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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