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Intelligent decision support for industrial systems performance optimization

This project will create an artificial intelligence (AI)-based platform to develop low-cost decision support systems and prototypes for pulp mills with industry partners. Learn how machine learning can enhance operations across several industries, including pulp and paper.

Project objectives

This project uses machine learning (ML) techniques to extract knowledge and identify patterns from industrial performance data. This knowledge will help engineers assess, diagnose and optimize process operation performance across several industries such as the pulp and paper, food and drink, oil refining and chemical sectors.

Digital and artificial intelligence techniques

  • Machine learning, including:
    • Support vector machines (SVM)
    • K-nearest neighbours (KNN)
    • Principal component analysis (PCA)
    • Neural nets
    • Reinforcement learning
  • Optimization techniques:
    • Direct search
    • Particle swarm optimization

Data requirements

  • Historical and continuous sensor data


Collaborators and Partners

  • IVADO - Institut de valorisation des données
  • Polytechnique Montréal
  • FPInnovations
  • OSISoft
  • Resolute Forest Products
  • White Birch Paper
  • National Research Council


Mouloud Amazouz

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