Reducing sugars produced from agro-industrial wastes by means of hydrolysis represent a promising alternative of chemicals and energy. Yet, large scale production still struggles with several factors involving process complexity, sugars degradation, corrosion, enzyme recyclability, and economic feasibility. More recently, sub and supercritical water hydrolysis has been reported for the production of reducing sugars as a readily available alternative to acid and enzymatic biomass hydrolysis. Accordingly, in this work, the results of batch and semicontinuous lab scale subcritical water hydrolysis experiments of agro-industrial wastes of pea pot and corn stover are discussed. Experiments were carried in the temperature range 250 to 300 °C, pressures up to 3650 psi, residence times up to 30 minutes in batch mode operation, or water flowrates up to 12 mL/min in semicontinuous mode operation. Produced sugars were assessed in the effluent of each experimental run by means of dinitrosalicilic acid method (DNS). A maximum total reducing sugar (TRS) yield of 21.8% was measured for batch pea pot subcritical water hydrolysis experiments at 300°C, 15 minutes, 3650 psi, and 1:6 biomass to water mass ratio. Semicontinuous subcritical water hydrolysis of corn stover showed a maximum TRS accumulated yield of 19% at 290 °C, 1500 psi, and water flowrate of 9 mL/min. The results showed the feasibility of producing reducing sugars from agro-industrial wastes currently discarded through subcritical hydrolysis.
The role of temperature in water is fundamental for the community aquatic dynamics once it regulates several processes on different scales. The spatial and temporal variability of water temperature can be assessed by satellite images, which allows a better understanding of ecosystems. In this work, we evaluated the surface temperature variation of Itapeva Lake, located in Rio Grande do Sul, Brazil, between 1985 and 2017, using MOD11A1 product and images Landsat 5, 7 and 8. An homogeneous seasonal variation pattern was identify between the two sensors used. The information provided by MODIS and Landsat has a coefficient R2 = 0.91 and RMSE = 2.32 ° C. The analysis between the Landsat series adjusted data and the original data allowed the smoothing of maximum and minimum temperatures of water, reducing biased records. Water temperature for the summer and autumn months increases, while for the winter season the regime decrease. However, the surface temperature response may be better understood by involving climatic variables in the study.
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