Prediction of product yields using fusion model from Co-pyrolysis of biomass and coal. (June 2022)

• Record Type: Journal Article
• Authors: Song, Jinling; Tang, Chuyang; Yu, Shiyao; Yang, Xinyu; Yang, Lei
• Is Part Of: Bioresource technology. Volume 353(2022)
• Publication Date: 2022-06
• Subjects: Co-pyrolysis -- Biomass -- Coal -- Fusion model -- Learning weight; Biomass -- Periodicals; Biomass energy -- Periodicals; Bioremediation -- Periodicals; Agricultural wastes -- Periodicals; Factory and trade waste -- Periodicals; Organic wastes -- Periodicals; Bioénergie -- Périodiques; Déchets agricoles -- Périodiques; Déchets industriels -- Périodiques; Déchets organiques -- Périodiques; Déchets (Combustible) -- Périodiques; 662.88
• DOI: 10.1016/j.biortech.2022.127132
• Languages: English
• ISSNs: 0960-8524

Abstract

Graphical abstract: A. Saw dust; B. Rice husk; C. Cotton stalk; D. Huainan coal; E. Shenmu coal; F. Heishan Coal. Highlights: A self-corrective fusion model was formed from SVM, RBF, and RF models. Fusion model could spontaneously optimize the outputs base on learning weight algorithm. Fusing model method improved the predicted performance (R 2 > 0.99, RMSE < 0.83%). Abstract: This study aimed to establish a self-corrective machine learning model base on co-pyrolysis data of biomass and coal. Proximate and ultimate analysis of raw materials were chosen as input parameters. Radial basis function (RBF), support vector machine (SVM), and random forest (RF) were used to build the base regression models for the fusion (FU) model. 96 sets of the experimental data were applied to train and test the base models. A learning weight were then determined by the predicted performance of base models. Based on the learning weight method, FU model spontaneously regulated and controlled the weight of base models to output the predicted result of co-pyrolysis products. The coefficient of determination (R 2 ) was more than 0.99 and the root-mean-squared error (RMSE) was lower than 0.88%. The results suggested that FU model was more accurately adequate to forecast the yields of co-pyrolysis products than any of the base models.