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Ensemble deep learning based semi-supervised soft sensor modeling method and its application on quality prediction for coal preparation process
Advanced Engineering Informatics ( IF 8.8 ) Pub Date : 2020-07-08 , DOI: 10.1016/j.aei.2020.101136
Xianhui Yin , Zhanwen Niu , Zhen He , Zhaojun(Steven) Li , Dong-hee Lee

Coal preparation is the most effective and economical technique to reduce impurities and improve the product quality for run-of-mine coal. The timely and accurate prediction for key quality characteristics of separated coal plays a significant role in condition monitoring and production control. However, these quality characteristics are usually difficult to directly measure online in industrial practices Although some computation intelligence based soft sensor modeling methods have been developed and reported in existing research for these quality variables estimation, some problems still exist, i.e., manual feature extraction, considerable unlabeled data, temporal dynamic behavior in data, which will influence the accuracy and efficiency for established soft sensor model. To address above-mentioned problem and develop an more excellent quality prediction model for coal preparation process, a novel deep learning based semi-supervised soft sensor modeling approach is proposed which combining the advantage of unsupervised deep learning technique (i.e., Stacked Auto-Encoder (SAE)) with the advantage of supervised deep bidirectional recurrent learner (i.e., Bidirectional Long Short-Term Memory (BLSTM)). More specifically, the unsupervised SAE networks are implemented to learn the representative features hidden in all available input data (labeled and unlabeled samples) and store them as context vector. Then, partial context vector with corresponding labels and the quality variable measure value at previous time are concatenated to form a new merged input feature vector. After that, the temporal and dynamic features are further extracted from the new merged input feature vector via BLSTM networks. Subsequently, the fully connected layers (FCs) are exploited to learn the higher-level features from the last hidden layer of the BLSTM. Lastly, the learned output features by FCs are fed into a supervised liner regression layer to predict the coal quality metrics. Meanwhile, to avoid over-fitting, some regularization techniques are utilized and discussed in proposed network. The application in ash content estimation for a real dense medium coal preparation process and some comparison experiment result demonstrate that the effectiveness and priority of proposed soft sensor modeling approach.



中文翻译:

基于集成深度学习的半监督软传感器建模方法及其在选煤质量预测中的应用

选煤是减少杂质和提高矿用煤产品质量的最有效,最经济的技术。对分离出的煤的关键质量特征进行及时,准确的预测在状态监测和生产控制中起着重要作用。但是,这些质量特征通常很难在工业实践中直接在线测量,尽管在现有研究中已经开发并报告了一些基于计算智能的软传感器建模方法来评估这些质量变量,但是仍然存在一些问题,例如,手动特征提取,未标记的数据,数据中的时间动态行为,这将影响已建立的软传感器模型的准确性和效率。为了解决上述问题并开发出更出色的选煤过程质量预测模型,结合无监督深度学习技术(即堆叠式自动编码器())的优点,提出了一种基于深度学习的新型半监督软传感器建模方法。 SAE))具有受监督的深度双向循环学习器(即双向长期短期记忆(BLSTM))的优势。更具体地说,实施无监督的SAE网络以了解隐藏在所有可用输入数据(标记和未标记样本)中的代表性特征,并将其存储为上下文向量。然后,将具有相应标签的部分上下文向量和先前时间的质量变量度量值连接起来,以形成新的合并输入特征向量。之后,然后通过BLSTM网络从新的合并输入特征向量中进一步提取时间和动态特征。随后,利用全连接层(FC)从BLSTM的最后一个隐藏层中学习更高级别的功能。最后,燃料电池公司学习到的输出特征被输入到有监督的线性回归层中,以预测煤炭质量指标。同时,为了避免过度拟合,在提出的网络中利用和讨论了一些正则化技术。在实际的重介质选煤过程灰分含量估算中的应用和一些对比实验结果表明,所提出的软传感器建模方法的有效性和优先级。利用全连接层(FC)从BLSTM的最后一个隐藏层中学习更高级别的功能。最后,燃料电池公司学习到的输出特征被输入到有监督的线性回归层中,以预测煤炭质量指标。同时,为了避免过度拟合,在提出的网络中利用和讨论了一些正则化技术。在实际的重介质选煤过程灰分含量估算中的应用和一些对比实验结果表明,所提出的软传感器建模方法的有效性和优先级。利用全连接层(FC)从BLSTM的最后一个隐藏层中学习更高级别的功能。最后,燃料电池公司学习到的输出特征被输入到有监督的线性回归层中,以预测煤炭质量指标。同时,为了避免过度拟合,在提出的网络中利用和讨论了一些正则化技术。在实际的重介质选煤过程灰分含量估算中的应用和一些对比实验结果表明,所提出的软传感器建模方法的有效性和优先级。在提议的网络中利用和讨论了一些正则化技术。在实际的重介质选煤过程灰分含量估算中的应用和一些对比实验结果表明,所提出的软传感器建模方法的有效性和优先级。在提议的网络中利用和讨论了一些正则化技术。在实际的重介质选煤过程灰分含量估算中的应用和一些对比实验结果表明,所提出的软传感器建模方法的有效性和优先级。

更新日期:2020-07-08
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