Intelligent optimization of solar-driven biomass-plastic co-gasification system based on limited data mapping

Tengyu Ma, Ruilin Wang, Haifeng Wu, Junhong Hao and Hongsheng Wang

Energy, 2025, vol. 336, issue C

Abstract: This study proposes a solar-driven biomass-plastic co-gasification system that not only achieves efficient coupling of renewable energy sources but also promotes the clean recycling of plastic waste. Compared with biomass gasification alone, the co-gasification system can significantly increase the higher heating value and yield of combustible gas products. However, operating parameters play a decisive role in the energy utilization and carbon conversion processes of the co-gasification system. To this end, this study systematically analyzes the effects of gasification temperature, gasification agent dosage, and plastic content in the feedstock on the system's energy utilization and carbon conversion processes. Further, it explores the interactions between these parameters in these conversion processes. Building on this, a finite data mapping-based parameter optimization method is proposed and applied. The optimization results show that under optimal operating parameters, the theoretical energy utilization efficiency of the system can reach 76.2%, and the theoretical carbon conversion efficiency is 98.0%. The optimization method proposed in this study significantly reduces the computational complexity of the optimization process and greatly improves optimization efficiency. Additionally, this study not only provides new pathways for the efficient coupling and utilization of clean renewable energy but also provides technical support for the clean recycling and treatment of plastic waste.

Keywords: Solar thermochemical; Co-gasification; Limited data; Neural network (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:336:y:2025:i:c:s0360544225042045

DOI: 10.1016/j.energy.2025.138562

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