Estonia has formally ratified the Paris Agreement on Climate Change, aligning with the EU commitment to reduce greenhouse gas (GHG) emissions. In accordance with the ‘Estonia 2035’ development strategy and the Long-term Development Programme for the Estonian Energy Sector up to the year 2035 (ENMAK 2035, 2023), the national objective by 2050 is to achieve an 80% reduction in GHG emissions compared to 1990 levels, aiming to become a climate-neutral country. To attain these ambitious goals, there is a significant emphasis on increasing the utilization of wood as a renewable energy source.
While the annual increment of Estonian forests is estimated at about 16 million cubic meters (Mm3), environmental constraints designate 33.2% of forest areas for protection (Forest, 2023), limiting the further growth of harvesting volume. Consequently, one solution to augment the share of energy wood in Estonia’s energy balance involves focusing on less exploited assortments of raw materials and meticulous logistics planning.
In recent years, milder winters have posed challenges to the extraction of residues and local transport of wood fuels on soft and unfrozen soils. This situation complicates the reliable supply of wood fuels. In instances where logging residues on unfrozen soils are inaccessible, there is a necessity to process older piles of logging residues. The quality of wood chips derived from such piles is lower compared to the norm.
Research on the storage of logging residues (Filbakk
In 2010, sample piles were established in the Järvselja Training and Experimental Forest Centre, specifically in compartments JS177 (subcompartment 8) and JS207 (sub-compartment 3), to monitor the degradation process of logging residues. The piles were created in final felling areas of birch (
Samples were systematically collected from four types of piles, totalling 17 times over an 8-year period from 2010 to 2018 (Table 1). The purpose was to analyse various properties of woody biomass during storage, including ash content, wood density, moisture content, and calorific value. The analyses adhered to established standards for determining the properties of solid biofuels (EVS-EN ISO 18122, 2015; EVS-EN ISO 18125, 2017; EVS-EN ISO 18134-2, 2017).
Dates of samples.
No | Date of sampling | Days from the start of the experiment |
---|---|---|
1 | 16.06.2010 | 0 |
2 | 02.09.2010 | 78 |
3 | 22.11.2010 | 159 |
4 | 07.03.2011 | 264 |
5 | 02.09.2011 | 443 |
6 | 15.02.2012 | 609 |
7 | 25.04.2012 | 679 |
8 | 04.06.2012 | 719 |
9 | 24.08.2012 | 800 |
10 | 23.11.2012 | 891 |
11 | 16.05.2013 | 1065 |
12 | 26.11.2013 | 1259 |
13 | 25.06.2014 | 1470 |
14 | 12.12.2014 | 1640 |
15 | 26.08.2015 | 1897 |
16 | 28.08.2016 | 2265 |
17 | 18.03.2018 | 2832 |
Additionally, the diameter drying shrinkage was computed for each sample. This involved measuring the diameter of the branch section in two directions before and after drying. The relative diameter shrinkage (Shr) was calculated by dividing the difference between the wet and dry wood diameter by the wet sample diameter. It was essential to establish a consistent moisture content for this calculation, and the decision was made during the initial sampling on 16 June 2010 (Table 1). The logging residues had been standing and drying in the clear-cutting area, and it was determined to use the base moisture content which was 24.53% for Norway spruces and 25.60% for silver birches.
To characterize the alterations in the properties of logging residues, ANCOVA or regression analysis was employed, utilizing the lm function within the statistical software R (R Core Team, 2019). Subsequently, the ANCOVA equation format was employed to estimate various properties of logging residues, including ash content, moisture content, bulk density, calorific value, shrinkage of diameter, or energy density, represented by the equation:
In order to determine the loss of wood mass, it is relevant to establish various relationships within the data. Initially, the correlation between the drying shrinkage of wood diameter (Shr) and the moisture content, as well as the duration of storage, was identified through regression analysis. This involved employing two distinct equations for Norway spruce (Equation 2) and silver birch (Equation 3):
In the regression analysis, an equation (Equation 4) was employed to explain the impact of the season on moisture content:
Subsequently, using the base moisture content values (Norway spruce – 24.53% and silver birch – 25.60%) and the number of days since the start of the experiment, the diameter drying shrinkage from base moisture to oven dry was computed for each sample utilizing Equations 2 or 3. Following this, the relative volume of logging residues was calculated at base moisture, considering oven-dry volume as 1:
The bulk density of the absolute dry material (ρ
Regression analysis was subsequently employed to investigate the correlation between storage time and the density of the samples, which was calculated based on dry weight and the initial moisture content volume:
The assessment of energy loss during the storage of logging residues involved the application of the aforementioned equations. Initially, the net calorific value of the dry matter was computed across various storage durations, ranging from 0 to 3,000 days (Equation 1). Subsequently, a relative net calorific value was derived for distinct storage durations, with the calorific value at the onset of the experiment (0 days) set as the reference point at 100% (
In the second step, the bulk density of the material at different time intervals was determined using Equation 7, employing oven-dry mass and volume at the base moisture. Next, a relative density was established, considering the material’s density at the commencement (0 days) as 100% (
The relative remaining energy content for diverse storage durations was ascertained by multiplying the relative net calorific value by the relative bulk density using the formula:
ANCOVA was employed to explain the temporal evolution of logging residues properties, as delineated by Equation 1. The outcomes of the analysis are detailed in Table 2, encompassing the statistical parameters associated with the calculation equations for diverse properties. Table 3 displays the parameter estimates derived from Equation 1, along with their corresponding significance probabilities.
