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AI-Based Real-Time Site-Wide Optimization for Process Manufacturing

Author

Listed:
  • Jayant Kalagnanam

    (IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598)

  • Dzung T. Phan

    (IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598)

  • Pavankumar Murali

    (IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598)

  • Lam M. Nguyen

    (IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598)

  • Nianjun Zhou

    (IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598)

  • Dharmashankar Subramanian

    (IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598)

  • Raju Pavuluri

    (IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598)

  • Xiang Ma

    (IBM Global Business Services, Calgary, Alberta T2R1R9, Canada)

  • Crystal Lui

    (IBM Global Business Services, Calgary, Alberta T2R1R9, Canada)

  • Giovane Cesar da Silva

    (IBM Global Business Services, Calgary, Alberta T2R1R9, Canada)

Abstract

In this paper, we propose a site-wide lead advisor, which is an artificial intelligence–based prediction and set-point recommendation engine, by combining the use of machine learning with optimization techniques. It provides operational set-point recommendations to continuously improve site-wide operations for throughput measured in additional barrels of oil produced per day. A key contribution and differentiator is the utilization of sensor data to continuously learn the behavior of all the subsystems of an oil-producing plant and use this within an optimization framework to provide advisory control in near real time. This is novel in that it does not require a model of the plant to be provided as input. The predictive model is learned automatically and continuously from data. This work required the development of a new prediction-optimization modeling framework that optimizes throughput while staying in the vicinity of the historical process behavior and employing the model’s structure in designing algorithms to solve it. This solution has been deployed at Suncor Energy, an oil-sands company, since January 2019 and is estimated to generate business value in the order of tens of millions of dollars per year. The generalized approach of this framework lends it the ability to be applied to any processing or manufacturing plant.

Suggested Citation

  • Jayant Kalagnanam & Dzung T. Phan & Pavankumar Murali & Lam M. Nguyen & Nianjun Zhou & Dharmashankar Subramanian & Raju Pavuluri & Xiang Ma & Crystal Lui & Giovane Cesar da Silva, 2022. "AI-Based Real-Time Site-Wide Optimization for Process Manufacturing," Interfaces, INFORMS, vol. 52(4), pages 363-378, July.
    • Handle: RePEc:inm:orinte:v:52:y:2022:i:4:p:363-378
    DOI: 10.1287/inte.2022.1121
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    References listed on IDEAS

    as
    1. Min, Qingfei & Lu, Yangguang & Liu, Zhiyong & Su, Chao & Wang, Bo, 2019. "Machine Learning based Digital Twin Framework for Production Optimization in Petrochemical Industry," International Journal of Information Management, Elsevier, vol. 49(C), pages 502-519.
    2. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
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    Cited by:

    1. Yi Wang & Yafei Yang & Zhaoxiang Qin & Yefei Yang & Jun Li, 2023. "A Literature Review on the Application of Digital Technology in Achieving Green Supply Chain Management," Sustainability, MDPI, vol. 15(11), pages 1-18, May.

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