A new method for analyzing particle attrition dynamics using three-dimensional profiling

Abstract

This method can be applied to samples, taken longitudinally in segments from a crash stopped finish ball mill, or segments through any grinding apparatus, or segments of timed intervals of particle size differentials, and can give insight to the performance of the grinding circuit and theoretically calculate the dynamic attrition of particles. For instance, it is a common cement industry practice to run Blaine and sieve analysis from those samples taken inside a ball mill after a crash stop to analyze the grinding process. For our new technique, we take the normal Blaine and sieve analysis, and use the particle size distribution (PSD) data from a particle size analyzer to show detail in three-dimensions. This profile is not about energy consumption but about considering the distribution of particles that is imperative to the quality of cement and efficiency of the grinding apparatus. There are many theories to determine the efficiency of any grinding equipment to grind materials of varying hardness to a particular fineness. This study is about the evaluation of what the grinding apparatus is accomplishing by observing the particle attrition dynamics from a starting point and ending point PSD. This technique is similar to the Blaine and sieve analysis but in more detail, allowing us to observe the data in a three-dimensional (3D) view and determine the rate of attrition on individual particle sizes. This is a continuing investigation into what others have already discovered. However, they were limited to the Blaine or total surface area with one data point per sample; or wet sieving with a small number of individual sizes; or dry sieving with possible material agglomeration of fine particles. Most of the data from particle size analyzers is not being used to the fullest potential. Many second-compartment mill studies of particles sampled "through the mill" look at about 10-100 plus data points longitudinally; we are looking at 300-800 plus data points longitudinally, three-dimensionally, and calculating the rates of attrition. Our current plans are to utilize this to calculate efficiency of the ball mill itself.