969 resultados para 710100 Electricity, Gas and Water Services and Utilities


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Molecular and isotopic measurements of gas and water obtained from a gas hydrate at Site 570, DSDP Leg 84, are reported. The hydrate appeared to be Structure I and was composed of a solid framework of water molecules enclosing methane and small amounts of ethane and carbon dioxide. Carbon isotopic values for the hydrate-bound methane, ethane, and carbon dioxide were -41 to about -44, -27, and -2.9 per mil, respectively. The d13C-C1 values are consistent with void gas values that were determined to have a biogenic source. A significant thermogenic source was discounted because of high C1/C2 ratios and because the d13C-CO2 values in these sections were also anomalously heavy (or more positive) isotopically, suggesting that the methane was formed biogenically by reduction of heavy CO2 . The isotopically heavy hydrate d13C-C2 is also similar to void gas isotopic compositions and is either a result of low-temperature diagenesis producing heavy C2 in these immature sediment sections or upward migration of deeper thermogenic gas. The salinity of the hydrate water was 2.6 per mil with dDH2O and d18OH2O values of +1 and +2.2 per mil, respectively.

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Mode of access: Internet.

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This paper presents some forecasting techniques for energy demand and price prediction, one day ahead. These techniques combine wavelet transform (WT) with fixed and adaptive machine learning/time series models (multi-layer perceptron (MLP), radial basis functions, linear regression, or GARCH). To create an adaptive model, we use an extended Kalman filter or particle filter to update the parameters continuously on the test set. The adaptive GARCH model is a new contribution, broadening the applicability of GARCH methods. We empirically compared two approaches of combining the WT with prediction models: multicomponent forecasts and direct forecasts. These techniques are applied to large sets of real data (both stationary and non-stationary) from the UK energy markets, so as to provide comparative results that are statistically stronger than those previously reported. The results showed that the forecasting accuracy is significantly improved by using the WT and adaptive models. The best models on the electricity demand/gas price forecast are the adaptive MLP/GARCH with the multicomponent forecast; their MSEs are 0.02314 and 0.15384 respectively.

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Produced water constitutes the largest volume of waste from offshore oil and gas operations and is composed of a wide range of organic and inorganic compounds. Although treatment processes have to meet strict oil in water regulations, the definition of “oil” is a function of the analysis process and may include aliphatic hydrocarbons which have limited environmental impact due to degradability whilst ignoring problematic dissolved petroleum species. This thesis presents the partitioning behavior of oil in produced water as a function of temperature and salinity to identify compounds of environmental concern. Phenol, p-cresol, and 4-tert-butylphenol were studied because of their xenoestrogenic power; other compounds studied are polycyclic aromatic hydrocarbon PAHs which include naphthalene, fluorene, phenanthrene, and pyrene. Partitioning experiments were carried out in an Innova incubator for 48 hours, temperature was varied from 4゚C to 70゚C, and two salinity levels of 46.8‰ and 66.8‰ were studied. Results obtained showed that the dispersed oil concentration in the water reduces with settling time and equilibrium was attained at 48 h settling time. Polycyclic aromatic hydrocarbons (PAHs) partitions based on dispersed oil concentration whereas phenols are not significantly affected by dispersed oil concentration. Higher temperature favors partitioning of PAHs into the water phase. Salinity has negligible effect on partitioning pattern of phenols and PAHs studied. Simulation results obtained from the Aspen HYSYS model shows that temperature and oil droplet distribution greatly influences the efficiency of produced water treatment system.