Coarse Return Prediction in a Cement Industry’s Closed Grinding Circuit System through a Fully Connected Deep Neural Network (FCDNN) Model

Milling operations in various production processes are among the most important factors determining quality, stability, and consumption of energy. Optimizing stabilizing milling process is a non-linear multivariable control problem. In specific that deal with natural materials (e.g., cement, pulp paper, beverage brewery water/wastewater treatment industries). A novel data-driven approach utilizing real-time monitoring technology proposed for purpose optimizing grinding cement processing. combined event modeling feature extraction fully connected deep neural network model to predict coarseness particles proposed. The resulting prediction allows look ahead strategy corrective actions. solution has been deployed number plants around world. resultant enabled operators take actions before coarse return increases, both autonomous manual mode. impact improved efficiency resource use by 10% resources, plant overall energy plant.