System development and testing of wind-powered reverse osmosis desalination for remote Pacific islands

2002 ◽  
Vol 2 (2) ◽  
pp. 123-129
Author(s):  
C.C.K. Liu ◽  
R. Migita ◽  
J.-W. Park
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Reverse Osmosis ◽  
Pacific Islands ◽  
System Development ◽  
Water Pressure ◽  
Small Island ◽  
Feed Water ◽  
Average Wind Speed ◽  
Short Supply

Reverse osmosis (RO) is one of the most feasible methods of desalination to produce a supplemental freshwater supply. Because traditional RO desalination is energy-intensive, it is not a viable solution for remote Pacific islands where electricity is also in short supply. The utilization of wind power holds promise as a solution to this problem, as most of these remote islands are subject to constant trade winds. RO desalination of brackish groundwater, which is available in many of these islands, requires low feed water pressure that can be delivered by wind power at a moderate wind speed. Testing of a prototype wind-powered RO desalination system constructed on Coconut Island, a small island off the windward coast of Oahu, Hawaii, indicated that at an average wind speed of 8.5 m/s, a freshwater flow of over 4000 L/d can be produced. This volume is sufficient to meet the freshwater needs of a typical remote island community.

scholarly journals The super-turbine wind power conversion paradox: using machine learning to reduce errors caused by Jensen's inequality

2019 ◽  
Vol 4 (2) ◽  
pp. 343-353 ◽  
Author(s):  
Tyler C. McCandless ◽  
Sue Ellen Haupt
Keyword(s):  
Machine Learning ◽  
Standard Deviation ◽  
Wind Speed ◽  
Wind Power ◽  
Power Conversion ◽  
Jensen’S Inequality ◽  
Jensen's Inequality ◽  
Average Wind Speed ◽  
Wind Speeds ◽  
Average Wind

Abstract. Wind power is a variable generation resource and therefore requires accurate forecasts to enable integration into the electric grid. Generally, the wind speed is forecast for a wind plant and the forecasted wind speed is converted to power to provide an estimate of the expected generating capacity of the plant. The average wind speed forecast for the plant is a function of the underlying meteorological phenomena being predicted; however, the wind speed for each turbine at the farm is also a function of the local terrain and the array orientation. Conversion algorithms that assume an average wind speed for the plant, i.e., the super-turbine power conversion, assume that the effects of the local terrain and array orientation are insignificant in producing variability in the wind speeds across the turbines at the farm. Here, we quantify the differences in converting wind speed to power at the turbine level compared with a super-turbine power conversion for a hypothetical wind farm of 100 2 MW turbines as well as from empirical data. The simulations with simulated turbines show a maximum difference of approximately 3 % at 11 m s−1 with a 1 m s−1 standard deviation of wind speeds and 8 % at 11 m s−1 with a 2 m s−1 standard deviation of wind speeds as a consequence of Jensen's inequality. The empirical analysis shows similar results with mean differences between converted wind speed to power and measured power of approximately 68 kW per 2 MW turbine. However, using a random forest machine learning method to convert to power reduces the error in the wind speed to power conversion when given the predictors that quantify the differences due to Jensen's inequality. These significant differences can lead to wind power forecasters overestimating the wind generation when utilizing a super-turbine power conversion for high wind speeds, and indicate that power conversion is more accurately done at the turbine level if no other compensatory mechanism is used to account for Jensen's inequality.

Optimum arrangement of two-stage plug and concentrate recycling RO systems using thermodynamic and exergy analysis

2019 ◽  
Vol 30 (6) ◽  
pp. 3323-3348
Author(s):  
Abbas Naeimi ◽  
Mohammad Hossein Ahmadi ◽  
Milad Sadeghzadeh ◽  
Alibakhsh Kasaeian
Keyword(s):  
Reverse Osmosis ◽  
Water Pressure ◽  
Optimization Method ◽  
Feed Water ◽  
Two Stage ◽  
Content Type ◽  
Optimum Arrangement ◽  
North Of Iran ◽  
Optimization Parameters ◽  
Seawater Reverse Osmosis

