In the present work, a strain of the basidiomycete fungus Trametes polyzona was used to decolorize the Amaranth dye. The decolorization was carried out in an Airlift reactor with three flow regimes: 1, 2, and 3 vvm. The results showed that the decolorization was a function of the flow regime. The decolorization times for the regimes of 1, 2, and 3 vvm were 30, 25, and 19 days, respectively. The COD (Chemical Oxygen Demand) decreased from 1600 to 72 mg COD/L. The enzymatic activity kinetics of laccase (Lcc), lignin peroxidase (LiP), and manganese peroxidase (MnP) were determined. In all the treatments, the enzyme LiP was expressed during the first 6 days, at which point 80% decolorization was observed, whereas Lcc and MnP enzymes were produced from day 6 until the end of the decolorization process. The effluent generated showed no inhibitory effects on the growth of the algae Nannochloropsis salina. T. polyzona showed great versatility in the decolorization of synthetic effluents containing the Amaranth dye, and the fungus was able to use this dye as its only carbon source starting at the beginning of the process. LiP was the enzyme that contributed the most to the decolorization process, and on average, 95% decreases in color and the COD were observed.
Barley straw is a lignocellulosic biomass that can be used to obtain value-added products for industrial applications. Barley straw hydrolysis with sodium sulfite facilitates the production of lignosulfonates. In this work, the delignification process of barley straw by solubilizing lignin through sulfite method was studied. Response surface methodology and artificial neural network were used to develop predictive models for simulation and optimization of delignification process of barley straw. The influence of parameters over sulfite concentration (1.0 to 10.0%), particle size (8 to 20), and reaction time (30 to 90 min) on total percentage of solubilized material was investigated through a three level three factor (3 3 ) full factorial central composite design with the help of Matlab® ver. 8.1. The results show that particle size and sulfite concentration have the most significant effect on delignification process. Both techniques, response surface methodology and artificial neural networks, predicted the lignosulfonate yield adequately, although the artificial neural network technique produced a better fit (R 2 = 0.9825) against the response surface methodology (R 2 = 0.9290). Based on these findings, this study can be used as a guide to forecast the potential production of lignosulfonates from barley straw using different experimental conditions.
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