Risk Assessment Platform Research Articles

Risk Analysis of Gas Turbines for Natural Gas Liquefaction

Procurement of process plant equipment involves decisions based not only on an economic agenda but also on long term plant capability, which in turn depends on equipment reliability. As the greater global community raises environmental concerns and pushes for economic reform, a tool is evermore required for a specific and critical selection of plant equipment. Risk assessments based on NASA’s Technology Readiness Level (TRL) scale have been employed in many previous risk models to map technology in terms of risk and reliability. The authors envisage a scale for quantifying the technical risk. The focus of this paper is the technical risk assessment of gas turbines as mechanical drivers for producing liquefied natural gas (LNG). This risk assessment is a cornerstone of the technoeconomic environmental and risk analysis (TERA) philosophy developed by Cranfield University’s Department of Power and Propulsion in U.K. Monte Carlo simulations are used in order to compare the risks of introducing new plant equipment against existing and established plant equipment. Three scenarios are investigated using an 87MW single spool, typical industrial machine, a baseline engine followed by an engine with increased firing temperature, and finally an engine with a zero staged compressor. The results suggest that if the baseline engine was to be upgraded, then the zero staging option would be a better solution than increasing the firing temperature since zero staging gives the lower rise in total time to repair (TTTR) or downtime. The authors suggest a scaling system based on NASA’s TRL but with modified definition criteria for the separate technology readiness levels in order to better relate the scale to gas turbine technology. The intention is to link the modified TRL to downtime, since downtime has been identified as a quantitative measure of technical risk. Latest developments of the modeling are looking at integrating risk analysis and a maintenance cost and scheduling model to provide a platform for total risk assessment. This, coupled with emissions modeling, is set to provide the overall TERA tool for LNG technology selection.

Hybrid causal methodology and software platform for probabilistic risk assessment and safety monitoring of socio-technical systems

This paper introduces an integrated framework and software platform for probabilistic risk assessment (PRA) and safety monitoring of complex socio-technical systems. An overview of the three-layer hybrid causal logic (HCL) modeling approach and corresponding algorithms, implemented in the Trilith software platform, are provided. The HCL approach enhances typical PRA methods by quantitatively including the influence of soft causal factors introduced by human and organizational aspects of a system. The framework allows different modeling techniques to be used for different aspects of the socio-technical system. The HCL approach combines the power of traditional event sequence diagram (ESD)event tree (ET) and fault tree (FT) techniques for modeling deterministic causal paths, with the flexibility of Bayesian belief networks for modeling non-deterministic cause–effect relationships among system elements (suitable for modeling human and organizational influences). Trilith enables analysts to construct HCL models and perform quantitative risk assessment and management of complex systems. The risk management capabilities included are HCL-based risk importance measures, hazard identification and ranking, precursor analysis, safety indicator monitoring, and root cause analysis. This paper describes the capabilities of the Trilith platform and power of the HCL algorithm by use of example risk models for a type of aviation accident (aircraft taking off from the wrong runway).

T-Cell Immune Monitoring in Organ Transplantation

Chronic allograft injury remains the leading cause of graft loss despite improvements in immunosuppression, clinical risk stratification, and state-of-the-art antibody testing. Emerging results indicate that T-cell immune monitoring by cytokine enzyme-linked immunosorbent spot, as part of a comprehensive risk assessment platform, has the potential to guide decision making and improve outcomes after transplantation.

Dynamic reliability: towards an integrated platform for probabilistic risk assessment

Abstract Dynamic reliability methods are powerful mathematical frameworks capable of handling interactions among components and process variables explicitly. In principle, they constitute a more realistic modeling of systems for the purposes of reliability, risk and safety analysis. Although there is a growing recognition in the risk community of the potentially greater correctness of these methods, no serious effort has been undertaken to utilize them in industrial applications. User-friendly tools would help foster usage of dynamic reliability methods in the industry. This paper defines the key components of such a platform and for each component, provides a detailed review of techniques available for their implementation. This paper attempts to provide milestones in the creation of a high level design of such tools. To achieve this purpose, a modular approach is used. For each part, various existing techniques are discussed with respect to their potential achievements. Issues related to expected future developments are also considered.