Self-Organizing Agent-Based Grade Transition in Distributed Chemical Reactor Networks

Tetiker, M. Derya; Tatara, Eric; North, Michael; Teymour, Fouad; Çınar, Ali

doi:10.3182/20070606-3-mx-2915.00148

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…The method is different from the previous work [7] not only algorithmically, but also by being able to reject disturbances as well as resuming the operation during maintenance periods.…”

Section: Use Of Perceptron-based Learning In Multi-agent Systemsmentioning

confidence: 81%

Reconfiguration of chemical reactor networks using a hierarchical agent-based system

Artel

Teymour

Çınar

2008

2008 American Control Conference

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Controlling reactors in distributed manufacturing processes producing different grades of a product requires intelligent reconfiguration strategies. Agent-based approaches are ideal for such cases, since they can provide flexible, robust, and emergent solutions during dynamically changing process conditions. A hierarchical, agent-based system with local and global control agents is developed to control networks of interconnected chemical reactors. This paper proposes a multilayered, multiagent framework based on a decentralized approach for the supervision of grade transitions in autocatalytic reactor networks. The values for the manipulated variables are chosen to give the least disturbance to the system. The case studies show that the approach is successful in controlling the reactor network and being able to keep the desired grade even after the need of shutting down some of the reactors.

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“…The method is different from the previous work [7] not only algorithmically, but also by being able to reject disturbances as well as resuming the operation during maintenance periods.…”

Section: Use Of Perceptron-based Learning In Multi-agent Systemsmentioning

confidence: 81%

Reconfiguration of chemical reactor networks using a hierarchical agent-based system

Artel

Teymour

Çınar

2008

2008 American Control Conference

Self Cite

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“…A generic introduction of the agent-based modelling (ABM) can be found in the book (Gilbert, 2008). Moreover, a few examples for computational implementations of agent-based models in diverse backgrounds were provided in Nikolai and Madey (2009), Parker et al (2003), Tetiker et al (2007), Zauner et al (2019) and Alharbi and Coates (2019). Since the dialysis process is seldom modelled mathematically, we collect the data on medical, technical, and physical details from first-hand technical reports or even from local dialysis centres.…”

Section: Introductionmentioning

confidence: 99%

Energy consumption models in dialysis clinics for agent-based decision support

Wandler

Bai

Raskob

2020

IJSPM

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This paper describes an approach to predict the consumptions of water and power in dialysis clinics by simulating the internal processes. Better understanding is essential to prevent and mitigate adverse health impact on patients. Our methods are based on literature about dialysis processes and dialysis clinics, which are first-hand technical reports. The nonlinear model comprises the energy consumption of the technical systems together with their working schedules and the time schedule of the number of patients to be treated. Our model will be used as sub-model in an agent-based simulation system aiming to optimise the use of resources in an emergency with power outages or brownouts. The results presented in this study can be treated as a benchmark for decision makers to estimate the performance of a dialysis clinic or centre based on the amount of electricity and water needed and available.

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A multi-agent approach using perceptron-based learning for robust operation of distributed chemical reactor networks

Artel

Teymour

North

et al. 2011

Engineering Applications of Artificial Intelligence

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Self-Organizing Agent-Based Grade Transition in Distributed Chemical Reactor Networks

Cited by 3 publications

References 6 publications

Reconfiguration of chemical reactor networks using a hierarchical agent-based system

Reconfiguration of chemical reactor networks using a hierarchical agent-based system

Energy consumption models in dialysis clinics for agent-based decision support

A multi-agent approach using perceptron-based learning for robust operation of distributed chemical reactor networks

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