Purpose This paper aims to determine the optimum arrangement of a reverse osmosis system in two methods of plug and concentrate recycling. Design/methodology/approach To compare the optimum conditions of these two methods, a seawater reverse osmosis system was considered to produce fresh water at a rate of 4,000 m3/d for Mahyarkala city, located in north of Iran, for a period of 20 years. Using genetic algorithms and two-objective optimization method, the reverse osmosis system was designed. Findings The results showed that exergy efficiency in optimum condition for concentrate recycling and plug methods was 82.6 and 92.4 per cent, respectively. The optimizations results showed that concentrate recycling method, despite a 36 per cent reduction in the initial cost and a 2 per cent increase in maintenance expenses, provides 6 per cent higher recovery and 19.7 per cent less permeate concentration than two-stage plug method. Originality/value Optimization parameters include feed water pressure, the rate of water return from the brine for concentrate recycling system, type of SW membrane, feedwater flow rate and numbers of elements in each pressure vessel (PV). These parameters were also compared to each other in terms of recovery (R) and freshwater unit production cost. In addition, the exergy of all elements was analyzed by selecting the optimal mode of each system.

Wind Power Generating Potential at the University of North Texas Campus

2012 ◽  
Author(s):  
Rambod Rayegan ◽  
Yong X. Tao ◽  
Frank Y. Fang
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Power Density ◽  
Wind Turbines ◽  
North Texas ◽  
University Of North Texas ◽  
Average Wind Speed ◽  
Wind Speed Data ◽  
Weather Stations ◽  
The University

This study utilizes two sets of wind speed data at 3 m above the ground surface level retrieved from two on-campus weather stations to study the wind power generating potential at the University of North Texas Campus. Weather stations have been installed approximately 5 miles away from each other. The mean wind speed data of 10 minute intervals in a one-year period from February 1st 2011 to January 31st 2012 has been adopted and analyzed. The numerical values of the dimensionless Weibull shape parameter (k) and Weibull scale parameter (c) have been determined. Monthly average wind speed and standard deviation, power generation, and power density at the sensor level for both locations has been discussed. Lower values of wind speed were found during summer months and higher during spring months. The results show that the wind power density in the area is fair enough to be considered as a renewable power source for the University. Thereafter annual energy production by using two wind turbines with nominal capacities of 100 and 3.5 kW for both weather stations has been studied. Initial costs of using each turbine to maintain power demands of selected buildings have been compared. In order to utilize wind energy, it is recommended to install highly efficient wind turbines for electricity supply of campus buildings with lower power demands. Using grant monies to maintain the initial costs of the installation of wind turbines make them economically more desirable. Since wind power potential is low during summer, PV panels as proper supplements to the power generating system are suggested.

Statistical analysis of wind speed and wind power potential of Port Elizabeth using Weibull parameters

2012 ◽  
Vol 23 (2) ◽  
pp. 30-38 ◽  
Author(s):  
Temitope R Ayodele ◽  
Adisa A. Jimoh ◽  
Josiah L. Munda ◽  
John T. Agee
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
South African ◽  
Shape Parameter ◽  
Scale Parameter ◽  
Average Wind Speed ◽  
Weibull Parameters ◽  
Average Wind ◽  
Port Elizabeth ◽  
Power Project

This paper analyses wind speed characteristics and wind power potential of Port Elizabeth using statistical Weibull parameters. A measured 5–minute time series average wind speed over a period of 5 years (2005 - 2009) was obtained from the South African Weather Service (SAWS). The results show that the shape parameter (k) ranges from 1.319 in April 2006 to 2.107 in November 2009, while the scale parameter (c) varies from 3.983m/s in May 2008 to 7.390 in November 2009.The average wind power density is highest during Spring (September–October), 256.505W/m2 and lowest during Autumn (April-May), 152.381W/m2. This paper is relevant to a decision-making process on significant investment in a wind power project.

scholarly journals Evaluation of Wind Potential for the Generation of Electricity in Aliero, Kebbi State

2020 ◽  
pp. 1-7
Author(s):  
A. A. Yahaya ◽  
I. M. Bello ◽  
N. Mudassir ◽  
I. Mohammed ◽  
M. I. Mukhtar
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Power ◽  
Power Density ◽  
Horizontal Wind ◽  
Average Wind Speed ◽  
Wind Power Density ◽  
North East ◽  
Average Wind ◽  
Wind Potential

