Recurrent Neural Network based Soft Sensor for Monitoring and Controlling a Reactive Distillation Column

Kataria, Gaurav; Singh, Kanhaiya

doi:10.1515/cppm-2017-0044

Cited by 13 publications

(5 citation statements)

References 20 publications

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“…To deal with the complex dynamics in the industrial processes, RNN and its variant LSTM have great potential in capturing the temporal dependence from continuous industrial processes. For example, Kataria and Singh proposed a RNN-based soft sensor to estimate the bottom product composition of the reactive distillation column [41]. However, due to the problems of gradient vanishing and gradient exploding, it is difficult for RNN to learn the long-term dependence.…”

Section: Feature Extraction In Soft Sensorsmentioning

confidence: 99%

A novel random subspace method considering complementarity between unsupervised and supervised deep representation features for soft sensors

Wang¹,

Zhu²,

Wu³

et al. 2022

Meas. Sci. Technol.

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Unsupervised and supervised deep learning extract effective and abstract features from different perspectives, which have been successfully applied in soft sensors. However, few studies have fused them and explored the complementary effect between the two kinds of features, which limits the utilization of comprehensive prediction information. To address the problem, a novel random subspace method with stacked auto-encoder (SAE) and bidirectional long short-term memory (Bi-LSTM), named RS-SBL, is proposed for soft sensors. Firstly, unsupervised and supervised deep representation features are extracted by SAE and Bi-LSTM, respectively. Secondly, to leverage the complementarity of the fusion features, an improved random subspcae (RS) method with a structure sparsity learning model is designed to discriminate the relative importance of different features and generate ensemble prediction results. Finally, the experiments on two real-world industrial nonlinear processes demonstrate that the proposed RS-SBL with the feature fusion strategy improves the prediction performance, and outperforms the other comparison soft sensor models.

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Section: Feature Extraction In Soft Sensorsmentioning

confidence: 99%

A novel random subspace method considering complementarity between unsupervised and supervised deep representation features for soft sensors

Wang¹,

Zhu²,

Wu³

et al. 2022

Meas. Sci. Technol.

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“…In recent years, with the advances in data acquisition and storage technologies and data analysis methods, the data-driven soft sensor modeling methods, especially deep neural networks, have received considerable attention in process modeling (Liu et al, 2018b; Yuan et al, 2019). For example, the deep belief networks (DBNs) (Shang et al, 2014), convolutional neural networks (CNNs) (Yuan et al, 2020b), recurrent neural networks (RNNs) (Kataria and Singh, 2018) have been introduced into soft sensor modeling.…”

Section: Introductionmentioning

confidence: 99%

Gaussian mixture model sample selection strategy–based active semi-supervised soft sensor for industrial processes

Luo,

Lei,

Wang

2023

Transactions of the Institute of Measurement and Control

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Soft sensors have become reliable tools for estimating difficult-to-measure target variables in modern industrial processes. In order to make full use of labeled and unlabeled samples, an active semi-supervised soft sensor modeling method is proposed, which combines active learning and semi-supervised learning to maximize model performance and minimize the laboratory analysis cost of expanding the labeled sample data set. First, manifold regularization is introduced into the deep extreme learning machine (DELM) algorithm to form a semi-supervised DELM that improves the performance of a model trained with unlabeled samples. Then, considering non-Gaussian processes and the error information between the predicted and true values, an active sample selection strategy based on error Gaussian mixture model is developed. Using this strategy, the most uncertain and representative unlabeled samples are selected for labeling, and thereby expanding the labeled sample data set. Finally, the effectiveness of the proposed method is verified using industrial debutanizer process data.

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“… 35 In addition, a recurrent neural network (RNN) has also been introduced to construct nonlinear dynamic soft sensors for quality prediction. 36 Although RNN is a mainstream deep-learning model, it still suffers from the problem of gradient vanishing and exploding due to the “tanh” activation function. For an improvement of the network structure, a long short-term memory (LSTM) neural network has been developed to overcome the deficiency of RNN.…”

Section: Introductionmentioning

confidence: 99%

“…Lately, a novel soft sensor development using an echo state network (ESN) integrated with a singular value decomposition was proposed and applied to complex chemical processes . In addition, a recurrent neural network (RNN) has also been introduced to construct nonlinear dynamic soft sensors for quality prediction . Although RNN is a mainstream deep-learning model, it still suffers from the problem of gradient vanishing and exploding due to the “tanh” activation function.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Dynamic Soft Sensor Development with a Supervised Hybrid CNN-LSTM Network for Industrial Processes

Zheng

et al. 2022

ACS Omega

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A soft sensor is a key component when a real-time measurement is unavailable for industrial processes. Recently, soft sensor models based on deep-learning techniques have been successfully applied to complex industrial processes with nonlinear and dynamic characteristics. However, the conventional deep-learning-based methods cannot guarantee that the quality-relevant features are included in the hidden states when the modeling samples are limited. To address this issue, a supervised hybrid network based on a dynamic convolutional neural network (CNN) and a long short-term memory (LSTM) network is designed by constructing multilayer dynamic CNN-LSTM with improved structures. In each time instant, data augmentation is implemented by dynamic expansion of the original samples. Moreover, multiple supervised hidden units are trained by adding quality variables as part of the layer input to acquire a better quality-related feature learning performance. The effectiveness of the proposed soft senor development is validated through two industrial applications, including a penicillin fermentation process and a debutanizer column.

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Recurrent Neural Network based Soft Sensor for Monitoring and Controlling a Reactive Distillation Column

Cited by 13 publications

References 20 publications

A novel random subspace method considering complementarity between unsupervised and supervised deep representation features for soft sensors

A novel random subspace method considering complementarity between unsupervised and supervised deep representation features for soft sensors

Gaussian mixture model sample selection strategy–based active semi-supervised soft sensor for industrial processes

Nonlinear Dynamic Soft Sensor Development with a Supervised Hybrid CNN-LSTM Network for Industrial Processes

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