A combined process of catalytic ozonation in the presence of a novel heterogeneous catalyst and biological activated carbon was investigated for the removal of priority control organic pollutants, the reduction of gen...A combined process of catalytic ozonation in the presence of a novel heterogeneous catalyst and biological activated carbon was investigated for the removal of priority control organic pollutants, the reduction of genotoxicity, and the improvement of biodegradable dissolved organic carbon (BDOC). Results confirm that the catalytic ozonation has higher effectiveness for the removal of refractory harmful organic pollutants, the reduction of genotoxicity and the increase of bio-degradability of organics than ozonation alone, which results in lower pollution load for subsequent biological activated carbon process, and then leads to less organic pollutants penetrating biological activated carbon. The novel catalytic ozonation with this combined process exhibits excellent performance to guarantee the safety of drinking water.展开更多
In the context of new risks and threats associated to nuclear, biological and chemical （NBC） attacks, and given the shortcomings of certain analytical methods such as principal component analysis （PCA）, a neural n...In the context of new risks and threats associated to nuclear, biological and chemical （NBC） attacks, and given the shortcomings of certain analytical methods such as principal component analysis （PCA）, a neural network approach seems to be more accurate. PCA consists in projecting the spectrum of a gas collected from a remote sensing system in, firstly, a three-dimensional space, then in a two-dimensional one using a model of Multi-Layer Perceptron based neural network. It adopts during the learning process, the back propagation algorithm of the gradient, in which the mean square error output is continuously calculated and compared to the input until it reaches a minimal threshold value. This aims to correct the synaptic weights of the network. So, the Artificial Neural Network （ANN） tends to be more efficient in the classification process. This paper emphasizes the contribution of the ANN method in the spectral data processing, classification and identification and in addition, its fast convergence during the back propagation of the gradient.展开更多
基金Sponsored by the National High Technology Research and Development Program (863) of China(Grant No. 2006AA06Z306)the National Natural Science Foundation of China(Grant No.50578051)
文摘A combined process of catalytic ozonation in the presence of a novel heterogeneous catalyst and biological activated carbon was investigated for the removal of priority control organic pollutants, the reduction of genotoxicity, and the improvement of biodegradable dissolved organic carbon (BDOC). Results confirm that the catalytic ozonation has higher effectiveness for the removal of refractory harmful organic pollutants, the reduction of genotoxicity and the increase of bio-degradability of organics than ozonation alone, which results in lower pollution load for subsequent biological activated carbon process, and then leads to less organic pollutants penetrating biological activated carbon. The novel catalytic ozonation with this combined process exhibits excellent performance to guarantee the safety of drinking water.
文摘In the context of new risks and threats associated to nuclear, biological and chemical （NBC） attacks, and given the shortcomings of certain analytical methods such as principal component analysis （PCA）, a neural network approach seems to be more accurate. PCA consists in projecting the spectrum of a gas collected from a remote sensing system in, firstly, a three-dimensional space, then in a two-dimensional one using a model of Multi-Layer Perceptron based neural network. It adopts during the learning process, the back propagation algorithm of the gradient, in which the mean square error output is continuously calculated and compared to the input until it reaches a minimal threshold value. This aims to correct the synaptic weights of the network. So, the Artificial Neural Network （ANN） tends to be more efficient in the classification process. This paper emphasizes the contribution of the ANN method in the spectral data processing, classification and identification and in addition, its fast convergence during the back propagation of the gradient.
