Abstract
Digital twins and digital shadows are frequently used terms by industry and academia to describe data-centric models that accurately depict a physical system intended for process monitoring and control. Processes restricted by a low degree of automation rely greatly on operator competencies in key decision-making; a digital shadow can here assist as a guidance tool [1-4]. This work presents a practical implementation of a digital shadow to support operators running a pilot scale-packed batch distillation column at the Technical University of Denmark (DTU) primarily used in education and teaching activities [5]. This operation is selected due to inherent unsteady process dynamics that are controlled by a set of manual valves, which the student operator must continuously balance to meet purity constraints without disrupting the operation. This realisation employ a modular software architecture, separated into four distinct modules compiled into Docker images and independently deployed. These facilitate real-time connection with the equipment via OPC-UA endpoints, process model analysis via HTTP/HTTPS requests, and a dashboard UI presenting the data accessible through any web browser. The final module is realised as a Python package that stores thermodynamic models and is published on the internal GitLab registry for more straightforward utilisation across multiple projects. The advantages of this implementation strategy are flexibility and speed, allowing for continuously updating process models and directly employing them in various projects.