Using process topology in plant-wide control loop performance assessment

Yim, Seongjin; Ananthakumar, H.G.; Benabbas, Lamia; Horch, Alexander; Drath, Rainer; Thornhill, Nina F.

doi:10.1016/j.compchemeng.2006.05.004

Cited by 56 publications

(28 citation statements)

References 32 publications

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“…The methods with greatest impact are data-driven [7][8] and are mainly supported by dimensionality reduction techniques like Principal Component Analysis [9] and Independent Component Analysis [10]. Recent work [11][12] has successfully integrated data-driven analysis with process knowledge without quantitative models of the process, by using an electronic process schematic to extract the causal information in an automated way. The use of quantitative model-based methods has, in fact, had little impact in process industries [5,7] due, in part, to the inherent nonlinear nature of chemical processes and the uncertainties of thermodynamic data [7][8].…”

Section: A Fault Detection and Diagnosis In The Context Of Process Imentioning

confidence: 99%

Importance of auxiliary systems for process fault detection and diagnosis

Cecı́lio

Chen

Thornhill

2011

2011 19th Mediterranean Conference on Control &Amp; Automation (MED)

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Abstract-In industrial processes, fault detection and diagnosis is traditionally applied separately to process and equipment without relating the information on both systems. This paper shows the interaction during fault propagation between the process and its auxiliary systems, i.e. mechanical, electrical and utility systems. Evidence is given with an industrial case study. The relevance of this finding is demonstrated by showing the advantages of including information from electrical and mechanical data in a root cause diagnosis test.

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Section: A Fault Detection and Diagnosis In The Context Of Process Imentioning

confidence: 99%

Importance of auxiliary systems for process fault detection and diagnosis

Cecı́lio

Chen

Thornhill

2011

2011 19th Mediterranean Conference on Control &Amp; Automation (MED)

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“…The paper builds on the work of Yim et al, (2006). In that work, a list of elements in the process schematic and the connections between them were parsed from its CAEX description.…”

Section: Contribution Of the Papermentioning

confidence: 99%

Cause-and-effect analysis in chemical processes utilizing XML, plant connectivity and quantitative process history

Thambirajah

Benabbas

Bauer

et al. 2009

Computers & Chemical Engineering

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Disturbances that spread plant-wide in a chemical process pose challenges to maintenance staff. Connections within the plant and the presence of multiple causal paths mean it is not straightforward to locate the root disturbance because the effects can propagate and be detected elsewhere. Measurementbased methods use quantitative process history to generate hypotheses about the root cause, while a separate strand of work in the literature has used causal maps and digraphs. It has been reported that both approaches can give spurious solutions, however. The idea behind this article is to reduce the number of spurious solutions by combining basic and readily-available information about the connectivity of the process with the results from causal measurement-based analysis. Connectivity information is captured from an XML description of the process schematic that complies with the CAEX schema. The capabilities of the approach and its potential for future development are discussed.

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“…This technique was comprehensively reviewed by Maurya et al (2003), who also showed how to derive systematically these qualitative models from model equations. Recently, some authors have extracted this knowledge from process schematics; Yim et al (2006) used schematics represented electronically under the CAEX standard, and Jiang et al (2009) converted the process schematic to a digraph based on the information of the controllers.…”

Section: Sources Of Information In Process Performance Analysismentioning

confidence: 99%

“…Recently, some progress has been done with the integration of different sources of information, with Cecilio et al (2011) demonstrating the value of integrating data from the mechanical, electrical and utility systems with process data, and Yim et al (2006) and Maurya et al (2003) who combined the connectivity information from process schematics with data-driven analysis methods. However, most contributions for process performance analysis are still based on process measurements alone.…”

Section: Introductionmentioning

confidence: 99%

Process performance analysis in large-scale systems integrating different sources of information

Cecı́lio

Rapp

Thornhill

2012

IFAC Proceedings Volumes

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Process auditing using historical data can identify causes for poor performance and reveal opportunities to improve process operation. To date, the data used has been limited to process measurements; however other sources hold complementary information about the process behavior. This paper proposes a new approach to root-cause diagnosis, which also takes advantage of the information in utility, mechanical and electrical data, alarms and diagrams. Its benefit is demonstrated in an industrial case study, by tackling an important challenge in root-cause analysis: large-scale systems. This paper also defines specifications for a semi-automated tool to implement the proposed approach.

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Using process topology in plant-wide control loop performance assessment

Cited by 56 publications

References 32 publications

Importance of auxiliary systems for process fault detection and diagnosis

Importance of auxiliary systems for process fault detection and diagnosis

Cause-and-effect analysis in chemical processes utilizing XML, plant connectivity and quantitative process history

Process performance analysis in large-scale systems integrating different sources of information

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