CX4017 Process Optimization Syllabus:
CX4017 Process Optimization Syllabus – Anna University PG Syllabus Regulation 2021
COURSE OBJECTIVES:
• To develop optimization target and objectives
• To learn mathematical tools for linear programming
• To learn the mathematical methods for NLP unconstrained optimization problems
• To learn the mathematical methods for constrained optimization problems
• To understand the techniques in multi objective optimization
UNIT I INTRODUCTION
Problem formulation, degrees of freedom analysis, objective functions, constraints and feasible region, Types of optimization problem
UNIT II LINEAR PROGRAMMING
Simplex method, Barrier method, sensitivity analysis, Examples
UNIT III NON-LINEAR UNCONSTRAINED OPTIMIZATION
Convex and concave functions unconstrained NLP, Newton‘s method Quasi-Newton’s method, Examples
UNIT IV CONSTRAINED OPTIMIZATION
Direct substitution, Quadratic programming, Penalty Barrier Augmented Lagrangian Methods
UNIT V MULTI OBJECTIVE OPTIMIZATION
Weighted Sum of Squares method, Epsilon constrains method, Goal attainment, Examples. Introduction to optimal control and dynamic optimization
TOTAL : 45 PERIODS
COURSE OUTCOMES:
The students will be able to
CO1: Understand the basics problem formulation and optimization.
CO2: Understand mathematical characteristics of Linear programming.
CO3: Learn computational solution techniques for nonlinear unconstrained optimization.
CO4: Understand various techniques used in constrained optimization
CO5: Understand the optimal and dynamic optimization
REFERENCES
1. Diwaker, U. W., Introduction to Applied Optimization, Kluwer, 2003
2. Edgar, T. F., Himmelblau, D. M. and Ladson, L. S., Optimization of Chemical Processes, 2nd Ed., McGraw Hill, New York, 2003
3. Joshi, M. C., and Moudgalya, K. M., Optimization, The Fory and Practice, Narosa, New Delhi, 2004
4. Rao, S. S., Engineering Optimization: Theory and Practice, New Age Publishers, 2000
5. Del Castilo., and Enrique., Process Optimization, Springer US, 2007