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基于ISSA−LSTM的浓缩池溢流浓度预测

张洋洋 樊玉萍 马晓敏 董宪姝 金伟 王大卫

张洋洋,樊玉萍,马晓敏,等. 基于ISSA−LSTM的浓缩池溢流浓度预测[J]. 工矿自动化,2022,48(11):63-72.  doi: 10.13272/j.issn.1671-251x.2022060084
引用本文: 张洋洋,樊玉萍,马晓敏,等. 基于ISSA−LSTM的浓缩池溢流浓度预测[J]. 工矿自动化,2022,48(11):63-72.  doi: 10.13272/j.issn.1671-251x.2022060084
ZHANG Yangyang, FAN Yuping, MA Xiaomin, et al. Prediction of overflow concentration of thickener based on ISSA-LSTM[J]. Journal of Mine Automation,2022,48(11):63-72.  doi: 10.13272/j.issn.1671-251x.2022060084
Citation: ZHANG Yangyang, FAN Yuping, MA Xiaomin, et al. Prediction of overflow concentration of thickener based on ISSA-LSTM[J]. Journal of Mine Automation,2022,48(11):63-72.  doi: 10.13272/j.issn.1671-251x.2022060084

基于ISSA−LSTM的浓缩池溢流浓度预测

doi: 10.13272/j.issn.1671-251x.2022060084
基金项目: 国家自然科学基金重点国际(地区)合作研究项目(51820105006);国家自然科学基金面上项目(52074189);国家自然科学基金青年科学基金项目(52004178)。
详细信息
    作者简介:

    张洋洋(1997—),男,安徽阜阳人,硕士研究生,研究方向为选煤智能化,E-mail:1278290544@qq.com

    通讯作者:

    樊玉萍(1988—),女,山西临汾人,副教授,硕士研究生导师,主要从事煤炭清洁高效利用方面的研究工作,E-mail:19880628fyp@163.com

  • 中图分类号: TD94

Prediction of overflow concentration of thickener based on ISSA-LSTM

  • 摘要: 浓缩池溢流浓度监测是实现煤泥水智能加药的关键。针对基于传感器的溢流浓度监测方式会导致絮凝剂调节滞后的问题,提出了一种基于改进麻雀搜索算法(ISSA)−长短期记忆(LSTM)的浓缩池溢流浓度预测方法。首先,对浓缩生产过程中的多参数时间序列进行相关性分析和预处理,得到输入变量。其次,采用多策略联合改进麻雀搜索算法(SSA):引入Tent混沌映射对麻雀种群进行初始化,以保证种群多样性,加快算法收敛速度;用螺旋捕食策略改进SSA的寻优过程,以兼顾局部开发和全局搜索能力;用萤火虫扰动策略对麻雀搜索结果进行扰动,以提高全局搜索能力,避免算法陷入局部最优。然后,采用ISSA优化双层LSTM网络模型的超参数。最后,构建基于ISSA−LSTM的浓缩池溢流浓度预测模型,进行在线监测。实验结果表明:① 选取Ackley函数和Rastrigin函数作为测试函数,得出ISSA的全局寻优性能和收敛速度均优于粒子群优化(PSO)算法、鲸鱼优化算法(WOA)和标准SSA。② 3种改进策略中,螺旋捕食策略对ISSA性能的提升起主导作用,混沌映射和萤火虫扰动策略协调算法的收敛速度和全局搜索能力,进一步提升算法寻优性能。③ 采用ISSA优化LSTM的超参数,解决了依靠主观经验取值时存在的欠拟合或过拟合问题,ISSA−LSTM模型的溢流浓度预测精度达97.26%,高于双层LSTM、SSA−LSTM、最小二乘支持向量机(LSSVM)等模型。④ 数据预处理可以提升模型的精度,降噪后溢流浓度预测精度比降噪前提升了30.25%。

     

  • 图  1  煤泥水浓缩工艺流程

    Figure  1.  Concentration process flow of coal slurry

    图  2  Spearman相关性分析热力图

    Figure  2.  Thermodynamic diagram of Spearman correlation analysis

    图  3  降噪前后入料流量对比

    Figure  3.  Comparison of feed flow before and after noise reduction

    图  4  降噪前后溢流浓度对比

    Figure  4.  Comparison of overflow concentration before and after noise reduction

    图  5  双层LSTM网络结构

    Figure  5.  Structure of double-layer LSTM network

    图  6  Tent映射混沌值与随机值对比

    Figure  6.  Comparison of chaotic value and random value of Tent map

    图  7  ISSA流程

    Figure  7.  Flow of improved sparrow search algorithm

    图  8  基于ISSA−LSTM的浓缩池溢流浓度预测模型

    Figure  8.  Prediction model of overflow concentration of thickener based on ISSA-LSTM

    图  9  Ackley函数

    Figure  9.  Ackley function

    图  10  Rastrigin函数

    Figure  10.  Rastrigin function

    图  11  不同优化算法的Ackley函数收敛曲线

    Figure  11.  Convergence curves of Ackley function of different optimization algorithms

    图  12  不同优化算法的Rastrigin函数收敛曲线

    Figure  12.  Convergence curves of Rastrigin function of different optimization algorithms

    图  13  不同改进策略的Ackley函数收敛曲线

    Figure  13.  Convergence curves of Ackley function of different improvement strategies

    图  14  不同改进策略的Rastrigin函数收敛曲线

    Figure  14.  Convergence curves of Rastrigin function of different improvement strategies

    图  15  适应度曲线对比

    Figure  15.  Comparison of fitness curves

    图  16  不同模型预测结果对比

    Figure  16.  Comparison of prediction results of different models

    表  1  S−G算法参数

    Table  1.   Parameters of S-G algorithm

    参数平滑因子多项式次数
    入料流量0.152
    入料浓度0.101
    加药量0.152
    下载: 导出CSV

    表  2  降噪前后预测结果

    Table  2.   Prediction results before and after noise reduction

    实验数据RMSEMAPE/%
    降噪前492.711 532.99
    降噪后55.788 32.74
    下载: 导出CSV

    表  3  SSA改进策略

    Table  3.   Improvement strategies of sparrow search algorithm

    算法混沌映射螺旋捕食萤火虫扰动
    ISSA1
    ISSA2
    ISSA3
    下载: 导出CSV

    表  4  设备硬件参数

    Table  4.   Equipment hardware parameters

    设备名称DESKTOP−3ER5HIS
    处理器11th Gen Intel(R) Core(TM)
    i7−11800H @ 2.30 GHz
    机带RAM32.0 GB
    系统类型64位操作系统
    下载: 导出CSV

    表  5  超参数设置

    Table  5.   Setting of hyperparameters

    超参数设定值
    第1层神经元个数${M}_{1}$[10,150]
    第2层神经元个数${M}_{2}$[10,60]
    初始学习率$ {I}_{1} $[10−5,1]
    L2正则化因子$ {I}_{2} $[10−6,1]
    最大迭代次数$t_{\rm{max}}$400
    下载: 导出CSV

    表  6  不同模型评价指标对比

    Table  6.   Comparison of evaluation indicator of different models

    模型RMSEMAPE/%
    ISSA−LSTM55.788 32.74
    SSA−LSTM56.237 03.08
    LSSVM74.490 23.67
    双层LSTM81.259 73.98
    下载: 导出CSV
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  • 收稿日期:  2022-06-22
  • 修回日期:  2022-10-29
  • 网络出版日期:  2022-08-09

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