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

• 过程系统工程 • 上一篇    下一篇

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()   

  1. 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()   

  1. 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:

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

图1

弧形静电传感器阵列"

图2

实验装置"

表1

实验条件"

入口空气温度/℃入口空气体积流量/(m3/h)
253035
45E1E4E7
60E2E5E8
75E3E6E9

图3

不同工况下生物质颗粒湿度变化"

图4

工况E3下初始流化阶段和平稳流化阶段时的传感器A-1、A-2和A-3的静电信号"

图5

RNN神经网络结构"

图6

LSTM神经网络结构图"

表2

生物质颗粒湿度与输入参数的相关性"

VCRHTDCACNSKEKURAFFENSF
-0.920.19-0.870.59-0.860.42-0.860.790.400.58-0.04

图7

隐含层神经元个数、隐含层层数与时间步长的优化"

图8

损失函数变化趋势"

图9

预测值与参考值对比与相对误差"

图10

LSTM与RNN模型预测效果对比"

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