4.6 Article

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

期刊

IEEE ACCESS
卷 6, 期 -, 页码 45217-45232

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2018.2865206

关键词

3D CAD model; 3D plant model; automatic model generation; first-principles model; process modeling; process simulation; thermal-hydraulic model

资金

  1. Engineering Rulez Research Project through Business Finland
  2. Technical Research Centre of Finland
  3. Aalto University
  4. Equa
  5. Fennovoima
  6. Fortum
  7. Masinotek
  8. Outotec
  9. Prosys OPC
  10. PSK
  11. Poyry
  12. Semantum

向作者/读者索取更多资源

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.

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