<p>Robust process monitoring in
real-time is a challenge for Continuous Pharmaceutical Manufacturing. Sensors
and models have been developed to help to make process monitoring more robust,
but they still need to be integrated in real-time to produce reliable estimates
of the true state of the process. Dealing with random and gross errors in the
process measurements in a systematic way is a potential solution. In this work,
we present such a systematic framework, which for a given sensor network and
measurement uncertainties will predict the most likely state of the process. As
a result, real-time process decisions, whether for process control, exceptional
events management or process optimization can be based on the most reliable
estimate of the process state.</p><p><br></p><p></p><p>Data reconciliation (DR) and gross
error detection (GED) have been developed to accomplish robust process
monitoring. DR and GED mitigate the effects of random measurement errors and
non-random sensor malfunctions. This methodology has been used for decades in
other industries (i.e., Oil and Gas), but it has yet to be applied to the
Pharmaceutical Industry. Steady-state data reconciliation (SSDR) is the
simplest forms of DR but offers the benefits of short computational times. However,
it requires the sensor network to be redundant (i.e., the number of
measurements has to be greater than the degrees of freedom).</p><p><br></p><p>In this dissertation, the SSDR
framework is defined and implemented it in two different continuous tableting
lines: direct compression and dry granulation. The results for two pilot plant
scales via continuous direct compression tableting line are reported in this
work. The two pilot plants had different equipment and sensor configurations.
The results for the dry granulation continuous tableting line studies were also
reported on a pilot-plant scale in an end-to-end operation. New measurements
for the dry granulation continuous tableting line are also proposed in this
work.</p><p><br></p><p></p><p>A comparison is made for the
model-based DR approach (SSDR-M) and the purely data-driven approach (SSDR-D)
based on the use of principal component constructions. If the process is linear or mildly nonlinear,
SSDR-M and SSDR-D give comparable results for the variables estimation and GED.
The reconciled measurement values generate using SSDR-M satisfy the model
equations and can be used together with the model to estimate unmeasured
variables. However, in the presence of nonlinearities, the SSDR-M and SSDR-D will
differ. SSDR successfully estimates the real state of the process in the
presence of gross errors, as long as steady-state is maintained and the
redundancy requirement is met. Gross errors are also detected whether using
SSDR-M or SSDR-D. </p><p><br></p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/7499273 |
| Date | 03 January 2019 |
| Creators | Mariana Moreno (5930069) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/Robust_Process_Monitoring_for_Continuous_Pharmaceutical_Manufacturing/7499273 |
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