Part 2: Phase I - Modeling Steady State Conditions

This is part 2. See part 1 here: https://youtu.be/4kNBxqG9jF4 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 -------------------------------------------------------------------------------------------------------- Get a free product trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See what's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2021 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.