(669a) Cause and Effect Dynamic Modeling of Real Processes Under Freely Existing Data Collection

Due to continual advancements of sensor technology, computer
technology and electronic data storage, the number of variables and their
frequency of sampling and storage are growing at tremendous rates. While the
plant data bases utilizing this growing technology have proven invaluable for
diagnostic and health monitoring, there are two critical reasons that they have
not been widely exploited in the development of cause and effect modeling which would allow direct implementation
into manual or automatic model-based control strategies. The first reason is
the low signal-to-noise ratio since the data are
collected under operating conditions where the objective is to minimize process
variability. The second reason is the highly correlated natural behavior of the
variables that is called multicollinearity that
impedes the ability to associate a specific change in one variable with a
specific amount of change in the response of interest. To overcome these
critical challenges, modelers rely on the use of experimental design where an
intelligent sequence of changes are made to the inputs to maximize information
content both by the size of the changes and type of changes. However, there are
two major drawbacks of this approach. The first one is the cost to run the
experiments due to production interruption; which often leads to unusable
products since the changes have to be outside of normal operating regions. The
second one is the limited duration of the plant test because cost increases
with duration. Limited duration impacts the ability to fully include the
cyclical nature of unmeasured disturbances in model development. This
limitation results in poor model performance as these disturbances move away
from the conditions when the model was developed.

To overcome these challenges, the
continuous-time Wiener block-oriented modeling approach developed by Bhandari
and Rollins (2003) is extended to discrete-time modeling and input correlation
is minimized by passing each input through a separate dynamic block and using a
static linear function on the outputs from these blocks to maintain accuracy
for mild extrapolation. By modeling over a sufficiently long period which is
possible from archived data, information content is strengthened and the impact
of unmeasured disturbances on measurement bias is minimized.

            This talk
will present results of this approach for a model of top tray temperature of a
pilot distillation column on at least 10 independent runs over a three year
period. The model was built from nine inputs, that
were highly correlated in some cases, under open loop conditions. The fitted
correlation coefficient (r_fit)
for training and validation were 0.96 and 0.97, respectively. For the test data
sets, which were run under closed-loop control, r_fit ranged from 0.61
to 0.93, with an average greater than 0.8, supporting the ability of this
approach to develop accurate models with long term stability from data under a
different correlation structure. In addition, a method based on principal
component analysis (PCA) is presented for elimination of cases representing
extreme extrapolation.

Bhandari, N. and D. K. Rollins, ?A Continuous-Time MIMO Wiener Modeling
Method,?Industrial and Engineering Chemistry
Research, 42(22), pp. 5583-5595
(2003).