Brewer's spent grain (BSG) is the main by‐product of the brewing industry. BSG can have diverse end‐users and has a high moisture level. To guarantee good conditions for storage and trade, it is necessary to remove the moisture from the material and use the proper method for the drying process. The phenomenon of water content removal is represented by mathematical models. Empirical and phenomenological models, as well as artificial neural networks (ANN) can be employed for this purpose. Here, we compare the fitted curves between empirical models (such as Page, Midilli–Kucuk, and Newton models) and an artificial neural network. The fits were investigated quantitatively by the analysis of the mean squared error (MSE) and determination coefficient (R2). The residues generated were analyzed qualitatively by the plots of histograms and qqplots (quantil vs. quantil). From the results that were obtained, it is possible to conclude that the ANN model had the best performance when compared to the empirical and semi‐empirical models studied, with the lowest values of MSE and the highest values of the R2 (0.999). The net presented adequate estimations for intermediate data as well, proving its use as a proper empirical model for the prediction of moisture data at several temperatures.
Carbon black is a high-interest industrial material due to its favorable characteristics and applications as nanoparticles. This substance is generated by combustion processes in diffusive or turbulent flames. Over the years, distinct models were developed and presented to model soot and carbon black formation kinetics in combustion chambers and reactors. One method to manufacture nanoparticles is the Flame Spray Pyrolysis process, with the advantage of offering a more controlled environment to tailor particle's properties. In this work, simulations of the FSP process are carried out considering the formation of carbon black nanoparticles. CFD simulations were performed approaching the continuous phase by an Eulerian framework and the dispersed phase (spray droplets) by a Lagrangian framework. A three-equation model is applied to predict the carbon black formation kinetics, and particle radiation is also considered. The injected fuel at the nozzle is composed of pure p-xylene. A 2D axisymmetric approach is considered to represent the enclosed FSP cylindrical reactor, and two different domains were investigated: with and without the surroundings of the reactor. Adiabatic and non-adiabatic wall cases are simulated to study the temperature and carbon black formation profiles. The influence of particle radiation is also analyzed. Results show that the insulated reactor (adiabatic wall) has a higher temperature profile along the reactor, affecting nucleation and oxidation rates of carbon black.
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