Prediction of combustion activation energy of NaOH/KOH catalyzed straw pyrolytic carbon based on machine learning

被引：27
作者：
Jiang, Wen [1 ,2 ]
Xing, Xianjun [2 ,3 ]
Zhang, Xianwen [2 ,3 ]
Mi, Mengxing [2 ,3 ]
机构：
[1] Hefei Univ Technol, Sch Food Sci & Engn, Hefei 230009, Anhui, Peoples R China
[2] Hefei Univ Technol, Adv Energy Technol & Equipment Res Inst, Hefei 230009, Anhui, Peoples R China
[3] Hefei Univ Technol, Sch Automot & Transportat Engn, Hefei 230009, Anhui, Peoples R China
来源：
RENEWABLE ENERGY | 2019年 / 130卷
关键词：
Combustion activation energy; Machine learning; Linear regression; Support vector regression; Random forest regression; ARTIFICIAL NEURAL-NETWORKS; RICE STRAW; BIOCHAR; TEMPERATURE; MECHANISMS; REGRESSION; ENZYMES; OIL; KOH;
D O I：
10.1016/j.renene.2018.08.089
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
Wheat straw, corn straw and sorghum straw were used as raw materials. KOH and NaOH were used as catalysts to prepare straw pyrolytic carbon (SPC) and the characteristics of combustion activation energy (AE) were analyzed by thermogravimetric analysis. The distributed modified Coats -Redfern integration method was used to compute the distributed AE. The predictive models of combustion AE based on Linear Regression (ER), Support Vector Regression (SVR) and Random Forest Regression (RFR) were proposed and compared. The results showed the AE variation trend of three kinds of SPCNaOH, SPCKOH and SPCNa-KOH were basically the same and obviously decreased. In the LR model, degree value was 2 and R-2 reached 0.8531. In the SVR model, the kernel function was Polynomial, C=3000, degree = 4, coef0 = 0.3 and R-2 reached 0.9048. In the RFR model, the n_estimators value was 400 and R-2 reached 0.9834. Compared with the LR and SVR model, the RFR model was more suitable for the AE prediction of alkali catalyzed SPC. (C) 2018 Elsevier Ltd. All rights reserved.
引用
收藏
页码：1216 / 1225
页数：10
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