Ubirajara Coutinho Filho

Neural network for fractal dimension evolution

The coagulation/flocculation process is an essential step in drinking water treatment. The process of formation, growth, breakage and rearrangement of the formed aggregates is key to enhancing the understanding of the flocculation process. Artificial neural networks (ANNs) are a powerful technique, which can be used to model complex problems in several areas, such as water treatment. This work evaluated the evolution of the fractal dimension of aggregates obtained through ANN modeling in the coagulation/flocculation process conducted in high apparent color water (100 ± 5 PtCo), using alum as coagulant in dosages varying from 1 to 12 mg Al3+ L-1, and shear rates from 20 to 60 s-1 for flocculation times from 1 to 60 minutes. Based on raw data, the ANN model resulted in optimized condition of 9.5 mg Al3+ L-1 and pH 6.1, for color removal of 90.5%. For fractal dimension evolution, the ANN was able to represent from 95% to 99% of the results.

Biopolymer production using fungus Mucor racemosus Fresenius and glycerol as substrate

This study evaluated extracellular production of biopolymer using fungus Mucor racemosus Fresenius and glycerol as a carbon source. Initially employing conical flasks of 500 mL containing 100 mL of cultive medium with 0.18 ± 0.03 g.L–1 of microorganisms, the results showed that the best conditions of the variables studied were: initial concentration of glycerol 50 g.L–1, fermentation time of 96 h, inoculum cultivation time of 120 h, and aeration in two stages–the first 24 hours without aeration and 72 hours fermentation with aeration of 2 vvm and 2 g.L–1 of yeast extract. The experiments conducted in a Biostat B fermenter with a 2.0 L capacity that contained 1.0 L of medium showed production of 16.35 g.L–1 gum formed and 75% glycerol consumption. These conditions produced a biopolymer with the molecular weight and total sugar content of 4.607×106 g.mol–1 (Da) and 89.5%, respectively.

Evaluation of Water Consumption and Neuro-Fuzzy Model of the Detergent Leavings Kinetics’ Removal in a Clean in Place System

This work has focused on describing the water consumption and the Neuro-Fuzzy model of the detergent leavings kinetics’ removal of a CIP System, based on the pH measured. The plant dynamics has been identified for different operational conditions. A flowrate value of 10.5 L.min−1 has been proved to be effective in order to provide the minimum required rinse water volume to execute the stage of the CIP system, which means that it is possible to optimize the process reducing energy, water and steam consumption as well as the time of unused machinery bringing productivity gains. The obtained models, allowed the prediction of the system dynamics behavior. The results were validated when compared with the experimental data. Three triangular membership functions for the input data modeled accordingly the pH dynamics with an error of 0.011 when comparing the validation data and the obtained model.

Experimental Investigation about Rinse Water Consumption of a CIP Process Applied to a Shell and Tube Exchanger

This paper focused the CIP (clean in place) rinse stage, typically applied to the food industry. The dynamic behavior of residuals removal kinetics was studied, to obtain mathematical models that describe adequately the system. The optimization of water, detergent, etc., is the next goal. The obtained results show that the temperature influence can be neglected in comparison with the rinse water flow rate considering the cleaning process. The systems dynamics are non linear and were adequately represented by 1st order plus dead time transfer functions. The flow models obtained with this paper were validated versus experimental data and the results were close with respect to the last ones.