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)

IF 4.1 4区 工程技术 Q3 ENERGY & FUELS
Mamadou Saliou Balde, Rukiye Karakış, Ayten Ateş
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引用次数: 0

Abstract

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.

Graphical Abstract

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
生物质在热解前的物理和化学活化对所生产的活性炭的性能有显著影响。本研究以生茶渣(TW)和榛子壳(HS)为原料,在不同的热解温度下进行热解,制备生物炭和活性炭样品,并进行化学和物理活化。随后,基于生物质原料和所得生物炭及活性炭的热解条件、近似分析和元素分析,建立了人工神经网络(ANN),预测了生物炭的较高热值(HHV)和比表面积(SSA)。为此,比较了人工神经网络(ANN)、高斯过程回归(GPR)、回归树(RT)和支持向量机(SVM)等机器学习算法,寻找预测生物炭HHV和SSA的最佳算法。基于人工神经网络的算法优于SVM、RT和GPR模型,具有较高的回归性和较低的预测误差。弹性反向传播(RProp)算法具有较低的均方误差(MSE)和平均绝对误差(MAE)百分比,是最适合的训练算法。ANN模型的相关系数(R)分别为0.82和0.95,决定系数(R2)分别为0.67和0.90,MAE和MSE较低,表明对生物炭HHV和SSA的预测精度合理。常规热解制得生物炭的能效范围为9.84% ~ 21.13%,而活性炭的能效范围为45.26% ~ 67.21%,在300℃时达到最大值。热力学分析结果表明,生物炭和活性炭的能量和火用产率与活化条件和温度有关。图形抽象
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来源期刊
Biomass Conversion and Biorefinery
Biomass Conversion and Biorefinery Energy-Renewable Energy, Sustainability and the Environment
CiteScore
7.00
自引率
15.00%
发文量
1358
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.
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