Hanna Haavikko, K. Kärhä, A. Poikela, Mika Korvenranta, T. Palander
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引用次数: 11
Abstract
The EU’s climate and energy framework and Energy Efficiency Directive drive European companies to improve their energy efficiency. In Finland, the aim is to achieve carbon neutrality by 2035. Stora Enso Wood Supply Finland (WSF) had a target, by 2020, to improve its energy efficiency by 4% from the 2015 level. This case study researches the use of the forest machine fleet contracted to Stora Enso WSF. The aims were to 1) clarify the forest machine fleet energy-efficiency as related to the engine power; 2) determine the fuel consumption and greenhouse gas (GHG) emissions from wood-harvesting operations, including relocations of forest machines by trucks; and 3) investigate the energy efficiency of wood-harvesting operations. The study data consisted of Stora Enso WSF’s industrial roundwood harvest of 8.9 million m3 (solid over bark) in 2016. The results illustrated that forest machinery was not allocated to the different cutting methods (thinning or final felling) based on the engine power. The calculated fuel consumption totalled 14.2 million litres (ML) for harvesting 8.9 million m3, and the calculated fuel consumption of relocations totalled 1.2 ML, for a total of 15.4 ML. The share of fuel consumption was 52.5% for harvesters (cutting), 39.5% for forwarders (forest haulage), and 8.0% for forest machine relocations. The average calculated cubic-based fuel consumption of wood harvesting was 1.6 L/m3, ranging from the lowest of 1.2 L/m3 for final fellings to the highest of 2.8 L/m3 in first thinnings. The calculated fuel consumption from machine relocations was, on average, 0.13 L/m3. The calculated carbon dioxide equivalent (CO2 eq.) emissions totalled 40,872 tonnes (t), of which 21,676 t were from cutting, 16,295 t were from forwarding, and 2,901 t from relocation trucks. By cutting method, the highest calculated CO2 eq. emissions were recorded in first thinnings (7340 g CO2 eq./m3) and the lowest in final fellings (3140 g CO2 eq./m3). The calculated CO2 eq. emissions in the forest machine relocations averaged 325 g CO2 eq./m3. The results underlined that there is a remarkable gap between the actual and optimal allocation of forest machine fleets. Minimizing the gap could result in higher work productivity, lower fuel consumption and GHG emissions, and higher energy efficiency in wood-harvesting operations in the future.
欧盟的气候和能源框架以及能源效率指令推动欧洲公司提高能源效率。在芬兰,目标是到2035年实现碳中和。芬兰斯道拉恩索木材供应公司(WSF)的目标是,到2020年,将其能源效率在2015年的基础上提高4%。本案例研究研究了与斯道拉恩索WSF签订合同的森林机群的使用情况。其目的是:1)阐明与发动机功率相关的森林机群能源效率;2) 确定木材采伐作业的燃料消耗量和温室气体排放量,包括卡车搬迁森林机械;和3)研究木材采伐作业的能源效率。研究数据包括斯道拉恩索WSF 2016年890万立方米的工业圆材产量(树皮上的固体)。结果表明,森林机械没有根据发动机功率分配给不同的采伐方法(间伐或最终采伐)。收割890万立方米的计算燃料消耗量总计1420万升,搬迁的计算燃料消费量总计120万升,总计15.4万升。收割机(切割)的燃料消耗量占52.5%,货代(森林运输)的燃料消费量占39.5%,森林机器搬迁的燃料消耗率占8.0%。木材采伐的平均计算立方燃料消耗量为1.6升/立方米,从最后一次砍伐的最低1.2升/立方米到第一次砍伐的最高2.8升/立方米不等。机器搬迁的计算油耗平均为0.13 L/m3。计算出的二氧化碳当量(CO2当量)排放总量为40872吨,其中21676吨来自切割,16295吨来自运输,2901吨来自搬迁卡车。通过采伐方法,第一次疏伐中计算的CO2当量排放量最高(7340 g CO2当量/m3),最后一次砍伐中计算的二氧化碳当量排放量最低(3140 g CO2当量g/m3)。森林机器搬迁中计算的CO2-当量排放量平均为325 g CO2当量m3。研究结果强调,森林机群的实际分配与最佳分配之间存在显著差距。最大限度地缩小差距可以提高工作生产率,降低燃料消耗和温室气体排放,并提高未来木材采伐作业的能源效率。
期刊介绍:
Croatian Journal of Forest Engineering (CROJFE) is a refereed journal distributed internationally, publishing original research articles concerning forest engineering, both theoretical and empirical. The journal covers all aspects of forest engineering research, ranging from basic to applied subjects. In addition to research articles, preliminary research notes and subject reviews are published.
Journal Subjects and Fields:
-Harvesting systems and technologies-
Forest biomass and carbon sequestration-
Forest road network planning, management and construction-
System organization and forest operations-
IT technologies and remote sensing-
Engineering in urban forestry-
Vehicle/machine design and evaluation-
Modelling and sustainable management-
Eco-efficient technologies in forestry-
Ergonomics and work safety