Research Article
Thermohaline Convection-enhanced Solar Membrane Distillation: A Sustainable Approach to Global Water Security
Issue:
Volume 12, Issue 2, June 2026
Pages:
27-38
Received:
23 April 2026
Accepted:
8 May 2026
Published:
18 May 2026
DOI:
10.11648/j.ajwse.20261202.11
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Abstract: The Thermohaline Convection-Enhanced Solar Membrane Distillation (TSMD) system is introduced as an innovative and sustainable solution to global water scarcity, designed to operate entirely off-grid using renewable energy sources, primarily solar thermal power, for an optimal eight-hour daily cycle. Unlike conventional desalination systems dependent on grid electricity or fossil fuels, the TSMD incorporates a 12 V DC submersible pump powered by a 15 W solar panel, a 12V-7Ah battery ensuring semi-autonomous operation in remote or resource-limited regions. The design integrates thermohaline convection and a pump to enhance heat and mass transfer, improving evaporation and condensation efficiency. Experimental results from hardware testing indicate a freshwater production rate of approximately 5.4 L of freshwater from 10 L of feedwater (54% water recovery), with brine discharge maintained at about 4.6 L. Total Dissolved Solids (TDS) levels were significantly reduced, reaching as low as 109 ppm, far below the WHO drinking water threshold of 300 ppm. Under peak sunlight conditions, the system achieved a thermal efficiency of approximately 62%. These findings demonstrate that the TSMD system is environmentally friendly, and its energy-independent design makes it a strong potential for solving the freshwater shortages in arid, semi-arid, and off-grid communities around the globe.
Abstract: The Thermohaline Convection-Enhanced Solar Membrane Distillation (TSMD) system is introduced as an innovative and sustainable solution to global water scarcity, designed to operate entirely off-grid using renewable energy sources, primarily solar thermal power, for an optimal eight-hour daily cycle. Unlike conventional desalination systems dependen...
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Research Article
Estimating Reference Evapotranspiration over Georgetown, Guyana, Using an Artificial Neural Network:
A Comparative Analysis with 32 Empirical Methods
Issue:
Volume 12, Issue 2, June 2026
Pages:
39-63
Received:
25 April 2026
Accepted:
8 May 2026
Published:
18 May 2026
DOI:
10.11648/j.ajwse.20261202.12
Downloads:
Views:
Abstract: This study investigates the capability of an Artificial Neural Network (ANN) model to estimate daily ETo using several meteorological variables in a data-limited region along Guyana’s coast. The ANN was trained on historical data from 2001–2018 and independently evaluated over 2019–2022, with ETo computed by the FAO Penman–Monteith (PM-56) method serving as the reference benchmark. Model performance was assessed using standard statistical indicators, including the root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE), and index of agreement (IoA). In addition, the ANN model was compared against 32 commonly used empirical ETo estimation methods, encompassing temperature-based, radiation-based, and mass transfer-based approaches. Results indicate that the ANN reproduced PM-56 ETo estimates with high accuracy and minimal bias, achieving R2 and NSE values exceeding 0.99 across the validation period. The ANN model also consistently outperformed all empirical methods across all performance metrics, demonstrating superior accuracy and robustness. Among conventional methods, the Hargreaves–Samani and Makkink approaches showed comparatively better performance, while mass transfer-based methods exhibited substantial deviations and poorer performance. These findings suggest that ANN-based models can serve as reliable alternatives for daily ETo estimation in regions where complete meteorological inputs for physically based methods are limited, thereby supporting improved water-resource and agricultural decision-making in Guyana and similar environments.
Abstract: This study investigates the capability of an Artificial Neural Network (ANN) model to estimate daily ETo using several meteorological variables in a data-limited region along Guyana’s coast. The ANN was trained on historical data from 2001–2018 and independently evaluated over 2019–2022, with ETo computed by the FAO Penman–Monteith (PM-56) method s...
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,
Sadman Shahriar Alam*
