Seyed Javad Hashemi

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.

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.

An Insurance Model for Risk Management of Process Facilities: Management of Process Facilities

Most existing risk management models for process industries do not consider the effect of insurance coverage, which results in an overestimation of overall risk. A model is presented in this article to study the effect of insurance coverage of health, safety, environmental, and business risks. The effect of insurance recovery is modeled through the application of adjustment factors by considering the stochastic factors affecting insurance recovery. The insurance contracts conditions, deductibles, and policy limits are considered in developing the insurance recovery adjustment factors. Copula functions and Monte Carlo simulations are used to develop the distribution of the aggregate loss by considering the dependence among loss classes. A case study is used to demonstrate both the practical application of the proposed insurance model to improve management decisions, and the mitigating effect of insurance to minimize the residual risk.

Bibliometric Analysis of Microbiologically Influenced Corrosion (MIC) of Oil and Gas Engineering Systems

Managing microbiologically influenced corrosion (MIC) is both an economic and technological challenge for the oil and gas industry. There are studies and data generated regarding the corrosion mechanism, microbial species involved, and chemicals that may enhance/inhibit MIC. However, these data are diffuse, sometimes having contradictory conclusions and ignoring one or more key factors that drive MIC. This paper investigates the evolution of MIC knowledge in the past decades by conducting a bibliometric analysis of the literature. The paper also identifies current knowledge gaps and proposes future research directions. Although MIC mechanisms, monitoring, and control have been active areas of research in recent years, linking microbiological activities, the chemical environment (e.g., produced water lines vs. crude lines), and the corrosion mechanisms is still an important knowledge gap. The importance of a coordinated multidisciplinary approach to develop integrated knowledge, MIC mechanistic models, and...