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A hierarchical data reconciliation based on multiple time-delay interval estimation for industrial processes.

Sen Xie1, Huaizhi Wang2, Jianchun Peng2

  • 1College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, PR China; College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, PR China.

ISA Transactions
|June 14, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new hierarchical data reconciliation method to improve industrial process monitoring. It addresses challenges with multiple, varying time delays in series reactors for better data quality and efficiency.

Keywords:
Data reconciliationHierarchical modelingImproved discrete state transition algorithmLong-running industrial processMultiple time-delay interval estimation

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Area of Science:

  • Chemical Engineering
  • Process Control
  • Data Analytics

Background:

  • High data quality is crucial for industrial efficiency and monitoring.
  • Data reconciliation enhances process data but faces challenges with time-varying delays in series reactors.
  • Accurate tracking of materials and their residence times is difficult in complex industrial setups.

Purpose of the Study:

  • To develop a novel data reconciliation method for industrial processes with multiple, time-varying delays.
  • To improve the accuracy of process data and parameter estimation in complex reactor systems.
  • To address the challenges of material tracing and time-delay variations in series reactors.

Main Methods:

  • Developed a multiple time-delay interval estimation technique based on process modeling.
  • Presented an improved discrete state transition approach for time-matching data across reactors.
  • Constructed a hierarchical data reconciliation framework tailored to data characteristics.

Main Results:

  • The proposed method effectively handles multiple and time-varying time delays in industrial processes.
  • Accurate data reconciliation was achieved despite complexities in series reactor systems.
  • The method demonstrated feasibility through successful industrial application verification.

Conclusions:

  • The novel hierarchical data reconciliation method provides a robust solution for industrial processes with complex time-delay dynamics.
  • This approach enhances operational management and monitoring by improving process data quality.
  • The technique offers significant potential for optimizing energy conservation and efficiency in industrial applications.