Salim Ahmed

Loss functions and their applications in process safety assessment

Process deviations, along with failure of control systems and protection layers, result in safety and quality loss in plant operations. This article proposes an operational risk‐based warning system design methodology based on overall system loss. Loss functions (LFs) are used to define the relationship between process deviations and system loss. For this purpose, properties associated with quadratic LF and a set of inverted probability LFs are investigated and compared. The results suggest that LFs can be used in a novel way to assess operational stability and system safety. The proposed consequence assessment methodology using LFs is then incorporated into a risk‐based warning system design model to analyze warnings associated with process deviations. A simulated case study is presented to demonstrate potential application of the proposed methodology; the study examines the response to a temperature surge for a reactor system.

LNG pool fire simulation for domino effect analysis

Abstract A three-dimensional computational fluid dynamics (CFD) simulation of liquefied natural gas (LNG) pool fire has been performed using ANSYS CFX-14. The CFD model solves the fundamental governing equations of fluid dynamics, namely, the continuity, momentum and energy equations. Several built-in sub-models are used to capture the characteristics of pool fire. The Reynolds-averaged Navier–Stokes (RANS) equation for turbulence and the eddy-dissipation model for non-premixed combustion are used. For thermal radiation, the Monte Carlo (MC) radiation model is used with the Magnussen soot model. The CFD results are compared with a set of experimental data for validation; the results are consistent with experimental data. CFD results show that the wind speed has significant contribution on the behavior of pool fire and its domino effects. The radiation contours are also obtained from CFD post processing, which can be applied for risk analysis. The outcome of this study will be helpful for better understanding of the domino effects of pool fire in complex geometrical settings of process industries.

Multivariate probabilistic safety analysis of process facilities using the Copula Bayesian Network model

Integrated safety analysis of hazardous process facilities calls for an understanding of both stochastic and topological dependencies, going beyond traditional Bayesian Network (BN) analysis to study cause-effect relationships among major risk factors. This paper presents a novel model based on the Copula Bayesian Network (CBN) for multivariate safety analysis of process systems. The innovation of the proposed CBN model is in integrating the advantage of copula functions in modelling complex dependence structures with the cause-effect relationship reasoning of process variables using BNs. This offers a great flexibility in probabilistic analysis of individual risk factors while considering their uncertainty and stochastic dependence. Methods based on maximum likelihood evaluation and information theory are presented to learn the structure of CBN models. The superior performance of the CBN model and its advantages compared to traditional BN models are demonstrated by application to an offshore managed pressure drilling case study.

Probabilistic modeling of business interruption and reputational losses for process facilities

This article presents probabilistic models to estimate business losses due to abnormal situations in process facilities. The main elements of business loss are identified as business interruption loss and reputational loss. The business interruption insurance approach is used to model business interruption loss. The subelements of business interruption loss are modeled based on expert knowledge using Program Evaluation Review Technique, which are then integrated using the Monte Carlo simulation approach. The reputational loss is considered as Weibull distributed, and the parameters are estimated by applying a scenario‐based approach. Copula functions are then used to develop the distribution of the aggregate loss, considering the correlation between business interruption and reputational losses. The application of the loss models is demonstrated using a distillation column case study. The models presented here provide a mechanism to monitor process facilitys business performance, with associated uncertainties, and to make swift operational and safety decisions. This will help to improve process facilities safety performance and optimal allocation of resources where they are needed the most.

Safety challenges in harsh environments: Lessons learned

Development of natural resources in harsh environments presents significant technical and logistical challenges. An industrial workshop on “safety and integrity management of operations in harsh environments” was organized by the safety and risk engineering group at Memorial University of Newfoundland to bring together industrial practitioners, regulatory authorities, and research and development institutions to identify the safety and integrity challenges in harsh environments, share experience, and develop a roadmap for desired solutions. This article summarizes the lessons learned from the workshop on safety issues in harsh environments. The workshop identified that there are safety challenges regarding construction and operation including a lack of detailed standards, optimization with respect to winterization, and data scarcity. The remoteness of operations in harsh environments is an additional challenge. Finally, human factors add another set of challenges that arise from the physical and psychological behavior of personnel in harsh and remote environments.

