Freitas, Júlio Cézar de OliveiraOliveira, José Francinaldo de2019-02-132019-02-132015-07-09OLIVEIRA, José Francinaldo de. Concepção de um modelo teórico “predição da resistência a compressão em cimentos Portland utilizando modelagem estatística”. 2015. 67f. Tese (Doutorado em Química) - Centro de Ciências Exatas e da Terra, Universidade Federal do Rio Grande do Norte, Natal, 2015.https://repositorio.ufrn.br/jspui/handle/123456789/26631Understand the physical and chemical properties of a given material is the key to a quality production, that meets the expectations or even norms established by normatizadoras. Portland cement production is a complex process that involves several steps during manufacturing. Endless modifications and additions can be made to this process, and when it comes to high-volume production errors during manufacture can happen and should be eliminated to ensure success on quality of production. During the preparation of the clinker, all components of flour are stoichiometrically balanced to ensure an appropriate composition of the phases after their merger and cooling. Before shipment, the cement is specified through regulations to determine their compliance with the application. If the application is aimed at oil wells, the cement should have left here for heology tests, compressive strength, take time and free water. This tests require time and qualified personnel to be carried out. A statistical model is about the simplified representation of reality, based on experimental data statistics is able to simulate and predict results. The use of mathematical and statistical models are a valuable alternative in the optimization of production processes, reliable simulations will facilitate the production of a desired features reliable material in a shorter time span. This work aims at making the use of experimental data for a statistical model able to predict resistance to compression, taking as input data the main constituents of Portland cement, C3S mineral, C3A and C4AF + 2 * C3A. The application of the model resulted in answers with values close to experimental, others with greater divergence, the size of the universe used as input data proved a limiting factor in the accuracy of our results where we had a discrepancy that can be assigned to the volume of the universe used, the divergence showed a minimum of variation value of 4% and the largest of 21%.Acesso AbertoCimento PortlandModelagem estatísticaAnálise multivariadaResistência à compressãodoctoralThesisCNPQ::CIENCIAS EXATAS E DA TERRA::QUIMICA