Digital Twin Concept for Risk Analysis of Oil Storage Tanks in Operations: a Systems Engineering Approach

This paper presents an approach to develop a risk monitoring tool for oil storage facilities. The suggested approach is derived from the existing dynamic risk analysis (DRA) methods and the digital twin concepts. One of the main challenges in practical applications of DRA methods is insufficient amount of relevant data, and it seems that digital twin models can overcome this challenge by offering increased availability of real-time data. It can be interesting to judge if their combination can provide the intended advantages with a structured and more holistic viewpoint. Therefore, this paper demonstrates how a representative systems engineering (SE) methodology may be used to facilitate the process of developing an improved risk monitoring tool.publishedVersio

A Risk Aspect of Periodic Testing on Pressure Relief Valves

A pressure relief valve (PSV) is a key safety barrier to prevent the catastrophic rupture of pressure equipment in a process plant. The safety function of a PSV is to open and relieve the pressure when the equipment pressure exceeds the predefined set point. To achieve the desired availability of the PSV function, periodic function testing is regularly performed to confirm the correct functioning of a PSV. If a fault of the PSV function is detected by a function test, the PSV is repaired to a functioning state. For this reason, the interval between function tests has a direct influence on the probability of failure on demand (PFD) of the PSV function. On the other hand, unwanted leakage can occur due to human errors made during the preparation prior to a test and the reinstatement after the test. Such leakage is not desired due to the potential for being ignited and causing a major accident, but this aspect is often not considered in the availability assessment of PSVs. Therefore, this paper suggests a multi-phase Markov approach that can estimate the PFD of a PSV as well as the frequency of the leaks induced by the periodic tests. The suggested approach may be suitable for supporting the decision about the test interval for a PSV, considering both reliability and risk effect of extending the function test interval.publishedVersio

An Approach to Update the Failure Rates of Safety Barriers Based on Operating Experience

Hazardous events in process plants like the leakage of dangerous substances can result in severe damage, and such an event is often defined as the TOP event of a fault tree analysis (FTA) in a quantitative risk analysis. The TOP event probability can then be calculated if the basic events probabilities are provided. These probabilities are often determined based on generic reliability data which do not necessarily reflect the operational and environmental characteristics of a plant of interest. This paper presents an approach based on Bayesian network (BN) analysis to explicitly include experience data collected during the plant operation to make the generic probabilities more plant specific. The approach is illustrated via a pressure vessel containing a toxic substance in an Ammonia production plant. In this case study, the failure rate distribution in the BN is updated as the new information becomes available during plant operation. The results show that the suggested approach effectively reflects the operating experience of a specific plant.publishedVersio

Safety and efficacy of fluoxetine on functional outcome after acute stroke (AFFINITY): a randomised, double-blind, placebo-controlled trial

Background Trials of fluoxetine for recovery after stroke report conflicting results. The Assessment oF FluoxetINe In sTroke recoverY (AFFINITY) trial aimed to show if daily oral fluoxetine for 6 months after stroke improves functional outcome in an ethnically diverse population. Methods AFFINITY was a randomised, parallel-group, double-blind, placebo-controlled trial done in 43 hospital stroke units in Australia (n=29), New Zealand (four), and Vietnam (ten). Eligible patients were adults (aged ≥18 years) with a clinical diagnosis of acute stroke in the previous 2–15 days, brain imaging consistent with ischaemic or haemorrhagic stroke, and a persisting neurological deficit that produced a modified Rankin Scale (mRS) score of 1 or more. Patients were randomly assigned 1:1 via a web-based system using a minimisation algorithm to once daily, oral fluoxetine 20 mg capsules or matching placebo for 6 months. Patients, carers, investigators, and outcome assessors were masked to the treatment allocation. The primary outcome was functional status, measured by the mRS, at 6 months. The primary analysis was an ordinal logistic regression of the mRS at 6 months, adjusted for minimisation variables. Primary and safety analyses were done according to the patient's treatment allocation. The trial is registered with the Australian New Zealand Clinical Trials Registry, ACTRN12611000774921. Findings Between Jan 11, 2013, and June 30, 2019, 1280 patients were recruited in Australia (n=532), New Zealand (n=42), and Vietnam (n=706), of whom 642 were randomly assigned to fluoxetine and 638 were randomly assigned to placebo. Mean duration of trial treatment was 167 days (SD 48·1). At 6 months, mRS data were available in 624 (97%) patients in the fluoxetine group and 632 (99%) in the placebo group. The distribution of mRS categories was similar in the fluoxetine and placebo groups (adjusted common odds ratio 0·94, 95% CI 0·76–1·15; p=0·53). Compared with patients in the placebo group, patients in the fluoxetine group had more falls (20 [3%] vs seven [1%]; p=0·018), bone fractures (19 [3%] vs six [1%]; p=0·014), and epileptic seizures (ten [2%] vs two [<1%]; p=0·038) at 6 months. Interpretation Oral fluoxetine 20 mg daily for 6 months after acute stroke did not improve functional outcome and increased the risk of falls, bone fractures, and epileptic seizures. These results do not support the use of fluoxetine to improve functional outcome after stroke

Safety performance of hazardous systems: Approaches to risk estimations for operations and design

