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School of Chemical and Environmental Engineering

Now offering two distinct diplomas: Chemical Engineering and Environmental Engineering

Optimization of Environmental and Energy Systems

1. COURSE INFORMATION:

SchoolChemical and Environmental Engineering
Course LevelUndergraduate
DirectionEnvironmental Engineering
Course IDENVE 335Semester6th
Course CategoryRequired
Course ModulesInstruction Hours per WeekECTS

Lectures and Tutorials

4
T=3, E=1, L=0

4
Course TypeGeneral Background
Prerequisites 
Instruction/Exam LanguageGreek
The course is offered to Erasmus studentsNo
Course URLhttps//www.eclass.tuc.gr/courses/MHPER216/  (in Greek)

 

2. LEARNING OUTCOMES

Learning Outcomes

Upon successful completion of the course the student will be able to:

  • Recall optimization methodologies, linear, non-linear, dynamic programming and advanced optimization methodologies.
  • Recognize the mathematical expression of optimization for an environmental problem and which methodology is most suitable for solving any kind of problem.
  • Select the appropriate solution method and determine the optimal solution
  • Αpply the taught methodologies to environmental problems.
 
General Competencies/Skills
 
  • Work Autonomously
  • Decision making
  • Project planning and management
 

3. COURSE SYLLABUS

 
  1. INTRODUCTION TO THE THEORY OF OPTIMIZATION: Introduction.
  2. Optimization Model Classification, Nonlinear Optimization, Hollow Sets and Functions.
  3. Mathematical Optimization Theorems.
  4. Geometry of the Mathematical Optimization Problem.
  5. CLASSIC OPTIMIZATION: Unlimited Optimization Problems, Lagrange Multipliers.
  6. LINEAR PROGRAMMING: Optimization in Linear Programming Problems
  7. LINEAR PROGRAMMING: Simplex Method.
  8. NON-LINEAR PROGRAMMING: Introduction, Unlimited Optimization Methods,
  9. NON-LINEAR PROGRAMMING Optimization Methods with Constraints, Dynamic Programming.
  10. DYNAMIC PROGRAMMING: Introduction and basic concepts.
  11. ADVANCED METHODS OF OPTIMIZATION: Genetic algorithms,
  12. ADVANCED METHODS OF OPTIMIZATION: Fuzzy Logic, Neural Networks.
  13. Matlab applications.
 

4. INSTRUCTION and LEARNING METHODS - ASSESSMENT

Lecture MethodDirect (face to face) in classrooms

Use of Information and Communication Technology

Specialized software; E-class support

Instruction OrganisationActivityWorkload per Semester
(hours)
- Lectures39
-Study and literature review18
- Projects 30
- Problem solving13
Course Total100

Assessment Method

Ι. Written final examination (80%).
- Theoretical problems with data to be resolved.

ΙΙ. Project 20(%).

5. RECOMMENDED READING

 
  • Μέθοδοι Βελτιστοποίησης Περιβαλλοντικών Συστημάτων, Καρατζάς Γεώργιος, Παπαδοπούλου Μαρία
  • Στοιχεία Βελτιστοποίησης, Ευστράτιος Ε. Τζιρτζιλάκης
 

6. INSTRUCTORS

Course Instructor:Professor G. Karatzas (Faculty - ChEnvEng), Professor D. Kolokotsa (Faculty - ChEnvEng)
Lectures:Professor G. Karatzas (Faculty - ChEnvEng), Professor D. Kolokotsa (Faculty - ChEnvEng)
Tutorial exercises:Professor G. Karatzas (Faculty - ChEnvEng), Professor D. Kolokotsa (Faculty - ChEnvEng)
Laboratory Exercises: