PURPOSE: Application of process models for estimation of parameters and immeasurable states of the process, process optimization, up-scaling of lab-scale model simulation results on the pilot-plant and industrial scale, process control and product quality control.
THE CONTENTS OF THE COURSE:
1st week
Basic concepts about process. Basic definitions for the model. Classification of models: analytical and non-analytical, deterministic and stochastic, distributed and homogeneous, linear and non-linear, static and dynamic.
2nd week
Applications and examples of models. Engineering analysis of physical, chemical, biological and environmental processes - development of process models: scheme of process streams, mass and energy balance, model parameters, numerical methods for model solving, selection of computer programs and simulation software, simulations, model application.
3rd week
Linearization of models. Non-linear models and their steady-states, numerical methods for assessment of steady-states of non-linear systems. Jacobi iterative method, Newton-Raphson method, method of secants.
4th week
Models and simulations of 1st and 2nd order dynamical systems. Analytical solutions.
5th week
Laplace transforms and transfer functions.
6th week
Mathematical methods for solving od differential equations: Euler method, Runge-Kutta method, Rosenbrock method.
7th week
Discretization methods: finite difference method, method of lines, collocation method.
8th week
Estimation of model parameters, linear and non-linear regression analysis: trial and error method, least square method, simplex method, Nelder-Mead method.
1st partial test
9th week
Model sensitivity analysis, stability of solutions. Model simulations.
10th week
Application of model simulation results for optimization, design and control of processes.
11th week
Experimental plan and process optimization: Evolutionary operation (EVOP), genetic algorithm, simplex method, Rosenbrock method.
12th week
Case study 1. Production of pyruvic acid.
13th week
Case study 2. Industrial aerobic waste-water treatment.
14th week
Case study 4. Treatment of air pollution caused by galvanizing industry.
15th week
Case study 4. Transport of pollutant in porous media.
2nd partial test
GENERAL AND SPECIFIC COMPETENCE:
Achieving of basic knowledge needed for solving of case studies - process analysis and modeling using chemical engineering methodology.
STUDENTS TEACHING OBLIGATIONS AND THEIR PERFORMANCE:
Students are required to attend lectures and seminars in the computer room. Students have the right to take the exam through partial tests. Students are required to complete a seminar paper.
CONDITIONS FOR TAKING THE EXAM:
Attendance at a minimum of 75% of all lectures and seminars to be held in the computer room.
TEACHING METHODS:
Lectures and seminars in the computer room.
METHOD OF EXAMINATION OF KNOWLEDGE AND EXAMINATION:
Partial tests or written exam.
METHOD OF MONITORING THE QUALITY AND PERFORMANCE OF COURSES:
Student survey.
METHODOLOGICAL PREREQUISITES:
Passed exam in the course Fundamentals of Environmental Statistics and Numerical Methods, Balance of Substances and Energy, Transfer of Substances and Energy, the right to take an exam from the course Reactors and Bioreactors.
COURSE LEARNING OUTCOMES:
1. apply the principles of mass and energy maintenance to physical, chemical and biochemical processes
2. define process space, system boundaries, and process input and output streams
3. set mathematical models of engineering processes and processes that take place in nature
4. apply numerical methods to solve process models
5. define an experiment plan
6. optimize the process by applying experimental results and process model simulation results
7. apply numerical methods to optimize the process
LEARNING OUTCOMES AT PROGRAM LEVEL:
1. analyze and optimize the processes of the chemical and related industries
2. apply the methodology of chemical engineering in process development
3. manage and plan processes
4. manage and plan time
5. apply mathematical methods, models and techniques in solving case studies
TEACHING UNITS WITH ASSOCIATED LEARNING OUTCOMES AND EVALUATION CRITERIA:
Teaching unit:
1. Mathematical process model
Learning outcomes
- apply the principle of mass maintenance to physical processes
- define process space, system boundaries, and process input and output streams
- set a mathematical model of experimental engineering processes and processes that take place in nature
Evaluation criteria
- for a given process, sketch a process diagram, and identify input and output process streams
- determine the basis for the calculation
- apply the law on conservation of mass and energy and set the mass and energy balances of a given process
Teaching unit:
2. Numerical methods in process modeling
Learning outcomes
- apply numerical methods for solving systems of independent linear equations
- apply numerical methods for solving systems of nonlinear equations
- apply numerical methods for solving systems of differential equations
- apply numerical methods for solving systems of partial differential equations
Evaluation criteria
- solve the system of independent linear, nonlinear, differential or partial differential equations
Teaching unit:
3. Process optimization
Learning outcomes
- define an experiment plan
- determine the characteristic variables of the process that need to be optimized
- define the target function
- apply an appropriate process optimization method using experimental results or process model simulation results
Evaluation criteria
- define an experiment plan for the given process
- optimize the process using the appropriate method
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1. B. Zelić: Nastavni materijali na mrežnim stranicama Fakulteta, 2009.,
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1. E. Holzbecher: Environmental Modeling using Matlab, Springer-Verlag, Berlin, 2007.
2. J. Mikleš, M. Fiklar: Process Modeling, Identification and Control, Springer-Verlag, Berlin, 2007.
3. I. Plazl, M. Lakner: Uvod v modeliranje procesov, Univerza v Ljubljani, Ljubljana, 2004.
4. J.B. Snape, I.J. Dunn, J. Ingham, J.E. Prenosil: Dynamics of Environmental Bioprocesses, VCH, Weinheim, 1995.
5. K.T. Valsaraj: Elements of Environmental Engineering, Thermodynamics and Kinetics, Lewis Publishers, Boca Raton, 2000.
6. W.W. Nazaroff: Environmental Engineering Science, John Wiley & Sons, New York, 2001.,
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