CIESC Journal ›› 2022, Vol. 73 ›› Issue (5): 2039-2051.DOI: 10.11949/0438-1157.20211646
• Process system engineering • Previous Articles Next Articles
Zihao QI1(),Wenqi ZHONG1(),Xi CHEN1,Guanwen ZHOU1,Xiaoliang ZHAO2,Meijing XIN2,Yi CHEN2,Yongchang ZHU2
Received:
2021-11-17
Revised:
2022-03-07
Online:
2022-05-24
Published:
2022-05-05
Contact:
Wenqi ZHONG
戚子豪1(),钟文琪1(),陈曦1,周冠文1,赵小亮2,辛美静2,陈翼2,朱永长2
通讯作者:
钟文琪
作者简介:
戚子豪(1996—),男,硕士研究生,CLC Number:
Zihao QI, Wenqi ZHONG, Xi CHEN, Guanwen ZHOU, Xiaoliang ZHAO, Meijing XIN, Yi CHEN, Yongchang ZHU. Research on dynamic characteristics of cement raw meal decomposition process based on hybrid modeling[J]. CIESC Journal, 2022, 73(5): 2039-2051.
戚子豪, 钟文琪, 陈曦, 周冠文, 赵小亮, 辛美静, 陈翼, 朱永长. 基于混合建模的水泥生料分解过程动态特性研究[J]. 化工学报, 2022, 73(5): 2039-2051.
Add to citation manager EndNote|Ris|BibTeX
名称 | 数值 |
---|---|
分解炉高度/mm | 78 |
分解炉主体直径/m | 8.4 |
轴向长度/m | 98.6 |
气体停留时间/s | 7~8 |
进料量/(t/h) | 360 |
生料温度/℃ | 800 |
分解炉喂煤量/(t/h) | 11 |
投煤温度/℃ | 60 |
喷氨量/(kg/吨熟料) | 3 |
氨水浓度/% | 20 |
排烟温度/℃ | 880 |
Table 1 Parameters of calciner
名称 | 数值 |
---|---|
分解炉高度/mm | 78 |
分解炉主体直径/m | 8.4 |
轴向长度/m | 98.6 |
气体停留时间/s | 7~8 |
进料量/(t/h) | 360 |
生料温度/℃ | 800 |
分解炉喂煤量/(t/h) | 11 |
投煤温度/℃ | 60 |
喷氨量/(kg/吨熟料) | 3 |
氨水浓度/% | 20 |
排烟温度/℃ | 880 |
元素分析/% | 工业分析/% | 低位发热量/(MJ/kg) | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Cad | Had | Oad | Sad | Nad | Aad | FCad | Vad | Mad | |||
67.73 | 3.89 | 3.59 | 1.03 | 1.41 | 22.34 | 54.32 | 20.21 | 2.11 | 23.93 |
Table 2 Parameters of fuel characteristic
元素分析/% | 工业分析/% | 低位发热量/(MJ/kg) | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Cad | Had | Oad | Sad | Nad | Aad | FCad | Vad | Mad | |||
67.73 | 3.89 | 3.59 | 1.03 | 1.41 | 22.34 | 54.32 | 20.21 | 2.11 | 23.93 |
预测对象 | 计算值 | 实际值 |
---|---|---|
出口温度/K | 1164 | 1160 |
出口压力/Pa | -900 | -851 |
碳酸钙分解率/% | 96 | 95 |
煤炭燃尽率/% | 98 | — |
O2含量/% | 2.43 | 2.27 |
CO含量/% | 0.0195 | 0.0217 |
NO x 含量/(mg/m3) | 75 | 66 |
Table 3 Calculation result
预测对象 | 计算值 | 实际值 |
---|---|---|
出口温度/K | 1164 | 1160 |
出口压力/Pa | -900 | -851 |
碳酸钙分解率/% | 96 | 95 |
煤炭燃尽率/% | 98 | — |
O2含量/% | 2.43 | 2.27 |
CO含量/% | 0.0195 | 0.0217 |
NO x 含量/(mg/m3) | 75 | 66 |
项目 | 训练集 | 测试集 | ||
---|---|---|---|---|
RMSE | MAPE | RMSE | MAPE | |
温度 | 4.06 K | 0.34% | 6.72 K | 0.50% |
NO x 浓度 | 6.22 mg/m3 | 7.27% | 7.35 mg/m3 | 9.01% |
Table 4 Comparison of training error and test error
项目 | 训练集 | 测试集 | ||
---|---|---|---|---|
RMSE | MAPE | RMSE | MAPE | |
温度 | 4.06 K | 0.34% | 6.72 K | 0.50% |
NO x 浓度 | 6.22 mg/m3 | 7.27% | 7.35 mg/m3 | 9.01% |
变量名称 | 方程 | 变量名称 | 方程 | ||
---|---|---|---|---|---|
挥 发 分 析 出 | 挥发分析出量 | 焦 炭 燃 烧 | 氧气扩散系数 | ||
Reynolds 数 | |||||
Schmidt 数 | |||||
挥发分含量 | |||||
Archimedes 数 | |||||
碳 酸 钙 分 解 | 分解速率 | ||||
焦 炭 燃 烧 | 焦炭燃烧化学机械因子 | 孔隙效率因子 | |||
分子扩散系数 | |||||
CO/CO2浓度 生成比 | |||||
颗粒表面温度 | |||||
单个焦炭颗粒反应速率 | 扩散系数校正因子 | ||||
焦炭燃烧反应速率 | CO2平衡 分压 | ||||
化学反应速率 | 化学反应速率 | ||||
扩散反应速率 | 扩散反应速率 | ||||
Sherwood 数 | 碳酸钙质量变化速率 |
Appendix 1 Calculation formulas for volatile matter, coke combustion and calcium carbonate decomposition
变量名称 | 方程 | 变量名称 | 方程 | ||
