Decision Making Process for Operational Neurocontrol of Mixture Grinding in Cement Production with Controversial Setting

Neurocontrol method of a mixture grinding process in cement production to increase its efficiency is proposed. The use of neural control is due to the fact that the quality of grinding and resource consumption depend on many factors that present great difficulties for their measurement and prediction. Reliable measurement of influencing factors required to solve the problem of determining the best combination of the ball grinding volume and the amount of solids to optimize the particle size reduction rate with a minimum specific energy consumption. Neurocontrol is based on the training of a neural network with a teacher, which is played by an experienced mill operator, who realizes effective control over the grinding process. The controller, built on the basis of the neural network, should work in real time and reflect the current state of grinding process. The choice of solutions for operational control problems using a neural network belongs to the class of multi-criteria tasks. The paper proposes a decision-making method based on the set of technical conditions imposed on the grinding process. Such a formulation of the problem is generally contradictory. The paper proposes an approach to solving this problem on the basis of determining the maximum number of joint weighted conditions imposed on the process. This approach allows organizing an interactive procedure for selecting a feasible solution to provide operational control over the grinding process. A computer model is proposed for operational control of the clinker grinding process in cement production.