Hybrid Artificial Photosynthesis and AdaBoost for Effective Solar-to-Fuel Optimization

Achieving optimal solar-to-fuel conversion is essential for developing sustainable energy technology. An innovative hybrid method is proposed in this work, which combines Artificial Photosynthesis (AP) with the AdaBoost algorithm to improve the optimization of solar-to-fuel conversion. AP systems imitate the process of AP to transform solar energy into chemical fuels. However, their effectiveness is often constrained by a range of issues. Using AdaBoost, a robust ensemble learning technique, the parameters and operating conditions of the AP system are optimized to enhance its performance. This hybrid model leverages AdaBoost's ability to refine predictions and dynamically adjust parameters based on performance metrics, yielding substantial improvements in fuel production efficiency. The experimental findings demonstrate that the hybrid technique outperforms conventional approaches, offering a more efficient and scalable alternative for converting solar energy into gasoline. This comprehensive technique not only progresses the domain of AP but also offers a strong foundation for enhancing renewable energy systems.