A New AdaBoost.IR Soft Sensor Method for Robust Operation Optimization of Ladle Furnace Refining

Tian, Huixin; Yu-dong, Liu; Li, Kun; Yang, Ranran; Meng, Bo

doi:10.2355/isijinternational.isijint-2016-371

Cited by 13 publications

(5 citation statements)

References 15 publications

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“…The second strategy is based on the intelligent algorithm to establish the nonlinear correlation between temperature and the influencing parameters. Tian et al [5][6][7][8] predicted the temperature of molten steel in LF by using extreme learning machine (ELM), back propagation (BP) neural network, and modified adaptive boosting (AdaBoost) algorithm. Lü et al 9) predicted the temperature of molten steel in LF based on optimally pruned bagging combined with partial linear extreme learning machine (PLELM), indicating the prediction accuracy of the model is higher by comparing with genetic algorithm-back propagation (GA-BP) model, partial least squares-support vector machine (PLS-SVM) model, and AdaBoost.…”

Section: A Hybrid Modeling Methods Based On Expert Control and Deep Neural Network For Temperature Prediction Of Molten Steel In Lfmentioning

confidence: 99%

“…Actually, the weight of molten steel is non-negligible, which not only affects the addition of alloy and slag making materials but also affects the refining heating duration and the heat loss. [6][7][8]34) Meanwhile, the initial energy of molten steel is determined by the weight of molten steel. Thus, the weight of molten steel was considered when the hybrid model was established.…”

Section: Correlation Analysis and Data Normalizationmentioning

confidence: 99%

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A Hybrid Modeling Method Based on Expert Control and Deep Neural Network for Temperature Prediction of Molten Steel in LF

et al. 2022

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The temperature control of molten steel in ladle furnace (LF) has a critical impact on steelmaking production. In this work, production data were collected from a steelmaking plant and a hybrid model based on expert control and deep neural network (DNN) was established to predict the molten steel temperature in LF. In order to obtain the optimal DNN model, the trial and error method was used to determine the hyperparameters. And the optimal architecture of DNN model corresponds to the hidden layers of 4, hidden layer neurons of 35, iterations of 3 000, and learning rate of 0.2. Compared with the multiple linear regression model and the shallow neural network model, the DNN model exhibits stronger generalisation performance and higher accuracy. The coefficient of determination (R 2 ), correlation coefficient (r), mean square error (MSE), and root-mean-square error (RMSE) of the optimal DNN model reached 0.897, 0.947, 2.924, 1.710, respectively. Meanwhile, in the error scope of temperature from − 5 to 5°C, the hit ratio of the hybrid model acquired 99.4%. The results demonstrate that the proposed model is effective to predict temperature of molten steel in LF.

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Section: A Hybrid Modeling Methods Based On Expert Control and Deep Neural Network For Temperature Prediction Of Molten Steel In Lfmentioning

confidence: 99%

Section: Correlation Analysis and Data Normalizationmentioning

confidence: 99%

A Hybrid Modeling Method Based on Expert Control and Deep Neural Network for Temperature Prediction of Molten Steel in LF

et al. 2022

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“…The authors applied the improved adaptive boosting (AdaBoost) algorithm to integrate multiple sub-models and predict steel temperature in LF. 6,7) Hybrid model (HM) combine multiple MMs and DMs, and a well-structured, high-performance HM can leverage the advantages of both. When predicting steel temperature, He et al took into account the effect of ladle heat status on temperature.…”

Section: Steel In Ladle Furnacementioning

confidence: 99%

An Error Correction Method Based on CBR for End Temperature Prediction of Molten Steel in Ladle Furnace

He,

Song,

Guo

et al. 2024

ISIJ Int.

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Accurately predicting the end temperature of molten steel is significant for controlling ladle furnace (LF) refining. This paper proposes an error correction method called EC-CBR based on case-based reasoning (CBR) to reduce errors in the prediction models caused by discrepancies between actual production data and training data. The proposed method combines the incremental learning advantage of CBR with the ability of other models to fit nonlinear relations. First, a prediction model is established, and historical heats similar to the new heat are retrieved by CBR. Then, the model error of the new heat is calculated by employing the errors of similar heats. The prediction result is calculated by subtracting the error from the predicted value. Testing and comparison are conducted on the models (support vector regression, backpropagation neural network, extreme learning machine and mechanism model) and general CBR using actual production data. Results show that the EC-CBR is effective for both data-driven and mechanism models, with an increase of approximately 5% in hit rate within the range of ±5℃ for data-driven models and an increase of 21.73% for mechanism model. Moreover, the corrected data-driven models show higher accuracy than the general CBR, further proving the effectiveness of the proposed method.

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“…Among various ensemble learning soft sensors, perturbing training data remains dominant for creating diversity, such as clustering [41], moving window [24,27], bootstrapping sampling [42,43], and sequential sampling [44]. However, such data manipulation strategies do not always function well for EJIT modeling because JIT learning only relies on a small subset of relevant samples for each run of prediction and is less sensitive to the randomness injected to the database.…”

Section: Generation Of Base Jit Learners Through Evolutionarymentioning

confidence: 99%

Ensemble Just-In-Time Learning-Based Soft Sensor for Mooney Viscosity Prediction in an Industrial Rubber Mixing Process

Jin

Wang

et al. 2020

Advances in Polymer Technology

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e lack of online sensors for Mooney viscosity measurement has posed significant challenges for enabling efficient monitoring, control, and optimization of industrial rubber mixing process. To obtain real-time and accurate estimations of Mooney viscosity, a novel soft sensor method, referred to as multimodal perturbation-(MP-) based ensemble just-in-time learning Gaussian process regression (MP-EJITGPR), is proposed by exploiting ensemble JIT learning. is method employs perturbations on similarity measure and input variables for generating the diversity of JIT learners. Furthermore, a set of accurate and diverse JIT learners are built through an evolutionary multiobjective optimization by balancing the accuracy and diversity objectives explicitly. Moreover, all base JIT learners are combined adaptively using a finite mixture mechanism. e proposed method is applied to an industrial rubber mixing process for Mooney viscosity prediction, and the experimental results demonstrate its effectiveness and superiority over traditional soft sensor methods.

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A New AdaBoost.IR Soft Sensor Method for Robust Operation Optimization of Ladle Furnace Refining

Cited by 13 publications

References 15 publications

A Hybrid Modeling Method Based on Expert Control and Deep Neural Network for Temperature Prediction of Molten Steel in LF

A Hybrid Modeling Method Based on Expert Control and Deep Neural Network for Temperature Prediction of Molten Steel in LF

An Error Correction Method Based on CBR for End Temperature Prediction of Molten Steel in Ladle Furnace

Ensemble Just-In-Time Learning-Based Soft Sensor for Mooney Viscosity Prediction in an Industrial Rubber Mixing Process

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