化工学报 ›› 2020, Vol. 71 ›› Issue (S1): 307-314.doi: 10.11949/0438-1157.20190692
Huangfeng FANG(),Yaoyao LIU,Wenbiao ZHANG(
)
摘要:
生物质作为一种储量丰富、环境友好且易于获取的可再生能源,日渐成为能源研究利用领域的热点。生物质湿度是影响生物质利用效率的关键因素,因此干燥是生物质利用之前的必要步骤。流化床由于其良好的传热传质特性,在干燥过程中得到了广泛的应用。为了实时监测生物质颗粒的干燥过程,利用弧形静电传感器阵列,结合用于时间序列建模的长短期记忆(LSTM)神经网络,实现了流化床干燥器内生物质颗粒湿度的预测。在实验室规模的流化床干燥器上进行了多工况实验获取训练和测试数据,通过模型参数优化确定了LSTM模型。通过与标准循环神经网络(RNN)模型的预测结果的对比表明,LSTM神经网络模型的平均相对误差较小,能够较为准确地预测流化床干燥器内生物质颗粒的湿度。
中图分类号:
1 | Vitousek P. Beyond global warming-ecology and global change[J]. Ecology, 1994, 75(7): 1862-1876. |
2 | Stevens C V. Thermochemical processing of biomass: conversion into fuels, chemicals and power[M]//Brown R C, ed. Thermochemical Processing of Biomass. New York: Wiley, 2011: 22-30. |
3 | 阎立峰, 朱清时. 以生物质为原材料的化学化工[J]. 化工学报, 2004, 55(12): 1938-1943. |
Yan L F, Zhu Q S. New chemical industry based on biomass[J]. Journal of Chemical Industry and Engineering (China), 2004, 55(12): 1938-1943. | |
4 | 常圣强, 李望良, 张晓宇, 等. 生物质气化发电技术研究进展[J]. 化工学报, 2018, 69(8): 27-39. |
Chang S Q, Li W L, Zhang X Y, et al. Progress in biomass gasification power generation technology[J]. CIESC Journal, 2018, 69(8): 27-39. | |
5 | Garcia R, Pizarro C, Lavin A G, et al. Biomass sources for thermal conversion. Techno-economical overview[J]. Fuel, 2017, 195: 182-189. |
6 | Dietz T, Rosa E A. Effects of population and affluence on CO2 emissions[J]. Proceedings of the National Academy of Sciences, 1997, 94(1): 175-179. |
7 | Mckendry P. Energy production from biomass (Ⅰ): Overview of biomass[J]. Bioresource Technology, 2002, 83(1): 37-46. |
8 | Donald L. Biomass for Renewable Energy, Fuels, and Chemicals[M]. San Diego: Academic Press, 1998: 53. |
9 | Verma M, Loha C, Sinha A N, et al. Drying of biomass for utilizing in co-firing with coal and its impact on environment – a review[J]. Renewable and Sustainable Energy Reviews, 2017, 71: 732-741. |
10 | Da Silva C A M, Butzge J J, Nitz M, et al. Monitoring and control of coating and granulation processes in fluidized beds – a review[J]. Advanced Powder Technology, 2014, 25(1): 195-210. |
11 | Wang H G, Senior P R, Mann R, et al. Online measurement and control of solids moisture in fluidized bed dryers[J]. Chemical Engineering Science, 2009, 64(12): 2893-2902. |
12 | Carter A, Briens L. Inline acoustic monitoring to determine fluidized bed performance during pharmaceutical coating[J]. International Journal of Pharmaceutics, 2018, 549: 293-298. |
13 | Aghbashlo M, Sotudeh-Gharebagh R, Zarghami R, et al. Measurement techniques to monitor and control fluidization quality in fluidized bed dryers: a review[J]. Drying Technology, 2014, 32: 1005-51. |
14 | Kadlec P, Gabrys B, Strandt S. Data-driven soft sensors in the process industry[J]. Computers and Chemical Engineering, 2009, 33(4): 795-814. |
15 | Momenzadeh L, Zomorodian A, Mowla D. Experimental and theoretical investigation of shelled corn drying in a microwave-assisted fluidized bed dryer using artificial neural network[J]. Food and Bio-products Processing, 2011, 89(1): 15-21. |
16 | Chokphoemphun S, Chokphoemphun S. Moisture content prediction of paddy drying in a fluidized-bed drier with a vortex flow generator using an artificial neural network[J]. Applied Thermal Engineering, 2018, 145: 630-636. |
17 | Zhang W B, Yan Y, Yang Y R, et al. Measurement of flow characteristics in a bubbling fluidized bed using electrostatic sensor arrays[J]. IEEE Transactions on Instrumentation and Measurement, 2016, 65(3): 703-712. |
18 | Yang Y, Zhang Q, Zi C, et al. Monitoring of particle motions in gas-solid fluidized beds by electrostatic sensors[J]. Powder Technology, 2017, 308: 461-471. |
19 | Shi Q, Zhang Q, Han G D, et al. Simultaneous measurement of electrostatic charge and its effect on particle motions by electrostatic sensors array in gas-solid fluidized beds[J]. Powder Technology, 2017, 312: 29-37. |
