Control of a closed dry grinding circuit with ball mills using predictive control based on neural networks

Abstract

Closed circuit dry grinding using ball mills is essential in mineral processing industries. This process is characterized by significant dead times, highly coupled variables, limitations by operating ranges and complex modeling, making it difficult for classical controls to obtain fast responses with low overshoot. An alternative to overcome these difficulties is intelligent controls, such as the artificial neural network model predictive control (NNMPC). This study aimed to develop a NNMPC for dry grinding in a closed circuit with a ball mill. Artificial Neural Network represented the multi-input-multi-output system for the model to predict the outputs in the predictive control strategy. Compared to the classical controls and linear model predictive control, the results obtained by the NNMPC were superior with lower errors and better robustness, reaching a reduction of up to 79% and 74.2% for overshoot and settling time, respectively, besides being more efficient in simulations with simultaneous disturbances of the controlled variables.

Keywords

• Grinding circuits
• Nonlinear model predictive control (NMPC)
• Artificial Intelligence (AI)
• Artificial neural network model (ANNM)
• MIMO system