Achieving a more sustainable future partially hinges on developing larger energy storage systems
25 Oca 2024
3 dk okuma süresi
Achieving a more sustainable future partially hinges on developing larger energy storage systems. Currently, the predominant method is pumped-storage hydropower. This technique involves storing surplus energy by elevating water and then utilizing it to power a turbine as energy needs increase.
However, imagine if this method could be integrated with desalination processes, such as reverse-osmosis water purification. This process entails forcing water through a membrane under high pressure to filter out impurities, resulting in drinkable water. Could merging these two systems amplify their benefits? Researchers at Cornell University explored this possibility using machine learning models.
Their studies concentrated on systems combining pumped-storage hydropower and reverse osmosis using seawater, as the system requires large amounts of water to be feasible. In this integrated system, water raised for storage purposes could simultaneously serve the water purification process. The inherent water pressure in this system would facilitate reverse osmosis. Such a dual-purpose system could provide electricity and potable water for coastal communities, potentially reducing the costs of constructing two separate systems.
Researchers noted that integrating these systems could lead to a 16 percent reduction in the time needed to reach cost-effectiveness.
The modeling accuracy is crucial for researchers aiming to construct a real-world prototype of a combined energy and water purification system. Since such a project involves substantial investment, models are vital to designing the most efficient system and optimizing its operation before commencing construction.
Modeling the pumped-storage aspect is relatively more straightforward. The energy output here depends chiefly on turbine efficiency. However, modeling the reverse osmosis component is significantly more complex. The efficiency of producing potable water through reverse osmosis is influenced by various factors such as pressure, flow rate, water salinity, and the specific types of reverse osmosis membranes used.
Current modeling techniques for reverse osmosis don't fully encompass its intricacies. Researchers combined existing models with insights from experimental data involving different reverse-osmosis membranes to enhance accuracy. They employed neural networks, a machine-learning approach, trained on this experimental data to draw deeper understandings. This hybrid modeling approach enabled them to predict the output flow rate based on variables like pressure, salinity, and input flow rate.
Through this refined model, researchers identified the system's optimal operating conditions. These include the ideal balance between electricity generation and freshwater production, considering that resources devoted to one aspect reduce the availability of the other. The model revealed that prioritizing one function over the other, rather than dividing resources equally, is more cost-effective. For instance, one optimal scenario that favored potable water production could satisfy the electricity needs of approximately 1.66 million people and the freshwater requirements of about 11.6 million people.
The model developed by researchers also proved beneficial in optimizing operations to minimize waste treatment expenses associated with the reverse-osmosis process. This process produces a concentrated salty brine as a by-product, which typically needs to be safely reintegrated into the ocean.
Compliance with regulations regarding ocean salinity levels is critical. Thus, the model was employed to determine the necessary operation level of the pumped-storage hydro component. This ensures adequate dilution of the briny waste with seawater, achieving a salinity level that meets legal requirements.
However, the journey to fully realize a combined pumped-storage hydropower and reverse-osmosis system is ongoing. Future efforts will concentrate on refining the pretreatment phase of water purification. This stage is crucial for eliminating significant contaminants from the water. Current pretreatment methods operate at lower pressures.
Integrating these into the system is not the easiest thing to do, as one of the key advantages of the combined system is leveraging the high-pressure conditions advantageous for reverse osmosis. Therefore, reducing the pressure before the seawater enters the reverse-osmosis system could lead to inefficiencies, necessitating further research and innovation in this area.
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