Caracterização de resíduos agroindustriais para produção de bio-óleo: composição química e potencial de pirólise

Abstract

Agro-industrial residues represent a promising source for the production of inputs such as bio-oil, which is widely used for various industrial applications. This study investigated the physicochemical and chromatographic characterization of bio-oils obtained from the pyrolysis of different biomasses — including coconut fiber, guava seeds, passion fruit seeds, acerola seeds, grape seeds, coffee grounds, corn cob and straw, bean pods, cassava, peanut, and pine shells — with the aim of understanding the relationship between feedstock composition and the chemical profile of the resulting bio-oils. Analyses of ash content, oil content, moisture, total extractives, elemental composition, and atomic O/C and H/C ratios revealed significant variations among the samples. The samples exhibited low ash (0.43% to 3.96%) and moisture (1.55% to 9.25%) contents, while oil and total extractives contents ranged from 0.15% to 23.26% and from 4.75% to 36.37%, respectively. Thermogravimetric analysis showed that, in the temperature range of 25°C to 1000°C, 82.7% to 100% of the samples were degraded, indicating the feasibility of pyrolysis processes at 600°C. Following micro-pyrolysis, the resulting bio-oils were analyzed by GC-MS, which identified different classes of organic compounds, notably phenols, carboxylic acids, ketones, alcohols, and nitrogen-containing compounds, whose predominance varied according to the biomass employed. The application of multivariate analysis techniques (PCA and HCA) was fundamental for interpreting the complexity of the obtained data. These approaches allowed the identification of patterns and the grouping of samples with similar chemical profiles, highlighting that bio-oils derived from oilseed biomasses exhibited higher carboxylic acid contents, whereas others were characterized by compositions more closely related to cellulose or lignin. This analytical strategy not only reinforced the robustness of the interpretations but also enhanced the understanding of the relationships between chemical composition and industrial application potential.