Characterization of the caloric properties of the residues
The characterization of the caloric properties presented a variation between forest biomass and agro-industrial biomass (Table 1). The caloric power of G. arborea and T. grandis showed an average value of 19059.50 kJ/Kg. comosus and S. officinarum was 16684.9 kJ/Kg. As for the moisture content, the forest species presented an average value of 80.33%, a lower value than the agro-industrial crops, which was 90.22%. Finally, no significant differences were found in the ash content between the four crops, showing an average value of 5.31%.
Table 1
Calorimetric assessment of forest and agro-industrial crops with biomass generation potential in the northern zone of Costa Rica.
Biomass type
|
Caloric power (kJ/kg)
|
Moisture content (%)
|
Ash (%)
|
G. arborea
|
18518.3 a (368.4)
|
81.53 b (9.44)
|
4.34 a (2.11)
|
T. grandis
|
19600.7 a (690.0)
|
79.13 b (9.89)
|
3.99 a (1.02)
|
A. comosus
|
17189.3 b (789.9)
|
92.30 a (7.34)
|
5.90 a (1.23)
|
S. officinarum
|
16180.5 b (867.6)
|
88.14 a (8.87)
|
6.99 a (1.53)
|
Note: values in parentheses correspond to standard deviation. |
The energy capacity of available biomass
When analyzing the generation of residual biomass (Table 2), it was determined that forest cultivation generated 16.5 ton/ha, which comes from the branches cut in the pruning processes, treetops, and trees that do not meet the minimum harvest diameters. With this, it is possible to find annually about 68 585.5 tons of residual biomass, representing in power the base of an electrical generation of 16.32 MW.
Table 2
Availability of dry biomass with potential for use in electricity generation in the northern zone of Costa Rica.
Biomass type
|
Dry residues generated (tons/ha/year)
|
Cultivated area (ha)
|
Available dry biomass (tons/year)
|
Energy potential (MW)
|
Forestry
|
16.5 (3.2)
|
4 156.70
|
68 585.55 (13 301.44)
|
16.32 (2.96)
|
A. comosus
|
34.1 (5.6)
|
24 794.72
|
845 500.00 (13 8850.42)
|
153.72 (28.34)
|
S. officinarum
|
24.6 (5.9)
|
5 790.33
|
142 442.12 (34 162.97)
|
26.37 (7.11)
|
Total
|
-
|
34 921.75
|
1 056 527.67
|
196.41
|
Note: Parentheses corresponds to standard deviation. |
In the case of A. comosus, the annual generation per hectare of dry residual biomass was 34.1 tons, coming from the handling and harvesting of the crop (the whole plant becomes a residue). These data annually represent 845,500 tons of dry biomass, which currently have no productive use (burned or buried). On the other hand, its use for the production of electrical energy could represent 153.72 MW. Finally, S. officinarum generated 24.6 tons/ha/year of residual biomass, coming from harvest residues such as the plant leaves. Taking advantage of these residues could mean an annual contribution of 26.37 MW to the national electrical system.
Determining the optimal radius of biomass supply.
When analyzing the optimal biomass supply radius for the proposed power plant (Fig. 2), the trend was that generation increased as the radius increased (due to increased biomass). The increase was 5% per kilometer; additionally, the same behavior was evident for the cost of transportation, with increases between 7% and 9%. Therefore, the maximum distance of biomass supply in a profitable way was 30 km, since greater distances would generate increases in transport cost that would only be justifiable with a decrease in the profit margin of the sale of biomass or increase in the market values of biomass.
When visualizing the points with the potential areas of biomass supply (Fig. 3), it was shown that 91% of the region under study was covered, taking into account areas where the zones overlap, which can generate errors in the estimation of biomass. To prevent this situation, in the selection processes, very close points should be avoided. In addition, it was noted that the western sector of the region was where there is a greater concentration of biomass but without forestry or agroindustrial development.
Optimal location of the bioelectric generation plant
In the analysis of the biomass availability in the 12 pre-selected points for the installation of the power plant (Table 3), it was determined that the availability of biomass varies between 19897.5 ton/year and 498 906.8 tons/year, and the capacity of electricity generation between 4.06MW to 101.82 MW. Point 11 was the one that presented the highest presence of biomass (especially from A. comosus and S. officinarum).
Table 3
Energy potential and available biomass at each potential point for establishing a power plant from lignocellulosic biomass in the northern area of Costa Rica.
Point
|
Forest biomass (tons/year)
|
A. comosus biomass (tons/year)
|
S. officinarum biomass (tons/year)
|
Available biomass (tons/year)
|
Energy potential (MW)
|
1
|
11385.2
|
43966.0
|
356.1
|
55707.3
|
11.37
|
2
|
3772.2
|
71022.0
|
0.0
|
74794.2
|
15.26
|
3
|
2126.2
|
37202.0
|
3789.0
|
43117.1
|
8.80
|
4
|
6309.9
|
173327.5
|
7976.8
|
187614.1
|
38.29*
|
5
|
1783.2
|
37202.0
|
0.0
|
38985.2
|
7.96
|
6
|
8641.8
|
27901.5
|
1709.3
|
38252.6
|
7.81
|
7
|
19821.2
|
180091.5
|
1566.9
|
201479.6
|
41.12
|
8
|
4197.4
|
18601.0
|
0.0
|
22798.4
|
4.65
|
9
|
1714.6
|
17755.5
|
427.3
|
19897.5
|
4.06
|
10
|
754.4
|
151344.5
|
9543.6
|
161642.6
|
32.99
|
11
|
823.0
|
408630.2
|
89453.7
|
498906.8
|
101.82*
|
12
|
3840.8
|
131898.0
|
71078.6
|
206817.4
|
42.21
|
In the case of points 7, 10, and 12 (with the generation of 32.99 to 42.21 MW), the high energy potential is due to the overlap of the radius compared to the area of point 11. If the overlap were eliminated, the energy potential would decrease from 35–65%. On the other hand, site 4 was the only point where there was no overlap with point 11, and it also had a high energy potential (38.29 MW).
