Monitoring of Processes with Multiple Operating Modes through Multiple Principle Component Analysis Models

Zhao, Shi Jian; Zhang, Jie; Xu, Yong

doi:10.1021/ie0497893

Cited by 204 publications

(134 citation statements)

References 27 publications

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“…This would seem to indicate that a distinct shift in process behaviour occurred on August 4th, coinciding with the large increase in steam flowrate that occurred at that time. Therefore, if statistical models were going to be built for prediction purposes, it would probably be best to identify separate models for the process in OP1 and OP2 [7].…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

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Modelling of a Naphtha Recovery Unit (NRU) with Implications for Process Optimization

Cluett

2018

Processes

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Abstract:The naphtha recovery unit (NRU) is an integral part of the processes used in the oil sands industry for bitumen extraction. The principle role of the NRU is to recover naphtha from the tailings for reuse in this process. This process is energy-intensive, and environmental guidelines for naphtha recovery must be met. Steady-state models for the NRU system are developed in this paper using two different approaches. The first approach is a statistical, data-based modelling approach where linear regression models have been developed using Minitab ® from plant data collected during a performance test. The second approach involves the development of a first-principles model in Aspen Plus ® based on the NRU process flow diagram. A novel refinement to this latter model, called "withdraw and remix", is proposed based on comparing actual plant data to model predictions around the two units used to separate water and naphtha. The models developed in this paper suggest some interesting ideas for the further optimization of the process, in that it may be possible to achieve the required naphtha recovery using less energy. More plant tests are required to validate these ideas.

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Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

“…This application highlights the need for multiple models and the ability to detect when a system has shifted from one operating mode to another. PCA could be used in an on-line manner for this purpose [7] as could a Bayesian approach [8]. …”

Section: Linear Regression Modelsmentioning

confidence: 99%

Modelling of a Naphtha Recovery Unit (NRU) with Implications for Process Optimization

Cluett

2018

Processes

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“…Therefore, its fault detection rate and fault diagnosis rate are the same. 13 All comparison results are listed in Table 1. In this table, the 20 types of faults are roughly divided into five groups.…”

Section: Overall Comparison Based On All Types Of Faultsmentioning

confidence: 99%

Mixture Discriminant Monitoring: A Hybrid Method for Statistical Process Monitoring and Fault Diagnosis/Isolation

Huang

Chen

Yao

2013

Ind. Eng. Chem. Res.

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In order to better utilize historical process data from faulty operations, supervised learning methods, such as Fisher discriminant analysis (FDA), have been adopted in process monitoring. However, such methods can only separate known faults from normal operations, and they have no means to deal with unknown faults. In addition, most of these methods are not designed for handling non-Gaussian distributed data; however, non-Gaussianity is frequently observed in industrial processes. In this paper, a hybrid multivariate approach named mixture discriminant monitoring (MDM) was proposed, in which supervised learning and statistical process control (SPC) charting techniques are integrated. MDM is capable of solving both of the above problems simultaneously during online process monitoring. Then, for known faults, a root-cause diagnosis can be automatically achieved, while for unknown faults, abnormal variables can be isolated through missing variable analysis. MDM was used on the benchmark Tennessee Eastman (TE) process, and the results showed the capability of the proposed approach.

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“…In addition, for nonlinear, non-Gaussian, and multimodal problems, there are corresponding improvements, such as kernel PCA 7 for nonlinear, independent component analysis (ICA) 8 for non-Gaussian, and multimodal PCA for multimode. 9 However, when these problems coexist, these methods are not satisfactory. To circumvent these difficulties, He and Wang 10 proposed a new method, termed fault detection using the k-nearest neighbor rule (FD-kNN).…”

Section: Introductionmentioning

confidence: 99%

A real-time monitoring method using random projection and k-nearest neighbor rule for batch process

Wen

Zhou

et al. 2017

International Journal of Advanced Robotic Systems

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As an important production method, the batch process is complex and flexible. Moreover, the modeling complexity and the spatial complexity of the storage model are higher, and the monitoring of the actual batch process is more difficult. To address this problem, this article proposes a fault detection method based on random projection, K-means clustering, and the k-nearest neighbor algorithm. First, a multiperiod division method is put forward based on the random projection and the K-means clustering algorithm. This reduces the computational complexity while ensuring the fault detection performance of the algorithm. Second, a real-time monitoring model is established based on each sub-period data using the k-nearest neighbor method to realize online monitoring of the batch production process. According to the premise that the fault detection performance is approximately equal, the proposed method reduces the complexity and computation of the model and realizes the real-time demand of fault detection.

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Monitoring of Processes with Multiple Operating Modes through Multiple Principle Component Analysis Models

Cited by 204 publications

References 27 publications

Modelling of a Naphtha Recovery Unit (NRU) with Implications for Process Optimization

Modelling of a Naphtha Recovery Unit (NRU) with Implications for Process Optimization

Mixture Discriminant Monitoring: A Hybrid Method for Statistical Process Monitoring and Fault Diagnosis/Isolation

A real-time monitoring method using random projection and k-nearest neighbor rule for batch process

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