Implementation of multi agents based system for process supervision in large-scale chemical plants

Natarajan, Sathish

doi:10.1016/j.compchemeng.2013.08.012

Cited by 29 publications

(14 citation statements)

References 23 publications

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“…18,19 Control theorists have proposed approaches by adopting traditional control theory for understanding systems. 20,21 Others have proposed agent based modeling 22 or domain-independent system safety principles. 23 Our prior work in this area has stressed the need for modeling cause-and-effect knowledge explicitly as well as the need for a multiscale modeling framework.…”

Section: Systemic Failures: Common Patterns Of Failures At Multiple Lmentioning

confidence: 99%

TeCSMART: A hierarchical framework for modeling and analyzing systemic risk in sociotechnical systems

Venkatasubramanian

Zhang

2016

AIChE Journal

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Recent systemic failures in different domains continue to remind us of the fragility of complex sociotechnical systems. Although these failures occurred in different domains, there are common failure mechanisms that often underlie such events. Hence, it is important to study these disasters from a unifying systems engineering perspective so that one can understand the commonalities as well as the differences to prevent or mitigate future events. A new conceptual framework that systematically identifies the failure mechanisms in a sociotechnical system, across different domains is proposed. Our analysis includes multiple levels of a system, both social and technical, and identifies the potential failure modes of equipment, humans, policies, and institutions. With the aid of three major recent disasters, how this framework could help us compare systemic failures in different domains and identify the common failure mechanisms at all levels of the system is demonstrated.

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Section: Systemic Failures: Common Patterns Of Failures At Multiple Lmentioning

confidence: 99%

TeCSMART: A hierarchical framework for modeling and analyzing systemic risk in sociotechnical systems

Venkatasubramanian

Zhang

2016

AIChE Journal

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“…A formal definition of ontology is given by [16] as an explicit specification of a conceptualization which was used to describe a specific domain knowledge where concepts and relationships are unambiguously defined and checked. Recent works extensively applied ontology to facilitate the context modeling, its applicability covers domain include risk management of cold chain logistics [17], enterprise application [18], process supervision [19] and so on.…”

Section: A Context-modeling Methodologymentioning

confidence: 99%

An Intelligent Context-aware System for Logistics Asset Supervision Service

Feng

Pang

Lodewijks

2016

Annals of Computer Science and Information Systems

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Abstract-The use of Information and Communication Technology (ICT) has touched various aspects in the domain of transport engineering and logistics (TEL). As the development of TEL tends to be more complex in operation and large in scale, recent practices start to pay more attentions on improving system robustness and reliability. In addition, current ICT innovations (such as WSN and IOT) could record and deliver system descriptors (physical measurements, virtual resources, operational configurations) in real time. Such large-stream and heterogeneous data requires an integrated framework to process and management. To address such challenges, in this paper, a novel concept of context-aware supervision is proposed. An intelligent system with integration of semantic web and agent technology is proposed to support the concept realization, which aims at providing condition-monitoring and maintenance service to relevant user. A generic ontology-agent based framework will be illustrated. Finally, it will be applied for the supervision of a large-scale material handling system-belt conveying system as a proof-of-concept.

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“…Digital diagnostic assistants for "human in the loop" realtime operations can be valuable in identifying root causes of failures [4], [5] or even predict the onset of disturbances that could lead to failures [6], [7], thereby reducing downtime and limiting stress for the operators. To establish a model of the system, technical documentation like P&IDs and process flow diagrams, are important [3], but it is also vital to "harvest" expertise and experience from engineers and operators [8] or empirical data [5].…”

Section: Introductionmentioning

confidence: 99%

Generating Diagnostic Bayesian Networks from Qualitative Causal Models

Kirchhübel

Jørgensen

2019

2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

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Safety and efficiency of modern industrial plants can be improved by providing operators with effective digital assistants to diagnose abnormal situations occurring in the plant. To make sense of a large number of alarms, root cause analysis can help pinpoint the origin of an abnormal situation. We investigate the translation of qualitative causal models into Bayesian belief networks (BBN) to utilize efficient tools for probability inference. The diagnosis result of a fault scenario of the Tennessee-Eastman-Process highlight the feasibility of the principle approach and the ongoing research aims to fully leverage the potential of BBN.

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Implementation of multi agents based system for process supervision in large-scale chemical plants

Cited by 29 publications

References 23 publications

TeCSMART: A hierarchical framework for modeling and analyzing systemic risk in sociotechnical systems

TeCSMART: A hierarchical framework for modeling and analyzing systemic risk in sociotechnical systems

An Intelligent Context-aware System for Logistics Asset Supervision Service

Generating Diagnostic Bayesian Networks from Qualitative Causal Models

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