Particle size distribution
- 1. Particle Size Distribution Function By- Rohit Dixit
- 2. Objectives Significance of Particle Size Distribution Understanding of Particle Size and Particle Shape Particle Size Distribution (PSD) Particle Size Distribution: Measurement Techniques Analytical distribution function
- 3. Who cares about particle size? • Pharmaceutical • Chemical • Ceramics • Cement • Food • Abrasives • Cosmetics • Mining • Powder metals • University • Others
- 4. Size terminology • The most common designation is micrometer or micron. • For very small size nanometer is used.
- 6. Which is the most meaningful size? Different size definition Different result
- 7. The Basic • What size can be measured?
- 8. Which size to measure? • Vol. based particle size equals the diameter of the sphere that has a same vol as the given particle. D=2*(3 3𝑉/4π) Weight based particle size equals the diameter of the sphere that has a same weight as the given particle. D=2*(3 3𝑊/4π𝑑 𝑔) Area based particle size equals the diameter of the sphere that has a same weight as the given particle. D=2(2 𝐴/4𝜋)
- 9. Particle Size Distribution “Particle size distribution (PSD) of a powder, or granular material, or particles dispersed in fluid, is a list of values or a mathematical function that defines the relative amounts of particles present, sorted according to size.”
- 12. Symmetric and Asymmetric distribution Distribution is along side of the center value Distribution is not same along side of the center value Mean: Sum of values of a data set divided by number of values Mode: most frequent set in data set Median: Middle value separating the greater and lesser halves of a data set
- 13. Analytical distribution function Some analytical distribution funtions are: • Normal distribution • Log-normal distribution • Weibull distribution
- 14. Normal distribution • Probability distribution function: Where µ is mean σ is standard deviation σ2 is variance
- 15. Log- normal distribution function: Probability distribution function: Where µ is mean σ is standard deviation σ2 is variance
- 16. Weibull distribution Probability distribution function: Where µ is mean σ is standard deviation σ2 is variance
- 17. Class example Mean size: 135.9992 nm Standard deviation : 37.38654
- 18. Normal distribution fitting R^2 : .604
- 19. Thanks