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Data-driven approach for time-delay estimation of industrial processes.
1Department of Automation, Xiamen University, 361005, China.
This study introduces a new data-driven method for estimating time delays in industrial processes using only output data. The approach accurately estimates delays without needing system identification or prior knowledge, validated on diverse examples.
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Area of Science:
- Control Engineering
- Signal Processing
- Industrial Automation
Background:
- Accurate time-delay estimation is critical for effective process control, performance assessment, and controller design.
- Industrial processes often operate under routine conditions with background disturbances, requiring methods that utilize available closed-loop output data.
- Existing methods may require system identification or prior process knowledge, limiting their applicability.
Purpose of the Study:
- To develop a novel, data-driven approach for estimating time delays in industrial processes.
- To provide practical solutions for time-delay estimation using only closed-loop output data under routine operating conditions.
- To validate the proposed method's effectiveness on various numerical and industrial examples.
Main Methods:
- A data-driven approach is proposed, estimating the closed-loop impulse response online from output data.
- For large time delays, direct estimation is performed without system identification or prior knowledge.
- For small time delays, estimation utilizes a stationarilized filter, pre-filter, and loop filter.
Main Results:
- The proposed method successfully estimates time delays using only routine closed-loop output data.
- The approach is effective for both large and small time-delayed processes.
- Validation on industrial examples like distillation columns and refinery heating furnaces demonstrates practical applicability.
Conclusions:
- The developed data-driven method offers an accurate and practical solution for time-delay estimation in industrial settings.
- The approach bypasses the need for system identification and prior process knowledge, enhancing its versatility.
- The method's effectiveness is confirmed across diverse industrial applications, highlighting its robustness.