Yazar "Hu, Jie" seçeneğine göre listele

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    Condition Recognition Strategy Based on Fuzzy Clustering With Information Granulation for Blast Furnace
    (Ieee-Inst Electrical Electronics Engineers Inc, 2023) Huang, Yuanfeng; Du, Sheng; Hu, Jie; Pedrycz, Witold; Wu, Min
    The temperature of the cooling stave (TCS) is an important state parameter to indicate the states of the slag crust during the blast furnace ironmaking process. The state of the slag crust affects the quality and production of pig iron, and the gas flow distribution in the blast furnace. Thus, it is necessary to recognize the states of the slag crust. This article proposes a condition recognition strategy based on fuzzy clustering endowed with a novel distance with information granulation for recognizing the states of the slag crust. First, the raw TCS time-series data are split into segments according to the appropriate segmentation length, and the segments are represented in a granular form by the information granulation method. Then, information granules are clustered using fuzzy clustering endowed with a novel distance. After completing the data representation, each information granule is compounded of a lower bound and an upper bound that indicate the dynamic characteristics of the corresponding segments. In the fuzzy clustering, information granulation distance, a new distance, is established to measure the similarity between two information granules. Finally, the data experiments using the datasets from the UCR time-series database and actual industrial data from the blast furnace demonstrate the effectiveness and superiority of the proposed condition recognition strategy.
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    Dynamic Modeling Framework Based on Automatic Identification of Operating Conditions for Sintering Carbon Consumption Prediction
    (Ieee-Inst Electrical Electronics Engineers Inc, 2024) Hu, Jie; Wu, Min; Cao, Weihua; Pedrycz, Witold
    In iron and steel industry, the sintering process requires huge carbon consumption. Achieving accurate and dynamic prediction of carbon consumption in this process is of evident significance to protect the environment and raise the economic efficiency of iron and steel industry. This article develops an original dynamic modeling framework, including automatic identification of operating conditions, modeling in different conditions, and dynamic prediction model of sintering carbon consumption. First, an automatic kernel-based fuzzy C-means algorithm is presented for the automatic identification of operating conditions. Dynamic relationships between the inputs and output of the model are analyzed by copula entropy, and then, the relevant production data in each operating condition are determined by a just-in-time learning method. Next, broad learning models are established under different operating conditions. Furthermore, a dynamic prediction model of sintering carbon consumption is designed, and the prediction error is adopted to quantify the performance of the model and as one criterion to determine the update of production database. Finally, results of experiments using actual production data demonstrate the advantage and validity of the developed model compared with some advanced modeling methods. The developed model considers complex characteristics of the sintering process and presents better dynamic performance and information mining performance.
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    Intelligent prediction and soft-sensing of comprehensive production indicators for iron ore sintering: a review
    (Elsevier b.v., 2025) Du, Sheng; Ma, Xian; Fan, Haipeng; Hu, Jie; Cao, Weihua; Wu, Min; Pedrycz, Witold
    Iron ore sintering is a critical process in iron and steel production, with a substantial impact on overall energy consumption and the emission of various environmental pollutants. Enhancing the efficiency of this process is crucial for achieving sustainability in the iron and steel industry. Accurate prediction and real-time monitoring of comprehensive production indicators are essential for optimizing production and improving energy efficiency. This paper provides a systematic review of intelligent prediction and soft-sensing techniques applied to the iron ore sintering process. It details the mechanisms and operational principles of these technologies, with a focus on key indicators such as quality, thermal state, yield, and energy consumption. This paper explores the current state-of-the-art in four prediction methodologies: mechanism analysis-based methods, data feature analysis-based methods, multi-model fusion-based methods, and operating mode recognition-based methods. Finally, the challenges to the current comprehensive production indicator prediction of the sintering process are pointed out, including the difficulty of dealing with the changing operating mode, the incomplete analysis of image features, and the insufficient consideration of the differences in data distribution. In the future, operating mode recognition approaches, deep learning approaches, transfer learning approaches, and computer vision techniques will have a broad prospect in the comprehensive production indicator prediction of the sintering process.
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    Relevance vector machine with hybrid kernel-based soft sensor via data augmentation for incomplete output data in sintering process
    (Pergamon-Elsevier Science Ltd, 2024) Hu, Jie; Li, Hongxiang; Li, Huihang; Wu, Min; Cao, Weihua; Pedrycz, Witold
    A ratio of CO and CO2 (CO/CO2) is a key indicator of sintering carbon consumption, which is difficult to be determined in real-time. Therefore, the establishment of its soft sensing model is of great practical significance. This paper proposes a novel CO/CO2 soft sensing model with incomplete output data based on relevance vector machine with hybrid kernel via data augmentation. First, a least absolute shrinkage and selection operator is employed for determining key input variables of the model, and an automatic fuzzy clustering framework is used to automatically identify multiple operating modes. Then, a relevance vector machine with hybrid kernel method is presented to model each operating mode separately. Meanwhile, considering the problem of incomplete input and output data, data augmentation is applied in modeling to enhance the model performance. Finally, the soft sensing model of CO/CO2 is formed. Experimental results and analyses using actual production data coming from the sintering production process demonstrate that the prediction performance and accuracy of the proposed model outperform some existing algorithms.
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    Soft-Sensing of burn-through point based on weighted kernel just-in-time learning and fuzzy broad-learning system in sintering process
    (IEEE, 2024) Hu, Jie; Wu, Min; Cao, Weihua; Pedrycz, Witold
    Burn-through point (BTP) is an essential thermal state parameter in a sintering process, which is a direct reflection of the stability of this process. However, it cannot be measured online. Soft-sensing technology offers a reliable method for estimating unmeasurable variables in industrial processes. Here, a soft-sensing model for BTP based on weighted kernel just-in-time learning (WKJITL) and fuzzy broad-learning system (FBLS) is built. First, an abnormal production data detection and correction strategy is employed to process the production data, and the mechanism analysis and mutual information analysis are utilized to specify the detectable process variables that are directly related to BTP. Then, the WKJITL method is proposed to obtain historical production data similar to the query data of BTP for local learning modeling, and the FBLS is utilized as an efficient modeling method for the soft-sensing prediction of BTP. Finally, the results of simulation experiments based on actual sintering production data reveal that the developed soft-sensing model of BTP exhibits better prediction accuracy and efficiency compared with some advanced modeling methods. Furthermore, the proposed method is of general nature and can also be easily applied to other industrial processes.