An Efficient Combination of Genetic Algorithm and Population BalancesModeling for Prediction of Droplet/Particle Size in Inverse Suspension

Controlling the particle size and its distribution is one of the crucial parameters to improve the hydrogel's efficacyin hemostasis and wound healing. To obtain an impressive and precise control of final particle size, it is essential toaddress a comprehensive evaluation. Cross-linked dextran microspheres' (CDMs) particle size behavior during theinverse suspension cross-linking process (SCP) was theoretically investigated. To carry out, a population balanceapproach was employed to predict the particle size and size distribution of CDMs in the SCP. Droplet breakage andcoalescence rates equations were incorporated into the population balance equation (PBE) to simplify the PBE.Moreover, tunable parameters for this particular system were optimized using Genetic Algorithm (GA). After that,the impact of agitation rate as a key factor on final particle size evolution was modeled well. Finally, a superbaccordance was observed between the modeling and empirical final particle size of CDMs with 50% dextran.

An Efficient Combination of Genetic Algorithm and Population BalancesModeling for Prediction of Droplet/Particle Size in Inverse Suspension Keywords:

Population Balance Modeling , Inverse suspension cross-linking process , Cross-linked Dextranmicrospheres , dextran droplet size