Bridging the Gap in Technology Transfer for Advanced Process Control with Industrial Applications.

Area of Science:

• Control Engineering
• Software Engineering
• Industrial Automation

Background:

• Model Predictive Control (MPC) is a powerful industrial control method with significant potential for performance enhancement.
• Bridging the gap between academic research and industrial application of MPC is challenging.
• Current industrial practices often lag behind academic advancements in control techniques.

Purpose of the Study:

• To present a versatile software framework for implementing Model Predictive Control (MPC).
• To facilitate seamless technology transfer of MPC from academic research to industrial settings.
• To enhance industrial control loop performance and promote energy efficiency.

Main Methods:

• Development of a versatile software framework for MPC implementation.
• Ensuring compatibility with various target systems like programmable logic controllers and distributed control systems.
• Validation through experimental deployment on a real industrial plant.

Main Results:

• Successful deployment of the MPC software framework on an industrial plant.
• Demonstration of the framework's versatility across different control systems.
• Evidence of improved industrial control loop performance and efficiency.

Conclusions:

• The proposed software framework effectively bridges the academic-industrial gap for MPC.
• The framework supports sustainable development goals by reducing energy waste.
• The solution offers a practical pathway for adopting advanced control strategies in industry.