?????? ? ??? Biological process simulation and optimization

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?????? ? ???Biological process simulation and
optimization

• Major Interdisciplinary program of the
  integrated biotechnology
• Graduate school of bio- information technology
• Youngil Lim (N110), Lab. FACS
• phone [phone] (secretary), 82 31 670
  5207 (direct)
• Fax [phone], mobile phone [phone]
  5207
• Email limyi_at_hknu.ac.kr, homepage 
  http//hknu.ac.kr/limyi/index.htm

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Outline
Course Course name Time Time Room
  Biological process simulation and optimization Thu. 9-12? N130/N116
Overview Optimization is a critical tool for all engineers and a key course of study at university and industry training levels. Though the techniques of optimization are relatively old, the tools for implementing optimization 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. This lecture includes mathematical theories on local/global optimization, a linear programming (LP), nonlinear programming (NLP), mixed-integer NLP (MINLP), multi-objective programming (MOP), successive quadratic programming (SQP), and genetic algorithm (GA). Matlab is used for computational practices. This lecture is given in English. Optimization is a critical tool for all engineers and a key course of study at university and industry training levels. Though the techniques of optimization are relatively old, the tools for implementing optimization 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. This lecture includes mathematical theories on local/global optimization, a linear programming (LP), nonlinear programming (NLP), mixed-integer NLP (MINLP), multi-objective programming (MOP), successive quadratic programming (SQP), and genetic algorithm (GA). Matlab is used for computational practices. This lecture is given in English. Optimization is a critical tool for all engineers and a key course of study at university and industry training levels. Though the techniques of optimization are relatively old, the tools for implementing optimization 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. This lecture includes mathematical theories on local/global optimization, a linear programming (LP), nonlinear programming (NLP), mixed-integer NLP (MINLP), multi-objective programming (MOP), successive quadratic programming (SQP), and genetic algorithm (GA). Matlab is used for computational practices. This lecture is given in English. Optimization is a critical tool for all engineers and a key course of study at university and industry training levels. Though the techniques of optimization are relatively old, the tools for implementing optimization 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. This lecture includes mathematical theories on local/global optimization, a linear programming (LP), nonlinear programming (NLP), mixed-integer NLP (MINLP), multi-objective programming (MOP), successive quadratic programming (SQP), and genetic algorithm (GA). Matlab is used for computational practices. This lecture is given in English.
Method   Lecture(?), Seminar (?), Computational practice (?),  Factory tour (?), Beam projector(?)   Lecture(?), Seminar (?), Computational practice (?),  Factory tour (?), Beam projector(?)   Lecture(?), Seminar (?), Computational practice (?),  Factory tour (?), Beam projector(?)   Lecture(?), Seminar (?), Computational practice (?),  Factory tour (?), Beam projector(?)
Evaluation   Attendance 8,  homework 20,  Mid-exam 30,  Final-exam 30, Presentation 12   Attendance 8,  homework 20,  Mid-exam 30,  Final-exam 30, Presentation 12   Attendance 8,  homework 20,  Mid-exam 30,  Final-exam 30, Presentation 12   Attendance 8,  homework 20,  Mid-exam 30,  Final-exam 30, Presentation 12
Text Main Edgar and Himmelblau, Optimization of Chemical Processes, 2nd ed. McGraw-Hill, 2001. Sub Bioseparation engineering, M.R. Ladisch, Wiley interscience, 2001. Main Edgar and Himmelblau, Optimization of Chemical Processes, 2nd ed. McGraw-Hill, 2001. Sub Bioseparation engineering, M.R. Ladisch, Wiley interscience, 2001. Main Edgar and Himmelblau, Optimization of Chemical Processes, 2nd ed. McGraw-Hill, 2001. Sub Bioseparation engineering, M.R. Ladisch, Wiley interscience, 2001. Main Edgar and Himmelblau, Optimization of Chemical Processes, 2nd ed. McGraw-Hill, 2001. Sub Bioseparation engineering, M.R. Ladisch, Wiley interscience, 2001.
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Weekly Lecture Plan
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 (???, 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   (???, 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  
15 Final exam.  
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Part I. Problem Formulation

• We knew and learned
• AE (algebraic equation) equation consisting of
  , -, ? and ? Newtons method
• ODE (ordinary differential equation) equation
  consisting of , -, ?, and dy/dx ? Gears
  method
• PDE (partial differential equation) , -, ?,
  and ?y/?x ? discretization Gears method
• IE (Integral equation)

Number of equations Number of variables Degree
of freedom 0
5
Part I. Problem Formulation

• We will learn
• Problem formulation Objective function
  constraints
• Objective function Maximization or Minimization
• Constraints AEODEPDEIE

Number of equations lt Number of variables Degree
of freedom gt 0
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Part I. Problem Formulation

• Example 1
• We have two variables x1 and x2
• We have one equation x1 x2 10
• We have two constrains on the two variablesx10,
  x20
• How many solutions are there ?
• We want to maximize the value, f2x1 x2
• How do we visualize this problem?

Number of equations1, Number of
variables2 Degree of freedom 1
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Part I. Problem Formulation

• Example 1
• x1 and x2 products
• x1 x2 10 we have 10kg of a raw material
• x10, x20 positive production
• f2x1 x2 the price of the products is known
• How do we visualize this problem?
• What may be the uncertain parameter?

Number of equations1, Number of
variables2 Degree of freedom 1