---|---|---|---|---|---|
挥 发 分 析 出 | 挥发分析出量 | 焦 炭 燃 烧 | 氧气扩散系数 | ||
Reynolds 数 | |||||
Schmidt 数 | |||||
挥发分含量 | |||||
Archimedes 数 | |||||
碳 酸 钙 分 解 | 分解速率 | ||||
焦 炭 燃 烧 | 焦炭燃烧化学机械因子 | 孔隙效率因子 | |||
分子扩散系数 | |||||
CO/CO2浓度 生成比 | |||||
颗粒表面温度 | |||||
单个焦炭颗粒反应速率 | 扩散系数校正因子 | ||||
焦炭燃烧反应速率 | CO2平衡 分压 | ||||
化学反应速率 | 化学反应速率 | ||||
扩散反应速率 | 扩散反应速率 | ||||
Sherwood 数 | 碳酸钙质量变化速率 |
化学反应 | 催化剂 | 反应速率 | |
---|---|---|---|
(1) | — | ||
(2) | — | ||
(3) | — | ||
(4) | — | ||
(5) | — | ||
CaO | |||
(6) | — | ||
char | |||
CaO | |||
(7) | char | ||
CaO | |||
(8) | char | ||
CaO | |||
(9) | Char | ||
CaO | |||
(10) | — |
Appendix 2 Homogeneous reaction rate in the furnace
化学反应 | 催化剂 | 反应速率 | |
---|---|---|---|
(1) | — | ||
(2) | — | ||
(3) | — | ||
(4) | — | ||
(5) | — | ||
CaO | |||
(6) | — | ||
char | |||
CaO | |||
(7) | char | ||
CaO | |||
(8) | char | ||
CaO | |||
(9) | Char | ||
CaO | |||
(10) | — |
1 | Gungor A. Simulation of NO x emission in circulating fluidized beds burning low-grade fuels[J]. Energy & Fuels, 2009, 23(5): 2475-2481. |
2 | Gungor A. One dimensional numerical simulation of small scale CFB combustors[J]. Energy Conversion and Management, 2009, 50(3): 711-722. |
3 | Wu H C, Yang C, He H X, et al. A hybrid simulation of a 600 MW supercritical circulating fluidized bed boiler system[J]. Applied Thermal Engineering, 2018, 143: 977-987. |
4 | Ke X W, Li D F, Li Y R, et al. 1-Dimensional modelling of in-situ desulphurization performance of a 550 MWe ultra-supercritical CFB boiler[J]. Fuel, 2021, 290: 120088. |
5 | 毛玉如. 循环流化床富氧燃烧技术的试验和理论研究[D]. 杭州: 浙江大学, 2003. |
Mao Y R. Theoretical and experimental study on oxygen-enriched combustion technology in circulating fluidized bed[D]. Hangzhou: Zhejiang University, 2003. | |
6 | 魏莉, 钟文琪, 邵应娟. 煤流化床加压富氧燃烧过程的动态特性[J]. 东南大学学报(自然科学版), 2020, 50(2): 358-367. |
Wei L, Zhong W Q, Shao Y J. Dynamic characteristics of pressurized oxy-fuel combustion in fluidized bed[J]. Journal of Southeast University (Natural Science Edition), 2020, 50(2): 358-367. | |
7 | 戚龙周. 600MW超临界直流锅炉热力性能建模与仿真研究[D]. 武汉: 华中科技大学, 2012. |
Qi L Z. Modeling and simulation research on thermodynamic performances of 600MW supercritical once through boiler[D]. Wuhan: Huazhong University of Science and Technology, 2012. | |
8 | Magnanelli E, Tranås O L, Carlsson P, et al. Dynamic modeling of municipal solid waste incineration[J]. Energy, 2020, 209: 118426. |
9 | 谢海立. 垃圾焚烧炉排炉的炉内过程动态特性数字仿真[D]. 南京: 东南大学, 2017. |
Xie H L. Numerical simulation on the dynamic characteristics of combustion in mechanical grate incinerator[D]. Nanjing: Southeast University, 2017. | |
10 | Jensen L S. NO x from cement production-reduction by primary measures[D]. Denmark: Technical University of Denmark, 1999. |
11 | Iliuta I, Dam-Johansen K, Jensen L S. Mathematical modeling of an in-line low-NO x calciner[J]. Chemical Engineering Science, 2002, 57(5): 805-820. |
12 | Iliuta I, Dam-Johansen K, Jensen A, et al. Modeling of in-line low-NO x calciners—a parametric study[J]. Chemical Engineering Science, 2002, 57(5): 789-803. |
