Impurity optimized silicon is needed for the advancement of terrestrial photovoltaic power generation. In this study the possibility of producing solar grade silicon from rice husks has been pursued. An integrated process flowsheet was developed and practiced that included initial leaching, reduction of Rice husk ash (RHA) and post-reduction purification of silicon. Metallothermic reduction of purified RHA with magnesium was investigated within the temperature range of 500-950 oC. The reduction product was purified by two stage acid leaching sequence. Analysis of the final silicon powder product by XRD and ICP-OES showed crystalline silicon with boron content to be less than 3ppm- corresponding to reduction by a factor greater than 10, whilst the phosphorus content was reduced by a factor of over 20 and reaching less than 73ppm. The effects of temperature, magnesium amount and leaching agents were optimized in this study. A one step test melting was also carried out to convert the silicon powder into silicon chunks.

Impurity optimized silicon is needed for the advancement of terrestrial photovoltaic power generation. In this study the possibility of producing solar grade silicon from rice husks has been pursued. An integrated process flowsheet was developed and practiced that included initial leaching, reduction of Rice husk ash (RHA) and post-reduction purification of silicon. Metallothermic reduction of purified RHA with magnesium was investigated within the temperature range of 500-950 oC. The reduction product was purified by two stage acid leaching sequence. Analysis of the final silicon powder product by XRD and ICP-OES showed crystalline silicon with boron content to be less than 3ppm- corresponding to reduction by a factor greater than 10, whilst the phosphorus content was reduced by a factor of over 20 and reaching less than 73ppm. The effects of temperature, magnesium amount and leaching agents were optimized in this study. A one step test melting was also carried out to convert the silicon powder into silicon chunks.

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-03-12T16:25:13Z No. of bitstreams: 1 ARIOSTON ARAÚJO DE MORAIS JÚNIOR - TESE PPGEQ 2015..pdf: 4075446 bytes, checksum: 58c7745e3f0cf289b5f5d0a0a774ed88 (MD5) / Made available in DSpace on 2018-03-12T16:25:13Z (GMT). No. of bitstreams: 1 ARIOSTON ARAÚJO DE MORAIS JÚNIOR - TESE PPGEQ 2015..pdf: 4075446 bytes, checksum: 58c7745e3f0cf289b5f5d0a0a774ed88 (MD5) Previous issue date: 2015-11-06 / Neste trabalho é proposta uma metodologia para construção de sensores virtuais implementados em software, com objetivo de estimar e prever o comportamento de impurezas na corrente de base de uma coluna de destilação de alta pureza, do processo produtivo do 1,2 Dicloroetano (C2H4Cl2). A aquisição dos dados utilizados na construção dos sensores virtuais foi realizada através do modelo matemático do processo, simulado com dados reais de uma planta industrial. O estudo específico engloba a modelagem matemática/termodinâmica e avaliação do comportamento estacionário e dinâmico dessa torre, simulada aqui no software Aspen Plus e DynamicsTM. Desse modo, o modelo fornece os dados necessários para inferência das impurezas relacionadas, que são os teores dos compostos tetracloreto de carbono (CCl4) e clorofórmio (CHCl3), ambos devem ser mantidos, respectivamente, em valores ≤3000 e ≤400 ppm (partes por milhão). A metodologia também aborda dois algoritmos de seleção de variáveis secundárias, que utilizam técnicas estatísticas multivariadas (algoritmo de todas as regressões possíveis-TRP e da análise de componentes principais-PCA). Verifica-se também nos dados gerados quanto a real ou não necessidade de remoção de erros grosseiros (outliers), por isso é também inserida na metodologia uma etapa de pré-processamento de dados. Foram selecionados os dez melhores modelos de inferência para cada uma das saídas. Diante dessa informação, os melhores modelos produzidos não utilizavam as concentrações dos compostos das correntes de alimentação e sim medições de temperaturas ao longo da torre. Uma importante conclusão do ponto de vista de construção de sensores virtuais, porque na maioria dos trabalhos desenvolvidos essas variáveis são cruciais na produção de bons resultados. O treinamento dos sensores virtuais foi efetuado em um ambiente ruidoso, haja vista que foram simulados ruídos inerentes às medições (ruídos brancos Gaussianos). Na etapa final, os sensores virtuais são construídos utilizando uma técnica de modelagem empírica, redes neurais artificiais (RNA), onde foram utilizadas RNA do tipo Perceptron Multicamadas (MLP). Foram também avaliadas diversas variações quanto ao número de neurônios e camadas ocultas das RNA, empregando como critério de parada a técnica de validação cruzada. Os sensores virtuais desenvolvidos apresentaram erros satisfatórios do ponto de vista de engenharia, uma boa análise de regressão e um bom erro médio quadrático. Logo, com essas estimativas espera-se a minimização e a previsão do comportamento transiente dos compostos no referido processo. / This work proposes a methodology for software implementation to make soft sensors. The goal is to estimate and predict the behavior of impurities in the bottom current of a highpurity distillation column, for 1,2-Dichloroethane or 1,2-DCE (C2H4Cl2) production. The data acquisition used in the construction of soft sensors was performed through a mathematical simulation of the process, with real industrial data taken from an industrial plant. A specific study involves the mathematical modeling, thermodynamics, evaluation of the steady state and the dynamic behavior of this process, simulated here in Aspen Plus and Aspen DynamicsTM software. Thus, the model provides the necessary data to infer the contents of the carbon impurities mentioned above, Tetrachloride (CCl4) and Chloroform (CHCl3), which are to be fixed approximately below 3000 and 400 ppm (parts per million) respectively. The methodology also covers selection algorithms of secondary variables, using multivariate statistical techniques: All Possible Regressions (TRP) and Principal Component Analysis (PCA). The data generated was checked in order to know whether to include or not a step for removal of outliers, so it was also included in the methodology one preprocessing data step. The ten best inference models were selected for each output concentration. With this information, these models do not use concentrations measurements in the feed streams but measurements of the temperature along the column. This is an important conclusion from the point of view of virtual sensors building, because in most of the literature reported these variables are crucial in getting good results. The training of soft sensors was done in a noisy environment, considering that simulated noise was inherent to measurements (Gaussian noise). In the final step, the soft sensors devices are constructed using an empirical modeling technique of artificial neural networks (ANN), which were generated ANN type Multilayer Perceptron (MLP). Several variations were also evaluated on the number of neurons and hidden layers of networks, employing as a stopping criterion the cross-validation technique. The developed soft sensors presented satisfactory errors from the engineering viewpoint, a good regression and a good mean square error. Finally, with these estimations it is expected to minimize and predict the transient behavior of the compounds in the referred process.

