https://repositorio.ufu.br/handle/123456789/5147 Sat, 27 Jun 2026 04:10:01 GMT 2026-06-27T04:10:01Z https://repositorio.ufu.br:443/retrieve/5ea0a001-5b05-4c47-9521-8d3feed4d8d9/ https://repositorio.ufu.br/handle/123456789/5147 https://repositorio.ufu.br/handle/123456789/48774 Title: Explorando a produção de hidrogênio a partir da co-gaseificação de biomassa–plástico: um estudo integrado de simulação e aprendizado de máquina Abstract: Biomass–plastic co-gasification is a promising route for producing low-carbon syngas and hydrogen; however, its optimization is challenged by nonlinear interactions among temperature, equivalence ratio, and feed composition. In this work, an integrated approach combining steady-state thermochemical process modelling and machine learning was developed to predict and optimize the performance of biomass–plastic systems. A phenomenological model was implemented in the AVEVA PRO/II simulator, structured into drying, pyrolysis, gasification, and restricted chemical-equilibrium stages, using air as the gasifying agent. The model was validated against experimental literature data, reproducing the order of magnitude of the molar fractions of H₂, CO, CO₂, and CH₄ under different operating conditions. Based on this validated platform, a synthetic dataset of 3,000 simulations was generated via Latin Hypercube Sampling, covering five plastics (HDPE, PE, PP, PS, and PET), 35 lignocellulosic biomasses, and representative ranges of temperature and equivalence ratio. Extreme Gradient Boosting (XGBoost) models were then trained to predict syngas composition (H₂, CO, and CO₂), total gas yield, lower heating value, and the H₂/CO ratio. The split into training, validation, and test sets was assessed using distance-based metrics (1-NN, MMD, and Energy Distance), ensuring representativeness and generalization. The models achieved high performance, with out-of-sample coefficients of determination above 0.98. Interpretability was examined using explainable AI techniques based on SHAP values, indicating that temperature and equivalence ratio are key drivers of H₂ formation, whereas carbon and fixed carbon contents govern CO generation. The applicability domain was verified using Mahalanobis distance, ensuring prediction reliability. Finally, Differential Evolution optimization identified synergistic biomass–plastic pairs and operating conditions that maximize hydrogen production and syngas quality. The optimal solutions favored polypropylene-rich blends with lignocellulosic biomasses, yielding H₂ fractions of approximately 28%, H₂/CO ratios close to 1.1, and lower heating values around 6.5 MJ·Nm⁻³. The proposed approach integrates mechanistic modelling, explainable machine learning, and optimization, supporting the rational design of hydrogen-oriented co-gasification systems. Fri, 13 Feb 2026 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/48774 2026-02-13T00:00:00Z https://repositorio.ufu.br/handle/123456789/48703 Title: Memorial descritivo Fri, 08 May 2026 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/48703 2026-05-08T00:00:00Z https://repositorio.ufu.br/handle/123456789/48559 Title: Análise técnica e de viabilidade de um processo de pirólise por microondas Abstract: This study investigated the pyrolysis processes of the jatobá fruit peel. First was made the proximate analysis of biomass; from the proximate analysis results, the moisture, ash, volatile matter, and fixed carbon content were determined. From these values, the elemental composition of the jatobá fruit peel was determined using the Parikh, Shen, and Nhuchhen methodologies, in addition to the arithmetic mean of the three methodologies. From the elemental composition and characterization of the jatobá fruit peel, the atomic classification of the jatobá fruit peel was performed, and the atomic ratio was calculated using the van Krevelen diagram. The moisture content was 8.67% ± 0.09%, the ash content was 1.52% ± 0.01% on a dry basis, the volatile content was 80.71% ± 2.63% on a dry basis, and the fixed carbon fraction was 17.77% ± 2.63%. From the average of the three methodologies, it was possible to determine that the carbon content was 48.27% ± 0.46%, the hydrogen content was 5.91% ± 0.01%, and the oxygen content was 43.86% ± 0.34%. From the micropyrolysis tests, it was observed that alkaline catalysts such as potassium hydroxide and calcium oxide presented the best results, since these compounds favored the formation of hydrocarbons, which was explained by the high hydrocarbon content in the volatiles. Tests conducted with calcium oxide at 650 °C showed a hydrocarbon content in the volatiles of 15.0%, while tests conducted at 650 °C using potassium hydroxide showed a hydrocarbon content in the volatiles of 17.1%. It was also observed that the absence of higher temperatures favors hydrocarbon formation. Experiments involving microwave-assisted pyrolysis were performed using particles with diameters between 0.250 mm and 0.355 mm and particles with diameters between 0.106 mm and 0.180 mm; larger particles favored charcoal formation, while smaller particles favored bio-oil formation. Long duration experiments and temperatures between 550 °C and 620 °C are favorable to produce bio-oil. While temperatures between 451 °C and 550 °C are favorable for producing charcoal. Scanning electron microscopy images showed that the charcoal is much more porous and has a more irregular surface than the original biomass, indicating a larger surface area compared to the original biomass. Mon, 16 Mar 2026 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/48559 2026-03-16T00:00:00Z https://repositorio.ufu.br/handle/123456789/48523 Title: Extração de proteínas das folhas de ora-pronóbis (Pereskia aculeata) para desenvolvimento de novos produtos alimentícios Abstract: Ora-pro-nóbis (Pereskia aculeata) is a non-conventional food plant (PANC) widely distributed in Brazil and recognized for its high nutritional value, especially for the high protein content present in its leaves. Given the growing demand for alternative sources of plant-based proteins and the interest in developing new food ingredients, this work aimed to evaluate and optimize the extraction of proteins from ora-pro-nóbis leaves, aiming to obtain a protein concentrate with potential application in the food industry. Initially, the physicochemical characterization of the fresh leaves was carried out, revealing a high moisture content and significant protein concentration on a dry basis. Subsequently, the leaves were subjected to freeze-drying, grinding, and mucilage removal. Protein extraction was performed by alkaline extraction followed by isoelectric precipitation, and the effects of pH, temperature, and extraction time were evaluated using a central composite rotatable design (CCRD). The results demonstrated that the recovery of proteins from ora-pro-nóbis was efficient, allowing the production of concentrates with protein content between 18.85% and 30.51%. Optimization of the extraction process using DCCR allowed the identification of favorable operating conditions to maximize the percentage of protein in the extract, yield, and protein recovery. Determining the isoelectric point was fundamental for the proper conduct of the precipitation step, contributing to increased process efficiency. The obtained protein concentrate was subjected to freeze-drying and characterized in terms of its physicochemical and functional properties. Thus, the results obtained indicated that ora-pro-nóbis has high potential as an alternative source of vegetable proteins, contributing to the valorization of unconventional food plants (PANCs) and the development of new food products with greater nutritional value and sustainable appeal. Thu, 05 Feb 2026 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/48523 2026-02-05T00:00:00Z