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ATLANTA-August 15, 2002-Computer models
known as neural networks are helping to improve productivity
and product quality in paper manufacturing, reports Dr.
Seppo Karrila of the Institute of Paper Science and Technology
(IPST) in Atlanta, GA. Karrila spoke at the Second International
Workshop on Intelligent Systems Design and Application
held on August 8 and 9 in Atlanta.
Karrila presented some results of
his work on neural networks, mathematical computer models
that are typically based only on input and output data.
Currently, the pulp and paper industry uses these methods
to assist in predicting paper properties or boiler stack
emissions, optimizing deinking processes, and analyzing
other industry operations. Neural network models act
as virtual sensors on paper machines and accurately estimate
on-line parameters, thus replacing slow or costly measurements.
They are also used as off-line models to support optimization
of steady operating conditions. The result is an improvement
in both product quality and productivity.
The focus of Karrila's novel work
is a method he calls nonlinear factor analysis (NLFA),
which is based on conventional feedforward networks.
This method enables the reduction of nonlinear data to
an implicit model, which can then be further inspected,
statistically analyzed or optimized in traditional ways.
The method can be implemented within many standard, low-cost
software packages, which makes it accessible to a wide
user base. Its major advantage is its proper treatment
of noisy input data; making it superior to typical modeling
methods that only consider error sums in the output variables.
The novel nonlinear data reduction method is particularly
useful with a large number of interdependent variables,
such as measured "curves" to be modeled in
conjunction with other process variables.
One case study using this approach,
explains Karrila, involved analyzing bubble-size distributions
in a flow column. In his research, he found that the
conventional approach to analysis by curve fitting the
bubble-size distributions required about two days of
labor, including data preparation, finding and programming
the trial function shapes, fitting the parameters and
then analyzing the results. However, the same results
were obtained in only two hours using his neural network
approach, and the quality of the "parameterization" found
was better.
"I have been impressed with the
ease by which laboratory data can be handled using neural
network methods," says Karrila. He speaks knowingly
of the frustration of using conventional statistics to
get results with nonlinear multivariable data.
To Karrila, one of the primary advantages
of these novel techniques is their ability to "learn
by example" through process history. Unlike other
modeling methods that operate according to a set of given
rules, these methods can use previous data to quickly
process large sets of data, even without much prior knowledge.
However, Karrila points out, neural
networks are just one way to approach the data analysis
applications of pulp and paper manufacturers. "Just
like you don't carry only screwdrivers in your toolbox," he
says, "it is useful to complement neural network
methods with other available tools, including traditional
first-principle modeling, various optimization methods
and traditional simulations." Likewise, he states,
there will always be a place for conventional statistics.
Still, in the growing arena of neural
networks, the future is bright. Karrila sees their proliferation
in both large and small applications and in everyday
uses. He predicts that the industry will continue to
improve its instrumentation and process monitoring, thus
making good data more readily available. He sees this
as an excellent way to optimize the manufacturing processes
with a minimum of capital expenditure.
Karrila believes that, in the not-so-distant
future, modular and hybrid methods will incorporate neural
networks so that the models learn faster with less data
and are more maintainable. Prior knowledge will enter
these models in various ways, allowing extrapolation
outside the span of the training data.
The Institute of Paper Science and
Technology is the premier institute for the advanced
study of pulp and papermaking processes in the United
States. As a privately funded graduate research institute,
IPST has developed a unique scientific and educational
mission based on its a relationship with the pulp and
paper industry.
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