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One of the most common methods for controlling SO2 is the process of desulfurization using spray tower. Due to the large number of parameters to be evaluated and the process complexity, there are difficulties in the proposal of mathematical models to predict the removal efficiency and the gas phase volumetric mass transfer coefficient (kga). The aim of this study was to obtain an artificial neural network (ANN) to predict the removal efficiency and the kga in a spray tower for SO2 removal. The results showed that the choosing of the best model from the training and validation steps did not generate reliable results. The best structure was defined by analyzing the results of a simulation step, which used independent data. The best model was obtained with the structure 5-9-2, trained using the Levenberg–Marquardt algorithm with Bayesian Regularization and having the softmax and linear transfer functions in the hidden and output layers, respectively. This network presented an average error of 8.44% for the outlet SO2 concentration and 4.53% for the kga. This work showed that the use of neural networks is promising in the prediction of important variables in the processes of removal of air pollutants in spray towers.

The blackberry fruit ( Rubus spp.) presents an attractive color ranging from purple red to blue, due to the high content of bioactive compounds, such as anthocyanins. Bioactive compounds have antioxidant properties, acting as free radical scavengers and are also frequently linked to the prevention of cardiovascular and other chronic diseases. Due to native-form anthocyanins instability against environmental stress, bioactive compounds are not always as effective in improving human health as they could be. Therefore, the production of powders using adjuvants is a promising approach for encapsulating anthocyanins-rich pastes, in order to improve their stability and application. Thus, the aim of the present study was to evaluate the influence of adjuvants on the physical properties of the powder, and on anthocyanin content and antioxidant activity of the pastes and freeze-dried milk-blackberry pulp mixtures. The pastes formulated were of milk-blackberry pulp (25%:75%, (weight/weight ( w /w)) and of milk-blackberry pulp-adjuvant (25%:70%:5%, (w/w/w)), and the adjuvants employed were maltodextrins 10DE and 20DE and Capsul®. The addition of maltodextrin 20DE and Capsul® resulted in pastes with high anthocyanins content (451.6 ± 80.0 μg/g and 453.1 ± 26.0 μg/g, respectively), similar to the paste without adjuvants (568.6 ± 3.4 μg/g). Also, maltodextrin 20DE and Capsul® showed a high protection capacity of the anthocyanins present on the powders (around 96% of anthocyanins retention). When no adjuvants were used, the anthocyanins retention was very low (40.1%). The freeze-drying process maintained the antioxidant capacity of the bioactive compounds and the powders obtained showed good quality with low moisture content and hygroscopicity.