Process Modeling, Simulation & Optimization
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Process Modeling, Simulation & Optimization Syllabus — Unit-wise Content
The official GTU curriculum for Process Modeling, Simulation & Optimization (subject code 2180503) as prescribed for Chemical · Semester 8 divides the course into 10 units, covering 62 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (2180503)Unit 1 Modeling Aspects
- Definition of process model
- physical and mathematical modeling
- deterministic and stochastic process
- classification of models
- model building
- black-box model
- white box model
- gray model
- classification of mathematical methods
Unit 2 Mathematical Models of Chemical Engineering Systems
- uses of mathematical models
- scope of coverage
- principles of formulation
- fundamental laws
- continuity equations
- energy equations
- equation of motion
- transport equation
- equation of state
Unit 3 Examples of Mathematical Models of Chemical Engineering Systems
- series of isothermal
- constant-hold up CSTR
- CSTR with variable holds up, two heated tanks
- pressurized CSTR
- non-isothermal CSTR
- single-component vaporizer
- batch reactor
- reactor with mass transfer
- ideal binary distillation column
- batch distillation with holdup
- CHEMICAL PROCESS SIMULATION
Unit 4 Partitioning and Tearing
- Steady state lumped system-partitioning equation
- tearing equation
- simultaneous equation
- modular approaches & equation solving approaches
- decomposition of networks
Unit 5 Introduction to Various Professional Simulators and Equation Solver Software
- CHEMICAL PROCESS OPTIMIZATION
Unit 6 The Nature and Organization of Optimization Problems
- Scope and hierarchy of optimization
- examples of applications of optimization
- the essential features of optimization problems
- general procedure for solving optimization problems
- obstacles to optimization
Unit 7 Developing Models for Optimization
- Classification of models
- how to build a model
- selecting functions to fit empirical data
- factorial experimental designs
- degrees of freedom
- examples of inequality and equality constrains in models
- formulation of the objective function
Unit 8 Basic Concepts of Optimization
- Continuity of function
- NLP problem statement
- convexity and its applications
- interpretation of the objective function in terms of its quadratic approximation
- necessary and sufficient conditions for an extremum of an unconstrained function
Unit 9 Optimization of Unconstrained Functions
- One-Dimensional search numerical methods for optimizing a function of one variable
- scanning and bracketing procedures
- Newton and Quasi-Newton methods of uni-dimensional search
- polynomial approximation methods
- how one-dimensional search is applied in a multidimensional problem
- evaluation of uni-dimensional search methods
Unit 10 Unconstrained Multivariable Optimization
- Methods using function values only
- methods that use first derivatives
- Newton’s method
- Quasi-Newton methods
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