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- W2000607103 abstract "Deep knowledge about process behaviors plays an important role in the diagnosis of chemical processes. Cause-and-effect reasoning using deep knowledge is useful especially for interacting malfunctions. This work explores the integration of deep knowledge into task-specific, knowledge-based architectures for resolving interacting multiple malfunctions and presents a novel methodology called diagnostically focused simulation (DFS). Invoked in an auxiliary manner, DFS uses deep knowledge and performs qualitative simulation in a highly constrained manner. The close integration with other problem solvers is an evolutionary approach to using qualitative simulation in diagnosis and manages a normally computationally-explosive procedure. Diagnostic results from the compiled problem solver provide a situation-specific assessment of the chemical process, identify possible malfunction scenarios, and focus on appropriate levels of process detail. DFS effectively demonstrates a balance between run-time simulation and compiled problem solving in diagnosis." @default.
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- W2000607103 date "1991-04-01" @default.
- W2000607103 modified "2023-10-14" @default.
- W2000607103 title "Managing qualitative simulation in knowledge-based chemical diagnosis" @default.
- W2000607103 doi "https://doi.org/10.1002/aic.690370410" @default.
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