The solar cooker represents a challenging scientific design. Its non-regular rechargeable system and the restriction imposed by the required availability quantity are the main issues. The use of a bar plate coated with nanolayer materials helps to stimulate and control the multifaceted performances for the cooker vessels. Further, it was noted that the traditional human methods are not capable to stimulate an efficient design for thermal applications, because the environment cannot adapt to the variable source. To overcome these challenges, we have used the approaches of adaptive neural network-based controls which further consider other parameters as the smaller family, measured conjunction, enormous period of feeding and below performances. Therefore, a novel solar cooker based on adaptive control through an online Sequential Extreme Learning Machine (OSELM) is presented and discussed. The use of OSELM enables also to detect an off-line physical activity process. The proposed solar cooker includes a bar plate coated with nanolayer materials (SiO2/TiO2 nanoparticles) which is responsible for physical accelerated activity of energy absorption. The feasibility scheme to validate this study is based on the calculation of extensive heat transfer process. By using the furious SiO2/TiO2 nanoparticles for the Stepped solar bar plate cooker (SSBC) the efficiency was increased by 37.69% and 49.21% using 10% and 15% volume fractions of nanoparticles.
- solar cooker
- stepped bar plate
- adaptive traversal tree
- online Sequential Extreme Learning Machine