Application Self-organizing Map Type in a Study of the Profile of Gasoline C Commercialized in the Eastern and Northern Parana Regions

Artificial neural networks self-organizing map type (SOM) was used to classify samples of automotive gasoline C marketed in the eastern and northern regions of the state of Paraná, Brazil. The input order of parameters in the network were the values of temperature of the first drop, the 10, 50 and 90% distilled bulk, the final boiling point, density, residue content and alcohol content. A network with a topology of 25x25 and 5000 training epochs was used. The weight maps of input parameters for the trained network identified that the most important parameters for classifying samples were the temperature of the first drop and the temperature of the 10% and 50% of the distilled fuel. DOI: http://dx.doi.org/10.17807/orbital.v7i2.73

Application Self-organizing Map Type in a Study of the Profile of Gasoline C Commercialized in the Eastern and Northern Parana Regions

Artificial neural networks self-organizing map type (SOM) was used to classify samples of automotive gasoline C marketed in the eastern and northern regions of the state of Paraná, Brazil. The input order of parameters in the network were the values of temperature of the first drop, the 10, 50 and 90% distilled bulk, the final boiling point, density, residue content and alcohol content. A network with a topology of 25x25 and 5000 training epochs was used. The weight maps of input parameters for the trained network identified that the most important parameters for classifying samples were the temperature of the first drop and the temperature of the 10% and 50% of the distilled fuel. DOI: http://dx.doi.org/10.17807/orbital.v7i2.73

Influence of film coefficient during multicomponent diffusion – KCl/NaCl in biosolid for static and agitated system using 3D computational simulation

Abstract The influence of film coefficient formed during the diffusion of inorganic salts (NaCl and KCl) in biosolids was studied using a 3D computer modeling by Finite Elements Method (FEM) in COMSOL Multiphysics® software combined with SOM-type Artificial Neural Networks (ANN). Such tools have shown that the influence of the film formed in the biosolid/solution interface occurs in a heterogeneous manner and is due to the matrix geometry, the type of system (agitated or static) and the ion size (Na+ or K+). The influence of film coefficient was more pronounced for K+ ion, and for a static system. Comparing the geometry of biosolids, ion diffusion was more pronounced in the (Y±) axis in relation to other axes, X (±) and Z (±), as well as in between the poles (±) of this axis. FEM simulation associated with SOM-type ANN were efficient tools to evaluate this complex and unknown biophysical phenomenon

Application of self-organising maps towards segmentation of soybean samples by determination of inorganic compounds content

CAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIORBACKGROUND: In this study, 20 samples of soybean, both transgenic and conventional cultivars, which were planted in two different regions, Londrina and Ponta Grossa, both located at Parana, Brazil, were analysed. In order to verify whether the inorganic compound levels in soybeans varied with the region of planting, K, P, Ca, Mg, S, Zn, Mn, Fe, Cu and B contents were analysed by an artificial neural network self-organising map. RESULTS: It was observed that with a topology 10 x 10, 8000 epochs, initial learning rate of 0.1 and initial neighbourhood ratio of 4.5, the network was able to differentiate samples according to region of origin. Among all of the variables analysed by the artificial neural network, the elements Zn, Ca and Mn were those which most contributed to the classification of the samples. CONCLUSION: The results indicated that samples planted in these two regions differ in their mineral content; however, conventional and transgenic samples grown in the same region show no difference in mineral contents in the grain. (C) 2015 Society of Chemical IndustryIn this study, 20 samples of soybean, both transgenic and conventional cultivars, which were planted in two different regions, Londrina and Ponta Grossa, both located at Parana, Brazil, were analysed. In order to verify whether the inorganic compound levels in soybeans varied with the region of planting, K, P, Ca, Mg, S, Zn, Mn, Fe, Cu and B contents were analysed by an artificial neural network self-organising map. It was observed that with a topology 10 x 10, 8000 epochs, initial learning rate of 0.1 and initial neighbourhood ratio of 4.5, the network was able to differentiate samples according to region of origin. Among all of the variables analysed by the artificial neural network, the elements Zn, Ca and Mn were those which most contributed to the classification of the samples. The results indicated that samples planted in these two regions differ in their mineral content; however, conventional and transgenic samples grown in the same region show no difference in mineral contents in the grain.961306310CAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIORCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIORsem informaçã

<b>The use of multilayer perceptron artificial neural networks for the classification of ethanol samples by commercialization region

Samples of automotive ethanol, marketed in the northern and eastern regions of the state of Paraná, Brazil, underwent physical and chemical tests. Rates were assessed by Multilayer Perceptron (MLP) neural network for classification. For network training, two hundred epochs, a 0.05 learning rate and a random subdivision of samples in three groups with 70 for training, 15 for test and 15% for validation were employed. Sixty networks were trained from three different initializations. Three networks, one at each start-up, were highlighted and the one with the best performance presented 8 neurons in the hidden layer, with 95 accuracy training, 96 in the test and 96% in validation. The most important variables in classifications, identified by the network, occurred in the following order: alcohol content, density, pH and electrical conductivity. Application of MLP segmented ethanol samples and identified the commercialization regions

Application of the simplex-centroid design with process variable in the optimization of production conditions of B100 biodiesel from sunflower oil

O delineamento simplex-centroide foi aplicado para otimizar as condições de obtenção de biodiesel B100 de óleo de girassol, utilizando diferentes catalisadores, com metanol e etanol como variável de processo. A reação de transesterificação, usando metanol, indicou o metóxido de sódio como o melhor catalisador apresentando um rendimento de 98,30%. Usando etanol como variável de processo e KOH como catalisador, o rendimento da reação foi de somente 89,50%. Os ensaios com os produtos obtidos, nas condições ótimas, indicou que ele estavam de acordo com os parâmetros estabelecidos pela legislação Brasileira e pela União européia.A simplex-centroid design was applied to optimize conditions for obtaining B100 biodiesel from sunflower oil using different catalysts with methanol and ethanol as process variable. Sodium methoxide was indicated as the best catalyst in the transesterification reaction with methanol at a 98.30% yield. Reaction yield was optimized only to 89.65% when ethanol as process variable and KOH as catalyst were employed. Tests with the obtained products, in optimal conditions, indicated that they were within the parameters established by Brazilian legislation and by the European Union.

APPLICATION OF THE MULTIRESPONSE OPTIMISATION SIMPLEX METHOD TO THE BIODIESEL - B100 OBTAINING PROCESS

The process of obtaining B100 biodiesel from vegetable oil and animal fat mixtures by transesterification under basic conditions was optimised using the super-modified simplex method. For simultaneous optimisation, yield, cost, oxidative stability and Cold Filter Plugging Point (CFPP), were used as responses, and the limits were established according to the experimental data and the conformity parameters established by legislations. Based on the predictive equations obtained from the simplex-centroid design-coupled functions, the multi-response optimisation showed an optimal formulation containing 38.34 % soybean oil, 21.90 % beef tallow and 39.25 % poultry fat. The validation showed that there are no significant differences between the predicted and experimental values. The simplex-centroid mixture design and simplex optimisation methods were effective tools in obtaining biodiesel B100, using a mixture of different raw materials.</p