Results of ANCOVA of different characteristics (Equation 1).
Dependent variable ( |
p-value | Standard error | R2 | Figure |
---|---|---|---|---|
Ash content | 0.0032 | 0.4825 | 0.1286 | 3 |
Net calorific value of dry matter | <0.0001 | 0.1026 | 0.2120 | 4 |
Net calorific value of moist fuel | <0.0001 | 0.6905 | 0.3940 | 5 |
Bulk density | <0.0001 | 0.0733 | 0.7789 | 6 |
Energy density | <0.0001 | 0.3962 | 0.7718 | 7 |
Shrinkage of diameter | 0.0015 | 1.3550 | 0.0989 | 8 |
Estimations and significance probabilities of parameters of ANCOVA Equation 1.
Dependent variable ( |
Figure | Parameter | Estimation | p-value |
---|---|---|---|---|
Ash content, % | 3 | 1.561477 | <0.0001 | |
0.000941 | <0.0001 | |||
0.315644 | 0.0230 | |||
−0.000310 | 0.0045 | |||
−0.000140 | 0.0341 | |||
−0.000122 | 0.0633 | |||
Net calorific value of dry matter, kWh/kg | 4 | 5.323000 | <0.0001 | |
0.000085 | 0.0081 | |||
−0.000039 | 0.0052 | |||
0.000021 | 0.1380 | |||
Net calorific value of moist fuel, kWh/kg | 5 | 4.162196 | <0.0001 | |
−0.001087 | <0.0001 | |||
−0.643537 | <0.0001 | |||
0.000423 | <0.0001 | |||
Bulk density, g/cm3 | 6 | 0.571200 | <0.0001 | |
−0.000160 | <0.0001 | |||
0.173900 | <0.0001 | |||
−0.042530 | 0.0013 | |||
0.000046 | 0.0069 | |||
Energy density, MWh/m3 | 7 | 3.055000 | <0.0001 | |
−0.000846 | <0.0001 | |||
0.925200 | <0.0001 | |||
−0.232900 | 0.0012 | |||
0.000248 | 0.0068 | |||
Shrinkage of diameter, % | 8 | 4.284253 | <0.0001 | |
−0.791850 | 0.0012 | |||
0.000150 | 0.0973 |
The subsequent figures illustrate the influence of storage duration on the characteristics of residues. As depicted in Figure 3, the ash content of all sample types did not surpass 2% within the initial three years of storage. Prolonged storage revealed a noticeable rise in the ash content of birch logging residues, with the highest levels observed in covered birch residues. In contrast, the ash content of spruce residues remained constant throughout the observation period. The lowest ash content was observed in uncovered spruce residues. The extended storage of wood chips derived from Mediterranean poplar plantations revealed a gradual increase in ash content, rising from 2.91% to 3.31% over an 18-month period (Pari
The net calorific value of dry matter in logging residues exhibits a consistent upward trend during storage, as illustrated in Figure 4. Conversely, the net calorific value of moist fuel demonstrates a decline, as depicted in Figure 5, with birch experiencing a more rapid decrease. Covered storage piles maintain a higher net calorific value for moist fuel in comparison to uncovered piles. Additionally, both bulk density and energy density decrease during storage, as evidenced by Figures 6 and 7.
Furthermore, prior research has addressed the issue of dry matter loss. In Norway, the dry matter loss in softwood logging waste was reported to be between 1 and 3% per month (Filbakk
Figure 8 demonstrates that storage leads to an increase in the drying shrinkage of wood samples over time.
As observed by Nilsson
Relationship between moisture content and month number (Equation 4).
Sample | p-value | Standard error | R2 | |||
---|---|---|---|---|---|---|
Birch, covered | 58.654 | −7.687 | 0.423 | 0.0235 | 8.289 | 0.415 |
Birch, uncovered | 78.481 | −11.882 | 0.863 | 0.0967 | 12.862 | 0.284 |
Spruce, covered | 57.448 | −9.080 | 0.561 | 0.0428 | 8.637 | 0.363 |
Spruce, uncovered | 65.693 | −10.981 | 0.722 | 0.0407 | 9.484 | 0.367 |
In Figures 10 and 11, the influence of storage duration and humidity on the shrinkage of diameter and alteration in the moisture content of birch and spruce branches is illustrated. To characterize the correlation between drying shrinkage, storage time, and moisture content, regression analysis was employed. Equation 2 was utilized for the Norway spruce data-set, while Equation 3 was applied to the silver birch data. The parameter estimates resulting from the regression analysis are presented in Table 5.
