| 00:00:00 | Sampling for particle size analysis - estimation of standard error |
| 00:01:51 | Sampling for particle size analysis - estimation of standard error |
| 00:03:14 | Overview |
| 00:04:12 | Obligatory Opening Quotation |
| 00:04:55 | Published Abstract |
| 00:06:07 | 4 days on sampling course in CSM, Golden, CO - after writing and delivering this presentation…. |
| 00:07:32 | Standing on the shoulders of previous kings of sampling… |
| 00:08:50 | Heinrich Oscar Hofman, MIT (August 13 1852 – April 28 1924) Heidelburg and Clausthal |
| 00:09:52 | David William Brunton (June 11 1849 – Dec. 20 1927) “Constr. Engr. Room 506, Boston Bldg., Denver, CO.” Joined AIME in 1883 |
| 00:11:24 | Professor Robert Richards We’ll be back to him later! |
| 00:13:34 | Pierre Maurice Gy (from Kim Esbensen) |
| 00:14:16 | Pierre Gy |
| 00:14:22 | Pierre Gy |
| 00:14:37 | The course material…… 4 days condensed into less than 45 minutes… |
| 00:15:10 | Assumptions of the listener |
| 00:16:09 | What do I need to think about and define before I even attempt a particle size measurement? |
| 00:18:18 | The 3 R’s |
| 00:19:11 | Pitard |
| 00:20:31 | “Your decisions are only as good as your samples” Francis F Pitard |
| 00:23:06 | Fundamental Sampling Error (FSE) |
| 00:24:47 | Sampling New notations (Pitard/Esbensen) |
| 00:25:34 | The taking of a representative sample |
| 00:26:18 | Esbensen – 2 SlideData |
| 00:27:00 | NASA lunar regolith |
| 00:27:54 | “How much sample do we need to take for any required degree of precision?” |
| 00:28:31 | Standard Error (Gy: Fundamental Sampling Error, FSE) A population has a true mean and standard deviation. When we're sampling we don't know what those 'true' values actually are. The standard error is the estimate of the true standard deviation based |
| 00:31:17 | “I wish to detect a small amount of agglomeration in my system” |
| 00:33:24 | “I wish to detect a small amount of agglomeration in my system” |
| 00:35:25 | “I wish to detect a small amount of agglomeration in my system” |
| 00:37:01 | Calculation of minimum mass Totally different approaches: Gy via s and Rawle via numbers of particles |
| 00:38:33 | Break out – back to the course – x95 |
| 00:38:53 | [(1/aL) – 2] |
| 00:40:41 | Vezin (yes he!) 1866 Quoted in R H Richards Ore Dressing Volume II Hill Publishing New York (1908) page 850 |
| 00:42:18 | So compare Rawle and Vezin – in the same breath! |
| 00:43:42 | “Gy's Formula: Conclusion of a New Phase of Research” D. Francois-Bongarçon, May 1998 |
| 00:44:19 | Professor Robert Richards, MIT Robert Hallowell Richards: August 26, 1844 – March 27, 1945 |
| 00:45:17 | We can turn the calculation ‘around’… |
| 00:46:02 | OK - up to 200mm |
| 00:46:57 | The big stuff |
| 00:48:14 | So you’re taking only 20mg………. |
| 00:49:44 | And how do you take your sample? With a spatula? Like a grocer? |
| 00:50:27 | The x100 represents the single largest particle in the (sampled) distribution |
| 00:51:35 | x100 – ISO 13320: 2009 |
| 00:51:57 | Sampling - summary |
| 00:52:34 | Untitled |
Sampling is one of the most important aspects of particle size analysis. In this presentation we will calculate the best possible standard error with a given mass of sample of known top end size, for a chosen size distribution parameter.