Practical on-line model validation for model predictive controllers (MPC)
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Date
2010
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Abstract
A typical petro-chemical or oil-refining plant is known to operate with hundreds if not
thousands of control loops. All critical loops are primarily required to operate at their
respective optimal levels in order for the plant to run efficiently. With such a large
number of vital loops, it is difficult for engineers to monitor and maintain these loops
with the intention that they are operating under optimum conditions at all times. Parts of
processes are interactive, more so nowadays with increasing integration, requiring the use
of a more advanced protocol of control systems. The most widely applied advanced
process control system is the Model Predictive Controller (MPC). The success of these
controllers is noted in the large number of applications worldwide. These controllers rely
on a process model in order to predict future plant responses.
Naturally, the performance of model-based controllers is intimately linked to the quality
of the process models. Industrial project experience has shown that the most difficult and
time-consuming work in an MPC project is modeling and identification. With time, the
performance of these controllers degrades due to changes in feed, working regime as well
as plant configuration. One of the causes of controller degradation is this degradation of
process models. If a discrepancy between the controller’s plant model and the plant itself
exists, controller performance may be adversely affected. It is important to detect these
changes and re-identify the plant model to maintain control performance over time.
In order to avoid the time-consuming process of complete model identification, a model
validation tool is developed which provides a model quality indication based on real-time
plant data. The focus has been on developing a method that is simple to implement but
still robust. The techniques and algorithms presented are developed as far as possible to
resemble an on-line software environment and are capable of running parallel to the
process in real time. These techniques are based on parametric (regression) and nonparametric
(correlation) analyses which complement each other in identifying problems
-iiwithin
on-line models. These methods pinpoint the precise location of a mismatch. This
implies that only a few inputs have to be perturbed in the re-identification process and
only the degraded portion of the model is to be updated. This work is carried out for the
benefit of SASOL, exclusively focused on the Secunda plant which has a large number of
model predictive controllers that are required to be maintained for optimal economic
benefit. The efficacy of the methodology developed is illustrated in several simulation
studies with the key intention to mirror occurrences present in industrial processes. The
methods were also tested on an industrial application. The key results and shortfalls of
the methodology are documented.
Description
Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, Durban, 2010.
Keywords
Predictive control., Prediction theory., Kalman filtering., Theses--Chemical engineering.