Abstract:
The field of quantification of nonlinearity has drawn much attention from researchers
in recent times. However, the scope of research on quantification of nonlinearity, especially
for process plants, remains a vast area to explore. Nonlinearity in process plants
may arise from two factors, namely, instruments and process characteristics. Instruments
can induce nonlinearity due to their nonlinear dynamics or faults such as stiction,
hysteresis and saturation. Process may themselves be nonlinear in nature or may show
nonlinear behavior due to violation of some physical limits or constraints. Nonlinearity
quantifications are useful for many purposes such as checking the adequacy of a linear
controller for a nonlinear process and finding the root cause of a fault that arose due to
increase in nonlinearity of the instruments. Model based quantification of nonlinearity
is difficult, expensive and time consuming. On the other hand, data-based nonlinearity
quantification methods are easy to use and becoming popular due to readily available
data from Distributed Control System (DCS) or data historian of the plants.
Four data-based nonlinearity measures, namely, Bicoherence based, Surrogate Data
based, Correlation Dimension and Maximal Lyapunov Exponent are studied and their
performances are compared in this thesis. Both simulations and experimental investigations
have been undertaken to compare and find the suitability of the data-based nonlinearity
measures for quantification of nonlinearity for chemical processes. Bicoherencebased
measure was found to be the most successful among them and the Surrogate
data-based measure was next to it.
