An Improved Machine Learning Model for Pure Component Property Estimation

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外文摘要：Information on the physicochemical properties of chemical species is an important prerequisite when performing tasks such as process design and product design.However,the lack of extensive data and high experimental costs hinder the development of prediction techniques for these properties.Moreover,accuracy and predictive capabilities still limit the scope and applicability of most property estimation methods.This paper proposes a new Gaussian process-based modeling framework that aims to manage a discrete and high-dimensional input space related to molecular structure representation with the group-contribution approach.A warping function is used to map discrete input into a continuous domain in order to adjust the correlation between different compounds.Prior selection techniques,including prior elicitation and prior predictive checking,are also applied during the building procedure to provide the model with more information from previous research findings.The framework is assessed using data-sets of varying sizes for 20 pure component properties.For 18 out of the 20 pure component properties,the new models are found to give improved accuracy and predictive power in comparison with other published models,with and without machine learning.

外文关键词：

Group contributionGaussian processWarping functionPrior predictive checking

作者：

Xinyu Cao、Ming Gong、Anjan Tula、Xi Chen、Rafiqul Gani、Venkat Venkatasubramanian

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作者单位：

State Key Laboratory of Industrial Control Technology,College of Control Science and Engineering,Zhejiang University,Hangzhou 310027,China

Department of Physics,Bard College at Simon's Rock,Great Barrington,MA 01230,USA

PSE for SPEED Company,Charlottenlund DK-2920,Denmark

Sustainable Energy and Environment Thrust,The Hong Kong University of Science and Technology(Guangzhou),Guangzhou 510000,China

Department of Applied Sustainability,Széchenyi István University,Györ 9026,Hungary

Complex Resilient Intelligent Systems Laboratory,Department of Chemical Engineering,Columbia University,New York,NJ 10027,USA

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基金：

National Natural Science Foundation of ChinaNational Natural Science Foundation of China

项目编号：

2215041033861973268

出版年：

2024

DOI：

10.1016/j.eng.2023.08.024

工程(英文)

CSTPCDEI

ISSN：2095-8099

年,卷(期)：2024.39(8)