An integrated risk prediction model for corrosion-induced pipeline incidents using artificial neural network and Bayesian analysis

Kumari, Pallavi; Halim, Syeda Zohra; Kwon, Joseph; Quddus, Noor

doi:10.1016/j.psep.2022.07.053

Cited by 33 publications

(13 citation statements)

References 39 publications

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“…A BN is a probabilistic graphical model utilized to represent causality among process variables in a process . Mathematically, a BN is represented as < G ,Θ>.…”

Section: Preliminariesmentioning

confidence: 99%

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A Direct Transfer Entropy-Based Multiblock Bayesian Network for Root Cause Diagnosis of Process Faults

Kumari

Wang

Khan

et al. 2022

Ind. Eng. Chem. Res.

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In chemical processes, Bayesian network (BN)-based approaches have been extensively applied for process fault diagnosis. Generally, BN is learned using score and search algorithms where search algorithms create candidate networks whose fitness to data is measured by scores. However, existing approaches cannot utilize cyclic loop knowledge while learning BN. Since cyclic loops are prevalent in chemical processes, their unaccountability results in inaccurate BN and reduces diagnosis accuracy. Therefore, for accurate diagnosis, we propose direct transfer entropy (DTE)-based multiblock BN to discover cyclic loops while learning BN. First, the process is segmented into multiple blocks. Next, block-level BNs are learned using DTE-based score and Greedy search. By eliminating the common source variable effect, DTE finds correct causality between process variables and obtains accurate block-level BNs, which are fused to discover significant cyclic loops that were otherwise unachievable when finding BN. The performance of the developed methodology is demonstrated through a benchmark process.

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“…A BN is a probabilistic graphical model utilized to represent causality among process variables in a process . Mathematically, a BN is represented as < G ,Θ>.…”

Section: Preliminariesmentioning

confidence: 99%

“…A BN is a probabilistic graphical model utilized to represent causality among process variables in a process. 35 Mathematically, a BN is represented as <G,Θ>. The graph G = <X,A > denotes the structure of BN where X is a set of process variables, X i ∈ X, that are represented by nodes.…”

Section: ■ Preliminariesmentioning

confidence: 99%

A Direct Transfer Entropy-Based Multiblock Bayesian Network for Root Cause Diagnosis of Process Faults

Kumari

Wang

Khan

et al. 2022

Ind. Eng. Chem. Res.

Self Cite

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“…A new ANN framework, the operable adaptive sparse identification of systems (OASIS), was developed for chemical processing and successfully applied to reactorseparation risk analysis [30]. Risk assessment using the OASIS of an oil and refining plant was conducted from incidents on the basis of atmospheric and internal conditions and fault prediction.…”

Section: Introductionmentioning

confidence: 99%

Threat and Risk Analysis-Based Neural Network for a Chemical Explosion (TRANCE) Model to Predict Hazards in Petroleum Refinery

Gabhane

Rao

2023

Toxics

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Risk analysis and prediction is a primary monitoring strategy to identify abnormal events occurring in chemical processes. The accidental release of toxic gases may result in severe problems for people and the environment. Risk analysis of hazardous chemicals using consequence modeling is essential to improve the process reliability and safety of the refineries. In petroleum refineries: toluene, hydrogen, isooctane, kerosene, methanol, and naphtha are key process plants with toxic and flammable chemicals. The major process plants considered for risk assessment in the refinery are the gasoline hydrotreatment unit, crude distillation, aromatic recovery, continuous catalytic reformer, methyl–tert–butyl–ether, and kerosene merox units. Additionally, we propose a threat and risk analysis neural network for the chemical explosion (TRANCE) model for refinery incident scenarios. Significantly, 160 attributes were collected for the modeling on the basis of the significance of failure and hazardous chemical leaks in the refinery. Hazard analysis shows that the leakages of hydrogen and gasoline at the gasoline hydrotreatment unit, kerosene at the kerosene merox plant, and crude oil at crude-distillation units were areas of profound concern. The developed TRANCE model predicted the chemical explosion distance with an R2 accuracy value of 0.9994 and MSE of 679.5343.

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“…Monte Carlo simulation methods were also employed to project the trajectory of the corrosion process, leveraging the probability distributions of corrosion growth rates [11,12]. A predictive model considering the variation of corrosion rate was developed using Bayesian inference methods [13]. However, exploring the relationship between corrosion damage and its aging remains challenging due to the limited target materials for each method.…”

Section: Introductionmentioning

confidence: 99%

An analysis on spatial properties of corroded surface of weathering steel by different environmental conditions

Feng,

Mikihito,

Kazuki

et al. 2023

Corrosion Engineering, Science and Technology

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This study examined the spatial characteristics of the corroded surface of weathering steel by different environmental conditions to evaluate the applicability of two accelerated corrosion tests. Two types of weathering steel were subjected to the two accelerated corrosion tests for 28, 84, and 168 days. The same materials were also exposed at two separate locations for 180 and 365 days of atmospheric exposure tests. The corrosion behavior of the weathering steel was investigated by spatial statistical analysis. The analysis suggested that the spatial properties of the corrosion of weathering steel experimented in the early stage of both accelerated corrosion tests were close to those obtained in the atmospheric exposure tests. Furthermore, the corroded surfaces experimented under the ISO 16539 Method B accelerated corrosion test demonstrated a closer resemblance to the corroded surfaces of weathering steel in real corrosion environments compared to those derived from the Cyclic Corrosion Test Method A.

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An integrated risk prediction model for corrosion-induced pipeline incidents using artificial neural network and Bayesian analysis

Cited by 33 publications

References 39 publications

A Direct Transfer Entropy-Based Multiblock Bayesian Network for Root Cause Diagnosis of Process Faults

A Direct Transfer Entropy-Based Multiblock Bayesian Network for Root Cause Diagnosis of Process Faults

Threat and Risk Analysis-Based Neural Network for a Chemical Explosion (TRANCE) Model to Predict Hazards in Petroleum Refinery

An analysis on spatial properties of corroded surface of weathering steel by different environmental conditions

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