Multiple-Step Kinetic Model for Biomass [18,42].

Thermochemical processes employ heat to transform biomass into energy. In these processes, heat supply and biomass type can affect the products, therefore understanding them is critical. Confirming these changes directly requires time and resources. Several hypotheses have been proposed to explain these changes. So, the purpose of this work was to investigate mass loss during thermochemical reactions utilising available kinetic parameters. This study comprised previously pyrolysed herbal agricultural biomass (soybean pod, corncob), woody agricultural biomass (pepper stem, grape pruning branch), and forestry biomass (wood pellet, bamboo). Temperature fluctuations were studied using a 1D temperature prediction model and evaluated using kinetic parameters. The findings anticipated using prior research’ kinetic parameters differed by up to 20% from the experimental results. As a result, some of the kinetic parameters were adjusted. The prediction model with the changed parameters outperformed the prior results, with an RMSE of 2.0607 for wood pellets and 5.9754 for soybean pods. The results obtained using grape pruning branches, bamboo, and corncobs confirmed the mass reduction predicted by prior studies. This study revealed the capacity to estimate mass loss without using thermogravimetric measurements, and future predictions should include a broader spectrum of biomass materials.

热化学过程（Thermochemical processes）通过热能将生物质（biomass）转化为能源。此类过程中，热源供给与生物质种类均会对产物生成产生影响，因此对其机理的深入理解至关重要。若要直接探究这些变化规律，需投入大量时间与资源。目前已有多项假说被提出，用以阐释此类变化。故此，本研究旨在借助已公开的动力学参数（kinetic parameters），探究热化学反应过程中的质量损失情况。

本研究的实验样品涵盖三类生物质：经预先热解的草本农业生物质（大豆荚、玉米芯）、木本农业生物质（辣椒茎秆、葡萄修剪枝条）以及林业生物质（木质颗粒、竹材）。研究采用一维温度预测模型（1D temperature prediction model）对温度波动进行分析，并结合动力学参数对结果展开评估。

基于已有研究的动力学参数所得到的预测结果，与实验实测值之间的最大偏差可达20%。据此，我们对部分动力学参数进行了修正。修正参数后的预测模型性能优于此前的研究结果：针对木质颗粒，其均方根误差（Root Mean Square Error, RMSE）为2.0607；针对大豆荚，该值为5.9754。针对葡萄修剪枝条、竹材与玉米芯所得到的实验结果，也验证了已有研究的质量减量预测结论。

本研究证实了无需借助热重测量即可估算质量损失的可行性，未来的预测研究应纳入更广泛的生物质材料品类。