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A Hybrid Missing Data Imputation Method for Batch Process Monitoring Dataset.

Qihong Gan1,2, Lang Gong2,3, Dasha Hu2,3

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This study introduces a hybrid method to accurately impute multi-type missing data in batch process monitoring. The novel approach enhances data quality for fault identification and optimal control.

Keywords:
LSTM neural networkbatch processdata qualitymissing data imputation

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Area of Science:

  • Chemical Engineering
  • Data Science
  • Process Control

Background:

  • Batch process monitoring datasets frequently suffer from missing data, hindering accurate fault identification and optimal control.
  • Existing data imputation methods often fail to address the complexities of multi-type missing data in sensor datasets, compromising data quality.

Purpose of the Study:

  • To develop a hybrid missing data imputation method tailored for batch process monitoring datasets with diverse missing data types.
  • To improve the performance of data-driven modeling for fault identification and optimal control by enhancing data quality.

Main Methods:

  • Missing data is categorized into five types based on duration and simultaneous variable loss.
  • A hybrid approach employs single-dimensional interpolation, iterative multivariate regression, and Long Short-Term Memory (LSTM) models for imputation.
  • Specific imputation strategies are applied based on the characteristics of each missing data category.

Main Results:

  • The proposed hybrid method demonstrated superior imputation accuracy across various missing data categories compared to existing methods.
  • Experiments on a real-world batch process monitoring dataset validated the effectiveness of the imputation technique.
  • The method successfully addresses transient isolated, short-term, and long-term missing data scenarios.

Conclusions:

  • The developed hybrid imputation method offers a robust solution for handling multi-type missing data in batch process monitoring.
  • Enhanced data quality through accurate imputation leads to improved performance in fault identification and optimal control applications.
  • This approach provides a valuable tool for leveraging data-driven modeling in industrial batch processes.