http://repositorio.ufc.br/handle/riufc/419 2026-06-12T14:21:24Z http://repositorio.ufc.br/handle/riufc/86725 Título: Platform for optimization and sizing of hybrid photovoltaic-battery systems for industrial decarbonization Autor(es): Guerra, Lucca Lemos Costa Abstract: Battery Energy Storage Systems (BESS) have been experiencing accelerated growth worldwide, driven by electrification, the expansion of intermittent renewable energy sources, and the increasing need for operational flexibility in the power sector. In this context, this work develops and implements a computational platform for the sizing and optimization of hybrid photovoltaicbattery systems (PV-BESS), aimed at industrial decarbonization. The methodology is based on a Mixed-Integer Linear Programming (MILP) optimization model, implemented in Python using the Pyomo library. The tool was validated using real data from a medium-sized industry located in the state of Ceará, Brazil, classified as a Group A consumer (high voltage), with an annual electricity consumption of approximately 1.4 GWh. The model simultaneously considers energy, operational, and economic aspects, including capital investment costs (CAPEX), electricity consumption tariffs, grid energy injection/compensation, and peakhour operation. As the main methodological differentiator, the sizing can be conducted according to two distinct operational profile approaches, associated with the user’s desired level of risk aversion. The f irst approach is based on a critical day, using the 95th percentile (P95) to model demand and restricting photovoltaic generation to the 10th percentile (P10) of historical solar irradiance, with an additional safety margin, resulting in a solution oriented toward maximum operational robustness. The second approach uses average hourly load and irradiance profiles, optimizing the project for typical day-to-day operating conditions while reducing implementation costs. For the analyzed case study, the sizing based on the average profile resulted in an 800 kW photovoltaic system combined with a 3,916 kWh BESS, providing 92.7% self-sufficiency and an estimated reduction of 608.37 tons of CO2 avoided annually. Additionally, the comparison between the “critical day” and “average day” strategies highlighted the tradeoff between operational robustness, energy resilience, and economic viability, reinforcing the practical applicability of the tool for energy transition and industrial decarbonization projects. Tipo: Dissertação 2026-01-01T00:00:00Z http://repositorio.ufc.br/handle/riufc/86656 Título: Métodos para treinamento rápido e esparso de máquinas de vetores-suporte de mínimos quadrados: uma abordagem dual Autor(es): Marinho, Felipe Pinto Abstract: The least squares support vector machine model is a variant of the classical support vector machine model that employs equality constraints in the formulation of its primal problem. This allows the derivation of a linear system when applying the Karush–Kuhn–Tucker optimality conditions, considerably simplifying the training of this model when compared to the adjustment of support vector machines. However, a drawback of this formulation lies in the fact that the optimal vector of Lagrange multipliers of the problem is dense. Thus, all training patterns are considered support vectors, making the prediction stage computationally expensive when working with large datasets. In many cases, the solution of the system is obtained through the use of iterative methods based on conjugate directions, which, on the one hand, is advantageous since it avoids numerical difficulties related to matrix inversion, but, on the other hand, makes the training stage slow for datasets with high volume, as it is necessary to operate with dense kernel matrices. In this context, two new methodologies are proposed for the fast and sparse training of least squares support vector machines. In the first approach, the dual problem of least squares support vector machines is solved via a sequential minimal optimization algorithm with a new three-term conjugate descent direction which, combined with a working set selection strategy based on functional gain, allows an acceleration in convergence, reducing the number of iterations when compared to the standard sequential minimal optimization algorithm. In addition, an iterative pruning process based on the functional gain of the optimization problem is adopted in order to sparsify the obtained Lagrange multipliers. Finally, the last proposal consists of the use of a new spectral conjugate gradient method for solving the corresponding dual problem and sparsification through iterative pruning using the proximity of the pattern to the decision hyperplane as the criterion for removal. Numerical experiments carried out on several real and artificial datasets demonstrate that both approaches present competitive performance, with fast training and a high level of sparsity of the Lagrange multipliers. For binary classification datasets, the sparsity gain reached approximately 80% when compared to the total number of training samples for the considered dataset. The reduction in training time was approximately 99.9% in relation to standard least squares support vector machines. For datasets with higher volume, the proposals were the only ones that provided feasible training time with stable convergence. The quality of the decision boundaries was further analyzed for synthetic datasets, where the results indicate the generation of boundaries similar to the considered benchmarking model, confirming the predictive capability of the new methodologies. Finally, the results for regression datasets indicate that the proposal based on the spectral conjugate gradient method may be a sparse and fast-training alternative to the least squares support vector machine regression model. Tipo: Tese 2025-01-01T00:00:00Z http://repositorio.ufc.br/handle/riufc/86649 Título: Enhancement of anaerobic azo dye decolorization under saline and sulfate-rich conditions through electron transfer-based strategies Autor(es): Oliveira Júnior, José