Application of Robust Model Identification Techniques in the Activated Sludge Process
Research Engineer: Anthony Wu
Supervisors: Tao Chen, Matt McEwan, Dave Lovett
E-mail: [email protected]
This Engineering Doctorate Research Project is a collaboration between the University of Surrey and Perceptive Engineering Limited, and is funded by the Engineering and Physical Sciences Research Council
ExperimentationGrey-Box Model
Identification
Sequential Design of Experiments
MPC Model
Process Knowledge
MPC models describe how the output responds to a change in an input signal
Automated controller uses the MPC model to predict the output andadjust the input to bring the output to the set point
3
1
2
Step tests on the input signals to observe how the output responds
Capturing more information-rich data from experiments
• Design of step tests to obtain better (more information-rich) data
• Better data → better MPC models → better process optimisation
• The Fisher Information Matrix (FIM) is used to assess the information
content of a dataset (the inputs and outputs)
• The step-tests for the next experiment is designed to maximise the
information content (the output is estimated from MPC model)
• Constraints are added so the designed input trajectory should not
violate output consents or damage process equipment
Enhancing black-box modelling with process knowledge
• Black-box models: built purely by statistical analysis
Without sufficient information-rich data, black-box
models overfit and perform poorly
• Grey-box models : process knowledge is incorporated
as constraints to black-box models
• Constraints: gain magnitude, minimum/non-minimum
phase, dead-time
[1] M. O’ Brien, J. Mack, B. Lennox, D. Lovett and A. Wall, “Model Predictive Control of an ActivatedSludge Process: A Case Study,” Control Engineering Practices, vol. 19, no. 1, pp. 54-61, 2011.
FRAMEWORK
Output
Input(s)
Noise(disturbance)
Timer
ASP simulation Slow process, disturbance dominated,Stringent output consents
MPCSet point changesDisturbance response
Target/Set Point
Output
InputPredicted Input
Output Prediction
What is my research? I am investigating modelling techniques to build more robust MPC models in wastewater treatment processes.
What is MPC? MPC stands for Model Predictive Control. It is used in automated controllers and improving process efficiency.
What are MPC models? They describe how the output signal responds to a change in input. MPC use these models to make output predictions.
How does MPC benefit the activated sludge process? The activated sludge process requires aeration and this consumes a lot of energy. MPC has
demonstrate energy savings of over 25% [1] in a typical plant without compromising the quality of treated water.
What are the challenges when building MPC models? While MPC is useful, building the MPC model is difficult due to a number of reasons,
including: high non-linearity, long time-delays, resource limitations, stringent output consent limits and process disturbances.
What solutions am I looking at? Two avenues are being explored: Grey-Box Model Identification and the Sequential Design of Experiments.
First principles knowledge and engineering experience
Dead time
Gain magnitude
Gain direction specified to be
negative
Grey-box
Black-box
Example: black-box vs grey-box model
Model overfit
Model Predictive Control
SUMMARY
OUTPUT