Proposal of an Original Methodology to Evaluate the Performance of Chipper Machines

• Published in: AgriEngineering 2024-06, Vol.6 (2), p.1619-1638
• Main Authors: Fanigliulo, Roberto, Stefanoni, Walter, naciari, Laura, Grilli, Renato, Benigni, Stefano, Scutaru, Daniela, Sperandio, Giulio, Pochi, Daniele
• Format: Article
• Language: English
• Subjects: Agricultural equipment; Agroforestry; Alternative energy sources; bioenergy; Biomass; Carbon dioxide; Carbon dioxide emissions; Chipping; class; Coppicing; Data analysis; data collection; Datasets; Diameters; energy; Energy consumption; Energy requirements; energy use and consumption; Forestry; Fuel consumption; Hardwoods; Hydraulics; Intervals; Lubricants & lubrication; Parameters; Performance evaluation; Populus; Real time; Renewable resources; Sensors; species; sustainable forest operations; torque; tractors; Trees; wood; wood chips; work productivity; Working hours
• ISSN: 2624-7402; 2624-7402
• DOI: 10.3390/agriengineering6020092

Wood fuel from the agroforestry sector is one of the main strategies cited by the EU for reducing energetic dependance on foreign markets. Its sustainability, both economic and environmental, can be improved through the optimization of harvesting and chipping operations. This study was focused on the dynamic and energetic balance of the chipping phase carried out by a chipper operated by the power-take-off (PTO) of a medium-power tractor. Both machines were equipped with sensors for real-time monitoring of fuel consumption, PTO torque and speed, trunk diameter and working time during the comminution of 61 poplar trees grown in a medium rotation coppice system. The data analysis was carried out on the entire dataset (about 29,000 records) without considering their belonging to different trees. By means of proper data ordinations, it has been possible to define all the intervals in which the chipping stopped (e.g., between two trees) and to exclude them from the intervals of actual chipping. This has allowed forcomputation of operative and actual working time, as well as of the basic power required to operate the chipper and the power for actual chipping. Subsequently, the parameter values observed during actual chipping were related to the cutting diameters measured at the same instant. Subsequently, the dataset was divided according to seven diameter classes, and, for each class, the descriptive statistical indices of working time, work productivity, CO2 emissions, energy requirement and fuel consumption were calculated. Eventually, the correlation between the variations in trunk diameter and other parameters was verified both on the whole dataset and based on the class average values. The analysis made it possible to identify the conditions of greatest efficiency for the chipper. More generally, the method could help to increase the accuracy of measurements aimed at characterizing the performance of chippers from the point of view of dynamic energy requirements as well as in relation to different wood species.