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SUMMARY:Data-Driven Optimization of Batch Processes: The Design of Dynamic
  Experiments
DTSTART:20110318T101500
DTSTAMP:20260407T043213Z
UID:a8b31f6fa9906c84b1701005d648804563913bf32f1d1bcdb352245f
CATEGORIES:Conferences - Seminars
DESCRIPTION:Pr. C. Georgakis\, Department of Chemical and Biological Engin
 eering\, Tufts University\, Medford\, USA.\nMany batch processes cannot be
  optimized using knowledge-driven process\nmodels\, because such models do
  not exist. This is due to our incomplete understanding of the inner worki
 ngs of many batch processes and unfavorable economics due to their small p
 roduction rates.  To resolve this impasse\, a new data-driven methodology 
 is presented for optimizing the operation of a variety of batch processes 
 when at least one time-varying operating condition needs to be selected. T
 his methodology calculates optimal time-varying conditions without the use
  of an a priori knowledge-driven model. The approach generalizes the class
 ical Design of Experiments (DoE) methodology\, limited by its consideratio
 n of time-invariant decision variables. The new approach\, called the Desi
 gn of Dynamic Experiments (DoDE)\, designs experiments that explore a set 
 of “dynamic signatures” of the unknown decision function(s). Constrain
 ed optimization of the interpolating response surface model\, calculated f
 rom the results of the performed experiments\, leads to the selection of t
 he optimal operating conditions. Results from two simulated examples and a
 n experimental pharmaceutical process demonstrate the powerful utility of 
 the method. The first examines a simple reversible reaction in a batch rea
 ctor\, where the time-dependant reactor temperature is the decision functi
 on. The second example examines the optimization of a penicillin fermentat
 ion process\, where the feeding profile of the substrate is the decision v
 ariable. In both cases\, a finite number of experiments leads to the effec
 tive approximation of the optimal operation of the process. The third exam
 ple examines an asymmetric catalytic hydrogenation reaction in the product
 ion of an active pharmaceutical ingredient. Here the best of the DoDE expe
 riments is 50% better than the best experiment of the DoE set.
LOCATION:MEC2405
STATUS:CONFIRMED
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