Publications by authors named "Xiu-tao Zhao"
Based on the prototype experiment of treating herb wastewater by Up flow Anaerobic Sludge Bed and Anaerobic Filter reactor (UASBAF), an artificial neural network (ANN) model which adopts a back propagation algorithm with momentum and adaptive learning rate was established. And the effect of each parameter to the performance of the reactor was compared, using the method of partitioning connection weights (PCW). The result is pH values>influent of chemical oxygen demand (COD)>hydraulic retention time (HRT)>alkalinity.
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- The experiment utilized anaerobic fermentation to treat kitchen waste and produce hydrogen, revealing a shift from mixed acid to ethanol fermentation after 22 days.
- The highest hydrogen production efficiency achieved was 4.77 LH(2)/(L reactor d) under specific operational conditions, including an organic loading rate of 32-50 kg COD/(m³ d) and a temperature around 35°C.
- An artificial neural network model indicated that the most significant factors affecting hydrogen yield were organic loading rate, pH, oxidation reduction potential, and alkalinity, with organic loading rate having the greatest influence; the production cost of hydrogen was found to be lower than that of water electrolysis.