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

Xin, Zicheng; Zhang, Jiangshan; Zheng, Jin; Jin, Yu; Liu, Qing

doi:10.2355/isijinternational.isijint-2021-251

Cited by 25 publications

(4 citation statements)

References 33 publications

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“…These models can currently be mainly classified into three categories in the literature: mechanism, data-driven, and hybrid. 4) The mechanism model (MM), also known as the first principle model, is mainly established according to the energy conservation, heat transfer, and mass conservation equations. Wu et al used the energy conservation equation and took molten steel and slag as research objects to derive the molten steel heating rate model.…”

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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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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“…Fang [16], Xin [17] 2014-2023 Prediction of molten steel temperature Zhou [18], Wang [19], Zang [20] 2022-2023 Prediction of oxygen demand Wang [21] 2017 Prediction of ladle furnace temperature Takalo-Mattila [22], Chen [23], Li [24], Wu [25], Zhao [26], Xie [27], He [28], Boto [29], Chen [30], Xu [31], Orta [32],…”

Section: Review Of Dynamic Problems In Complex Industrial Processesmentioning

confidence: 99%

Dynamic Operation Optimization of Complex Industries Based on a Data-Driven Strategy

Tian,

Zhao,

Xie

et al. 2024

Processes

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As industrial practices continue to evolve, complex process industries often exhibit characteristics such as multivariate correlation, dynamism, and nonlinearity, making traditional mechanism modeling inadequate in terms of addressing the intricacies of complex industrial problems. In recent years, with advancements in control theory and industrial practices, there has been a substantial increase in the volume of industrial data. Data-driven dynamic operation optimization techniques have emerged as effective solutions for handling complex industrial processes. By responding to dynamic environmental changes and utilizing advanced optimization algorithms, it is possible to achieve dynamic operational optimization in industrial processes, thereby reducing costs and emissions, improving efficiency, and increasing productivity. This correlates nicely with the goals set forth by conventional process operation optimization theories. Nowadays, this dynamic, data-driven strategy has shown significant potential in complex process industries characterized by multivariate correlations and nonlinear behavior. This paper approaches the subject from a data-driven perspective by establishing dynamic optimization models for complex industries and reviewing the state-of-the-art time series forecasting models to cope with changing objective functions over time. Meanwhile, aiming at the problem of concept drift in time series, this paper summarizes new concept drift detection methods and introduces model update methods to solve this challenge. In addressing the problem of solving dynamic multi-objective optimization problems, the paper reviews recent developments in dynamic change detection and response methods while summarizing commonly used as well as the latest performance measures for dynamic multi-objective optimization problems. In conclusion, a discussion of the research progress and challenges in the relevant domains is undertaken, followed by the proposal of potential directions for future research. This review will help to deeply understand the importance and application prospects of data-driven dynamic operation optimization in complex industrial fields.

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“…Additional developments include the comparison of the predictive performances from three ML trained on data from five different EAF [17] and the evaluation of multiple ML models predicting the EE consumption with the aim to retrieve the optimal melting time for a given heat [18]. The published papers on ML models predicting the LRF end-point temperature are numerous [19][20][21][22][23][24][25][26][27]. However, the research primarily concerns the comparison of the predictive performance of models created using existing or newly developed ML modeling frameworks.…”

Section: Previous Workmentioning

confidence: 99%

A Proposed Methodology to Evaluate Machine Learning Models at Near-Upper-Bound Predictive Performance—Some Practical Cases from the Steel Industry

Carlsson,

Samuelsson

2023

Processes

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The present work aims to answer three essential research questions (RQs) that have previously not been explicitly dealt with in the field of applied machine learning (ML) in steel process engineering. RQ1: How many training data points are needed to create a model with near-upper-bound predictive performance on test data? RQ2: What is the near-upper-bound predictive performance on test data? RQ3: For how long can a model be used before its predictive performance starts to decrease? A methodology to answer these RQs is proposed. The methodology uses a developed sampling algorithm that samples numerous unique training and test datasets. Each sample was used to create one ML model. The predictive performance of the resulting ML models was analyzed using common statistical tools. The proposed methodology was applied to four disparate datasets from the steel industry in order to externally validate the experimental results. It was shown that the proposed methodology can be used to answer each of the three RQs. Furthermore, a few findings that contradict established ML knowledge were also found during the application of the proposed methodology.

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

Cited by 25 publications

References 33 publications

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

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

Dynamic Operation Optimization of Complex Industries Based on a Data-Driven Strategy

A Proposed Methodology to Evaluate Machine Learning Models at Near-Upper-Bound Predictive Performance—Some Practical Cases from the Steel Industry

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