Soft sensing method for load parameter of ball mill

Abstract

The invention relates to a soft sensing method for load parameters of a ball mill. The method is that a hardware supporting platform is used to obtain vibration signals, vibration sound signals and current signals of a ball mill cylinder to soft sense ball mill internal parameters (ratio of material to ball, pulp density and filling ratio) characterizing ball mill load. The method comprises the following steps that: the vibration, the vibration sound, the current data and the time-domain filtering of the ball mill cylinder are acquired, time frequency conversion is conducted to the vibration and the vibration sound data, kernel principal component analysis based nonlinear features of the sub band of the vibration and the vibration sound data in frequency domain are extracted, nonlinear features of the time domain current data are extracted, feature selection is conducted to the fused nonlinear feature data and a soft sensing model based on a least squares support vector machine is established. The soft sensing method of the invention has the advantages that the sensitivity is high, the sensed results are accurate, the practical value and the popularization prospect are very good, and the realization of the stability control, the optimization control, the energy saving and the consumption reduction of the grinding production process is facilitated.

Description

Technical field: [0001] The invention belongs to the field of automatic technology measurement, and in particular relates to a soft measurement method for the load parameters of a wet ball mill in the grinding production process, that is, the material-ball ratio inside the ball mill, the pulp concentration and the filling rate. Background technique: [0002] Tubular ball mills are widely used in grinding operations in metal ore and non-metal concentrators, building materials, refractory materials, cement, coal, chemical, electric power and metallurgy industries. The power consumption of grinding operation accounts for 3-4% of the world's total power generation, and accounts for 30%-70%, 15%, 60%-70% of the energy consumption of their respective industrial processes in mineral processing, electric power, cement and other industries. The consumption of ore medium and liner reaches 0.4-3.0kg / t. According to the experimental research, the ball mill has at least 10% energy savin...

AI Technical Summary

Problems solved by technology

[0004] The determination of the existing ball mill load mainly depends on the long-term work experience of the on-site operators, it is difficult to measure the internal parameters that characterize the ball mill load, and the existing detection methods are difficult to fully extract the nonlinearity of the signal, which leads to the inability to ensure the safety of the equipment in the industrial process. However, the ball mill often runs under the load state, which affects the processing capacity and product quality of the ball mill. It is impossible to carry out targeted production according to market demand and obtain the maximum economic benefits. The present invention provides a method based on the Ball mill load soft sensing method based on multi-source data feature fusion

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