Nishaben Desai Dholakiya, Ranjan Dey* and Anirban Roy,
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引用次数: 0
Abstract
This study investigates the thermodynamic feasibility of establishing desalination plants along the Arabian Sea coast of Goa (India). Based on the experimental values of ultrasonic velocity (u) and density (u) at varying temperature and salinity conditions, isothermal compressibility (βT), adiabatic compressibility (βs), acoustic impedance (Z), and molecular free length (Lf) were derived. Utilizing the real seawater data, we developed robust machine learning models, including random forest (RF), gradient boosting (GB), AdaBoost (AB), and stack ensemble machine learning (SEML), to predict these thermodynamic properties solely on the basis of temperature and salinity. Our models exhibited high accuracy, enabling reliable predictions that inform energy and resource management strategies for desalination in India’s coastal regions, highlighting the importance of understanding the estuarine system.
期刊介绍：
The Journal of Chemical & Engineering Data is a monthly journal devoted to the publication of data obtained from both experiment and computation, which are viewed as complementary. It is the only American Chemical Society journal primarily concerned with articles containing data on the phase behavior and the physical, thermodynamic, and transport properties of well-defined materials, including complex mixtures of known compositions. While environmental and biological samples are of interest, their compositions must be known and reproducible. As a result, adsorption on natural product materials does not generally fit within the scope of Journal of Chemical & Engineering Data.
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