The energy consumption of chipping, transportation and drying is as follows:

a. Chipping: The energy consumption of the chipping process is due to electricity and diesel. The specific units of energy consumption are 13.6 kWh/material-t (122.4 MJ/material-t) and 1.23 L-diesel/material-t (43.7 MJ/material-t), respectively (Hashimoto et al., 2000).

b. Transportation: The chopped biomass materials are delivered to the plant by 10 ton diesel trucks. CO2 emissions and/or energy intensities on a given transportation run would be affected by the weight of biomass materials. That is, the weight of which the materials can be carried is restricted to bulk density. We measured the bulk density (=0.14 t/m3) in the atmosphere. The bulk density is dependent upon the moisture content. Thus, assuming that the bulk density is at a moisture content of 15 wt. % (p15 ), the bulk density pMC at any moisture content (MC wt.%) is

_ 0.85

PMC _ 1-MC P15

Next, the loading platform of 10t-trucks is to be approximately 24.7 m3 (Suri-Keikaku Co. Ltd., 2005). Consequently, even a truck with 10 ton’s volume cannot always carry that in full weight. Here, CO2 emissions and/or energy intensities are assumed to be due to the fuel consumption of truck, which is indicated as a function of the loading rate of weight. That is, using the loading rate of X, the fuel consumption rate of a 10t — truck fFC (X) is

fFC (X) _ aX + b (14)

where, a(=714 g-CO2/km) and b(=508 g-CO2/km) are constants on the fuel consumption of the truck (Dowaki et al., 2008b).

The definition of the loading rate of X is as follows: Assuming that the plant scale is Ps dry-t/ d, and that the annual operating time is 300 days, the annual material balance on the feed materials is 300Ps t-dry/yr. Since the throughput per year at MC wt.% is 300Ps/(1 — MC), the total number of transportation by 10 t trucks at MC wt.% (Nmat) is

Where, [a] is represented as the maximum integer, so as not to exceed a. Thus, the average loading rate of a 10 t-truck (Xave) is

Providing the average loading rate, and multiplying fFC (X) by the transportation distance and the specific CO2 emissions or the energy consumption of diesel, we can estimate CO2 emissions or fuel consumption in the transportation sub-process. In this paper, the transportation distance is the range between 5 (Distmin) and 50 km (Distmax ), because the wooden materials in Japan are distributed widely. That is, it is assumed that the feed materials are collected within a radius of 50 km.

c. Drying: Next, on the sub-process of drying, the energy consumption was estimated under the condition that the moisture content of feed materials would decrease to 20 wt. %. Here, assuming that the initial moisture contents are from 20 (MCmin) to 50 wt. % ( MCmax the raw materials are dried by a boiler. Also, the auxiliary power of a pump in a boiler is included in the energy consumption of the sub-process. The operational specification of a wood-chip dryer (boiler) is the energy efficiency of 80 %, and the auxiliary power of a pump of 0.195 kWh/t-water (1.75 MJ/t-water). Note that the moisture content of feedstock can be reduced by the hot exhausted gas to some extent.

d. Monte Carlo simulation on the uncertainties: As the above, in this paper, we estimated CO2 emissions and/ or energy intensities, considering the uncertainties of the transportation distance and the moisture content. In this paper, the following two uncertainties of the distance and the moisture content were considered by the Monte Carlo simulation.

That is, the uncertainties on the transportation distance ( Dist km) and the moisture content (MC wt.%) are represented by uniform random numbers Rndi between 0 and 1 in Eqs. (17) and (18). Note that Rnd1 and Rnd2 are independent and identically distributed.

Dist = Ch’s^in + Rnd2 (Distmax — Distmn ) (17)

MC = MCmin + Rnd1 (MCmax — MCmin ) (18)

An iteration count in the simulation was executed up to 10,000. The range within a 95 % significant level was adopted as the uncertain data on the distance and the moisture content, in order to estimate CO2 emissions. In this case, the gross distributions on CO2 emissions would be normal distributions.