# Towards Semi-Automatic Generation of a Steady State Digital Twin of a Brownfield Process Plant
## 7 Conclusion and Further Work
### Summary of Results and Further Work  
In this paper, a 7-step methodology was proposed for the generation of steady state digital twins for process plants. Related works were positioned along the steps 1-2, so the focus of the paper was on steps 3-7. The findings and topics of further work for these steps are discussed next.  
In our case study, the result of steps 3 and 4 was an automatically generated flowsheet of a steady state model that required only minor manual changes by an expert modeler. Specifically, the following changes were made to two of the tanks: changing the type of the tank to another type of tank from the library of the steady state modelling tool and reconnecting the makeup flows of these tanks. It may be concluded that the generation significantly reduced the manual modelling effort and that the methodology is ready for further research on larger and more complicated processes.  
The rationale for the manual changes done in step 5 was presented in detail. The modelling decisions related to a makeup flow of a tank required the consideration of several parts of the process upstream of the said tank. The general formulation of such modelling decisions as rules is a nontrivial problem. However, one direction of further research would be the formulation and implementation of such rules, and validating them across a wide range of case studies.  
In step 6, the modelling decisions related to selecting calculation modules were discussed. It was noted that the decisions depend on the properties of incoming flows, which in turn depend on how other parts of the process were modelled. The automation of this work was left for further research.  
In step 7, concrete examples of steady state model parameterization were given, and the case model was parameterized manually based on known typical operating parameters of the process. In further work, the developed toolchain could be integrated to the process automation system and its history database in order to retrieve recent sensor values and to use them to automatically parameterize the steady state model. It is proposed that such a capability for automatic parameterization would turn a steady state simulation model to a steady state digital twin. It is notable that there is a lack of research specifically about digital twins based on steady state models, so there is no established definition for a steady state digital twin. Significant further research questions arise related to the development of the automatic parameterization capability, so it is not only an industrial information integration task. Knowledge about the recent operating conditions of the process is required to select and preprocess a suitable time period of recent process history, in order to parameterize a steady state model that will be relevant for answering the specific questions related to the unique retrofit project at hand.  
Conceptualization, S.S., L.S., and M.A.; Data curation, S.S. and L.S.; Formal analysis, S.S. and L.S.; Funding acquisition, S.S. and E.H.; Investigation, S.S.; Methodology, S.S. and L.S.; Project administration, S.S. and E.H.; Resources, S.S., L.S., and V.V.; Software, S.S. and A.V.; Supervision, S.S., E.H., and V.V.; Validation, S.S., L.S., and A.V.; Visualization, S.S. and M.A. Writing--original draft, S.S., L.S., M.A., and A.V.; Writing--review & editing, S.S., L.S., M.A., E.H., and V.V. All authors have read and agreed to the published version of the manuscript. This research was funded by Business Finland grants 3915/31/2019 and 4153/31/2019. The authors declare no conflict of interest. The funder had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.