article · Journal of Water Reuse and Desalination
Rising demand for freshwater has highlighted the need for more dependable and efficient seawater treatment operations. Deep learning and machine learning systems present opportunities to improve how salt particles in seawater are analysed, directly benefiting water treatment facility performance. This study presents a method to model and optimise saline water treatment processes using water level data analysis. The modelling and optimisation steps apply molecular separation based reverse osmosis Bayesian optimisation. Saline particle analysis is then conducted using back propagation combined with a kernelised support swarm machine. When evaluated on water salinity data, the machine learning methodology achieved 92 percent accuracy, alongside balanced performance across precision, recall, specificity, computational cost, and Kappa metrics.
Growing freshwater demand requires seawater treatment and desalination facilities to operate with higher consistency and efficiency. Integrating advanced machine learning into saline water processing provides more accurate particle analysis and improved operational control, which helps treatment facilities better manage desalination workflows to secure essential clean water resources.
This work could inform the development of algorithmic control and monitoring software for operators of seawater desalination plants. By improving the precision of salt particle analysis, such tools could assist in plant process tuning. Based on the abstract, the technology is at an early research stage, having been evaluated experimentally on algorithmic performance metrics without reported field deployment or pilot testing.
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Abstract Water is a vital resource that makes it possible for human life forms to exist. The need for freshwater consumption has significantly increased in recent years. Seawater treatment facilities are less dependable and efficient. Deep learning systems have the potential to increase the efficiency as well as the accuracy of salt particle analysis in saltwater, which will benefit water treatment plant performance. This research proposed a novel method for optimization and modelling of the treatment process for saline water based on water level data analysis using machine learning (ML) techniques. Here, the optimization and modelling are carried out using molecular separation-based reverse osmosis Bayesian optimization. Then the modelled water saline particle analysis has been carried out using back propagation with Kernelized support swarm machine. Experimental analysis is carried out based on water salinity data in terms of accuracy, precision, recall, and specificity, computational cost, and Kappa coefficient. The proposed technique attained an accuracy of 92%, precision of 83%, recall of 78%, specificity of 81%, computational cost of 59%, and Kappa coefficient of 78%.
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DOI: 10.2166/wrd.2022.069
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