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Der Einsatz von Mixed-Reality-(MR)-Anwendungen in der chemischen Prozessindustrie bietet das Potenzial für sicherere und effizientere Prozessauslegung, Anlagenwartung und Betriebsabläufe sowie für eine vereinfachte Schulung von Bedienpersonal. In der Praxis wird die Umsetzung jedoch häufig durch hohen Backend- und Frontend-Entwicklungsaufwand, die heterogene Landschaft an Datenquellen (z. B. Automatisierungssysteme und Engineering-Daten) sowie Anforderungen an Datenschutz, Pseudonymisierung und Rechtemanagement erschwert – insbesondere bei kleinen und mittleren Unternehmen. Diese Arbeit stellt den MR-Datenhub als wiederverwendbares Entwicklungs-Framework und Integrationsrückgrat für industrielle MR-Anwendungen vor. Der Ansatz kombiniert (1) ein vorkonfiguriertes, wiederverwendbares Backend- und Frontend-Framework, (2) standardisierte Schnittstellen zur Datenintegration sowie (3) einen Pseudonymisierungsdienst für personenbezogene und potenziell gesundheitsbezogene Daten, um DSGVO-konforme MR-Anwendungen zu unterstützen. Der Datenhub setzt auf eine GraphQL-basierte Serverarchitektur und ermöglicht die systematische Modellierung und Anbindung relevanter Datenquellen. Ergänzend wird eine Datenlandkarte vorgestellt, die domänenspezifische Standards und technische Frameworks systematisch erfasst, einordnet und strukturiert. Sie dokumentiert zudem die Mappings zwischen den Standards und spezifiziert deren Zuordnungsregeln. Der MR-Datenhub wird bereits in Projekten wie MR4SafeOperations und KeepCool eingesetzt und dient dort als Integrationsbasis für MR-gestützte Arbeitsabläufe.
BPMN 2.0 is a widely adopted standard across various industries, primarily used for business process management outside of the engineering sphere [1]. Its long history and widespread use have contributed to a mature ecosystem, offering advanced software tools for editing and optimizing business workflows.
DEXPI Process, a newly developed standard for early-phase chemical process design, focuses on representing Block Flow Diagrams (BFDs) and Process Flow Diagrams (PFDs), both crucial in the conceptual design phase of chemical plants. It provides a standardized way to document design activity, offering engineers a clear rationale for design decisions [2], which is especially valuable during a plant’s operational phases. While DEXPI Process offers a robust data model, it currently lacks an established serialization format for efficient data exchange. As Cameron et al. note in [2], finding a suitable format for DEXPI Process remains a key research area, essential for enhancing its usability and adoption. So far, Cameron et al. have explored several serialization formats for exchanging DEXPI Process information, including AutomationML, an experimental XML, and UML [2].
This work aims to map the DEXPI Process data model to BPMN 2.0, providing a standardized serialization for the newly developed standard. Mapping DEXPI Process to BPMN 2.0 also unlocks access to BPMN’s extensive software toolset. We investigate and validate the effectiveness of this mapping and the enhancements it brings to the usability of DEXPI Process through a case study based on the Tennessee-Eastman process, described in [3]. We then compare our approach with those of Cameron et al. in [2].
We conclude by presenting our findings and the key benefits of this mapping, such as improved interoperability and enhanced toolset support for chemical process engineers. Additionally, we discuss the challenges encountered during the implementation, including aligning the differences in data structures between the two models. Furthermore, we believe this mapping serves as a bridge between chemical process design engineers and business process management teams, unlocking opportunities for better collaboration and integration of technical and business workflows.
Usage of mixed reality (MR) applications in the chemical process industry can offer safer and more efficient process design, plant maintenance, and operational workflows, as well as easier operator training. Despite this potential, the development of MR applications is still slow and often impeded by high costs – especially for small and medium-sized companies. To this end, this contribution presents the MR DataHub, a framework that simplifies MR application development by providing a pre-configured backend environment for data integration and by automating frontend code generation.
The MR DataHub consists of a containerized backend server that enables connections to external data sinks and sources through standard interfaces such as REST API, MQTT, and OPC UA. Hence, it can link automation systems, enterprise resource management, and / or engineering data to the MR frontend environment. It enables the exchange of live process data and control commands, providing the MR application with essential operational information. Based on the linked sinks and sources, the MR DataHub automatically generates type and class definitions for a frontend Unity engine. Furthermore, MR DataHub offers predefined, extensible GraphQL schemas for various industry domain standards, including DEXPI P&ID and DEXPI Process, enabling faster integration of standardized data into industrial MR applications.
The benefits of the MR DataHub are demonstrated in the MR4SafeOperations project (https://mr4b.de/wir-buendnis-mr4b-projekte/mr4safeoperations/), where an MR application was developed using the MR DataHub to guide plant operators in liquid sampling procedures by providing step-by-step visual guidance integrated with live process and engineering data – enhancing operational safety. Preliminary evaluations indicate significant reductions in development time of the MR application by relying on MR DataHub’s pre-configured backend environment, predefined interfaces, and extensible GraphQL schemas.
Integration of Information Models Using Semantic Web Technologies Within the Mixed Reality Data Hub
(2026)
Engineering information is distributed across heterogeneous data models, ontologies, and reference data libraries (RDLs), which complicates interoperability, hinders workflow automation, and limits the deployment of industrial Mixed Reality (MR) applications. This work uses DEXPI Process semantics and Semantic Web technologies to align and integrate these heterogeneous sources into a single knowledge graph. The resulting graph provides a queryable semantic layer that enables consistent cross-source access to engineering information and supports MR application development. Beyond integration, the semantic graph enables automation of process design workflows. We demonstrate this using a basic engineering use case in which alternative flowsheet design options are generated from a semantically modeled block flow diagram (BFD).
Chemical process design begins with a pen and paper draft of a block flow diagram (BFD). This BFD is then further specified into a process flow diagram (PFD), which contains more detailed design decisions and elaborates on the type of unit operation or equipment to realize the functional process steps. Currently, most of this design workflow is done manually and relies on heuristics and engineering expertise. Instead, this contribution automates the generation of design alternatives in the early stages of process design using semantic web technologies.
DEXPI Process, a newly developed standard, offers a semantic data model designed to formalize design information relevant for BFDs and PFDs. Using the DEXPI Process standard to formalize basic BFDs, our approach queries publicly available standards, reference data libraries (RDLs), and ontologies to generate design options that shall then be further investigated in simulation tools. The queries retrieve lists of equipment types capable of realizing respective functions. Throughout this process, our framework makes use of the SPARQL query language as part of the semantic web technology stack. The key benefit of this approach is its reliance on dynamic, linked open data rather than on hard-coded relationships between process steps and equipment. This ensures that data can be regularly updated and sourced from multiple providers. In doing so, our work showcases the importance of RDLs and linked open data, encouraging engineers, equipment vendors, and other stakeholders to adopt technologies for machine-accessible data.
We demonstrate the functionality of this workflow by generating design alternatives for the well-known Tennessee Eastman process. In this case study, our system successfully identifies a range of feasible design alternatives, illustrating both the potential of the semantic web approach and the current limitations of available RDLs. While this work focuses on generating design options in basic engineering, future research aims to use semantic web technologies to generate P&ID base structures starting with information from BFDs and PFDs.