Reinforcement learning-based autonomous control of bench-scale primary separation vessel
Bibliographic record
Abstract
In the process industry, smart automation of complex operations has great potential for efficient and safe operation, making it a key component for unlocking economic and sustainable large-scale production. However, real-world process units such as primary separation vessels (PSVs) pose numerous challenges, such as sensory uncertainty, nonlinear dynamics, and operational variability. This study introduces a novel autonomous control framework integrating model predictive control (MPC), reinforcement learning (RL), and state estimation techniques for building an adaptive, optimal, and safe control strategy. The proposed framework is demonstrated in a real-world scenario using a bench-scale experimental setup of the PSV that mimics the actual process. The implemented closed-loop control system accurately predicted a crucial process variable, optimized the operating point in real time, and achieved robust set-point tracking performance by tuning the controller for real process conditions. The results indicate that incorporating adaptive and data-driven techniques such as reinforcement learning into feedback control approaches is promising for building robust autonomous control strategies that maximize efficiency while respecting physical constraints, paving the way for autonomous control systems that are deployable in complex real-world scenarios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".