Abstract:
Determination of sugar grain size parameters has a great significance in quality control
monitoring of sugar grain size distributions. It is usually performed using sieve analysis
method. In this research work, investigation of numerical models with different order of
polynomial approximation of Gaussian cumulative curve distribution were used to determine
the grain size parameters from the best curve fit. 100g of sugar grain sample was taken from
Metahara sugar industry were selected purposively. After weighing the mass of sugar grain,
the numerical models were applied to determine the grain size parameters. For Powers
method determination of MA and CV, the six term polynomial approximation of Gaussian
cumulative distribution curve was used. For Rens, RRSB and Butler’s methods determination
of MA and CV, empirical equation, logarithmic equation and usual statistical equations were
used respectively. The graph of the Aperture size versus cumulative mass retained curve
analysis was used; and it was found that the best S-shaped curve approximately linear in the
central section. Powers method was approximately best fit experimental data points for grain
size distribution that follows a normal probability distribution function which is fully
represented by standard deviations and mean of grain size distributions. Also power method
has been shown to produce grain size determination parameters for dry sugar grain size that
give linear relation to the standard sieve method currently used in the Metahara sugar
industry
