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In this section, please find abstracts of the publications of the Computer Integrated Food Manufacturing Center.
To find out more information or to order a reprint of a publication please
contact us.
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Publications by Steven J. Mulvaney
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Authors: Haley, T.A., Mulvaney S.J. (1999)
Title: On-line System Identification and Control Design of an Extrusion Cooking Process. Part I - System Identification.
Abtsract: A systems analysis of an extrusion cooking process for puffed corn snack products
revealed that the specific mechanical energy (SME) and screw speed (SS) was a
desirable pairing of measured and manipulated variables respectively for regulating extrudate density.
To facilitate the design of an SME model-based control system, a discrete-time transfer function that
depicts the dynamic response of motor load (ML) to changes in SS is required.
The research literature describes several off-line techniques for developing such transfer function models
but no methods for on-line system identification were found.
This paper represents the first of two articles that describe our investigations into the use of on-line
system identification for automatic tuning and adaptive control of a high-shear twin-screw extrusion process.
This paper reports results for using various system identification schemes in combination with
relay-feedback as a way to derive, in real-time, a transfer function model that accurately depicts the
dynamical behavior of an extrusion cooking process.
A Wenger TX-52 co-rotating twin screw extruder was subjected to relay feedback during
the processing of cornmeal for a breakfast cereal formula under different moisture and
screw speed conditions. The data obtained from these experiments were used to derive first,
second and third-order discrete-time transfer functions.
An analysis of the resulting transfer functions
revealed that a first-order lead-lag transfer function structure adequately described the
dominant dynamic behavior of the process in all cases. Next, batch and recursive
implementations of least squares, extended least squares, output error, maximum likelihood,
Box-Jenkins and predictive error algorithms were used to derive parameters for the first-order
transfer function. Overall, the batch output error method provided good transfer function
estimates over the range of product and process conditions studied.
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Authors: Haley, T.A., Mulvaney S.J. (1999)
Title: On-line System Identification and Control Design of an Extrusion Cooking Process. Part II -
Model Predictive and Inferential Control Design.
Abtsract: A non-linear model-based predictive control law to regulate specific mechanical
energy (SME) using screw speed is developed. Operating variable setpoints are
determined using an inferential model that correlates SME with product density and
melt moisture content. Additionally, a ratio control strategy is used to regulate melt
moisture content and an output noise filter is added to attenuate sensor noise. When
combined with an online system identification procedure, the resulting system provides
good servo and regulatory control response that is robust to modeling errors and distubances.
This paper represents the second of two articles that describe our investigation into the use of
on-line system identification for automatic tuning and adaptive control of high-shear twin-screw
extrusion processes. The resulting control design addresses the need for a comprehensive
system that can be used to regulate extrudate density in extrusion cooking and puffing processes.
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Authors: Haley, T.A., Mulvaney S.J. (1995)
Title: Advanced Process Control Techniques for the Food Industry
Abtsract: The proper design and application of process controllers is essential for
minimizing both operating costs and out-of-specification waste product in food
processing operations. In this regard, advanced process controllers may provide significantly
better performance than classical controllers.
This article describes how advanced process
controllers, particularly model-based controllers, are created and the design criteria used to
tune these controllers in the context of controlling food processes. A number of examples are
reviewed which demonstrate the feasibility of applying model-based controllers to a variety of
food processes. Guidelines for selecting appropriate advanced control strategies for particular
types of dynamic processes are also presented.
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You can find more information on our current research activities in the
Current Projects section.
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