13 | Iliuta I, Dam-Johansen K, Jensen A. Modelling of in-line low-NO x calciners-NO x emission[J]. Chemical Engineering Research and Design, 2003, 81(5): 537-548. |
14 | Nieuwland J J, Delnoij E, Kuipers J A M, et al. An engineering model for dilute riser flow[J]. Powder Technology, 1997, 90(2): 115-123. |
15 | Fellaou S, Harnoune A, Seghra M A, et al. Statistical modeling and optimization of the combustion efficiency in cement kiln precalciner[J]. Energy, 2018, 155: 351-359. |
16 | Hao X C, Guo T T, Huang G L, et al. Energy consumption prediction in cement calcination process: a method of deep belief network with sliding window[J]. Energy, 2020, 207: 118256. |
17 | Hao X C, Xu Q Q, Shi X, et al. Prediction of nitrogen oxide emission concentration in cement production process: a method of deep belief network with clustering and time series[J]. Environmental Science and Pollution Research International, 2021, 28(24): 31689-31703. |
18 | He W, Li J F, Tang Z H, et al. A novel hybrid CNN-LSTM scheme for nitrogen oxide emission prediction in FCC unit[J]. Mathematical Problems in Engineering, 2020, 2020: 8071810. |
19 | Hvala N, Kocijan J. Design of a hybrid mechanistic/Gaussian process model to predict full-scale wastewater treatment plant effluent[J]. Computers & Chemical Engineering, 2020, 140: 106934. |
20 | Bangi M S F, Kwon J S I. Deep hybrid modeling of chemical process: application to hydraulic fracturing[J]. Computers & Chemical Engineering, 2020, 134: 106696. |
21 | Bhadriraju B, Bangi M S F, Narasingam A, et al. Operable adaptive sparse identification of systems: application to chemical processes[J]. AIChE Journal, 2020, 66(11): e16980. |
22 | Nielsen R F, Nazemzadeh N, Sillesen L W, et al. Hybrid machine learning assisted modelling framework for particle processes[J]. Computers & Chemical Engineering, 2020, 140: 106916. |
23 | 华丰, 方舟, 邱彤. 乙烯裂解炉反应与传热耦合的智能混合建模与模拟[J]. 化工学报, 2018, 69(3): 923-930. |
Hua F, Fang Z, Qiu T. Recirculation and reaction hybrid intelligent modeling and simulation for industrial ethylene cracking furnace[J]. CIESC Journal, 2018, 69(3): 923-930. | |
24 | von Stosch M, Oliveira R, Peres J, et al. Hybrid semi-parametric modeling in process systems engineering: past, present and future[J]. Computers & Chemical Engineering, 2014, 60: 86-101. |
25 | Venkatasubramanian V. The promise of artificial intelligence in chemical engineering: is it here, finally? [J]. AIChE Journal, 2019, 65(2): 466-478. |
26 | Zendehboudi S, Rezaei N, Lohi A. Applications of hybrid models in chemical, petroleum, and energy systems: a systematic review[J]. Applied Energy, 2018, 228: 2539-2566. |
27 | Hill S C, Smoot L D. Modeling of nitrogen oxides formation and destruction in combustion systems[J]. Progress in Energy and Combustion Science, 2000, 26(4/5/6): 417-458. |
28 | Yang Y, Zhang Y, Li S J, et al. Numerical simulation of low nitrogen oxides emissions through cement precalciner structure and parameter optimization[J]. Chemosphere, 2020, 258: 127420. |
29 | Mickley H S, Trilling C A. Heat transfer characteristics of fluidized beds[J]. Industrial & Engineering Chemistry, 1949, 41(6): 1135-1147. |
