Introduction: Real-time monitoring and control are critical components of modern industrial processes, enabling operators to optimize production efficiency, ensure product quality, and minimize operational costs. This session focuses on the latest advancements in real-time monitoring and control technologies, aiming to enhance the efficiency and reliability of processes across various industries.

Methods: Utilizing state-of-the-art sensors, data acquisition systems, and control algorithms, this study explores innovative approaches to real-time process monitoring and control. Advanced process models and machine learning techniques are employed to predict process behavior and identify optimal control strategies. The integration of the Industrial Internet of Things (IIoT) platform into these models enables the remote monitoring and control of processes, enhancing their flexibility and scalability.

Results: This research yields significant advancements in real-time monitoring and control. The implementation of advanced sensing technologies enables high-resolution data acquisition, facilitating real-time process monitoring and anomaly detection. The integration of predictive analytics and control algorithms enables the proactive adjustment of process parameters to optimize performance and prevent disruptions. Case studies demonstrate the practical application of real-time monitoring and control techniques to improve productivity, reduce waste, and ensure regulatory compliance across diverse industrial sectors.

Conclusions: In conclusion, this session highlights the transformative impact of real-time monitoring and control on industrial processes. By harnessing the power of advanced technologies and analytics, we can achieve greater operational efficiency, sustainability, and competitiveness in today's dynamic market environment.