Egg hatchability prediction by multiple linear regression and artificial neural networks

An artificial neural network (ANN) was compared with a multiple linear regression statistical method to predict hatchability in an artificial incubation process. A feedforward neural network architecture was applied. Network trainings were made by the backpropagation algorithm based on data obtained from industrial incubations. The ANN model was chosen as it produced data that fit better the experimental data as compared to the multiple linear regression model, which used coefficients determined by minimum square method. The proposed simulation results of these approaches indicate that this ANN can be used for incubation performance prediction

Continuous polymerization in tubular reactors with prepolymerization: analysis using two-dimensional phenomenological model and hybrid model with neural networks

Continuous polymerization processes have advantages when large amounts of product are required; moreover, higher quality can be obtained because of the elimination of variability between batches. Tubular reactors are economically attractive because of their simple geometry and high heat exchange area; however, they are not commonly used for commercial purposes, mainly because of the large radial profiles. This study elucidates the operation of this kind of reactors in three different ways: first a detailed two-dimensional mathematical model was developed, in which a complete visualization of all axial and radial profiles is possible, allowing a safe analysis at different operating conditions. In a second step a system composed of a continuously stirred tank reactor in series with a tubular reactor was used. A reduction in radial profiles can be clearly observed when prepolymerization is taken into account, improving both the homogeneity and the end properties of the polymer. In a third approach neural networks (NNs) were used in parallel with a one-dimensional model. The objective of this study was to illustrate how NNs can improve the prediction capability when it is not possible to build a reliable model because of uncertainties in parameters and incomplete knowledge of the system. The NNs generated good results, showing that the hybrid model was able to accurately simulate the reactor, even when uncertainty in kinetic and diffusional parameters was imposed to the model. (C) 2003 Wiley Periodicals, Inc.91287188

Performance of reverse osmosis and nanofiltration membranes in the fractionation and retention of patchouli essential oil

Patchouli essential oil consists of over 24 different components. Patchoulol has been known for over a century as the most important component of this essential oil, being widely used in the perfumery and cosmetics industries. Recent research has demonstrated that another component of patchouli essential oil, alpha-bulnesene, has pharmaceutical properties, providing a decrease in thromboxane formation. In this study, three different membranes were evaluated in terms of their fractionation capability and retention of patchouli oil in supercritical media, aiming at the separation and concentration of the main oil components (patchoulol and a-bulnesene) and regeneration of CO2. The membranes tested showed good resistance under the experimental conditions used, but did not show good fractionation and concentration of the patchouli oil components. The reverse osmosis membrane gave the highest oil retention (0.95) and lowest reduction in the permeate flux of the CO2 in the presence of the essential oil

The green ocean amazon experiment (GOAMAZON2014/5) observes pollution affecting gases, aerosols, clouds, and rainfall over the rain forest

The susceptibility of air quality, weather, terrestrial ecosystems, and climate to human activities was investigated in a tropical environment