双循环流化床混合颗粒流率实验研究与核极限学习机模型预测
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摘要
两床间颗粒循环流率是保证双循环流化床正常运行的关键。在自行设计的双循环流化床(DCFB)实验系统上进行循环流率(G_(s,mix))与各控制参数(气化室风速、提升管风速、初始床层物料量、混合物料中石英砂粒径以及稻壳质量分数)变化关系的研究,并对循环物料中稻壳质量分数(X_r)变化进行分析。此外,根据实验测量数据,建立核极限学习机(KELM)模型并进行G_(s,mix)和循环物料中Xr的预测,同时与极限学习机(ELM)模型进行比较,发现KELM模型具有更小的预测平均绝对值误差和均方根误差且预测所需时间较短,表明该模型可实现各控制参数下DCFB系统中G_(s,mix)和X_r的良好预测,为DCFB系统及类似气化系统的数据模型研究提供一种新方法。
The particles circulation flow rate between the two beds is the key to ensure the normal operation of the dual circulating fluidized bed. The study of relationship between circulating flow rate(G_(s,mix)) and various control parameters(gasification chamber gas velocity, riser gas velocity, initial bed material mass, quartz sand particle diameter and rice husk quality in the mixture) were carried out on a self-designed dual circulating fluidized bed(DCFB) experimental system,and the change of rice husk mass fraction(X_r) in circulating materials was analyzed. In addition, based on the experimental measurement data, the KELM model was established to predict the G_(s,mix) and the Xrof the circulating materials, and compared with the ELM model. It was found that the KELM model had smaller predicted MAPE and RMSE values, and the required time of complete prediction was shorter than ELM. This indicated that KELM model can achieve good prediction of G_(s,mix) and Xrunder various control parameters and provided a new method for the research of DCFB system and similar gasification system.
The particles circulation flow rate between the two beds is the key to ensure the normal operation of the dual circulating fluidized bed. The study of relationship between circulating flow rate(G_(s,mix)) and various control parameters(gasification chamber gas velocity, riser gas velocity, initial bed material mass, quartz sand particle diameter and rice husk quality in the mixture) were carried out on a self-designed dual circulating fluidized bed(DCFB) experimental system,and the change of rice husk mass fraction(X_r) in circulating materials was analyzed. In addition, based on the experimental measurement data, the KELM model was established to predict the G_(s,mix) and the Xrof the circulating materials, and compared with the ELM model. It was found that the KELM model had smaller predicted MAPE and RMSE values, and the required time of complete prediction was shorter than ELM. This indicated that KELM model can achieve good prediction of G_(s,mix) and Xrunder various control parameters and provided a new method for the research of DCFB system and similar gasification system.
引文
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[20]张雷,张小刚,陈华.基于Gath-Geva算法和核极限学习机的多阶段间歇过程软测量[J].化工学报,2018,69(6):2576-2585.ZHANG L,ZHANG X G,CHEN H.Soft sensors for multi-stage batch processes based on Gath-Geva algorithm and kernel extreme learning machine[J].CIESC Journal,2018,69(6):2576-2585.
[21]李琨,韩莹,佘东生,等.基于IFOA-KELM-MEA模型的游梁式抽油机采油系统井下工况的短期预测[J].化工学报,2017,68(1):188-198.LI K,HAN Y,SHE D S,et al.IFOA-KELM-MEA model based transient prediction on down-hole working conditions of beam pumping units[J].CIESC Journal,2017,68(1):188-198.
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[2]CHU C Y,HWANG S J.Attrition and sulfation of calcium sorbent and solids circulation rate in an internally circulating fluidized bed[J].Power Technology,2002,127:185-195.
[3]韩超一,吴文龙,陶蕾,等.双循环流化床提升管中气固流动特性及接触效率研究[J].石油炼制与化工,2016,47(3):16-24.HAN C Y,WU W L,TAO L,et al.Study on gas-solid flow characteristics and contact efficiency in double-circulating fluidized bed riser[J].Petroleum Processing and Petrochemicals,2016,47(3):16-24.
[4]耿察民,钟文琪,邵英娟,等.双循环流化床颗粒分布特性的三维数值模拟[J].中国科学院大学学报,2016,33(2):258-264.GENG C M,ZHONG W Q,SHAO Y J,et al.Three-dimensional numerical simulation of particle distribution characteristics of doublecirculating fluidized bed[J].Journal of University of Chinese Academy of Sciences,2016,33(2):258-264.
