Lab2Plant
Digital twins for the Lab2Plant transfer in modular plants
Project Information
Project Duration: 01/2023 - 12/2026
Project Manager: Dipl.-Ing. Jonathan Mädler, (Substitute: Prof. Dr.-Ing.-habil. Leon Urbas)
Project Contact: Dipl.-Ing Amy Koch
Financing: Merck KGaA, Siemens AG
Project Partners:
Project Description
Merck KGaA Process Development Lab Facilities
In the specialty chemical and pharmaceutical industries there is the ongoing challenge of specialized products and shorter product lifecycles. Here, the increasingly important technology of modular process plants plays a crucial role. Modular plants consisting of Process Equipment Assemblies (PEAs) offer a faster time-to-market by reducing the time required for construction and commissioning. The combination of PEAs with Digital Twins, so-called smart PEAs, are a key technology which must be developed in order to answer this problem Further potential lies in the integration of these smart PEAs into the process development ecosystem, here termed the Lab2Plant Transfer.
The Lab2Plant projects aims to transfer current engineering workflows from the lab ecosystem into a human augmented smart factory ecosystem consisting of smart PEAs in lab and production scales. For this purpose, Digital Twins, created from structural and behavioral models must be semantically linked to support the exchange of information between the different Digital Twin models.
Lab2Plant Concept from Koch, A. (2022)
As part of Lab2Plant, a concept is developed for creating structural and behavioral models for PEAs, which make up the Digital Twin. To integrate these Digital Twins into process development workflows as part of a Smart-Manufacturing ecosystem, new tools and interfaces, based on open standards for information models, must be created to support the semantic connection between the models which make up a Digital Twin. Further aspects include creating a functional engineering toolchain between the Process Orchestration Layer (POL) and simulation tool for capturing and utilizing data from both lab and production scale equipment in accordance with FAIR data principals.