Safety of chemical and petroleum process installations have received increased legislative and academicattention in most countries, to enhance protection for people from adverse effect of activities involving materials with dangerous properties. This may represent vigilance of current society against the potential for major process accidents such as explosion, fire, and toxic release which may result in fatalities and injuries. Despite knowledge on what characterizes a major accident is enhanced through experiences with past accidents, severe process accidents continue to occur until recently. This PhD study focuses on the safety challenges associated with the risk contribution from changes in plant design and operations, and suggests advanced methodologies for safety and risk analysis. The models and methods developed aim to support both industry practitioners and risk analysts. Operators of systems handling hazardous materials may confront with difficulties when making safety-related decisions. The cost for risk control and protective measures can be high, and therefore the control measures should be selected based on plant-specific contexts. For this reason, the industry ought to aim for better understanding of risk, instead of focusing on compliance to standards and regulations. However, this is challenging, because event scenarios involving major hazards are rare. This work therefore suggests representative case studies where available information and data are prerequisite for both constructing models and running the model for obtaining results. The results of this PhD study show how safety can be enhanced by continuous monitoring of safety barrier performance, based on forward-looking risk indicators as well as retrospective risk indicators for Dynamic Risk Analysis. However, modeling works based on Bayesian Networks, Multi-Phase Markov model and Petri Nets can be relatively challenging for a company and their adoption in practical application may still be in question. Thoughtful discussion on uncertainties and sensitivity analyses represent further required works for this research. In addition, operational modes of technical systems and their interaction with human operators should also be addressed in a more integrated manner. Despite these limitations, the proposed methodologies may provide insights on how to select and apply prevalent techniques of risk analysis in real industry cases. Furthermore, illustrations of the models and approaches in example cases lays the foundations for advances in safety and risk analysis. More importantly, this PhD study is expected to encourage continual learning about risk and safety analysis in the relevant industry sectors

Validation of Dynamic Risk Analysis Supporting Integrated Operations Across Systems

Dynamic risk analysis (DRA) is a novel industrial approach that aims to capture changes in operational conditions over time and quantify their effect on risk. This aspect may be advantageous for providing insight into the causal factors that have substantial risk contributions and supporting decisions related to risk control. Some DRA methods were developed by the oil and gas industry to support the integration of work processes and the cooperation across virtual clusters, e.g., between offshore and onshore systems and/or oil company and supplier. However, DRA has not been extensively adopted and limited attention is given to its validity in practical applications. The objective of this article is to illustrate how this validity can be established based on common validation approaches for risk analysis. The case study focuses on a DRA method named risk barometer that was developed to support integrated operations across the oil and gas industrial systems. The outcome of this study may serve as a basis for the validation of other DRA methods, the use of DRA in practical cases, and ultimately the achievement of integrated operations (IO) capabilities

Digital Twin Concept for Risk Analysis of Oil Storage Tanks in Operations: a Systems Engineering Approach

This paper presents an approach to develop a risk monitoring tool for oil storage facilities. The suggested approach is derived from the existing dynamic risk analysis (DRA) methods and the digital twin concepts. One of the main challenges in practical applications of DRA methods is insufficient amount of relevant data, and it seems that digital twin models can overcome this challenge by offering increased availability of real-time data. It can be interesting to judge if their combination can provide the intended advantages with a structured and more holistic viewpoint. Therefore, this paper demonstrates how a representative systems engineering (SE) methodology may be used to facilitate the process of developing an improved risk monitoring tool

Validation of Dynamic Risk Analysis Supporting Integrated Operations Across Systems

Dynamic risk analysis (DRA) is a novel industrial approach that aims to capture changes in operational conditions over time and quantify their effect on risk. This aspect may be advantageous for providing insight into the causal factors that have substantial risk contributions and supporting decisions related to risk control. Some DRA methods were developed by the oil and gas industry to support the integration of work processes and the cooperation across virtual clusters, e.g., between offshore and onshore systems and/or oil company and supplier. However, DRA has not been extensively adopted and limited attention is given to its validity in practical applications. The objective of this article is to illustrate how this validity can be established based on common validation approaches for risk analysis. The case study focuses on a DRA method named risk barometer that was developed to support integrated operations across the oil and gas industrial systems. The outcome of this study may serve as a basis for the validation of other DRA methods, the use of DRA in practical cases, and ultimately the achievement of integrated operations (IO) capabilities

A Risk Aspect of Periodic Testing on Pressure Relief Valves

A pressure relief valve (PSV) is a key safety barrier to prevent the catastrophic rupture of pressure equipment in a process plant. The safety function of a PSV is to open and relieve the pressure when the equipment pressure exceeds the predefined set point. To achieve the desired availability of the PSV function, periodic function testing is regularly performed to confirm the correct functioning of a PSV. If a fault of the PSV function is detected by a function test, the PSV is repaired to a functioning state. For this reason, the interval between function tests has a direct influence on the probability of failure on demand (PFD) of the PSV function. On the other hand, unwanted leakage can occur due to human errors made during the preparation prior to a test and the reinstatement after the test. Such leakage is not desired due to the potential for being ignited and causing a major accident, but this aspect is often not considered in the availability assessment of PSVs. Therefore, this paper suggests a multi-phase Markov approach that can estimate the PFD of a PSV as well as the frequency of the leaks induced by the periodic tests. The suggested approach may be suitable for supporting the decision about the test interval for a PSV, considering both reliability and risk effect of extending the function test interval