Pierre Gy, the inventor of the Theory of Sampling (TOS), pioneered applications of variography to understanding large-scale variability in process plants and process control from as early as the 1950s and devoted a major part of his TOS development period to this subject. The variogram allows one to identify sources of variability and provides valuable insight into correlations between successive samples. Neglect or poor understanding of the data analytical capabilities of the variogram means that it has not been widely applied in process control until now, except in industry sectors which have embraced TOS (mining, cement and certain parts of the process industries) because of the overwhelming consequences of making wrong decisions when treating vast tonnages—the consequences of wrong decisions are simply too great. Failure to address stream heterogeneity means that conventional statistics and Statistical Process Control (SPC) too often fail to identify and distinguish the true sources of variability in a process stream. For each type of heterogeneity, there is a matching variety of process variability. Although the method is powerful in terms of the insights one is able to gain in regard to plant performance and management, examples of the application of this particular method have been suspiciously little notable in the literature.
After the previous column’s introduction to the why, the how and the technicalities involved in process sampling and variographic analysis, it is time for a bonanza of applications and case histories covering as broad a practical scope as possible. In this column, we introduce the critical prerequisites for the variographic experiment, by focusing on the importance of TOS-correct increment extraction for proper variographics. This issue cannot be overemphasised.
This is the first of a number of columns dealing with process sampling, i.e. sampling from moving streams of matter. As will become clear there is a great deal of redundancy regarding how to sample both stationary and moving lots, but it is the specific issues pertaining to dynamic lots that will be highlighted.
Kim Esbensen and Claas Wagner have produced an extensive Sampling column, on “Representative mass reduction in the laboratory: riffle splitting galore (with or without errors)”. They guide readers through the choice of mass reduction equipment and what needs to be done to ensure representative sampling.
Kim Esbensen and Claas Wagner continue to stress that grab sampling is still an absolute no-no regardless of the size of the sampling device or the sample.
This column now turns its attention to sampling using a very popular tool, the “sampling spear”. There is much good to be said about spear sampling—and only one thing which is bad. But this is bad enough: spear samplers are very, very difficult to get to produce representative samples! The spear sampling principle can be made representative, but in most practical situations in which spear sampling is used today it manifestly is not. WHY? And more importantly, WHAT can be done about it? This column also turns out to touch on one of TOS’ six governing principles: SSI, Sampling Scale Invariance.
In their Sampling Column, Kim Esbensen and Claas Wagner stray into Quality Matters territory as they look at standards and how they work with the Theory of Sampling. Kim and Claas are concerned that many international standards do not comply with the TOS and that this compromises the results.
Another side of ensuring that our results are valid is correct sampling. In the latest Sampling Column on “Sampling quality criteria (SQC)” Kim Esbensen and Claas Wagner continue our education in the use of the Theory of Sampling. The fundamental step in ensuring representative sampling is sampling quality criteria, and the authors describe why and how.
In the Sampling Column, Kim Esbensen and Claas Wagner continue our education about representative sampling. In “Sampling quality assessment: the replication experiment”, they provide an overview of the issue of replication, which may not be as straightforward as might be expected at first.
Kim Esbensen and Claas Wagner’s Sampling Column also addresses issues around ensuring valid results, from the sampling perspective. “Composite sampling II: lot dimensionality transformation” continues to address the problem of heterogeneity and how to overcome it. If you can find a time when your bulk (3D) sample becomes a 1D sample, the job is possible. Interesting examples from unloading a grain ship to emptying a fishing boat hold are described.
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