| 00:00:00 | Keys for Successful Analysis -Sampling for particle size analysis Estimation of standard error |
| 00:02:22 | The presenters |
| 00:02:30 | Overview |
| 00:04:13 | Obligatory Opening Quotation (O2Q) |
| 00:05:21 | Why? Mining examples |
| 00:07:50 | Sampling: The impact on costs and decision making |
| 00:08:39 | Sampling in the 1890’s |
| 00:10:12 | LinkedIn - Mining – GEOSTATISTICS group |
| 00:10:40 | Published Abstract |
| 00:11:15 | (ALL) My University Notes on Sampling |
| 00:11:48 | University Notes (continued) |
| 00:12:22 | This is not the time to decide on Ms…… |
| 00:13:10 | Adapted from: F J Flanagan “Reference Samples in Geology And Geochemistry” U.S. Geological Survey Bulletin # 1582, (1986) |
| 00:14:24 | Hofman (1901) – units? |
| 00:15:06 | Math will be based up that of Pierre Gy |
| 00:16:15 | Assumptions of the listener |
| 00:17:07 | What do I need to think about and define before I even attempt a particle size measurement? |
| 00:20:27 | What do we mean by representative sampling? |
| 00:22:16 | The 3 R’s |
| 00:23:23 | Francis Pitard |
| 00:24:08 | Cliché time |
| 00:24:38 | Fundamental Sampling Error (FSE) |
| 00:25:27 | Sampling |
| 00:26:04 | Sampling - segregation |
| 00:26:36 | T R Woodridge ‘Ore-Sampling Conditions in the West’ |
| 00:27:25 | The taking of a representative sample |
| 00:27:56 | Esbensen – 2 SlideData |
| 00:28:21 | NASA lunar regolith |
| 00:28:40 | ‘How much sample do we need to take for any required degree of precision?’ |
| 00:29:00 | Standard Error (Gy: Fundamental Sampling Error, FSE) |
| 00:31:03 | Fundamental Sampling Error (FSE) |
| 00:32:01 | Fundamental Sampling Error (FSE) |
| 00:32:22 | ‘I wish to detect a small amount of agglomeration in my system’ |
| 00:33:39 | Minimum Mass |
| 00:33:58 | ‘I wish to detect a small amount of agglomeration in my system’ |
| 00:34:47 | Minimum Mass |
| 00:35:58 | Calculation of minimum mass |
| 00:36:28 | Break out – back to the course – x95 |
| 00:36:44 | [(1/aL) – 2] |
| 00:36:54 | Vezin (yes he!) 1866 (?) |
| 00:37:09 | So compare Rawle and Vezin |
| 00:37:42 | ‘Gy's Formula: Conclusion of a New Phase of Research’ |
| 00:38:05 | Professor Robert Richards, MIT |
| 00:38:39 | We can turn the calculation ‘around’… |
| 00:39:14 | OK - up to 200 mm |
| 00:40:02 | The big stuff |
| 00:40:36 | So you’re taking only 20 mg………. |
| 00:40:56 | So how do you take a sample/specimen? |
| 00:41:21 | The x100 represents the single largest particle in the (sampled) distribution |
| 00:41:50 | The largest particle….x100 |
| 00:41:59 | The largest particle….x 100 |
| 00:42:18 | x100 – ISO 13320: 2009 |
| 00:42:31 | Sampling - summary |
| 00:42:56 | Cliché time |
| 00:43:09 | Charles Babbage |
| 00:43:30 | References |
| 00:43:49 | Sampling personality webinars |
| 00:44:08 | Contact Information and Q&A |
| 00:48:28 | Thanks for listening |
In any analytical technique you get out what you put in – that is, the instrument and technique measure what is given to them.
In this webinar we’ll explore (from a theoretical aspect, the Theory of Sampling (TOS), as first formulated by Pierre Gy) the variation seen when different samples are extracted from the bulk lot. In many instances analytical techniques get superb results on tiny amounts of sample but this has repercussions from a representative sampling perspective (think fruit cake and just sampling the raisins).
The variation caused by the inherent heterogeneity of the material represents the best variation that can be achieved and is easily calculable.
Our examples will be based on particle size distribution analysis but the conclusions are transferable to all other metrologies including x-ray fluorescence where elemental content variation is repeated samplings is the norm. The sampling formulae also have implications for count length in x-ray diffraction.
Palestrante
Alan Rawle -C.Sci., B.Tech., Ph.D, C.Chem., FRSC
Applications Manager/CoChair E56.02 Characterization SubCommittee of ASTM E56 Committee on Nanotechnology
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Who will benefit: anyone taking measurements on an instrument and wants to understand why each sample taken from a bulk lot is different. This allows achievable specifications to be set based on the heterogeneity of the material.