# Towards Semi-Automatic Generation of a Steady State Digital Twin of a Brownfield Process Plant
## 3 Proposed Methodology  
### Methodology Overview  
Figure 1 shows a vision for a methodology for the semi-automatic generation of a steady state digital twin for a brownfield process plant. The methodology consists of the following seven steps. Since several research works exist for steps 1 and 2, this article proposes solutions for the remaining steps.  
1. The main process design document that is generally available at a brownfield process plant is a P&ID. Some leading P&ID CAD vendor's tools are able to export P&IDs in a machine readable format according to the standardized Proteus XML schema, but this capability is present only in the most recent tool versions and is thus not applicable to brownfield plants [6,59]. In general, the P&IDs at a brownfield plant are paper documents that have been scanned to pdf-format, so various image recognition techniques are needed to extract information from them [12,48,60,61].
2. Graphs have emerged as an intermediate format for abstracting key information from a process plant design [6,54,55,56]. The information that is relevant for building a steady state simulation model is extracted from the digitalized P&ID into a directed graph, as described in [6]. If Proteus XML is used as the digitalized P&ID format, the methodology will be able to support also modern plants for which the P&ID could be exported directly into this format. However, if the compatibility is not required, any proprietary format for a digitalized P&ID can be used as long as the graph is generated according to the following guidelines. Process equipment such as tanks, pumps, and valves are represented with nodes, and node labels capture the type of the component as well as the tag. Flows are represented with directed edges between the components, and the type of flow (e.g., water or broke) is captured by the edge label.
3. The graph should be transformed until it is at a level of abstraction in which the steady state model can be generated by performing a one-to-one mapping from the graph nodes and edges to the equipment and flows of a steady state model.
4. Simulation tool specific rules should be defined and implemented for generating a flowsheet of the steady state model automatically from the graph generated in step 3. The rules should be implemented by a custom software tool that writes its output into a format that can be imported to the selected simulation tool.
5. A steady state modelling expert should manually finalize the flowsheet of the generated steady state model, using his or her expert modelling knowledge that could not be formalized as rules in step 4.
6. A steady state modelling expert should manually initialize the steady state simulation model by defining the chemical components (i.e., water, pulp, air, steam, etc.) and selecting the calculation modules for each process equipment using his or her expert modelling knowledge that could not be formalized as rules in step 4. Further research could try to automate this step.
7. The selected calculation module defines the needed input values for parameterizing the process equipment. The steady state modelling expert performs the parameterization manually. Further research could try to automate this step. If the parameterization is performed according to recent sensor data from the process, the steady state model may be considered as a digital twin.  
This article is scoped as follows. Previous research exists for steps 1 and 2 as has been cited above. This article will provide solutions for steps 3, 4, and 5 and apply them to a case study. Steps 6 and 7 will be addressed as further work in the final section of the article.