When analyzing the distribution of biomass in the pre-established points, the wide distribution of forest biomass and A. Comosus was noted, with the biomass of S. officinarum being the most restricted (absent in points 2 and 5). It was only the primary energy source in forest biomass in point 7, slightly exceeding agro-industrial crops. In the remaining points, the high presence of A. comosus was significant, and the fact that its distribution is closer, which allowed the creation of harvesting clusters.
By analyzing the conditions for establishing the plant at each site (Table 4), with the road network, only points 3, 4, and 6 showed significantly lower values because they were in areas with ballast roads, while the rest of the points were asphalted. The electricity network connection in points 1, 3, 4, 5, and 6 presented supply problems due to their distance from the distribution networks, which would require the installation of connection towers that would increase the project's costs.
Table 4
Potential values analyzed to establish an electricity generation plant from lignocellulosic biomass in the northern zone of Costa Rica.
Point
|
Road network conditions
|
Electrical network connection
|
Closeness of towns and industries
|
Industry growth potential
|
Distance to conservation areas
|
Total
|
1
|
15.3 a (1.2)
|
10.2 b (1.1)
|
12.2 b (1.5)
|
18.8 a (2.3)
|
16.5 a (1.2)
|
73.0
|
2
|
14.4 a (1.2)
|
12.2 a (1.2)
|
13.5 b (1.4)
|
17.7 a (2.4)
|
14.4 b (1.4)
|
72.2
|
3
|
8.5 c (1.2)
|
8.4 c (1.1)
|
17.7 a (1.6)
|
12.5 a (2.5)
|
8.8 c (1.3)
|
55.9
|
4
|
12.2 b (1.4)
|
9.3 c (1.3)
|
18.8 a (1.3)
|
14.4 a (2.5)
|
12.2 b (1.2)
|
66.9
|
5
|
14.4 a (1.2)
|
11.1 b (1.2)
|
16.6 a (1.5)
|
13.3 a (2.4)
|
20.0 a (1.3)
|
75.4
|
6
|
11.1 b (1.2)
|
12.2 b (1.0)
|
16.7 a (1.8)
|
11.5 a (2.4)
|
17.8 a (1.4)
|
69.3
|
7
|
13.3 a (1.3)
|
16.9 a (1.1)
|
18.9 a (1.4)
|
18.8 a (2.3)
|
16.6 a (1.4)
|
84.5
|
8
|
17.6 a (1.2)
|
18.0 a (1.2)
|
17.0 a (1.3)
|
16.0 a (2.2)
|
20.0 a (1.5)
|
88.6
|
9
|
18.8 a (1.1)
|
19.0 a (1.2)
|
17.1 a (1.6)
|
15.7 a (2.2)
|
18.2 a (1.2)
|
88.8
|
10
|
16.6 a (1.1)
|
19.0 a (1.3)
|
18.8 a (1.7)
|
15.6 a (3.2)
|
16.6 a (1.4)
|
86.6
|
11
|
17.9 a (1.1)
|
19.0 a (1.1)
|
18.8 a (1.5)
|
13.3 a (2.2)
|
18.2 a (1.5)
|
87.2
|
12
|
16.9 a (1.2)
|
18.4 a (1.0)
|
19.9 a (1.6)
|
14.4 a (2.2)
|
11.3 b (1.4)
|
80.9
|
Note: Parenthesis value is standard deviation. Different letters show statistical significance at 0.05. |
Regarding the variable proximity to towns and industries, only points 1 and 2 showed statistically lower values due to the remoteness of towns and industries. In addition, no significant differences were found in the growth potential of industry between the twelve points of study, which presented conditions for the increase of agro-industrial activities. Finally, points 3, 4, and 12 presented proximity to protected areas, which meant that lower scores were given in the assessment.
When establishing the optimal installation point of the power plant (Table 5), it was determined that point 11 (Fig. 4) had the ideal environmental conditions and enough biomass for the installation of a bioelectricity generation plant. Then, points 7, 10, and 12 were found as sites with installation potentiality higher than 52%, with the limitation that they overlapped areas with point 11. Therefore, the three points' energy potential was reduced from 22 to 45% by eliminating the overlapping area. Later, point 4 was found to have a potentiality of 49.32% and not overlap with point 11. The other points had feasibility of less than 40% for the installation of the plant, which was considered not very viable for its development.
Table 5
Values of biomass availability and potential for establishing a power plant from lignocellulosic biomass in the northern zone of Costa Rica.
Point
|
Biomass availability (%)
|
Site conditions (%)
|
Total (%)
|
1
|
6.69
|
29.20
|
35.90
|
2
|
8.99
|
28.88
|
37.87
|
3
|
5.18
|
22.36
|
27.55
|
4
|
22.56
|
26.76
|
49.32*
|
5
|
4.68
|
30.16
|
34.85
|
6
|
4.60
|
27.72
|
32.32
|
7
|
24.23
|
33.80
|
58.03
|
8
|
2.74
|
35.44
|
38.18
|
9
|
2.39
|
35.52
|
37.91
|
10
|
19.43
|
34.64
|
54.08
|
11
|
59.99
|
34.88
|
94.88*
|
12
|
24.87
|
32.36
|
57.23
|