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From Then to Now and Beyond: Exploring How Machine Learning Shapes Process Design Problems. 从过去到现在,再到未来:探索机器学习如何塑造流程设计问题。
Pub Date : 2024-01-01 Epub Date: 2024-07-10 DOI: 10.69997/sct.116002
Burcu Beykal

Following the discovery of the least squares method in 1805 by Legendre and later in 1809 by Gauss, surrogate modeling and machine learning have come a long way. From identifying patterns and trends in process data to predictive modeling, optimization, fault detection, reaction network discovery, and process operations, machine learning became an integral part of all aspects of process design and process systems engineering. This is enabled, at the same time necessitated, by the vast amounts of data that are readily available from processes, increased digitalization, automation, increasing computation power, and simulation software that can model complex phenomena that span over several temporal and spatial scales. Although this paper is not a comprehensive review, it gives an overview of the recent history of machine learning models that we use every day and how they shaped process design problems from the recent advances to the exploration of their prospects.

继勒让德尔(Legendre)于 1805 年、高斯(Gauss)于 1809 年发现最小二乘法之后,代用建模和机器学习取得了长足的进步。从识别工艺数据中的模式和趋势到预测建模、优化、故障检测、反应网络发现和工艺操作,机器学习已成为工艺设计和工艺系统工程各个方面不可或缺的一部分。这得益于从工艺中随时可获得的大量数据、数字化程度的提高、自动化程度的提高、计算能力的增强以及可对跨越多个时间和空间尺度的复杂现象进行建模的仿真软件。虽然本文不是一篇全面的综述,但它概述了我们日常使用的机器学习模型的近代史,以及这些模型是如何从最近的进步到对其前景的探索中塑造工艺设计问题的。
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Systems & control transactions
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