Mathematical Modeling of Different Breakage PBE Kernels Using Monte Carlo Simulation Results

Ashok Das () and Jitendra Kumar ()
Additional contact information
Ashok Das: Indian Institute of Technology Kharagpur
Jitendra Kumar: Indian Institute of Technology Kharagpur

A chapter in Optimization of Pharmaceutical Processes, 2022, pp 79-101 from Springer

Abstract: Abstract This chapter discusses the development of some breakage PBE kernels. Three different types of breakage processes are considered, namely linear breakage, nonlinear collisional breakage, and sonofragmentation of rectangular shaped crystals. Mathematical models of monovariate linear breakage selection function and nonlinear collisional breakage kernel are presented, which are dependent on particle volume and process time simultaneously. Furthermore, a bivariate breakage PBE is considered for a particular set of sonofragmentation experiments on δ-form of pyrazinamide (rectangular shaped) crystals. Consequently, the corresponding bivariate breakage selection function and breakage distribution function were discussed to predict the experimental results. The developed bivariate breakage selection function takes care of the time dependence in particle selections along with the size dependence. For the verification of the presented models, the Monte Carlo (MC) method is used. For the first two processes, MC merely acts as the replication tool to produce experimental results. However, for the sonofragmentation process, the MC technique was used to understand the breakage behavior accurately.

Keywords: Population balance equation; Monte Carlo; Linear breakage; Collision breakage; Sonofragmentation (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-030-90924-6_4

Ordering information: This item can be ordered from
http://www.springer.com/9783030909246

DOI: 10.1007/978-3-030-90924-6_4

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().