ABSTRACT
A novel advanced oxidation process (AOP) of ultraviolet/chlorite-ammonia (UV/NaClO2-NH4OH) was developed to remove Hg0 from flue gas. The distribution of mercury concentration in three solutions of NaClO2-NH4OH, KCl, and H2SO4-KMnO4 was determined by cold atom fluorescence spectrometry (AFS). The role of NH4OH was to help NaClO2 preserving and/or stabilizing Hg2+ meanwhile inhibiting the photo-production of ClO2. In the absence of UV, decreasing pH promoted the release of Hg2+ from NaClO2-NH4OH; introducing NO, SO2, O2, Br-, Cl-, and HCO3- suppressed Hg0 oxidation. In the presence of UV, rising temperature accelerated the release of Hg2+ from NaClO2-NH4OH; while SO2, Br- and HCO3- facilitated Hg0 oxidation. In the absence and presence of UV, Hg0 oxidation was controlled by ClO2- and by ClO/Cl2O2/HO/ClO2, respectively. The formations of ClO/HO/ClO2 were confirmed by electron spin resonance (ESR). X-ray photoelectron spectroscopy (XPS) revealed that the products of Hg0 and ClO2- were HgCl2, and ClO2, Cl-, ClO3-, Cl2, and ClO4-, respectively. Analysis of kinetics showed that the Hatta numbers were 23-133 and 69-305 without and with UV, respectively, thus, the gas-film mass transfer was the rate-determining step. This paper gives a new insight in radical behavior in Hg0 oxidation.
ABSTRACT
Artificial neural networks have been applied for predicting the hydrophobic parameters of alkylbenzene. Compared with traditional methods it has the advantages of simple operation and wide applications. Based on error back propagation neural networks the relationship among the molecular connectivity index (chi), van der Waals surface area (Aw) and hydrophobic parameter was studied, meanwhile the mathematical model was established and used to predict the hydrophobic parameters. By comparing the hydrophobic parameters of experimental values with those calculated by neural networks, we found they had good agreement. The average relative deviation was less than 1%. Because traditional back propagation network is generally time consuming, resilient backpropagation (RPROP) algorithm was used to solve this problem. By using RPROP algorithm, the hydrophobic parameters were obtained precisely by fast training and simple parameter's selection. It needed less than 1,000 iterations to reach the goal on the computer operated at 1.4 GHz. The present work shows that the artificial neural network is a new powerful tool to predict the physicochemical parameters.
