Implementation and comparison of prediction models in Periodic Disturbance Micromixer (PDM)

Abstract

In recent years, the use of micromixers to produce liposomes has increased in the research field. They are an economical alternative, helping reactants waste, and allowing control of liposomes size. However, micromixer technology is not still viable for the industry. Some reasons are: a low production rate, no protocol existing to know the operating parameters for liposome size, and existing prediction models for liposome size do not have the desired accuracy. This dissertation focused on implementing and comparing different prediction models used in Periodic Disturbance Micromixer (PDM). Three models are focused on predicting liposome size with two operating parameters. All the models were implemented in MATLAB and compared through correlation coefficient (R). They were experimentally validated and subsequently compared with data analysis (DA) models. This work concluded that artificial intelligence (AI) techniques to predict operating parameters and liposome size show a significant improvement in correlation coefficients compared to the ones obtained by DA-based models