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Improving mill efficiency and meeting quality targets through Multivariate Data Analysis
Organized by the PAPTAC Energy Community / Organisé par la Communauté PAPTAC de l'énergie

About the energy Community / À propos de la Communauté de l'énergie

Join this Community / Joindre cette Communauté

  When / quand: Jan. 17, 2017
13:00 – 14:00 ET

 
Registration / inscription: Free for PAPTAC members / Gratuit pour les membres
50 $ for non-members / pour les non-membres
  Speaker / Conférencier:
Mouloud Amazouz, Ph.D.
CanmetENERGY,
Natural Resources Canada 		Click here to register

Multivariate Data Analysis (aka Data Mining) refers to multiple advanced techniques for examining relationships among multiple variables at the same time. It can be applied to turn historical data from pulp and paper mills into knowledge and actionable information to maximize yield and profits.

Using this technique can help you to:
• Troubleshoot process operation problems.
• Improve process performance and product quality.
• Make real-time predictions of KPIs.
• Develop soft-sensors for product quality and emissions estimation.
• Monitor process performance and diagnose inefficiencies.

This webinar will introduce the theory behind multivariate data analysis and make an overview of the CanmetENERGY EXPLORE software through a demonstration using a real case from a pulp mill. The EXPLORE software will be made available to registered participants during the upcoming NRCan technical courses.
 
Mouloud Amazouz, Ph.D.
Senior Project Manager
Industrial Systems Optimization CanmetENERGY, Natural Resources Canada 		 

Mr. Mouloud Amazouz holds a PhD degree in Mechanical engineering from École Polytechnique de Montréal.

He has more than 25 years' experience in leading, performing and managing research & development and technology demonstration and deployment in the area of multivariate data analysis (data mining) techniques and methods for process performance monitoring, optimization and diagnosis.

  
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