A Fault-Tolerant Soft Sensor Algorithm Based on Long Short-Term Memory Network for Uneven Batch Process

Liu, Yujun; Ni, Dong; Wang, Zongyi

doi:10.3390/pr12030495

Cited by 2 publications

(1 citation statement)

References 54 publications

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“…LSTM neural networks are excellent variants of the recurrent neural network (RNN) [25,26]. LSTM neural networks were developed to address the limitations of conventional RNNs, such as vanishing gradients and the inability to capture long-time dependencies in sequences.…”

Section: Methodology Of T-sne-woa-lstmmentioning

confidence: 99%

Predicting Alloying Element Yield in Converter Steelmaking Using t-SNE-WOA-LSTM

Liu,

Qu,

Xie

et al. 2024

Processes

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The performance and quality of steel products are significantly impacted by the alloying element control. The efficiency of alloy utilization in the steelmaking process was directly related to element yield. This study analyses the factors that influence the yield of elements in the steelmaking process using correlation analysis. A yield prediction model was developed using a t-distributed stochastic neighbor embedding (t-SNE) algorithm, a whale optimization algorithm (WOA), and a long short-term memory (LSTM) neural network. The t-SNE algorithm was used to reduce the dimensionality of the original data, while the WOA optimization algorithm was employed to optimize the hyperparameters of the LSTM neural network. The t-SNE-WOA-LSTM model accurately predicted the yield of Mn and Si elements with hit rates of 71.67%, 96.67%, and 99.17% and 57.50%, 89.17%, and 97.50%, respectively, falling within the error range of ±1%, ±2%, and ±3% for Mn and ±1%, ±3%, and ±5% for Si. The results demonstrate that the t-SNE-WOA-LSTM model outperforms the backpropagation (BP), LSTM, and WOA-LSTM models in terms of prediction accuracy. The model was applied to actual production in a Chinese plant. The actual performance of the industrial application is within a ±3% error range, with an accuracy of 100%. Furthermore, the elemental yield predicted by the model and then added the ferroalloys resulted in a reduction in the elemental content of the product by 0.017%. The model enables accurate prediction of alloying element yields and was effectively applied in industrial production.

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Section: Methodology Of T-sne-woa-lstmmentioning

confidence: 99%

Predicting Alloying Element Yield in Converter Steelmaking Using t-SNE-WOA-LSTM

Liu,

Qu,

Xie

et al. 2024

Processes

View full text Add to dashboard Cite

show abstract

Unsupervised Hybrid Models Integrating Deep Autoencoders and Process Controllers’ Models for Enhanced Process Monitoring and Fault Detection

Aghaee,

Krau,

Tamer

et al. 2024

Ind. Eng. Chem. Res.

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This paper introduces a novel hybrid process monitoring model that integrates long short-term memory autoencoders with process controllers’ models. The parameters of the hybrid model are optimized by minimizing a novel loss function, which combines the mean square error (MSE) between controlled variables and their reconstructions from the LSTM-AE model, along with the MSE of manipulated variables and their reconstructions obtained with the numerically implemented and exactly a priori known controller equations. The effectiveness of the proposed method is evaluated on the benchmark of an industrial-scale penicillin process as a batch case study and the Tennessee Eastman plant process under a decentralized control strategy as a continuous case study. A comparative analysis of the proposed hybrid model with an equivalent nonhybrid LSTM-AE model, which does not utilize process controllers’ equations, highlights the superiority of the proposed hybrid monitoring model in fault detection. These improvements result from the use of an LSTM-AE network with fewer parameters, thus making it less susceptible to overfitting.

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A Fault-Tolerant Soft Sensor Algorithm Based on Long Short-Term Memory Network for Uneven Batch Process

Cited by 2 publications

References 54 publications

Predicting Alloying Element Yield in Converter Steelmaking Using t-SNE-WOA-LSTM

Predicting Alloying Element Yield in Converter Steelmaking Using t-SNE-WOA-LSTM

Unsupervised Hybrid Models Integrating Deep Autoencoders and Process Controllers’ Models for Enhanced Process Monitoring and Fault Detection

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