GOT- Refinement of reused wood through technology development

Purpose and goal:

The aim is to develop a "hybrid material" and study process flow parameters, calculate LCA, material flow costs and market potential for a new supply chain. It includes material collection, sorting, handling of side-stream solid wood/residual solid from demolition that becomes material for part of a wooden facade system. Comparison with new material will be performed by automating the process of categorizing the properties of the recovered material using non-destructive scanning techniques and machine learning (AI) models.

Expected results and effects:

The result is trained machine learning models that categorize reclaimed wood with images and properties based on proven technology on new raw material. In the long term, the analysis of the alternative material flow will be an important tool in the hub to provide incentives to actors to minimize waste generation and find circular solutions for the material flows. The LCA study can be used as a basis for calculations in environmental certifications such as NollCO2 and “the Swan”.

Approach and implementation:

The project is carried out in five steps. The new process flow includes collecting residual products, sorting and determining their quality with a scanner and then manufacturing facade prototypes which are evaluated to assess if they meet the set requirements. The demands and requirements are analyzed together with the parties in the project, several of whom are future potential suppliers, manufacturers and clients.

A case is used as a point of departure: the construction project of Arctic Center of Energy, Skellefteå.