DEVELOPMENT OF AN AUTOMATED AND INTELLIGENT CONTROL SYSTEM FOR THE POLYVINYL CHLORIDE DRYING PROCESS
Abstract
The drying process is a critical stage in polyvinyl chloride production, directly influencing product quality, energy consumption, and operational efficiency. Conventional drying systems often rely on fixed control strategies that are unable to effectively handle the nonlinear and dynamic nature of polymer drying. This study presents the development of an automated and intelligent control system for the PVC drying process aimed at improving energy efficiency and moisture uniformity.
The proposed system integrates real-time process monitoring, programmable logic controller-based automation, and intelligent control techniques, including fuzzy logic and neural network-based moisture prediction. Key drying parameters such as air temperature, humidity, and airflow rate are continuously monitored and adaptively regulated. Comparative experimental analysis was conducted between conventional control and intelligent control modes under identical operating conditions.
The results demonstrate that the intelligent control system significantly reduces drying time and energy consumption while maintaining stable operating conditions and achieving uniform final moisture content. The predictive capability of the neural network model enables proactive control actions, enhancing process reliability and product consistency. Overall, the findings confirm the effectiveness of intelligent control technologies in optimizing industrial PVC drying processes and supporting the implementation of smart manufacturing principles.
Keywords
Polyvinyl chloride; drying process; automation; intelligent control; fuzzy logic; neural networks; energy efficiency
References
- W. V. Titow, PVC Technology, 4th ed., Oxford, UK: Elsevier Applied Science, 1984.
- C. E. Wilkes, J. W. Summers, and C. A. Daniels, PVC Handbook, Munich, Germany: Hanser Publishers, 2005.
- A. Mujumdar, Handbook of Industrial Drying, 4th ed., Boca Raton, FL, USA: CRC Press, 2015.
- J. R. Couper, W. R. Penney, J. R. Fair, and S. M. Walas, Chemical Process Equipment: Selection and Design, 3rd ed., Oxford, UK: Elsevier, 2012.
- K. J. Åström and T. Hägglund, Advanced PID Control, Research Triangle Park, NC, USA: ISA, 2006.
- S. Skansi, Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence, Cham, Switzerland: Springer, 2018.
- L. Wang, A Course in Fuzzy Systems and Control, Upper Saddle River, NJ, USA: Prentice Hall, 1997.
- J. Zhang, J. Morris, and E. Martin, “Process monitoring and fault diagnosis using multivariate statistical techniques,” Control Engineering Practice, vol. 12, no. 8, pp. 899–911, 2004.
- L. Monostori et al., “Cyber-physical systems in manufacturing,” CIRP Annals – Manufacturing Technology, vol. 65, no. 2, pp. 621–641, 2016.
- G. Wypych, PVC Degradation and Stabilization, 3rd ed., Toronto, Canada: ChemTec Publishing, 2015.
- S. J. Kowalski and P. Rajewska, “Moisture transport and drying of polymeric materials,” Drying Technology, vol. 27, no. 10, pp. 1183–1192, 2009.
- M. A. Henson and D. E. Seborg, Nonlinear Process Control, Upper Saddle River, NJ, USA: Prentice Hall, 1997.
- J. M. Mendel, Fuzzy Logic Systems for Engineering: A Tutorial, New York, NY, USA: IEEE Press, 1995.
- S. Haykin, Neural Networks and Learning Machines, 3rd ed., Upper Saddle River, NJ, USA: Pearson, 2009.
- H. Kagermann, W. Wahlster, and J. Helbig, Recommendations for Implementing the Strategic Initiative Industrie 4.0, Frankfurt, Germany: acatech – National Academy of Science and Engineering, 2013.