Knowledge-based diagnostic systems for continuous process operations based upon the task framework

Ramesh, T.S.; Davis, Jim; Schwenzer, G.M.

doi:10.1016/0098-1354(92)80009-x

Cited by 26 publications

(9 citation statements)

References 13 publications

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“…Henley's inference algorithm used a set of if-then rules to determine the state of a system and provide a possible source of the fault. Ramesh et al [13] introduced a hierarchical classification to improve Henley's approach by focusing on the information processing tasks underlying diagnostic reasoning. Their framework was successfully applied to the development of knowledge-based diagnostic systems for several processes.…”

Section: Related Workmentioning

confidence: 99%

Robust model-based fault diagnosis for air handling units

Mulumba¹,

Afshari²,

Yan³

et al. 2015

Energy and Buildings

110

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Section: Related Workmentioning

confidence: 99%

Robust model-based fault diagnosis for air handling units

Mulumba¹,

Afshari²,

Yan³

et al. 2015

Energy and Buildings

110

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“…Its early success, combined with its on-line implementation, sparked interest in knowledge-based technology in chemical engineering. Examples of diagnostic applications include Diad-Kit/Boiler, an on-line expert system for performance monitoring and diagnosis of steam boilers (17), and CatCracker, a knowledge-based system for diagnosing operating problems in fluidized catalytic cracking units (18). Examples of design applications include BioSep Designer, which can automatically configure a process flow sheet and size the equipment involved in downstream separation of fermentation products (19); and Still, a tool for automated process and mechanical design of distillation columns (20).…”

Section: Knowledge-based Systems In Chemical Engineeringmentioning

confidence: 99%

“…This is the brittleness problem. One way to address the brittleness problem within the framework of mainstream knowledge-based systems, is to structure knowledge at successive levels of detail, so that additional detail is available when needed (18). Nevertheless, the details are still represented in compiled form, ie, they are not derived at problem-solving time.…”

Section: Model-based Reasoningmentioning

confidence: 99%

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Expert Systems

Ramesh

2000

Kirk-Othmer Encyclopedia of Chemical Technology

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Expert systems, or more appropriately knowledge‐based systems, are essentially computer programs that encode the expertise of human problem solvers in some well‐defined domain. A practical offshoot of the field of artificial intelligence, knowledge‐based technology has emerged as an important area of computer applications, with significant business impact on the industry. This is because knowledge‐based systems can automate problem‐solving tasks that hitherto could not be computerized by conventional, numeric modeling techniques. In the domain of chemical engineering, knowledge‐based systems have been used to automate such tasks as equipment and process design, control of complex processes, troubleshooting process operations, scheduling batch processes under uncertain conditions, and simulation of discrete event operations. This article discusses the motivations and benefits of knowledge‐based technology as applied to chemical engineering problems; the differences between knowledge‐based systems and quantitative, numerical techniques; the theoretical underpinnings of the technology; approaches to selecting, designing, and implementing knowledge‐based system applications; and some of the tools available to implement such systems. Alternatives to expert systems such as neural networks (nets) and case‐based reasoning are also briefly discussed. Throughout, the concepts are illustrated using examples from chemical engineering.

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“…Expert system based on rule-based reasoning [6] and expert system based on case-based reasoning [7] are the two most common kinds. Expert system based on rule-based reasoning has strong explanatory as well as simple forms of expression [8], but the rules are difficult to be obtained. Expert system based on case-based reasoning can obtain answers to questions by case retrieval without rule match, but case coverage and system efficiency is a pair of irreconcilable contradictions [9].…”

Section: Introductionmentioning

confidence: 99%

Application of Expert System in the Analysis of Aluminum Reduction Cell

Yu¹

2015

Proceedings of the 2015 International Conference on Electrical, Computer Engineering and Electronics

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Aluminum reduction cell is a non-linear, multivariable coupling time-varying system, inside which a large number of physical and chemical reactions occur. Meanwhile, aluminum reduction cell is often influenced by disturbance outside, which can easily cause instability of aluminum reduction cell. For the analysis of aluminum reduction cell, a kind of expert system based on blackboard structure is proposed, which can analyze aluminum reduction cell by combination of rule-based reasoning and case-based reasoning based on the data of blackboard structure and information from knowledge base. For an aluminum plant, experiment result has shown that expert system based on blackboard structure can analyze the aluminum reduction cell effectively, and it has certain engineering practicability.

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Knowledge-based diagnostic systems for continuous process operations based upon the task framework

Cited by 26 publications

References 13 publications

Robust model-based fault diagnosis for air handling units

Robust model-based fault diagnosis for air handling units

Expert Systems

Application of Expert System in the Analysis of Aluminum Reduction Cell

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