VR-BASED KNOWLEDGE PRESERVATION IN CHEMICAL PROCESS INDUSTRY
1 Fraunhofer Institute for Factory Operation and Automation IFF (GERMANY)
2 Otto-von-Guericke University Magdeburg (GERMANY)
3 Solvay Chemicals GmbH (GERMANY)
About this paper:
Conference name: 12th International Conference on Education and New Learning Technologies
Dates: 6-7 July, 2020
Location: Online Conference
Abstract:The availability of well-trained staff in companies is currently a major challenge. Qualified employees are about to retire, so that they cannot pass on their knowledge or experience to the following employees , . In particular, the knowledge of how to deal with highly complex, old systems in the process industry is difficult to capture textually. With Virtual Reality (VR) technologies, extensive information can be depicted clearly, comprehensibly and tangible , .
In process engineering there are a large number of existing technical systems, the operation of which is very complicated and which can benefit considerably from a VR-based knowledge transfer. An example of this is the process plant for ammonia recovery in soda production. The process produces the basic soda, which is very important for the production of detergents and glass.
As part of a project cooperation between the Fraunhofer IFF Magdeburg and a German company in the chemical process industry, a digital twin of an ammonia recovery system was developed, with which the process knowledge relating to this system can be compactly archived, trained and thus passed on to subsequent generations more quickly.
In the first project phase, the 3D models required for the digital twin were identified by the system and the processes required for this to be mapped in order to derive the necessary functions and animations. A didactic concept was also developed for optimal use, in which the 3D models, their functions and animations were embedded. In the second phase, the implementation phase, the geometries were modeled in the form of 3D objects and provided with realistic materials and textures. These geometries were then transferred to the VR environment, so that the result is a static model.
On this basis, the necessary dynamics in the form of functionalities and animations were integrated in the last phase. They map the behavior of the complex system and present the contents, how this system is composed and how these components interact with one another in various learning levels to ensure functionality. The didactic concept provides not only the presentation of the structure and the mode of operation of the system, but also the demonstration of malfunctions and their solutions for restarting. Finally, the knowledge learned can be checked and evaluated by the system.
The resulting VR system in its entirety represents an extendible knowledge archive and a learning environment for employee qualification, which can be adapted in essential core components by existing experts.
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Keywords: Virtual Reality, Knowledge Preservation, Knowledge Transfer.