Fault Detection and Diagnosis in Chemical and Petrochemical Process

Overview

Early and accurate fault detection and diagnosis for chemical production plants minimizes downtime, increases the safety of plant operations, and reduces manufacturing costs. Univariate charts have a limited ability to detect and diagnose faults in multivariable processes.

This has led industry to develop more effective process monitoring methods leveraging the advances in artificial intelligence and the increase in computing power.

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Highlights:

In this session you will be able to learn: 

  • Learn about the industrial applications of multivariate analysis and success stories.
  • Create a PCA model from steady state conditions using a GUI
  •  Perform fault detection and diagnosis using the PCA model.

 

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Speaker

Kevin Chng


Kevin Chng is the LTC Application Engineer at Techsource Systems where he successfully assists customers from financial sector to adopt finance and enterprise solutions. He specializes in finance modelling, application deployment and data analytics. Kevin has conducted seminars and workshops at various banks/hedge funds, government research agency and universities. He is one of the active contributors to MATLAB community.

He holds a Bachelor of Chemical Engineering with Honors (1st Class) Degree from Universiti Malaysia Sabah, Malaysia. His recent focuses are MATLAB finance application and Enterprise Solution. Before joining TechSource System, during his study, He had MATLAB experiences in modelling chemical processes and handled a project collaborated with a Wastewater Treatment Plant in Penampang, Sabah. Besides, You may find his MATLAB community profile in Mathworks website, he is one of active contributors there.



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