In this study,the impact of different reaction times on the preparation of powdered activated carbon(PAC)using a one-step rapid activation method under flue gas atmosphere is investigated,and the underlying reaction m...In this study,the impact of different reaction times on the preparation of powdered activated carbon(PAC)using a one-step rapid activation method under flue gas atmosphere is investigated,and the underlying reaction mechanism is summarized.Results indicate that the reaction process of this method can be divided into three stages:stage I is the rapid release of volatiles and the rapid consumption of O_(2),primarily occurring within a reaction time range of 0-0.5 s;stage II is mainly the continuous release and diffusion of volatiles,which is the carbonization and activation coupling reaction stage,and the carbonization process is the main in this stage.This stage mainly occurs at the reaction time range of 0.5 -2.0 s when SL-coal is used as material,and that is 0.5-3.0 s when JJ-coal is used as material;stage III is mainly the activation stage,during which activated components diffuse to both the surface and interior of particles.This stage mainly involves the reaction stage of CO_(2)and H2O(g)activation,and it mainly occurs at the reaction time range of 2.0-4.0 s when SL-coal is used as material,and that is 3.0-4.0 s when JJ-coal is used as material.Besides,the main function of the first two stages is to provide more diffusion channels and contact surfaces/activation sites for the diffusion and activation of the activated components in the third stage.Mastering the reaction mechanism would serve as a crucial reference and foundation for designing the structure,size of the reactor,and optimal positioning of the activator nozzle in PAC preparation.展开更多
针对火电机组SO_(2)排放质量浓度的影响因素众多,难以准确预测的问题,提出一种改进向量加权平均(weighted mean of vectors,INFO)算法与双向长短期记忆(bi-directional long short term memory,Bi-LSTM)神经网络相结合的预测模型(改进IN...针对火电机组SO_(2)排放质量浓度的影响因素众多,难以准确预测的问题,提出一种改进向量加权平均(weighted mean of vectors,INFO)算法与双向长短期记忆(bi-directional long short term memory,Bi-LSTM)神经网络相结合的预测模型(改进INFO-Bi-LSTM模型)。采用Circle混沌映射和反向学习产生高质量初始化种群,引入自适应t分布提升INFO算法跳出局部最优解和全局搜索的能力。选取改进INFO-Bi-LSTM模型和多种预测模型对炉内外联合脱硫过程中4种典型工况下的SO_(2)排放质量浓度进行预测,将预测结果进行验证对比。结果表明:改进INFO算法的寻优能力得到提升,并且改进INFO-Bi-LSTM模型精度更高,更加适用于SO_(2)排放质量浓度的预测,可为变工况下的脱硫控制提供控制理论支撑。展开更多
基金supported by the Qingdao Postdoctoral Program Funding(QDBSH20220202045)Shandong provincial Natural Science Foundation(ZR2021ME049,ZR2022ME176)+1 种基金National Natural Science Foundation of China(22078176)Taishan Industrial Experts Program(TSCX202306135).
文摘In this study,the impact of different reaction times on the preparation of powdered activated carbon(PAC)using a one-step rapid activation method under flue gas atmosphere is investigated,and the underlying reaction mechanism is summarized.Results indicate that the reaction process of this method can be divided into three stages:stage I is the rapid release of volatiles and the rapid consumption of O_(2),primarily occurring within a reaction time range of 0-0.5 s;stage II is mainly the continuous release and diffusion of volatiles,which is the carbonization and activation coupling reaction stage,and the carbonization process is the main in this stage.This stage mainly occurs at the reaction time range of 0.5 -2.0 s when SL-coal is used as material,and that is 0.5-3.0 s when JJ-coal is used as material;stage III is mainly the activation stage,during which activated components diffuse to both the surface and interior of particles.This stage mainly involves the reaction stage of CO_(2)and H2O(g)activation,and it mainly occurs at the reaction time range of 2.0-4.0 s when SL-coal is used as material,and that is 3.0-4.0 s when JJ-coal is used as material.Besides,the main function of the first two stages is to provide more diffusion channels and contact surfaces/activation sites for the diffusion and activation of the activated components in the third stage.Mastering the reaction mechanism would serve as a crucial reference and foundation for designing the structure,size of the reactor,and optimal positioning of the activator nozzle in PAC preparation.
文摘针对火电机组SO_(2)排放质量浓度的影响因素众多,难以准确预测的问题,提出一种改进向量加权平均(weighted mean of vectors,INFO)算法与双向长短期记忆(bi-directional long short term memory,Bi-LSTM)神经网络相结合的预测模型(改进INFO-Bi-LSTM模型)。采用Circle混沌映射和反向学习产生高质量初始化种群,引入自适应t分布提升INFO算法跳出局部最优解和全局搜索的能力。选取改进INFO-Bi-LSTM模型和多种预测模型对炉内外联合脱硫过程中4种典型工况下的SO_(2)排放质量浓度进行预测,将预测结果进行验证对比。结果表明:改进INFO算法的寻优能力得到提升,并且改进INFO-Bi-LSTM模型精度更高,更加适用于SO_(2)排放质量浓度的预测,可为变工况下的脱硫控制提供控制理论支撑。
