{"title":"基于人工神经网络(ANN)的生物质热解及物化助热解生物炭和活性炭比表面积及较高热值估算","authors":"Mamadou Saliou Balde, Rukiye Karakış, Ayten Ateş","doi":"10.1007/s13399-025-06728-w","DOIUrl":null,"url":null,"abstract":"<div><p>The physical and chemical activation of biomass prior to pyrolysis significantly affects the properties of the activated carbon produced. In this study, raw tea waste (TW) and hazelnut shells (HS) were used to produce biochar and activated carbon samples by pyrolysis at different pyrolysis temperatures with and without chemical and physical activation. Subsequently, an artificial neural network (ANN) was developed based on the pyrolysis conditions, proximate and elemental analyses of the biomass feedstocks and the obtained biochar and activated carbon to predict the higher heating value (HHV) and specific surface area (SSA) of the biochar. For this purpose, machine learning algorithms such as ANN, Gaussian process regression (GPR), regression trees (RT), and support vector machines (SVM) were compared to find the best-performing algorithm for the prediction of HHV and SSA of biochar. Algorithms based on ANNs performed better than SVM, RT, and GPR models, with higher regressions and lower prediction errors. The resilient backpropagation (RProp) algorithm proved to be the most suitable training algorithm as it provided satisfactory results with a low percentage of mean squared error (MSE) and mean absolute error (MAE). The ANN models showed moderate to strong performance in the tests, with correlation coefficient (R) values of 0.82 and 0.95, coefficient of determination (R<sup>2</sup>) values of 0.67 and 0.90, and low MAE and MSE, indicating reasonable prediction accuracy for HHV and SSA of the biochar. The energy efficiency of biochar produced with conventional pyrolysis ranged from 9.84% to 21.13%, while the energy efficiency of activated carbon ranged from 45.26% to 67.21%, with the maximum reached at 300 °C. Based on the results of the thermodynamic analysis, it was found that the energy and exergy yields of the biochar and activated carbon produced depend on the activation conditions and temperature.</p><h3>Graphical Abstract</h3><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":488,"journal":{"name":"Biomass Conversion and Biorefinery","volume":"15 16","pages":"23047 - 23070"},"PeriodicalIF":4.1000,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13399-025-06728-w.pdf","citationCount":"0","resultStr":"{\"title\":\"Estimation of specific surface area and higher heating value of biochar and activated carbon produced by pyrolysis and physico-chemically assisted pyrolysis of biomass using an artificial neural network (ANN)\",\"authors\":\"Mamadou Saliou Balde, Rukiye Karakış, Ayten Ateş\",\"doi\":\"10.1007/s13399-025-06728-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The physical and chemical activation of biomass prior to pyrolysis significantly affects the properties of the activated carbon produced. In this study, raw tea waste (TW) and hazelnut shells (HS) were used to produce biochar and activated carbon samples by pyrolysis at different pyrolysis temperatures with and without chemical and physical activation. Subsequently, an artificial neural network (ANN) was developed based on the pyrolysis conditions, proximate and elemental analyses of the biomass feedstocks and the obtained biochar and activated carbon to predict the higher heating value (HHV) and specific surface area (SSA) of the biochar. For this purpose, machine learning algorithms such as ANN, Gaussian process regression (GPR), regression trees (RT), and support vector machines (SVM) were compared to find the best-performing algorithm for the prediction of HHV and SSA of biochar. Algorithms based on ANNs performed better than SVM, RT, and GPR models, with higher regressions and lower prediction errors. The resilient backpropagation (RProp) algorithm proved to be the most suitable training algorithm as it provided satisfactory results with a low percentage of mean squared error (MSE) and mean absolute error (MAE). The ANN models showed moderate to strong performance in the tests, with correlation coefficient (R) values of 0.82 and 0.95, coefficient of determination (R<sup>2</sup>) values of 0.67 and 0.90, and low MAE and MSE, indicating reasonable prediction accuracy for HHV and SSA of the biochar. The energy efficiency of biochar produced with conventional pyrolysis ranged from 9.84% to 21.13%, while the energy efficiency of activated carbon ranged from 45.26% to 67.21%, with the maximum reached at 300 °C. Based on the results of the thermodynamic analysis, it was found that the energy and exergy yields of the biochar and activated carbon produced depend on the activation conditions and temperature.</p><h3>Graphical Abstract</h3><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>\",\"PeriodicalId\":488,\"journal\":{\"name\":\"Biomass Conversion and Biorefinery\",\"volume\":\"15 16\",\"pages\":\"23047 - 23070\"},\"PeriodicalIF\":4.1000,\"publicationDate\":\"2025-03-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://link.springer.com/content/pdf/10.1007/s13399-025-06728-w.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biomass Conversion and Biorefinery\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s13399-025-06728-w\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomass Conversion and Biorefinery","FirstCategoryId":"5","ListUrlMain":"https://link.springer.com/article/10.1007/s13399-025-06728-w","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
Estimation of specific surface area and higher heating value of biochar and activated carbon produced by pyrolysis and physico-chemically assisted pyrolysis of biomass using an artificial neural network (ANN)
The physical and chemical activation of biomass prior to pyrolysis significantly affects the properties of the activated carbon produced. In this study, raw tea waste (TW) and hazelnut shells (HS) were used to produce biochar and activated carbon samples by pyrolysis at different pyrolysis temperatures with and without chemical and physical activation. Subsequently, an artificial neural network (ANN) was developed based on the pyrolysis conditions, proximate and elemental analyses of the biomass feedstocks and the obtained biochar and activated carbon to predict the higher heating value (HHV) and specific surface area (SSA) of the biochar. For this purpose, machine learning algorithms such as ANN, Gaussian process regression (GPR), regression trees (RT), and support vector machines (SVM) were compared to find the best-performing algorithm for the prediction of HHV and SSA of biochar. Algorithms based on ANNs performed better than SVM, RT, and GPR models, with higher regressions and lower prediction errors. The resilient backpropagation (RProp) algorithm proved to be the most suitable training algorithm as it provided satisfactory results with a low percentage of mean squared error (MSE) and mean absolute error (MAE). The ANN models showed moderate to strong performance in the tests, with correlation coefficient (R) values of 0.82 and 0.95, coefficient of determination (R2) values of 0.67 and 0.90, and low MAE and MSE, indicating reasonable prediction accuracy for HHV and SSA of the biochar. The energy efficiency of biochar produced with conventional pyrolysis ranged from 9.84% to 21.13%, while the energy efficiency of activated carbon ranged from 45.26% to 67.21%, with the maximum reached at 300 °C. Based on the results of the thermodynamic analysis, it was found that the energy and exergy yields of the biochar and activated carbon produced depend on the activation conditions and temperature.
期刊介绍:
Biomass Conversion and Biorefinery presents articles and information on research, development and applications in thermo-chemical conversion; physico-chemical conversion and bio-chemical conversion, including all necessary steps for the provision and preparation of the biomass as well as all possible downstream processing steps for the environmentally sound and economically viable provision of energy and chemical products.