Browsing by Author "Theron, Douglas Arnoldus"

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    The diagnostic monitoring of the acoustic emission from a laboratory ball mill
    (Stellenbosch : Stellenbosch University, 1999-12) Theron, Douglas Arnoldus; Aldrich, C.; Weber, O. M.; Stellenbosch University. Faculty of Engineering. Dept. of Process Engineering.
    ENGLISH ABSTRACT: The harsh interior environment of mills makes on-line monitoring of these grinding systems difficult. Not only are conventional contact sensors expensive, but the nature of the grinding process makes their application impractical. Unfortunately few accurate quantitative measures are in place in industry to describe or assist in the operation and diagnosis of ball mills. In the South African context operators learn to control the mill based on a priori knowledge of the system gathered from years of process experience. It is common knowledge in industry that these operators associate the sound emission from the system with certain process conditions, and adjust the mill set points to obtain optimal grinding conditions. Unfortunately the high turnover of manpower in the mining industry has led to a drain of knowledge from many operations, leading to a loss of valuable control information. In this work the acoustic emission from a ball mill was studied making use of a laboratory ball mill, acoustic microphones and a personal computer, equipped with a sound card. The mill signal was recorded for a series of batch experiments. These consisted of single parameter experiments where single parameters such as percentage filling, mill speed, percentage water and percentage charge mass were varied, while keeping all other parameters constant. A second series of experiments were conducted with two platinum ore types, namely UG2 and Merensky, to study the influence of changing particle size on the acoustic emission from the mill. The acoustic signal was transformed into the frequency domain from the time domain by using Welch's averaged periodogram method. Hereby the power spectral density function for each acoustic sample was obtained and used as the basis for further data analysis. The structure of the data was investigated with a Sammon map obtained from the power spectral density data. This method confirmed that specific conditions in the mill each had a unique fingerprint which enabled differentiation of the acoustic information. Feature vectors were obtained by principal component analysis of the power spectrum density function extracted from the original mill signal. These feature vectors were used for the modelling of different data sets. Linear regression was applied to the Single parameter experiments yielding modelling results with r² values above 0.95. With the platinum- ore data both linear regression and feed forward neural networks were used for modelling. However, the linear regression model was unable to predict the ore particle size from the acoustic data. The non-linear neural network models achieved accurate particle size predictions for both ore types on both known and unknown validation data sets. r² values greater than 0.93 for the test data and 0.97 for the training data were obtained.