Automatic generation of qualitative models of chemical process units

The concept of qualitative modeling is briefly described. This paper focuses on the use of signed directed graphs to model fault propagation in process plants. It is common to use a unit-based approach for modeling. Two kinds of problems, which arise when constructing unit-based qualitative models, are identified: (1) The models are difficult to construct. (2) Ambiguities due to multiple causal paths may occur when the models are combined. A method that allows models to be built simply and correctly is proposed. The method incorporates features from the knowledge acquisition field, and a computer-aided modeling tool, Equipment Model Builder, has been built to support this method. A new modular approach is presented which overcomes the problem of ambiguous inference when qualitative models are combined. Two case studies were used to evaluate the method developed and the support tool.

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“…When the unit models are combined, the behavior of the whole plant can be analyzed. This unit-based approach is widely used. ,, …”

Section: Sdgsmentioning

confidence: 99%

“…By combining the unit models the behaviour of the whole plant can be analysed. This unit-based approach is widely used (Chung, 1993;Vaidhyanathan and Venkatasubramanian, 1995;Catino et al, 1991). The SDG and functional equation representations can be used to create unit models.…”

Section: Plant Models From Unit Modelsmentioning

confidence: 99%

Creating Signed Directed Graph Models for Process Plants

Palmer

Chung

2000

Ind. Eng. Chem. Res.

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“…An advantage of QPT is its focusing of our attention on vocabulary details and structures. A basic "chemical plant" vocabulary has been presented by Catino et al (1991).…”

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confidence: 99%

Automatic synthesis of batch plant procedures: Process‐oriented approach

1994

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A strategy for the automatic synthesis of plant operating procedures, through the integration of artificial intelligence and mathematical modeling tools is discussed. This "predicative/numerical" modeling IntroductionThe chemical process industry is increasingly interested in relatively small-scale, high-value, short-life-cycle products. This shift in industry perspective, justifies a fresh look at the way processing equipment is designed, programmed, and operated in order to ensure its flexibility. Flexibility of operation allows for the manufacture of different products using the same basic equipment. It also prolongs the useful life of a production facility beyond the short life cycle of a particular product. Exploiting the inherent flexibility of such plants gives rise to difficult operation problems. Although programmable logic controllers provide effective automation and sequencing of tasks, they are "hardwired" in the sense that they must be reprogrammed for each new production cycle. This raises the cost of flexibility and limits their effective capacity to adjust to plant upsets. In addition, as the size of the plant increases, the programming of these controllers become more involved. The automatic generation of batch plant procedures has therefore emerged as an important issue.The analysis and synthesis of process operations require models of the considered problem. The models developed for the operation of flexible production facilities should be flexible themselves (that is, they should be easily modified in order to mirror changes in the plant or manufacturing conditions). Traditional modeling techniques are not explicit enough to meet Correspondence concerning this article should be addressed to R. Lavie. this requirement, while those based on artificial intelligence show promise (Stephanopoulos et al., 1990;Piela et al., 1991). The need for explicit methods becomes apparent when the size of mathematical models grows and makes their development and understanding difficult. This is the case when we must deal with models consisting of thousands of variables and constraints.Recently, there has been interest in the integration of artificial intelligence (AI) and operations research (OR) techniques for the solution of MI(N)LP [mixed integer (non)linear programming] models. OR methods are useful for the efficient solution and analysis of numerical optimization problems. However, A1 methods may provide two main avenues of potential benefit: (1) they provide modeling methodologies to simplify the generation, correction, and reuse of the process OR models; (2) they may reduce the computational load by systematically exploiting the problems logical structure and the accumulated experience. Previous works were based on propositional logic and concentrated on aspects related with the second avenue. Two examples are the generation of propositional logic models from the design superstructures considered, or the use of the logic relationships to aid the branch and bound search in the solution of MI(N)LP proble...

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Bayesian Networks in Decision Support

Carlsson

Wennersten

2002

Power Systems

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Automatic generation of qualitative models of chemical process units

Cited by 16 publications

References 7 publications

Creating Signed Directed Graph Models for Process Plants

Creating Signed Directed Graph Models for Process Plants

Automatic synthesis of batch plant procedures: Process‐oriented approach

Bayesian Networks in Decision Support

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