基于LSTM神经网络的流化床干燥器内生物质颗粒湿度预测

doi:10.11949/0438-1157.20190692

化工学报 ›› 2020, Vol. 71 ›› Issue (S1): 307-314.doi: 10.11949/0438-1157.20190692

基于LSTM神经网络的流化床干燥器内生物质颗粒湿度预测

方黄峰(),刘瑶瑶,张文彪()

华北电力大学控制与计算机工程学院，北京 102206

收稿日期:2019-06-19 修回日期:2019-08-13 出版日期:2020-04-25 发布日期:2020-05-22
通讯作者: 张文彪 E-mail:hf_fang@ncepu.edu.cn;wbzhang@ncepu.edu.cn
作者简介:方黄峰（1994—），男，硕士研究生，hf_fang@ncepu.edu.cn
基金资助:
国家自然科学基金项目(61403138);北京市自然科学基金项目(3202028);中央高校基本科研业务费专项资金项目(2017MS032)

Biomass moisture content prediction in fluidized bed dryer based on LSTM neural network

Huangfeng FANG(),Yaoyao LIU,Wenbiao ZHANG()

School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China

Received:2019-06-19 Revised:2019-08-13 Online:2020-04-25 Published:2020-05-22
Contact: Wenbiao ZHANG E-mail:hf_fang@ncepu.edu.cn;wbzhang@ncepu.edu.cn

摘要/Abstract

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。

关键词: LSTM模型, 生物质, 湿度, 神经网络, 静电传感器阵列, 流化床

Abstract:

As a common form of renewable energy with abundant reserves, environmental friendliness and easy access, biomass has aroused general interests in the field of energy research and utilization. Biomass moisture content is a significant factor for the efficient utilization of biomass resources, therefore drying becomes a necessary step before the biomass utilization.Fluidized bed has been widely used in the drying process due to its good heat and mass transfer characteristics. For the online monitoring of the biomass drying process, arc-shaped electrostatic sensor arrays along with a LSTM (long short-term memory) neural network are deployed to establish a neural network model for the prediction of biomass moisture content in the fluidized bed dryer. Experimental tests under different conditions are carried out on a lab-scale fluidized bed dryer to obtain data for model training and testing. Besides, the LSTM neural network model is determined through the optimization of the model parameters. Compared with normal RNN (recurrent neural network) model,the prediction results from the LSTM neural network model have smaller mean relative error, which can predict the biomass moisture content in the fluidized bed dryer with certain accuracy.

Key words: LSTM model, biomass, moisture content, neural network, electrostatic sensor arrays, fluidized bed

中图分类号:

TK 6

方黄峰, 刘瑶瑶, 张文彪. 基于LSTM神经网络的流化床干燥器内生物质颗粒湿度预测[J]. 化工学报, 2020, 71(S1): 307-314.

Huangfeng FANG, Yaoyao LIU, Wenbiao ZHANG. Biomass moisture content prediction in fluidized bed dryer based on LSTM neural network[J]. CIESC Journal, 2020, 71(S1): 307-314.

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参考文献 33

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.

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入口空气温度/℃	入口空气体积流量/(m³/h)
入口空气温度/℃	25	30	35
45	E1	E4	E7
60	E2	E5	E8
75	E3	E6	E9

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基于LSTM神经网络的流化床干燥器内生物质颗粒湿度预测

Biomass moisture content prediction in fluidized bed dryer based on LSTM neural network

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