Diagnóstico computacional e otimização operacional da unidade de dessulfurização industrial semi-seco (FGD-SDA) em plantas de geração termelétricas

• Ano de defesa: 2019
• Autor(a) principal: Pereira, Andréa da Silva
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Palavras-chave em Português: Engenharia química; Processos químicos - Otimização; Modelagem matemática; Mathematical modeling; Process optimization
• Link de acesso: http://www.repositorio.ufc.br/handle/riufc/44505

The use of fossil fuels represents about 65% of the global electric energy generation and one estimates that 38% of this production is obtained from the coal. However, due to more rigorous environmental legislation and growing awareness about sulphurous compounds emissions on the atmosphere, many desulphurization technologies have emerged over the last decades, generating the necessity of heavy investments on R&D in order to access alternatives and more efficient solutions. This research project has as main objective to diagnose computationally and to optimize a semidry industrial desulphurization unit (FGD-SDA) in thermoelectric power plants to make them more efficient in the sulphur content lowering (SO2), aiming at a better performance in using lime (CaO) and additive (NH4NO3). The analysis using process modelling allows it to be evaluated by simulating experimentally non tested conditions, due to time or cost constraints in performing these tests in pilot unit built for this project. For this, a mathematical model was developed to represent the desulphurization, using a Spray Dry Absorber, in one-dimensional steady state flow. Three other models were as well developed to predict absorption/reaction of SOX, where the main difference is the location of the reaction front on the atomized droplet. To estimate the parameters of the models, the nonlinear least squares method (Levenberg-Marquardt) was implemented in Python and used in predictions. The mathematical models were submitted to validation in, order to evaluate their relevance in predicting the experimental data, and to statistical analysis, as alternative to discern the models employed. As main results, the model with central reaction front presented the best prediction to the desulphurization efficiency using a solution of Ca(OH)2 plus additive, with apparent experimental error of 6.8%. The optimization this model in order to maximize removal efficiency of SO2 is obtained at Ca(OH)2 and additive concentrations of 18% and 50% (CaO molar basis), respectively. The development of the software for the FGD treatment unit allows a preventive diagnosis of the unit once it is capable to estimate all the process currents, aiming at analysing operational conditions of the unit to maintain the SO2 emissions between environmental limits, using coal with higher levels of sulphur and or less concentrate solutions.