Optimization of Energy Consumption of Conveyor Belts in Self-Unloading Ships Using Machine Learning Models
Bibliographic record
Abstract
The energy consumption of conveyor belts in self-unloading ships is a significant concern in the maritime industry due to its impact on costs and sustainability. This study demonstrates the potential of machine learning in managing conveyor belt energy consumption, resulting in substantial savings and improved operational efficiency. Introducing intelligence in this process changes operational conditions, achieves significant energy savings, and improves efficiency and environmental sustainability by reducing greenhouse gas emissions associated with bulk cargo transportation. This study is designed in collaboration with our industrial partner based in North America. For this, a comparative study of different analytical approaches, such as Decision Trees (DT), Support Vector Regressor (SVR), and Random Forest (RF), was conducted to choose the most efficient to optimize conveyor belt energy consumption. Through careful data pre-processing and hyperparameter tuning, we demonstrate that RF yielded the best results, with an average train R-squared of 0.930 and a test R-squared of 0.89.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".