Marcelo Medre Nobrega

Mixture design applied for the study of the tartaric acid effect on starch/polyester films.

Tartaric acid (TA), a dicarboxylic acid, can act as a compatibiliser in starch/polyester blends. A mixture design was proposed to evaluate the effect of TA on the properties of starch/poly (butylene adipate co-terephthalate) (PBAT) blown films plasticised with glycerol. The interaction between the starch/PBAT and the TA has a positive effect on the tensile strength and puncture force. Additionally, greater proportions of TA increased Youngs modulus. The starch+PBAT/TA and Gly/TA interactions contributed to a reduction in the water vapour permeability of the films. The inclusion of TA did not change the crystallinity of the samples. Formulations with intermediate proportions of TA (0.8 g/100 g) were shown to produce the best compatibilising effect. This was observed by DMA analysis as a consequence of the perfect equilibrium between the contributions of TA as a compatibiliser and in the acidolysis of starch, resulting in films with a tensile strength of 5.93 MPa, a possible alternative to non-biodegradable packaging.

Modelagem e simulação da desidratação osmótica em pedaços de abacaxi utilizando o método de elementos finitos

Water loss and sugar gain were modelling during the osmotic dehydration process of pieces of pineaplle. The transfer of solute to the fruit and the water to the solution was based on Ficks 2nd law. The three dimensional model was solved by the finite element method with the usage of the software COMSOL Multiphysics 3.2. The main and cross diffusion coefficients and the Biot number were determined on the simulation and the deviation between the experimental and the simulated data were 4,28% to sucrose and 1,66 to the water.

Aplicação de redes neurais artificiais na identificação de gasolinas adulteradas comercializadas na região de Londrina - Paraná

The multilayer perceptron network was used to classify the gasoline. The main parameters used in the classification were established by the Ordinance no 309 of the Agencia Nacional do Petroleo, but without informing the network the legal limits of these parameters. The network used had 10 neurons in a single hidden layer, learning rate of 0.04 and 250 training epochs. The application of artificial neural network served classify 100% of the commercialized gas in the region of Londrina-PR and to identify the tampered gasoline even those suspected of tampering.

An artificial neural network model for the prediction of mechanical and barrier properties of biodegradable films

Nowadays, the production of biodegradable starch-based films is of great interest because of the growing environmental concerns regarding pollution and the need to reduce dependence on the plastics industry. A broad view of the role of different components, added to starch-based films to improve their properties, is required to guide the future development. The self-organizing maps (SOMs) provide comparisons that initially were complicated due to the large volume of the data. Furthermore, the construction of a model capable of predicting the mechanical and barrier properties of these films will accelerate the development of films with improved characteristics. The water vapor permeability (WVP) analysis using the SOM algorithm showed that the presence of glycerol is very important for films with low amounts of poly (butylene adipate co-terephthalate) and confirms the role of the equilibrium relative humidity in the determination of WVP. Considering the mechanical properties, the SOM analysis emphasizes the important role of poly (butylene adipate co-terephthalate) in thermoplastic starch based films. The properties of biodegradable films were predicted and optimized by using a multilayer perceptron coupled with a genetic algorithm, presenting a great correlation between the experimental and theoretical values with a maximum error of 24%. To improve the response of the model and to ensure the compatibility of the components more information will be necessary.