Batch reactors are often used to produce high quality products because any batch that
does not meet quality speci cations can be easily discarded. However, for high-value
products, even a few wasted batches constitute substantial economic loss. Fortunately,
databases of historical data that can be exploited to improve operation are often
readily available. Motivated by these considerations, this thesis addresses the problem
of direct, data-based quality control for batch processes. Speci cally, two novel datadriven
modeling and control strategies are proposed.
The rst approach addresses the quality modeling problem in two steps. To begin,
a partial least squares (PLS) model is developed to relate complete batch trajectories
to resulting batch qualities. Next, the so called missing-data problem, encountered
when using PLS models partway through a batch, is addressed using a data-driven,
multiple-model dynamic modeling approach relating candidate input trajectories to
future output behavior. The resulting overall model provides a causal link between
inputs and quality and is used in a model predictive control scheme for direct quality
control. Simulation results for two di erent polymerization reactors are presented
that demonstrate the e cacy of the approach.
The second strategy presented in this thesis is a state-space motivated, timeinvariant
quality modeling and control approach. In this work, subspace identi cation
methods are adapted for use with transient batch data allowing state-space dynamic
models to be identifi ed from historical data. Next, the identifi ed states are related
through an additional model to batch quality. The result is a causal, time-independent
model that relates inputs to product quality. This model is applied in a shrinking
horizon model predictive control scheme. Signi cantly, inclusion of batch duration
as a control decision variable is permitted because of the time-invariant model. Simulation
results for a polymerization reactor demonstrate the superior capability and
performance of the proposed approach. / Thesis / Doctor of Philosophy (PhD) / High-end chemical products, ranging from pharmaceuticals to specialty plastics, are
key to improving quality of life. For these products, production quality is more
important than quantity. To produce high quality products, industries use a piece
of equipment called a batch reactor. These reactors are favorable over alternatives
because if any single batch fails to meet a quality specifi cation, it can be easily discarded.
However, given the high-value nature of these products, even a small number
of discarded batches is costly.
This motivates the current work which addresses the complex topic of batch quality
control. This task is achieved in two steps: first methods are developed to model
prior reactor behavior. These models can be applied to predict how the reactor
will behave under future operating policies. Next, these models are used to make
informed decisions that drive the reaction to the desired end product, eliminating
o -spec batches.
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/20664 |
| Date | January 2016 |
| Creators | Corbett, Brandon |
| Contributors | Mhaskar, Prashant, Chemical Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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