Yazar "Cao, Weihua" seçeneğine göre listele

Listeleniyor 1 - 6 / 6
  • Küçük Resim Yok
    Öğe
    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.
  • Küçük Resim Yok
    Öğe
    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.
  • Küçük Resim Yok
    Öğe
    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.
  • Küçük Resim Yok
    Öğe
    Robust control of feeding speed for coal mine tunnel drilling machines
    (Institute of electrical and electronics engineers inc., 2024) Liu, Xiao; Chen, Luefeng; Wu, Min; Cao, Weihua; Lu, Chengda; Pedrycz, Witold
    Changes in coal seam hardness cause fluctuations in the feed resistance at the drill bit during the drilling process, leading to unstable feeding speed. This paper proposes a robust dynamic output feedback controller to suppress disturbances caused by the variations in coal seam hardness in the feed system. Firstly, an unknown parameter measuring coal seam hardness is introduced, and an uncertain model of the feeding system is established based on the finite element model of the drill string. By designing weighted functions based on industrial field requirements and constructing a generalized plant, the controller achieves loop shaping, reducing the low-frequency impact of coal seam hardness variations on the feed system and suppressing the systems resonance peak. Simulation results demonstrate that the controller effectively suppresses parameter variations and external disturbances caused by changes in coal seam hardness, achieving stable control of the drilling speed.
  • Küçük Resim Yok
    Öğe
    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.
  • Küçük Resim Yok
    Öğe
    Time series anomaly detection via rectangular information granulation for sintering process
    (Institute of electrical and electronics engineers inc., 2024) Du, Sheng; Ma, Xian; Wu, Min; Cao, Weihua; Pedrycz, Witold
    Time series anomaly in the sintering process is a direct manifestation of equipment failure and abnormal operating mode, and effective detection of time series anomaly is important to improve the stability of the sintering process. This article presents a time series anomaly detection via rectangular information granulation, whose originality is to apply the similarity of information granules as a reference for anomaly detection. It converts time series into rectangular granules, and the similarity of time series is measured with rectangular granules. The one-way analysis of variance method is used to detect the difference for the similarity between the time series to be detected and the historical time series and the similarity between any two historical time series, thus achieving the anomaly detection of the time series. The experiment is conducted on real-world data from an enterprise. The result shows that the proposed method outperforms the probability density analysis method and can effectively detect abnormal time series.