Coupled risk analysis of hospital infection: A multimethod-fusion model combining association rules with complex networks

Zhou, Ying; Wang, Yu; Li, Chenshuang; Ding, Lieyun; Mei, Yaqin

doi:10.1016/j.cie.2023.109720

Cited by 2 publications

(2 citation statements)

References 43 publications

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“…Additionally, the results obtained from the ARM need to satisfy a minimum support threshold (minsup) and a minimum confidence threshold (mincon). The formula for calculating the support, confidence and lift is described in Equations ( 30)- (32).…”

Section: Association Rules Algorithmmentioning

confidence: 99%

“…Data mining is widely applied in various fields, such as the biomedical field and the field of energy construction, by domestic and foreign scholars. It extracts rules from complex data, discovers association patterns between different features, and achieves functions such as fault diagnosis [25,26], behavior prediction [27,28], pattern recognition [29,30], and disease research [31,32]. Data mining also has some successful applications in the iron and steel industry.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Energy Utilization Efficiency and Optimal Energy Matching Model of EAF Steelmaking Based on Association Rule Mining

Yang,

Li,

et al. 2024

Metals

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In the iron and steel industry, evaluating the energy utilization efficiency (EUE) and determining the optimal energy matching mode play an important role in addressing increasing energy depletion and environmental problems. Electric Arc Furnace (EAF) steelmaking is a typical short crude steel production route, which is characterized by an energy-intensive fast smelting rhythm and diversified raw charge structure. In this paper, the energy model of the EAF steelmaking process is established to conduct an energy analysis and EUE evaluation. An association rule mining (ARM) strategy for guiding the EAF production process based on data cleaning, feature selection, and an association rule (AR) algorithm was proposed, and the effectiveness of this strategy was verified. The unsupervised algorithm Auto-Encoder (AE) was adopted to detect and eliminate abnormal data, complete data cleaning, and ensure data quality and accuracy. The AE model performs best when the number of nodes in the hidden layer is 18. The feature selection determines 10 factors such as the hot metal (HM) ratio and HM temperature as important data features to simplify the model structure. According to different ratios and temperatures of the HM, combined with k-means clustering and an AR algorithm, the optimal operation process for the EUE in the EAF steelmaking under different smelting modes is proposed. The results indicated that under the conditions of a low HM ratio and low HM temperature, the EUE is best when the power consumption in the second stage ranges between 4853 kWh and 7520 kWh, the oxygen consumption in the second stage ranges between 1816 m3 and 1961 m3, and the natural gas consumption ranges between 156 m3 and 196 m3. Conversely, under the conditions of a high HM ratio and high HM temperature, the EUE tends to decrease, and the EUE is best when the furnace wall oxygen consumption ranges between 4732 m3 and 5670 m3, and the oxygen consumption in the second stage ranges between 1561 m3 and 1871 m3. By comparison, under different smelting modes, the smelting scheme obtained by the ARM has an obvious effect on the improvement of the EUE. With a high EUE, the improvement of the A2B1 smelting mode is the most obvious, from 24.7% to 53%. This study is expected to provide technical ideas for energy conservation and emission reduction in the EAF steelmaking process in the future.

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Section: Association Rules Algorithmmentioning

confidence: 99%