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  • This document is downloaded from DR‑NTU (https://dr.ntu.edu.sg) Nanyang Technological University, Singapore.

    Surrogate modeling applications in chemical and biomedical processes

    Kazemzadeh Farizhandi, Amir Abbas

    2017

    Kazemzadeh Farizhandi, A. A. (2017). Surrogate modeling applications in chemical and biomedical processes. Doctoral thesis, Nanyang Technological University, Singapore.

    http://hdl.handle.net/10356/72705

    https://doi.org/10.32657/10356/72705

    Downloaded on 25 Jun 2021 06:53:50 SGT

  • Surrogate modeling applications in chemical and biomedical processes

    Amir Abbas Kazemzadeh Farizhandi

    SCHOOL OF CHEMICAL AND BIOMEDICAL ENGINEERING

    2017

  • Surrogate modeling applications in chemical and biomedical processes

    Amir Abbas Kazemzadeh Farizhandi

    School of Chemical and Biomedical Engineering

    A thesis submitted to the Nanyang Technological University

    In partial fulfillment of the requirement for the degree of

    Doctor of Philosophy

    2017

  • i

    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

  • ii

    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.

  • iii

    Acknowledgement

    I would like to express my sincere thanks and appreciation to my supervisor, Dr.

    Lau Wai Man, Raymond for his invaluable guidance, support and suggestions.

    His knowledge, suggestions, and discussions help me to become a capable

    researcher. His encouragement also helps me to overcome the difficulties

    encountered in my research. I also want to thank my colleagues in the lab, for

    their generous help. I want to thank Dr. Wang ke for her explanation of the

    surrogate modeling, which saved me a lot of time, and Zhao, for his generous

    help in my experiments in fluidized bed. I am very grateful to my lovely wife

    who always supports me. Last but not least, I want to thank my parents in Iran,

    for their constant love and encouragement.

  • iv

    Table of content

    Abstract ................................................................................................................ i

    Acknowledgement ............................................................................................. iii

    Table of content ................................................................................................. iv

    List of figures .................................................................................................... vii

    List of tables ........................................................................................................ x

    Chapter 1 Introduction ........................................................................................ 1

    1.1 Background ................................................................................................... 1

    1.2 Motivation of this research............................................................................ 4

    1.3 Objectives and scope ..................................................................................... 8

    1.4 Organization of the thesis............................................................................ 10

    Chapter 2 Literature Survey .............................................................................. 12

    2.1 Review of surrogate modeling .................................................................... 12

    2.2 Data distribution methods ........................................................................... 17

    2.3 Surrogate modeling techniques ................................................................... 20

    2.4 Surrogate model fitting methods ................................................................. 26

    2.5 Surrogate model validation and accuracy ................................................... 27

    2.6 Review of surrogate modeling applications in chemical engineering ......... 29

    Chapter 3 Modeling Techniques ....................................................................... 33

    3.1 Preface ......................................................................................................... 33

    3.2 Artificial neural network (ANN) as a surrogate modeling technique ......... 33

    3.3 Variables selection ...................................................................................... 35

    3.4 Sensitivity analysis (SA) ............................................................................. 37

    3.5 Symbolic regression (SR) ........................................................................... 41

    3.6 Genetic algorithms (GA) ............................................................................. 43

    3.7 Accuracy and validation of surrogate model............................................... 45

    3.8 ANN-GA as an integrated approach for process modeling ......................... 46

    3.9 Particle size distribution (PSD) ................................................................... 52

    Chapter 4 Modeling the change in particle size distribution in a gas-solid

    fluidized bed due to particle attrition using a hybrid artificial neural network-

    genetic algorithm approach ............................................................................... 55

    4.1 Preface ......................................................................................................... 55

    4.2 Experimental setup ...................................................................................... 58

    4.3 Data collection ............................................................................................ 60

    4.4 Design of the ANN model for prediction of PSD ....................................... 61

  • v

    4.5 Results and Discussion ................................................................................ 62

    4.5.1 Application of the Rosin–Rammler model to the IBA particle size