Revealing prediction of perched cum off-centered wick solar still performance using network based on optimizer algorithm

A sincere effort has been made to engineer a perched cum off-centered wick solar still (PCWSS) in the present work. The daily average efficiency with hourly prediction distillate yield of the PCWSS has used those artificial neural networks (ANNs) tool with Harris Hawk's Optimizes (HHOs) technique. HHO performance with ANN simulated as an optimal parameter to grab preyed. An experimental performance predicting the system's productivity is associated by dual supplementary mockups as vectors gadget, tradition ANN. HHO-ANN approach results are compared with the experimental observations (one year) of the solar still. Radial Basis Function (RBF) and Feed Forward (FF) have been used ANN structures to estimate hourly distillate yield and efficiency of the system is 59.78%. Evaluating the R2, RMSE, MRE, MAE, EC, OI, CRM analysis of prediction models was based on numerical error conditions. Optimized the analysis of PCWSS with a model as HHO-ANN used optimal parameter values has prediction accuracy associated with ANN and the competence for HHO. Annual analysis based on the HHO - ANN structures predicted the hourly distillate yield with mean error varying from 8.13% and 6.1%. The error for the monthly average prediction of distillate yield is from 0.95% to 1.12%, respectively. HHO - ANN has been used with the best accuracy in predicting the PCWSS invention associated with tangible experimental outcomes. (c) 2022 Institution of Chemical Engineers. Published by Elsevier Ltd. All rights reserved.

Automated summary

In this study, a sincere effort has been made to engineer a perched cum off-centered wick solar still (PCWSS). Artificial neural networks (ANNs) and optimization techniques were used to predict the efficiency and distillate yield of the system. The experimental results show that the system exhibits good prediction accuracy and productivity.