Model based Analysis, Design, Optimization and Control of Complex (Bio)Chemical Conversion Processes...
-
Upload
june-parker -
Category
Documents
-
view
226 -
download
4
Transcript of Model based Analysis, Design, Optimization and Control of Complex (Bio)Chemical Conversion Processes...
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
Better and more
robust performance
practical implementationoptimization and control
manageabilityaccuracy
complex enough to cover main dynamics
Prelude: complexity trade-off
MODEL
Complexity related to …
… # of states
Carbon and nitrogen removing activated sludge systems- biodegradation- sedimentation
Theme #1:
Fast & reliable simulations
Optimization & control
Objectives:
ProcessControl
InfluentWastewater
Aeration TankEnvironment
Microbial Community
Selection
Effluent WaterQuality
Improvement
Long term objectives
Image AnalysisProcedure
Image AnalysisProcedure
Experimental set-up @ BioTeC
Influent
EffluentEFFLUENTEFFLUENT
TurbidityQuality
SLUDGESLUDGE
ConcentrationLoading
SettleabilityCharacteristics
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 with
highhigh predictipredictiveve quality quality (input or state driven) (input or state driven) Significant Significant reduction inreduction in computation timecomputation time due to analytic due to analytic
solution of LTI state space modelsolution of LTI state space model (within 1 class)(within 1 class)
Simple Simple linear modellinear model for forrisk assessmentrisk assessment and and 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
[%]
Mal
ate
[g/L
]
OD
578
GU
S a
ctiv
ity
[M.U
.]D
[1/h]
Primary modeling: validation
EFT [h] EFT [h]
Co
[%]
Mal
ate
[g/L
]
OD
578
GU
S a
ctiv
ity
[M.U
.]D
[1/h]
Sensitivity function based model reduction
Sensitivity functionsSensitivity functions reflect the sensitivity of model predictions to (small) variations in model parameters with given inputs
time
0
5
-5
j
i
p
y
time
0
0.001
-0.001
j
i
p
y
Essential
Reduced model: identification experiment
EFT [h] EFT [h]
Co
[%]
Mal
ate
[g/L
]
OD
578
GU
S a
ctiv
ity
[M.U
.]D
[1/h]
Reduced model: validation experiment
EFT [h] EFT [h]
Co
[%]
Mal
ate
[g/L
]
OD
578
GU
S a
ctiv
ity
[M.U
.]D
[1/h]
max
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
SQUARE ROOT MODEL [Ratkowsky et al., 1982]
b
b minT
Tmin
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
• bio-informatics,bio-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:
Controller (on-line Cx measurements)
Feedforward (OC) Stabilizing feedback
observer
I-action
P-action
= +1 = -1 or
Stabilizing feedback controller for fed-batchStabilizing feedback controller for fed-batchnon-monotonic growth processesnon-monotonic growth processes
Only based on on-line biomass Only based on on-line biomass concentration measurementsconcentration measurements
Adaptive: no detailed kinetics information Adaptive: no detailed kinetics information needed (needed ( observer) observer)
Conclusions
Complexity related to …
…time & space dependency
Tubular chemical reactorsCase study:
Optimal jacket fluid temperature control of - classical reactors, and - novel type reactors
Objective:
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
maximum-singular-minimummaximum-singular-minimum profile profile optimal, but optimal, but
singular part difficult to implementsingular part difficult to implement maximum-minimummaximum-minimum profile profile
not optimal, but not optimal, but practically realizablepractically realizable
how much how much optimality optimality is lost?is lost?
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…