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采用用户自定义函数(UDF)对煤粉MILD燃烧的数值模型进行了优化，包括挥发分的多组分比拟、脱挥发分极限的温度依赖优化和煤粉颗粒燃烧过程的算法优化。通过与IFRF的煤粉MILD燃烧经典实验的对比，验证了模型优化所产生的效果。研究发现，挥发分的多组分比拟能更准确地预测炉内O2浓度分布；脱挥发分极限的温度依赖优化，一方面能够使挥发分在低温区间开始脱附，改善模拟中着火延迟过长的问题，另一方面，能够使煤粉颗粒在高温区间脱附出更多的挥发分，使得温度分布的预测更为准确；煤粉颗粒燃烧过程的算法优化能够刻画挥发分脱附过程和焦炭反应过程在温度区间(780~1200K)上的重叠现象，能更准确地预测炉内CO分布，与真实的煤粉MILD燃烧过程更为相符。“,”Based on the user-defined functions (UDF), the present work optimized the numerical models of MILD combustion for pulverized coal by considering the components of volatile, temperature dependence of the devolatilization limit and combustion process of the pulverized coal particles. The IFRF experiment of MILD coal combustion was taken as the reference. Results suggest that the multicomponent volatile module improves the predictions of the O2 distribution. After considering the temperature dependence of the limit of the devolatilization, the volatile starts devolatilization in low-temperature region and more volatile is desorbed in high-temperature region. The optimized combustion process of the pulverized coal depicts the overlap phenomenon of the volatile devolatilization and char combustion over 780~1200K. This improves the CO prediction in the furnace and agrees with the actual process of the coal MILD combustion.