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.
This article investigates how graph matching can be applied to process plant design data in order to support the reuse of previous designs. A literature review of existing graph matching algorithms is performed, and a group of algorithms is chosen for further testing. A use case from early phase plant design is presented. A methodology for addressing the use case is proposed, including graph simplification algorithms and node similarity measures, so that existing graph matching algorithms can be applied in the process plant domain. The proposed methodology is evaluated empirically on an industrial case consisting of design data from several pulp and paper plants.
The benefits of the use of modeling and simulation in engineering are acknowledged widely. It has proven its advantages e.g., in virtual prototyping i.e., simulation aided design and testing as well as in training and R&D. It is recognized to be a tool for modern decision making. However, there are still reasons that slow down the wider utilization of modeling and simulation in companies. Modeling and simulation tools are separate and are not an integrated part of the other engineering information management in the company networks. They do not integrate well enough into the used CAD, PLM/PDM and control systems. The co-use of the simulation tools themselves is poor and the whole modeling process is considered often to be too laborious. In this article we introduce an integration solution for modeling and simulation based on the semantic data modeling approach. Semantic data modeling and ontology mapping techniques have been used in database system integration, but the novelty of this work is in utilizing these techniques in the domain of modeling and simulation. The benefits and drawbacks of the chosen approach are discussed. Furthermore, we describe real industrial project cases where this new approach has been applied.
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