in both the commercial and military aerospace industries. The promise of reduced costs and reduced risk to human life is one of their major attractions, however these low-cost systems are yet to gain acceptance as a safe alternate to manned solutions. The absence of a thinking, observing, reacting and decision making pilot reduces the UAVs capability of managing adverse situations such as faults and failures. This paper presents a review of techniques that can be used to track the system health onboard a UAV. The review is based on a year long literature review aimed at identifying approaches suitable for combating the low reliability and high attrition rates of today’s UAV. This research primarily focuses on real-time, onboard implementations for generating accurate estimations of aircraft health for fault accommodation and mission management (change of mission objectives due to deterioration in aircraft health). The major task of such systems is the process of detection, identification and accommodation of faults and failures (FDIA). A number of approaches exist, of which model-based techniques show particular promise. Model-based approaches use analytical redundancy to generate residuals for the aircraft parameters that can be used to indicate the occurrence of a fault or failure. Actions such as switching between redundant components or modifying control laws can then be taken to accommodate the fault. The paper further describes recent work in evaluating neural-network approaches to sensor failure detection and identification (SFDI). The results of simulations with a variety of sensor failures, based on a Matlab non-linear aircraft model are presented and discussed. Suggestions for improvements are made based on the limitations of this neural network approach with the aim of including a broader range of failures, while still maintaining an accurate model in the presence of these failures

Predicting Alarm And Safety System Performance Using Simulation

Safety is paramount to the chemical process industries. Because many processes operate at high temperatures and/or pressures, involving hazardous chemicals at high concentrations, the potential for accidents involving adverse human health and/or environmental impacts is significant. Thanks to research and operational efforts, both academically and industrially, the occurrences of such incidents are rare. However, disastrous events in the chemical manufacturing industry are still of relevant concern and garner further attention – the Deepwater Horizon incident (2010) and the Texas City refinery explosion (2005) being two recent examples. Many techniques have been developed to understand, quantify, and predict alarm and safety system failures. In practice, hazards are identified using Hazard and Operability (HAZOP) analysis, and a network of independently-acting safety systems works to maintain the probabilities of such events below a Safety Integrity Level (SIL). The network of safety systems is studied with Layer of Protection Analysis (LOPA), which uses failure probability estimates for individual subsystems to project the failures of entire safety system networks. With few alarm and safety system activations over the lifetime of a chemical process, particularly the critical last-line-of-defense systems, the failure probabilities of these systems are difficult to estimate. Statistical techniques have been developed, attempting to decrease the variances of such predictions despite few supporting data. This thesis develops methods to estimate the failure probabilities of rarely activated alarm and safety systems using process and operator models, enhanced by process, alarm, and operator data. Two repeated simulation techniques are explored involving informed prior distributions and transition path sampling. Both use dynamic process models, based upon first-principles, along with process, alarm, and operator data, to better understand and quantify the probability of alarm and safety system failures and the special-cause events leading to those failures. In the informed prior distribution technique, process and alarm data are analyzed to extract information regarding operator behavior, which is used to develop models for repeated simulation. With alarm and safety system failure probabilities estimated for specific special-cause events, near-miss alarm data are used, in real-time, to enhance the predictions. The transition path sampling method was originally developed by the molecular simulation community to understand better rare molecular events. Herein, important modifications are introduced for application to understand better how rare safety incidents evolve from rare special-cause events. This method uses random perturbations to identify likely trajectories leading to system failures – providing a basis for potential alarm and safety system design

System effectiveness model formulation with application to nuclear safeguards systems

Evaluation of a given system\u27s effectiveness has numerous pitfalls. System objectives may be poorly defined, may shift during the system life, or may be hard to quantify. Further, individual perceptions of the quantifications may differ. Whatever the cause, system effectiveness has been an elusive term to quantitatively define. This research posits a quantitative system effectiveness model and establishes a utilitarian approach for use with an illustrative application to n operating nuclear safeguards system.The Department of Energy (DOE) defines domestic safeguards, which are applied to nuclear material as; an integrated system of physical protection, material accounting, and material control measures designed to deter, prevent, detect, and respond to unauthorized possession, use, or sabotage of nuclear materials. This research includes the investigation of the utility coefficients and simulation of a domestic nuclear safeguards system, as well as simulation of an airport passenger screening system consisting of: an identification screening system; an X-ray system for checking bags and computers; and a walk through metal detector. Expert judgment was used to determine the relative importance (utility) of the individual subsystems through a statistically analyzed web survey. The survey population is nuclear material protection, control, accounting, and plant management experts.The mean utility coefficients determined during the survey were applied to the system components developed assigned randomly generated values of component effectiveness and combined to produce an overall system effectiveness. Simulated Type I and Type II error rates are used for illustration of the probabilistic methodology currently used by DOE (calculating protection effectiveness) and the posited and heuristically based methodology (system effectiveness). Use of the heuristically based system effectiveness methodology illustrates an approach that combines the subsystem components of plant management, physical protection, material accounting, and material control for a domestic safeguards system. The system effectiveness methodology is complimentary to and more robust than the protection effectiveness calculation and can offer opportunities for cost savings during the system lifecycle

