Building a scalable digital infrastructure for a (bio)chemical engineering pilot plant: A case study from DTU

Abstract

The digitalization of pilot-scale chemical engineering facilities offers significant potential for enabling e.g. data-driven research, process modeling, closed loop process control, and digital twin development, but the implementation of robust and maintainable infrastructure remains a practical challenge. This case study presents the digitalization of the Pilot Plant at DTU Chemical Engineering, with a focus on building a scalable and reproducible architecture for real-time data access, structured data storage, and unified system control.

A key feature of the infrastructure is the use of standardized OPC UA gateways to establish encrypted connections to a diverse set of legacy and modern unit operations. While the supervisory control and data acquisition (SCADA) system communicates directly with the OPC UA gateways, the data streams are also structured using an intermediate data broker. Here, each tag is organized by unit operation and type (e.g., sensors, controls, configurations) aligned with the underlying database schema. The broker then publishes all real-time data via MQTT. Containerized Python applications deployed on a dedicated server subscribe to the MQTT data streams and whenever experiments are active, write the real-time data to an SQL database. The system is fully extensible: new units or sensors can be added without modifying the database schema or Python code.

Unified operation and metadata collection are enabled through a web-based SCADA system, while version-controlled CI/CD pipelines ensure reproducible deployment of all services on the server. This workflow avoids manual modifications to the server and simplifies long-term maintenance. The use of open communication protocols minimizes dependency on proprietary services and ensures that individual components can be replaced or extended without vendor lock-in.

The resulting infrastructure provides both real-time and historical access to high-quality experimental data, supporting applications ranging from digital twin development and process optimization to machine learning. It serves as an educational resource used annually by approximately 150–200 students across five courses, in addition to student and Ph.D. projects. The SCADA system is routinely applied during pilot-scale unit operation exercises, while advanced courses make use of live data access and interaction with the SQL database. Beyond education, the infrastructure has been adopted across multiple research centers: for example, it underpins recent work on hybrid modeling and digital twins for pilot-scale bubble column and distillation units, and its modular components (CI/CD pipelines, database, MQTT broker, data broker) are being reused in other digitalization initiatives. These developments highlight both the scalability of the approach and its value as a transferable reference for academic and industrial pilot plants.