Risk-based fault prediction of chemical processes using operable adaptive sparse identification of systems (OASIS)

Bhadriraju, Bhavana; Kwon, Joseph; Khan, Faisal

doi:10.1016/j.compchemeng.2021.107378

Cited by 45 publications

(14 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Researchers have used various forms of CSTRs for process monitoring in a variety of studies. 22 , 39 , 47 A simplified diagram of the three-state closed-loop CSTR process is depicted in Figure 4 . The following equations primarily describe the mechanism of the CSTR process: …”

Section: Case Studiesmentioning

confidence: 99%

Nonlinear Dynamic Process Monitoring Based on Ensemble Kernel Canonical Variate Analysis and Bayesian Inference

Wang

2022

ACS Omega

View full text Add to dashboard Cite

By considering autocorrelation among process data, canonical variate analysis (CVA) can noticeably enhance fault detection performance. To monitor nonlinear dynamic processes, a kernel CVA (KCVA) model was developed by performing CVA in the kernel space generated by kernel principal component analysis (KPCA). The Gaussian kernel is widely adopted in KPCA for nonlinear process monitoring. In Gaussian kernel-based process monitoring, a single learner is represented by a certain selected kernel bandwidth. However, the selection of kernel bandwidth plays a pivotal role in the performance of process monitoring. Usually, the kernel bandwidth is determined manually. In this paper, a novel ensemble kernel canonical variate analysis (EKCVA) method is developed by integrating ensemble learning and kernel canonical variate analysis. Compared to a single learner, the ensemble learning method usually achieves greatly superior generalization performance through the combination of multiple base learners. Inspired by the ensemble learning method, KCVA models are established by using different kernel bandwidths. Further, two widely used T 2 and Q monitoring statistics are constructed for each model. To improve process monitoring performance, these statistics are combined through Bayesian inference. A numerical example and two industrial benchmarks, the continuous stirred-tank reactor process and the Tennessee Eastman process, are carried out to demonstrate the superiority of the proposed method.

show abstract

Section: Case Studiesmentioning

confidence: 99%

Nonlinear Dynamic Process Monitoring Based on Ensemble Kernel Canonical Variate Analysis and Bayesian Inference

Wang

2022

ACS Omega

View full text Add to dashboard Cite

show abstract

“…In other words, the authors did not address how the multimode process operations can be handled. Recently, Bhadriraju et al , proposed methods for multimode process FDD and DRA using several local models. For risk assessment, the authors have adopted the Gaussian CDF.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Process Safety Assessment Using Adaptive Bayesian Network with Loss Function

Amin

Khan

2022

Ind. Eng. Chem. Res.

Self Cite

View full text Add to dashboard Cite

Fault detection and diagnosis (FDD) is crucial for dynamic process safety analysis. Integrated with failure prediction models, it enables us to realize how a deviation in process variable(s) can affect system safety (measured as risk). This work aims to overcome the challenges of nonlinear, non-Gaussian, and multimodal behavior of the processing systems to detect abnormal process operations, predict dynamic operational risk, and diagnose root cause of the abnormal situation. A methodology is proposed here by integrating different techniques. The artificial neural network (ANN) is used to identify process modes, while the Bayesian network (BN) is used for fault detection. How a fault will lead to a process failure is modeled using the event tree (ET), whereas time-dependent losses associated with the failure scenarios are assessed using the inverted normal loss function (INLF). A probability adaption mechanism is used to estimate the conditional probabilities in each time slice. The complexity of estimating conditional probabilities is handled using the copula theory. The proposed framework is validated using numerical, simulated, and industrial datasets. The results suggest that the developed framework can provide greater flexibility and wider applications.

show abstract

“…Rare events in the CPI results from poorly managed process faults, which are defined as deviations of observed process variables from their normal operating conditions (NOCs) . Diagnosis of these process faults assists in identifying and isolating the faults before they propagate to significant process failures . Therefore, performing root cause diagnostics of process faults is critical for making suitable troubleshooting decisions and returning the process to its NOC…”

Section: Introductionmentioning

confidence: 99%

“…6 Diagnosis of these process faults assists in identifying and isolating the faults before they propagate to significant process failures. 7 Therefore, performing root cause diagnostics of process faults is critical for making suitable troubleshooting decisions and returning the process to its NOC. 8 For process monitoring in the CPI, the multivariate statistical process control (MSPC) approach based on methods such as principal component analysis (PCA) is one of the most dominant techniques.…”

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.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Risk-based fault prediction of chemical processes using operable adaptive sparse identification of systems (OASIS)

Cited by 45 publications

References 51 publications

Nonlinear Dynamic Process Monitoring Based on Ensemble Kernel Canonical Variate Analysis and Bayesian Inference

Nonlinear Dynamic Process Monitoring Based on Ensemble Kernel Canonical Variate Analysis and Bayesian Inference

Dynamic Process Safety Assessment Using Adaptive Bayesian Network with Loss Function

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

Contact Info

Product

Resources

About