DATA IDENTIFICATION OF BIOMASS FOR ENERGY USING NEURAL NETWORKS APPLIED TO THERMOGRAVIMETRIC CURVES

This work evaluates the use of TGA curve markers (similar to genetic markers) to obtain information on biomass. That is, multiple values of percentage of residual weight with respect to the initial one at specific temperatures of the TGA curve are used to identify specie and wood and leaf mixtures. The data obtained from the TGA analyzes for different mixtures of leaves and wood (100% leaf, 50% leaf and 50% wood and 100% wood) of 7 species are presented (poplar (Populus sp.), caper (Euphorbia laurifolia), alder (Alnus acuminata), arupo (Chionanthus pubescens), cypress (Cupressus macrocarpa), eucalyptus (Eucalyptus globulus), linden (Sambucus nigra L.) and pine (Pinus radiata)) From each of the thermogravimetric curves, the residual weights of the sample at fixed temperatures have been selected (100ºC, 175ºC, 200ºC, 325ºC, 400ºC, 475ºC and 550ºC). These weights represent the input to a neural network to identify both the species and the percentage of leaves and wood. The last sheet of the file shows the assignment of values to each species and the final combination of the mixture of leaves and wood, output of the neural network.本研究评估了热重分析（Thermogravimetric Analysis, TGA）曲线标记（类似遗传标记）在获取生物质相关信息中的应用。具体而言，本研究利用TGA曲线在特定温度下相对于初始质量的剩余质量百分比的多个数值，来识别物种以及木材与叶片的混合物料。 本研究呈现了针对7个物种的叶片与木材不同混合比例物料（100%叶片、50%叶片+50%木材、100%木材）的TGA分析所得数据，涉及的物种包括：杨树（Populus sp.）、月桂叶大戟（Euphorbia laurifolia）、桤木（Alnus acuminata）、阿鲁普木（Chionanthus pubescens）、大果柏木（Cupressus macrocarpa）、蓝桉（Eucalyptus globulus）、接骨木（Sambucus nigra L.）以及辐射松（Pinus radiata）。 从每条热重曲线中，本研究选取了样品在固定温度（100℃、175℃、200℃、325℃、400℃、475℃及550℃）下的剩余质量数值。上述剩余质量将作为神经网络的输入，用于识别物料的物种组成以及叶片与木材的混合比例。 数据集文件的最后一页展示了各物种的数值赋值结果，以及神经网络输出的叶片与木材混合物料的最终组合类别。