Biological process simulation and optimization

생물공정모사 및 최적화

Biological process simulation and optimization

Major: Interdisciplinary program of the integrated biotechnology

Graduate school of bio- & information technology Youngil Lim (N110), Lab. FACS

Youngil Lim (N110), Lab. FACS

phone: +82 31 670 5200 (secretary), +82 31 670 5207 (direct) phone: +82 31 670 5200 (secretary), +82 31 670 5207 (direct)

Fax: +82 31 670 5445, mobile phone: +82 10 7665 5207 Fax: +82 31 670 5445, mobile phone: +82 10 7665 5207 Email:

Email: [email protected][email protected], homepage: , homepage: http://http://hknu.ac.kr/~limyi/index.htmhknu.ac.kr/~limyi/index.htm

Course # Course name Time Room # Biological process simulation and optimization Thu. 9-12시 N130/N116

Overview

Optimization is a critical tool for all engineers and a key course of study at university and industry train ing levels. Though the techniques of optimization are relatively old, the tools for implementing optimiz ation have advanced greatly in recent years. Non-linear problems encountered in the real world include huge numbers of variables that under normal circumstances could never be tested but are now analyzed via computer.

This lecture presents optimization theories and their application to the bioseparation process design. Thi s lecture includes mathematical theories on local/global optimization, a linear programming (LP), nonli near programming (NLP), mixed-integer NLP (MINLP), multi-objective programming (MOP), successi ve quadratic programming (SQP), and genetic algorithm (GA). Matlab is used for computational practic es. This lecture is given in English.

Method Lecture(●), Seminar (●), Computational practice (●), Factory tour (●), Beam projector(●)

Evaluation Attendance: 8%, homework: 20%, Mid-exam: 30%, Final-exam: 30%, Presentation: 12%

Text

Main : Edgar and Himmelblau, Optimization of Chemical Processes, 2^nd ed. McGraw-Hill, 2001.

Sub: Bioseparation engineering, M.R. Ladisch, Wiley interscience, 2001.

Outline

Week Contents Remarks 1 Introduction

2 Part I. problem formulation, Ch. 1 Application examples 3 Part I. problem formulation, Ch. 1 Application examples 4 Part I. problem formulation, Ch. 2 Fitting models to data 5 Field trip (Factory tour): Samsung Research Institute

(Physical vapor deposit (PVD) factory tour)

Homework 1: field trip report

(김국윤 , 031-280-9076, 019-446-0517) 6 Part I. problem formulation, Ch. 3 Objective functions

7 Part I. problem formulation, Ch. 3 Objective functions Presentation 1: ch. 3 8 Mid-term exam.

9 Part II. Theory, Ch. 4 Basic concepts

10 Part II. Theory, Ch. 4 One dimensional search 11 Field trip (Factory tour): WooJin ACT

(Solvent cleaning factory tour)

Homework 2 : field trip report

( 이 재 용 , 031-678-8930, 011-9982- 5757)

12 Part II. Theory, Ch. 5 Multivariable optimization

13 Part II. Theory, Ch. 5 Multivariable optimization Presentation 2: ch 5.

14 Part II. Theory, Ch. 6 LP, NLP, MOP, MINLP, GA

Weekly Lecture Plan

Part I. Problem Formulation

We knew and learned :

• AE (algebraic equation): equation consisting of +, -,  an d ÷  Newton’s method

• ODE (ordinary differential equation): equation consistin g of +, -, , ÷ and dy/dx  Gear’s method

• PDE (partial differential equation): +, -, , ÷ and ∂y/∂x

 discretization + Gear’s method

• IE (Integral equation):

Number of equations = Number of variables Degree of freedom = 0

 _x ^x

^{f ( x ) dx}

Part I. Problem Formulation

We will learn :

• Problem formulation: Objective function + constraints

• Objective function: Maximization or Minimization

• Constraints: AE+ODE+PDE+IE

Number of equations < Number of variables Degree of freedom > 0

) , ( 0

) , ( 0

.) . ( ) , (

p x h

p x g t

s to subject

p x f Min





Part I. Problem Formulation Example 1:

• We have two variables: x

and x

• We have one equation: x

+ x

= 10

• We have two constrains on the two variables:x

≥0, x

≥0

• How many solutions are there ?

• We want to maximize the value, f=2x

+ x

• How do we visualize this problem?

Number of equations=1, Number of variables=2 Degree of freedom = 1

2 1

2 1

2 , 1

0 0

10 0

. .

) 2

(

2 1

x x

x x

t s

x x

f Min x x

















Part I. Problem Formulation Example 1:

• x

and x

: products

• x

+ x

= 10: we have 10kg of a raw material

• x

≥0, x

≥0: positive production

• f=2x

+ x

: the price of the products is known

• How do we visualize this problem?

• What may be the uncertain parameter?

Number of equations=1, Number of variables=2 Degree of freedom = 1

2 1

2 1

2 , 1

0 0

10 0

. .

) 2

(

2 1

x x

x x

t s

x x

f Min x x















