TY - JOUR
T1 - Evaluation of carrier size and surface morphology in carrier-based dry powder inhalation by surrogate modeling
AU - Farizhandi, Amir Abbas Kazemzadeh
AU - Pacłaawski, Adam
AU - Szlęk, Jakub
AU - Mendyk, Aleksander
AU - Shao, Yu Hsuan
AU - Lau, Raymond
N1 - Publisher Copyright:
© 2018
PY - 2019/1/16
Y1 - 2019/1/16
N2 - In this work, design parameters of carrier-based dry powder inhalation were studied using surrogate modeling technique. The surrogate models constructed were then used to evaluate the key design parameters independently, which were otherwise difficult to determine based on experimental studies alone. Artificial neural network (ANN) was chosen as the surrogate modeling technique and models were constructed based on experimental data obtained from the literature. Twenty-eight variables describing the carrier size distribution, density, surface characteristics and operating conditions of dry powder inhaler were used as the input variables and emitted dose (ED) and fine particle fraction (FPF) were used as the output variables. Carrier surface characteristics were evaluated by applying image analysis on carrier SEM images. Genetic algorithm (GA) was used for the selection of important variables to be included in the surrogate models. Sensitivity analysis was also performed to determine the key variables affecting ED and FPF. Key design criteria for carrier-based dry powder inhalation were proposed based on the surrogate models constructed.
AB - In this work, design parameters of carrier-based dry powder inhalation were studied using surrogate modeling technique. The surrogate models constructed were then used to evaluate the key design parameters independently, which were otherwise difficult to determine based on experimental studies alone. Artificial neural network (ANN) was chosen as the surrogate modeling technique and models were constructed based on experimental data obtained from the literature. Twenty-eight variables describing the carrier size distribution, density, surface characteristics and operating conditions of dry powder inhaler were used as the input variables and emitted dose (ED) and fine particle fraction (FPF) were used as the output variables. Carrier surface characteristics were evaluated by applying image analysis on carrier SEM images. Genetic algorithm (GA) was used for the selection of important variables to be included in the surrogate models. Sensitivity analysis was also performed to determine the key variables affecting ED and FPF. Key design criteria for carrier-based dry powder inhalation were proposed based on the surrogate models constructed.
KW - Artificial neural network
KW - Dry powder inhalation
KW - Emitted dose
KW - Fine particle fraction
KW - Sensitivity analysis
KW - Surface roughness
KW - Variable selection
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U2 - 10.1016/j.ces.2018.09.007
DO - 10.1016/j.ces.2018.09.007
M3 - Article
AN - SCOPUS:85053056326
SN - 0009-2509
VL - 193
SP - 144
EP - 155
JO - Chemical Engineering Science
JF - Chemical Engineering Science
ER -