Design and Implementation of a Web-Based SCADA for Closed-Loop PID Control Optimized by AI with PLC Integration
A Multi-Layer Architecture Integrating Real-Time Monitoring, Intelligent PID Tuning, and Industrial Automation
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This journal aims to design and implement a Web Based SCADA system integrated with PLC and using the Firefly algorithm for PID control parameter optimization in a closed loop system. The system is developed following the ISA-95 architecture on 1st Layer to 3rd Layer, including data collection via PLC, monitoring via SCADA, and AI-based control and analysis. The Firefly algorithm is used to automatically refine the Kp, Ki, and Kd parameters so that the control system works optimally. Testing was carried out on a liquid level control system using the LCV-01 valve control. The results showed that the Firefly method produced faster steady state time and lower overshoot compared to the manual and Ziegler-Nichols methods. The developed Web Based SCADA interface supports real time monitoring, multiuser, historical data trending, and is flexible to access via a browser without special installation. This SCADA-AI-PLC integration provides an adaptive, efficient, and economical solution for modern industrial automation.
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[1] Altbawi, S. M. A., et al. "Tuning of PID Controller Using Hybridized Modified Firefly-Chaos Optimization Algorithm." International Journal of Intelligent Systems and Applications in Engineering, vol. 9, no. 3, pp. 2224–2230, 2021.
[2] Rustamova, S., and F. Rustamov. "Problems of Integrating Artificial Intelligence with SCADA Systems." International Journal of Energy Applications and Technologies, vol. 10, no. 2, pp. 123–130, 2023.
[3] Irawan, D., Syah, S. A. A., & Cahyani, D. O. "Tuning PID Controller Berbasis Algoritma Kecerdasan Buatan." Multitek Indonesia: Jurnal Ilmiah, vol. 18, no. 1, pp. 72–82, 2024.
[4] Zhang, Y., et al. "AI-Enhanced SCADA Systems: A Review of Trends and Future Direc-tions." IEEE Access, vol. 11, pp. 105344–105361, 2023.
[5] Kumar, R., & Singh, A. "Metaheuristic Algorithms for PID Tuning in Industrial Applica-tions." Journal of Control Engineering and Applied Informatics, vol. 25, no. 2, pp. 33–44, 2023.
[6] Chen, L., et al. "Implementation of Cloud-Based SCADA Systems for Smart Manufactur-ing." Sensors, vol. 21, no. 18, 2021.
[7] El-Hawary, M. E., & Ahmed, K. "Digital Twin-Enabled Web-Based SCADA Systems." IEEE Transactions on Industrial Informatics, vol. 18, no. 12, pp. 8921–8932, 2022.
[8] Ali, H., & Rahman, M. "An Improved Firefly Algorithm for Nonlinear PID Controller Tun-ing." Applied Soft Computing, vol. 136, 2023.
[9] Singh, P., et al. "PLC-SCADA Integration with IoT for Smart Industrial Control." Interna-tional Journal of Electrical and Computer Engineering, vol. 14, no. 1, pp. 55–63, 2024.
[10] Nguyen, T., et al. "Web-Based Supervisory Control Systems in Industry 4.0." Journal of Industrial Information Integration, vol. 30, 2022.
[11] Suryawan, A., et al. "Optimization of PID Controller Parameters Using Firefly Algorithm in Temperature Control System." Journal of Physics: Conference Series, vol. 1869, 2021.
[12] Prakash, J., & Verma, A. "Artificial Intelligence for SCADA Data Analytics." IEEE Access, vol. 12, pp. 55422–55433, 2024.
[13] Li, X., et al. "Hybrid Firefly Algorithm for Robust PID Control in Power Systems." Energies, vol. 15, no. 6, 2022.
[14] Ghosh, A., & Banerjee, S. "Advances in SCADA for Industrial Automation Using AI." Inter-national Journal of Automation and Smart Technology, vol. 13, no. 4, pp. 281–292, 2023.
[15] Zhang, J., & Zhao, Y. "Adaptive PID Tuning via Firefly Algorithm in Cyber-Physical Sys-tems." Frontiers in Control Engineering, vol. 5, 2024.
[16] Santos, M., et al. "Web-SCADA with Cloud and Edge Computing Integration." IEEE Inter-net of Things Journal, vol. 10, no. 7, pp. 5990–6001, 2023.
[17] Wang, H., et al. "Data-Driven SCADA System Design for Process Optimization." Computers & Chemical Engineering, vol. 162, 2022.
[18] Kumar, S., et al. "IoT and AI-Based PID Tuning for Industrial Control." International Jour-nal of Electrical Power & Energy Systems, vol. 140, 2022.
[19] Rahman, M., et al. "AI-Based Optimization Algorithms for PID Control in Industry 4.0." Applied Sciences, vol. 14, no. 2, 2024.
[20] Yilmaz, B., et al. "Real-Time Web-Based SCADA with Machine Learning Integration." Journal of Industrial Engineering International, vol. 19, pp. 421–432, 2023.
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