https://repositorio.ufba.br/handle/ri/4910 2026-05-05T07:34:31Z https://repositorio.ufba.br/handle/ri/44432 Título: Modificação da fibra da luffa cylindrica (l. aegyptiacal) com ácidos graxos para aplicação como biossorvente de óleo diesel Autor(es): Melo, Luciana Lima Primeiro Orientador: Vidal, Rosangela Regia Lima Abstract: The high demand for and widespread use of petroleum and its derivatives, such as diesel oil, across various economic sectors have caused significant impacts on the environment and human health. In the environment, these compounds have become major pollutants, primarily due to spills. In this context, sorption processes emerge as a promising alternative for removing oily pollutants from water, underscoring the need for biodegradable, low-cost, and highly efficient materials. Luffa cylindrica fiber (vegetable sponge) shows potential as a biosorbent due to its porous structure and mechanical stability. However, its sorptive capacity still requires improvement. Thus, this work proposes the surface modification of Luffa cylindrica fiber with fatty acids (lauric, palmitic, stearic, oleic, and linoleic acids) to increase its affinity for oily compounds. The main objective was to evaluate the effect of these modifications on the sorption capacity of diesel oil. Sample characterization was performed using Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM), and thermogravimetric analysis (TGA). Sorption studies were conducted by varying sorbent mass, process time, agitation speed, and temperature. Batch experiments were carried out using reference systems (diesel oil, distilled water, or saline water) and systems containing diesel oil spilled into distilled or saline water. The results indicated that oleic acid modification provided the most significant increase in diesel oil sorption capacity among the evaluated systems. The pseudo-second-order model showed the best fit to the kinetic data, indicating that the process is controlled by surface interactions. The predominant mechanism is driven primarily by hydrophobic interactions and van der Waals forces, reflecting the nonpolar affinity between diesel oil and the modified fiber surface. It is concluded that modifying Luffa cylindrica fiber with oleic acid increases its hydrophobicity and sorption efficiency, highlighting the material's potential for oil spill remediation. Editora / Evento / Instituição: Universidade Federal da Bahia Tipo: Dissertação 0002-02-27T00:00:00Z https://repositorio.ufba.br/handle/ri/44297 Título: Determinação do coeficiente de transferência de massa na remoção de propanona por absorção Autor(es): Barbosa, Isabel Barbosa e Primeiro Orientador: Góis, Luiz Mário Nelson de Abstract: Air pollution from industries has been a frequently discussed topic today, generating the need to carry out studies on ways to reduce these impacts. Propanone is classified as a VOC (volatile organic compound) because it is a substance that in high concentrations generates risks to human health; and as it is present in industrial gas effluents, it is necessary to search for appropriate means to purify these gases. To carry out this type of treatment, studies focused on mass transfer through absorption were developed. The present work aims to determine the volumetric mass transfer coefficient of the liquid phase, KLa, in the removal of propanone present in the air by water using a stuffed absorption tower. The equipment used is 1,00 m high, 0,07 m in diameter and 0,79 m high filling glass rings. 33 experiments were carried out, at room temperature, at flow rates varying between 0,86 and 1,04 L min-1 of water and 0,4 to 1,2 L min-1 of air, in which, after contact between the liquid and gaseous streams, the Water samples are taken at an interval of 0 to 3,5 min to quantify the transferred propanone. The volumetric mass transfer coefficients on the liquid side obtained belonged to a range of 0,0044 to 0,0277 s-1, with a greater influence of the air flow on the KLa values in relation to the influence of the liquid flow. The equation developed in the dimensional analysis proved to be coherent, presenting an average error of 24%, and, when compared with some methods available in the literature, a better performance was observed, evidenced by the average error difference equivalent to more than 9%. Therefore, this study proved to be effective in the propanone transfer process, adding new data and empirical equations for the study of KLa. Editora / Evento / Instituição: Universidade Federal da Bahia Tipo: Dissertação 2024-07-12T00:00:00Z https://repositorio.ufba.br/handle/ri/44288 Título: Otimização do cultivo de chlorolobion braunii em associação de