30 | 苏亚欣, 骆仲泱, 岑可法. 循环流化床颗粒团更新传热模型的修正[J]. 动力工程, 2001, 21(5): 1426-1429, 1416. |
Su Y X, Luo Z Y, Cen K F. Modification to the CFB cluster-renewal heat transfer model[J]. Power Engineering, 2001, 21(5): 1426-1429, 1416. | |
31 | Smoot L D, Smith P J. Coal Combustion and Gasification[M]. New York: Plenum Press, 1985. |
32 | Liu Z C, Zhong W Q, Shao Y J, et al. Exergy analysis of supercritical CO2 coal-fired circulating fluidized bed boiler system based on the combustion process[J]. Energy, 2020, 208: 118327. |
33 | Field M A, Gill D W, Morgan B B, et al. Combustion of Pulverized Coal[M]. Leatherhead: BCURA, 1967. |
34 | Zhong W Q, Yu A B, Zhou G W, et al. CFD simulation of dense particulate reaction system: approaches, recent advances and applications[J]. Chemical Engineering Science, 2016, 140: 16-43. |
35 | Basu P. Combustion of coal in circulating fluidized-bed boilers: a review[J]. Chemical Engineering Science, 1999, 54(22): 5547-5557. |
36 | Mikulčić H, Vujanović M, Duić N. Improving the sustainability of cement production by using numerical simulation of limestone thermal degradation and pulverized coal combustion in a cement calciner[J]. Journal of Cleaner Production, 2015, 88: 262-271. |
37 | Fidaros D K, Baxevanou C A, Dritselis C D, et al. Numerical modelling of flow and transport processes in a calciner for cement production[J]. Powder Technology, 2007, 171(2): 81-95. |
38 | Mikulčić H, von Berg E, Vujanović M, et al. Numerical modelling of calcination reaction mechanism for cement production[J]. Chemical Engineering Science, 2012, 69(1): 607-615. |
39 | 胡芝娟, 刘志江, 王世杰. 模拟分解炉中煤焦燃烧生成NO的特性[J]. 化工学报, 2005, 56(3): 545-550. |
Hu Z J, Liu Z J, Wang S J. NO formation from coal char combustion in cement precalciner[J]. Journal of Chemical Industry and Engineering (China), 2005, 56(3): 545-550. | |
40 | 黄来, 陆继东, 李卫杰, 等. 分解炉中NO生成模拟与优化[J]. 化工学报, 2006, 57(11): 2624-2630. |
Huang L, Lu J D, Li W J, et al. Numerical simulation of NO in precalciner and its optimization[J]. Journal of Chemical Industry and Engineering (China), 2006, 57(11): 2624-2630. | |
41 | 胡道和, 徐德龙, 蔡玉良. 气固过程工程学及其在水泥工业中的应用[M]. 武汉: 武汉理工大学出版社, 2003. |
Hu D H. Xu D L, Cai Y L. Gas Solid Process Engineering and Its Application in Cement Industry[M]. Wuhan: Wuhan University of Technology Press, 2003. | |
42 | 金涌. 流态化工程原理[M]. 北京: 清华大学出版社, 2001. |
Jin Y. Fluidization Engineering Principles[M]. Beijing: Tsinghua University Press, 2001. | |
43 | Hornik K, Stinchcombe M, White H. Multilayer feedforward networks are universal approximators[J]. Neural Networks, 1989, 2(5): 359-366. |
44 | 李昌勇, 金春强, 胡道和. SLC-S分解炉气固两相运动规律研究[J]. 燃烧科学与技术, 2003, 9(3): 239-243. |
Li C Y, Jin C Q, Hu D H. Synthetic study of the motion patterns of gas and solid phases in SLC-S calciner[J]. Journal of Combustion Science and Technology, 2003, 9(3): 239-243. | |
45 | 李相国, 马保国, 吴贝, 等. 喷腾型分解炉内冷态流场的模拟与优化设计[J]. 哈尔滨工业大学学报, 2009, 41(4): 226-228. |
Li X G, Ma B G, Wu B, et al. Numerical simulation and optimization of cold airflow field in sprayed calciners[J]. Journal of Harbin Institute of Technology, 2009, 41(4): 226-228. |
