Performance prediction of aluminum and polycarbonate solar stills with air cavity using an optimized neural network model by golden jackal optimizer

Abstract

Solar stills (SS) are simple eco-friendly desalination devices that exploit solar energy to obtain freshwater from seawater. In this study, a hybrid artificial intelligence model is proposed to predict the thermal behavior of two designs of SSs. Two SSs with a basin and absorber plate made of aluminum for the first SS (ALSS)and polycarbonate for the second SS (PCSS) were established and tested. Both SSs have a modified absorber plate with an air cavity. The hybrid model was composed of an optimized Artificial Neural Network (ANN) model by Golden Jackal Optimizer (GJO). To prove the capability of the proposed model to predict the SSs performance, it was compared with the conventional ANN model as well as two other optimized models with Genetic Algorithm (GA) or Particle Swarm Optimizer (PSO). The results showed that ANN-GJO had better accuracy than ANN, ANN-GA, and ANN-PSO to predict overall heat transfer coefficient, energy efficiency, exergy efficiency, and distillate output. Moreover, ALSS showed better thermal performance compared with PCSS regarding water productivity, exergy efficiency, and energy efficiency. The average exergy efficiency and energy efficiency of PCSS and ALSS were 2.30%, 42.40%, and 3.44%, 48.80%, respectively. The maximum distillate output for PCSS and ALSS were 3.40 l/m2/day and 3.80 l/m2/day, respectively.

Keywords

• Solar still
• Artificial neural network
• Golden jackal optimizer
• Distillate output
• Prediction