The research on Constructed Soil Rapid Infiltration(CSRI) system is in its infancy at home and abroad.There are several details about the mechanism and application of CSRI system needed to be further studied.A major l...The research on Constructed Soil Rapid Infiltration(CSRI) system is in its infancy at home and abroad.There are several details about the mechanism and application of CSRI system needed to be further studied.A major limitation in the current research is the absence of degradation dynamics of pollutants,and the height of filtration bed in CSRI system currently determined by empirical judgment lacks accuracy and logicality.To solve these two prob-lems,the soil column of CSRI system was utilized to treat domestic wastewater,meanwhile,the NH3-N degradation dynamics were studied according to the Monod equation,the research of Mann A T and the NH3-N degradation law.Then the mathematical model of filtration bed height was built based on NH3-N degradation dynamics equation in the soil column.It has been proven that within a limited range this model can calculate the appropriate height of filtration bed accurately in order to optimize technological parameters of hydraulic load and the concentration of influent NH3-N,improving the effluent quality of CSRI system.展开更多
One of the problems encountered in the operation of a leachate treatment in a landfill is the quantity of the fluctuating leachate. Therefore, information on the precise prediction about the quantity of leachate produ...One of the problems encountered in the operation of a leachate treatment in a landfill is the quantity of the fluctuating leachate. Therefore, information on the precise prediction about the quantity of leachate produced in a landfill is required. This information can be obtained by using an ANN （artificial neural networks） model. In this study, a prediction on a leachate generation for a period of 15 days was made. The input for the ANN model consists of data such as rainfall, temperature, humidity, duration of solar radiation, and the landfill characteristics, while the output is the leachate landfills production in Minamiashigara, Japan. The ANN algorithm uses a BP （back propagation） with LM （Levenberg-Marquadrt） training type. By using the input-output data pairs, the training of ANN model was conducted in order to obtain the values of the weights that describe the relationship between the input-output data. Furthermore, with the trained ANN model, the prediction of leachate generation for a period of 15 days was made. The study result shows that the prediction accuracy ofleachate generation of ANN-C model, with a correlation coefficient （r） of 0.924, is quite good. Thus, the prediction of leachate generation using artificial neural network model can be recommended for predicting leachate generation in the future. In this study, a prediction on a leachate generation for a period of 15 days was made. The quantity of leachate generation in a landfill can be obtained by using ANN for future periods. By entering data for future periods （t ＋1） in ANN models, the leachate generation for the period （t ＋1） can be predicted.展开更多
The shift in the percolation threshold of compressed composites was studied by a 3D continuum percolation model. A Monte Carlo (MC) method was employed in the simulations. The percolation threshold was found to rise w...The shift in the percolation threshold of compressed composites was studied by a 3D continuum percolation model. A Monte Carlo (MC) method was employed in the simulations. The percolation threshold was found to rise with the compression strain, which captures the basic trend in compression-induced conductivity variation from the experiments. Both fiber bending and texture formation contribute to the percolation threshold. The results suggest that fillers with a high aspect ratio are more desirable for sensor and electrical switch applications.展开更多
基金Under the auspices of Foundational Research Fund of Science Application in Sichuan Province (No. 05J029-098)
文摘The research on Constructed Soil Rapid Infiltration(CSRI) system is in its infancy at home and abroad.There are several details about the mechanism and application of CSRI system needed to be further studied.A major limitation in the current research is the absence of degradation dynamics of pollutants,and the height of filtration bed in CSRI system currently determined by empirical judgment lacks accuracy and logicality.To solve these two prob-lems,the soil column of CSRI system was utilized to treat domestic wastewater,meanwhile,the NH3-N degradation dynamics were studied according to the Monod equation,the research of Mann A T and the NH3-N degradation law.Then the mathematical model of filtration bed height was built based on NH3-N degradation dynamics equation in the soil column.It has been proven that within a limited range this model can calculate the appropriate height of filtration bed accurately in order to optimize technological parameters of hydraulic load and the concentration of influent NH3-N,improving the effluent quality of CSRI system.
文摘One of the problems encountered in the operation of a leachate treatment in a landfill is the quantity of the fluctuating leachate. Therefore, information on the precise prediction about the quantity of leachate produced in a landfill is required. This information can be obtained by using an ANN （artificial neural networks） model. In this study, a prediction on a leachate generation for a period of 15 days was made. The input for the ANN model consists of data such as rainfall, temperature, humidity, duration of solar radiation, and the landfill characteristics, while the output is the leachate landfills production in Minamiashigara, Japan. The ANN algorithm uses a BP （back propagation） with LM （Levenberg-Marquadrt） training type. By using the input-output data pairs, the training of ANN model was conducted in order to obtain the values of the weights that describe the relationship between the input-output data. Furthermore, with the trained ANN model, the prediction of leachate generation for a period of 15 days was made. The study result shows that the prediction accuracy ofleachate generation of ANN-C model, with a correlation coefficient （r） of 0.924, is quite good. Thus, the prediction of leachate generation using artificial neural network model can be recommended for predicting leachate generation in the future. In this study, a prediction on a leachate generation for a period of 15 days was made. The quantity of leachate generation in a landfill can be obtained by using ANN for future periods. By entering data for future periods （t ＋1） in ANN models, the leachate generation for the period （t ＋1） can be predicted.
基金Project supported by the National Natural Science Foundation of China (No 10832009)the National Basic Research Program (973) of China (No 2004CB619304)the Science Foundation of Chinese University (No 2009QNA4034)
文摘The shift in the percolation threshold of compressed composites was studied by a 3D continuum percolation model. A Monte Carlo (MC) method was employed in the simulations. The percolation threshold was found to rise with the compression strain, which captures the basic trend in compression-induced conductivity variation from the experiments. Both fiber bending and texture formation contribute to the percolation threshold. The results suggest that fillers with a high aspect ratio are more desirable for sensor and electrical switch applications.
