Please use this identifier to cite or link to this item:
|Title:||Surrogate modeling applications in chemical and biomedical processes||Authors:||Kazemzadeh Farizhandi, Amir Abbas||Keywords:||DRNTU::Engineering::Chemical engineering::Chemical processes
DRNTU::Engineering::Chemical engineering::Processes and operations
|Issue Date:||2017||Source:||Kazemzadeh Farizhandi, A. A. (2017). Surrogate modeling applications in chemical and biomedical processes. Doctoral thesis, Nanyang Technological University, Singapore.||Abstract:||Surrogate modeling is an efficient alternative for computation-intensive process simulations in engineering problems. Surrogate model is developed using experimental or computer data, which are collected from experiments or simulation runs. The use of surrogate model allows efficient and cost-effective computation for different applications. With this purpose, two systems: 1) particle size distribution (PSD) in gas-solid fluidized beds and 2) carrier-based dry powder inhalation (DPI) efficiency have been considered as case studies. In this study, artificial neural network (ANN) coupled with genetic algorithm (GA) was employed as a surrogate modeling tool. PSD plays a crucial role in performance and operation of the fluidized bed. Since monitoring of the change in PSD in computational fluid dynamic (CFD) simulation is computationally expensive, PSD usually considers being constant during fluidization in CFD simulation. Therefore, surrogate modeling has been proposed as a fast and cheap computation method to estimate PSD during fluidization. Planetary ball milling is employed to derive descriptive parameters to account for the effect of material properties in the particle attrition process. Gas-solid fluidized bed experiments have been conducted to provide required data for surrogate model construction. The results show that the Rosin-Rammler (RR) distribution is able to describe the PSD reasonably well (R-square > 0.97) for fluidization and ball milling processes. Two ANN-GA models were developed based on the RR parameters (d and n) obtained from least-square fitting of experimental PSD results. R-square values of leave-one-out cross-validation for the developed ANN-GA models were more than 0.9589 which shows that the surrogate model can estimate PSD during fluidization reasonably well. With adding the developed surrogate model to CFD simulation, more accurate and reliable results can be provided in the simulation of gas-solid fluidized beds. On the other hand, finding the effect of variables interaction on the efficiency of DPI by experiments is not possible because usually changing a variable will change the other variables inevitably. Therefore, ANN-GA approach as a surrogate model has been employed to evaluate the effect of different variables on DPI efficiency. In vitro aerosolization performance and drug delivery efficiency of a DPI are generally represented by emitted dose (ED) and fine particle fraction (FPF). Image analysis is employed to obtain various descriptive parameters for surface morphologies of carriers based on scanning electron microscopy (SEM) images. Variable selection is used to reduce the number of input variables needed for surrogate model development. R-square values of leave-one-out cross-validation for the developed surrogate models were more than 0.7546 in prediction of ED and FPF. Sensitivity analysis was also performed to determine the key variables affecting ED and FPF. With this developed model, one variable can be isolated and its effect on DPI efficiency can be evaluated. In fact, it provides a tool for better understanding of DPI formulation and it can be used for the design and optimization of DPI.||URI:||http://hdl.handle.net/10356/72705||DOI:||10.32657/10356/72705||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCBE Theses|
Updated on May 14, 2021
Updated on May 14, 2021
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.