Autonomous Control of Primary Separation Vessel using Reinforcement Learning
Bibliographic record
Abstract
Reinforcement Learning (RL) adapts to a dynamic environment through exploration and continual learning. However, the risks associated with exploration have limited RLC applications in industrial settings. This research designed a safe RLC framework for the bitumen extraction process from oil sands. We explore transfer learning techniques through Behavioral Cloning (BC), Generative Adversarial Imitation Learning (GAIL), and Simulation-to-Reality (Sim2Real) pretraining, which allow the RL agent to acquire fundamental knowledge before real-world exploration. This transferred knowledge steers exploration towards near-optimal and safer regions. GAIL and Sim2Real pretrained agents deliver satisfactory control performance right after pretraining, cutting process trips during online training by factors of 8 and 27, respectively. Further online training reduces Integral Squared Error (ISE) for interface tracking by 71% with GAIL and 22% with Sim2Real. ISE for tailings density tracking also decreases by 73% and 33%, respectively. This significant reduction demonstrates RL's continual learning ability suitable for autonomous control. This work highlights the potential for safely deploying RL in process automation and reveals that RLC can handle complex control problems in multivariate, multimodal, partially observable, and uncertain environments. RLC achieves Model Predictive Control (MPC)-level performance but with less controller effort, and computation time faster by a factor of 10.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".