Part 2: Phase I - Modeling Steady State Conditions
17.02.2021 · MathWorks ·
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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
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