Batch process performance monitoring has been achieved primarily using process measurements with the extracted information being associated with the physical parameters of the process. More recently, there has been an increase in the implementation of process spectroscopic instrumentation in the processing industries. By integrating the process and spectroscopic measurements for multivariate st...

in some statistical process control applications, the quality of the product is characterized by thecombination of both correlated variable and attributes quality characteristics. in this paper, we propose anovel control scheme based on the combination of two multi-layer perceptron neural networks forsimultaneous monitoring of mean vector as well as the covariance matrix in multivariate-attribu...

Statistical process control ($SPC$), an internationally recognized technique for improving product quality and productivity, has been widely employed in various industries. $SPC$ relies on the use of control charts to monitor a manufacturing process for identifying causes of process variation and signaling the necessity of corrective action for the process. Fuzzy data exist ubiquitously in the ...

in this paper a novel process monitoring scheme for reducing the type і and type іі error rates in the monitoring phase is proposed. first, the proposed approach uses an augmented data matrix to implement the process dynamic. then, we apply independent component analysis (ica) transformation to the augmented data matrix, and eliminate the outliers using the local outlier factor (lof) algorithm....

The use of statistics in the daily operation of test laboratories tends to be very formal. Six Sigma for Design, Design of Experiments, Gauge R&R, and ANOVA may be used to help design and analyze product and process characteristics in research labs. The use of statistical approaches can provide important preventive actions since many of the common SPC (Statistical Process Control) tools can det...

On-line monitoring and control of particle shape and size distribution is important in pharmaceutical and fine chemical manufacturing. In this paper, we describe a methodology for real-time estimation of particle quality variables during crystallization using inline process video-images. Our approach integrates image analysis techniques with multivariate statistical techniques to quantify parti...