Simple descriptor based machine learning model development for synergy prediction of different metal loadings and solvent swellings on coal pyrolysis. (28th April 2022)

• Record Type: Journal Article
• Authors: Ma, Duo; Yao, Qiuxiang; Wang, Jing; Hao, Qingqing; Chen, Huiyong; Ma, Li; Sun, Ming; Ma, Xiaoxun
• Is Part Of: Chemical engineering science. Volume 252(2022)
• Publication Date: 2022-04-28
• Subjects: Catalytic pyrolysis -- Elemental descriptor -- Machine learning -- ANOVA -- Symbolic transformation; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; Chemical engineering; Periodicals; Electronic journals; 660
• DOI: 10.1016/j.ces.2022.117538
• Languages: English
• ISSNs: 0009-2509

Abstract

Graphical abstract: Highlights: The synergy of metal loading and solvent swelling was reveled by machine learning. Analysis of variance screened out various responses affected by the two pretreatments. Symbolic transformation (ST) shown obvious improvement on model performance. Formula of ST provided possibility to optimizing multiple objectives simultaneously. Abstract: The co-effect of solvent swelling and metal loading on coal pyrolysis was investigated through statistical analysis and machine learning. The distributions and properties of pyrolysis products, and the pyrolysis parameters were all considered. 22 targets were screened out by analysis of variance (ANOVA). Both linear and non-linear regression models aiming to predict these values were constructed, in which the swelling ratio and atomic descriptors collected from handbooks were taken as inputs. The symbolic transformation (ST) algorithm was involved to assemble a new feature and the model built on the advanced feature set displays higher prediction accuracy for all targets. The result of leave-one-out cross validation shows acceptable performance(R 2 > 0.8) for most targets (19 of 22), and good performance (R 2 > 0.9) for half of them (12 of 22). The importance of ST feature was verified, and the contribution of each single feature was clearly reflected in the formula of ST.