This thesis demonstrates a parameter estimation technique for bioprocesses that utilizes
measurement noise in experimental data to determine credible intervals on parameter
estimates, with this information of potential use in prediction, robust control,
and optimization. To determine these estimates, the work implements Bayesian inference
using nested sampling, presenting an approach to develop neural network (NN)
based surrogate models. To address challenges associated with non-uniform sampling
of experimental measurements, an NN structure is proposed. The resultant surrogate
model is utilized within a Nested Sampling Algorithm that samples possible parameter
values from the parameter space and uses the NN to calculate model output
for use in the likelihood function based on the joint probability distribution of the
noise of output variables. This method is illustrated against simulated data, then
with experimental data from a Sartorius fed-batch bioprocess. Results demonstrate
the feasibility of the proposed technique to enable rapid parameter estimation for
bioprocesses. / Thesis / Master of Applied Science (MASc) / Bioprocesses require models that can be developed quickly for rapid production of desired
pharmaceuticals. Parameter estimation is necessary for these models, especially
first principles models. Generating parameter estimates with confidence intervals is
important for model based control. Challenges with parameter estimation that must
be addressed are the presence of non-uniform sampling and measurement noise in
experimental data. This thesis demonstrates a method of parameter estimation that
generates parameter estimates with credible intervals by incorporating measurement
noise in experimental data, while also employing a dynamic neural network surrogate
model that can process non-uniformly sampled data. The proposed technique
implements Bayesian inference using nested sampling and was tested against both
simulated and real experimental fed-batch data.
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/29967 |
| Date | January 2024 |
| Creators | Weir, Lauren |
| Contributors | Mhaskar, Prashant, Chemical Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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