The Estimation of the Higher Heating Value of Biochar by Data-Driven Modeling

作     者：Jiefeng Chen Lisha Ding Pengyu Wang Weijin Zhang Jie Li Badr A.Mohamed Jie Chen Songqi Leng Tonggui Liu Lijian Leng Wenguang Zhou 

作者机构：Key Laboratory of Poyang Lake Environment and Resource UtilizationMinistry of Education and School of ResourcesEnvironmental&Chemical EngineeringNanchang UniversityNanchangChina School of Energy Science and EngineeringCentral South UniversityChangshaChina Department of Chemical and Biomolecular EngineeringNational University of SingaporeSingapore Department of Agricultural EngineeringCairo UniversityGizaEgypt 

出 版 物：《Journal of Renewable Materials》 (可再生材料杂志（英文）)

年 卷 期：2022年第10卷第6期

页      面：1555-1574页

核心收录：

学科分类：09[农学] 0903[农学-农业资源与环境] 090301[农学-土壤学] 

基　　金：The work was supported by the National Natural Science Foundation of China(No.51808278) the Science Foundation for Youths of Jiangxi Province,China(20192BAB213012) This research was also supported by the College Students’Innovative Entrepreneurial Training Plan Program,China(No.201910403049) 

主　　题：Biochar higher heating value machine learning prediction proximate analysis ultimate analysis 

摘      要：Biomass is a carbon-neutral renewable energy *** produced from biomass pyrolysis exhibits preferable characteristics and potential for fossil fuel *** time-and cost-saving,it is vital to establish predictive models to predict biochar ***,limited studies focused on the accurate prediction of HHV of biochar by using proximate and ultimate analysis results of various ***,the multi-linear regression(MLR)and the machine learning(ML)models were developed to predict the measured HHV of biochar from the experiment data of this *** detail,52 types of biochars were produced by pyrolysis from rice straw,pig manure,soybean straw,wood sawdust,sewage sludge,Chlorella Vulgaris,and their mixtures at the temperature ranging from 300 to 800℃.The results showed that the co-pyrolysis of the mixed biomass provided an alternative method to increase the yield of biochar *** contents of ash,fixed carbon(FC),and C increased as the incremental pyrolysis temperature for most *** Pearson correlation(r)and relative importance analysis between HHV values and the indicators derived from the proximate and ultimate analysis were carried out,and the measured HHV was used to train and test the MLR and the ML ***,ML algorithms,including gradient boosted regression,random forest,and support vector machine,were also employed to develop more widely applicable models for predicting HHV of biochar from an expanded dataset(total 149 data points,including 97 data collected from the published literature).Results showed HHV had strong correlations(|r|0.9,p*** ML models showed better performance with test R^(2)around 0.95(random forest)and 0.97–0.98 before and after adding extra data for model construction,*** importance analysis of the ML models showed th