A risk‐based methodology to estimate shutdown interval considering system availability

This article presents a risk‐based methodology to estimate shutdown inspection and maintenance interval considering system availability. Most inspection and maintenance activities are performed when the plant/unit is in the operational state. However, some inspection and maintenance activities require the plant to be in a nonoperational or shutdown state. In most cases, operating companies adopt a shutdown schedule based on the original equipment manufacturers (OEM) suggested recommended periods. However, this may not be the best strategy as OEM recommended duration is general and may not reflect the current state of operation. The proposed methodology is unique in the sense that it identifies a shutdown interval by identifying the critical equipment in terms of risk considering availability and safety of the operating unit. It optimizes process plant shutdown interval to minimize the risk (in dollar terms). The Markov process is used to establish the state diagram to calculate system availability. The proposed methodology is comprised of three steps namely, risk‐based equipment selection, shutdown availability modeling of a complex system using the Markov process, and risk‐based shutdown inspection and maintenance interval modeling. It can be applied to process plants such as those for liquefied natural gas processing, petrochemicals, and refineries. The key elements for the success of the proposed methodology are the plant‐specific data and identification of critical equipment.

Conceptual framework for an event-based plant alarm system

An event-based plant alarm system is proposed as an alternative to the current variable-based alarm system used in the process industry. While a traditional variable-based process alarm indicates an unsafe deviation of a process variable, an event alarm will be a direct indicator of an undesired event. A conceptual framework for the design of such an alarm processor is outlined in this article. A methodology for allocation of alarms based on the information content of variables is highlighted. The annunciation philosophy for event alarms is detailed, and alarm prioritization method and operator guidance are discussed. The method uses Bayesian network and Bayesian inference to estimate probability of an event and to identify its root causes.

Alarm allocation for event-based process alarm systems

Abstract The ability to monitor large numbers of variables and the flexibility to assign alarms to each variable led to a substantial increase in the numbers of alarms in industrial plants. This, in turn, increased the numbers of false and redundant alarms. In plant operations, the numbers of annunciated alarms regularly exceed the acceptable rates that operators can handle. To reduce the number of assigned alarms, a risk-based alarm system has been proposed in the literature (Ahmed et al. (2011); Chang et al. (2011)) where alarms are assigned to groups of variables instead of individual variables. This articles explores the options for grouping variables for alarm allocation. Several grouping methods are discussed and an event-based grouping procedure is detailed. Selection of the key variables for a group is performed using the information that the variables can have to distinguish between an abnormal and a normal condition. The concept of mutual information is used to quantify the information. Variables with high information gain are grouped together for each respective abnormal event. To identify the redundant variables within the groups to further reduce the number of variables to be monitored, the maximum cross-correlation between pairs of key variables are used. A case study using the example of a continuous stirred tank reactor is used to demonstrate the methodology.

Process Parameter and Delay Estimation from Non-uniformly Sampled Data

Time-delay estimation is an important part of system identification. In process industries, it is even more important to consider the delay because of its common occurrence and significant impact on limiting the achievable performance of control systems. However, both in continuous-time (CT) and discrete-time (DT) model identification, the development of time delay-estimation methods lags behind the advancement of the estimation techniques for other model parameters. For example, linear filter methods are commonly used for continuous-time identification and significant developments have taken place in this field over the last few decades, see, e.g., [5,7,31,35,38,40]. In the linear filter approach, the most commonly used algorithm to estimate the time delay is based on a comprehensive search routine as used in [30,31,40] where process parameters are estimated for a set of time delays within a certain range and a predefined cost function is calculated for every set of estimated parameters corresponding to each delay term. Finally, the delay that gives the optimum value of the cost function is chosen. This procedure is computationally expensive especially for rapidly sampled data. Another popular approach is approximation of the delay by a polynomial or by a rational transfer function such as the Pad’e approximation as in [1] or by the use of the Laguerre expansion. Such an approach requires estimation of more parameters and an unacceptable approximation error may occur for systems having large delays [36]. Most of the methods to directly estimate the delay along with other model parameters are based on the step test [16, 22, 25, 36], the so-called piecewise step test [23] or the pulse test [13].

Layout Optimization of a Floating Liquefied Natural Gas Facility Using Inherent Safety Principles

This paper presents a layout optimization methodology for the topside deck of a floating liquefied natural gas facility (FLNG) using inherent safety principles. Natural gas is emerging as a clean energy, and a large amount of natural gas exists in the proven offshore area, thus making it an energy source with huge potential in todays and the future market. FLNG facilities tap natural gas from an offshore well by floating, compressing it into liquefied natural gas (LNG), and offloading it to LNG carriers after temporary storage. In addition, FLNG facilities enable long-distance as well as multilocation transportation. The FLNG facility requires compact design due to limited space and high construction costs and thus faces a more challenging situation where the design has to concurrently guarantee economic profits and a safe operational environment. Therefore, the layout of the topside deck, which includes production, storage, and other functions, plays a paramount role in designing an FLNG facility. This paper optimizes the layout of an FLNG topside deck by implementing inherent safety principles. The objective is to design a topside deck layout which achieves the largest extent of inherent safety with optimal costs. The details of the principles and their application for layout optimization are also provided.