ADVANCED PROCESS CONTROL TECHNIQUES IN AUTOMATED PRODUCTION

Authors

  • Khankeldiyeva Zebinisa Khabibovna Teacher, Bukhara state technical university

Keywords:

Advanced process control (APC), Automated production, model predictive control (MPC), feedforward control, multivariable control, soft sensors, real-time optimization (RTO), process automation.

Abstract

This article explores Advanced Process Control (APC) techniques in automated production systems, emphasizing their role in optimizing industrial processes. It discusses core methods such as Model Predictive Control (MPC), feedforward control, multivariable control, soft sensors, and real-time optimization. The article highlights implementation challenges, benefits, and emerging trends, including AI integration, digital twins, and edge computing, demonstrating how APC enhances efficiency, product quality, and sustainability in modern manufacturing.

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References

Camacho, E. F., & Bordons, C. (2004). Model Predictive Control. Springer.

Marlin, T. E. (2000). Process Control: Designing Processes and Control Systems for Dynamic Performance. McGraw-Hill.

Morari, M., & Lee, J. H. (1999). Model Predictive Control: Past, Present and Future. Computers & Chemical Engineering, 23(4–5), 667–682.

Qin, S. J., & Badgwell, T. A. (2003). A Survey of Industrial Model Predictive Control Technology. Control Engineering Practice, 11(7), 733–764.

Seborg, D. E., Edgar, T. F., Mellichamp, D. A., & Doyle, F. J. (2010). Process Dynamics and Control (3rd ed.). Wiley.

Zhang, Y., Li, X., & Zhou, Q. (2021). AI-Enhanced Advanced Process Control and Digital Twin Applications in Smart Manufacturing. Journal of Process Control, 101, 1–15.

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Published

2026-02-19

How to Cite

Khankeldiyeva Zebinisa Khabibovna. (2026). ADVANCED PROCESS CONTROL TECHNIQUES IN AUTOMATED PRODUCTION. Journal of Applied Science and Social Science, 16(02), 926–930. Retrieved from https://www.internationaljournal.co.in/index.php/jasass/article/view/3379