Transparency:The Missing Link to Boosting AI Transformations in Chemical Engineering  

透明度——促进化学工程领域人工智能变革的缺失环节

作　　者：Yue Yuan Donovan Chaffart Tao Wu Jesse Zhu 

机构地区：[1]Ningbo Institute of Digital Twin,Eastern Institute of Technology,Ningbo 315200,China [2]Department of Chemical and Biochemical Engineering,The University of Western Ontario,London,ON N6G 5B8,Canada [3]College of Engineering,Eastern Institute of Technology,Ningbo 315200,China [4]China Beacons Institute,University of Nottingham Ningbo China,Ningbo 315100,China

出　　处：《Engineering》2024年第8期45-60,共16页工程（英文）

摘　　要：The issue of opacity within data-driven artificial intelligence(AI)algorithms has become an impediment to these algorithms’extensive utilization,especially within sensitive domains concerning health,safety,and high profitability,such as chemical engineering(CE).In order to promote reliable AI utilization in CE,this review discusses the concept of transparency within AI utilizations,which is defined based on both explainable AI(XAI)concepts and key features from within the CE field.This review also highlights the requirements of reliable AI from the aspects of causality(i.e.,the correlations between the predictions and inputs of an AI),explainability(i.e.,the operational rationales of the workflows),and informativeness(i.e.,the mechanistic insights of the investigating systems).Related techniques are evaluated together with state-of-the-art applications to highlight the significance of establishing reliable AI applications in CE.Furthermore,a comprehensive transparency analysis case study is provided as an example to enhance understanding.Overall,this work provides a thorough discussion of this subject matter in a way that—for the first time—is particularly geared toward chemical engineers in order to raise awareness of responsible AI utilization.With this vital missing link,AI is anticipated to serve as a novel and powerful tool that can tremendously aid chemical engineers in solving bottleneck challenges in CE.

关 键 词：TRANSPARENCY Explainable AI Reliability CAUSALITY Explainability INFORMATIVENESS Hybrid modeling Physics-informed 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TQ02[自动化与计算机技术—控制科学与工程]