One of the major developments in the technology today is the wind turbine that generates electricity and feed it directly to the grid which is used in many part of the world. The main purpose of this work is to determine the wind potential for electricity generation in Aliero, Kebbi state. Five years Data (2014-2018) was collected from the metrological weather station (Campell Scientific Model), the equipment installed at Kebbi State University of Science And Technology Aliero The data was converted to monthly and annual averages, and compared with the threshold average wind speed values that can only generate electricity in both vertical and horizontal wind turbines. The highest average wind speed 2.81 m/s was obtained in the month of January and the minimum average wind speed of 1.20 m/s in the month of October. Mean annual wind speed measured in the study area shows that there has been an increase in the wind speed from 2014 which peaked in 2015 and followed by sudden decrease to a minimum seasonal value in the year 2016. The highest wind direction is obtained from the North North-East (NNE) direction. From the results of wind power density it shows that we have highest wind power density in month of January and December with  0.8635 w/ m2 and 0.8295 w/ m2 respectively, while lowest wind power density in the month of October and September with 0.6780 w/ m2 and 0.6575 w/ m2  respectively. Result of the type Wind Turbine to be selected in the study area shows that the site is not viable for power generation using a horizontal wind turbine but the vertical wind turbine will be suitable for the generation of electricity.

scholarly journals Assessment of Wind Energy Potential In Zwara, Libya

2021 ◽  
Vol 8 (2) ◽  
Author(s):  
Alhassan A. Teyabeen ◽  
Fathi R. Akkari ◽  
Ali E. Jwaid ◽  
Ashraf Zaghwan ◽  
Rehab Abodelah
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Ground Level ◽  
Energy Potential ◽  
Average Wind Speed ◽  
Wind Power Density ◽  
Wind Energy Potential ◽  
Wind Resource

To assess the wind energy potential at any site, the wind power density should be estimated; it evaluates the wind resource and indicates the amount of available wind energy. The purpose of this study is to estimate the monthly and annual wind power density based on the Weibull distribution using wind speed data collected in Zwara, Libya during 2007. The wind date are measured at the three hub heights of 10m, 30m, and 50m above ground level, and recorded every 10 minutes. The analysis showed that the annual average wind speed are 4.51, 5.86, 6.26 m/s for the respective mentioned heights. The average annual wind power densities at the mentioned heights were 113.71, 204.19, 243.48 , respectively.

scholarly journals Wind Farming Feasibility Assessment in 16 Locations of Nepal

2020 ◽  
Vol 15 (3) ◽  
pp. 205-215
Author(s):  
Raju Laudari ◽  
Bal Krishna Sapkota ◽  
Kamal Banskota
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Power Density ◽  
Wind Rose ◽  
Average Wind Speed ◽  
Wind Power Density ◽  
Feasibility Assessment ◽  
Average Wind ◽  
Acceptance Range ◽  
Study Sites

The paper assesses the feasibility of wind farming at the 16 sites scattered in different ecological regions of Nepal. The wind speed, the hourly and seasonal variation of wind, the wind-rose, the wind turbulence rate, the wind power density, the Weibull probability distribution and the frequency of the wind speed above cut in speed were computed. The average wind speed at all the sites was found to be higher during the dry season from March to May. The wind speed of the modern turbine for power generation at eight sites was found to be above cut-in speed. However, the wind power density was found to be good only at the two sites and fairly good at the six sites. More than 50 % time of a year at these eight sites had over 3.5 m/s wind speed. However, the turbulence rate at all the studied sites was found to be above the acceptance range of 25 %. Among the study sites, Kagbeni, Thini, Jumla, Ramechhap, Vorleni, Patan west, Hansapur and Baddanda were found to be technically feasible sites for wind energy generation in Nepal.

scholarly journals Aggravated biofouling caused by chlorine disinfection in a pilot-scale reverse osmosis treatment system of municipal wastewater

2021 ◽  
Author(s):  
Li-Wei Luo ◽  
Yin-Hu Wu ◽  
Yun-Hong Wang ◽  
Xin Tong ◽  
Yuan Bai ◽  
...  
Keyword(s):  
Reverse Osmosis ◽  
Relative Abundance ◽  
Membrane Fouling ◽  
Municipal Wastewater ◽  
Water Pressure ◽  
Pilot Scale ◽  
Resistant Bacteria ◽  
Feed Water ◽  
Chlorine Disinfection ◽  
Key Species

