Handling sensor faults in economic model predictive control of batch processes

The problem of sensor fault detection and isolation (FDI) and fault‐tolerant economic model predictive control (FT‐EMPC) for batch processes is addressed. To this end, we first model batch processes using subspace‐based system identification techniques. The analytical redundancy within the identifie...

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Bibliographic Details
Published in:AIChE journal 2019-02, Vol.65 (2), p.617-628
Main Authors: Rashid, Mudassir M., Patel, Nikesh, Mhaskar, Prashant, Swartz, Christopher L. E.
Format: Article
Language:English
Subjects:
batch process control
Batch processes
Batch processing
Case studies
Computer simulation
Data processing
Economic analysis
economic model predictive control
Economic models
electric arc furnace
Electric arc furnaces
Fault detection
fault detection and isolation
fault‐tolerant control
Mathematical models
Organic chemistry
Predictive control
Redundancy
subspace identification
System identification
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Summary:The problem of sensor fault detection and isolation (FDI) and fault‐tolerant economic model predictive control (FT‐EMPC) for batch processes is addressed. To this end, we first model batch processes using subspace‐based system identification techniques. The analytical redundancy within the identified model is subsequently exploited to detect, isolate, and handle the faulty measurements. The reconciled fault‐free measurements are then incorporated in an economic model predictive controller formulation. Simulation case studies involving the application of the proposed data‐driven FDI and FT‐EMPC algorithms to the energy intensive electric arc furnace process illustrate the improvement in economic performance under various fault scenarios. © 2018 American Institute of Chemical Engineers AIChE J, 65: 617–628, 2019
ISSN:0001-1541
1547-5905
DOI:10.1002/aic.16460