Abstract
Diversified operating conditions, input-output constraints, and parametric variations in the Vertical Roller Mill (VRM) make it to have complicated dynamics and closed-loop instability. Existing traditional controllers are not superlative and may lead to an uneven plant shutdown. Model predictive controller with adaptive models can track these parametric variations and ensure the plant’s smooth running, which has been addressed in this paper. Data from the real-time VRM is acquired, and correlation analysis is carried out, which illustrates the use of outlet temperature and differential pressure as the output variables with tensile pressure and booster fan speed as the input variables. The base model for VRM is identified using the selected variables by data-driven system identification methods. The fourth-order state-space model was found to be optimal in capturing the dynamic behavior of VRM. Dual Adaptive Model Predictive Controller (DAMPC) is designed to handle each output variable individually. The use of DAMPC minimizes the complexity involved in the on-line parametric estimation for higher-order models by distributing the control authority to different controllers. The performance of the proposed DAMPC is compared with the existing Proportional Integral (PI) controller and Model Predictive Controller (MPC). Simulation experiments for reference tracking and rejection of slowly varying internal disturbances by considering parametric variations are carried out. Results illustrate DAMPC provides lesser overshoot and faster settling time amidst parametric variations.
Highlights
The sustainability of cement manufacturing has been struggling with high energy consumption and the gap between supply and demand
Data-based modeling and investigation of Dual Adaptive Model Predictive Controller (DAMPC) for industrial Vertical Roller Mill (VRM) process with on-line parameter estimation are considered the novelty of the proposed work, which has not been attempted in the literature
The traditional Proportional Integral (PI) controller used in the VRM machine is designed in this work
Summary
The sustainability of cement manufacturing has been struggling with high energy consumption and the gap between supply and demand. Some investigations using intelligent control divided the VRM process into many single loops like pressure, temperature, vibration, etc., and applied individual control algorithms. These control mechanisms produced satisfactory results only when all the process inputs are readily available within the constraints if any variable out of range causes the plant’s uneven shutdown [7, 12, 15]. Data-based modeling and investigation of DAMPC for industrial VRM process with on-line parameter estimation are considered the novelty of the proposed work, which has not been attempted in the literature. As per the grinding machine control engineer’s suggestion, the data is preprocessed and using different linear and nonlinear system identification methods, the satisfactory VRM model is identified.
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