Abstract

The elevated conditions needed for hydrothermal carbonization of biomass require a special high-pressure reactor which makes it expensive and time-consuming. The soft computing approaches as proposed here can predict the conversion of any feedstock based on the composition and operating conditions without the need for any kinetic modeling. In this study, Extreme Gradient Boosting method (XGBoost), Multilayer Perceptron Artificial Neural Network (MLPANN) and Support Vector Machine (SVM) were trained in python programming language using the data available from the literature for hydrothermal carbonization of different biomass. Statistically, XGBoost showed a higher accuracy among all studied approaches with R² of 0.999 and 0.964 for training and testing data, respectively. The conversion was sensitive to temperature, time, lignin, moisture content, cellulose and hemicellulose, respectively, for the range of conditions applied. It was also revealed that none of the parameters were negligible, however operating conditions were more influential followed by lignin content. This proposed approach can be extended to include liquefaction and gasification processes, where the distribution of products can be estimated for any lignocellulosic biomass.

摘要

生物质水热碳化所需的升高条件需要特殊的高压反应器，这使得它既昂贵又耗时。这里提出的软计算方法可以根据成分和操作条件预测任何原料的转化率，而无需任何动力学建模。在这项研究中，使用来自不同生物质的热液碳化文献中的数据，用 Python 编程语言对极端梯度提升方法 (XGBoost)、多层感知器人工神经网络 (MLPANN) 和支持向量机 (SVM) 进行了训练。从统计上看，XGBoost 在所有使用 R ^{2的研究方法中显示出更高的准确度}训练和测试数据分别为 0.999 和 0.964。对于所应用的条件范围，转化率分别对温度、时间、木质素、水分含量、纤维素和半纤维素敏感。还发现没有一个参数可以忽略不计，但是操作条件的影响更大，其次是木质素含量。这种提议的方法可以扩展到包括液化和气化过程，其中可以估计任何木质纤维素生物质的产品分布。