Energy efficiency of a hybrid cable yarding system: A case study in the North-Eastern Italian Alps under real working conditions
Bibliographic record
Abstract
In order to reduce greenhouse gas emissions, low emission or zero-emission technologies have been applied to light and heavyduty vehicles by adopting electric propulsion systems and battery energy storage. Hybrid cable yarders and electrical slack-pulling carriages could represent an opportunity to increase the energy efficiency of forestry operations leading to lower impact timber harvesting and economic savings thanks to reduced fuel consumption. However, given the limited experience with hybrid-electric systems applied to cable yarding operations, these assumptions remain uncertain. This study assessed an uphill cable yarding operation using a hybrid cable yarder and an active slack-pulling electric power carriage over thirty working days. A total of 915 work cycles on four different cable lines were analysed. Longterm monitoring using Can-BUS data and direct field observations were used to evaluate the total energy efficiency, total energy efficiency (%), and fuel consumption per unit of timber extracted (L/m3). The use of the electric-hybrid system with a 700 V supercapacitor to store the recovered energy made it possible to reduce the running time of the engine by about 38% of the total working time. However, only 35% to 41% of the Diesel-based mechanical energy was consumed by the mainline and haulback winches. Indeed, the remaining energy was consumed by the other winches of the cable line system (skyline, strawline winches and carriage recharging or breaking during outhaul) or dissipated by the system (e.g., by the haulback blocks). With reference to all work cycles, the highest net energy consumption occurred during the inhaulunload work element with a maximum of 1.15 kWh, consuming 70% of total net energy consumption to complete a work cycle. In contrast, lower energy consumption was recorded for lateral skid and outhaul, recording a maximum of 23% and 32% of the total net energy consumption, respectively. The estimated recovered energy, on average between the four cable lines, was 2.56 kWh. Therefore, the reduced fuel need was assessed to be approximately 730 L of fuel in the 212.5 PMH15 of observation, for a total emissions reduction of 1907 kg CO2 eq, 2.08 kg CO2 eq for each work cycle.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".