Novel Thermal Desalination Brine Reject-Sewage Effluent Salinity Gradient for Power Generation and Dilution of Brine Reject

Salinity gradient resource presents an essential role for power generated in the process of pressure-retarded osmosis (PRO). Researchers proposed several designs for coupling the PRO process with the desalination plants, particularly reverse osmosis technology for low-cost desalination but there is no study available yet on the utilization of the concentrated brine reject from a thermal desalination plant. This study evaluates the feasibility of power generation in the PRO process using thermal plant brine reject-tertiary sewage effluent (TSE) salinity gradient resource. Power generation in the PRO process was determined for several commercially available FO membranes. Water flux in Oasys Forward Osmosis membrane was more than 31 L/m2h while the average water flux in the Oasys module was 17 L/m2h. The specific power generation was higher in the thin film composite (TFC) membranes compared to the cellulose triacetate (CTA) membranes. The specific power generation for the Oasys membrane was 0.194 kWh/m3, which is 41% of the maximum Gibbs energy of the brine reject-TSE salinity gradient. However, the Hydration Technology Innovation CTA membrane extracted only 0.133 kWh/m3 or 28% of Gibbs free energy of mixing for brine reject-TSE salinity gradient. The study reveals the potential of the brine reject-TSE salinity gradient resource for power generation and the dilution of brine reject

A review of the key sensitive parameters on the aerodynamic performance of a horizontal wind turbine using Computational Fluid Dynamics modelling

Renewable energy technologies are receiving much attention to replacing power plants operated by fossil and nuclear fuels. Of all the renewable technologies, wind power has been successfully implemented in several countries. There are several parameters in the aerodynamic characteristics and design of the horizontal wind turbine. This paper highlights the key sensitive parameters that affect the aerodynamic performance of the horizontal wind turbine, such as environmental conditions, blade shape, airfoil configuration and tip speed ratio. Different turbulence models applied to predict the flow around the horizontal wind turbine using Computational Fluid Dynamics modeling are reviewed. Finally, the challenges and concluding remarks for future research directions in wind turbine design are discussed

Evaluation of wind resource potential using statistical analysis of probability density functions in New South Wales, Australia

Wind energy is a vital part of Australia's energy mix. The first step in a wind power project at a particular site is to assess the wind resource potential and feasibility for wind energy production. Research on wind potential and statistical analysis has been done throughout the world. Currently, recent potential wind studies are lacking, especially in New South Wales (NSW), Australia. This study highlighted the feasibility of wind potential at four sites in NSW, namely Ballina, Merriwa, Deniliquin, and the Bega region. The type of wind speed distribution function dramatically affects the output of the available wind energy and wind turbine performance at a particular site. Therefore, the accuracy of four probability density functions was evaluated, namely Rayleigh, Weibull, Gamma, and Lognormal distributions. The outcomes showed Weibull provided the most accurate distribution. The annual average scale and shape parameters of Weibull distribution varied between 2.935-5.042 m/s and 1.137-2.096, respectively. The maximum shape and scale factors were at Deniliquin, while the minimum shape and scale factors were at Bega area. Assessment of power density indicated that Deniliquin had a marginal wind speed resource, while Ballina, Bega, and Merriwa had poor wind resources

XGBoost model as an efficient machine learning approach for PFAS removal: Effects of material characteristics and operation conditions.

Due to the implications of poly- and perfluoroalkyl substances (PFAS) on the environment and public health, great attention has been recently made to finding innovative materials and methods for PFAS removal. In this work, PFAS is considered universal contamination which can be found in many wastewater streams. Conventional materials and processes used to remove and degrade PFAS do not have enough competence to address the issue particularly when it comes to eliminating short-chain PFAS. This is mainly due to the large number of complex parameters that are involved in both material and process designs. Here, we took the advantage of artificial intelligence to introduce a model (XGBoost) in which material and process factors are considered simultaneously. This research applies a machine learning approach using data collected from reported articles to predict the PFAS removal factors. The XGBoost modeling provided accurate adsorption capacity, equilibrium, and removal estimates with the ability to predict the adsorption mechanisms. The performance comparison of adsorbents and the role of AI in one dominant are studied and reviewed for the first time, even though many studies have been carried out to develop PFAS removal through various adsorption methods such as ion exchange, nanofiltration, and activated carbon (AC). The model showed that pH is the most effective parameter to predict PFAS removal. The proposed model in this work can be extended for other micropollutants and can be used as a basic framework for future adsorbent design and process optimization

