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Energies, Volume 4, Issue 8 (August 2011), Pages 1112-1257

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Research

Open AccessArticle Performance of a Polymer Flood with Shear-Thinning Fluid in Heterogeneous Layered Systems with Crossflow
Energies 2011, 4(8), 1112-1128; doi:10.3390/en4081112
Received: 13 May 2011 / Revised: 23 June 2011 / Accepted: 27 July 2011 / Published: 2 August 2011
Cited by 6 | PDF Full-text (378 KB) | HTML Full-text | XML Full-text
Abstract
Assessment of the potential of a polymer flood for mobility control requires an accurate model on the viscosities of displacement fluids involved in the process. Because most polymers used in EOR exhibit shear-thinning behavior, the effective viscosity of a polymer solution is [...] Read more.
Assessment of the potential of a polymer flood for mobility control requires an accurate model on the viscosities of displacement fluids involved in the process. Because most polymers used in EOR exhibit shear-thinning behavior, the effective viscosity of a polymer solution is a highly nonlinear function of shear rate. A reservoir simulator including the model for the shear-rate dependence of viscosity was used to investigate shear-thinning effects of polymer solution on the performance of the layered reservoir in a five-spot pattern operating under polymer flood followed by waterflood. The model can be used as a quantitative tool to evaluate the comparative studies of different polymer flooding scenarios with respect to shear-rate dependence of fluids’ viscosities. Results of cumulative oil recovery and water-oil ratio are presented for parameters of shear-rate dependencies, permeability heterogeneity, and crossflow. The results of this work have proven the importance of taking non-Newtonian behavior of polymer solution into account for the successful evaluation of polymer flood processes. Horizontal and vertical permeabilities of each layer are shown to impact the predicted performance substantially. In reservoirs with a severe permeability contrast between horizontal layers, decrease in oil recovery and sudden increase in WOR are obtained by the low sweep efficiency and early water breakthrough through highly permeable layer, especially for shear-thinning fluids. An increase in the degree of crossflow resulting from sufficient vertical permeability is responsible for the enhanced sweep of the low permeability layers, which results in increased oil recovery. It was observed that a thinning fluid coefficient would increase injectivity significantly from simulations with various injection rates. A thorough understanding of polymer rheology in the reservoir and accurate numerical modeling are of fundamental importance for the exact estimation on the performance of polymer flood. Full article
(This article belongs to the Special Issue Advances in Petroleum Engineering)
Open AccessArticle Water Transfer Characteristics during Methane Hydrate Formation Processes in Layered Media
Energies 2011, 4(8), 1129-1137; doi:10.3390/en4081129
Received: 7 June 2011 / Revised: 26 July 2011 / Accepted: 27 July 2011 / Published: 2 August 2011
Cited by 3 | PDF Full-text (328 KB) | HTML Full-text | XML Full-text
Abstract
Gas hydrate formation processes in porous media are always accompanied by water transfer. To study the transfer characteristics comprehensively, two kinds of layered media consisting of coarse sand and loess were used to form methane hydrate in them. An apparatus with three [...] Read more.
Gas hydrate formation processes in porous media are always accompanied by water transfer. To study the transfer characteristics comprehensively, two kinds of layered media consisting of coarse sand and loess were used to form methane hydrate in them. An apparatus with three PF-meter sensors detecting water content and temperature changes in media during the formation processes was applied to study the water transfer characteristics. It was experimentally observed that the hydrate formation configurations in different layered media were similar; however, the water transfer characteristics and water conversion ratios were different. Full article
(This article belongs to the Special Issue Natural Gas Hydrate 2011)
Open AccessArticle Entropy-Based Bagging for Fault Prediction of Transformers Using Oil-Dissolved Gas Data
Energies 2011, 4(8), 1138-1147; doi:10.3390/en4081138
Received: 28 February 2011 / Revised: 21 July 2011 / Accepted: 1 August 2011 / Published: 4 August 2011
Cited by 1 | PDF Full-text (315 KB) | HTML Full-text | XML Full-text
Abstract
The development of the smart grid has resulted in new requirements for fault prediction of power transformers. This paper presents an entropy-based Bagging (E-Bagging) method for prediction of characteristic parameters related to power transformers faults. A parameter of comprehensive information entropy of [...] Read more.
The development of the smart grid has resulted in new requirements for fault prediction of power transformers. This paper presents an entropy-based Bagging (E-Bagging) method for prediction of characteristic parameters related to power transformers faults. A parameter of comprehensive information entropy of sample data is brought forward to improve the resampling process of the E-Bagging method. The generalization ability of the E-Bagging is enhanced significantly by the comprehensive information entropy. A total of sets of 1200 oil-dissolved gas data of transformers are used as examples of fault prediction. The comparisons between the E-Bagging and the traditional Bagging and individual prediction approaches are presented. The results show that the E-Bagging possesses higher accuracy and greater stability of prediction than the traditional Bagging and individual prediction approaches. Full article
(This article belongs to the Special Issue Future Grid)
Open AccessArticle Analysis of Wind Generator Operations under Unbalanced Voltage Dips in the Light of the Spanish Grid Code
Energies 2011, 4(8), 1148-1162; doi:10.3390/en4081148
Received: 29 June 2011 / Revised: 20 July 2011 / Accepted: 22 July 2011 / Published: 8 August 2011
Cited by 4 | PDF Full-text (1789 KB) | HTML Full-text | XML Full-text
Abstract
Operation of doubly fed induction generators subjected to transient unbalanced voltage dips is analyzed in this article to verify the fulfillment of the Spanish grid code. Akagi’s p-q theory is not used for this study, because control of the electronic converter is [...] Read more.
