基于人工智能的过程制造实时现场优化

J. Kalagnanam, D. Phan, Pavankumar Murali, Lam M. Nguyen, Nianjun Zhou, D. Subramanian, Raju Pavuluri, Xiang-Qi Ma, C. Lui, Giovane Cesar da Silva
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引用次数: 2

摘要

在本文中,我们提出了一个站点范围内的lead advisor,这是一个基于人工智能的预测和设定点推荐引擎,通过结合使用机器学习和优化技术。它提供了操作设定点建议,以持续改善整个现场的操作,以每天生产的额外石油桶来衡量吞吐量。一个关键的贡献和区别是利用传感器数据来持续学习采油厂所有子系统的行为,并在优化框架内使用这些数据来提供近乎实时的咨询控制。这是新颖的,因为它不需要提供植物的模型作为输入。预测模型从数据中自动连续学习。这项工作需要开发一种新的预测优化建模框架,在保持历史过程行为附近的同时优化吞吐量,并在设计算法时使用模型的结构来解决它。自2019年1月以来,该解决方案已在油砂公司Suncor Energy部署,估计每年可产生数千万美元的商业价值。该框架的一般化方法使其能够应用于任何加工或制造工厂。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AI-Based Real-Time Site-Wide Optimization for Process Manufacturing
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.
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