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 R۲=۰.۹۹, validation R۲=۰.۹۱, and test R۲=۰.۹۲. For optimization, Nelder-Mead and differential evolution methods are used. Results predict that suspending ۰.۰۱۵ weight-percent of GNP-MWCNT to water achieves the maximum efficiency under ۱ kW/m radiation.

کلیدواژه ها:

optimization ، machine learning ، solar direct evaporation ، desalination