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This paper studies on the field application of microbial technology used in bioremediation of urban polluted river. Xuxi River, a heavily polluted urban river, had been maken the treatment as the research object. In this treatment, Xuxi River was directly added indigenous microorganism agent and microbiological accelerating agent into the river water and sediment in the special inoculation way, without artificial aeration and sediment dredging. After this treatment， the removal rates of COD, TP and NH3-N are up to 43%、56% and 58%, DO increases to 2mg/L, the Nemerow index reduces to 2.2 from 6.4, and a lot of water earthworms and a school of fish reappear. The results indicate that this microbial technology can effectively alleviate the pollution, promote the water quality, and restore the river ecosystem.

To complete the previously information issued on the feasibility study and some technical challenges identi-fied from the application of bacterial technology, this study presents another characteristics of numerical output as the bacterial growth is now also limited to ammonium nitrogen and water temperature. Based on the results obtained, it is found that the degradation of readily biodegradable COD will be much slower be-cause of lower bacterial growth. At certain period, the COD concentration will increase and be plotted higher later on compared to the model which is limited only to substrate and oxygen. Besides the ammonium nitro-gen, other parameters i.e. particulate products from COD decay and particulate degradable organic nitrogen will also increase soon after certain time. Considering the increase of ammonium nitrogen as it is also con-verted to nitrate nitrogen, it can be predicted that some algae may show up during the treatment processes. When the model is simulated under different water temperature, slower biodegradation process is presented at lower water temperature. Because the bacteria grow better at higher water temperature, more oxygen is then required. Finally, from this study, it is also identified that the artificial mixing and addition of oxygen at initial stage of treatment will considerably influence the restoration.

Correct calculation of sediment carrying capacity in natural rivers is of great significance to the simulation of sediment movement and river-bed deformation by mathematical model. Peak recognition support vector machines, an improved support vector machines, was proposed considering the complication and nonlinearity between sediment carrying capacity and its impact factors; peak recognition least square support vector machines sediment carrying capacity prediction model, which was based on chaos optimization, was built combining with accelerating chaos optimization against questions of support vector machines regression such as parameter optimization, training and test speed. The test data of 30 sets of water tanks with high, medium and low sediment concentrations were trained, and training values agreed well with measured values; four sets of test data were predicted by trained support vector machines model, and training values were pretty much the same with measured values. Theoretical analysis and experimental results show that sediment carrying capacity studying method based on peak recognition support vector machines is more accurate in predication and more reliable than common support vector machines and BP neural network.

Phosphorus is one of the most important nutrients required to support various kinds of biodegradation processes. As this particular nutrient is not included in the activated sludge model no. 1 (ASM1), this study extended this model in order to determine the fate of phosphorus during the biodegradation processes. When some of the kinetics parameters are modified using observed data from the restoration project of the Xuxi River in Wuxi City, China, from August 25 to 31 in 2009, the extended model shows excellent results. In order to obtain optimum values of coefficients of nitrogen and phosphorus, the mass fraction method was used to ensure that the final results were reasonable and practically relevant. The temporal distribution of the data calculated with the extended ASM1 approximates that of the observed data.