Impact of hydrodynamic conditions on optimum power generation in dual stage pressure retarded osmosis using spiral-wound membrane

The Dual Stage Pressure Retarded Osmosis technique is considered for power generation. The influence of feed flow rates, hydraulic pressure, and pressure drop on mass transfer and solute diffusion in a full-scale membrane model was investigated for the first time to maximize power generation. Dead Sea-seawater, Dead Sea-reverse osmosis brine, reverse osmosis brine-wastewater, and seawater-wastewater salinity gradient resources were investigated for power generation. Results revealed a 71.07% increase in the specific power generation due to the dual-stage pressure retarded osmosis process optimization using Dead Sea-seawater salinity gradient resources. The increase in the specific power generation due to the dual-stage pressure retarded osmosis optimization was 108.8%, 63.18%, and 133.54%, respectively, for Dead Sea-reverse osmosis brine, reverse osmosis brine-wastewater, and seawater-wastewater salinity gradient resources. At optimum operating conditions, using the dual-stage pressure retarded osmosis process as an alternative to the single pressure retarded osmosis process achieved up to a 22% increase in the energy output. Interestingly, the hydraulic pressure at optimum operating conditions was slightly higher than the average osmotic pressure gradients in the dual-stage pressure retarded osmosis process. The study also revealed that power generation in the dual-stage pressure retarded osmosis process operating at constant mass transfer and solute resistivity parameters was overestimated by 2.8%

Optimization of a Small Wind Turbine for a Rural Area: A Case Study of Deniliquin, New South Wales, Australia

The performance of a wind turbine is profoundly affected by wind conditions. Small wind turbines usually achieve the demand for electricity in rural areas. The shape of the blade greatly influences the performance of the wind turbine. The present study aims to optimize the performance of a 20 kW horizontal-axis wind turbine (HAWT) under local wind conditions at Deniliquin, New South Wales, Australia. ANSYS Fluent was used to investigate the aerodynamic performance of the 20 KW HAWT. The effects of four Reynolds Averaged Navier Stokes (RANS) turbulence models on predicting the flow over the wind turbine under separation condition were examined. Transition SST model had the best agreement with NREL CER data, which was used to investigate the mechanical output at different rotational speeds and variable pitch angles. Then the aerodynamic shape of the rotor of the wind turbine was optimized to maximize the annual energy production (AEP) in the Deniliquin region. Statistical wind analysis was applied to define the Weibull function and scale parameters which were 2.096 and 5.042 m/s, respectively. HARP_Opt was enhanced with design variables concerning the shape of the blade, rated rotational speed, and pitch angle. Pitch angle remained at 0ᵒ while the rising wind speed improved rotor speed to 148.4482 rpm at rated speed. This optimization improved the AEP rate by 9.068% when compared to the original NREL design

Aquaporin–graphene interface: relevance to point-of-care device for renal cell carcinoma and desalination

© 2018 The Author(s) Published by the Royal Society. All rights reserved. The aquaporin superfamily of hydrophobic integral membrane proteins constitutes water channels essential to the movement of water across the cell membrane, maintaining homeostatic equilibrium. During the passage of water between the extracellular and intracellular sides of the cell, aquaporins act as ultra-sensitive filters. Owing to their hydrophobic nature, aquaporins self-assemble in phospholipids. If a proper choice of lipids is made then the aquaporin biomimetic membrane can be used in the design of an artificial kidney. In combination with graphene, the aquaporin biomimetic membrane finds practical application in desalination and water recycling using mostly Escherichia coli AqpZ. Recently, human aquaporin 1 has emerged as an important biomarker in renal cell carcinoma. At present, the ultra-sensitive sensing of renal cell carcinoma is cumbersome. Hence, we discuss the use of epitopes from monoclonal antibodies as a probe for a point-of-care device for sensing renal cell carcinoma. This device works by immobilizing the antibody on the surface of a single-layer graphene, that is, as a microfluidic device for sensing renal cell carcinoma