Similarity concept for case-based design in process engineering

Avramenko, Yuri; Krasławski, Andrzej

doi:10.1016/j.compchemeng.2005.10.011

Cited by 57 publications

(37 citation statements)

References 8 publications

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“…This aspect, in a way, is the converse to the work of Surma et al, (1996) and Avramenko et al (2006) where one of their foci is retrieval based on similarity and as such the issue of similarity measurements are vital for the quality of the retrieved results. Here, the decision as to how to carry out the versioning is based on the quantity of detected changes between two artefacts.…”

Section: Model Evolution Management Using Scmmentioning

confidence: 81%

“…Flowsheets are retrieved based on the criteria of either: possessing the largest similar sub-graph to the query flowsheet or possessing the smallest least-cost set of transformation operations required to transform it to the query flowsheet. A review of similarity measures which can be used for case-based design in process engineering have been detailed by Avramenko and Kraslawski (2006). They discussed various approaches where the degree of similarities between features is calculated based on structural comparison, quantitative distance, location in a hierarchy as well as qualitative comparisons of defined terms.…”

Section: Page 7 Of 63mentioning

confidence: 99%

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A model-centric approach for the management of model evolution in chemical process modelling

Lam

2007

Computers & Chemical Engineering

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Abstract. This paper explores the development of an automatic model centric version control approach for managing the evolution of chemical models and to support model reuse. Unlike traditional versioning which is text-based, the basis of versioning in the proposed approach is based on structural changes of the chemical models. An implemented prototype tool incorporating the proposed approach and the use of an example XML-based representation of chemical models was used to illustrate the associated concepts. Some results associated with a case study are presented. Given that chemical modelling tools like HYSYS has just delivered an XML infrastructure that aims to support collaborative engineering, this proposed technique is timely and relevant. The benefit of the approach is that it provides a way of automatic storage and retrieval of the chemical models as well as the management of the different versions of the model as it evolves, thus allowing chemical process engineers to concentrate on the modelling and simulation.

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Section: Model Evolution Management Using Scmmentioning

confidence: 81%

Section: Page 7 Of 63mentioning

confidence: 99%

A model-centric approach for the management of model evolution in chemical process modelling

Lam

2007

Computers & Chemical Engineering

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“…Well-structured and simple problems can be solved with regular rules and principles. They have knowable and comprehensible solutions where the relationship between decision choices and all problem states is known or probabilistic (Avramenko & Kraslawski, 2006;Han, Lee, & Jo, 2005;Meacham & Emont, 1989). Ill-structured and complex problems possess multiple solutions, solution paths, or no solution at all.…”

Section: The Cognitive Processes In Problem Solvingmentioning

confidence: 98%

An ontology-based similarity measurement for problem-based case reasoning

Lau

Tsui

Lee

2009

Expert Systems with Applications

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a b s t r a c tTraditional case-based reasoning uses a table/frame or scenario to represent a case. It assumed that similar input/event results in similar output/event state. However, similar cases may not have similar output/event states since problem solver may have different way to break down the problem. Thus, authors previously proposed problem-based case reasoning to overcome the limitation of the traditional approaches and used clustered ontology to represent the problem spaces of a case. However, synonym problem causes the mismatch of similar sub-problems of historical cases for new case. Thus, this paper proposed ontology-based similarity measurement to retrieve the similar sub-problems that overcomes the synonym problems on case retrieval. The recall and precise of ontology-based similarity measurement were higher than that of the traditional similarity measurement.

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“…, Euclidean and Manhattan Distance[11][12][13][14][15]) to the replica of all samples for the linked population, and to the mean values of 43 samples for the non-linked population. The results were evaluated by histogram representations schematised inFig.…”

mentioning

confidence: 99%