Operation of doubly fed induction generators subjected to transient unbalanced voltage dips is analyzed in this article to verify the fulfillment of the Spanish grid code. Akagi’s p-q theory is not used for this study, because control of the electronic converter is not the main goal of the paper, but rather to know the physical phenomena involved in the wind turbine when voltage dips occur. Hence, the magnetizing reactive power of the induction generators and their components, which are related with the magnetic fields and determine operation of these machines, are expressed through the reactive power formulations established in the technical literature by three well-known approaches: the delayed voltage (DV) method, Czarnecki’s Current’s Physical Components (CPC) theory and Emanuel’s approach. Non-fundamental and negative-sequence components of the magnetizing reactive power are respectively established to define the effects of the distortion and voltage imbalances on the magnetic fields and electromagnetic torques. Also, fundamental-frequency positive-sequence and negative-sequence reactive powers are decomposed into two components: due to the reactive loads and caused by the imbalances. This decomposition provides additional information about the effects of the imbalances on the main magnetic field and electromagnetic torque of the induction generator. All the above mentioned reactive powers are finally applied to one actual wind turbine subjected to a two-phase voltage dip in order to explain its operation under such transient conditions. Full article
(This article belongs to the Special Issue Wind Energy 2011)
Open AccessArticle Removal and Conversion of Tar in Syngas from Woody Biomass Gasification for Power Utilization Using Catalytic Hydrocracking
Energies 2011, 4(8), 1163-1177; doi:10.3390/en4081163
Received: 27 June 2011 / Revised: 4 August 2011 / Accepted: 4 August 2011 / Published: 12 August 2011
Cited by 7 | PDF Full-text (892 KB) | XML Full-text
Abstract
Biomass gasification has yet to obtain industrial acceptance. The high residual tar concentrations in syngas prevent any ambitious utilization. In this paper a novel gas purification technology based on catalytic hydrocracking is introduced, whereby most of the tarry components can be converted [...] Read more.
Biomass gasification has yet to obtain industrial acceptance. The high residual tar concentrations in syngas prevent any ambitious utilization. In this paper a novel gas purification technology based on catalytic hydrocracking is introduced, whereby most of the tarry components can be converted and removed. Pilot scale experiments were carried out with an updraft gasifier. The hydrocracking catalyst was palladium (Pd). The results show the dominant role of temperature and flow rate. At a constant flow rate of 20 Nm3/h and temperatures of 500 °C, 600 °C and 700 °C the tar conversion rates reached 44.9%, 78.1% and 92.3%, respectively. These results could be increased up to 98.6% and 99.3% by using an operating temperature of 700 °C and lower flow rates of 15 Nm3/h and 10 Nm3/h. The syngas quality after the purification process at 700 °C/10 Nm3/h is acceptable for inner combustion (IC) gas engine utilization. Full article
(This article belongs to the Special Issue Biomass and Biofuels)
Open AccessArticle Model Predictive Control-Based Fast Charging for Vehicular Batteries
Energies 2011, 4(8), 1178-1196; doi:10.3390/en4081178
Received: 13 June 2011 / Revised: 2 August 2011 / Accepted: 4 August 2011 / Published: 17 August 2011
Cited by 6 | PDF Full-text (1066 KB) | HTML Full-text | XML Full-text
Abstract
Battery fast charging is one of the most significant and difficult techniques affecting the commercialization of electric vehicles (EVs). In this paper, we propose a fast charge framework based on model predictive control, with the aim of simultaneously reducing the charge duration, [...] Read more.
Battery fast charging is one of the most significant and difficult techniques affecting the commercialization of electric vehicles (EVs). In this paper, we propose a fast charge framework based on model predictive control, with the aim of simultaneously reducing the charge duration, which represents the out-of-service time of vehicles, and the increase in temperature, which represents safety and energy efficiency during the charge process. The RC model is employed to predict the future State of Charge (SOC). A single mode lumped-parameter thermal model and a neural network trained by real experimental data are also applied to predict the future temperature in simulations and experiments respectively. A genetic algorithm is then applied to find the best charge sequence under a specified fitness function, which consists of two objectives: minimizing the charging duration and minimizing the increase in temperature. Both simulation and experiment demonstrate that the Pareto front of the proposed method dominates that of the most popular constant current constant voltage (CCCV) charge method. Full article
(This article belongs to the Special Issue Electric and Hybrid Vehicles)
Open AccessArticle A Carbon Footprint of an Office Building
Energies 2011, 4(8), 1197-1210; doi:10.3390/en4081197
Received: 4 May 2011 / Revised: 10 August 2011 / Accepted: 17 August 2011 / Published: 19 August 2011
Cited by 11 | PDF Full-text (231 KB) | HTML Full-text | XML Full-text
Abstract
Current office buildings are becoming more and more energy efficient. In particular the importance of heating is decreasing, but the share of electricity use is increasing. When the CO2 equivalent emissions are considered, the CO2 emissions from embodied energy make [...] Read more.
