Navegando por Assunto "Metodologia de superfície de resposta"

Agora exibindo 1 - 3 de 3

Acesso aberto (Open Access)
Ethanolysis optimisation of Jupati (Raphia taedigera Mart.) oil to biodiesel using response surface methodology
(2015-07) CONCEIÇÃO, Leyvison Rafael Vieira da; COSTA, Carlos Emmerson Ferreira da; ROCHA FILHO, Geraldo Narciso da; PEREIRA FILHO, Edenir Rodrigues; ZAMIAN, José Roberto
In this work, the transesterification of jupati (Raphia taedigera Mart.) oil using ethanol and acid catalyst was examined. The production of biodiesel was performed using a central composite design (CCD). A range of values for catalyst concentration (1 to 4.21%), temperature (70-80 °C), and the molar ratio of alcohol to oil (6:1-13.83:1) were tested, and ester content, viscosity, and yield were the response variables. The synthesis process was optimised using response surface methodology (RSM), resulting in the following optimal conditions for the production of jupati ethyl esters: a catalyst concentration of 3.85% at 80 °C and an alcohol-to-oil molar ratio of 10:1.
Acesso aberto (Open Access)
Identificação de sistemas multiforças a partir de dados de vibração e técnicas de aprendizado de máquinas
(Universidade Federal do Pará, 2024-11-07) PINHEIRO, Giovanni de Souza; NUNES, Marcus Vinícius Alves; http://lattes.cnpq.br/9533143193581447
The emergence of defects in dynamic components tends to produce changes in the forces generated, which can be detected through alterations in the vibration response spectrum of the equipment. Understanding the forces acting on a structure is extremely important, especially in cases where measurement points are limited or inaccessible, as it allows for assessing, among other things, whether the component's lifespan is compromised by the current condition of the machine. In such cases, an inverse problem needs to be solved. Machine Learning techniques have been standing out as a powerful tool for prediction among the solutions developed for this type of problem, being increasingly applied to engineering problems. Therefore, this work aims to evaluate different machine learning models for the identification of a system, composed of a suspended plate with one or more applied forces, based on measured vibration data. In this regard, a computational model was generated and calibrated using vibration responses measured in the laboratory. A robust database was created using Response Surface Methodology together with the Design of Experiment (DOE) and then used to assess the ability of machine learning models to predict the location, excitation frequency, magnitude, and number of forces acting on the structure. Among the six machine learning models evaluated, k-NN was able to predict with an error of 0.013%, and random forests showed a maximum error of 0.2%. Finally, a database, containing a line of experimental data, was used to evaluate the k-NN and Random Forest models, obtaining a score of 0.96 and 0.93, respectively. The innovation of the study lies in the application of the proposed method for parameter identification in multiforce systems.
Acesso aberto (Open Access)
Optimization of annatto (Bixa orellana L.) drying in fixed bed
(2000-12) FARIA, Lênio José Guerreiro de; ROCHA, Sandra Cristina dos Santos
The drying of annatto seeds (Bixa orellana L.), red piave cultivate, was studied in a fixed bed dryer. The best conditions were estimated to minimize the loss of coloring and to obtain final moisture of the seeds in appropriate levels to its conservation and maintenance of quality. The quantification of the influence of entrance variables in the final contents of bixin and moisture seeds and the identification of the optimal point was performed through the techniques of factorial design, response surfaces methodology, canonical analysis and desirability function. It was verified that the final moisture of the seeds may be estimated by a second-order polynomial model and that the final content of bixin is only significantly influenced by the time of drying being described properly by a linear model, for the seeds used in this study.