Download Particle Size Measurements: Fundamentals, Practice, Quality by Henk G. Merkus PDF
By Henk G. Merkus
Following my commencement in actual natural chemistry on the collage of Amst- dam, i began to paintings on the Royal Dutch Shell Laboratories in Amsterdam. My first task was once examine within the box of detergents and business chemical compounds. It used to be via improvement paintings on thermal wax cracking for construction of C – C 2 14 olefins and on acid-catalyzed synthesis of carboxylic acids from C – C olefins. three 6 Then, I made an important swap to analytical chemistry, first at Shell’s procedure improvement division and later within the chemical engineering division of Delft college of know-how. In either departments, there has been a wide number of analy- cal innovations and improvement of recent equipment for automatic research of small technique streams. It was once the time that fuel chromatography conquered the realm. during this box, a company foundation was once given by means of Henk Boer, Arie Kwantes and Frits Zuiderweg at Shell learn Laboratories in Amsterdam, either for packed and for capillary c- umns. the potential for fuel chromatography was once large and, accordingly, additionally in Delft, its use elevated tremendously. additionally, the expansion of this system used to be facilitated considerably by way of the swiftly constructing electronics undefined. It not just ended in electronic height integrators and private pcs but in addition enabled advanced size recommendations. furthermore, I turned excited about floor sector and porosity characteri- tion of catalysts and adsorbents, on which subject the study were initiated via Prof. J. H. de Boer.
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Extra resources for Particle Size Measurements: Fundamentals, Practice, Quality
Sample text
The Rosin–Rammler distribution (also called Rosin–Rammler–Sperling–Bennett or RRSB or Weibull distribution) was originally used for representation of sieve analysis results of crushed coal. It is often applicable to the wide size distributions of milled products. 5–80% part of the PSD. The Gaudin–Schumann distribution (also called Gates–Gaudin–Schumann or power fit distribution) is included, as it is an example of a simple power law fit. It usually fits best in the middle part of size distributions.
Sometimes, such experimental studies have to be based on experience or intuition and the best parameters selected, preferably based on robust regression analysis. Always, acceptance limits for these parameters should be determined in relation to product performance and, thus, the precision for their determination has to be established. Many candidates for these parameters exist, for example: • particle size: a weighted mean size or moment of the PSD, or D10, D50, D90, or maximum size (for a given type of particle size) • PSD width: D90/D10, (D90-D10)/D50, or a characteristic width parameter of a modeled distribution (See Chap.
Usually, detailed information is obtained from a PSD measurement. This may be useful to reveal details of the relationship between product performance and particle 24 2 Particle Size, Size Distributions and Shape size (for example in a property function) or of the mechanism in a production process (cf. Chap. 1). Full details are, however, most often redundant if the data are used for a quality check of a product or for process control. Then, characteristic PSD data are extracted from the given distributions.