Machine Learning in Chemical Engineering: A Perspective
Machine Learning in Chemical Engineering: A Perspective
复制标题
DOI：
10.1002/cite.202100083
复制
发表时间：
2021-10
影响因子：
1.9
通讯作者：
Artur M. Schweidtmann;E. Esche;Asja Fischer;M. Kloft;J. Repke;S. Sager;A. Mitsos
中科院分区：
文献类型：
--
作者：
Artur M. Schweidtmann;E. Esche;Asja Fischer;M. Kloft;J. Repke;S. Sager;A. Mitsos
化学工业向可再生能源和原料供应的转型需要灵活工厂、（生物）催化剂和功能材料设计的新范式。机器学习（ML）的最新突破提供了独特的机会，但只有ML和化学工程（CE）社区之间的联合跨学科研究才能充分发挥潜力。我们确定了六个挑战，这些挑战将为CE开辟新的方法，并为ML制定新类型的问题：（1）最佳决策，（2）在ML中引入和实施物理学，（3）信息和知识表示，（4）数据的异构性，（5）ML应用程序的安全性和信任，以及（6）创造力。在这些挑战的保护伞下，我们讨论了未来跨学科研究的前景，这将使CE的转型。
The transformation of the chemical industry to renewable energy and feedstock supply requires new paradigms for the design of flexible plants, (bio‐)catalysts, and functional materials. Recent breakthroughs in machine learning (ML) provide unique opportunities, but only joint interdisciplinary research between the ML and chemical engineering (CE) communities will unfold the full potential. We identify six challenges that will open new methods for CE and formulate new types of problems for ML: (1) optimal decision making, (2) introducing and enforcing physics in ML, (3) information and knowledge representation, (4) heterogeneity of data, (5) safety and trust in ML applications, and (6) creativity. Under the umbrella of these challenges, we discuss perspectives for future interdisciplinary research that will enable the transformation of CE.