Introduction: Industry 4.0, characterized by the integration of cyber–physical systems, data analytics, and automation, is driving a paradigm shift in process control and monitoring. This session explores the latest innovations in process automation and adaptive control techniques, aiming at enabling agile and responsive manufacturing operations in the era of digital transformation.

Methods: Drawing upon principles from control theory, machine learning, and artificial intelligence, this study investigates advanced process automation and adaptive control strategies. Autonomous control systems, equipped with sensor networks and intelligent algorithms, enable real-time adaptation to changing process conditions and demand fluctuations. The integration of digital twins and simulation-based optimization techniques enables virtual commissioning and predictive maintenance, reducing time-to-market and operational risks.

Results: This research yields significant advancements in process automation and adaptive control. The implementation of autonomous control systems enhances process agility and responsiveness, enabling adaptive production scheduling and resource allocation. The integration of digital twins facilitates the virtual prototyping and optimization of control strategies, reducing commissioning time and improving process reliability. Case studies demonstrate the practical application of adaptive control techniques in improving throughput, quality, and energy efficiency in manufacturing operations.

Conclusions: In conclusion, this session highlights the transformative potential of process automation and adaptive control in enabling agile and intelligent manufacturing systems. By embracing Industry 4.0 principles and leveraging advanced technologies, we can unlock new opportunities for innovation, efficiency, and competitiveness in the digital age.