DROWNING IN DATA: Informatics and modeling challenges in a data‐rich networked world

Venkatasubramanian, Venkat

doi:10.1002/aic.11756

Cited by 67 publications

(32 citation statements)

References 26 publications

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“…Such a desirable methodology may also act as preconditioning module of any integrated design and control framework and will mostly concern the structure of the problem formulation rather than fine details. The required developments potentially will benefit from "expanding the scope of modeling options…" to new modelling techniques such as "graph theoretical models, Petri nets, rule-based systems, semantic networks, ontology's, agents,…" as discussed by Stephanopoulos and Reklaitis (2011) and Venkatasubramanian (2009Venkatasubramanian ( , 2011. Suggestion 3.…”

Section: Suggestions For Future Researchmentioning

confidence: 99%

Integration of process design and control: A review

Sharifzadeh

2013

Chemical Engineering Research and Design

116

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There is a large variety of methods in literature for process design and control, which can be classified into two main categories. The methods in the first category have a sequential approach in which, the control system is designed, only after the details of process design are decided. However, when process design is fixed, there is little room left for improving the control performance. Recognizing the interactions between process design and control, the methods in the second category integrate some control aspects into process design. With the aim of providing an exploration map and identifying the potential areas of further contributions, this paper presents a thematic review of the methods for integration of process design and control. The evolution paths of these methods are described and the advantages and disadvantages of each method are explained. The paper concludes with suggestions for future research activities. KeywordsIntegrated design and control, Sequential design and control, Control structure selection, Simultaneous optimization of a process and its controllers, inversely controlled process model, controllability.

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Section: Suggestions For Future Researchmentioning

confidence: 99%

Integration of process design and control: A review

Sharifzadeh

2013

Chemical Engineering Research and Design

116

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“…Intelligent systems are now well poised to make significant contributions [80,81] to AEM and PHA in real-life industrial settings and revolutionize the prognostic and diagnostic monitoring of complex systems in the coming decade in a wide variety of industries.…”

Section: Intelligent Systems For Process Hazards Analysismentioning

confidence: 99%

Systemic risks management in complex process plants: Challenges, opportunities, and emerging trends

Venkatasubramanian

2010

2010 Conference on Control and Fault-Tolerant Systems (SysTol)

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The BP Deepwater Horizon oil spill disaster has reminded us, once again, the potential for systemic failures in complex engineered systems. But such systemic failures are not limited to the chemical and petrochemical industries alone. The 2003 Northeast electrical power blackout was a systemic failure. Financial disasters such as Enron, WorldCom, subprime derivatives market, and so on, also belong to the same class. Given the size, scope, and complexity of these modern engineered systems and their interactions, it is becoming increasingly difficult for people to anticipate, diagnose and control serious abnormal events in a timely manner. Practitioners in the process industries view this as the next major challenge in control systems research and application. There are two different, but related, components of the overall systemic risks management problem. One deals with the problem of process safety during real-time operations. The other deals with safety issues during the design and/or modifications of the plant or the processes. In this paper, I will present an overview of the challenges and the opportunities. Recent progress has promising implications on the use of intelligent systems for a variety of applications in the chemical, petrochemical, and pharmaceutical industries for inherently safer design, operator training, abnormal events management, and optimal process operations.

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“…Depending on plant size, records of the time evolution of a few hundreds to that of tens of thousands of variables (tags) may be collected (often with sampling intervals of less than 1 min) and made available in data historians. Process operators thus often find themselves "drowning in data" 6 due to the lack of time and resources required to analyze and generate value from these large volumes of information. Nevertheless, tools such as principal component analysis (PCA) and partial least squares regression have been successfully used to detect and isolate faults pertaining to individual process variables and units.…”

Section: Introductionmentioning

confidence: 99%

Process fault detection using time‐explicit Kiviat diagrams

et al. 2015

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Significant amounts of data are collected and stored during chemical process operations. The corresponding datasets are typically difficult to represent and analyze using traditional visualization methods. This article introduces time-explicit Kiviat diagrams as a means to visualize the multidimensional time series data acquired from plant operations. This framework is then used to build multivariate control charts for large scale time series datasets, and to develop a fault detection mechanism that lends itself to real-time implementation. The proposed methodology is applied to an industrial case study as well as to data obtained from the Tennessee Eastman process simulator, showing very good performance.

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DROWNING IN DATA: Informatics and modeling challenges in a data‐rich networked world

Cited by 67 publications

References 26 publications

Integration of process design and control: A review

Integration of process design and control: A review

Systemic risks management in complex process plants: Challenges, opportunities, and emerging trends

Process fault detection using time‐explicit Kiviat diagrams

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