Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats
Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats
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DOI：
10.1016/j.eng.2021.03.019
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发表时间：
2021-11-12
期刊：
影响因子：
12.8
通讯作者：
Van Geem, Kevin M.
中科院分区：
文献类型：
--
作者：
Dobbelaere, Maarten R.;Plehiers, Pieter P.;Van Geem, Kevin M.
化学工程师依赖模型进行设计、研究和日常决策，通常会带来潜在的巨大财务和安全影响。几十年前，联合收割机将人工智能和化学工程结合起来进行建模的努力无法实现预期。在过去的五年里，数据和计算资源的可用性不断增加，导致了基于机器学习的研究的复苏。最近的许多努力促进了机器学习技术在研究领域的推广，为化学应用和新的机器学习框架开发了大型数据库，基准和表示。与传统建模技术相比，机器学习具有显著的优势，包括灵活性、准确性和执行速度。这些优势也伴随着弱点，例如这些黑箱模型缺乏可解释性。最大的机会涉及在时间有限的应用中使用机器学习，例如实时优化和规划，这些应用需要高精度，并且可以建立在具有自我学习能力的模型上，以识别模式，从数据中学习，并随着时间的推移变得更加智能。当今人工智能研究的最大威胁是不恰当的使用，因为大多数化学工程师在计算机科学和数据分析方面的培训有限。尽管如此，机器学习肯定会成为化学工程师建模工具箱中值得信赖的元素。(C)2021年的走廊。由爱思唯尔有限公司代表中国工程院和高等教育出版社有限公司出版。
Chemical engineers rely on models for design, research, and daily decision-making, often with potentially large financial and safety implications. Previous efforts a few decades ago to combine artificial intelligence and chemical engineering for modeling were unable to fulfill the expectations. In the last five years, the increasing availability of data and computational resources has led to a resurgence in machine learning-based research. Many recent efforts have facilitated the roll-out of machine learning techniques in the research field by developing large databases, benchmarks, and representations for chemical applications and new machine learning frameworks. Machine learning has significant advantages over traditional modeling techniques, including flexibility, accuracy, and execution speed. These strengths also come with weaknesses, such as the lack of interpretability of these black-box models. The greatest opportunities involve using machine learning in time-limited applications such as real-time optimization and planning that require high accuracy and that can build on models with a self-learning ability to recognize patterns, learn from data, and become more intelligent over time. The greatest threat in artificial intelligence research today is inappropriate use because most chemical engineers have had limited training in computer science and data analysis. Nevertheless, machine learning will definitely become a trustworthy element in the modeling toolbox of chemical engineers. (C) 2021 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company.