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Part 2: Phase I - Modeling Steady State Conditions



The goal of process monitoring is to ensure that the planned operations are successful. You can achieve this goal by recognizing process upsets and faults using data-driven measures, such as PCA. These measures are derived directly from process data and aid in fault detection and diagnosis by transforming the high dimensional data into a lower dimension, and thereby capturing important information in the process.

Create a PCA model to describe the normal variability in the operation of a methanol-ethanol distillation column. Building an effective monitoring system requires a good data set that represents the steady state, normal operating conditions. Use an application developed in MATLAB® by GIEM as an aid for understanding the PCA-based MSPC strategy.


Additional Resources:

- MATLAB for the Chemical and Petrochemical Industry: https://bit.ly/2Mxc91a - MATLAB and Simulink for Predictive Maintenance: https://bit.ly/3opdXqt - MATLAB for Machine Learning: https://bit.ly/2YlIQRY - A Benchmark Software for MSPC: https://bit.ly/2KR2GRZ



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