Method for establishing load soft measurement model of cement combined grinding ball mill and application thereof

Abstract

The invention discloses a method for establishing a load soft measurement model of a cement combined grinding ball mill, which is established on the basis of a fuzzy clustering cognitive network, andcomprises the following specific steps of: determining fuzzy cognitive network nodes, and performing normalized representation on node state values; determining node weights according to causal relationships among the nodes; establishing a function relationship between the node state value and the weight and the state value at the previous moment; wherein the node weight is updated along with thesystem feedback value; calculating a node state estimation value by adopting a possibility kernel clustering algorithm; and establishing a soft measurement rule base, and replacing the system feedbackvalue of the fuzzy cognitive network with the node state estimation value according to the rule. The invention further discloses a soft measurement method applying the model. According to the soft measurement technology, by means of the function relation between the auxiliary variables easy to measure and the dominant variables difficult to measure, online measurement of the dominant variables, namely the mill load, is achieved, and the actual working condition of the mill can be accurately reflected.

Description

technical field [0001] The invention relates to the field of combined cement grinding, in particular to a method for establishing a load soft sensor model of a cement combined grinding ball mill and its application. Background technique [0002] Cement combined grinding is currently the most mature technology and is adopted by most cement enterprises. The ball mill system is an important part of cement combined grinding. Cement combined grinding process such as figure 1 shown. In the cement combined grinding process, the main equipment of the ball mill system includes a ball mill, a post-separator, a post-main exhaust fan, a dust collection fan at the end of the mill, and a mill elevator. After the pre-grinding is completed, the raw materials are mixed with fly ash and enter the ball mill to continue grinding. Part of the materials with smaller particles in the materials discharged from the tail of the ball mill are collected by the dust collector 3 and then enter the mixe...

AI Technical Summary

Problems solved by technology

Restricted by physical conditions and working characteristics, it is difficult to accurately detect the mill load

At present, in the actual production process of cement grinding in a cement plant, the mill load is mainly judged by the on-site operator based on past experience and by one or some of the variables related to the mill load. The following problems: (1) Different operators have different operating levels, and it is often difficult for inexperienced operators to accurately judge the load status of the mill; (2) The load of the mill is related to many variables, often a single variable or part of the variables It cannot accurately reflect the actual working conditions of the mill. When a certain variable fluctuates, it is easy to cause the operator to misjudge and make inappropriate adjustments, thereby affecting cement production; (3) Due to the characteristics of large inertia and large lag of the ball mill system , the change of the mill load can only appear within a certain period of time, and it is difficult for the on-site operator to perform corresponding operations in a timely manner, so that the ball mill cannot be maintained under a stable working condition; (4) The classification definition of the mill load (such as underload, normal load, overload, etc.) are not quantified into numerical values, and the range is too wide, which brings great uncertainty to the subsequent control. The identification framework of the mill load is too simple

Images