CONTROLLING AND MANAGEMENT SYSTEMS IN DRYING PROCESSES
Keywords:
drying process control, industrial automation, model predictive control, fuzzy logic, neural networks, real-time monitoring, energy efficiency, SCADA systems, sensor integration, process optimizationAbstract
Drying processes play a vital role across multiple industries, yet they remain among the most energy-intensive and technically complex operations. The effective control and management of drying systems are essential for ensuring product quality, operational efficiency, and energy conservation. This paper explores the evolution, current practices, and emerging technologies in the control and automation of drying processes. Beginning with traditional PID-based control, the study reviews the shift toward model-based strategies such as Model Predictive Control (MPC) and soft computing approaches including fuzzy logic and neural networks. It also highlights the importance of sensor integration, real-time monitoring, and the adoption of SCADA and IoT frameworks for intelligent process management. Despite significant advancements, challenges such as modeling complexity, cost of implementation, and skill requirements continue to hinder widespread adoption, particularly in small and medium-sized enterprises. The discussion emphasizes that future solutions should prioritize scalability, user-friendliness, and energy efficiency. Ultimately, the research underscores the transformative potential of intelligent control systems in optimizing industrial drying operations within the context of digitalization and sustainable production.
Downloads
References
Mujumdar, A. S. (2007). Handbook of Industrial Drying (3rd ed.). CRC Press.
Krzysztofowicz, R., & Zhang, Y. (2009). Model Predictive Control in Drying Systems. Drying Technology, 27(5), 625–637.
Çengel, Y. A., & Boles, M. A. (2015). Thermodynamics: An Engineering Approach (8th ed.). McGraw-Hill Education.
Zhang, Y., Li, B., & Wang, J. (2020). ANN-based adaptive control of infrared drying for agricultural products. Journal of Food Engineering, 278, 109966.
Chen, X., Li, Z., & Deng, J. (2017). Advanced sensor technology for monitoring drying processes. Sensors and Actuators B: Chemical, 245, 1142–1150.
Strumiłło, C. (2006). Energy Aspects in Drying. Drying Technology, 24(8), 1033–1047.
Janjai, S., Bala, B. K., & Mahayothee, B. (2011). Solar drying of fruits and vegetables: Retrospective and future prospects. Renewable and Sustainable Energy Reviews, 15(7), 3220–3231.
Voogt, J., & Roblin, N. P. (2012). A comparative analysis of international frameworks for 21st century competences. Journal of Curriculum Studies, 44(3), 299–321.
Bi, J., Zhang, Y., & Sun, X. (2018). SCADA-based control strategies for industrial drying. Control Engineering Practice, 77, 1–12.
Vázquez-Canteli, J. R., & Nagy, Z. (2019). Reinforcement learning for demand response: A review of algorithms and modeling techniques. Applied Energy, 235, 1072–1089.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
All content published in the Journal of Applied Science and Social Science (JASSS) is protected by copyright. Authors retain the copyright to their work, and grant JASSS the right to publish the work under a Creative Commons Attribution License (CC BY). This license allows others to distribute, remix, adapt, and build upon the work, even commercially, as long as they credit the author(s) for the original creation.
