Statistical Techniques for Monitoring Industrial Processes

Unlock the power of Statistical Process Monitoring (SPM) for industrial applications! This course delves into the core statistical techniques used to ensure the health and efficiency of complex process plants. Learn to implement robust process monitoring solutions using real-world industrial datasets.

Why is Statistical Process Monitoring Essential?

Modern industrial plants demand continuous oversight. 24/7 plant monitoring of process performance and equipment health is no longer optional – it's crucial for predicting and preventing costly failures. This course provides the tools you need for fault detection (identifying abnormal process behavior) and fault diagnosis (pinpointing the specific variables causing issues).

The Enduring Value of Classical Statistics

While advanced methods like artificial neural networks and deep learning are popular, classical statistical approaches remain fundamental to effective industrial process monitoring. Techniques like PCA (Principal Component Analysis) and PLS (Partial Least Squares) offer a compelling alternative: they are easier to understand, more interpretable, and simpler to develop and maintain, often delivering comparable or even superior performance.

What You Will Learn:

• Univariate Statistical Process Monitoring: Master techniques for monitoring single process variables.
• Multivariate Statistical Process Monitoring: Gain expertise in monitoring multiple interacting variables.
• Practical Implementation: Follow step-by-step guidance for developing industrial-level SPM solutions.
• Real-World Case Studies: Apply learned techniques to analyze data from systems like stirred tank reactors, catalytic cracking units, furnaces, chemical plants, and polymer reactors.
• Python Introduction: Begin with a quick introduction to Python programming and the development environment (no prior experience required).

Course Outcomes:

Upon completion, you will be equipped to address the monitoring needs of a wide range of industrial processes.

Prerequisites:

No prior Python programming or machine learning experience is required. The course includes a beginner-friendly Python introduction.