Utilização de rede neural artificial para controle de variáveis que causam encanoamento diagonal de papéis, em máquina industrial de papel cartão

• Ano de defesa: 2013
• Autor(a) principal: Schneid, Guinter Neutzling
• Orientador(a): Oliveira, Rubens Chaves de
• Banca de defesa: Silva, Vanessa Lopes
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Viçosa
• Programa de Pós-Graduação: Mestrado em Ciência Florestal
• Departamento: Manejo Florestal; Meio Ambiente e Conservação da Natureza; Silvicultura; Tecnologia e Utilização de
• País: BR
• Palavras-chave em Português: Celulose; Papel; Cartão; Qualidade; Rede neural artificial; Modelagem; Encanoamento diagonal
• Palavras-chave em Inglês: Cellulose; Paper; Card; Quality; Artificial neural network; Modeling; Diagonal cupping
• Área do conhecimento CNPq: CNPQ::CIENCIAS AGRARIAS::RECURSOS FLORESTAIS E ENGENHARIA FLORESTAL::TECNOLOGIA E UTILIZACAO DE PRODUTOS FLORESTAIS
• Link de acesso: http://locus.ufv.br/handle/123456789/3155

The dimensional stability of the paper may change due to exchange moisture middle, releasing the stress acquired in the manufacturing process. One result of this release of tension is the diagonal curl, or also known as twist. In the paper industry, laboratory measurements are important to ensure the final quality of the material delivered to the customer. Particularly in twist measurement, sampling are made at the end of each jumbo roll, demanding time for any corrections, by operation. With these assumptions, this study aims to do a sensitivity analysis of the different input variables of an industrial board machine, along with some laboratory measurements, related with the property of the paper called diagonal curl (twist) and propose artificial neural networks for, in the future, be used as a virtual sensor. Initially, a survey was made of the production history, to observe the product with the largest quality losses. From this, correlated with the critical points of measurement profile in the CD direction. Were made three artificial neural networks, Multi-Layer Perceptron (MLP) type to predict the three sectors of the profile of the paper machine. Being, the front side (FS), middle of profile (M) and drive side (DS). We found some changes in the order in which variables correlated with twist, from the sensitivity analysis, it was revealed that the most important variable and sensitive, respectively for the FS, MS and DS were total flow from the top layer headbox, vapor pressure in the 6th group and flow lateral mass of the paperboard bottom layer (Module Edge). The correlation coefficients for RNA's were 0.426, 0.557 and 0.597, respectively, in this order, for the sectors of the FS, M and DS. In conclusion, the results allow us to indicate that the models are able to represent the process.