Enhanced grinding process of a cement ball mill through a generalised predictive controller integrated with a CARIMA...

Area of Science:

• Industrial Process Control
• Chemical Engineering
• Energy Management

Background:

• Cement ball mills are major energy consumers in cement production.
• Optimizing control is crucial for energy efficiency, productivity, and product quality.
• Existing controllers may not meet the demands for reduced energy consumption and improved performance.

Purpose of the Study:

• To design and implement generalised predictive controllers (GPC) for cement ball mill grinding.
• To analyze the performance of GPC in terms of servo and regulatory responses.
• To compare GPC performance against conventional controllers using error metrics.

Main Methods:

• Developed a process model from step response data using an industrial simulator.
• Designed generalised predictive controllers (GPC) based on the derived model.
• Implemented GPC in a closed-loop system, evaluating performance with and without constraints.

Main Results:

• The designed GPC demonstrated effective servo and regulatory control for the cement ball mill.
• Performance analysis showed that GPC achieves superior results compared to traditional controllers.
• Error metrics indicate a significant improvement in control accuracy and efficiency with GPC.

Conclusions:

• Generalised predictive control (GPC) is a highly effective strategy for optimizing cement ball mill operations.
• GPC offers significant advantages in reducing energy consumption and enhancing product quality.
• The study confirms GPC's superiority over conventional controllers for cement grinding processes.