A basin solar still precision design is regularly not reachable. To solve this issue, the basin area is coated with a nanolayer which allows to stimulate and control the multifaceted of the fast evaporations of physiognomies. The use of adaptive neural network-based approaches leads to better design cause permits detecting the conjunction, gigantic period feed, lower performances parameters which can be detrimental to system production. Further, an online Sequential Extreme Learning Machine (OSELM) system can be used to obtain the latest solar still based on adaptive control. Here, the solar still has been created at physical scale activity for haste of energy absorption. The performance of solar still is defined by the uniform occurrence with time series of dynamics transfer from basin liner to saline water. The feasibility scheme to authenticate was studied by applying calculation to the extensive heat transfer process. The furious SiO₂/TiO₂ nanoparticles used for the stepped basin solar still (SBSS) efficiency shows an increase of performances by 37.69% and 49.21%, respectively using 20% and 30% of SiO₂/TiO₂ coating. It is comparable higher when equated against an SBSS coating either SiO₂ or TiO₂, and/or no nanoparticles coatings. The binary search tree enabled to find the optimal cost for the solar still investigated and obtaining a superior design with higher performances.

盆地太阳能蒸馏器的精确设计通常无法实现。为了解决这个问题，盆地区域涂有纳米层，可以刺激和控制生理学快速蒸发的多面性。基于自适应神经网络的方法的使用导致了更好的设计原因，使得可以检测到合点，巨大的周期馈送，较低的性能参数，这可能对系统生产有害。此外，可以使用在线顺序极限学习机（OSELM）系统获取基于自适应控制的最新太阳能静止图像。在这里，太阳能仍然是在物理规模的活动中产生的，以加快能量吸收。太阳静止器的性能由从盆衬到盐水的动力学时间序列的均匀发生来定义。通过将计算应用于广泛的传热过程中，研究了验证的可行性方案。愤怒的SiO使用20％和30％的SiO ₂ / TiO ₂涂层，用于阶梯式盆式太阳能蒸馏器（SBSS）效率的₂ / TiO ₂纳米颗粒的性能分别提高了37.69％和49.21％。当相当于SiO ₂或TiO ₂的SBSS涂层和/或没有纳米颗粒涂层时，它的可比性更高。二进制搜索树能够找到仍在研究中的太阳能的最佳成本，并获得性能更高的卓越设计。