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Abstract
By deploying the online Oil-in-Water (OiW) sensors in a de-oiling hydrocyclone system used for produced water treatment processes in offshore oil & gas production, this work investigated modelling of the complicated separation dynamics inside the hydrocylone system using the Long-Short-Term Memory Neural Network (LSTM-NN). The purpose of this modelling is to predict the hydrocyclone's transient de-oiling efficiency in a high level of accuracy. Thereby the hydrocyclone system can be optimally controlled subject to different operating conditions. The acquisition and analysis of the data obtained from a lab-scaled pilot plant is introduced. Two types of LSTM-NN configurations are proposed, and the hyper-parameter tuning as well as training and validation results, are discussed in details. The results exhibit that the relative concentration of OiW, which correlated with the de-oiling efficiency, can be predicted in a quite accurate level using two types of measurements, i.e., the opening degrees of cyclone's underflow and overflow control valves, both the hydrocyclone's inlet/water-outlet OiW concentration measurements. The best model can achieve a normalized RMSE 83,62% accuracy in the validation test. One of our next step is to cooperate the LSTM-NN model into the model predictive control framework to design some optimal control solution for de-oiling hydrocyclone systems.
Original language | English |
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Title of host publication | IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society |
Number of pages | 6 |
Publisher | IEEE |
Publication date | Oct 2023 |
Article number | 10311791 |
ISBN (Print) | 979-8-3503-3183-7 |
ISBN (Electronic) | 979-8-3503-3182-0 |
DOIs | |
Publication status | Published - Oct 2023 |
Event | IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society - , Singapore Duration: 16 Oct 2023 → 19 Oct 2023 |
Conference
Conference | IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society |
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Country/Territory | Singapore |
Period | 16/10/2023 → 19/10/2023 |
Series | Proceedings of the Annual Conference of the IEEE Industrial Electronics Society |
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ISSN | 1553-572X |
Keywords
- LSTM-NN
- Oil-in-Water
- hydrocyclone
- modelling
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