Predictive machine learning models for optimization of direct solar steam generation

Direct solar steam generation (DSSG) has gained significant consideration in the recent decade because of its ability to generate freshwater, relying on renewable solar energy. Despite experimental data abundance, it is still difficult to optimize DSSG under certain conditions regarding fluid surface temperature changes (Ttop) and evaporation efficiency (eta). This study investigates six predictive machine learning models, including multilayer perceptron (MLP), support vector regression (SVR), decision tree (DT), random forest (RF), adaptive boosting ensemble (ADA-BE), and combinations of them, to model Ttop and eta in interfacial and volumetric DSSG systems. The models are trained on experimental data, and their performance is evaluated using various metrics. Based on the findings of the study, the DT (total R2 = 0. 9900) and DT-SVR combo (total R2 = 0.9829) are the best models to predict eta in interfacial and volumetric systems, respectively. Results show that interfacial DT-MLP combo (total R2 = 0.9964) and volumetric DT-ADA-BE (total R2 = 0.9870) models predict Ttop more accurately. The study predicts that the eta max of 85 +/- 5 % and 90.91 +/- 5 % will be obtained under one sun (1 kW/m2) using GNPMWCNT with 0.015 weight percentage in volumetric and using Au-HT-wood with a thickness of 14.78 mm in interfacial approaches, respectively.

Automated summary

This study investigates six predictive machine learning models for modeling temperature changes and evaporation efficiency in solar steam generation systems. Decision tree and decision tree-support vector regression combo models perform best in predicting evaporation efficiency, while interfacial decision tree-multilayer perceptron combo model and volumetric decision tree-adaptive boosting ensemble model predict temperature changes more accurately.