Parameter estimates for the relationship between drying shrinkage, storage time, and moisture content (Equation 2 and 3).
Species | Parameter | Estimation | p-value |
---|---|---|---|
Silver birch | 3.851 | <0.0001 | |
0.02156 | 0.0432 | ||
3.572·10−283 | <0.0001 | ||
−1.944·10−7 | 0.0175 | ||
R2 | 0.240 | ||
SE | 1. 098 | ||
p | 0.0010 | ||
Norway spruce | 0.15284 | <0.0001 | |
0.37387 | <0.0001 | ||
R2 | 0.937 | ||
SE | 0.990 | ||
p | <0.0001 |
The drying shrinkage percentage of the diameters of silver birch logging residues decreases during storage, with the rate of decrease initially slow and progressively accelerating over time (Figure 10). Conversely, for spruce, an opposite trend is observed, where the drying shrinkage of diameters increases with time (Figure 11). Initially, the shrinkage undergoes a faster change, followed by a slower rate of change.
The disparity in shrinkage between birch and spruce can be attributed to the distinct characteristics of these tree species. Shrinkage is known to be influenced by specimen dimensions, drying speed, and density (Bowyer
Analysing the changes in the bulk density of residues during storage (see Figures 12 and 13), the decline in the energy content of logging residues over long-term storage was calculated, and the results are illustrated in Figure 14.
The energy loss incurred during the storage of logging residues was determined by applying the net calorific value of the dry matter (Equation 1, Figure 4). Additionally, the bulk density, calculated using the oven-dry mass and volume at the base moisture, was assessed at various time points (Equation 7, Figures 12 and 13). Utilizing Equation 9, the residual energy content for different storage periods was computed and presented in Figure 14 and Table 6.
Decline of energy content of logging residues during long-term storage by species and place in pile.
Storage time, year | Spruce, top | Spruce, middle | Birch, top | Birch, middle |
---|---|---|---|---|
0 | 100.0 | 100.0 | 100.0 | 100.0 |
1 | 95.9 | 95.8 | 90.5 | 90.4 |
2 | 92.0 | 91.7 | 81.9 | 81.7 |
3 | 88.2 | 87.9 | 74.2 | 73.9 |
4 | 84.6 | 84.2 | 67.1 | 66.8 |
5 | 81.2 | 80.6 | 60.8 | 60.4 |
6 | 77.9 | 77.2 | 55.0 | 54.5 |
7 | 74.7 | 73.9 | 49.8 | 49.3 |
8 | 71.6 | 70.8 | 45.1 | 44.6 |
In their study, Anerudi
The data illustrates the decline in energy content of the logging residues during long-term storage, differentiated by species and pile placement over an 8-year period. Notably, birch exhibits a higher loss of dry matter compared to spruce. The 8-year observation indicates approximately a 30% drop for spruce and over 50% for birch. This degradation, attributed to micro-organisms and various processes, also results in a decline in the energy density of the fuel.
Despite the extensive degradation during extra long-term storage, it is noteworthy that the quality of dry fuels remains compliant with standards and remains acceptable for use in boiler houses.
The combustion properties of logging residues as fuel are significantly influenced by their moisture content and bulk density. These two factors have a cascading effect on other properties. Notably, the moisture content of covered piles was found to be lower than in uncovered piles. The ash content, however, exhibited different trends for silver birch and Norway spruce logging residues during the storage period. Silver birch residues showed an increase, while Norway spruce residues demonstrated a decrease. Overall, the ash content did not exhibit a strong dependence on storage time.
Examining the relationship between the net calorific value of dry matter and storage time revealed that silver birch logging residues experienced a greater increase in calorific value compared to Norway spruce. The increase in the net calorific value was attributed to the breakdown of hemicellulose and cellulose, leading to a higher proportion of lignin with a higher calorific value.
The bulk density and energy density of silver birch logging residues decreased at a faster rate than those of Norway spruce. The uncovered logging residues of both tree species had a lower energy density compared to the covered ones. Additionally, the moisture content was lower in summer months than in winter.
Diameter drying shrinkage had a significant correlation with storage time, with silver birch residues experiencing greater reduction than Norway spruce. Organic matter loss during storage was more pronounced in silver birch compared to Norway spruce. The decrease in the density of dry mass per volume unit was significantly higher in silver birch over the storage period, leading to a faster decline in energy density.
In practical terms, it is recommended that the storage time for logging residues should not exceed one or two years due to extensive degradation and dry matter loss. Despite this, the quality of dry fuels remains acceptable and meets standard requirements even after longer storage, making them suitable for use in boiler houses.