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Optimization problems of the residual biomass value chain

Dealing with agricultural residues can present significant extra costs to farmers as these residues are usually processed on the field either by burning or mulching. Meanwhile, recent growth of the bio-based industrial sector can provide an alternative use for these materials. Because bio-based industries need a stable supply of high-quality raw materials, connecting these industries with sources that can supply this type of raw material at the best possible price is important. However, this process is not a simple one. The availability of certain materials changes significantly based on location and industrial demand for different residues shows high variance. Development of an efficient supply chain would be beneficial for both farmers and industries. As a result of this project, basic research will be carried out looking into the value chain of residual biomass and possible optimization questions will be studied and identified. Because this value chain includes multiple actors, there are multiple possible problem sets, which can range from collecting and sorting the residues on site to transporting them to bio-refineries for processing. Selected problems will be explored and modelled in more detail, and solution algorithms will be developed to solve them. As the arising problems are NP-hard, these will most likely be approximation algorithms. Models and algorithms will not only consider basic theoretical aspects of the problems but application-oriented characteristics will also be studied in more detail. Dealing with these characteristics is important as we intend these algorithms to be efficiently applicable in a real-life scenario. Efficiency of these methods will be tested on artificial instance sets.

InnoRenew CoE main activities in the project

InnoRenew CoE will contribute to the project with: analysis of the industrial problem and selection of the problem set to be studied; specification of the basic model for the selected problem; and development of artificial instance sets for testing the solution methods.