Izvorni znanstveni članak
https://doi.org/10.15255/KUI.2020.048
Modelling Drying Time of Candesartan Cilexetil Powder Using Computational Intelligence Technique
Sonia Keskes
; Quality Control Laboratory, SAIDAL Complex, Médéa Unit, Médéa 26 000, Algeria
Mohamed Hentabli
orcid.org/0000-0002-6693-0708
; Quality Control Laboratory, SAIDAL Complex, Médéa Unit, Médéa 26 000, Algeria
Mamaar Laidi
orcid.org/0000-0002-8977-9895
; Laboratory of Biomaterials and Transport Phenomena (LBMPT), Faculty of Technology, University Yahia Fares of Médéa, Algeria
Salah Hanini
; Laboratory of Biomaterials and Transport Phenomena (LBMPT), Faculty of Technology, University Yahia Fares of Médéa, Algeria
Sažetak
The aim of this work was to use two computational intelligence techniques, namely, artificial neural network (ANN) and support vector regression (SVR), to model the drying time of a pharmaceutical powder Candesartan Cilexetil, which is used for arterial hypertension treatment and heart failure. The experimental data set used in this work has been collected from previously published paper of the drying kinetics of Candesartan Cilexetil using vacuum dryer and under different operating conditions. The comparison between the two models has been conducted using different statistical parameters namely root mean squared error (RMSE) and determination coefficient (R2). Results show that SVR model shows high accuracy in comparison with ANN model to predict the non-linear behaviour of the drying time using pertinent variables with {R2 = 0.9991, RMSE = 0.262} against {R2 = 0.998, RMSE = 0.339} for SVR and ANN, respectively.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Ključne riječi
<i>Candesartan Cilexetil</i>; response surface methodology; vacuum drying; artificial neural networks; support vector regression
Hrčak ID:
254682
URI
Datum izdavanja:
27.3.2021.
Posjeta: 1.165 *