[5]陈鸿伟,刘焕志,李晓伟,等.双循环流化床颗粒循环流率与BP神经网络预测[J].中国电机工程学报,2010,33(32):25-29.CHEN H W,LIU H Z,LI X W,et al.Circulating flow rate of double circulating fluidized bed particles and BP neural network prediction[J].Proceedings of the CSEE,2010,33(32):25-29.
[6]韩磊,于旷世,朱治平,等.双循环流化床冷态实验研究[J].锅炉技术,2011,42(1):26-30.HAN L,YU Y S,ZHU Z P,et al.Study on cold state experiment of double circulating fluidized bed[J].Boiler Technology,2011,42(1):26-30.
[7]BI H,ZHU J X.Static instability analysis of circulating fluidized beds and concept of high-density risers[J].AIChE Journal,1998,39(8):1272-1280.
[8]MIAO Q,JESSE Z,BARGHI S,et al.A hydrodynamic model prediction of a circulating fluidized bed with low-density particles[J].The Canadian Journal of Chemical Engineering,2002,90:1027-1032.
[9]SIDDHARTHA S,BRAHIM S A,BADRUL M J,et al.Hydrodynamic properties of a cold model of dual fluidized bed gasifier:a modeling and experimental investigation[J].Chemical Engineering Research and Design,2016,109:791-805.
[10]王伟,范晓旭,那永杰,等.循环流化床多联供试验台的冷态实验研究[J].锅炉技术,2006,37(4):40-43.WANG W,FAN X X,NA Y J,et al.Cold state experimental study of circulating fluidized bed multi-joint test bench[J].Boiler Technology,2006,37(4):40-43.
[11]韩磊.双循环流化床冷态实验研究[D].北京:中国科学院工程热物理研究所,2010:32-41.HAN L.Experimental study on cold state of double circulating fluidized bed[D].Beijing:Institute of Engineering Thermophysics,Chinese Academy of Sciences,2010:32-41.
[12]李佑楚,陈丙瑜,王凤鸣,等.快速流态化流动[J].化工学报,1979,30(2):143-152.LI Y C,CHEN B Y,WANG F M,et al.Fast fluidization flow[J].Journal of Chemical Industry and Engineering(China),1979,30(2):143-152.
[13]RYU H J,LIM N Y,BAE D H,et al.Minimum fluidization velocity and transition velocity of fast fluidization of oxygen carrier particle for chemical-looping combustor[J].Hwahak Konghak,2003,41(5):624-631.
[14]金涌,祝京旭,汪展文,等.流态化工程原理[M].北京:清华大学出版社,2001:25-63.JIN Y,ZHU J X,WANG Z W,et al.Principle of fluidization engineering[M].Beijing:Tsinghua University Press,2001:25-63.
[15]WEI F,CHENG Y,JIN Y,et al.Axial and lateral dispersion of fine particles in a binary-solid riser[J].The Canadian Journal of Chemical Engineering,1998,76:19-26.
[16]HSINHONG S,CHEN Y C,SHYH J H.Solids circulation and attrition rate and gas bypassing in an internally circulating fluidized bed[J].Industrial&Engineering Research,2003,42:5915-5923.
[17]PAUDEL B,FENG Z.Prediction of minimum fluidization velocity for binary mixtures of biomass and inert particles[J].Powder Technology,2013,237:134-140.
[18]SHARMA M,KUMAR A,PATIL K,et al.Fluidization characteristics of a mixture of gasifier solid residues,switchgrass and inert material[J].Powder Technology,2013,235:661-668.
[19]HUANG G B,ZHOU H M,DING X J,et al.Extreme learning machine for regression and multiclass classification[J].IEEE Transactions on Systems,Man,and Cybernetics,Part B(Cybernetics),2012,42(2):513-529.
[20]张雷,张小刚,陈华.基于Gath-Geva算法和核极限学习机的多阶段间歇过程软测量[J].化工学报,2018,69(6):2576-2585.ZHANG L,ZHANG X G,CHEN H.Soft sensors for multi-stage batch processes based on Gath-Geva algorithm and kernel extreme learning machine[J].CIESC Journal,2018,69(6):2576-2585.
[21]李琨,韩莹,佘东生,等.基于IFOA-KELM-MEA模型的游梁式抽油机采油系统井下工况的短期预测[J].化工学报,2017,68(1):188-198.LI K,HAN Y,SHE D S,et al.IFOA-KELM-MEA model based transient prediction on down-hole working conditions of beam pumping units[J].CIESC Journal,2017,68(1):188-198.
[22]ZHANG M,LIU X G,ZHANG Z Y.A soft sensor for industrial melt index prediction based on evolutionary extreme learning machine[J].Chinese Journal of Chemical Engineering,2016,24(8):1013-1019.