Artificial neural networks modelling the prednisolone nanoprecipitation in microfluidic reactors

• Main Authors: Ali, Hany S.M., Blagden, Nicholas, York, Peter, Amani, Amir, Brook, Toni
• Language: en
• Published: 2011
• Subjects: Artificial neural networks; Crystal engineering; Modelling; Microfluidics; Nanoprecipitation; Prednisolone
• Online Access: http://hdl.handle.net/10454/4850

no === This study employs artificial neural networks (ANNs) to create a model to identify relationships between variables affecting drug nanoprecipitation using microfluidic reactors. The input variables examined were saturation levels of prednisolone, solvent and antisolvent flowrates, microreactor inlet angles and internal diameters, while particle size was the single output. ANNs software was used to analyse a set of data obtained by random selection of the variables. The developed model was then assessed using a separate set of validation data and provided good agreement with the observed results. The antisolvent flow rate was found to have the dominant role on determining final particle size.