Using Artificial Neural Networks for Classification of Composition and Biomass Species for Energy Based on Thermogravimetric Data

The thermogravimetric analysis (TGA) data of wood present a very similar layout, which makes it difficult to obtain information on components or types of biomass with traditional techniques, which are generally based on the decrease in weight of the sample at specific temperature values. In this work, artificial neural networks are applied as an innovative technique to identify species or differences in components more effectively. This work evaluates the use of TGA data markers (similar to genetic markers) to obtain information on biomass. That is multiple values of the percentage of residual weight with respect to the initial one at specific temperatures of the TGA data. These networks achieve classification by automatically adjusting weights based on training patterns with input marker data. By replicating the curve for the same sample and having unique characteristics, TGA becomes a valuable tool to identify species and characterize biomass composition. The application of artificial intelligence techniques makes it possible to provide detailed information about the components and improve the accuracy of sample classification. The results demonstrate that the neural network successfully classified 95% of wood samples from eight different species and accurately determined the percentage composition with 98% precision.