Digital Twin-Based Virtual Sensing for Real-Time Process Monitoring of Marine Reciprocating Natural Gas Compressor Packages
Bibliographic record
Abstract
Marine reciprocating compressor packages are critical in offshore oil and gas production but face inherent challenges for process monitoring due to explosion-proof requirements and limited onboard sensing. Many key process variables, such as flow rates and stage-wise efficiencies, remain unmeasured in practice, hindering comprehensive and accurate real-time monitoring. This study proposes a digital twin-based virtual sensing approach that integrates process mechanism modeling with real-time data streams to infer unmeasured variables from limited measurements and reconstruct package-level operating states. A full package-level process mechanism model was established in HYSYS, incorporating compression, heat exchange, and recycle control modules, and further generalized through a cascaded-stage modeling strategy that supports scalable deployment across packages with different configurations. A DT framework was constructed to couple the model with real-time sensor inputs, enabling continuous model updating, virtual-physical synchronization, and visualization through a service platform. Validation on a two-stage marine compressor package shows that predicted data closely match operating and design values, with MAPE typically <2% for compressor metrics, heat exchanger water flow MAPE≈1.4–1.5%, and recycle flow MAPE≈9.6%. Additional testing on a small-scale compressor package confirms the scalability of the cascaded-stage modeling approach and the DT system, maintaining flow-prediction errors within 5% while requiring only minimal interface configuration and parameter adjustment for direct deployment across packages. These results indicate that the proposed method can effectively extend monitoring capability beyond limited sensor coverage, providing a practical pathway for DT-enabled monitoring and supervisory optimization of marine reciprocating compressor packages.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".