Anticipatory monitoring and control in a process environment

L. Tsoukalas, G. W. Lee, M. Ragheb

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A methodology for the synthesis of engineering Anticipatory Systems is presented. It accounts for both random and fuzzy aspects of uncertainty introduced in process control, and develops mathematical measures of performance. The random, probabilistic component quantifies the uncertainty of occurrence of an event. The fuzzy, possibilistic component quantifies the imprecision in the meaning of an event. Facts about the system are represented as fuzzy information granules of the form: g = (number of carts carrying parts) is (small) is (likely). The performance measures are used as an input to the diagnostic and control functions which are assumed by a Production-Rule System. Using a fuzzified Bayes formula as the link between present and future states, a model-based system estimates at any time t, current performance as well as anticipated performance at time t + At. A control action can be taken at time t, based on both current and anticipated performance. Accordingly, the system can change its current state on the basis of both the current and the anticipated future state. The agency for the prediction is a model of the system and/or its environment which is internal to the system. The synthesis of such system is demonstrated using a model of a nuclear reactor. The model of the reactor is constructed using procedural programming methods and is coupled to a symbolic program which assumes the overall control function.

Original languageEnglish (US)
Title of host publicationProceedings of the 2nd International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 1989
EditorsMoonis Ali
PublisherAssociation for Computing Machinery, Inc
Pages278-287
Number of pages10
ISBN (Electronic)0897913205, 9780897913201
DOIs
StatePublished - Jun 6 1989
Event2nd International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 1989 - Tullahoma, United States
Duration: Jun 6 1989Jun 9 1989

Publication series

NameProceedings of the 2nd International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 1989

Other

Other2nd International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 1989
CountryUnited States
CityTullahoma
Period6/6/896/9/89

Fingerprint

Monitoring
Information granules
Control Function
Nuclear reactors
Systems engineering
Quantify
Process control
Synthesis
Uncertainty
Bayes' Formula
Production Rules
Nuclear Reactor
Fuzzy Information
Imprecision
Systems Engineering
Process Control
Performance Measures
Reactor
Diagnostics
Programming

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Control and Optimization
  • Modeling and Simulation

Cite this

Tsoukalas, L., Lee, G. W., & Ragheb, M. (1989). Anticipatory monitoring and control in a process environment. In M. Ali (Ed.), Proceedings of the 2nd International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 1989 (pp. 278-287). (Proceedings of the 2nd International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 1989). Association for Computing Machinery, Inc. https://doi.org/10.1145/66617.66651

Anticipatory monitoring and control in a process environment. / Tsoukalas, L.; Lee, G. W.; Ragheb, M.

Proceedings of the 2nd International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 1989. ed. / Moonis Ali. Association for Computing Machinery, Inc, 1989. p. 278-287 (Proceedings of the 2nd International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 1989).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Tsoukalas, L, Lee, GW & Ragheb, M 1989, Anticipatory monitoring and control in a process environment. in M Ali (ed.), Proceedings of the 2nd International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 1989. Proceedings of the 2nd International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 1989, Association for Computing Machinery, Inc, pp. 278-287, 2nd International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 1989, Tullahoma, United States, 6/6/89. https://doi.org/10.1145/66617.66651
Tsoukalas L, Lee GW, Ragheb M. Anticipatory monitoring and control in a process environment. In Ali M, editor, Proceedings of the 2nd International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 1989. Association for Computing Machinery, Inc. 1989. p. 278-287. (Proceedings of the 2nd International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 1989). https://doi.org/10.1145/66617.66651
Tsoukalas, L. ; Lee, G. W. ; Ragheb, M. / Anticipatory monitoring and control in a process environment. Proceedings of the 2nd International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 1989. editor / Moonis Ali. Association for Computing Machinery, Inc, 1989. pp. 278-287 (Proceedings of the 2nd International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 1989).
@inproceedings{a34ab0c05cf6405fb416901e128e43cd,
title = "Anticipatory monitoring and control in a process environment",
abstract = "A methodology for the synthesis of engineering Anticipatory Systems is presented. It accounts for both random and fuzzy aspects of uncertainty introduced in process control, and develops mathematical measures of performance. The random, probabilistic component quantifies the uncertainty of occurrence of an event. The fuzzy, possibilistic component quantifies the imprecision in the meaning of an event. Facts about the system are represented as fuzzy information granules of the form: g = (number of carts carrying parts) is (small) is (likely). The performance measures are used as an input to the diagnostic and control functions which are assumed by a Production-Rule System. Using a fuzzified Bayes formula as the link between present and future states, a model-based system estimates at any time t, current performance as well as anticipated performance at time t + At. A control action can be taken at time t, based on both current and anticipated performance. Accordingly, the system can change its current state on the basis of both the current and the anticipated future state. The agency for the prediction is a model of the system and/or its environment which is internal to the system. The synthesis of such system is demonstrated using a model of a nuclear reactor. The model of the reactor is constructed using procedural programming methods and is coupled to a symbolic program which assumes the overall control function.",
author = "L. Tsoukalas and Lee, {G. W.} and M. Ragheb",
year = "1989",
month = "6",
day = "6",
doi = "10.1145/66617.66651",
language = "English (US)",
series = "Proceedings of the 2nd International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 1989",
publisher = "Association for Computing Machinery, Inc",
pages = "278--287",
editor = "Moonis Ali",
booktitle = "Proceedings of the 2nd International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 1989",

