Abstract: Existing approaches for building digital twins specific to industrial plants require industry domain experts, process modeling engineers, data scientists, and solution developers to spend considerable time and effort to build the right solution. This is not an easily reproducible process. For each type of industry and for each specific plant, the design, and development process must start all over, more or less from scratch and the effort needs to be reinvested. Hence this is not a scalable proposition. Method and system disclosed herein provide a knowledge-based plant monitoring and optimization approach. In this approach, for a given high-level problem statement, a detailed problem definition is derived, a plant view of interest is identified using the knowledge based approach, and in turn plant data of interest is identified. Further, a digital twin is generated using the plant data of interest, which is then used for the plant monitoring and optimization.

Abstract: The disclosure relates generally to knowledge retrieval using ontology-based context matching. Conventional knowledge retrieval methods and systems are based on generative context that is modeled loosely as some meta data annotations. The meta data annotations do not provide sufficient semantic basis for meaningful context matching, compromising safety aspect. Other typical systems are designed such that the context is encoded into knowledge elements themselves, thus matching is too strict and the knowledge can only be used in contexts that match exactly, compromising the reuse aspect. The disclosed method and system explicitly models the context in which knowledge is generated, and matches the problem context with the generative context of knowledge, with respect to the underlying domain ontology, to assess the degree of similarity between the two, which then provides the basis for ranking the suitability of different knowledge elements for the problem context.

Abstract: The disclosure relates generally to knowledge retrieval using ontology-based context matching. Conventional knowledge retrieval methods and systems are based on generative context that is modeled loosely as some meta data annotations. The meta data annotations do not provide sufficient semantic basis for meaningful context matching, compromising safety aspect. Other typical systems are designed such that the context is encoded into knowledge elements themselves, thus matching is too strict and the knowledge can only be used in contexts that match exactly, compromising the reuse aspect. The disclosed method and system explicitly models the context in which knowledge is generated, and matches the problem context with the generative context of knowledge, with respect to the underlying domain ontology, to assess the degree of similarity between the two, which then provides the basis for ranking the suitability of different knowledge elements for the problem context.