Predicting chattering alarms: A machine Learning approach

Abstract Alarm floods represent a widespread issue for modern chemical plants. During these conditions, the number of alarms may be unmanageable, and the operator may miss safety-critical alarms. Chattering alarms, which repeatedly change between the active and non-active states, are responsible for most of the alarm records within a flood episode. Typically, chattering alarms are only addressed and removed retrospectively (e.g. during periodic performance assessments). This study proposes a Machine-Learning based approach for alarm chattering prediction. Specifically, a method for dynamic chattering quantification has been developed, whose results have been used to train three different Machine Learning models – Linear, Deep, and Wide&Deep models. The algorithms have been employed to predict future chattering behavior based on actual plant conditions. Performance metrics have been calculated to assess the correctness of predictions and to compare the performance of the three models

Implementing Industry Best Practice Alarm Management

This dissertation investigates how alarm management practices can be implemented at a large industrial facility, and what effect this could have on alarm system performance. Poor performing alarm systems create an environment where alarms of high importance can be missed or operators are desensitised to the alarm system. Alarm system performance has been a factor in several high consequence industrial accidents. The project was conducted at a large coal fired power plant which uses modern Process Control and Human Machine Interface systems. The project site has never employed any alarm management practices in the past and is seeking to implement alarm management practices based on ISA-18.2-2009 Management of Alarm Systems for the Process Industries. The requirements of industry best practice alarm management were researched and identified. Alarm management processes were created and adopted by the project site. New software tools were developed to support the alarm management processes. The alarm system performance at the project site was measured using these software tools. The worst performing alarms over a six month period were identified. By investigating the causes of the worst performing alarms improvements were identified that could reduce the alarm rate by 40%

Improving Barrier Effectiveness using Human Factors Methods

PresentationThe Process Industry has an established practice of identifying barriers to credit as IPLs (Independent protection layers) through the use of methods such as PHA (Process Hazard Analysis) and LOPA (Layer of Protection Analysis) type studies. However, the validation of IPLs and barriers to ensure their effectiveness especially related to human and organization factors is lagging. The concept of barriers as discrete onion layers comprised of administrative controls, alarms, instruments, mechanical devices, and post-release mitigation is highly idealized. Even worse it is misleading because it blinds us to the reality that all barriers are human. Further, this human base is often made up of small groups of people, comprised of operations, maintenance, and technical staff, with a management layer. The groups of people that maintain and manage all barriers is the most critical factor to ensuring good performance of those barriers in the threat path of a hazard scenario. The methods of PHA and LOPA as currently practiced are not addressing this issue. There is not even awareness of this issue, because the mantra to “ensure independence between protection layers” creates the illusion that barriers can be made independent. The two related issues this paper will address are, (1) the human and organization impact on effectiveness of a single barrier, and (2) the human and organization impact on all barriers in the same threat path. The first issue can be addressed with established human factors and human reliability tools such as Task Analysis, coupled with a public domain human reliability model. The second issue is more complex and requires analyzing the groups of people that cross barrier types and can negatively influence multiple barriers. The methods and concepts will be explained by considering the following barrier types, in a common threat path. The approach described in this paper has been in use for the past two years applied to actual barriers. Critical Alarm with Operator Response Safety Instrumented System Mechanical Pressure Relief Device Demonstrating barrier effectiveness involves both qualitative and quantitative considerations. Demonstrating qualitative effectiveness is done by performing a Task Analysis to identify the degradation factors (human and organization) and degradation factor controls related to the barrier. Demonstrating quantitative effectiveness of the same requires use of a Human Reliability method. Neither of these approaches has been widely adopted in the Process Industry and so there exists a competency gap related to their use. However the need for these tools is evident by the incidents arising in industry due to human and organization factors. Finally, documenting the results on a Bow-tie diagram (the left-hand side) will be demonstrated. Identifying leading process safety indicators embedded in the Bow-tie will be discussed

Rule-based integrated building management systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The introduction of building management systems in large buildings have improved the control of building services and provided energy savings. However, current building management systems are limited by the physical level of integration of the building's services and the lack of intelligence provided in the control algorithms. This thesis proposes a new approach to the design and operation of building management systems using rule-based artificial intelligence techniques. The main aim of is to manage the services in the building in a more co-ordinated and intelligent manner than is possible by conventional techniques. This approach also aims to reduce the operational cost of the building by automatically tuning the energy consumption in accordance with occupancy profile of the building. A rule-based design methodology is proposed for building management systems. The design adopts the integrated structure made possible by the introduction of a common communications network for building services. The 'intelligence' is coded in the form of rules in such a way that it is both independent of any specific building description and easy to facilitate subsequent modification and addition. This is achieved using an object-oriented approach and classifying the range of data available into defined classes. The rules are divided into two knowledge-bases which are concerned with the building's control and its facilities management respectively. A wide range of rule-based features are proposed to operate on this data structure and are classified in terms of the data classes on which they operate. The concepts presented in this thesis were evaluated using software simulations, mathematical analysis and some hardware implementation. The conclusions of this work are that a rule-based building management system could provide significant enhancements over existing systems in terms of energy savings and improvements for both the building's management staff and its occupants