}

TY - GEN

T1 - Anticipatory monitoring and control in a process environment

AU - Tsoukalas, L.

AU - Lee, G. W.

AU - Ragheb, M.

PY - 1989/6/6

Y1 - 1989/6/6

N2 - A methodology for the synthesis of engineering Anticipatory Systems is presented. It accounts for both random and fuzzy aspects of uncertainty introduced in process control, and develops mathematical measures of performance. The random, probabilistic component quantifies the uncertainty of occurrence of an event. The fuzzy, possibilistic component quantifies the imprecision in the meaning of an event. Facts about the system are represented as fuzzy information granules of the form: g = (number of carts carrying parts) is (small) is (likely). The performance measures are used as an input to the diagnostic and control functions which are assumed by a Production-Rule System. Using a fuzzified Bayes formula as the link between present and future states, a model-based system estimates at any time t, current performance as well as anticipated performance at time t + At. A control action can be taken at time t, based on both current and anticipated performance. Accordingly, the system can change its current state on the basis of both the current and the anticipated future state. The agency for the prediction is a model of the system and/or its environment which is internal to the system. The synthesis of such system is demonstrated using a model of a nuclear reactor. The model of the reactor is constructed using procedural programming methods and is coupled to a symbolic program which assumes the overall control function.

AB - A methodology for the synthesis of engineering Anticipatory Systems is presented. It accounts for both random and fuzzy aspects of uncertainty introduced in process control, and develops mathematical measures of performance. The random, probabilistic component quantifies the uncertainty of occurrence of an event. The fuzzy, possibilistic component quantifies the imprecision in the meaning of an event. Facts about the system are represented as fuzzy information granules of the form: g = (number of carts carrying parts) is (small) is (likely). The performance measures are used as an input to the diagnostic and control functions which are assumed by a Production-Rule System. Using a fuzzified Bayes formula as the link between present and future states, a model-based system estimates at any time t, current performance as well as anticipated performance at time t + At. A control action can be taken at time t, based on both current and anticipated performance. Accordingly, the system can change its current state on the basis of both the current and the anticipated future state. The agency for the prediction is a model of the system and/or its environment which is internal to the system. The synthesis of such system is demonstrated using a model of a nuclear reactor. The model of the reactor is constructed using procedural programming methods and is coupled to a symbolic program which assumes the overall control function.

UR - http://www.scopus.com/inward/record.url?scp=84941538962&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84941538962&partnerID=8YFLogxK

U2 - 10.1145/66617.66651

DO - 10.1145/66617.66651

M3 - Conference contribution

AN - SCOPUS:84941538962

T3 - Proceedings of the 2nd International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 1989

SP - 278

EP - 287

BT - Proceedings of the 2nd International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 1989

A2 - Ali, Moonis

PB - Association for Computing Machinery, Inc

ER -