Ball grinding mill load detecting method for optimizing BP neural network based on genetic algorithm

Abstract

The invention discloses a ball grinding mill load detecting method for optimizing the BP neural network based on the genetic algorithm. The method comprises the first step of collecting starting grinding sound and the stopping grinding sound of a ball grinding mill, the second step of carrying out off-line processing on the grinding sound, the third step of selecting an effective frequency band of the grinding sound, the fourth step of calibrating load data of the ball grinding mill, and the fifth step of optimizing BP neural network modeling based on the genetic algorithm and carrying out predicting on loads of the ball grinding mill to obtain a load detecting result of the ball grinding mill. According to the method, a plurality of sets of experiment data are obtained by analyzing the grinding sound frequency spectrum and selecting the effective frequency band range of the grinding sound, the experiment data are utilized for training the load model, and after training, the model can be used for predicting the loads of the ball grinding mill. The method is convenient to achieve, high in adaptability and capable of providing related detection data for optimization and control of the ball grinding mill, and automatic control and energy-saving and consumption-reducing capabilities of the ball grinding mill are improved.

Description

technical field [0001] The invention belongs to the technical field of automatic detection and relates to a ball mill load detection method based on genetic algorithm optimization BP neural network. Background technique [0002] The ball mill is widely used in the material crushing industry. The determination of the load of the existing ball mill mainly depends on the long-term work experience of the on-site operators. The error is large and the accuracy is not high, resulting in certain waste or loss. The safe operation of the ball mill cannot be guaranteed. The long-term operation of the ball mill in the under-load state affects the processing capacity and product quality of the ball mill, and also causes the problem of energy waste. The energy consumption of ball mills accounts for a large proportion of the entire industry. Therefore, for ball mills, a nonlinear and complex system with large time delays, accurate detection of the mill load is of great significance for ene...

AI Technical Summary

Problems solved by technology

[0002] The ball mill is widely used in the material crushing industry. The determination of the load of the existing ball mill mainly depends on the long-term work experience of the on-site operators. The error is large and the accuracy is not high, resulting in certain waste or loss. The safe operation of the ball mill cannot be guaranteed. The long-term operation of the ball mill in the under-load state affects the processing capacity and product quality of the ball mill, and also causes energy waste.

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