Enhancing glucose classification in continuous flow hydrothermal biomass liquefaction streams through generative AI and IR spectroscopy

Abstract

Energy from fossil fuels is forecasted to contribute to 28% of the energy demand by 2050. Shifting to renewable, green energy is desirable to mitigate the adverse effects on the climate posed by resultant gases. Continuous flow hydrothermal liquefaction holds promise to convert biomass into renewable energy. However, sustainable conversion of biomass feedstocks remains a considerable challenge, and more process optimization studies are necessary to achieve positive net energy ratios (NERs). To fast-track this process development, we investigated the integration of Fourier transform infrared spectroscopy (FTIR) for data collection coupled with a support vector machine classifier (SVC). We trained the model on data labeled after the analysis of the aqueous stream by high-performance liquid chromatography (HPLC). Multiple test data, such as liquified wood and cotton, and dissolved glucose, were used to classify the aqueous streams. The results showed that fused original data achieves 84% accuracy. The accuracy increased to 93% after merging synthetic data from generative adversarial networks (GANs) and hand-crafted statistical features. The effect of Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP) on accuracy was also studied. We noticed that UMAP increases accuracy on some variations of the datasets, but it does not exceed the highest reported value. Shapely Additive Explanations (SHAP) were used to investigate the contribution of the top 20 features. We discovered that features representative of glucose contribute positively to the model's performance, whereas those found in water have a negative influence.