Mitigating carbon emissions through AI-driven optimization of zeolite structures: A hybrid model approach

The escalating hazard of weather change necessitates pressing advancements in carbon-mitigating technologies. The COQ capture with zeolites involves working by physical and chemical adsorption, taking advantage of the high surface area, porosity, and the crystalline structure zeolites possess. Zeolites, with their incredible adsorption properties, have emerged as promising materials for carbon capture. However, optimizing zeolite structures to maximize carbon capture efficiency is a complicated and useful resource-extensive technique. Traditional optimization techniques, at the same time effective, are regularly restricted using their computational demands and incapability to fully discover the considerable design space, leading to suboptimal answers. This study introduces a novel integrated model that mixes Genetic Algorithms (GA) with Generative Adversarial Networks (GANs) to enhance the layout and optimization of zeolite systems for carbon capture. GAN stands for generative adversarial networks, which are actual AIs that create realistic data, whereas GA resembles the process of natural selection to get the best solution. They both enhance the zeolite design in this study. The GA component successfully searches the design space by iteratively choosing and evolving promising zeolite systems, even as the GAN aspect generates new, high-capacity systems based totally on learned styles from present facts. This GA-GAN hybrid approach addresses the constraints of modern-day strategies by enabling a complete exploration of feasible zeolite configurations and improving the probability of identifying the optimal approach. Optimization of the zeolite structure is done by a blend of genetic algorithms in conjunction with Generative Adversarial Networks for improved adsorption capacity, surface area, and selectivity of COQ capture. The latest research report has found that the GA-GAN mix model completely outperforms classical optimization methods, a promising tool for advanced carbon capture materials development to this end. The study introduces a brandnew approach to finding the best zeolite designs by merging the characteristics of genetic algorithms and generative adversarial networks. The findings and analysis imply that it is indeed this combined model that will push the carbon capture drive in the future, thus helping global climate change.