Conversion of particle size distribution data from mass to number-based and its application to biomass processing. (December 2018)

• Record Type: Journal Article
• Authors: Ghasemi, Mohammad; Alexandridis, Paschalis; Tsianou, Marina
• Is Part Of: Biosystems engineering. Volume 176(2018)
• Page Start: 73
• Page End: 87
• Publication Date: 2018-12
• Subjects: Particle size distribution -- Polydisperse system -- Large-scale processing -- Sieving analysis -- Population model; Bioengineering -- Periodicals; Agricultural engineering -- Periodicals; Biological systems -- Periodicals; Génie rural -- Périodiques; Systèmes biologiques -- Périodiques; 631
• DOI: 10.1016/j.biosystemseng.2018.10.007
• Languages: English
• ISSNs: 1537-5110

Abstract

Abstract : Standard sieving analysis is a simple and common method for determining the mass-based particle size distributions of systems with high polydispersity. Conversion of mass-based particle size distributions to finite number of particles having different sizes (number-based particle size distribution) is required in order to predict the physical characteristics of a polydisperse system or to investigate the behaviour of such a system with computational models that consider the impact of individual particles during large-scale processing. A general method for this conversion from size distribution to number of particles is developed here, that determines the minimal total number of particles that is required for capturing a broad particle size distribution, and identifies the factors that impact the generated mass-based size distribution. As an example, this algorithm is utilised for semi-crystalline biomass particles to demonstrate the effect of different algorithm parameters on the dissolution behaviour of this highly polydisperse system. Both the upper and lower limits of the particle size have an important impact on the physical characteristics of the generated polydisperse system. Considering a low value for the lower limit (minimum particle diameter) causes coarser particles not to be captured well, while considering a very high value for the upper limit results in fine particles not being present in the system. The proposed method and the obtained insights are useful in the numerical non-continuum (discrete) population modelling of polydisperse systems undergoing changes that are physical (e.g., dissolution, drying), chemical (e.g., combustion, gasification), or biological (e.g., enzymatic hydrolysis). Highlights: Mass size distribution is converted to number distribution using a newly developed method. Total number of particles and minimum/maximum particle size are important parameters. Biomass dissolution is used as a case study to demonstrate the utility of this method.