Automatic Generation of a High-Fidelity Dynamic Thermal-hydraulic Process Simulation Model from a 3D Plant Model

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Santillán Martínez, Gerardo
dc.contributor.author Sierla, Seppo
dc.contributor.author Karhela, Tommi
dc.contributor.author Lappalainen, Jari
dc.contributor.author Vyatkin, Valeriy
dc.date.accessioned 2018-09-06T10:16:45Z
dc.date.available 2018-09-06T10:16:45Z
dc.date.issued 2018-08-13
dc.identifier.citation Santillán Martínez , G , Sierla , S , Karhela , T , Lappalainen , J & Vyatkin , V 2018 , ' Automatic Generation of a High-Fidelity Dynamic Thermal-hydraulic Process Simulation Model from a 3D Plant Model ' IEEE Access . DOI: 10.1109/ACCESS.2018.2865206 en
dc.identifier.issn 2169-3536
dc.identifier.other PURE UUID: 7fd57046-184d-4f06-9aab-33099edc3049
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/automatic-generation-of-a-highfidelity-dynamic-thermalhydraulic-process-simulation-model-from-a-3d-plant-model(7fd57046-184d-4f06-9aab-33099edc3049).html
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/27673844/ELEC_Martinez_Automated_Genration_IEEE_ACCESS_2018.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/33863
dc.description.abstract 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 3D 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 3D plant model. Process structure, dimensioning and component connection information included in the 3D plant model is extracted from the machine-readable export of the 3D 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 3D plant model and used to calculate head loss coefficients of the pipelines and to 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 which are able to accurately predict the targeted system, even during operational transients. en
dc.format.extent 16
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries IEEE Access en
dc.rights openAccess en
dc.subject.other 113 Computer and information sciences en
dc.title Automatic Generation of a High-Fidelity Dynamic Thermal-hydraulic Process Simulation Model from a 3D Plant Model en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Electrical Engineering and Automation
dc.contributor.department VTT Technical Research Centre of Finland
dc.subject.keyword 3D CAD modelling
dc.subject.keyword 3D Plant Model
dc.subject.keyword automatic model generation
dc.subject.keyword first principles model
dc.subject.keyword process modelling
dc.subject.keyword process simulation
dc.subject.keyword thermal-hydraulic model
dc.subject.keyword ditigal twin
dc.subject.keyword 113 Computer and information sciences
dc.identifier.urn URN:NBN:fi:aalto-201809064974
dc.identifier.doi 10.1109/ACCESS.2018.2865206
dc.type.version publishedVersion


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