中南大学学报（自然科学版）2025，Vol.56Issue(3)：868-880,13.DOI:10.11817/j.issn.1672-7207.2025.03.005
脱硫废水旋转喷雾干燥塔烟气分布器优化设计
Optimization design of flue gas distributor for rotary spray drying tower of desulfurization wastewater
摘要
Abstract
To optimize the design of flue gas distributors in desulfurization wastewater rotary spray drying towers,reduce tower resistance,and enhance evaporation performance,an optimization method integrating CFD and LG-GPR-CIAL models was proposed.Firstly,CFD technology was employed to simulate flow field distribution within the drying tower.A steady-state gas-liquid coupling model was established to generate training samples.Secondly,the LG-GPR-CIAL framework was introduced for optimization.A Gaussian process regression(GPR)surrogate model was constructed.Genetic algorithm(GA)was applied to minimize leave-one-out mean squared error(LOO-MSE)for optimizing GPR hyperparameters.An active learning strategy combining confidence intervals and importance boundary sampling was implemented,where only one sample with maximum confidence interval and optimal objective function value was selected per iteration for model retraining,ensuring minimal sample size and improved accuracy.Thirdly,geometric parameters of internal/external guide vanes were adjusted to optimize the flue gas distributor.New sample points were iteratively added through the active learning strategy to refine model precision,ultimately determining the optimal solution.Finally,the method was validated by using a 600 MW coal-fired unit's desulfurization wastewater rotary spray evaporation system.The results show that the optimized distributor achieves enhancing uniformity of outlet airflow,optimizes swirl number,and reduces turbulence intensity.The design reduces drying tower resistance by 10.05%and maintains evaporation performance.关键词
旋转喷雾干燥塔/烟气分布器/数值模拟/优化设计/主动学习Key words
rotary spray drying tower/flue gas distributor/numerical simulation/optimal design/active learning分类
能源与动力引用本文复制引用
陈沾兴,詹凌霄,陈海杰,杨林军..脱硫废水旋转喷雾干燥塔烟气分布器优化设计[J].中南大学学报（自然科学版）,2025,56(3):868-880,13.基金项目
国家自然科学基金资助项目(52076046)(Project(52076046)supported by the National Natural Science Foundation of China) （52076046）
