Leon Urbas

Predictive maintenance is a promising approach to increase safety and productivity in chemical plants. One notoriously difficult problem in predictive maintenance are hard to predetermine, non-deterministic changes such as drifts. The term "drift" can be found with different definitions in this context. Therefore, it is defined here as changes in variables and parameters that occur orders of magnitude slower than the nominal process dynamics and are not directly measurable. Previous research resulted in a hybrid method that detects and diagnoses drifts from two sources: process and equipment. This method combines model-based and statistical approaches and additional information from the equipment, such as measurement gain or power consumption, is envisioned to reduce uncertainty about the drift cause [1]. First case studies revealed significant problems regarding economically viable integration of additional information. These problems arise due to the amount of information in scenarios with multiple devices, making analysis costly and time-consuming. For solving these problems, an automated evaluation of the information is introduced. It analyzes which additional equipment information is most relevant to distinguish between different drifts, based on sensitivity analyses. Additionally, a drift index - a unique mathematical label for drifts - is defined. The extended method is then successfully applied to a scenario closer to the reality in chemical plants with multiple adjacent devices. It is shown that the additional information is evaluated automatically and drifts are diagnosed more efficiently than in the previous method.

The use of equipment digital twins of standardized, multi-purpose units can accelerate process development and reduce experimental effort. Experimental data are essential not only for identifying critical process parameters and enabling model-based methods within a Quality by Design framework, but also for constructing and validating the simulation models that describe digital twin behavior. To achieve high-fidelity and robust predictive models, structured concepts are required to manage metadata and process-, product-, and resource-specific information exchanged between physical and digital twins. Electronic lab notebooks (ELNs), which contextualize experimental data, must therefore be structured and standardized to ensure interoperability and seamless data exchange. For integration into digital twin workflows and process transfer between equipment instances of the same category, ELNs must comply with FAIR (Findable, Accessible, Interoperable, Reusable) data principles. This work proposes a Semantic Integration Pattern for FAIR-compliant ELNs to document simulated experiments using domain-specific, vendor-independent standards such as DEXPI 2.0 and ISA 88/IEC 61512-1. DEXPI 2.0 supports standardized descriptions of equipment and process steps, while ISA 88/IEC 61512-1 structures experimental recipes. The resulting FAIR ELN Semantic Integration Pattern enables standardized data exchange between ELNs and digital twin tools. Its applicability is demonstrated through an illustrative example documenting simulated saponification experiments in gPROMS® using simple batch reactor model. The results will support subsequent real experiments conducted in a 2L stirred tank unit.

Modular production concepts enable flexible process plants composed of standardized process equipment assemblies (PEAs). However, the practical implementation of modular plants across the entire engineering lifecycle, including planning, approval and operation, remains challenging. Within the ENPRO REUNION project, semantic PEA datasheets (SPEADs) were developed to enable manufacturer‐independent and machine‐readable module descriptions. The approach was validated through the commissioning of a modular pilot plant integrating modules from different manufacturers and through the development of a modular crystallization unit. In addition, energy monitoring via digital twins and a knowledge‐graph‐based hazard and operability study (HAZOP) analysis were implemented. The results demonstrate key technological building blocks for modular production while highlighting remaining challenges regarding semantic integration and regulatory approval.

In the pharmaceutical and specialty chemical industries, faster time‐to‐process is a significant measure of success. Modular Plants made up of Process Equipment Assemblies are of significant interest due to high flexibility and good scalability. Additional benefit can be leveraged from equipment‐based Digital Twins combined with methodologies such as Quality by Design (QbD). In 2024, a small survey, aimed at stakeholders in the modular plant community, was launched to address FAIR information in process development domains. Responses from professionals in multiple disciplines revealed critical gaps in information during the lifecycle of modular manufacturing systems. These insights informed the development of a Digital Twin‐based workflow for scale‐up in Modular Plants, incorporating principles of QbD. Key aspects of this workflow are illustrated for transfer of an esterification process from a 2‐L to a 50‐L reactor module.

Modulare Prozessanlagen bieten eine aufstrebende Möglichkeit, flexible Produktionsanlagen zu schaffen. Das volle Potenzial erfordert ein Ökosystem, das die Flexibilität insbesondere bei der Anbindung von Modulen unterstützt. In dieser Publikation wird die Konzeption, das Engineering und der Bau einer Infrastruktur für modulare verfahrenstechnische Anlagen beschrieben. Diese Infrastruktur zeigt eine Beispielrealisierung eines BlueFields für ein Forschungslabor.

In current practice, linear planning structures dominate in the chip industry. However, these plans do not meet the complex requirements of semiconductor production. These plans often contain branched processes, control processes and feedback loops that are not adequately captured by traditional linear representations, resulting in an incomplete understanding of the processes, which leads to errors in the planning and implementation of changes. Therefore, an UI prototype was developed that is adaptive to the user role, level of user knowledge and the specific use case. Through the contextual adaptation of the information, the non-linear structure of the production plans becomes more transparent for the user, which minimizes errors in the work process. This approach improves not only visualization and comprehensibility of the complex processes, but also reduces dependence on expert knowledge.

H2Giga ist eines der drei führenden Wasserstoffleitprojekte, die vom Bundesministerium für Bildung und Forschung (BMBF) im Rahmen des Ideenwettbewerbs „Wasserstoffrepublik Deutschland“ finanziert werden. Das Ziel von H2Giga ist es, Elektrolyseure für die Produktion von Grünem Wasserstoff in die Massenproduktion zu bringen. Der vorliegende Artikel beschreibt die Potenziale interagierender Digitaler Zwillinge und präsentiert die Ergebnisse einer gemeinsam durchgeführten Umfrage zur Entwicklung und Nutzung von Digitalen Zwillingen im Rahmen von H2Giga.