Performance and revolving analysis of Solar box cooker using PCM with prediction Hybrid deep Algorithms

Abstract

Abstract Human health is an important main part of the food to consideration in the performance analysis of PCM (Magnesium chloride hexahydrate - MgSO4. 7H2O) covered plastic balls (PBs) were augmented in Solar box-type cooker (SBC). The Artificial Neural Network (ANN) prediction analysis of thermal behavior in SBC is simulated and integrated using a tree and seed metaheuristic algorithm (TSA) an accuracy level was achieved in predicting SBC's efficiency. Hence, the enhancements entailed by introducing a variant may depend on improving ANN's concert. Engineering design found the optimal weights of the neurons using the TSA and includes a copper bar plate (CBP) with 50% higher thermal performance comparable to a silver bar plate (SBP). The functioning of the ANN/TSA technique using SBC has been simulated in the direction of predicting hourly variation by CBP & SBP with ANN/ANN/TSA is verified from food cooking efficiency related to predicting improvements of the SBC is applied as R2, RMSE, MRE, and MAE values like 0.99, 0.0475, 0.228, and 0.05 for the CBP design, while for the SBP design, they were 0.98, 0.086, 0.007, and 0.053, respectively. The R morals working out, testing, and whole statistics set of CBP design were 0.999, 0.995, and 0.997, respectively. For the SBP design, they were 1, 0.964, and 0.996, respectively. It is concluded that the SBC design with PCM-covered PBs and CBP improves cooking performance and increases the system's efficiency in preparing rice and eggs within 2 to 3 hrs.