Xiao Kai, Zhu Xianghui
Wuhan Railway Vocational College of Technology, Wuhan 430205, China.
Wuhan Institute of Technology, Wuhan 430205, China.
ACS Omega. 2024 Nov 25;9(49):48125-48136. doi: 10.1021/acsomega.4c04649. eCollection 2024 Dec 10.
In the present work, artificial neural network (ANN)-based machine learning models are developed to predict biomass pyrolysis kinetics. Data sets of thermogravimetric analysis and feedstock characterization from a diverse range of biomasses were used to build and test the networks. The composition of the raw biomass material was classified and used as input parameters of ANN models. Three models, which use ultimate analysis, proximate analysis, and three components as input parameters, were developed in this study. A total of 32 types of biomass raw materials were used, and 270 sets of kinetic data were obtained according to different pyrolysis conversion rates ranging from 0.1 to 0.9. Results show that increasing the number of neurons can improve the prediction accuracy. The optimized neuron number is 7-11. The largest relative deviation between experimental and modeling results for the three models are 20.80%, 14.06% and 12.85%, respectively, which proves that using cellulose, hemicellulose, and lignin as input parameters of the neural network model can better predict the activation energy of pyrolysis at each reaction stage. The particle swarm optimization algorithm could significantly improve the prediction accuracy of the BP-ANN model. The largest deviation for activated energy prediction decreases from 12.85% to 6.72%.
在本研究中，开发了基于人工神经网络（ANN）的机器学习模型来预测生物质热解动力学。利用来自多种生物质的热重分析数据集和原料特性来构建和测试网络。对原始生物质材料的成分进行分类，并将其用作ANN模型的输入参数。本研究开发了三种模型，分别使用元素分析、工业分析和三种成分作为输入参数。总共使用了32种生物质原料，并根据0.1至0.9的不同热解转化率获得了270组动力学数据。结果表明，增加神经元数量可以提高预测精度。优化后的神经元数量为7 - 11。三种模型实验结果与建模结果之间的最大相对偏差分别为20.80%、14.06%和12.85%，这证明将纤维素、半纤维素和木质素用作神经网络模型的输入参数可以更好地预测每个反应阶段热解的活化能。粒子群优化算法可以显著提高BP - ANN模型的预测精度。活化能预测的最大偏差从12.85%降至6.72%。
