# Towards Semi-Automatic Generation of a Steady State Digital Twin of a Brownfield Process Plant
)1  
[PERSON]  
1 Department of Electrical Engineering and Automation, Aalto University, 00076 Aalto, Finland; [PERSON]SS] (S.S.); [PERSON] (V.V. )1  
[PERSON]  
1 Department of Electrical Engineering and Automation, Aalto University, 00076 Aalto, Finland; [PERSON]SS] (S.S.); [PERSON] (V.V. )1  
[PERSON]  
1 Department of Electrical Engineering and Automation, Aalto University, 00076 Aalto, Finland; [PERSON]SS] (S.  
A _digital twin_ is a special kind of plant model that has been synchronized with the physical process using recent sensor information. Thus, a digital twin would be especially suited for designing retrofits for brownfield plants that have been in operation for a long time. The following requirements are identified for the digital twin:  
1. It accurately captures aspects of the plant relevant to the retrofit
2. It can be generated from source information commonly available at brownfield plants
3. Minimal manual engineering effort should be involved  
Despite much recent research on digital twins, there is a lack of research addressing these requirements. Numerous definitions for digital twins have been proposed. An _experimentable_ digital twin, based on a simulation model of the plant, is suited for assessing impacts of a retrofit [8]. Simulation approaches for industrial process plants can be categorized into _steady state_ and _dynamic_. Dynamic simulation has the special capability of determining how the process state changes over time in response to an event such as the closing of a valve or a setpoint change. Such capabilities are essential for investigating modernization of automation systems. A dynamic simulation model can be extended to an experimentale digital twin through tracking approaches that synchronize the simulation model with the process state measured by sensors [9]. Essential source information for generating a high-fidelity dynamic simulation model includes pipeline routing details, which are used to determine pressure head losses [10]. If such information is available, for example from 3D CAD models, a dynamic simulation model can be automatically generated [10], and it can be extended to an experimentable digital twin with tracking simulation [11]. However, such source information is generally not available for brownfield plants [7; 12]. Point clouds from 3D scanning of industrial plants support use cases such as detection of whether a factory layout is collision free [13], but this does not capture essential information for creating a dynamic simulation model, namely individual components and their connections. Thus, the focus of this article is experimentale digital twins based on steady state simulation models. Such twins could be used for supporting the operators in their daily decision-making or the management in strategic decision-making [14]. These twins may also be used for \"what if\"-studies, i.e., for the assessment of retrofits involving physical process configuration changes (e.g., process stream re-arrangements; removing, replacing or installing a new process equipment such as a purification step or heat recovery system) or changes in the key process parameters (e.g., temperature, consistency). As an outcome, the digital twin would evaluate the impact of design alternatives for the retrofit in terms of the process's fresh water, energy, chemical or utility consumption. It could also be used to determine the chemical state of the process by modelling the pH, COD levels (Chemical Oxygen Demand), TSS levels (Total Suspended Solids) or trace component amounts in the process streams. A steady state digital twin would be a powerful tool to improve understanding of the process, investigate abnormal situations in the plant or train process operators [14]. This article is structured as follows. Section 2 reviews related research in the fields of steady state simulation, digital twin research in the context of brownfield plants, and automatic generation of digital twins.