Current issue: 53(2)
Under compilation: 53(3)
Industrial chipping is becoming increasingly popular, as the result of a growing demand for woody biomass. Industrial chippers are large, powerful machines that generate much noise and vibration. This study explored some factors that may affect exposure to noise and vibration, namely: feedstock type (branches vs. logs), work station characteristics (truck cab vs. separate cab) and knife wear (new knives vs. blunt knives). Exposure to noise was significantly affected by all three factors, and it was higher for branch feedstock, separate cabs and blunt knives. The higher exposure levels recorded for the separate cab were especially insidious, because they were below and above the hearing threshold and would elude immediate perception. Exposure to whole-body vibration (WBV) was significantly higher for branch feedstock and for the separate cab. Knife wear seemed to determine an increase in WBV, but this effect had no statistical significance and the result could not be taken as conclusive. Among the three factors studied, work station characteristics had the strongest effect. Further studies may extend the comparison to a wider range of options, as well as explore the use of exposure variation for machine diagnostics.
Chain flail delimbing and debarking may improve value recovery from small tree harvests, without renouncing the benefits of multi-tree processing. The technology is mature and capable of excellent performance, which has been documented in many benchmark studies. This paper offers new insights into the relationship between the performance of chain flail delimbing and debarking and such factors as tree volume, load volume, tree form and bark-wood bond strength (BWBS). The study was conducted in Chile, during the commercial harvesting of a Eucalyptus globulus Labill. plantation. In an observational study, researchers collected production data from over 780 work cycles, and work quality data from over 1000 individual trees. The analysis of these data shows that productivity is affected primarily by load volume. Work quality is affected by BWBS and by the number of trees in a load. Work quality degrades with increasing BWBS and tree number, since more trees tend to shield each other. Tree form has no effect on either productivity or work quality. Regression and probability functions are provided, and can be used for predictive purposes when trying to optimize current operations or to prospect the introduction of chain flail technology to new work environments.
A time study was conducted to determine whether stem crowding had any impact on harvester productivity in Eucalyptus grandis stands. This represents an important element when trying to balance the advantages and disadvantages of coppice management in fast growing plantations designated for mechanized harvesting (i.e. machine felling, delimbing, debarking and cross-cutting). The study material consisted of 446 coppice stems, half of which grew as single stems per stool and half as double stems per stool as a result of different coppice reduction strategies. The dataset was balanced and randomized, with both subsets replicating exactly the same stem size distribution and the single and double stems alternating randomly. Harvester productivity ranged between 6 and 50 m3 under bark per productive machine hour, following the variation of tree diameter from 10 to 40 cm at breast height (1.37 m according to South African standards). Regression analysis indicated that both tree size and stem crowding (e.g. one or two stems per stool) had a significant effect on harvester productivity, which increased with stem size and decreased with stem crowding. However, operator experience may overcome the effect of stem crowding, which was not significant when the harvester was manned by a highly experienced operator. In any case, the effect of stem size was much greater than that of stem crowding, which resulted in a cost difference of less than 10%. However, this figure excludes the possible effects of stem crowding on volume recovery and stem development, which should be addressed in the future.
The present research focuses on the productivity of energy wood chipping operations at several sites in Italy. The aim was to assess the productivity and specifically the effect attributed to the operator in the chipping of wood biomass. The research included 172 trials involving 67 operators across the country that were analysed using a mixed model approach, in order to assess productivity, and to isolate the operator effect from other potential variables. The model was constructed using different predictors aiming to explain the variability due to the machines and the raw-materials. The final model included the average piece weight of raw material chipped as well as the power of the machine. The coefficients of determination (R2) were 0.76 for the fixed part of the model, and 0.88 when the effects due to the operators were included. The operators’ performance compared to their peers was established, and it was compared to a subjective classification based on the operator’s previous experience. The results of this study can help to the planning and logistics of raw material supply for bioenergy, as well as to a more effective training of future forest operators.