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

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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

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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

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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

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Controller Validation (Experiment)

PPO Intro D

NYDDNNYY2014/15– 1. 19

Disturbance Scenario

PPO Intro D

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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