Kleber Sousa Abstract: Azo dyes are among the most persistent pollutants in textile wastewaters due to the high stability of their aromatic structures and resistance to conventional biological treatment processes. Reactive Black 5 (RB5), one of the most widely used azo dyes, represents a significant environmental challenge when present in industrial effluents. In this context, this doctoral thesis investigated strategies to intensify the anaerobic decolorization of RB5 under conditions representative of real textile wastewaters, with emphasis on modulating extracellular electron transfer through the use of soluble and insoluble redox mediators and iron-based additives. Batch experiments, with 10h of duration, confirmed that RB5 decolorization occurs predominantly via biological reduction, with abiotic controls showing removals below 5%. The addition of the soluble redox mediator anthraquinone-2-sulfonate (AQS) increased decolorization efficiency from approximately 70% to up to 87% and enhanced the first-order kinetic constant from 0.19 to 0.33 h-1 . Sulfate exerted a limited effect on decolorization (~72- 73%), whereas salinity caused moderate kinetic inhibition. The combined presence of chloride and sulfate constituted the most restrictive condition, reducing RB5 removal to approximately 65-68% and the kinetic constant to 0.15-0.17 h-1 ; nevertheless, AQS maintained a positive effect under all tested scenarios. In continuous anaerobic reactors, RB5 decolorization under control conditions typically ranged from 66 to 75% and decreased to approximately 60-68% in the presence of sulfate due to competition for reducing equivalents. Salinity had a more pronounced impact on organic matter removal, decreasing COD removal to approximately 35-40%, while exerting a comparatively smaller effect on dye decolorization. AQS addition consistently improved RB5 removal by about 5-7%, although at the expense of COD removal. Recovery tests demonstrated rapid restoration of dye decolorization, whereas COD removal exhibited partial and slower recovery, indicating greater resilience of dye-reducing pathways compared to methanogenic processes. The evaluation of iron-based materials revealed strong speciation- dependent effects. Zero-valent iron (Fe0 ) emerged as the most effective additive, increasing RB5 removal to approximately 80-82% in continuous reactors and raising the kinetic constant to up to 0.24 h-1 in batch assays, while providing high operational stability even under reduced hydraulic retention time. Magnetite and soluble iron species (Fe2+/Fe3+) produced more moderate improvements (~69-77%), strongly dependent on dosage and bioavailability. In addition, insoluble carbonaceous materials, such as activated carbon and biochar, particularly when functionalized with AQS, enabled RB5 removals above 80% and promoted enrichment of electroactive microbial consortia. Overall, this thesis demonstrates that intensification of anaerobic azo dye decolorization in complex textile wastewaters can be achieved through targeted modulation of extracellular electron transfer, resulting in significant gains in efficiency, kinetics, and operational robustness. These findings provide scientific and technological foundations for the development of more efficient and resilient anaerobic treatment systems applicable to real textile effluents. Tipo: Tese 2026-01-01T00:00:00Z http://repositorio.ufc.br/handle/riufc/86643 Título: Imobilização da anidrase carbônica de Sulfurihydrogenibium azorense no suporte a base de sílica SBA-15 com poros expandidos para captura de CO 2 Autor(es): Barboza, José Anderson Lucas Abstract: The increase in carbon dioxide (CO 2 ) emissions, mainly from the use of fossil fuels, has intensified the search for sustainable technologies aimed at mitigating environmental impacts, such as global warming. In this context, biological carbon capture and storage processes have stood out due to their higher selectivity and operation under mild conditions, reducing energy consumption compared to conventional methods, such as chemical absorption. Therefore, this work aimed to evaluate the immobilization of carbonic anhydrase, derived from the heterologous expression of the ArticExpress (DE3) strain of the thermophilic microorganism Sulfurihydrogenibium azorense, on mesoporous silica SBA-15 (Santa Barbara Amorphous-15) with expanded pores, aiming to obtain a biocatalyst with higher stability and efficiency for CO 2 capture. For this purpose, the enzyme was expressed in Escherichia coli, extracted by cell lysis, and analyzed in terms of catalytic activity and zeta potential. The SBA-15 support with expanded pores was synthesized via a hydrothermal route and characterized by nitrogen adsorption/desorption isotherms, Fourier-transform infrared spectroscopy, and point of zero charge, resulting in the formation of a structure with ordered pores. Meanwhile, for the biocatalyst obtained by physical adsorption (SBA-AC), the effects of pH, ionic strength, protein load, and contact time during immobilization were measured. Protein yields were higher than 90% under almost all conditions, indicating strong affinity of the enzyme extract for the support, and the condition of 50 mg of protein per gram of support showed the best overall performance, with recovered activity above 80%. In addition, the support not only promoted enzyme immobilization but also provided partial purification of the extract, selectively retaining carbonic anhydrase over other proteins present. Furthermore, the storage stability of SBA-AC biocatalysts and those chemically modified with glutaraldehyde (SBA-AC-GA) was monitored over 90 days, showing not only maintenance but also an increase in catalytic activity during this period, with an approximate 25% increment in relative activity for both cases. Therefore, the results of this work demonstrate that the immobilization of carbonic anhydrase on SBA-15 with expanded pores is a promising strategy for CO 2 capture, presenting high catalytic performance and elevated storage stability. Tipo: Dissertação 2028-01-01T00:00:00Z