Synthetic data derived from a digital twin for an error compensation algorithm of hydrogen tube fitting assembly

Christ, Lukas; Adler, Patrick; Schulte, Laura; Kuhlenkötter, Bernd

doi:10.1016/j.procir.2023.09.017

Cited by 1 publication

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To make this process as efficient as possible, a digital twin can be implemented to make the training data generation possible even before the physical production system is available so that the training can be carried out in parallel with the development of the production system. After implementing the physical production system, it is possible to supplement the training with data acquired in the physical system to maximize performance [55].…”

Section: Ai-based Error Compensationmentioning

confidence: 99%

HyPLANT100: Industrialization from Assembly to the Construction Site for Gigawatt Electrolysis

Büsch,

Jakschik,

Syniawa

et al. 2024

Hydrogen

Self Cite

View full text Add to dashboard Cite

The global push for sustainable energy has heightened the demand for green hydrogen, which is crucial for decarbonizing heavy industry. However, current electrolysis plant capacities are insufficient. This research addresses the challenge through optimizing large-scale electrolysis construction via standardization, modularization, process optimization, and automation. This paper introduces H2Giga, a project for mass-producing electrolyzers, and HyPLANT100, investigating large-scale electrolysis plant structure and construction processes. Modularizing electrolyzers enhances production efficiency and scalability. The integration of AutomationML facilitates seamless information exchange. A digital twin concept enables simulations, optimizations, and error identification before assembly. While construction site automation provides advantages, tasks like connection technologies and handling cables, tubes, and hoses require pre-assembly. This study identifies key tasks suitable for automation and estimating required components. The Enapter Multicore electrolyzer serves as a case study, showcasing robotic technology for tube fittings. In conclusion, this research underscores the significance of standardization, modularization, and automation in boosting the electrolysis production capacity for green hydrogen, contributing to ongoing efforts in decarbonizing the industrial sector and advancing the global energy transition.

show abstract

Section: Ai-based Error Compensationmentioning

confidence: 99%