2 research outputs found
Just-in-Time Kernel Learning with Adaptive Parameter Selection for Soft Sensor Modeling of Batch Processes
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
Fuzzy Phase Partition and Hybrid Modeling Based Quality Prediction and Process Monitoring Methods for Multiphase Batch Processes
A novel fuzzy phase
partition method and a hybrid modeling strategy
are proposed for quality prediction and process monitoring in batch
processes with multiple operation phases. The fuzzy phase partition
method is proposed on the basis of a sequence-constrained fuzzy c-means
(SCFCM) clustering algorithm. It divides the batch process into several
fuzzy operation phases by performing the SCFCM algorithm on trajectory
data of phase-sensitive process variables. This SCFCM-based partition
method not only has high computation efficiency and good partition
accuracy but also is easy to implement and popularize. In addition,
it generates “soft” partition results, where a “transition”
phase exists between two adjacent “steady” operation
phases. A hybrid modeling strategy is developed to build appropriate
models for all operation phases according to their own characteristics.
Phase-based multiway PLS models are built for regular steady phases
that have longer durations and stable process behaviors. Just-in-time
PLS models are built for those phases with shorter durations but time-varying
or nonlinear process behaviors, including all transition phases and
several irregular steady phases. This hybrid modeling strategy significantly
enhances the modeling accuracy, resulting in better quality prediction
and process monitoring performance. Advantages of proposed methods
are illustrated by case studies in a fed-batch penicillin fermentation
process