سال:1391 | دوره: | شماره: |تعداد مقالات:7

Development of wind energy is gaining a great interest nowadays with over 25% annual growth in any configurations and sizes. Designing a wind turbine with improved performance remains the ultimate research and development goal. Therefore, understanding the influence of the different wind turbine key parameters, e.g., tip speed ratio (TSR), twist and pitch angles, number of blades, and airfoil chord distribution, is critical. In this work, an improved blade element model (BEM) is developed by correcting the code with tip loss, Buhl empirical correction, skewed wake, and rotational effect. These corrections are necessary to extend the application of the method to the turbulent wake regime for the horizontal axis wind turbine (HAWT) configuration. Results from the developed code are compared with the NREL measured data of the two-bladed Unsteady Aerodynamics Experiment (UAE) phase-VI turbine. They indicate an improved trend with the incorporation of these corrections. The performance of the three-bladed 3.5-kW HAWT was tested and found to follow closely the experimental trend. As the mismatch between these low fidelity analyses (BEM) and the experimental work persists, the undertaken analysis demonstrates its limitation, and it emphasizes the role of high fidelity wind tunnel and flow simulations. BEM analysis, however, shows that designing blades with proper twist and pitch angles, and targeting suitable TSR could lead to substantial gain (over 10%) in the performance.

Accurate wind speed modeling is critical in estimating wind energy potential for harnessing wind power effectively. The quality of wind speed assessment depends on the capability of chosen probability density function (PDF) to describe the measured wind speed frequency distribution. The objective of this study is to describe (model) wind speed characteristics using three mixture probability density functions Weibull-extreme value distribution (GEV), Weibull-lognormal, and GEV-lognormal which were not tried before. Statistical parameters such as maximum error in the Kolmogorov-Smirnov test, root mean square error, Chi-square error, coefficient of determination, and power density error are considered as judgment criteria to assess the fitness of the probability density functions. Results indicate that Weibull-GEV PDF is able to describe unimodal as well as bimodal wind distributions accurately whereas GEV-lognormal PDF is able to describe familiar bell-shaped unimodal distribution well. Results show that mixture probability functions are better alternatives to conventional Weibull, two-component mixture Weibull, gamma, and lognormal PDFs to describe wind speed characteristics.

An effort has been made to develop an equation with the given data for all seasons for a location (Lucknow, India) consisting of composite climate, which is further helpful in developing a relation between difference in efficiencies of module with respect to thicknesses of dust collected on the module. This equation that is developed mathematically is in good correlation with the measured data. Here, data are shown for a whole year (from 2010 to 2011); the study done gives us a broad view of finding out the difference in efficiencies of module when dust collects on it.

Natural resource scarcity and the effects of environmental destruction have pushed societies to use and reuse resources more efficiently. Waste should no longer be seen as a burden but rather as another source of material such as energy fuel. This study analyzes the potential of three waste management scenarios that include the combination of four waste management technologies - incineration with energy recovery, composting, anaerobic digestion, and sanitary landfill gas collection - as ways to recover energy and material from municipal solid waste. The study applies the environmental load point (ELP) method and utilizes municipal waste characteristics and composition from India, Indonesia, and China as case studies. The ELP methodology employs integrated weighting in the quantification process to get a one-unit result. This study particularly uses analytic hierarchical process questionnaires to get the weighting value of the nine impact categories: energy depletion, global warming, ozone depletion, resource consumption, ecosystem influence, water pollution, waste disposal, air pollution, and acid rain. The results show that the scenario which includes composting organic waste and sanitary landfill with gas collection for energy recovery has medium environmental impact and the highest practicability. The optimum material and energy potential is from the Chinese case study in which 254 tonnes of compost fertilizer and 60 MWh of electricity is the estimated output for every 1,000 tonnes of waste treated.

This work studies the biodegradation of spent engine oil in soil using Box-Behnken design under response surface methodology. NPK fertilizer (inorganic nutrient), Tween 80 (nonionic surfactant), and pig manure (organic nutrient) concentrations were used as independent biostimulant variables, while total petroleum hydrocarbon (TPH) and hexavalent chromium (Cr (VI)) reductions as dependent variables (response) in a 42-day remediation period. A statistically significant second-order quadratic regression model for TPH and Cr (VI) removal was obtained. The coefficient of determination (R2=0.9995 for TPH and 0.9988 for Cr (VI)) and probability value (P<0.0001) demonstrated significance for the regression model. Numerical optimization technique based on desirability function was carried out for initial spent engine oil concentration of 10% w/w to optimize the biodegradation process. The optimum values for biostimulation agents to achieve a predicted maximum TPH and Cr (VI) removal of 67.20% and 53.20%, respectively, were found to be as follows: NPK fertilizer, 4.22 g; Tween 80, 10.69 mg/l; and pig manure, 47.76 g. At this optimum point, the observed TPH and Cr (VI) reductions were found to be 66.47% and 52.33%, respectively. The statistical analyses and the closeness of the experimental results and model predictions show the reliability of the regression model, and thus, biostimulation of indigenous microbial density and activity can reduce remediation period of petroleum hydrocarbon and heavy metal-contaminated environment and subsequently the cost of remediation.

Access to safe drinking water is important as a health and development issue at national, regional, and local levels. About one billion people do not have healthy drinking water. More than six million people (about two million children) die because of diarrhea which is caused by polluted water. Developing countries pay a high cost to import chemicals including polyaluminium chloride and alum. This is the reason why these countries need low-cost methods requiring low maintenance and skill. The use of synthetic coagulants is not regarded as suitable due to health and economic considerations. The present study was aimed to investigate the effects of alum as coagulant in conjunction with bean, sago, and chitin as coagulants on the removal of color, turbidity, hardness, and Escherichia coli from water. A conventional jar test apparatus was employed for the tests. The study was taken up in three stages, initially with synthetic waters, followed by testing of the efficiency of coagulants individually on surface waters and, lastly, testing of blended coagulants. The experiment was conducted at three different pH conditions of 6, 7, and 8. The dosages chosen were 0.5, 1, 1.5, and 2 mg/l. The results showed that turbidity decrease provided also a primary E. coli reduction. Hardness removal efficiency was observed to be 93% at pH 7 with 1-mg/l concentration by alum, whereas chitin was stable at all the pH ranges showing the highest removal at 1 and 1.5mg/l with pH 7. In conclusion, using natural coagulants results in considerable savings in chemicals and sludge handling cost may be achieved.

There is a pressing need in the energy industry to develop technologies capable of reducing the environmental impact during oil and gas drilling operations. However, these technologies have not been fully integrated into a decision-making system that can reflect a quantitative effort toward this goal. This paper introduces two quantitative decision methods for the selection of environmentally friendly drilling systems. One is based on a multi-attribute utility approach and the other one is based on the analysis of interventions or causal approach. To illustrate the applicability of the proposed methods and to contract their benefits and limitations, a case study is presented using data collected from Green Lake at McFaddin, TX, USA.