Modelagem e simulação da adsorção de H2S em colunas de leito fixo visando a otimização de condições operacionais e o scale-up
Due to the increase in energy demand, new sustainable technologies for energy supply are being developed not only to supply this demand, but to reduce environmental impacts, since much of the energy used by human being is through the fossil fuels burning, which leds to greenhouse gases emission. Thi...
Autor principal: | Silva, Pedro Guilherme Costacurta da |
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Formato: | Trabalho de Conclusão de Curso (Graduação) |
Idioma: | Português |
Publicado em: |
Universidade Tecnológica Federal do Paraná
2023
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Assuntos: | |
Acesso em linha: |
http://repositorio.utfpr.edu.br/jspui/handle/1/30654 |
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Resumo: |
Due to the increase in energy demand, new sustainable technologies for energy supply are being developed not only to supply this demand, but to reduce environmental impacts, since much of the energy used by human being is through the fossil fuels burning, which leds to greenhouse gases emission. This adverse scenario provides concerns about air pollution and climate change. In this context, the present work consisted in the mathematical modeling development to investigate the behavior of kinetics, equilibrium, and the influence of operational variables on the performance of the adsorption of H2S in fixed bed columns, as well as to model and simulate its dynamic behavior adjusted to experimental lab-scale data coupled with analysis of the influence of operational parameters, aiming to establish optimal operating conditions to support the dimensioning and scale-up of biogas desulfurization units. Therefore, lab-scale experimental data aimind to obtain equilibrium and kinetic data from the Langmuir isotherm (qmax (45°C) = 23.903 mg g-1; qmax (25°C) = 146.5 mg g-1 and b = 12.372 L mg-1), were obtained in a fixed bed adsorption column equipment present at Labmater-UFPR, linked to the ANEEL/COPEL PD RD&I UFPR 80-2018 project. Afterwards, for the description of the mass transfer rate, three models were considered assuming different mechanisms as the controlling step of mass transfer, namely, two diffusional models: Resistance to external mass transfer – Diffusion in the film; Resistance to internal mass transfer – Intraparticle diffusion using the LDF approach; and a reaction model: Adsorption at the adsorbent sites. From this, the following operational parameters were evaluated: Feed concentration of H2S in the bed (C0) in 50 ppmmol, 500 ppmmol and 1000 ppmmol; volumetric feed rate (Q) in 300 mL min-1, 1050 mL min-1 and 1800 mL min-1; Temperature (T) at 25 °C, 35 °C and 45 °C). From the obtained models’ fitting performance results, it was identified that the LDF model was the one that best described phenomenologically the fixed bed adsorption process. In this way, different simulations were carried out with the actual operating data of the adsorption column present in the Entre Rios do Oeste Biogas Mini Power Plant, varying the following parameters: C0 (191 ppmmol, 769.1 ppmmol and 1773 ppmmol); Q (187.50 L min-1,656, 25 L min-1 and 1125 L min-1); Bed height (HB) (82 cm and 164 cm). In addition, on large scale (real scale), sudden variations in H2S concentration and feed flowrate were identified as critical variables, because despite being variables that are not easily controllable and intrinsic to the process, these (especially in some non-ideal bed operation configurations) caused a “concentration peak” effect, impairing the bed’s efficiency. In this sense, in order to avoid underutilization of the bed and to overcome such problems, the best operating parameters for the bed were found from the simulations (C0 = 769 ppmmol, Q = 656.25 L min-1 and HB = 164 cm), but due to the increase in the pressure drop (head loss) in the bed caused by the increase in the bed length, it is also suggested to operate in parallel by using two beds with the same optimal parameters, however, considering half the flowrate (Q = 328.13 L min-1). In general, through the development of this work, the phenomenological mathematical modeling stood out as a powerful tool in the analysis and optimization of processes, in which, through simulations of different operating conditions or configurations, it allowed the optimization of these, the identification of possible operational problems (through reduced experimentation), and the scale-up of the process, which was confronted and validated based on real operating data. |
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