Application of Bayesian network for root cause diagnosis of chemical process fault

Gharahbagheri, H.; Imtiaz, Syed; Khan, Faisal

doi:10.1109/indiancc.2017.7846473

Cited by 6 publications

(5 citation statements)

References 21 publications

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“…The qualitative part is presented by a directed acyclic graph and the structure of dependence between them and the quantitative part is formed from the variable states and the relationships between them . BNs are widely used and appreciated in many disciplines such as civil design , strategic decision making ; root cause analysis , salinity management ; industrial discharge limit ; shipping ; performance analysis ; and chemical process .…”

Section: The Rbi Approachmentioning

confidence: 99%

Implementing risk‐based inspection approach: Is it beneficial for pressure equipment in Malaysia industries?

Mohamed

Hassan

Hamid

2017

Process Safety Progress

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The transition from time-based inspection to risk-based inspection (RBI) in Malaysian industry began in June 2014 with the gazette of Factories and Machinery (Special Scheme of Inspection) (Risk-Based Inspection) Regulations 2014 (SSI 2014). Under the new regulations, owners shall express their commitment and provide proof of their ability to ensure safety, integrity, and operability of relevant equipment. The regulation does not prescriptively mention methods to implement the RBI approach, but does spell out minimum requirements. Upon successful demonstration of compliance to the authority, the Certificate of Fitness of pressurized equipment may be extended to maximum 72 months.This paper aims to establish the benefits which may accrue from the adoption of RBI approach and ascertain the potential difficulties associated with the implementation of this approach. The paper first presents a review of the literature, establishing the major reasons for, and the benefits of, RBI implementation. This is followed by the results of a Malaysian survey on the implementation of RBM approach. An audit was conducted in the Malaysian petrochemical and chemical industries in April 2016. The audit results highlight the reasons for and benefits of implementing RBI. The paper concludes with a discussion and recommendations for the implementation of the RBI approach.

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Section: The Rbi Approachmentioning

confidence: 99%

Implementing risk‐based inspection approach: Is it beneficial for pressure equipment in Malaysia industries?

Mohamed

Hassan

Hamid

2017

Process Safety Progress

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“…However, the stochastic nature of these models can lead to over-reliance on data quality. Integrated approaches are more scalable by applying statistical principles to AI methods, with Bayesian networks [19] and hidden Markov models [20] being examples of integrated approaches. Existing warning methods do not consider the characteristics of unlabeled dynamic data, resulting in weak generalization ability, poor stability, and limited effectiveness, accuracy, and early warning capabilities.…”

Section: Introductionmentioning

confidence: 99%

Fused Data-Driven Approach for Early Warning Method of Abnormal Conditions in Chemical Process

2023

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The utilization of data-driven methods in chemical process modeling has been extensively acknowledged due to their effectiveness. However, with the increasing complexity and variability of chemical processes, predicting and warning of anomalous conditions have become challenging. Extracting valuable features and constructing relevant warning models are critical problems that require resolution. This research proposed a novel fused method that integrates K-means density-based spatial clustering of applications with noise (DBSCAN) clustering and bi-directional long short-term memory multilayer perceptron (Bi-LSTM-MLP) to enable early warning of abnormal conditions in chemical processes. The paper applied the proposed method to analyze the early warning using actual process data from Eastman Tennessee and the atmospheric pressure reduction unit as an example. In the TE model and example, the root mean square error (RMSE) of this method is 0.006855 and 0.052546, respectively, which is quite low when compared to other methods. The experimental results confirmed the effectiveness of our approach.

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“…Here, the accuracy of root cause diagnosis depends on the causal network. For an accurate causal network, it is important to consider cyclic loops into the causal network that are prevalent in the CPI due to intensive material and heat integration, feedback control, and coupling among process variables. , To this end, recently, several works have proposed methods to meticulously incorporate cyclic loops in the causal network. , It is to be noted that cyclic loops incorporated into the causal network through existing methods are known beforehand based on process knowledge. However, it is possible, especially in a complex chemical process, that cyclic loops are not known due to inadequate process knowledge.…”

Section: Introductionmentioning

confidence: 99%

“…17,18 To this end, recently, several works have proposed methods to meticulously incorporate cyclic loops in the causal network. 19,20 It is to be noted that cyclic loops incorporated into the causal network through existing methods are known beforehand based on process knowledge. However, it is possible, especially in a complex chemical process, that cyclic loops are not known due to inadequate process knowledge.…”

Section: ■ Introductionmentioning

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.

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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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Application of Bayesian network for root cause diagnosis of chemical process fault

Cited by 6 publications

References 21 publications

Implementing risk‐based inspection approach: Is it beneficial for pressure equipment in Malaysia industries?

Implementing risk‐based inspection approach: Is it beneficial for pressure equipment in Malaysia industries?

Fused Data-Driven Approach for Early Warning Method of Abnormal Conditions in Chemical Process

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

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