• Course Information
  • Course Title Code Semester Laboratory+Practice (Hour) Pool Type ECTS
    Advanced Process Optimization KMÜ515 FALL-SPRING 3+0 Faculty E 6
    Learning Outcomes
    1-Uses experimental and semi-experimental multi-response mathematical model development methods required for optimization.
    2-Solves multi-answer optimization problems related to the field.
    3-Uses advanced optimization techniques for various applications in chemical engineering.
    4-Understands the place and importance of heuristic techniques in solving complex optimization problems in the chemical industry.
  • ActivityPercentage


    NumberTime (Hours)Total Workload (hours)
    Course Duration (Weeks x Course Hours)14342
    Classroom study (Pre-study, practice)14684
    Short-Term Exams (exam + preparation) 0000
    Midterm exams (exam + preparation)2011515
    Laboratory 0000
    Final exam (exam + preparation) 4011515
    Other 0000
    Total Workload (hours)   188
    Total Workload (hours) / 30 (s)     6,27 ---- (6)
    ECTS Credit   6
  • Course Content
  • Week Topics Study Metarials
    1 Modeling and optimization concepts
    2 Graphical optimization
    3 Development of theoretical mathematical models
    4 Development of experimental mathematical models
    5 Development of quasi-experimental mathematical models
    6 Classical optimization techniques
    7 Linear programming
    8 Midterm exams
    9 Nonlinear programming
    10 Nonlinear programming
    11 Integer Programming
    12 Stochastic Programming
    13 Intuitive Approaches
    14 Intuitive Approaches
    15 Applications of advanced optimization techniques in chemical engineering
    Prerequisites -
    Language of Instruction Turkish
    Coordinator Associate Prof. Dr. Barış Şimşek
    Instructors -
    Assistants -
    Resources 1. Baker, K.R., ?Optimization Modeling with Spreadsheets?, 2. Ed., John-Wiley&Sons, 2011. 2. Bangert, P., ?Optimization for Industrial Problems?, Springer, 2012. 3. Arora, J.S., ?Introduction to Optimum Design?, McGrawHill, 1989. 4. Edgar, T. F., Himmelblau, D. M., Laston, L.S., ?Optimization of Chemical Process?, SE, McGrawHill, 2001. 5. Taha, H. A., ?Operations Research: An Introduction, Nineth edition?, Prentice Hall, 2011. 6. Rao, S. S., ?Engineering Optimization: Theory and Practice, Third edition?, John Wiley & Sons, NY, 1996.
    Supplementary Book -
    Goals Teaching experimental modeling techniques based on experimental design, giving information about advanced multi-response linear, nonlinear, heuristic optimization techniques and applying these techniques to various chemical engineering problems
    Content The development of theoretical, experimental and quasi-experimental meta-models includes constrained and unconstrained optimization concepts and techniques, linear programming and constrained nonlinear programming, integer and mixed integer programming, heuristic algorithms, and optimization applications in the chemical industry.
  • Program Learning Outcomes
  • Program Learning Outcomes Level of Contribution
    1 To make scientific researches and reach the knowledge in depth; analyze interpret and apply the knowledge. 4
    2 To have knowledge about current technics, methods and their limitations applied in engineering. 5
    3 To have the ability to define and practice the knowledge by using scientific methods and limited or restricted data and to use the knowledge from other disciplines. 4
    4 To have awareness about the new and developing implementations in engineering and to research and learn them when required. 4
    5 To define and formulate problems concerning chemical engineering , to develop methods for solution and to apply innovative methods for solutions. 4
    6 To develop new and/or original ideas and methods, to design complex systems and processes and to improve alternative/innovative solutions. 5
    7 To design and apply theoretical, applied and simulative researches, to analyse and solve complicated problems encountered during these processes. 5
    8 To lead in multidisciplinary teams, improve solutions in complex situation and to work independently and take responsibility. 4
    9 To use English at least in European Language Portfolio B2 level for both oral and written skills. 5
    10 To declare the results and processes of studies both orally and written in national and international platforms with a systematically and concisely manner. 4
    11 To have awareness about the social, enviromental, health, security and law perspectives and project management and career applications of engineering practices and restrictions of all these. 3
    12 To regard social, scientific liabilities, and ethics during the collection, evaluation, and publication steps of data. 4
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