Current office buildings are becoming more and more energy efficient. In particular the importance of heating is decreasing, but the share of electricity use is increasing. When the CO2 equivalent emissions are considered, the CO2 emissions from embodied energy make up an important share of the total, indicating that the building materials have a high importance which is often ignored when only the energy efficiency of running the building is considered. This paper studies a new office building in design phase and offers different alternatives to influence building energy consumption, CO2 equivalent emissions from embodied energy from building materials and CO2 equivalent emissions from energy use and how their relationships should be treated. In addition this paper studies how we should weight the primary energy use and the CO2 equivalent emissions of different design options. The results showed that the reduction of energy use reduces both the primary energy use and CO2 equivalent emissions. Especially the reduction of electricity use has a high importance for both primary energy use and CO2 emissions when fossil fuels are used. The lowest CO2 equivalent emissions were achieved when bio-based, renewable energies or nuclear power was used to supply energy for the office building. Evidently then the share of CO2 equivalent emissions from the embodied energy of building materials and products became the dominant source of CO2 equivalent emissions. The lowest primary energy was achieved when bio-based local heating or renewable energies, in addition to district cooling, were used. The highest primary energy was for the nuclear power option. Full article
(This article belongs to the Special Issue Energy Savings in the Domestic and Tertiary Sectors 2011)
Open AccessArticle A General Mathematical Framework for Calculating Systems-Scale Efficiency of Energy Extraction and Conversion: Energy Return on Investment (EROI) and Other Energy Return Ratios
Energies 2011, 4(8), 1211-1245; doi:10.3390/en4081211
Received: 14 April 2011 / Revised: 29 July 2011 / Accepted: 17 August 2011 / Published: 19 August 2011
Cited by 20 | PDF Full-text (508 KB) | HTML Full-text | XML Full-text
Abstract
The efficiencies of energy extraction and conversion systems are typically expressed using energy return ratios (ERRs) such as the net energy ratio (NER) or energy return on investment (EROI). A lack of a general mathematical framework prevents inter-comparison of NER/EROI estimates between [...] Read more.
The efficiencies of energy extraction and conversion systems are typically expressed using energy return ratios (ERRs) such as the net energy ratio (NER) or energy return on investment (EROI). A lack of a general mathematical framework prevents inter-comparison of NER/EROI estimates between authors: methods used are not standardized, nor is there a framework for succinctly reporting results in a consistent fashion. In this paper we derive normalized mathematical forms of four ERRs for energy extraction and conversion pathways. A bottom-up (process model) formulation is developed for an n-stage energy harvesting and conversion pathway with various system boundaries. Formations with the broadest system boundaries use insights from life cycle analysis to suggest a hybrid process model/economic input output based framework. These models include indirect energy consumption due to external energy inputs and embodied energy in materials. Illustrative example results are given for simple energy extraction and conversion pathways. Lastly, we discuss the limitations of this approach and the intersection of this methodology with “top-down” economic approaches. Full article
Open AccessArticle Forecasting Electricity Demand in Thailand with an Artificial Neural Network Approach
Energies 2011, 4(8), 1246-1257; doi:10.3390/en4081246
Received: 3 May 2011 / Revised: 27 July 2011 / Accepted: 9 August 2011 / Published: 22 August 2011
Cited by 22 | PDF Full-text (475 KB) | HTML Full-text | XML Full-text
Abstract
Demand planning for electricity consumption is a key success factor for the development of any countries. However, this can only be achieved if the demand is forecasted accurately. In this research, different forecasting methods—autoregressive integrated moving average (ARIMA), artificial neural network (ANN) [...] Read more.
Demand planning for electricity consumption is a key success factor for the development of any countries. However, this can only be achieved if the demand is forecasted accurately. In this research, different forecasting methods—autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and multiple linear regression (MLR)—were utilized to formulate prediction models of the electricity demand in Thailand. The objective was to compare the performance of these three approaches and the empirical data used in this study was the historical data regarding the electricity demand (population, gross domestic product: GDP, stock index, revenue from exporting industrial products and electricity consumption) in Thailand from 1986 to 2010. The results showed that the ANN model reduced the mean absolute percentage error (MAPE) to 0.996%, while those of ARIMA and MLR were 2.80981 and 3.2604527%, respectively. Based on these error measures, the results indicated that the ANN approach outperformed the ARIMA and MLR methods in this scenario. However, the paired test indicated that there was no significant difference among these methods at α = 0.05. According to the principle of parsimony, the ARIMA and MLR models might be preferable to the ANN one because of their simple structure and competitive performance Full article
(This article belongs to the Special Issue Intelligent Energy Demand Forecasting)

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