Towards a Systematic Data Harmonization to Enable AI Application in the Process Industry

Wiedau, Michael; Tolksdorf, Gregor; Oeing, Jonas; Kockmann, Norbert

doi:10.1002/cite.202100203

Cited by 15 publications

(14 citation statements)

References 18 publications

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“…However, many data issues still exist, some connected with the volume and speed of data acquisition, some connected with reliability and uncertainty, some to do with dynamic model updating, and others related to data sharing and exchange standards, all of these hindering the chemical process industry to efficiently apply data-driven technologies for their assets in large scale. There need improve the data integration across the whole asset life cycle from process design over the functional design and asset specification up to the operation of the actual assets, building an Industrial Internet of Things (IIoT) platform to harness data integration and enable mashup applications like digital twins [41].…”

Section: Dt Datamentioning

confidence: 99%

Process Digital Twin and Its Application in Petrochemical Industry

Gao¹,

Jia²,

Liu³

2022

JSEA

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Digital twin (DT) is drawing significant attention both from the academia, industry and government. However, people from different fields have different understandings and cognitions about DT. In addition, most of the DT application scenarios discussed belong to discrete manufacturing and are not suitable for process manufacturing. Petrochemical industry is a typical process manufacturing with multi-scale hierarchical and functional structure in space and time. This contribution focuses on topics on the application of DT in petrochemical industry including: 1) The specific DT definition by process industry. 2) The three key elements and design of chemical DT. 3) Features and application scenarios of chemical DT from the view of model precision, model scale and asset life cycle. 4) The Four P's maturity framework of chemical DT, and 5) Prospects for the development of chemical DT.

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Section: Dt Datamentioning

confidence: 99%

Process Digital Twin and Its Application in Petrochemical Industry

Gao¹,

Jia²,

Liu³

2022

JSEA

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“…Furthermore, dependencies and interrelationships between elements in information models need to be modeled. Here, the data exchange in the process industry (DEXPI) activities should be mentioned [21], which are intended to facilitate the data exchange between simulation and P&ID programs in process industries. These models support the greenfield and brownfield engineering of hybrid plants by providing information about the monolithic and modular parts.…”

Section: Conceptual Models Of Modularity From Different System Viewsmentioning

confidence: 99%

“…In the future, artificial intelligence (AI) tools may assist process steps in the engineering workflow [71]. The seamless data integration from process simulation with the export to DEXPI can enable their import into P&ID packages and AI-assisted engineering tools [21]. Machine-readable formats and structures are important for data transfer and analysis, which are currently under development [69].…”

Section: Simulation Models In the Engineering And Operation Of Hybrid...mentioning

confidence: 99%

“…In some way, seamless engineering is currently not possible due to different departments of process development in companies, engineering with different disciplines, and separated operation and production. A common database and machine-readable structure are essential for this approach [21]. There is a high effort for applying modules to other, already existing or conventionally planned, monolithic plant types, particularly to dedicated single-product plants.…”

Section: Preliminary Conclusionmentioning

confidence: 99%

“…Standards play a crucial role in the design and operation of such systems. Today, standards in cyber-physical systems and systems engineering are the key to higher efficiency in data analysis with consistent access [20,21]. Industrial standards were formed during the Industrial Revolution, e.g., the tube diameters or the Whitworth thread in 1841 based on company standards [22].…”

Section: Introduction To Modularity Modularization and Modular Systemsmentioning

confidence: 99%

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Definition, Characterization, and Modeling of Hybrid Modular‐Monolithic Process Plants

Markaj

Fay

Kockmann

2022

Chemie Ingenieur Technik

Self Cite

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The modularization of production plants and their automation in the pharmaceutical and (bio-)chemical industry is currently gaining attention and is subject of standardization efforts. The combination of modular and monolithic (bio-)chemical plants and related engineering processes, i.e., hybrid process plants and hybrid engineering, respectively, offers advantages in production flexibility and time to operation. However, combining the different approaches is full of hurdles and unsolved issues. In this paper, hybrid process plants are defined and characterized in a process systems framework. Modeling and simulation requirements are elaborated for the engineering workflow of hybrid process plants. The contribution concludes with summarizing current research topics and pointing to a future research agenda.

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Developing an Ontology on Battery Production and Characterization with the Help of Key Use Cases from Battery Research

Nebel,

Beran,

Königer

et al. 2024

Adv Eng Mater

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Materials science research faces challenges due to diverse and evolving measurements, materials, and methods. Managing research data in a way that is understandable, comparable, and reproducible is essential for high data quality, particularly for data science and machine learning applications. In Li‐ion batteries research data storage concepts and structures vary widely between institutions and researchers, leading to difficulties in data comparison and understanding. To address the issue of data structuring, battery production and characterization ontology (BPCO) is developed. The ontology builds on existing ontologies like the Platform MaterialDigital core ontology and quantities, units, dimensions, and types ontology to model standard battery production processes, characterization methods, and materials. The BPCO is based on a workflow structure to be accessible to nonexperts and, unlike highly specialized existing ontologies, models the whole production process removing the need for separate data structures and enabling the identification of dependencies between parameters. This work builds upon a previously published paper in which the taxonomy and fundamental strategies for ontology development are established. The article presents the developed ontology and its use for structuring research data in three key use cases, that is, different experiments performed to validate the ontology's capabilities, provide feedback, and ensure its applicability.

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Towards a Systematic Data Harmonization to Enable AI Application in the Process Industry

Cited by 15 publications

References 18 publications

Process Digital Twin and Its Application in Petrochemical Industry

Process Digital Twin and Its Application in Petrochemical Industry

Definition, Characterization, and Modeling of Hybrid Modular‐Monolithic Process Plants

Developing an Ontology on Battery Production and Characterization with the Help of Key Use Cases from Battery Research

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