DSpace Community:https://repositorio.ufu.br/handle/123456789/51472026-07-17T03:19:34Z2026-07-17T03:19:34ZAAnálise da Aplicação de Processos de Filtração por Membranas para Separação de Dióxido de Carbono: Uma Revisão Bibliográficahttps://repositorio.ufu.br/handle/123456789/488402026-07-15T06:25:15Z2025-05-08T00:00:00ZTitle: AAnálise da Aplicação de Processos de Filtração por Membranas para Separação de Dióxido de Carbono: Uma Revisão Bibliográfica2025-05-08T00:00:00ZDesenvolvimento e caracterização de geleias de morango contendo edulcorantes e fruto-oligossacarídeo (FOS)https://repositorio.ufu.br/handle/123456789/488122026-07-14T06:20:29Z2026-02-26T00:00:00ZTitle: Desenvolvimento e caracterização de geleias de morango contendo edulcorantes e fruto-oligossacarídeo (FOS)
Abstract: This work addresses the development of diet strawberry-flavored jams that utilize fructooligosaccharide (FOS), a sweetened dietary fiber, as a substitute for sucrose or other sweeteners. The literature review article, including studies from 2014 to 2025, aimed to capture studies with different sweetener alternatives and describe the findings regarding the consequences of consuming these substances on human health and industrial applications. As a result, the included articles found an association between the consumption of artificial sweeteners and health alterations, mainly in animals, such as a higher risk of cancer incidence and alterations in the intestinal microbiota. Further studies are needed to confirm these negative consequences in humans. In the consumer market, a large portion of foods (66.7%) contained added sugars or non-nutritive sweeteners, notably acesulfame-k. Sweeteners are used in greater quantities in non-alcoholic beverages (37.2%) and foods with high energy value (25%). The most common natural sweeteners, such as Stevia and polyols, have shown successful applications in foods like chocolate, especially when used in combination, but their physicochemical attributes in bakery products were further from the control. Short-chain fruit sweeteners, such as fructooligosaccharide (FOS) and oligofructose (OF), are soluble fibers with a sweetness of 35 to 55% compared to sucrose and, according to studies, need to be used in conjunction with other sweeteners to achieve good sensory acceptance and adequate technological properties (pH, TSS, moisture) in foods, including jams. The experimental article aimed to develop two strawberry jam formulations (GSA and GFOS) containing FOS as a sweetening agent and to analyze their physicochemical (Brix degrees, water activity, moisture, pH, acidity, texture, viscosity, soluble solids content, determination of reducing, non-reducing and total sugars, total monomeric anthocyanins, phenolic compounds, antioxidant activity), sensory (descriptive and acceptance tests and purchase intention) and microbiological (molds and yeasts and total and thermotolerant coliforms) characteristics when compared to a control jam (GT). The results showed that the jams with FOS had higher instrumental color parameters a*, b*, C and h than GT. The values for moisture, water activity (Aw), and soluble solids (SST) were similar between the GSA (33.24% ± 1.04; 0.748 ± 0.01; 66°Brix ± 1.17) and control sample GT (33.26% ± 0.86; 0.763 ± 0.01; 66°Brix ± 0.98). GFOS was less hard, adhesive, and gummy than the others and had an intermediate viscosity (22,044 cP ± 102). Light jams made with FOS had a higher quantity of phenolic compounds (GSA: 329.08 ± 9.50 mg of EAG/100g; GFOS: 365.64 ± 12.00 mg of EAG/100g), antioxidants (GSA: 2,344.38 ± 165.73 µmol of ET/100g; GFOS: 2,365.20 ± 89.31 µmol of ET/100g) and anthocyanins (GSA: 1.04 mg/100g ± 0.08; GFOS: 2.31 mg/100g ± 0.14) than the commercial GT jam (69.39 mg of EAG/100g ± 7.04; 388.64 µmol of ET/100 g ± 39.06; 0.52 mg/100 g ± 0.02). Regarding sugars, the jams with FOS had more sucrose than the commercial one because the cooking process converted part of the fiber into its carbohydrates (fructose and sucrose), but with a 25% reduction in total sugars when compared to the GT. The research concluded that the production of light jam with the addition of FOS, xylitol and Stevia (GSA) presented adequate physicochemical characteristics, similar to other studies, and with good sensory acceptance in the tests performed. Furthermore, all samples showed no microbial growth of fungi, yeasts, and coliforms, thus proving safe for consumption.2026-02-26T00:00:00ZExplorando a produção de hidrogênio a partir da co-gaseificação de biomassa–plástico: um estudo integrado de simulação e aprendizado de máquinahttps://repositorio.ufu.br/handle/123456789/487742026-06-24T06:19:05Z2026-02-13T00:00:00ZTitle: 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.2026-02-13T00:00:00ZMemorial descritivohttps://repositorio.ufu.br/handle/123456789/487032026-05-19T06:34:57Z2026-05-08T00:00:00ZTitle: Memorial descritivo2026-05-08T00:00:00Z