Published 2006 | Version v1
Publication

A decision support system for energy production from renewable resources: Logistics aspects of sustainable forest biomass collection

Description

The consequences of the use of traditional fossil fuels has led, in the last years, the European Union to promote and encourage the development and the use of renewable energies rather than the traditional ones. Energy production from forest biomass does not involve the CO 2 increase in the atmosphere, contributing in this way to the duties assumed by the European Community during the International Conference of Kyoto (1997). The developed DSS, that is a generalization and the further research activity presented in a previous work by the same authors (Freppaz et al. [2004]), is based on the integration of Geographic Information Systems tools, a relational database, and decision models (in terms of decision variables, objectives, and constraints). Specifically, two decision models have been created in order to face two sustainability verifications. The first one regards the planning of biomass collection and transport considering the biomass as a constant over time. In this case, the objective is to find the forest parcels from where it is convenient to collect biomass, for certain fixed plant sizes, and establish the quantity to take from each of them and to send to a specific plant, considering the geographical characteristics and the legislative constraints. This problem corresponds to a static decision model. The second decision model has a dynamic structure and it considers the biomass collection planning over five years, on the basis of a preliminary plant size, found solving the previous mathematical programming problem. The structure of the dynamic decision model is strictly connected with the growth model of the trees that are present in the different forest parcels. A user friendly interface is used to link the optimization models, the GIS, and the metadata necessary for the problem solution.

Additional details

Created:
March 27, 2023
Modified:
November 29, 2023