ProMetaS – A Metadata Schema for Process Engineering and Industry

Sherpa, Lincoln; Müller-Pfefferkorn, R.; Tolksdorf, Gregor; Khaydarov, Valentin; Wiedau, Michael; Urbas, Leon

doi:10.1002/cite.202200225

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…Due to the early beginning of the tool chain implementation, the management platform and the metadata schema ProMetaS were not already available but conceptually envisaged in the overall tool chain approach. ProMetaS is described in detail in a KEEN-related paper that is in being published in parallel to this article [22].…”

Section: Application Resultsmentioning

confidence: 99%

Tool Chain to Extract and Contextualize Process Data for AI Applications

Sherpa

Müller-Pfefferkorn

Enste³

et al. 2023

Chemie Ingenieur Technik

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Summarizing a key use case of a research workstream of the German publicly funded KEEN project, methods and tool chains are demonstrated to extract and to contextualize process data in an automated way based on engineering information. The contextualized process data serves as a high-quality data source for machine learning methods. The article covers the applied basic methodical approaches, design decisions and the results of a successful pilot installation of the developed tool chain.

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Section: Application Resultsmentioning

confidence: 99%

Tool Chain to Extract and Contextualize Process Data for AI Applications

Sherpa

Müller-Pfefferkorn

Enste³

et al. 2023

Chemie Ingenieur Technik

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“…ProMetaS, the Process Engineering/Industry Metadata Schema [41], defines various modular metadata categories (e.g., for plants, processes, or sensors) adhering to available metadata standards and extending it as needed (Fig. 2).…”

Section: Enabling Sharing Of Datamentioning

confidence: 99%

AI in Process Industries – Current Status and Future Prospects

Bortz

Dadhe

Engell

et al. 2023

Chemie Ingenieur Technik

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The chemical industry is one of the key industrial sectors in Germany and at the same time one of the largest consumers of energy and raw materials. A successful energy transition and the development of a circular economy can only succeed if they are actively supported and shaped by the chemical industry – through the redesign of existing production processes and the exploration and implementation of new process routes. The challenge is to realize this transformation within a very short time and for many production processes, whereby a much larger number of process routes must be explored. Digital technologies are key to master this transformation towards more sustainability, climate, and environmental protection. The KEEN project aims to explore and leverage artificial intelligence (AI) opportunities in process industry. The newly developed AI methods are tested wherever possible in real working environments and production plants to prove the economic benefit, applicability, and reliability of the methods and technologies.

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Towards Standardizing the Library Circulation Metadata

Dutta,

Bhuvaneshwari

2024

Metadata and Semantic Research

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ProMetaS – A Metadata Schema for Process Engineering and Industry

Cited by 3 publications

References 13 publications

Tool Chain to Extract and Contextualize Process Data for AI Applications

Tool Chain to Extract and Contextualize Process Data for AI Applications

AI in Process Industries – Current Status and Future Prospects

Towards Standardizing the Library Circulation Metadata

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