Just-in-Time Kernel Learning
with Adaptive Parameter
Selection for Soft Sensor Modeling of Batch Processes
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Abstract
An efficient nonlinear just-in-time learning (JITL) soft
sensor
method for online modeling of batch processes with uneven operating
durations is proposed. A recursive least-squares support vector regression
(RLSSVR) approach is combined with the JITL manner to model the nonlinearity
of batch processes. The similarity between the query sample and the
most relevant samples, including the weight of similarity and the
size of the relevant set, can be chosen using a presented cumulative
similarity factor. Then, the kernel parameters of the developed JITL-RLSSVR
model structure can be determined adaptively using an efficient cross-validation
strategy with low computational load. The soft sensor implement algorithm
for batch processes is also developed. Both the batch-to-batch similarity
and variation characteristics are taken into consideration to make
the modeling procedure more practical. The superiority of the proposed
soft sensor approach is demonstrated by predicting the concentrations
of the active biomass and recombinant protein in the streptokinase
fed-batch fermentation process, compared with other existing JITL-based
and global soft sensors