One of the biggest economic challenges to using forest residuals as a feedstock for biofuel production is the cost of residue collection, processing and transport. Based on NARA’s preliminary analysis, nearly 18% of the manufacturing cost for biojet fuel is associated with getting the feedstock to the biorefinery.
To help keep feedstock costs to a minimum, NARA researchers are developing tools and recommendations that will help land managers reduce costs. One such tool is a model developed by NARA researchers at Oregon State University used to evaluate options for forest residual transport and processing.
In a recently published paper partially funded by NARA and titled Economic Optimization of Forest Biomass Processing and Transport in the Pacific Northwest USA, authors Rene Zamora-Cristales, John Sessions, Kevin Boston and Glen Murphy describe the economic estimation model and provide examples of its use.
In the paper, the authors identify previous studies designed to assist forest managers and landowners recover biomass. This work, however, differs from previous studies by considering detailed factors such as multiple slash pile locations, road access and type, terrain, turn around and turnout locations, and equipment selection and interaction. This simulation model should help forest managers and landowners determine the most cost effective locations and operational layouts to process forest residuals and select the most cost-effective machinery to do the job.
The researchers tested their model on a harvest unit located near Sutherlin in Oregon where forest residues were processed in the field and transported to a bioenergy facility. In their analysis, they asked the following questions:
- Which pile locations could be used as a centralized landing?
- Should a centralized yard be established?
- What type of processing option was the most cost-effective under the problem circumstances?
- What type of truck configuration is most cost-effective?
- What is the maximum investment that can be justified in road improvement and turnaround construction to allow for larger trucks?
The program selected the best machinery options (grinders, bundlers and trucks) from the available options based upon the operating and transport costs. The program estimates the processing and transport cost of each slash pile allowing the manager to evaluate piles are profitable and those that should be left in the field. When the optimal approach predicted by the model was compared to the actual harvesting operation, the modeled approach suggested a potential cost savings of 21%. The authors then compared the outcomes of their model to six different operations in western Oregon and Washington. In the six simulations, potential savings ranged from 3 to 34%.
The authors highlighted that one of the model’s strengths is the flexibility to changes in productivity and cost. A current model weakness is that it only considers slash piles located at the roadside. Future improvements will allow for cost estimation of slash piles that are distant from the road. The next step is to develop a software application for public use. A collaboration with other Oregon State University faculty members is being considered to develop the application.