Power-law relationships for estimating mass, fuel consumption and costs of energy conversion equipments.
To perform life-cycle assessment studies, data on the production and use of the products is required. However, often only few data or measurements are available. Estimation of properties can be performed by applying scaling relationships. In many disciplines, they are used to either predict data or to search for underlying patterns, but they have not been considered in the context of product assessments hitherto. The goal of this study was to explore size scaling for commonly used energy conversion equipment, that is, boilers, engines, and generators. The variables mass M, fuel consumption Q, and costs C were related to power P. The established power-law relationships were M = 10(0.73.. 1.89)P(0.64.. 1.23) (R(2) ≥ 0.94), Q = 10(0.06.. 0.68)P(0.82.. 1.02) (R(2) ≥ 0.98) and C = 10(2.46.. 2.86)P(0.83.. 0.85) (R(2) ≥ 0.83). Mass versus power and costs versus power showed that none of the equipment types scaled isometrically, that is, with a slope of 1. Fuel consumption versus power scaled approximately isometrically for steam boilers, the other equipments scaled significantly lower than 1. This nonlinear scaling behavior induces a significant size effect. The power laws we established can be applied to scale the mass, fuel consumption and costs of energy conversion equipments up or down. Our findings suggest that empirical scaling laws can be used to estimate properties, particularly relevant in studies focusing on early product development for which generally only little information is available.
Empa, Swiss Federal Laboratories for Materials Testing and Research, Technology and Society Laboratory, Duebendorf, Switzerland. marloes.caduff@empa.ch
Marloes
M
Caduff
Mark A J
MA
Huijbregts
Hans-Joerg
HJ
Althaus
A Jan
AJ
Hendriks
D003246:Conservation of Energy Resources / Q000379:methods*; D011211:Electric Power Supplies / Q000191:economics* / Q000706:statistics & numerical data; D005579:Fossil Fuels / Q000032:analysis* / Q000706:statistics & numerical data; D018986:Motor Vehicles / Q000191:economics / Q000706:statistics & numerical data; D001335:Vehicle Emissions / Q000032:analysis*
D005579:Fossil Fuels; D001335:Vehicle Emissions