Paper Titles
Developing an EnergyPlus-Based Simulation Model for Energy and Comfort Analysis of an Office Building in Debrecen
p.379
Modeling the Breakage of Agricultural Particulate Materials: Review
p.385
Impact of Machined Surface Quality Level on Electrical Energy Consumption in WEDM
p.399
History and Manufacturing Analysis of Single – Purpose Gear Manufacturing Working Machines
p.411
Comparative Analysis of Manufacturing Technologies for Cycloidal Drive Production: FDM 3D Printing vs CNC Laser-Cut Aluminum
p.419
Quantitative Evaluation of Process Discovery Enhancement Using Clustering-Based Techniques
p.435
Applying AI to Predict High-Cost Rental Prices: A Comparative Modelling Approach
p.451
AI Powered KPI Optimization: Bridging the Gap between Industry Data and Operational Excellence
p.459
Process Simulation Using Parametrized Models
p.465

Process Simulation Using Parametrized Models

Article Preview

Abstract:

The application of virtual factory simulation plays a pivotal role in the design and optimization of modern production systems. By enabling the evaluation and refinement of manufacturing processes prior to physical implementation, process simulation significantly enhances decision-making efficiency. Real-time optimization has attracted considerable attention in the process industry and is widely adopted, as it typically relies on external databases and parameter sets. This study investigates the import, use, and management of such external parameters, providing a comprehensive overview. A detailed case study is developed and solved to demonstrate the proposed approach. The results show that process simulation with parameterized models improves system efficiency and enables real-time optimization capabilities.

You might also be interested in these eBooks
International Scientific Conference on Advances in Mechanical Engineering (11th ISCAME) View Preview

Info:

Periodical:

Advances in Science and Technology (Volume 181)

Pages:

465-472

DOI:

https://doi.org/10.4028/p-8MARBs

Citation:

Cite this paper

Online since:

August 2026

Authors:

Gábor Ruzicska*, Levente Czégé, Tamás Mankovits

Keywords:

External Database, Parameters, Parametrized Models, Process Simulation, Real Time Optimization

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

© 2026 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

References

[1] H.-P. Lu, C.-I. Weng, Smart manufacturing technology, market maturity analysis and technology roadmap in the computer and electronic product manufacturing industry, Technological Forecasting and Social Change, 133 (2018) 85-94.

DOI: 10.1016/j.techfore.2018.03.005

Google Scholar

[2] S. Chandra Sekaran, H. J. Yap, S. N. Musa, K. E. Liew, C. H. Tan, A. Aman, The implementation of virtual reality in digital factory — a comprehensive review, The International Journal of Advanced Manufacturing Technology, 115 (5-6) (2021) 1349-1366.

DOI: 10.1007/s00170-021-07240-x

Google Scholar

[3] J.W. Fowler, O. Rose, Grand challenges in modeling and simulation of complex manufacturing systems, Simulation, 80 (9) (2004) 469-476.

DOI: 10.1177/0037549704044324

Google Scholar

[4] J. Wang, Q. Chang, G. Xiao, N. Wang, S. Li, Data driven production modeling and simulation of complex automobile general assembly plant, Computers in Industry, 62 (7) (2011) 765-775.

DOI: 10.1016/j.compind.2011.05.004

Google Scholar

[5] F. J. Vuzem, M. Pipan, H. Zupan, M. Šimic, N. Herakovič, Automated Generation of Simulation Models and a Digital Twin Framework for Modular Production, Systems, 13(9) (2025) 800.

DOI: 10.3390/systems13090800

Google Scholar

[6] G. Zhou, C. Zhang, Z. Li, K. Ding, C. Wang, Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing, International Journal of Production Research, 58 (4) (2020) 1034-1051.

DOI: 10.1080/00207543.2019.1607978

Google Scholar

[7] P. Georgiadis, C. Michaloudis, Real-time production planning and control system for job-shop manufacturing: A system dynamics analysis, European Journal of Operational Research, 216 (1) (2012) 94-104.

DOI: 10.1016/j.ejor.2011.07.022

Google Scholar

[8] E. M. Frazzon, M. Kück, M. Freitag, Data-driven production control for complex and dynamic manufacturing systems, CIRP Annals, 67(1) (2018) 515-518.

DOI: 10.1016/j.cirp.2018.04.033

Google Scholar

[9] L. Li, Bottleneck detection of manufacturing systems using a data-driven method, International Journal of Production Research, 47(24) (2009) 6929-6940.

DOI: 10.1080/00207540802427894

Google Scholar

[10] S. Nagahara, T. A. Sprock, M. M. Helu, Toward data-driven production simulation modeling: dispatching rule identification by machine learning techniques, Procedia CIRP, 81 (2019) 222-227.

DOI: 10.1016/j.procir.2019.03.039

Google Scholar

[11] K. Mykoniatis, G. A. Harris, A digital twin emulator of a modular production system using a data-driven hybrid modeling and simulation approach, Journal of Intelligent Manufacturing, 32(7) (2021) 1899-1911.

DOI: 10.1007/s10845-020-01724-5

Google Scholar

[12] D. Antonelli, P. Litwin, D. Stadnicka, Multiple system dynamics and discrete event simulation for manufacturing system performance evaluation, Procedia CIRP, 78 (2018) 178-183.

DOI: 10.1016/j.procir.2018.08.312

Google Scholar

[13] T. Tvrdon, P. Fedorko, Usage of Dynamic Simulation in Pressing Shop Production System Design, Int. J. Simul. Model., 19(2) (2020) 185-196.

DOI: 10.2507/ijsimm19-2-494

Google Scholar

[14] A. Skoogh, T. Perera, B. Johansson, Input data management in simulation – Industrial practices and future trends, Simulation Modelling Practice and Theory, 29 (2012) 181-192.

DOI: 10.1016/j.simpat.2012.07.009

Google Scholar

[15] N. Robertson, T. Perera, Automated data collection for simulation?, Simulation Practice and Theory, 9 (2002) 349-364.

DOI: 10.1016/s0928-4869(01)00055-6

Google Scholar