Next-generation machine learning optimized graphene-based solar absorber design using Zr-Cr-Fe3O4-TiC for renewable energy mining applications

The widespread adoption of renewable energy can reduce carbon emissions and promote a greener environment. With a primary focus on the thermal conversion of sunlight, solar energy is an important renewable energy solution. A solar absorber is one of the important devices using solar energy and is made with a multilayered design of Zirconium – Chromium, Iron (III) oxide, and Titanium carbide across 2800 nm. With the graphene-based part in the developed design, near-perfect absorption (99%) is achieved over 600 nm (2.0–2.6 μm) and 94.43% with the wide band at 2800 nm, respectively. The resonance design is built with two plus-shaped and a cylinder over a thin graphene layer for the developed absorber investigation, which is named PCGSA. An AI–driven (ML) technique is used for the developed PCGSA work by the Random Forest method with a 0.25 test size and polarization-insensitive for wide–angle variation (0–80°). With the main focus on the thermal conversion by the developed PCGSA structure, it can be mainly used in some heating applications such as swimming pools, solar cooking, and space heating in home use, and large-scale electricity generation in industries can also be applied.