Cement particle size distribution prediction method based on random distribution

Abstract

The invention belongs to the technical field of cement fineness prediction, in particular to a cement particle size distribution prediction method based on random distribution. The method includes thesteps of obtaining the feed quantity of mill, working current of mill, temperature of inlet and outlet of mill, pressure difference of mill, current of circulating lifting machine, rotational speed of separator, current of circulating fan and probability distribution of cement particle size at corresponding time, and storing all parameter signals as historical data set; S200 establishing the basis function expression model of probability distribution density function of cement particle size; S300 screening out abnormal data, and assigning sample weights according to classification to form newdata samples; S400 establishing the nonlinear prediction model between the input variables and the previous n-1 weight vectors to predict the cement particle size distribution at the next time; S500updating the basis function of the cement particle size probability distribution density function to represent the model parameters through the output error value of the model. The method of the invention can detect the cement particle size distribution in the cement grinding system in real time.

Description

technical field [0001] The invention belongs to the technical field of cement fineness prediction research, in particular to a method for predicting cement particle size distribution based on random distribution. Background technique [0002] Cement is a powder product composed of particles of different sizes in a certain proportion. The cement grinding system is the process of grinding cement clinker into cement products that meet national quality standards. It is the last link in the process of producing cement, but also a crucial link. At present, the evaluation of cement quality standards is mainly based on cement fineness, which has a great influence on cement strength, setting time, and water consumption for quasi-consistency. Cement fineness mainly includes three indicators of sieve residue, specific surface area, and particle gradation. In enterprise production, sieve residue or specific surface area is usually used to measure and judge cement fineness. In fact, s...

AI Technical Summary

Problems solved by technology

These soft measurement techniques all use the percentage of cement fineness as the prediction result, which cannot fully reflect the actual quality and performance of cement.

In addition, the non-Gaussian characteristic of cement particle size distribution is not considered, resulting in low prediction accuracy

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