01 Process Performance Optimization Intro 2014
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Transcript of 01 Process Performance Optimization Intro 2014
Department of Biochemical and Chemical Engineering
Process Dynamics and Operations Group (DYN)
DNYDDNNYY
Process Performance Optimization
Introduction
PPO Intro D
NYDDNNYY2014/15– 1. 2
Performance Goals for Production Processes
Economic success = profit maximize
Consumption of energy and resources minimize
Environmental impact (emissions, waste) minimize
Risk minimize
Partly congruent, partly contradictory
• Less energy consumption ~ higher profit
• Less environmental impact possibly lower profit
Ways to incorporate non-economic goals:
• Regulations profit maximization as an constrained
optimization problem
• Economic incentives or penalties e.g. CO2-emissions
• Long-term effects corporate policy
PPO Intro D
NYDDNNYY2014/15– 1. 3
What Determines Process Performance?
Plant design
• Chemical or biochemical production route
• Catalyst efficiency
• Equipment design
• Heat integration
• Material recovery
• Control structure, safety logic
Design can be upgraded during the lifetime of the plant
How is the design optimized?
• Experience
• Lab and pilot plant experiments
• Trial and error in simulation studies
• Numerical optimization
PPO Intro D
NYDDNNYY2014/15– 1. 4
Process Operations
How can the performance of the plant be maintained or
improved during its daily operations
• Building a car vs. driving a car to win races
Nothing works in reality as planned!
• Reaction rates change, catalysts deactivate
• Microorganisms evolve genetically
• Raw materials change
• Outer conditions change, utilities fluctuate
• Prices for raw materials and for products change
• Demands change
These deviations are not always negative, may create potential for better operations
PPO Intro D
NYDDNNYY2014/15– 1. 5
The Essence of Process Operations:
How to Handle Uncertainty
Uncertainty is a pervasive aspect of (bio)chemical
process design and operations
Design: Strive for robustness
• Important issue in the selection and combination of steps
• Rarely addressed systematically
• Safety margins based upon experience
• Trade-off between performance and robustness
Operations:
• REACT to uncertainties feedforward and feedback
• Requires margins, “room to move”
• The closed-loop operation should be explicitly taken into
account in plant and process design
currently not industrial practice
PPO Intro D
NYDDNNYY2014/15– 1. 6
Feedback Control: The Main Means to
Counteract Uncertainties
Feedback control is mostly taught as methods to achieve good
responses to reference signals
However its main function is to counteract uncertanties
• Model errors
• Disturbances
(Only) since the 1980s, the issue of the robustness of feedback
loops with respect to model errors has been treated systematically
in the scientific literature, starting with:
George Zames: Feedback and Optimal Sensitivity - Model-Reference
Transformations, Multiplicative Seminorms, and Approximate Inverses
IEEE Tr. on Automatic Control 26 (1981)
Mature theory for linear systems
PPO Intro D
NYDDNNYY2014/15– 1. 7
0 0.1 0.2 0.3 0.4 0.5 0.6
4
3
2
1
0
-1
-2
0.1 0.2 0.3 0.4 0.5 0.6
0 0.1 0.2 0.3 0.4 0.5 0.6
Example: Plant PT1 with P-controller
Plant Step Response
Closed Loop Step Response with P-controller (Kc = 70)
2)200)(10()(
ss
KsG
p
P
Kp = 5
10)(
s
KsG
p
P
0 0.1 0.2 0.3 0.4 0.5 0.6
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
Time
Am
plit
ud
e
Kp = 2,5
Kp = 7,5
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
Time
Am
plit
ud
e
Kp = 5
Kp = 2,5
Kp = 7,5
PPO Intro D
NYDDNNYY2014/15– 1. 8
Example: Operation of a Reactive Distillation Column
Integration of reaction and
separation, azeotropes are
overcome by reaction
Semi-batch mode
Manipulated variables
• Reflux ratio (RT)
• Feed (HAc)
• Heat flow
Measured variables
• xMeAc
• xH2O
• Temperatures along the
column
RefluxSplit
Acetic AcidTop Product
CoolantCoolant
GT1
T2
T6
T5
T10
T9
T3
T7
T11
T4
T8
T12
X
L
Heat supply
PPO Intro D
NYDDNNYY2014/15– 1. 9
The Reactive Distillation Column
Pilot plant:
• Diameter: 100 mm
• Height: 9 m
Semi-batch operation
Cooperation
• Fluid Separation Processes Group
• Dynamics and Operations Group
PPO Intro D
NYDDNNYY2014/15– 1. 10
Product concentration (MeAc)
xM
eA
c [m
ol/m
ol
Reflux ratio [-] Feed (HAc) [mol/s]
Quasi steady-state behavior
PPO Intro D
NYDDNNYY2014/15– 1. 11
Semi-batch operation (I)
Concentrations in the
condensate (top of the c.)
