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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. Mathematical model. Prelude …. Design, optimization and control of (bio)chemical conversion processes based on. Historical experience.

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Model based Analysis, Design, Optimization and Control of

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  1. Model based Analysis, Design, Optimization and Control of Complex (Bio)Chemical Conversion Processes Bioprocess Technology and Control - KULeuven

  2. Mathematical model 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 Better and more robust performance

  3. Prelude: complexity trade-off manageability accuracy complex enough to cover main dynamics practical implementationoptimization and control MODEL

  4. manageability accuracy Prelude: methodology Primary model

  5. accuracy manageability Prelude: methodology Model complexity reduction

  6. accumulation reaction transport Balance type equations

  7. … # of states … reaction kinetics … time & space dependency Complexity related to …

  8. Theme #1: Objectives: Carbon and nitrogen removing activated sludge systems- biodegradation- sedimentation Fast & reliable simulationsOptimization & control Complexity related to … … # of states

  9. Complexity related to … … # of states

  10. Theme #1: Unit operationsASM1 model

  11. Complexity: ASM1 model (…)

  12. input output Complexity reduction

  13. Xs Ss Xbh Xba Xp So Snh Sno Xnd Snd Aerated tank time[day] time[day]

  14. Activated sludge Effluent Influent Theme #2: Filamentous bulking Sedimentation tank Aeration tank

  15. Image Analysis Procedure Long term objectives Process Control Microbial Community Selection Effluent Water Quality Improvement Influent Wastewater Aeration Tank Environment

  16. SLUDGE Concentration Loading Settleability Characteristics EFFLUENT Turbidity Quality Experimental set-up @ BioTeC Influent Effluent

  17. Robustness test Influence of microscope, camera and sludge type ?

  18. ARX model

  19. Rotating Biological Contactor Submerged Aerated Filter Theme #3: sWWTPS

  20. Milestones • Model complexity reduction for unit operations • Linear Multi (or Fuzzy)Model approach withhighpredictive quality (input or state driven) • Significant reduction incomputation time due to analytic solution of LTI state space model(within 1 class) • Simple linear model forrisk assessment andfeedback (MPC) control • Microbial dynamics: exploiting image analysis information… • Application to (s)WWTPS…

  21. Case studies: * Metabolism of bacterium Azospirillum brasilense* Quorum sensing of bacterium Salmonella typhimurium* Lag/growth/inactivation/survival… Objective: Macroscopic/microscopic cell metabolism modeling Complexity related to … … reaction kinetics

  22. Complexity • High added value of specialty chemicals(food additives, vaccins, enzymes, …) • Quantification of the influence of external signals on • cell metabolism (A. brasilense), and • quorum sensing (S. typhimurium). • Optimal experimental design of bioreactor experiments

  23. OD578 Co [%] D [1/h] Malate [g/L] GUS activity [M.U.] EFT [h] EFT [h] Primary modeling: identification of 14 parameters

  24. OD578 Co [%] D [1/h] Malate [g/L] GUS activity [M.U.] EFT [h] EFT [h] Primary modeling: validation

  25. 0.001 5 0 0 -0.001 -5 time time Sensitivity function based model reduction • Sensitivity functions • reflect the sensitivity ofmodel predictionsto (small) variations in modelparameters with given inputs Essential

  26. OD578 Co [%] D [1/h] Malate [g/L] GUS activity [M.U.] EFT [h] EFT [h] Reduced model: identification experiment

  27. OD578 Co [%] D [1/h] Malate [g/L] GUS activity [M.U.] EFT [h] EFT [h] Reduced model: validation experiment

  28. Nmax Stationary phase max Exponential phase Lag phase  Microbial growth @ constant temperature Escherichia coli K12 (MG1655), Brain Heart infusion, 36.3ºC

  29. b Tmin sub-optimal temperature range Topt Tmax Tmin SQUARE ROOT MODEL [Ratkowsky et al., 1982] Estimation of microbial growth kinetics as function of temperature

  30. Constrained input optimisation

  31. Constrained input optimisation 1st experiment: based on po

  32. Constrained input optimisation 2nd experiment: based on p1

  33. Constrained input optimisation Global identification of experiment 1 & 2

  34. Milestones • Macroscopic modeling: Sensitivity function analysis as a powerful tool to reduce the complexity of a physiology based, first principles model • Microscopic modeling: IBM (Individual based Modeling) linking • bio-informatics, with • macroscopic mass balance type models • Optimal experimental design of computer controlled bioreactor experiments

  35. Case study: Fed-batch growth process with non-monotonic kinetics Objective: Feedback stabilization: keep Cs constant Complexity related to … … reaction kinetics

  36. Two valued function! time Case study u

  37. Two valued function! time Case study u

  38. Two valued function! time Case study u

  39. P-action Feedforward (OC) Stabilizing feedback observer = +1 = -1 I-action or Controller (on-line Cx measurements)

  40. Conclusions • Stabilizing feedback controller for fed-batchnon-monotonic growth processes • Only based on on-line biomass concentration measurements • Adaptive: no detailed kinetics information needed ( observer)

  41. Case study: Tubular chemical reactors Objective: Optimal jacket fluid temperature control of - classical reactors, and - novel type reactors Complexity related to … …time & space dependency

  42. Tubular chemical reactor

  43. Model for tubular reactor: PDE/DPS C = reactant concentration [mole/L] T = reactor concentration [oK] Tw = jacket fluid temperature [oK]

  44. Combined terminal/integral objective Determine optimal jacket fluid temperature profile 2 ( ) Conversion Hot spots Temperature run-away

  45. Comparison with suboptimal profiles • maximum-singular-minimum profile • optimal, but singular part difficult to implement • maximum-minimum profile • not optimal, but practically realizable • how much optimality is lost?

  46. 0.3 Comparison with suboptimal profiles (I): Conversion

  47. 0.7 Comparison with suboptimal profiles (II): Hot Spots

  48. Milestones: optimal control theory for … • … optimal analytical jacket fluid temperature profiles for classical chemical reactors • steady state • transient • … optimization of novel type reactors • cyclically operated reverse flow reactors • circulation loop reactors • … optimal reactor design

  49. Postludium … • Dealing with complexity during modeling for optimization and control of (bio)chemical processes: a multimodal problem at the interface of various disciplines • We will pass several cases in review over the years to come…

  50. … emerging generic results • Development of widely applicable and transferable quantitative tools for complex (bio)chemical processes

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