Data-driven process monitoring and fault analysis of reformer units in hydrogen plants: Industrial application and perspectives

Kumar, Ankur; Bhattacharya, Apratim; Flores‐Cerrillo, Jesus

doi:10.1016/j.compchemeng.2020.106756

Cited by 27 publications

(18 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…And the T 2 statistics obtained by QR-KPCR algorithm is very large, which is more conducive to fault detection. We can also see that IDV (1) has not only a quality-related fault, but also a quality-unrelated fault. Both of them can be effectively detected.…”

Section: Numerical Examplementioning

confidence: 82%

See 1 more Smart Citation

Quality-Related Process Monitoring Based on Improved Kernel Principal Component Regression

Li¹,

Yao

et al. 2021

IEEE Access

View full text Add to dashboard Cite

To date, quality-related multivariate statistical methods are extensively used in process monitoring and have achieved admirable effects. However, most of them contain recursive processes, which result in higher time complexity and are not suitable for increasingly complex industrial processes. Therefore, this paper embeds singular value decomposition (SVD) into the kernel principal component regression (KPCR) to accomplish Quality-related process monitoring with a lower computational cost. Specifically, the kernel technique is devoted to map the original input into the higher dimensional space to boost the nonlinear ability of the principal component regression (PCR), and then the KPCR is employed to capture the correlation between the input kernel matrix and the output matrix. At the same time, the kernelized input space is decomposed into two orthogonal quality-related and quality-unrelated spaces by SVD, and the statistics of the two spaces are calculated to detect the faults respectively. Compared with other multivariate statistical methods, it has the following advantages: 1) A quality-related kernel principal component analysis (QR-KPCR) algorithm is proposed. 2) Compared with partial least squares method, the recursive process is omitted and the training time is shortened. 3) The model is more concise and the fault detection process is faster. 4) By contrast with other multivariate statistical process monitoring, it has a higher fault detection rate. Experimental results on a widespread example and an industry benchmark verify the effectiveness and reliability of the proposed method.INDEX TERMS Process monitoring, fault detection, kernel principal component regression, multivariate statistical process monitoring.

show abstract

Section: Numerical Examplementioning

confidence: 82%

“…W ITH the arrival of the era of big data, data-driven fault detection has attracted more and more visibility in fault detection [1], [2]. Considering that data usually contains information in the form of multivariate, multivariate methods are widely used to capture the relationship between variables.…”

Section: Introductionmentioning

confidence: 99%

Quality-Related Process Monitoring Based on Improved Kernel Principal Component Regression

Li¹,

Yao

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…However, the analysis method based on the kernel function does not need to calculate the eigenvector as the PCA method but to convert it into the eigenvalue and eigenvector of the kernel matrix. Thus, it avoids the calculation for obtaining the eigenvector in the high-dimensional space and converting it into projection, solving the linear combination of kernel functions, and by capturing the data dynamic matrix [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ], it also solves the dynamic matching problem of the PCA model. Hence, the calculation is greatly simplified.…”

Section: Fault Diagnosis Methodsmentioning

confidence: 99%

Integrated Diagnostic Framework for Process and Sensor Faults in Chemical Industry

Zhang

Luo

Dai

2021

Sensors

View full text Add to dashboard Cite

This study considers the problem of distinguishing between process and sensor faults in nonlinear chemical processes. An integrated fault diagnosis framework is proposed to distinguish chemical process sensor faults from process faults. The key idea of the framework is to embed the cycle temporal algorithm into the dynamic kernel principal component analysis to improve the fault detection speed and accuracy. It is combined with the fault diagnosis method based on the reconstruction-based contribution graph to diagnose the fault variables and then distinguish the two fault types according to their characteristics. Finally, the integrated fault diagnosis framework is applied to the Tennessee Eastman process and acid gas absorption process, and its effectiveness is proved.

show abstract

“…The use of PCA and PLS for process monitoring is well established, especially for continuous processes [13][14][15][16]. When it comes to the monitoring and troubleshooting of batch processes, methodologies and applications typically refer to individual units rather than to plant-wide systems [17][18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Backstepping Methodology to Troubleshoot Plant-Wide Batch Processes in Data-Rich Industrial Environments

et al. 2021

View full text Add to dashboard Cite

Troubleshooting batch processes at a plant-wide level requires first finding the unit causing the fault, and then understanding why the fault occurs in that unit. Whereas in the literature case studies discussing the latter issue abound, little attention has been given so far to the former, which is complex for several reasons: the processing units are often operated in a non-sequential way, with unusual series-parallel arrangements; holding vessels may be required to compensate for lack of production capacity, and reacting phenomena can occur in these vessels; and the evidence of batch abnormality may be available only from the end unit and at the end of the production cycle. We propose a structured methodology to assist the troubleshooting of plant-wide batch processes in data-rich environments where multivariate statistical techniques can be exploited. Namely, we first analyze the last unit wherein the fault manifests itself, and we then step back across the units through the process flow diagram (according to the manufacturing recipe) until the fault cannot be detected by the available field sensors any more. That enables us to isolate the unit wherefrom the fault originates. Interrogation of multivariate statistical models for that unit coupled to engineering judgement allow identifying the most likely root cause of the fault. We apply the proposed methodology to troubleshoot a complex industrial batch process that manufactures a specialty chemical, where productivity was originally limited by unexplained variability of the final product quality. Correction of the fault allowed for a significant increase in productivity.

show abstract

Data-driven process monitoring and fault analysis of reformer units in hydrogen plants: Industrial application and perspectives

Cited by 27 publications

References 42 publications

Quality-Related Process Monitoring Based on Improved Kernel Principal Component Regression

Quality-Related Process Monitoring Based on Improved Kernel Principal Component Regression

Integrated Diagnostic Framework for Process and Sensor Faults in Chemical Industry

Backstepping Methodology to Troubleshoot Plant-Wide Batch Processes in Data-Rich Industrial Environments

Contact Info

Product

Resources

About