THESIS
2014
xxiv, 157 pages : illustrations ; 30 cm
Abstract
Batch processes have been widely employed in chemical and other manufacturing industries,
due to their capabilities to meet the requirements of fast changing markets and to manufacture
high-value-added products. In batch manufacturing, operation safety and product quality
consistency are pivotal problems to guarantee high productivity, arousing attentions of many
researchers. To deal with these two problems, many multivariate statistical analysis
techniques such as principal component analysis (PCA) and partial least squares (PLS) have
been adopted in the field of multivariate statistical process control (MSPC), and have been
extended to different forms by taking the natures of batch processes into consideration. The
related methods include multiway PCA/PLS (MPCA/MPLS), phase-ba...[
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Batch processes have been widely employed in chemical and other manufacturing industries,
due to their capabilities to meet the requirements of fast changing markets and to manufacture
high-value-added products. In batch manufacturing, operation safety and product quality
consistency are pivotal problems to guarantee high productivity, arousing attentions of many
researchers. To deal with these two problems, many multivariate statistical analysis
techniques such as principal component analysis (PCA) and partial least squares (PLS) have
been adopted in the field of multivariate statistical process control (MSPC), and have been
extended to different forms by taking the natures of batch processes into consideration. The
related methods include multiway PCA/PLS (MPCA/MPLS), phase-based PCA/PLS, etc.
In the time direction within each batch, many existing methods assume that the similar
characteristics within each phase can be captured by a single statistical model, which ignore
the process variations between phases or within a phase. In the batch direction throughout the
whole process, traditional methods handle process variations by adjusting the monitoring
models consecutively in a direct way, which leads to the increase in probability of introducing
disturbances and faults. In fact, process evolution, a typical kind of process characteristic
variation caused by process dynamics or long term external factors, should be discriminated
from the random variation caused by noises.
In this thesis, several novel strategies have been proposed to improve both batch process
monitoring and quality prediction by tracing both inner-batch and inter-batch evolutions.
Using the proposed methods, the specific issues contained in batch process data such as
transitions, uneven durations, time-varying behaviors and multiple modes can be handled
effectively.
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