machine learning model-based optimization of solar-powered direct volumetric steam generation

In recent years solar-powered desalination systems have received much attention as a clean and sustainable solution to freshwater demand. one challenge in the field is to maximize the system’s efficiency. this study focuses on the volumetric direct solar steam generation and provides a model-based optimization using support vector regression and decision tree regression ensemble molding. the model achieves train r2=0.99, validation r2=0.91, and test r2=0.92. for optimization, nelder-mead and differential evolution methods are used. results predict that suspending 0.015 weight-percent of gnp-mwcnt to water achieves the maximum efficiency under 1 kw/m2 radiation.