Abstract
A de-oiling system consisting of a set of gravity separators and hydrocyclonesis used to separate oil from water in O&G production, to ensure low OiW concentration in the discharge. PID is currently used for de-oiling system control, but it is not always effective to guarantee separation efficiency. Control has been verified its effectiveness comparing with PID controllers in our previous works. However, the current control is model-based, requiring a lot of work for system identification. Therefore, it is difficult to transfer the developed control algorithms into different industrial facilities. In this work, we aim to develop an automatic control generation method such that the de-oiling control can automatically learn the optimal control policy from its behaviour in an online manner, i.e., learning from data without requiring system identification.
Original language | English |
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Publication date | 2019 |
Number of pages | 1 |
Publication status | Published - 2019 |
Event | Kick-off: AI for the people - Rendsburggade 14, Aalborg, Denmark Duration: 19 Nov 2019 → 19 Nov 2019 |
Other
Other | Kick-off: AI for the people |
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Location | Rendsburggade 14 |
Country/Territory | Denmark |
City | Aalborg |
Period | 19/11/2019 → 19/11/2019 |
Keywords
- De-oiling
- O&G production
- automatic control generation method