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http://hdl.handle.net/11375/29967
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DC Field | Value | Language |
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dc.contributor.advisor | Mhaskar, Prashant | - |
dc.contributor.author | Weir, Lauren | - |
dc.date.accessioned | 2024-07-17T17:39:56Z | - |
dc.date.available | 2024-07-17T17:39:56Z | - |
dc.date.issued | 2024 | - |
dc.identifier.uri | http://hdl.handle.net/11375/29967 | - |
dc.description.abstract | 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. | en_US |
dc.language.iso | en | en_US |
dc.subject | Bayesian Inference | en_US |
dc.subject | Surrogate Modelling | en_US |
dc.subject | Bioprocess | en_US |
dc.subject | Parameter Estimation | en_US |
dc.subject | Non-uniform sampling | en_US |
dc.title | NOISE AWARE BAYESIAN PARAMETER ESTIMATION IN BIOPROCESSES: USING NEURAL NETWORK SURROGATE MODELS WITH NON-UNIFORM DATA SAMPLING | en_US |
dc.title.alternative | NOISE AWARE BAYESIAN PARAMETER ESTIMATION IN BIOPROCESSES | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Chemical Engineering | en_US |
dc.description.degreetype | Thesis | en_US |
dc.description.degree | Master of Applied Science (MASc) | en_US |
dc.description.layabstract | 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. | en_US |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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Weir_Lauren_JL_finalsubmission202406_MASc.pdf | 1.84 MB | Adobe PDF | View/Open |
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