Automatic Generation of a High-Fidelity Dynamic Thermal-Hydraulic Process Simulation Model From a 3D Plant Model

• Main Authors: Gerardo Santillan Martinez, Seppo A. Sierla, Tommi A. Karhela, Jari Lappalainen, Valeriy Vyatkin
• Format: Article
• Language: English
• Published: IEEE 2018-01-01
• Series: IEEE Access
• Subjects: 3D CAD model; 3D plant model; automatic model generation; first-principles model; process modeling; process simulation
• Online Access: https://ieeexplore.ieee.org/document/8434288/

Dynamic thermal-hydraulic simulation models have been extensively used by process industry for decision support in sectors, such as power generation, mineral processing, pulp and paper, and oil and gas. Ever-growing competitiveness in the process industry forces experts to rely even more on dynamic simulation results to take decisions across the process plant lifecycle. However, time-consuming development of simulation models increases model generation costs, limiting their use in a wider number of applications. Detailed 3-D plant models, developed during early plant engineering for process design, could potentially be used as a source of information to enable rapid development of high-fidelity simulation models. This paper presents a method for automatic generation of a thermal-hydraulic process simulation model from a 3-D plant model. Process structure, dimensioning, and component connection information included in the 3-D plant model are extracted from the machine-readable export of the 3-D design tool and used to automatically generate and configure a dynamic thermal-hydraulic simulation model. In particular, information about the piping dimensions and elevations is retrieved from the 3-D plant model and used to calculate head loss coefficients of the pipelines and configure the piping network model. This step, not considered in previous studies, is crucial for obtaining high-fidelity industrial process models. The proposed method is tested using a laboratory process, and the results of the automatically generated model are compared with experimental data from the physical system as well as with a simulation model developed using design data utilized by existing methods on the state of the art. Results show that the proposed method is able to generate high-fidelity models that are able to accurately predict the targeted system, even during operational transients.