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Modeling and Control of Batch Processes presents state-of-the-art
techniques ranging from mechanistic to data-driven models. These
methods are specifically tailored to handle issues pertinent to
batch processes, such as nonlinear dynamics and lack of online
quality measurements. In particular, the book proposes: a novel
batch control design with well characterized feasibility
properties; a modeling approach that unites multi-model and partial
least squares techniques; a generalization of the subspace
identification approach for batch processes; and applications to
several detailed case studies, ranging from a complex simulation
test bed to industrial data. The book's proposed methodology
employs statistical tools, such as partial least squares and
subspace identification, and couples them with notions from
state-space-based models to provide solutions to the quality
control problem for batch processes. Practical implementation
issues are discussed to help readers understand the application of
the methods in greater depth. The book includes numerous comments
and remarks providing insight and fundamental understanding into
the modeling and control of batch processes. Modeling and Control
of Batch Processes includes many detailed examples of industrial
relevance that can be tailored by process control engineers or
researchers to a specific application. The book is also of interest
to graduate students studying control systems, as it contains new
research topics and references to significant recent work. Advances
in Industrial Control reports and encourages the transfer of
technology in control engineering. The rapid development of control
technology has an impact on all areas of the control discipline.
The series offers an opportunity for researchers to present an
extended exposition of new work in all aspects of industrial
control.
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