Hybrid modeling and dynamic simulation of automated batch plants

Area of Science:

• Chemical Engineering
• Automation and Control Systems
• Computer Science

Background:

• Automated batch processing plants often exhibit complex hybrid dynamics, combining continuous and discrete-event behaviors.
• Existing modeling techniques may not adequately capture the interplay between continuous process dynamics and discrete-event control logic.
• Industrial standards for batch control require robust simulation methods for design and verification.

Purpose of the Study:

• To propose a hybrid modeling and dynamic simulation approach for automated batch processing plants.
• To integrate continuous-time/discrete-event process modeling with discrete-event controller synthesis.
• To demonstrate the feasibility of this approach using industrial standards and a realistic case study.

Main Methods:

• Developed a hierarchical-modular modeling framework to separate process and control tasks.
• Established implementation rules for a dynamic simulator capable of handling hybrid systems.
• Utilized industrial standards for batch control in the modeling process.

Main Results:

• A complete, verified hybrid model of an automated milk pasteurization plant was created.
• The proposed approach proved capable of handling problems of realistic complexity.
• Clear differentiation between process and control tasks was achieved within the hierarchical model.

Conclusions:

• The proposed hybrid modeling and dynamic simulation method is effective for automated batch processing plants.
• State-of-the-art technology can successfully address complex challenges in batch plant simulation.
• The approach facilitates robust design and verification of automated batch control systems.