Blast furnace hot metal temperature and silicon content prediction using soft sensor based on fuzzy C-means and exogenous nonlinear autoregressive models

Fontes, Diane Otília Lima.; Vasconcelos, Luís Gonzaga Sales; Brito, Romildo Pereira

doi:10.1016/j.compchemeng.2020.107028

Cited by 45 publications

(16 citation statements)

References 30 publications

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“…Complementarily, the Pearson correlation coefficient (R) was calculated for each neural network and it was found that the results presented are better than those predicted in the literature [2][3][4][5][6][7][8], and [22][23][24][25][26][27][28][29][30][31][32][33][34]. The analysis of Fig.…”

Section: Resultsmentioning

confidence: 95%

Prediction of silicon content in the hot metal using Bayesian networks and probabilistic reasoning

Cardoso

Felice

2021

Int. J. Adv. Intell. Informatics

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The blast furnace is the principal method of producing cast iron. In the production of cast iron, the control of silicon is vital because this impurity is harmful to almost all steels. Artificial neural networks with Bayesian regularization are more robust than traditional back-propagation networks and can reduce or eliminate the need for tedious cross-validation. Bayesian regularization is a mathematical process that converts a nonlinear regression into a "well-posed" statistical problem in the manner of ridge regression. The main objective of this work was to develop an artificial neural network to predict silicon content in hot metal by varying the number of neurons in the hidden layer by 10, 20, 25, 30, 40, 50, 75, and 100 neurons. The results show that all neural networks converged and presented reliable results, neural networks with 20, 25, and 30 neurons showed the best overall results. However, In short, Bayesian neural networks can be used in practice because the actual values correlate excellently with the values calculated by the neural network.

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Section: Resultsmentioning

confidence: 95%

Prediction of silicon content in the hot metal using Bayesian networks and probabilistic reasoning

Cardoso

Felice

2021

Int. J. Adv. Intell. Informatics

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“…To make full use of the quality data, a soft sensor model based on a dynamic neural network named nonlinear autoregression with exogenous input (NARX) was designed. 40 Under the supervised framework, the correlations between ordinary process variables and quality variables can be extracted properly by hidden layers. When a multilayer perceptron is implemented with NARX, the quality variables are utilized as part of the model input.…”

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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“…A part of the blast furnace gas is burned in the hot stoves to heat the air jet entering the furnace to about 1050°C. The air jet is enriched with oxygen [7][8][9][10] .…”

Section: Introductionmentioning

confidence: 99%

“…High values of silicon content indicate increased heat input to the furnace and in some cases may indicate excess coke in the furnace. Since coke costs predominate in the production of cast iron, tighter control of silicon content therefore clearly has economic advantages 7,[15][16][17][18][19] .…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network for Predicting Silicon Content in the Hot Metal Produced in a Blast Furnace Fueled by Metallurgical Coke

2022

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The main production route for cast iron and steel is through the blast furnace. The silicon content in cast iron is an important indicator of the thermal condition of a blast furnace. High silicon contents indicate an increase in the furnace's thermal input and, in some cases, may indicate an excess of coke in the reactor. As coke costs predominate in the production of cast iron, tighter control of the silicon content therefore has economic advantages. The main objective of this article was to design an artificial neural network to predict the silicon content in hot metal, varying the number of neurons in the hidden layer by 10, 20, 25, 30, 40, 50, 75, 100, 125 , 150, 170 and 200 neurons. In general, all neural networks showed excellent results, with the network with 30 neurons showing the best results among the 12 modeled networks. The validation of the models was confirmed using the Mean Square Error (MSE) and Pearson's correlation coefficient. The cross-validation technique was used to re-evaluate the performance of neural networks. In short, neural networks can be used in practical operations due to the excellent correlations between the real values and those calculated by the neural network.

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Blast furnace hot metal temperature and silicon content prediction using soft sensor based on fuzzy C-means and exogenous nonlinear autoregressive models

Cited by 45 publications

References 30 publications

Prediction of silicon content in the hot metal using Bayesian networks and probabilistic reasoning

Prediction of silicon content in the hot metal using Bayesian networks and probabilistic reasoning

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

Artificial Neural Network for Predicting Silicon Content in the Hot Metal Produced in a Blast Furnace Fueled by Metallurgical Coke

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