Handling Uncertainty in the Development and Design of Chemical Processes

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Handling Uncertainty in the Development and
Design of Chemical Processes

• David Bogle,
• David Johnson and
• Sujan Balendra
• Centre for Process Systems Engineering
• Dept of Chemical Engineering
• University College London
• Collaborating Company Pharmacia

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Summary

• Objectives
• Process Development
• Methodology
• A Multiphase reactor
• Complete Manufacturing Processes
• Conclusions

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Dealing with Uncertainty in Process Development

• Utilise the available information
• What can be done?
• manipulate available decisions
• improve the model - reduce uncertainty
• alternative process/route

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Methodology
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Methodology
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Uncertainty Analysis

• Estimate characteristics based on local
  linearisation and approx. confidence region
• Hammersley sampling procedure
• Solve stochastic model to give expected values
  of statistics for output variables
• Continue sampling until mean and variance are
  unchanging (lt1)

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Validation and Sensitivity Analysis

• Estimate ranking priority of inputs contributing
  to uncertainty
• Use
• Correlation coefficients linear measures of
  input contribution
• Standardised regression coefficient fraction
  of output variability explained by input
  variability not due to any of the other inputs

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Optimal reduction in uncertainty
d decision variables fractions of original
values of parameters which characterise spread of
uncertainties Max S dst S ddt Subject
to deterministic model uncertainty space
characterisation stochastic inequality
constraint FW(F) lt a FW (F) (width between 5
and 95 fractiles)
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Optimal isothermal conditions
Criteria Nominal optimal operation Uncertain optimal operation
Scenarios 456 418
Mean-variance YC 0 0.234
EYC (), VarYC 94.35, 50.4 94.30, 26.9
EYD () 2.75 2.77
Etf (hr) 9.34 2.35
FWYC () 18.73 15.92
FWYD () 8.83 7.55
FWtf (hr) 24.97 5.17
PrpassYD ? 2.0, Prpasstf ? 8.0 0.59, 0.59 0.53, 0.98
EviolYD ? 2.0, Evioltf ? 8.0 1.39, 4.49 1.25, 0.05
Decisions
tadd (hr) 1.79 1.12
Tiso (K) 296.8 312.4
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Process development methodology
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Typical Pharmaceutical Plant
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Process design systems are typically modular and
data comes as error bounds around a data point
use interval methods
But how conservative?
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Conclusions

• Process development
• Methodology for quantifying and minimising
  uncertainty
• Uncertainty analysis and identification of
  potential uncertainty reduction
• Case study of semi batch reactor
• Contrast of stochastic and interval approaches

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Acknowledgements
EPSRC, Pharmacia, Aspentech