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Model-Based Process Optimization in the Presence of Parameter Uncertainty
Taylor & Francis
In model-based process optimization one uses a mathematical model
to optimize a certain criterion, for example the product yield
of a chemical process. Models often contain
parameters that have to be estimated from data. Typically,
a point estimate (e.g. the least squares estimate) is
used to fix the model for the optimization stage. However,
parameter estimates are uncertain due to incomplete and noisy
data. In this paper, we show how parameter uncertainty can be
taken into account in process optimization. To quantify the
uncertainty, we use Markov Chain Monte Carlo (MCMC) sampling, an
emerging standard approach in Bayesian estimation. In the
Bayesian approach, the solution to the parameter estimation
problem is given as a distribution, and the optimization criteria
are functions of that distribution. We study how to formulate and implement the optimization and show by numerical examples that parameter uncertainty can have a large effect in optimization
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