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22MOP - Process optimization

Course specification
Course titleProcess optimization
Acronym22MOP
Study programme
Module
Lecturer (for classes)
Lecturer/Associate (for practice)
    Lecturer/Associate (for OTC)
      ESPB5.0Status
      ConditionОблик условљености
      The goalThe objectives of the course is to introduce students to process system optimization and application of optimization methods, through practical problems from chemical process industries. Focus is in understanding the needs for optimization, problem setup and solving, through selection and application of appropriate methods for a desired process system.
      The outcomeAfter completing and passing the exam, master students will be able to: 1) Determine the needs and goals of optimization; 2) Select the approach, numerical method and software or programming language for solving; 3) Define optimization structure - objective function, constraints and optimization variables (degrees-of-freedom), 4) Setup optimization problem - algorithm and code; 5) Solve the problem; 6) Improve solution convergence (if needed); 7) Present, visualize and analyze the results in relation to the goals.
      Contents
      Contents of lecturesTheoretical classes present elements and objectives of process systems optimization, providing approaches and overview of optimization methods, algorithms and recommendations for utilization of methods for different systems and problems. Lectures cover deterministic methods (unconstrained, gradient, linear and nonlinear programming), stochastic methods (genetic algorithm, simulated annealing, tabu search, etc.), dynamic and sequential methods and game theory. Approaches in multi-objective optimization are also presented (Pareto). Different applications of process optimization are discussed, for: 1) estimation of physical and chemical constants and parameters, 2) process and plant economical optimization, 3) process synthesis and conceptual design, 4) superstructures optimization.
      Contents of exercisesPractical classes are conducted in a computer lab, where students with a guidance of professors / teaching assistants and/or individually, set up and solve optimization problems through examples relevant for process systems and industry. The examples and assignments illustrate different use-cases of process optimization (economical cost-benefit, process synthesis, process and equipment design, constants estimation) and utilization of various optimization methods, covered at theoretical classes. Python, GAMS, and other software are used.
      Literature
      1. Edgar. T.F., Himmelblau, Optimization of Chemical Processes, McGraw-Hill, 2001
      2. Dutta S. Optimization in Chemical Engineering, Cambbridge Uni. Press, 2016
      3. Biegler L. Nonlinear Programming: Concepts, Algorithms, and Applications to Chemical Processes, Society for Industial and Applied Mathematics, 2010
      4. Станимировић З. Нелинеарно програмирање, Математички факултет – Универзитет у Београду, 2014.
      5. Floudas C.A. Nonlinear and Mixed-Integer Optimization, Fundamentals and Applications, Oxford University Press, 1995
      Number of hours per week during the semester/trimester/year
      LecturesExercisesOTCStudy and ResearchOther classes
      13
      Methods of teachingLectures (1h weakly) Computer exercises (3h weakly)
      Knowledge score (maximum points 100)
      Pre obligationsPointsFinal examPoints
      Activites during lecturesTest paper30
      Practical lessons70Oral examination
      Projects
      Colloquia
      Seminars