Application of AI-based Data Analysis and Processing Technology in Process Industry

Yaqian Qi; Yuan Feng; Jingxiao Tian; Xiangxiang Wang; Hanzhe Li

doi:10.5281/zenodo.11003949

Authors

Yaqian Qi Baruch College
Yuan Feng Duke University
Jingxiao Tian San Diego State University
Xiangxiang Wang University of Texas at Arlington
Hanzhe Li New York University

DOI:

https://doi.org/10.5281/zenodo.11003949

References:

29

Keywords:

Process Industry, Data Processing and Analysis, Correlation Analysis, Artificial Intelligence

Abstract

In recent years, the rapid advancement of artificial intelligence (AI) has spurred progress across various industries, including manufacturing, propelled further by the advent of big data. Notably, the processing industry, encompassing sectors like metal smelting, chemical production, cement manufacturing, and glass production, generates vast amounts of data due to the nature of its operations. This paper employs AI-driven data analysis techniques, specifically utilizing association rule data analysis and dynamic clustering-based discretization technology, to comprehensively study and summarize this data. Test data from cement clinker and cement grinding processes are employed to validate the efficacy of these methods, with the analysis results visualized and outputted for use by enterprise analysts. This approach offers significant advantages over traditional data analysis methods, notably in terms of time, efficiency, and cost savings, thereby bridging the gap in intelligent development within the manufacturing industry.

Author Biographies

Yaqian Qi, Baruch College

Interdisciplinary Data Science , Duke University , North Carolina, USA.

Yuan Feng, Duke University

Interdisciplinary Data Science , Duke University , North Carolina, USA.

Jingxiao Tian, San Diego State University

Electrical and Computer Engineering, San Diego State University, CA, USA.

Xiangxiang Wang, University of Texas at Arlington

Computer Science, University of Texas at Arlington, Arlington, TX, USA.

Hanzhe Li, New York University

Computer Engineering, New York University, NY, USA.

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