Modelling the Oil-in-Water Separation Dynamics in a De-Oiling Hydrocyclone System Using LSTM Neural Network
Author:
Affiliation:
1. Aalborg University,AAU Energy,Denmark
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10311571/10311610/10311791.pdf?arnumber=10311791
Reference14 articles.
1. Performance Evaluation of De-oiling Processes Controlled by PID, H(X) and MPC;jespersen;Proc of 47th Annual Conference of the IEEE Industrial Electronics Society (IECON21),2021
2. Model-free H∞ tracking control for de-oiling hydrocyclone systems via off-policy reinforcement learning
3. Uncertainty Analysis of Fluorescence-Based Oil-In-Water Monitors for Oil and Gas Produced Water
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1. Flood Forecast and Control for Urban Rivers Using LSTM Neural-Network;Lecture Notes in Civil Engineering;2023-08-31
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