concentrado salino e água salobra: um bioprocesso para obtenção de biomassa e produção de biodiesel Autor(es): Medeiros, Ravena Maria de Almeida Primeiro Orientador: Cardoso, Lucas Guimarães Abstract: Effluents used as a culture medium for microalgae represent a strategy that combines wastewater treatment—promoting nutrient removal—with the generation of high value-added biomass. The ability of microalgae to accumulate lipids and carbohydrates in their biomass makes them a promising source for biofuel production. The aim of this study was to optimize the production and characterize the biomass of the microalga Chlorolobion braunii using a combination of brackish water and saline concentrate, a condition not previously explored. In addition, the potential of this biomass for biodiesel production was evaluated. The experiments were carried out in Erlenmeyer-type bioreactors (1 L), and the best biomass production condition was determined using a central composite rotational design with the following factors: volume of saline concentrate in brackish water (0–100%) and urea concentration (0.00– 0.08 g/L). The optimized condition was defined as 36.6% saline concentrate, 63.4% brackish water, and 0.02 g/L urea. The best treatment was the experiment with 50% brackish water and 50% saline concentrate, which produced 0.556 g/L of biomass with an average productivity of 0.014 g/L/day, and high levels of biomolecules such as 21.14% carbohydrates and 29.40% lipids. It also achieved bioremediation of 86.25% of nitrates (NO₃⁻), 79.17% of phosphates (PO₄³⁻), and a 23.18% reduction in salinity. The biomass showed high fatty acid content, with 24.94% C16:0 (palmitic acid) and 38.44% C18:1n9c (oleic acid), producing biodiesel that meets the quality standards of the American Society for Testing and Materials (ASTM D6751) and the Brazilian National Agency of Petroleum, Natural Gas and Biofuels (ANP). This study optimized the biomass production of Chlorolobion braunii and demonstrated the feasibility of using only a combination of brackish water and saline concentrate—without prior treatment and without the addition of synthetic medium—resulting in high levels of biomolecules that can serve as feedstock for the generation of high value-added bioproducts. Editora / Evento / Instituição: Universidade Federal da Bahia Tipo: Dissertação 2025-03-14T00:00:00Z https://repositorio.ufba.br/handle/ri/44130 Título: Otimização da transesterificação do óleo de rícino utilizando aprendizagem de máquina Autor(es): Santos, Vivian Lima dos Primeiro Orientador: Santos, Luiz Carlos Lobato dos Abstract: The global demand for energy and the environmental impacts associated with fossil fuels have driven the search for renewable sources, with a highlight on biodiesel. Castor oil (Ricinus communis L.) is a promising raw material because it is non-edible and has adaptable cultivation, although its transesterification faces common process challenges. In this context, machine learning (ML) emerges as a tool to model and optimize this complex and non-linear process. The general objective of this work was to develop and compare the performance of different ML architectures for the predictive modeling and optimization of the operational parameters of the homogeneous transesterification of castor oil, aiming to maximize biodiesel yield. Six architectures were analyzed: Multilayer Perceptron (MLP-logsig and MLP-tansig), Radial Basis Function Network (RBF), a hybrid model (RBF+MLP), Random Forest (RF), and Adaptive Neuro-Fuzzy Inference System (ANFIS), using a database of 406 labeled experimental sets from the literature. The models were evaluated using metrics such as the correlation coefficient (R), mean square error (MSE), and root mean square error (RMSE). The MLP-tansig model demonstrated the best predictive performance, with R > 0.98 in all phases and a test RMSE of 3.03%. For the reverse optimization stage, a Genetic Algorithm (GA) was coupled to the models, and the GA-RBF combination yielded the operational conditions most consistent with the literature, despite the superior point prediction performance of the MLP-tansig model: basic catalyst, alcohol/oil molar ratio of 19.35:1, catalyst concentration of 1.13% (w/w), temperature of 49.91 °C, reaction time of 70.44 min, and stirring at 548.32 rpm, achieving a predicted yield of 100% methyl esters. It is concluded that the proposed methodology is robust and effective, integrating artificial intelligence into process engineering to optimize biodiesel production, with potential application to other biomasses. Editora / Evento / Instituição: Universidade Federal da Bahia Tipo: Dissertação 2026-01-14T00:00:00Z