Slide background

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:

School Chemical and Environmental Engineering
Course Level Undergraduate
Direction Environmental Engineering
Course ID ENVE 335 Semester 6th
Course Category Required
Course Modules Instruction Hours per Week ECTS

Lectures and Tutorials

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

4
Course Type General Background
Prerequisites  
Instruction/Exam Language Greek
The course is offered to Erasmus students No
Course URL https//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 Method Direct (face to face) in classrooms

Use of Information and Communication Technology

Specialized software; E-class support

Instruction Organisation Activity Workload per Semester
(hours)
- Lectures 39
-Study and literature review 18
- Projects  30
- Problem solving 13
Course Total 100

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: