model based analysis, design, optimization and control of...

Model based

Analysis, Design, Optimization and Control of

Complex (Bio)Chemical Conversion Processes

Bioprocess Technology and Control - KULeuven

Prelude …

Design, optimization and control

of (bio)chemical conversion processes

based on

Historical

experience

• time consuming

• capital intensive

• operation/operator

specific

• on-line measurements

• in silico design,

optimization,

and control studies

Mathematical

model

practical implementation

optimization and control

manageabilityaccuracy

complex enough to

cover main dynamics

Prelude: complexity trade-off

MODEL

accuracy

manageability

Primary model

Prelude: methodology

accuracy manageability

Model

complexity

reduction

Prelude: methodology

reaction transportaccumulation

Balance type equations

Complexity

related to �

� # of states

� time & space

dependency

� reaction

kinetics

Complexity

related to �

� # of states

Carbon and nitrogen

removing activated

sludge systems

- biodegradation

- sedimentation

Theme #1:

Fast & reliable

simulations

Optimization &

control

Objectives:

Complexity

related to �

� # of states

Theme #1: Unit operations

ASM1 model

Complexity: ASM1 model

(…)

input output

Complexity reduction

Data

generation

Identification

ASM1

linearization

Model

interpolation

T=11 C

Dlow

o T=11 C

Dhigh

o T=22 C

Dlow

o T=22 C

Dhigh

o

Σ

λT11,Dlow λT11,Dhigh λT22,Dlow λ T22,Dhigh

[A B C D]T11,Dlow

[A B C D]T22,Dhigh

[A B C D]T22,Dlow

[A B C D]T11,Dhigh

Temperature & influent rate variation

Dlow Dhigh

Dlowλ0

1

oC11 22

11Tλ

22Tλ

T =

0

1

Dhighλ

Aerated tank

Ss

Xbh

Xp

Sno

Snd

Xs

Xba

So

Snh

Xnd

time [day] time [day]

Theme #2: Filamentous bulking

Influent Effluent

Aeration tank Sedimentation tank

Activated sludge

Process

Control

Influent

Wastewater

Aeration Tank

Environment

Microbial

Community

Selection

Effluent Water

Quality

Improvement

Long term objectives

Image Analysis

Procedure

Image Analysis

Procedure

Experimental set-up @ BioTeC

Influent

Effluent

EFFLUENTEFFLUENT

Turbidity

Quality

SLUDGESLUDGE

Concentration

Loading

Settleability

Characteristics

Robustness test

Influence of microscope, camera and sludge type ?

ARX model

Theme #3: sWWTPS

�� Rotating Biological Rotating Biological

ContactorContactor

�� Submerged Aerated Submerged Aerated

FilterFilter

Milestones

�� Model complexity reduction for unit operationsModel complexity reduction for unit operations

�� Linear Linear MultiMulti ((or Fuzzyor Fuzzy)) MModel odel approach withapproach withhighhigh predictipredictiveve qualityquality (input or state driven)(input or state driven)

�� Significant Significant reduction inreduction in computation timecomputation time due to due to analytic solution of LTI state space modelanalytic solution of LTI state space model(within 1 class)(within 1 class)

�� Simple Simple linear modellinear model forforrisk assessmentrisk assessment andand feedback feedback (MPC) (MPC) controlcontrol

�� Microbial dynamics: Microbial dynamics: exploiting image analysis information…exploiting image analysis information…

�� Application to (s)WWTPS…Application to (s)WWTPS…

Complexity

related to �

� reaction kinetics

* Metabolism of bacterium

Azospirillum brasilense

* Quorum sensing of bacterium

Salmonella typhimurium

* Lag/growth/inactivation/survival �

Case studies:

Macroscopic/microscopic

cell metabolism modeling

Objective:

�� High added value of specialty chemicalsHigh added value of specialty chemicals(food additives, vaccins, enzymes, …)(food additives, vaccins, enzymes, …)

�� Quantification of the influence of external signals onQuantification of the influence of external signals on

�� cell metabolism (cell metabolism (A. brasilenseA. brasilense), and ), and

�� quorum sensing (quorum sensing (S. typhimuriumS. typhimurium).).

