Trabalho de Conclusão de Curso - TCC https://repositorio.ifpe.edu.br/xmlui/handle/123456789/437 2026-09-20T01:05:03Z 2026-09-20T01:05:03Z https://repositorio.ifpe.edu.br/xmlui/handle/123456789/2312 2026-09-05T06:02:27Z 2026-03-13T00:00:00Z

Simulação CFD de um reator de leito fluidizado para captura do Co2 Since the 1970s, monitoring and mitigating greenhouse gas (GHG) emissions has been a topic of global interest, especially in light of increasing climate change and its consequences. This movement intensified in contemporary society, particularly with the signing of the Paris Agreement during the 21st United Nations Climate Change Conference (COP21) in 2015. In this context, the goal of reducing GHG emissions was established, with emphasis on carbon neutrality by 2060. Now, with COP30 in Belém (PA), new targets are being developed based on the previously agreed objectives. Therefore, the need to promote research and the development of technologies capable of attenuating or mitigating these emissions, especially carbon dioxide (CO₂), is evident. Given this scenario, the advancement of technologies focused on carbon sequestration, such as Carbon Capture and Storage (CCS), becomes even more urgent. CCS's main function is to remove CO₂ generated in industrial and energy processes. Multiphase reactors are a suitable piece of equipment to meet the necessary requirements of these processes. However, a challenge in modeling new technologies is the high cost and time inherent in the process. Therefore, one way to overcome these problems is through the use of computational simulation. Thus, this work aims to perform a computational simulation of a fluidized bed reactor using CFD modeling for CO₂ capture. The study made it possible to capture carbon dioxide through an adsorption reaction with potassium carbonate by varying the inlet velocity of the gas mixture. At lower velocities, a greater reduction in CO₂ was observed due to the longer residence time of the mixture.

2026-03-13T00:00:00Z https://repositorio.ifpe.edu.br/xmlui/handle/123456789/2206 2026-06-03T06:00:54Z 2024-09-30T00:00:00Z

Design computacional e impressão de eletrodos porosos para confecção de gerador de hidrogênio do tipo Membraneless The combination of computational design and additive manufacturing is transforming the production of energy devices, such as membraneless hydrogen generators. Computational design involves the use of simulations and digital modeling to optimize the characteristics of porous electrodes. Through 3D modeling, it is possible to predict and adjust the structure of the electrodes, enhancing their efficiency. This process includes the optimization of porosity, geometry, and flow channel distribution, which are essential for improving the electrochemical reaction and electrode conductivity. Using 3D printers and specific materials, it is possible to create electrodes with high surface area and a controlled porous network. The main goal of this thesis was to develop topological structures based on TPMS-type functions to generate porous electrodes for the assembly of membraneless electrolyzers. By combining computational modeling, 3D printing, and conductive composite preparation, it was possible to fabricate electrodes with Schwarz P-type geometries, exhibiting good electrical properties. Furthermore, through an electrochemical electroplating process, the electrode was coated with a nickel layer over copper (Ni@Cu), which showed better hydrogen generation results from alkaline water electrolysis compared to the uncoated electrode. In conclusion, the process proved to be promising for the fabrication of electrodes for membraneless systems.

2024-09-30T00:00:00Z https://repositorio.ifpe.edu.br/xmlui/handle/123456789/2204 2026-06-01T06:02:47Z 2026-03-10T00:00:00Z

Uso da análise de dados e Machine Learning aplicado ao processo de separação e expedição em home centers: estudo de caso The order picking and customer service process in the expedition area of home centers presents high operational variability, resulting in delays in meeting internal service targets and directly affecting customer experience. Factors such as total load weight, number of items, delivery type, and operator productivity contribute to fluctuations in separation and service times, making it difficult to efficiently control operational flow. In this context, it becomes necessary to quantitatively understand the factors that influence delays and propose data-driven improvements. This Final Term Paper aims to analyze the separation and service process in the expedition area, identifying operational bottlenecks and proposing solutions through statistical analysis and Artificial Intelligence techniques. A realistically structured fictitious database containing 3,000 simulated records was developed according to the company’s operational rules. Descriptive statistical analyses were conducted to identify patterns, variability, and compliance with internal service targets. Additionally, supervised classification models, such as Decision Tree and Random Forest, were applied to predict service delays. The results showed that approximately 40% of orders exceeded the established service-time targets. Feature importance analysis indicated that total weight, operator productivity, and number of items are the main factors associated with delays. The study concludes that integrating process engineering, statistics, and Artificial Intelligence is an effective approach to support logistics optimization and improve operational decision-making.

2026-03-10T00:00:00Z https://repositorio.ifpe.edu.br/xmlui/handle/123456789/2057 2026-03-17T06:01:43Z 2025-01-01T00:00:00Z

Aplicação de ferramentas da qualidade e de análise de dados de telemetria: um estudo de caso em escavadeira hidráulica In industrial processes, quality management tools are used to ensure efficiency. The use of industrial statistics together with data from control and telemetry systems helps engineers in decision-making, with the objective of achieving product quality or improving performance indicators. Techniques such as PDCA (Plan, Do, Check, and Act) provide a robust structure for implementing improvements. In the present study, a process is improved using these tools. Telemetry data from a hydraulic excavator operating in ore loading are analyzed. A continuous improvement action using the PDCA cycle is implemented to impact three fundamental aspects of the operation: consumption, performance, and sustainability. After implementation, it was possible to reduce the annual fuel consumption cost of a single piece of equipment by approximately R$ 2.646,00. With the expansion of the improvement to the equipment fleet, savings can reach R$ 31.752,00 per year. Another outcome was the reduction in the operation cycle time, which decreased by up to 51.04% after the implementation of the process improvement. Finally, it was also possible to achieve an average reduction of 2.03% in the equipment’s emission rate between the adoption and implementation phases of the proposed procedure.

2025-01-01T00:00:00Z