Syllabus

Design of experiments, data capture, presentation, pre-processing (signal processing, outlier detection, smoothening, filtering, and reconciliation of data). Feature extraction (mathematical processors/using transforms), and pattern recognition (using SPC charts, multivariate statistical software/PCA, PLS LDA etc.) as the frameworks of data based approach in quality assessment of process.

Extracting meaningful information from process plant data using techniques, which help in process identification, monitoring and Control.

For highly non-linear and complex processes, quality monitoring can be through data driven techniques.

Information distilled from process historical database can be used to identify the process and diagnose fault, if any.

1. Enable the students to apply Design experts for data generation.
2. Enable the students to apply SPC and MVSPCin process monitoring using commercial software like Statistica.
3. Enable the students to apply MATLAB in data pre-processing and data based modelling in identifying process faults and design of process quality monitors.

Douglas C. Montgomery,, Statistical Quality Control, John Wiley & Sons, Inc , 7 th Edition, . 2012

John S Oakland, Statistical Process Control, Butterworth-Heinemann, , Fifth Edition,-2003

M.Kundu,P. Kundu, and S. K. Damarla, A Chemometric Approach to Monitoring: Product Quality Assessment, Process Fault Detection and Miscellaneous Applications, CRC Press, Taylor & Francis Group, , 1 st Edition, 2017

Shardt, Yuri A.W, Statistics for Chemical and Process Engineers, Springer , Marcel Dekker, New York, 3rd 1 1 st Edition, 2015

Journal of Chemometrics, Elsevier Publication

Chemometrics and Intelligent Laboratory Systems, Elsevier Publication