20 | Qian X C, Shi D P, Yan Y, et al. Effects of moisture content on electrostatic sensing based mass flow measurement of pneumatically conveyed particles[J]. Powder Technology, 2017, 311: 579-588. |
21 | Zhang W B, Cheng X F, Hu Y H, et al. Measurement of moisture content in a fluidized bed dryer using an electrostatic sensor array[J]. Powder Technology, 2018, 325: 49-57. |
22 | Zhang W B, Cheng X F, Hu Y H, et al. Online prediction of biomass moisture content in a fluidized bed dryer using electrostatic sensor arrays and the random forest method[J]. Fuel, 2019, 239: 437-445. |
23 | He C, Bi X T, Grace J R. Simultaneous measurements of particle charge density and bubble properties in gas-solid fluidized beds by dual-tip electrostatic probes[J]. Chemical Engineering Science, 2015, 123: 11-21. |
24 | He C, Bi X T, Grace J R. Measurement of particle charge-to-mass ratios in a gas–solids fluidized bed by a collision probe[J]. Powder Technology, 2003, 135/136: 181-191. |
25 | Zachary C L, John B, Charles E. A critical review of recurrent neural networks for sequence learning[J]. Computer Science, 2015, arXiv: 15060019V4. |
26 | Hochreiter S, Schmidhuber J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735. |
27 | Bengio Y. Learning long-term dependencies with gradient descent is difficult[J]. IEEE Transactions on Neural Networks, 1994, 5(2): 157-166. |
28 | Colah. Understanding LSTM Networks[EB/OL].[2015-8-27]. https: //colah.github.io/posts/2015-08 |
-Understanding-LSTMs/.[29] Peng L, Liu S, Liu R, et al. Effective long short-term memory with differential evolution algorithm for electricity price prediction[J]. Energy, 2018, 162: 1301-1314. | |
30 | Gers F A, Schmidhuber J, Cummins F. Learning to forget: continual prediction with LSTM[C]//International Conference on Artificial Neural Networks. 2002. |
31 | 窦珊, 张广宇, 熊智华. 基于LSTM时间序列重建的生产装置异常检测[J]. 化工学报, 2019, 70(2): 61-66. |
Dou S, Zhang G Y, Xiong Z H. Anomaly detection of process unit based on LSTM time series reconstruction[J]. CIESC Journal, 2019, 70(2): 61-66. | |
32 | Yao K, Cohn T, Vylomova K, et al. Depth-gated recurrent neural networks[J]. Computer Science, 2015, arXiv: 1508.03790. |
33 | 李泽龙, 杨春节, 刘文辉, 等. 基于LSTM-RNN模型的铁水硅含量预测[J]. 化工学报, 2018, 69(3): 992-997. |
Li Z L, Yang C J, Liu W H, et al. Research on hot metal Si-content prediction based on LSTM-RNN[J]. CIESC Journal, 2018, 69(3): 992-997. |
[1] | 张兵, 魏利平, 滕海鹏. 隔板式内循环流化床压力脉动信号递归分析[J]. 化工学报, 2020, 71(S1): 106-113. |
[2] | 毛海涛, 王璐, 许志颖, 解万翠, 都健, 张磊. 基于分子表面电荷密度分布与机器学习的混合物设计方法研究[J]. 化工学报, 2020, 71(S1): 282-292. |
[3] | 吴延鹏, 赵薇, 陈凤君. 不同相对湿度下亲疏水纳米纤维膜空气过滤性能实验研究[J]. 化工学报, 2020, 71(S1): 471-478. |
[4] | 方书起, 石崇, 李攀, 白净, 常春. Fe-Zn共改性ZSM-5催化作用下生物质快速热解特性研究[J]. 化工学报, 2020, 71(4): 1637-1645. |
[5] | 田凤国, 朱田, 孔德正, 雷鸣. 非均匀布风流化床内大颗粒停留时间特性[J]. 化工学报, 2020, 71(4): 1520-1527. |
[6] | 田瑞超, 王淑彦, 邵宝力, 李好婷, 王玉琳. 基于粗糙颗粒动理学流化床内颗粒与幂律流体两相流动特性的数值模拟研究[J]. 化工学报, 2020, 71(4): 1528-1539. |
[7] | 贺彦林, 田业, 顾祥柏, 徐圆, 朱群雄. 基于正则化的函数连接神经网络研究及其复杂化工过程建模应用[J]. 化工学报, 2020, 71(3): 1072-1079. |
[8] | 张璐, 张嘉成, 韩红桂, 乔俊飞. 基于模糊神经网络的污水处理生化除磷过程控制[J]. 化工学报, 2020, 71(3): 1217-1225. |
[9] | 李扬, 张扬, 陈宣龙, 龚勋. 钙基吸附剂循环吸附性能对增强式生物质气化制氢的影响研究[J]. 化工学报, 2020, 71(2): 777-787. |
[10] | 黄正梁, 王超, 李少硕, 杨遥, 孙婧元, 王靖岱, 阳永荣. 基于深度学习的气液固三相反应器图像分析方法及应用[J]. 化工学报, 2020, 71(1): 274-282. |
[11] | 张楠, 陈龙祥, 胡芃. 混合工质临界性质的推算研究[J]. 化工学报, 2019, 70(S2): 1-7. |
[12] | 杨宁, 周云龙, 马书生. 喷嘴结构改进及其液体射流过程颗粒团聚研究[J]. 化工学报, 2019, 70(S2): 169-180. |
[13] | 曹晨鑫, 杜玉鹏, 王昕, 王振雷. 基于Ms-LWPLS的化工过程网络化性能分级评估方法[J]. 化工学报, 2019, 70(S1): 141-149. |
[14] | 陈虎, 陈倩, 刘长军, 黄卡玛, 龙卓. 基于SIW的介电系数宽带测量装置[J]. 化工学报, 2019, 70(S1): 182-185. |
[15] | 徐杰, 陈新, 王玲玲. 用过期切片面包制备环保超级电容器活性炭电极材料[J]. 化工学报, 2019, 70(9): 3582-3589. |
|