化学工程中机器学习的优势、限制、机会和挑战
Maarten R. Dobbelaere , Pieter P. Plehiers , Ruben Van de Vijver , Christian V. Stevens , Kevin M. Van Geem
工程（英文） ›› 2021, Vol. 7 ›› Issue (9) : 1201 -1211.
化学工程中机器学习的优势、限制、机会和挑战
Maarten R. Dobbelaere , Pieter P. Plehiers , Ruben Van de Vijver , Christian V. Stevens , Kevin M. Van Geem
工程（英文） ›› 2021, Vol. 7 ›› Issue (9) : 1201 -1211.
化学工程中机器学习的优势、限制、机会和挑战
Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats
化学工程师依靠模型进行工程设计、研究和日常决策制定，因为这些工作通常会伴有较大的财务和安全方面的风险。数十年来，将人工智能和化学工程进行有机结合用于建模的努力仍未满足预期效果。在过去的五年中，数据和计算资源的可获性不断提高，使基于机器学习的研究再度兴起。研究者最近努力为化学应用和新的机器学习框架开发大型数据库、基准测试集和表征，这些努力促进了机器学习技术在研究领域的推广。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、精度和执行速度。但有利也有弊，比如机器学习中黑盒模型就缺乏可解释性。其最大的机遇包括在时间有限的应用场合中使用机器学习，比如需要高精度的实时优化和规划技术，并且可以建立具有自学习能力的模型去识别模式，从数据中学习，并随着时间的推移变得更加智能。然而，现在人工智能研究最大的挑战是不恰当的使用，因为大多数化学工程师只在计算机科学和数据分析方面受到有限的培训。尽管如此，机器学习肯定也会成为化学工程师建模工具箱中值得信赖的基础工具。
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.
Artificial intelligence / Machine learning / Reaction engineering / Process engineering
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