THE ROLE OF MATHEMATICAL MODELING FOR MANAGING TECHNOLOGICAL PROCESS MODELS
Article Sidebar
Main Article Content
Abstract
This article examines the crucial role of mathematical modeling in managing technological process models. It explores various theoretical approaches—including differential, integral, and algebraic models—and demonstrates how these methods facilitate process simulation, optimization, and control. The discussion highlights the benefits of integrating mathematical modeling with advanced technologies for predictive maintenance and adaptive control, while also addressing challenges such as model complexity, data quality, and computational demands. Future directions for research, including the incorporation of artificial intelligence and digital twin systems, are also outlined.
Downloads
Article Details
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain the copyright of their manuscripts, and all Open Access articles are disseminated under the terms of the Creative Commons Attribution License 4.0 (CC-BY), which licenses unrestricted use, distribution, and reproduction in any medium, provided that the original work is appropriately cited. The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations.
How to Cite
References
1.Biegler, L. T. (2010). Nonlinear Programming: Concepts, Algorithms, and Applications to Chemical Processes. SIAM.
2.Grossmann, I. E. (2002). Review of Nonlinear Mixed-Integer and Disjunctive Programming Techniques. Optimization and Engineering, 3(3), 227–252.
3.Rawlings, J. B., & Mayne, D. Q. (2009). Model Predictive Control: Theory and Design. Nob Hill Publishing.
4.Aris, R. (1965). Introduction to the Analysis of Chemical Reactors. Prentice Hall.
5.Lewis, F. L., Vrabie, D., & Syrmos, V. L. (2012). Optimal Control. John Wiley & Sons.
6.Scott, R. L. (2008). Computer Simulation of Industrial Processes: Principles and Applications. Wiley.
7.Seydel, R. (2010). Tools for Nonlinear Optimization: A Practical Guide. Springer.