Introduction & Context

Sieve analysis is the oldest and still most widely used method for determining the particle size distribution (PSD) of granular solids. In process engineering it underpins the design and troubleshooting of any unit operation in which size matters: comminution circuits, fluidised-bed reactors, pneumatic conveying lines, crystallisers, cyclones, filters, hoppers and silos. The test separates a representative sample into discrete size fractions by mechanical shaking on a stack of woven-wire sieves. The resulting retained mass on each sieve is converted into a cumulative percentage, from which key descriptors such as d10, d50, d90 or the uniformity coefficient Cu = d60/d10 are interpolated. These descriptors feed directly to:

  • mass-balancing and population-balance models,
  • selection of mill type and closing screen,
  • prediction of pressure drop and heat/mass transfer coefficients,
  • assessment of flowability, segregation tendency and storage stability.

Methodology & Formulas

  1. Total sample mass
    The sum of all retained masses must equal the original test portion: \[ M_{\text{tot}} = \sum_{i} m_{i} \] where mi is the mass retained on sieve i (including the pan).
  2. Cumulative retained mass
    Starting with the coarsest sieve, add the retained masses successively: \[ M_{i} = M_{i-1} + m_{i} \] with M0 = 0 by definition.
  3. Cumulative percentage retained
    Convert each cumulative mass to a percentage of the total: \[ C_{i} = \frac{M_{i}}{M_{\text{tot}}} \times 100\ \% \]
  4. Validity criteria
    Check ISO 3310-1 threshold Consequence if violated
    Minimum total mass \( M_{\text{tot}} \geq 50\ \text{g} \) Excessive weighing error; repeat test with larger split.
    Pan residue \( \frac{m_{\text{pan}}}{M_{\text{tot}}} \times 100\ \% \leq 5\ \% \) Fine end poorly defined; interpolation of dx unreliable.
  5. Interpolation of characteristic sizes
    Plot Ci versus sieve opening di on a semi-log grid. Any percentile dx (where x is the percentage passing) is obtained by linear interpolation in log-space: \[ \log d_{x} = \log d_{\text{upper}} - \frac{x - P_{\text{upper}}}{P_{\text{upper}} - P_{\text{lower}}} \left( \log d_{\text{upper}} - \log d_{\text{lower}} \right) \] with P the cumulative percentage passing (= 100 − C).