Design Optimization of Direct Combustion Process in Regenerative Thermal Oxidizer at Low Concentration of Coal Mine Gas Using Advanced Computational Models
Year: 2025
Type: article
Abstract: Coal mine gas with methane concentrations below 8% cannot sustain stable self-combustion, posing significant challenges for safe utilization and greenhouse gas mitigation. To address this limitation, we developed a large-scale industrial square rotary regenerative thermal oxidizer (RTO) capable of high-efficiency oxidation under ultra-low methane conditions. This work integrates multi-scale computational fluid dynamics (CFD) modeling, laboratory and pilot-scale physical experiments, and multi-physics coupled simulations to capture the complex interactions of fluid flow, species transport, and thermal response in regenerative ceramics. Compared with conventional circular or three-bed RTOs, the proposed square rotating design achieves 13% higher heat storage utilization, 15% smaller floor area, and enhanced spatial uniformity of the temperature field. Multi-scale simulations reveal that increasing methane molar fraction (CH4) from 0.012 to 0.017 raises the peak temperature from 1280 K to 1350 K, reduces the burnout height from 1.18 m to 1.15 m, and, under constant oxygen supply, extends the high-temperature zone to 1450 K with a stabilized burnout position at 1.06 ± 0.01 m. Incorporating a 15° conical expansion combustion chamber increases local turbulent kinetic energy by 17.4%, accelerating oxidation while maintaining methane removal rates > 98% within an optimized bottom blowing time of 30–90 s. This study not only provides validated design thresholds for ultra-low concentration methane oxidation—such as temperature windows, buffer zones, and switching cycles—but also offers an engineering framework for scaling RTO systems to industrial coal mine applications. This advances both energy recovery efficiency and methane emission control, demonstrating clear advantages over existing RTO configurations.
Source: Fluids
Authors: Jida Zhang, Dong Wang, Zhongkuan Wei, Sheng Li, Junhui Yang, Shiyang Jia, Zhongcheng Ma, Chengmin Chen, Krishnaswamy Nandakumar
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,172
Award: G1493245663
OpenAlex ID: W4416124987
