Synergistic heliostat field design with intensity and homogenization matching MW-scale solar-driven dry reforming of methane

Abstract

Escalating atmospheric greenhouse gas concentrations and rising global energy demands have posed significant challenges to global sustainability. Solar driven dry reforming of methane assisted by carbon dioxide emerges as a promising technology for simultaneously mitigating greenhouse gas emissions and supplying low-carbon syngas. However, current solar-driven dry reforming of methane systems are mainly based on parabolic dish concentrators with solar power limited to tens of kilowatts. Here, a novel synergistic heliostat field design with intensity and homogenization matching MW-scale solar-driven dry reforming of methane is proposed. An optical-thermal-chemical model is developed for system performance evaluation by combining Monte Carlo ray tracing, the Finite Volume Method, and an Artificial neural network. Multi-objective optimization using Non-dominated Sorting Genetic Algorithm-II is further employed to balance methane conversion rate and system efficiency. High-efficiency solar concentration is achieved by optimizing heliostat field parameters and refining the aiming strategies to ensure the homogenization of solar flux among the sub-reactors. Compared to conventional heliostat field layout, the system solar-to-fuel efficiency is enhanced by 49.68 % with heliostat field land area reduced by 58.58 %. This work pioneers heliostat field design for MW-scale solar-driven dry reforming of methane, and paves the way for scalable industrial applications of solar-driven dry reforming of methane technologies.