THESIS
2015
xvi, 188 pages : illustrations ; 30 cm
Abstract
Modeling a new process can be challenging and time-consuming due to either a lack of
understanding of the true behavior of the process, costly experimentation, or scarce data.
Oftentimes, there are past processes related to but not exactly the same as the new process,
in which abundant knowledge has already existed. Although the process is new,
mathematical models from similar yet non-identical past processes can provide insights
into the new process and, hence, careful integration of the existing models will potentially
improve the new predictive model as well as reducing modeling effort. Over the last few
years, little has been studied on the combination of past models and expert judgments in
the discipline of chemical engineering. Therefore, this study will propose model mig...[
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Modeling a new process can be challenging and time-consuming due to either a lack of
understanding of the true behavior of the process, costly experimentation, or scarce data.
Oftentimes, there are past processes related to but not exactly the same as the new process,
in which abundant knowledge has already existed. Although the process is new,
mathematical models from similar yet non-identical past processes can provide insights
into the new process and, hence, careful integration of the existing models will potentially
improve the new predictive model as well as reducing modeling effort. Over the last few
years, little has been studied on the combination of past models and expert judgments in
the discipline of chemical engineering. Therefore, this study will propose model migration
methodologies to address the issue.
The purpose of this study is to use similarity, which is referred to as the phenomenon that
structurally similar, yet not-identical processes perform the similar function or yield the
similar output. Consequently, model migration aims to integrate data of the new process
with existing model of the related past processes. This study systematically investigates
model migration methodologies based on process similarity which widely exists in engineering
problems and many others. The overall theme of the proposed methods is the
statistical adjustments of the known past models, and the incorporation of prior knowledge
into new process data under Bayesian framework. A series of migration implementations
are studied where the objective is to develop a migration model for the new process that
is better than using the data of the new process only. The merits of model migration are
demonstrated on various test functions as well as on real-world chemical processes. It is
shown that the proposed methods are promisingly effective and efficient modeling tools
for complex chemical systems.
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