The RAYMOND simulation package — Generating RAYpresentative MONitoring Data to design advanced process monitoring and control algorithms

•New modular simulation package, implemented in MATLAB, freely downloadable.•Easy implementation of custom models, controls strategies, sensor properties.•Free specification of process variability and process disturbances.•Inclusion of process variability is important for process monitoring and cont...

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Bibliographic Details
Published in:Computers & chemical engineering Vol. 69; pp. 108 - 118
Main Authors: Gins, Geert, Vanlaer, Jef, Van den Kerkhof, Pieter, Van Impe, Jan F.M.
Format: Journal Article
Language:English
Published: Kidlington Elsevier Ltd 03.10.2014
Elsevier
Subjects:
Adaptative systems
Applied sciences
Benchmarking
Computer science; control theory; systems
Computer simulation
Control system synthesis
Control theory
Control theory. Systems
Data generation
Exact sciences and technology
Matlab
Modelling and identification
Modular design
Monitoring
Noise measurement
Packages
Process control
Process control. Computer integrated manufacturing
Process monitoring
Process simulation
Process monitoring
Data generation
Process control
Process simulation
Modular design
Computer integrated manufacturing
Variability
Measurement sensor
Control program
Control synthesis
Surveillance
Batch process
Robustness
Monitoring
Signal to noise ratio
ISSN:0098-1354, 1873-4375
Online Access:Get full text
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Summary:•New modular simulation package, implemented in MATLAB, freely downloadable.•Easy implementation of custom models, controls strategies, sensor properties.•Free specification of process variability and process disturbances.•Inclusion of process variability is important for process monitoring and control. This work presents the RAYMOND simulation package for generating RAYpresentative MONitoring Data. RAYMOND is a free MATLAB package and can simulate a wide range of processes; a number of widely-used benchmark processes are available, but user-defined processes can easily be added. Its modular design results in large flexibility with respect to the simulated processes: input fluctuations resulting from upstream variability can be introduced, sensor properties (measurement noise, resolution, range, etc.) can be freely specified, and various (custom) control strategies can be implemented. Furthermore, process variability (biological variability or non-ideal behavior) can be included, as can process-specific disturbances. In two case studies, the importance of including non-ideal behavior for monitoring and control of batch processes is illustrated. Hence, it should be included in benchmarks to better assess the performance and robustness of advanced process monitoring and control algorithms.
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ISSN:0098-1354
1873-4375
DOI:10.1016/j.compchemeng.2014.07.010