Application of machine learning to evaluating and remediating models for energy and environmental engineering

• Published in: Applied energy Vol. 320; no. C; p. 119286
• Main Authors: Chen, Hao, Zhang, Chao, Yu, Haizeng, Wang, Zhilin, Duncan, Ian, Zhou, Xianmin, Liu, Xiliang, Wang, Yu, Yang, Shenglai
• Format: Journal Article
• Language: English
• Published: United Kingdom Elsevier Ltd 15.08.2022; Elsevier
• Subjects: Energy and environmental engineering; Machine learning; Minimum miscible pressure; Model validation; Energy and environmental engineering; Minimum miscible pressure; Model validation; Machine learning
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2022.119286

[Display omitted] •A Common Trap of machine learning in engineering is presented.•It provides two ways to address the shortcomings of current machine learning applications.•Three optimal prediction models of minimum miscibility pressure are selected.•Countermeasures in application of machine learning in engineering is demonstrated.•It provides ideas for rational application of machine learning in various fields. Machine learning (ML) algorithms have been increasingly successful in their applications to solve energy and environmental engineering problems. ML algorithms have the advantage of being able to solve highly nonlinear issues effectively. Furthermore, considering the limited sample size of data collected in energy and environmental engineering, obtaining a ML model with reasonable accuracy is simple. Unfortunately, the vast majority of the current applications of ML algorithms lack effective screening of dominant factors and comprehensive model validation, which weakens the predictive ability of the models. The present study takes the minimum miscible pressure (MMP) of CO2 - oil systems as an example. It establishes a systematic and robust predictive model to address this issue. Based on 147 sets of slim tube tests, the predictive models of the MMPs are investigated by application of eight ML algorithms. The paper concludes that most of the published ML models in the field of energy and environmental engineering prediction are not reliable. Furthermore, it addresses the main reasons for the poor performance of some predictive models built by ML and provides guidelines on how to make such models robust. To the best of our knowledge, this is the first study to point out the defects of current ML modeling methods and propose countermeasures for their application in energy and environmental engineering problems.