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31.
Scum formation is a widespread problem in activated sludge nutrient removal plants. It often comes along with an excessive development of the filamentous bacterium “Microthrix parvicella” stabilizing the flotation process. As “M. parvicella” was found to depend on long‐chain fatty acids (LCFA) as sole carbon source not only in vitro but also in situ, some options of in‐situ substrate supply are discussed. Wastewater concentrations of fatty acids in the range of 2 to 15 mg L‐1 and homologue concentrations from synthetic surfactant degradation below 10 mg L‐1 rule out these substrates as source for excessive biomass production. They might, however, well be suitable for start‐up of a “M. parvicella” population. Build‐up of excessive biomass might rely on fatty acid supply originating in cell walls of lysed stationary phase bacteria of long residence time sludge fractions such as scum layers. Moreover, biogenic surfactants such as rhamnolipids have been proved to be an excellent carbon source for excessive biomass production in vitro. 相似文献
32.
Knowledge about saturation and pressure distributions in a reservoir can help in determining an optimal drainage pattern, and in deciding on optimal well designs to reduce risks of blow‐outs and damage to production equipment. By analyzing time‐lapse PP AVO or time‐lapse multicomponent seismic data, it is possible to separate the effects of production related saturation and pressure changes on seismic data. To be able to utilize information about saturation and pressure distributions in reservoir model building and simulation, information about uncertainty in the estimates is useful. In this paper we present a method to estimate changes in saturation and pressure from time‐lapse multicomponent seismic data using a Bayesian estimation technique. Results of the estimations will be probability density functions (pdfs), giving immediate information about both parameter values and uncertainties. Linearized rock physical models are linked to the changes in saturation and pressure in the prior probability distribution. The relationship between the elastic parameters and the measured seismic data is described in the likelihood model. By assuming Gaussian distributed prior uncertainties the posterior distribution of the saturation and pressure changes can be calculated analytically. Results from tests on synthetic seismic data show that this method produces more precise estimates of changes in effective pressure than a similar methodology based on only PP AVO time‐lapse seismic data. This indicates that additional information about S‐waves obtained from converted‐wave seismic data is useful for obtaining reliable information about the pressure change distribution. 相似文献