[1] | Xin YANG, Wen WANG, Kai XU, Fanhua MA. Simulation analysis of temperature characteristics of the high-pressure hydrogen refueling process [J]. CIESC Journal, 2023, 74(S1): 280-286. |
[2] | Jiahao SONG, Wen WANG. Study on coupling operation characteristics of Stirling engine and high temperature heat pipe [J]. CIESC Journal, 2023, 74(S1): 287-294. |
[3] | Siyu ZHANG, Yonggao YIN, Pengqi JIA, Wei YE. Study on seasonal thermal energy storage characteristics of double U-shaped buried pipe group [J]. CIESC Journal, 2023, 74(S1): 295-301. |
[4] | Gang YIN, Yihui LI, Fei HE, Wenqi CAO, Min WANG, Feiya YAN, Yu XIANG, Jian LU, Bin LUO, Runting LU. Early warning method of aluminum reduction cell leakage accident based on KPCA and SVM [J]. CIESC Journal, 2023, 74(8): 3419-3428. |
[5] | Linqi YAN, Zhenlei WANG. Multi-step predictive soft sensor modeling based on STA-BiLSTM-LightGBM combined model [J]. CIESC Journal, 2023, 74(8): 3407-3418. |
[6] | Xuejin GAO, Yuzhuo YAO, Huayun HAN, Yongsheng QI. Fault monitoring of fermentation process based on attention dynamic convolutional autoencoder [J]. CIESC Journal, 2023, 74(6): 2503-2521. |
[7] | Cheng YUN, Qianlin WANG, Feng CHEN, Xin ZHANG, Zhan DOU, Tingjun YAN. Deep-mining risk evolution path of chemical processes based on community structure [J]. CIESC Journal, 2023, 74(4): 1639-1650. |
[8] | Xinyuan WU, Qilei LIU, Boyuan CAO, Lei ZHANG, Jian DU. Group2vec: group vector representation and its property prediction applications based on unsupervised machine learning [J]. CIESC Journal, 2023, 74(3): 1187-1194. |
[9] | Mengbo ZHANG, Linjin LOU, Yirong FENG, Yuting ZHENG, Haomiao ZHANG, Jingdai WANG, Yongrong YANG. Research progress on synthesis of alkylaluminoxanes [J]. CIESC Journal, 2023, 74(2): 525-534. |
[10] | Xuejin GAO, Kun CHENG, Huayun HAN, Huihui Gao, Yongsheng QI. Fault diagnosis of chillers using central loss conditional generative adversarial network [J]. CIESC Journal, 2022, 73(9): 3950-3962. |
[11] | Le ZHOU, Chengkai SHEN, Chao WU, Beiping HOU, Zhihuan SONG. Deep fusion feature extraction network and its application in chemical process soft sensing [J]. CIESC Journal, 2022, 73(7): 3156-3165. |
[12] | Jiahui REN, Yu LIU, Chao LIU, Lang LIU, Ying LI. Critical temperature prediction of working fluids using molecular fingerprints and topological indices [J]. CIESC Journal, 2022, 73(4): 1493-1500. |
[13] | Cheng ZHANG, Lizhi PAN, Yuan LI. Fault detection and diagnosis method based on weighted statistical feature KICA [J]. CIESC Journal, 2022, 73(2): 827-837. |
[14] | Zhibin LU, Yimeng LI, Chang HE, Bingjian ZHANG, Qinglin CHEN, Ming PAN. Integrating physics-informed neural networks with partitioned coupling strategy for modeling conjugate heat transfer [J]. CIESC Journal, 2022, 73(12): 5483-5493. |
[15] | Zhuang YUAN, Yiqun LING, Zhe YANG, Chuankun LI. Critical parameters prediction based on TA-ConvBiLSTM for chemical process [J]. CIESC Journal, 2022, 73(1): 342-351. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||