“…Based on the shortcomings of existing methods for implementing SBDTs, it is clear that although these systems can bring significant benefits to the operation of industrial process plants, their development remains laborious and expensive. Although research on the reuse of existing simulation models could reduce implementation effort and cost [33], development of FPMs remains time-consuming, and thus expensive [10]. Moreover, wider industrial adoption of SBDTs is limited by the lack of integrated approaches for SBDT implementation, which could address complex integration of the process with simulation systems and the various simulation methods required over their lifecycle [26].…”
Section: Proposed Methods 31 Overview Of the Proposed Methodsmentioning
confidence: 99%
“…However, since the model development thereof is carried out manually, creating and maintaining industrial FPMs becomes time-consuming and thus expensive [32]- [35]. Reusing existing simulation models created for process engineering could significantly reduce model development effort and cost [33]. However, FPMs development remains laborious [10], making them less attractive than lower fidelity options based on data-driven approaches, which can be developed with less engineering effort [10], [36].…”
Section: Simulation Model Development In the Process Industrymentioning
confidence: 99%
“…Consequently, FPMs are less attractive than other lower fidelity modelling approaches based on data-driven modelling, as DDMs are generally faster to develop [10], [36]. Reusing models created for process design can reduce the maintainability costs of simulation-based applications [33], [37], [66]. However, the actual development of FPMs remains laborious, limiting broader adoption of these systems in the process industry [10].…”
Section: Development Of Fpms In the Process Industrymentioning
confidence: 99%
“…In the industrial process domain, although some research has been dedicated to the automatic generation of plant control applications during plant design 13 [33], [34], [88], [95], [96], these methods have mainly been targeted at developing the control application configuration of their application for virtual commissioning. Consequently, these techniques do not tackle the AMG of the process to be controlled.…”
Section: Amg Of Industrial Process Simulation Modelsmentioning
The author wrote the manuscript in collaboration with Mr. Miettinen, Mr. Aikala and Mr. Savolainen. The author implemented and tested the proposed method on the Aalto University laboratory process under the guidance of Dr. Karhela and Prof. Vyatkin. Publication IV: "Sliding Mode SISO Control of Model Parameters for Implicit Dynamic Feedback Estimation of Industrial Tracking Simulation Systems" The author wrote the manuscript in collaboration with Mr. Ruusu. Mr. Ruusu developed the conceptual designed of the proposed method and implemented the parameter controller. The author implemented and tested the proposed method on the Aalto University laboratory process under the guidance of Dr. Karhela and Prof. Vyatkin.
“…Based on the shortcomings of existing methods for implementing SBDTs, it is clear that although these systems can bring significant benefits to the operation of industrial process plants, their development remains laborious and expensive. Although research on the reuse of existing simulation models could reduce implementation effort and cost [33], development of FPMs remains time-consuming, and thus expensive [10]. Moreover, wider industrial adoption of SBDTs is limited by the lack of integrated approaches for SBDT implementation, which could address complex integration of the process with simulation systems and the various simulation methods required over their lifecycle [26].…”
Section: Proposed Methods 31 Overview Of the Proposed Methodsmentioning
confidence: 99%
“…However, since the model development thereof is carried out manually, creating and maintaining industrial FPMs becomes time-consuming and thus expensive [32]- [35]. Reusing existing simulation models created for process engineering could significantly reduce model development effort and cost [33]. However, FPMs development remains laborious [10], making them less attractive than lower fidelity options based on data-driven approaches, which can be developed with less engineering effort [10], [36].…”
Section: Simulation Model Development In the Process Industrymentioning
confidence: 99%
“…Consequently, FPMs are less attractive than other lower fidelity modelling approaches based on data-driven modelling, as DDMs are generally faster to develop [10], [36]. Reusing models created for process design can reduce the maintainability costs of simulation-based applications [33], [37], [66]. However, the actual development of FPMs remains laborious, limiting broader adoption of these systems in the process industry [10].…”
Section: Development Of Fpms In the Process Industrymentioning
confidence: 99%
“…In the industrial process domain, although some research has been dedicated to the automatic generation of plant control applications during plant design 13 [33], [34], [88], [95], [96], these methods have mainly been targeted at developing the control application configuration of their application for virtual commissioning. Consequently, these techniques do not tackle the AMG of the process to be controlled.…”
Section: Amg Of Industrial Process Simulation Modelsmentioning
The author wrote the manuscript in collaboration with Mr. Miettinen, Mr. Aikala and Mr. Savolainen. The author implemented and tested the proposed method on the Aalto University laboratory process under the guidance of Dr. Karhela and Prof. Vyatkin. Publication IV: "Sliding Mode SISO Control of Model Parameters for Implicit Dynamic Feedback Estimation of Industrial Tracking Simulation Systems" The author wrote the manuscript in collaboration with Mr. Ruusu. Mr. Ruusu developed the conceptual designed of the proposed method and implemented the parameter controller. The author implemented and tested the proposed method on the Aalto University laboratory process under the guidance of Dr. Karhela and Prof. Vyatkin.
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