Chapter 1 Process Integration and Optimization: 1.1 What Optimization Is All About
Chapter 1 Process Integration and Optimization: 1.1 What Optimization Is All About
Chapter 1 Process Integration and Optimization: 1.1 What Optimization Is All About
Optimization
1.1 WHAT OPTIMIZATION IS ALL
ABOUT
of two evils, always choose the lesser
1. The field of statistics treats various principles termed
"maximum likelihood,“ "minimum loss," and
"least squares," and
2. business makes use of "maximum profit," "minimum
cost," "maximum use of resources," "minimum effort,"
in its efforts to increase profits.
A typical engineering problem can be posed as
follows:
• A process can be represented by some
equations or perhaps solely by experimental
data.
• a single performance criterion in mind such as
minimum cost.
• The goal of optimization is to find the values
of the variables in the process that yield the
best value of the performance criterion.
• The described factors-process or model and
the performance criterion-constitute the
optimization "problem.“
• Typical problems in chemical engineering
process design or plant operation have many
(possibly an infinite number) solutions.
• Optimization is concerned with selecting the
best among the entire set by efficient
quantitative methods.
1. Introduction
Introduction
• OPTIMIZATION is the use of specific methods to determine
the most cost effective and efficient solution to a problem or
design for a process or
• Optimization is the act of obtaining the best result under given
circumstances.
• optimization can be defined as the process of finding the
conditions that give the maximum or minimum value of a
function.
• There is no single method available for solving all
optimization problems efficiently.
What optimization is all about?
1.2 WHY OPTIMIZE?
• Engineers work to improve the initial design of equipment
and strive to enhance the operation of that equipment once
it is installed so as to realize the largest production, the
greatest profit, the minimum cost, the least energy usage,
and so on
• Optimization can also lead to reduced maintenance costs, less
equipment wear, and better staff utilization
• It is extremely helpful to systematically identify the objective,
constraints, and degrees of freedom in a process or a plant,
leading to such benefits as improved quality of design, faster
and more reliable troubleshooting, and faster decision
making
1.3 SCOPE AND HIERARCHY OF
OPTIMIZATION
• Optimization can take place at many levels in a
company, ranging from a complex combination of
plants and distribution facilities down through
individual plants, combinations of units, individual
pieces of equipment, subsystems in a piece of
equipment, or even smaller entities
• the scope of an optimization problem can be the entire
company, a plant, a process, a single unit operation, a
single piece of equipment in that operation, or any
intermediate system between these.
• In a typical industrial company optimization
can be used in three areas (levels):
(1)management,
(2) process design and equipment specification,
and
(3) plant operations
.
Hierarchy of levels of optimization
Attributes of processes affecting
costs or profits make them attractive
for the application of optimization:
1. Sales limited by production
2. Sales limited by market:
3. Large unit throughputs
4. High raw material or energy consumption:
5. Product quality exceeds product specfications
6. Losses of valuable components through waste
streams:
7. High labor costs:
• Two valuable sources of data for identifying
opportunities for optimization include
(1) profit and loss statements for the plant or the
unit and
(2) the periodic operating records for the plant.
1.4 EXAMPLES OF APPLICATIONS OF OPTIMIZATION
f(x)
f(xi) B
f(xi-1) C
E D A X
xi+1 xi-1 xi
Quasi-Newton (secant)Method
• In the quasi-Newton method (secant method) the approximate
model to be solved is
• where m is the slope of the line connecting the point and a
second point given by
Is always positive-definite.
Finite difference Newton method.
POLYNOMIAL APPROXIMATION METHODS
where x0, x1, and x2 are the initial guesses, and x3 is the value
of x that corresponds to the maximum value of the parabolic fit
to the guesses
Chapter 4
Chapter 5 Linear Programing(LP)
Graphical solution example 1
Example 2
Example 3
Example 3
Standard form of LP
Simplex method
Simplex method
Simplex method
Simplex method
Simplex method
Simplex method pivoting
Simplex method
Simplex method
Simplex method pivoting
Simplex method pivoting
Simplex method example
Simplex method example
Simplex method example
Chapter 6 Non linear programing
Non linear programing
Non linear programming direct substitution
Non linear programming direct substitution
Non linear programming lagrangian multiplier method
Non linear programming lagrangian multiplier method
Non linear programming-problems containing only equality constraints
Non linear programming-problems containing only
equality constraints
Non linear programming-problems containing only inequality
constraints
Non linear programming-problems containing only inequality
constraints
Non linear programing example
Non linear programing solution
Non linear programing solution
Non linear programing examples
N
.
Non linear programing examples
Non linear programing examples
Non linear programing examples
Non linear programing exercise
Mixed integer programing reading assignment
Classification of the types of problems that are encountered in
optimization with discrete variables
some integer programming models
some integer programming models
Chapter 7
Process integration
Process integration techniques
heat exchanger networks
heat exchanger networks
heat exchanger networks
heat exchanger networks
heat exchanger networks
heat exchanger networks
heat exchanger networks
heat exchanger networks
heat exchanger networks
heat exchanger networks
heat exchanger networks
heat exchanger networks
heat exchanger networks
heat exchanger networks
heat exchanger networks
heat exchanger networks
heat exchanger networks
heat exchanger networks
Basic concepts in process heat integration
Basic concepts in process heat integration
Basic concepts in process heat integration
Basic concepts in process heat integration
Basic concepts in process heat integration
Basic concepts in process heat integration
Basic concepts in process heat integration
Basic concepts in process heat integration
Basic concepts in process heat integration
Simple two stream problem
Four stream problem
The problem table method
The problem table method procedure
The problem table method procedure
The problem table method procedure
The problem table method procedure
The problem table method summary
heat exchange network
heat exchange network -network design for maximum heat recovery
heat exchange network -network design above the pinch
heat exchange network -network design above the pinch
heat exchange network -network design below the pinch
heat exchange network -network design below the pinch
heat exchange network summary
heat exchange network summarizing
The problem table method
The problem table method
The problem table method
Mass exchanger network reading assignment