In order to meet ambitious emission reduction goals in California, greenhouse gas emissions from direct combustion of fossil fuels for residential space and water heating will need to be largely eliminated. If those emission reductions come from fuel switching (natural gas to electricity) then the electricity grid needs to be prepared for the additional load. This paper presents a new method fo...

It is notable that over the past decade, proven reserves of natural gas have dramatically increased as higher prices and advances in technology have turned previously unrecoverable resources into major sources of domestic production. Moreover, the decline in crude oil reserves has significantly slowed over the past decade. Therefore, there would be an end to the conventional hydrocarbon resourc...

in this study a pseudo comprehensive carbon footprint model for fossil fuel power plants is presented. parameters which their effects are considered in this study include: plant type, fuel type, fuel transmission type, internal consumption of the plant, degradation, site ambient condition, transmission and distribution losses. investigating internal consumption, degradation and site ambient con...

in this paper, a model based on gmdh type neural network, is used to predict gas price in the spot market while using oil spot market price, gas spot market price, gas future market price, oil future market price and average temperature of the weather. the results suggest that gmdh neural network model, according to the root mean squared error (rmse) and direction statistics (dstat) statistics ...

This work aims to present a numerical analysis of the natural gas which flows through a high-pressure pipeline and an orifice plate, through the use of CFD methods. The paper contains CFD calculations for the flow of natural gas in a pipe with different geometry used for the orifice plates. One of them has a standard geometry and a shape without any deformation and the other is deformed by the ...

This paper proposes an effective model based on the least squares support vector machines (LSSVM) and the particle swarm optimization (PSO), termed PSO-LSSVM, for prediction of natural gas consumption, as an important energy resource. The salient feature of mapping nonlinear data into high dimension feature space, distinguishes LS-SVM as a powerful approach for forecasting and estimation. Optim...

Large-scale U.S. dependence on thermoelectric (steam electric) generation requiring water for cooling underscores the need to understand controls on this water use. The study objective was to quantify water consumption and withdrawal for thermoelectric generation, identifying controls, using Texas as a case study. Water consumption for thermoelectricity in Texas in 2010 totaled ∼0.43 million ac...