Abstract The reverse osmosis (RO) system is widely applied to produce reclaimed water for high-standard industrial use. Chlorine disinfection is the main biofouling control method in the RO systems for wastewater reclamation. However, researchers reported the adverse effects of chlorine disinfection which aggravated biofouling in laboratory-scale RO systems. In this study, four parallel 4-inch spiral wound RO membranes were used to study the effect of chlorine on biofouling in a pilot-scale RO system. The free chlorine dosages in four experimental groups were 0, 1, 2 and 5 mg/L, respectively. After continuous chlorination and dechlorination, the feed water entered the RO system. It was found that chlorine pretreatment caused a 1.9–36.7% increase in relative feed water pressure of the RO system, suggesting that chlorine aggravated the membrane fouling in the pilot-scale RO system. The microbial community structures of living bacteria in the feed water of the RO system were determined by the PMA (propidium monoazide)-PCR method and showed that the relative abundance of chlorine-resistant bacteria (CRB) was significantly increased after disinfection. Nine major genera which maintained higher relative abundance in experimental groups with high chlorine dosage were considered as possible key species causing membrane fouling, including Pedobacter, Clostridium and Bradyrhizobium.

scholarly journals The Super-Turbine Wind Power Conversion Paradox: Using Machine Learning to Reduce Errors Caused by Jensen’s Inequality

2019 ◽  
Author(s):  
Tyler C. McCandless ◽  
Sue Ellen Haupt
Keyword(s):  
Machine Learning ◽  
Standard Deviation ◽  
Wind Speed ◽  
Wind Power ◽  
Power Conversion ◽  
Jensen’S Inequality ◽  
Jensen's Inequality ◽  
Average Wind Speed ◽  
Wind Speeds ◽  
Average Wind

Abstract. Wind power is a variable generation resource and therefore requires accurate forecasts to enable integration into the electric grid. Generally, the wind speed is forecast for a wind plant and the forecasted wind speed is converted to power to provide an estimate of the expected generating capacity of the plant. The average wind speed forecast for the plant is a function of the underlying meteorological phenomena being predicted; however, the wind speed for each turbine at the farm is also a function of the local terrain and the array orientation. Conversion algorithms that assume an average wind speed for the plant, i.e., the super-turbine power conversion, assume that the effects of the local terrain and array orientation are insignificant in producing variability in the wind speeds across the turbines at the farm. Here, we quantify the differences in converting wind speed to power at the turbine level compared to a super-turbine power conversion for a hypothetical wind farm of 100 2-MW turbines as well as from empirical data. The simulations with simulated turbines show a maximum difference of approximately 3 % at 11 m s−1 with 1 m s−1 standard deviation of wind speeds and 8 % at 11 m s−1 with 2 m s−1 standard deviation of wind speeds as a consequence of Jensen’s Inequality. The empirical analysis shows similar results with mean differences between converted wind speed to power and measured power of approximately 68 kW per 2 MW turbine. However, using a random forest machine learning method to convert to power reduces the error in the wind speed to power conversion when given the predictors that quantify the differences due to Jensen’s Inequality. These significant differences can lead to wind power forecasters over-estimating the wind generation when utilizing a super-turbine power conversion for high wind speeds, and indicates that power conversion is more accurately done at the turbine level if no other compensatory mechanism is used to account for Jensen’s Inequality.

A Study of Estimation of Wind Potential and Prediction of Grid-Connected Wind Power Development in Vietnam

2020 ◽  
Vol 11 (2) ◽  
pp. 27-37
Author(s):  
Co Xuan Hoang ◽  
Linh Thi Hai Dang ◽  
Da Van Ta ◽  
Cuong Manh Dinh ◽  
Chinh Van Kim ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Power Plants ◽  
Wind Farms ◽  
Ground Level ◽  
Correlation Equation ◽  
Electricity Production ◽  
Average Wind Speed ◽  
Average Wind ◽  
Wind Potential

The construction of grid-connected wind power plants has increased sharply in Vietnam due to the rapid rise of energy demands. Previous studies of wind energy have shown that the wind potential of Vietnam compared to other countries of Southeast Asia and examined wind speed levels of each region of Vietnam. In this study, the annual electricity production (AEP), which is an important factor of project's cost and benefit calculation, was calculated for 13 study areas. A correlation equation between AEP and the average wind speed at 60m above ground level was also developed to estimate AEP where there exists only data of the annual average wind speed. Moreover, other resources of the development of grid-connected wind power were discussed in this research such as the Vietnamese supporting mechanism, international co-operation, turbine technology development, etc. The article then predicts the trend, and proposes some recommendations of developing grid-connected wind farms in Vietnam.

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