New approach for risk based inspection of H2S based Process Plants

Vinod, Gopika; Sharma, Pradip Kumar; Santosh, T.V.; Prasad, M. A.; Vaze, K.K.

doi:10.1016/j.anucene.2013.08.042

Cited by 17 publications

(7 citation statements)

References 4 publications

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“…Another RBM study is performed by Vinod et al (2014) on an H 2 S based process plant. A qualitative approach to estimate consequences on leakage of H 2 S using influence factors from the American Petroleum Institute (API) is proposed.…”

Section: Maintenance Planning and Schedulingmentioning

confidence: 99%

Asset maintenance optimisation approaches in the chemical and process industries – A review

Chin

Varbanov

Benjamin

et al. 2020

Chemical Engineering Research and Design

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Section: Maintenance Planning and Schedulingmentioning

confidence: 99%

Asset maintenance optimisation approaches in the chemical and process industries – A review

Chin

Varbanov

Benjamin

et al. 2020

Chemical Engineering Research and Design

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“…Subsequently, the risk-based inspection (RBI) methodology was applied widely in different industrial contexts. For example, the RBI has been used in the nuclear industry to prioritize maintenance [7][8][9], its application in gas pipelines has helped guide the allocation of maintenance resources to the most at-risk pipeline areas [10], it has been used to optimize the maintenance of water supply networks [11], it has been used to develop an optimal inspection and repair strategy for structural systems [12], and it has also been applied to the risk ranking procedure for bridges [13].…”

Section: Introductionmentioning

confidence: 99%

A Spatial Statistic Based Risk Assessment Approach to Prioritize the Pipeline Inspection of the Pipeline Network

Hou

Dong

2020

Energies

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The identification of high risk regions is an important aim of risk-based inspections (RBIs) in pipeline networks. As the most vital part of risk-based inspections, risk assessment makes a significant contribution to achieving this aim. Accurate assessment can target high risk inspected regions so that limited resources can mitigate considerable risks in the face of increased spatial distribution of a pipeline network. However, the existing approaches for risk assessment face grave challenges due to a lack of sufficient data and an assessment’s vulnerability to human biases and errors. This paper attempts to tackle those challenges through spatial statistics, which is used to estimate the uncertainty of risk based on a dataset of locations of pipeline network failure events without having to acquire additional data. The consequence of risk in each inspected region is measured by the total cost caused by the failure events that have occurred in the region, which is also calculated in the assessment. Then, the risks of the different inspected regions are obtained by integrating the uncertainty and consequences. Finally, the feasibility of our approach is validated in a case study. Our results in the case study demonstrate that uncertainty is less instructive for prioritizing pipeline inspections than the consequences of risk due to the low significant difference in risk uncertainty in different regions. Our results also have implications for understanding the correlation between the spatial location and consequences of risk.

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“…Risk-based prioritization originates from a risk-based inspection project, which was started by the American Petroleum Institute (API) [22][23][24]. Subsequently, the idea has been applied widely in different industrial contexts, such as in the nuclear industry in order to prioritize maintenance [25][26][27][28], gas pipelines to guide the allocation of maintenance resources on the most risky stretches of pipeline [29], the optimization of the maintenance of water supply networks [30], and the risk ranking procedure for bridges [31]. However, limitations can be observed when they are applied in the wireless sensor network deployment of pipeline networks.…”

Section: Introductionmentioning

confidence: 99%

A Novel Risk-Based Prioritization Approach for Wireless Sensor Network Deployment in Pipeline Networks

Hou

Dong

2020

Energies

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In the face of increased spatial distribution and a limited budget, monitoring critical regions of pipeline network is looked upon as an important part of condition monitoring through wireless sensor networks. To achieve this aim, it is necessary to target critical deployed regions rather than the available deployed ones. Unfortunately, the existing approaches face grave challenges due to the vulnerability of identification to human biases and errors. Here, we have proposed a novel approach to determine the criticality of different deployed regions by ranking them based on risk. The probability of occurrence of the failure event in each deployed region is estimated by spatial statistics to measure the uncertainty of risk. The severity of risk consequence is measured for each deployed region based on the total cost caused by failure events. At the same time, hypothesis testing is used before the application of the proposed approach. By validating the availability of the proposed approach, it provides a strong credible basis and the falsifiability for the analytical conclusion. Finally, a case study is used to validate the feasibility of our approach to identify the critical regions. The results of the case study have implications for understanding the spatial heterogeneity of the occurrence of failure in a pipeline network. Meanwhile, the spatial distribution of risk uncertainty is a useful priori knowledge on how to guide the random deployment of wireless sensors, rather than adopting the simple assumption that each sensor has an equal likelihood of being deployed at any location.

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New approach for risk based inspection of H2S based Process Plants

Cited by 17 publications

References 4 publications

Asset maintenance optimisation approaches in the chemical and process industries – A review

Asset maintenance optimisation approaches in the chemical and process industries – A review

A Spatial Statistic Based Risk Assessment Approach to Prioritize the Pipeline Inspection of the Pipeline Network

A Novel Risk-Based Prioritization Approach for Wireless Sensor Network Deployment in Pipeline Networks

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