Part 2: Phase I - Modeling Steady State Conditions

part 1 here: https://www.matlabcoding.com/2021/02/part-1-multivariate-analysis-for.html

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