Abstract：To solve the problem of difficult optimization control caused by the long-time lag of fineness test delay and long-time lag in the cement combined grinding system, this paper proposed fineness soft measurement and online fineness optimization control method based on radial basis neural network (RBF-NN) and model predictive control (MPC) algorithm. First, relevant characteristic variables in the cement combined grinding system were extracted and data cleaning was completed. The K-Means clustering algorithm was introduced to optimize the parameter selection method of the radial basis neural network, and a cement fineness soft measurement model of the radial basis neural network was constructed. Through periodic scheduling of the model, online prediction of fineness was achieved. Secondly, an online quality control loop was constructed to perform real-time online optimal control of cement fineness based on the cement fineness prediction results and MPC algorithm. Finally, the effectiveness of this method was verified based on a certain cement taking the factory's combined grinding system as an example.
2024, Vol. 0 