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Colunas de destilação de alta pureza são sistemas de difícil controle. Apresentam longo tempo de resposta, comportamento altamente não linear e grande interação entre as variáveis. Os controladores preditivos são muito utilizados para o controle de colunas de destilação. Porém, em colunas de alta pureza, a incorporação de um único modelo linear geralmente acarreta em um controle de fraco desempenho. Isto pois, a representação do processo é deficiente, já que não considera variações nos ganhos e nas dinâmicas, típicas de um comportamento não linear. Estas incertezas podem, inclusive, provocar a instabilidade do controle o que resultaria em produtos que não atendam à especificação. Este trabalho tem por objetivo avaliar o desempenho dos controladores de horizonte de predição infinito com um modelo interno e com múltiplos modelos tendo o HYSYS(TM) como simulador de uma coluna de separação benzeno/tolueno e o MATLAB(TM) como ambiente para o controle supervisório. Observa-se que o controlador com apenas um modelo não é capaz de estabilizar o processo para perturbação nos valores de referência das variáveis controladas, ao contrário do controlador com múltiplos modelos. / High-purity distillation columns are systems which are typically difficult to control. The main reason for this is a strongly nonlinear and interactive system associated with a very sluggish response. Model Predictive Control is widely used for control of distillation columns. However, for high-purity columns, the use of a single linear model in the controller usually leads to a poor performance of the control system. The reason for this is the poor system representation, since variation in the system gains and time constants are not taken into account in the computation of the control law. Model uncertainties can produce instability in the control system and consequent deterioration of the product quality. The goal of this work is to evaluate the performance of infinite horizon MPC with a single internal model and with multiple models. HYSYS(TM) is used as simulator for the benzene/toluene column, and MATLAB(TM) is used as a platform for supervisory control. It is observed that the controller with a single model is not capable of stabilizing the process for disturbance in the set point of the controlled variables. Opposite to that behavior the controller with multiple models has a good performance.

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