Re
flu
x r
atio
C
om
po
sitio
n o
f t
he
pro
du
ct s
tre
am
Time [h]
R
D
PPO Intro D
NYDDNNYY2014/15– 1. 12
Re
flu
x r
atio
V
ap
or
co
mp
os
itio
n
time [h]
Concentrations in the vapor
from the reboiler
Semi-batch operation (II)
PPO Intro D
NYDDNNYY2014/15– 1. 13
Control structure selection
Product concentration must be controlled
Second controlled variable must ensure that enough water gets to the upper part of the column to overcome the distillation boundary • Temperatures in the upper part or water concentration
• H2O-concentration chosen because plant-model mismatch is smaller
Three possible actuated variables • Reflux ratio
• Feed flow
• Heating power
• Ratio HAc/MeOH is crucial, i.e. feed/heating power
Reaction to changes in heating power is slow, hard to set
Reflux ratio and feed flow
PPO Intro D
NYDDNNYY2014/15– 1. 14
Choice of the operating point
Feed (HAc) [mole/s] Feed (HAc) [mole/s]
Reflux ratio [-]
• Feed 0.035± 0.005 mole/s
• Reflux ratio 0.575±0.05
PPO Intro D
NYDDNNYY2014/15– 1. 15
Input Multiplicities
Fixed feed, varying reflux ratio Fixed reflux ratio, varying feed
PPO Intro D
NYDDNNYY2014/15– 1. 16
Changes of the Concentrations Over a Batch Run
R
D
PPO Intro D
NYDDNNYY2014/15– 1. 17
Data-based Modelling
0 2 4 6 8 10 120.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85
time [h]
[mo
le/m
ole
]methyl acetate molar fraction
data
simulated
0 2 4 6 8 10 120.52
0.54
0.56
0.58
0.6
0.62
0.64
time [h]
[-]
RT
0 2 4 6 8 10 120.032
0.034
0.036
0.038
0.04
0.042
0.044
time [h]
[mo
le/s
ec]
FEED
0 2 4 6 8 10 120
0.02
0.04
0.06
0.08
0.1
0.12
0.14
time [h]
[mo
le/m
ole
]
water molar fraction
data
simulated
PPO Intro D
NYDDNNYY2014/15– 1. 18
Controller Validation (Experiment)
PPO Intro D
NYDDNNYY2014/15– 1. 19
Disturbance Scenario
PPO Intro D
NYDDNNYY2014/15– 1. 20
Process Performance Optimization
Optimization (design, operating point, during operation)
• Scalar, multivariable; linear, nonlinear, constrained
Good process control
• Control structure selection
• Controller tuning
Advanced Control
• State Estimation for Monitoring and Control
• Model predictive control
Process Performance Monitoring
Simulation, Operator Training Systems
Management Execution Systems
SPC / Six Sigma
PPO Intro D
NYDDNNYY2014/15– 1. 21
Course Schedule
Week Date Topic Lecturer
1 06.10.2014 Overview, Intro and scalar optimization, Nonlinear unconstrained
optimization (3V)
Engell + Paulen
2 13.10. Tutorial optimization 1 (3Ü) Assistenten
3 20.10. Linear programming, Nonlinear constrained multivariable optimization
(2V)
Paulen
4 27.10. Tutorial optimization 2 Assistenten
5 03.11. Evolutionary algoritmns (2V + 1Ü) Urselmann + Ass.
6 10.11. Tutorial control recap (3Ü) Assistenten
7 17.11. Control structures (3V) Dünnebier
8 24.11. Control structures (3V) Dünnebier
9 01.12. Control structures (3Ü) Engell + Ass.
10 08.12. State estimation (2V + 1Ü) Engell
11 15.12. State estimation, NMPC, Optimizing control (3V) Engell + Ass.
12 05.01.2015 Tutorial State estimation, NMPC, Optimizing control (3Ü) Assistenten
13 12.01. Process performance monitoring (3V/Ü)) Dünnebier
14 19.01. Process simulation, operator training systems, MES (3V/Ü) Dünnebier
15 26.01. Additional exam preparation Assistenten
16 02.02. SPC ( Six Sigma (3V/Ü) Dünnebier