Artificial neural networks modelling the prednisolone nanoprecipitation in microfluidic reactors
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, microreact...
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ndltd-BRADFORD-oai-bradscholars.brad.ac.uk-10454-48502019-08-31T03:02:48Z Artificial neural networks modelling the prednisolone nanoprecipitation in microfluidic reactors Ali, Hany S.M. Blagden, Nicholas York, Peter Amani, Amir Brook, Toni Artificial neural networks Crystal engineering Modelling Microfluidics Nanoprecipitation Prednisolone 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. 2011-03-23T16:15:13Z 2011-03-23T16:15:13Z 28/06/2009 Article Ali, H.S.M., Blagden, N., York, P., Amani, A. and Brook, T. (2009). Artificial neural networks modelling the prednisolone nanoprecipitation in microfluidic reactors. European Journal of Pharmaceutical Sciences. Vol. 37, No. 3-4, pp. 514-522. http://hdl.handle.net/10454/4850 en http://dx.doi.org/10.1016/j.ejps.2009.04.007 |
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en |
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Artificial neural networks Crystal engineering Modelling Microfluidics Nanoprecipitation Prednisolone |
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Artificial neural networks Crystal engineering Modelling Microfluidics Nanoprecipitation Prednisolone Ali, Hany S.M. Blagden, Nicholas York, Peter Amani, Amir Brook, Toni Artificial neural networks modelling the prednisolone nanoprecipitation in microfluidic reactors |
description |
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. |
author |
Ali, Hany S.M. Blagden, Nicholas York, Peter Amani, Amir Brook, Toni |
author_facet |
Ali, Hany S.M. Blagden, Nicholas York, Peter Amani, Amir Brook, Toni |
author_sort |
Ali, Hany S.M. |
title |
Artificial neural networks modelling the prednisolone nanoprecipitation in microfluidic reactors |
title_short |
Artificial neural networks modelling the prednisolone nanoprecipitation in microfluidic reactors |
title_full |
Artificial neural networks modelling the prednisolone nanoprecipitation in microfluidic reactors |
title_fullStr |
Artificial neural networks modelling the prednisolone nanoprecipitation in microfluidic reactors |
title_full_unstemmed |
Artificial neural networks modelling the prednisolone nanoprecipitation in microfluidic reactors |
title_sort |
artificial neural networks modelling the prednisolone nanoprecipitation in microfluidic reactors |
publishDate |
2011 |
url |
http://hdl.handle.net/10454/4850 |
work_keys_str_mv |
AT alihanysm artificialneuralnetworksmodellingtheprednisolonenanoprecipitationinmicrofluidicreactors AT blagdennicholas artificialneuralnetworksmodellingtheprednisolonenanoprecipitationinmicrofluidicreactors AT yorkpeter artificialneuralnetworksmodellingtheprednisolonenanoprecipitationinmicrofluidicreactors AT amaniamir artificialneuralnetworksmodellingtheprednisolonenanoprecipitationinmicrofluidicreactors AT brooktoni artificialneuralnetworksmodellingtheprednisolonenanoprecipitationinmicrofluidicreactors |
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