�� Optimal experimental design of Optimal experimental design of

bioreactor experimentsbioreactor experiments

Complexity

Primary modeling: identification of 14 parameters

EFT [h] EFT [h]

Co [%]

Malate [g/L]

OD578

D [1/h]

Primary modeling: validation

EFT [h] EFT [h]

Co [%]

Malate [g/L]

OD578

D [1/h]

Sensitivity function based model reduction

�� Sensitivity functionsSensitivity functions

� reflect the sensitivity of model predictionsto (small) variations in model parameterswith given inputs

time

0

5

-5

j

i

p

y

∂

∂

time

0

0.001

-0.001

j

i

p

y

∂

∂

Reduced model: identification experiment

EFT [h] EFT [h]

Co [%]

Malate [g/L]

OD578

D [1/h]

Reduced model: validation experiment

EFT [h] EFT [h]

Co [%]

Malate [g/L]

OD578

D [1/h]

λλλλ

µµµµmax

Nmax

Escherichia coli K12 (MG1655), Brain Heart infusion, 36.3ºC

Microbial growth @ constant

temperature

Stationary phase

Exponential phase

Lag phase

Estimation of microbial growth kinetics as

function of temperature

Tmin Topt Tmaxsub-optimal temperature range

)()( minmax TTbT −⋅=µ

SQUARE ROOT MODEL [Ratkowsky et al., 1982]

b

b minT

Tmin

Constrained input optimisation

To avoid lag

(at most)

C5 T°

≤∆

Single small temperature step ⇒⇒⇒⇒ low information contentmax12low1 TTTTT ∆+==

Constrained input optimisation

1st experiment: based on po

Constrained input optimisation

2nd experiment: based on p1

Constrained input optimisation

Global identification of experiment 1 & 2

Constrained input optimisation

Milestones

�� Macroscopic modelingMacroscopic modeling: Sensitivity function : Sensitivity function analysis as a powerful tool to reduce the complexity analysis as a powerful tool to reduce the complexity of a physiology based, first principles modelof a physiology based, first principles model

�� Microscopic modelingMicroscopic modeling: : IBM (Individual based Modeling) linking IBM (Individual based Modeling) linking

•• biobio--informatics,informatics, with with

••macroscopic mass balance type modelsmacroscopic mass balance type models

�� Optimal experimental designOptimal experimental design of computer of computer controlled bioreactor experimentscontrolled bioreactor experiments

Complexity

related to �

� reaction

kinetics

Fed-batch growth

process with non-

monotonic kinetics

Case study:

Feedback stabilization:

keep Cs constant

Objective:

Case study

u

time

Two valued function!

Case study

u

time

Two valued function!

Case study

u

time

Two valued function!

Controller (on-line Cx measurements)

Feedforward (OC) Stabilizing feedback

observer

I-action

P-action

= +1 = -1 or

�� Stabilizing feedback controller for fedStabilizing feedback controller for fed--batchbatchnonnon--monotonic growth processesmonotonic growth processes

�� Only based on onOnly based on on--line biomass line biomass concentration measurementsconcentration measurements

�� Adaptive: no detailed kinetics information Adaptive: no detailed kinetics information needed (needed (µµ observer)observer)

Conclusions

Complexity

related to �

�time & space

dependency

Tubular chemical reactors

Case study:

Optimal jacket fluid

temperature control of

- classical reactors, and

- novel type reactors

Objective:

Tubular chemical reactor

C = reactant concentration [mole/L]

T = reactor concentration [oK]

Tw = jacket fluid temperature [oK]

Model for tubular reactor: PDE/DPS

Combined terminal/integral objective

Conversion

Hot spots

Temperature

run-away

Determine optimal jacket fluid temperature profile

( )2

Comparison with suboptimal profiles

�� maximummaximum--singularsingular--minimumminimum profileprofile

��optimal, but optimal, but

singular part difficult to implementsingular part difficult to implement

�� maximummaximum--minimumminimum profileprofile

��not optimal, but not optimal, but

practically realizablepractically realizable

�� how much how much optimality optimality is lost?is lost?

0.3

Comparison with suboptimal profiles (I):

Conversion

0.7

Comparison with suboptimal profiles (II):

Hot Spots

Milestones: optimal control theory for …

�� … optimal … optimal analyticalanalytical jacket fluid temperature jacket fluid temperature

profiles for profiles for classical classical chemical reactorschemical reactors

�� steady statesteady state

�� transienttransient

�� … optimization of … optimization of novel typenovel type reactors reactors

�� cyclically operated reverse flow reactorscyclically operated reverse flow reactors

�� circulation loop reactorscirculation loop reactors

�� … optimal reactor … optimal reactor designdesign

Postludium …

�� Dealing with complexityDealing with complexity during modeling for during modeling for

optimization and control of optimization and control of

(bio)chemical processes: (bio)chemical processes:

a multimodal problem at the interface of a multimodal problem at the interface of

various disciplinesvarious disciplines

�� We will pass several cases in review over the We will pass several cases in review over the

years to come…years to come…

… emerging generic results

�� Development of widely applicable and Development of widely applicable and

transferable quantitative tools for complex transferable quantitative tools for complex

(bio)chemical processes(bio)chemical processes

WP3

WP1 WP2

WP4