Abstract
There is a movement towards implementation of 2nd generation biorefineries producing chemicals from renewable sources or residual feedstock such as woody biomass or woodchips. Value chain optimisation is used as a decision making tool to aide in the development and implementation of biorefining. To this end, an optimisation model for the value chain assessment of a lignocellulosic biorefinery was developed using mixed integer linear programming and verified with a softwood case study. The model allows for the comparison of different feedstock sources with different characteristics such as moisture content and size. Each source may be subjected to alternate pre-processing prior to biorefining to up to three product streams. Optimisation identified the most profitable locations for each process stage, including, collection points and intermediate storages, pre-processing locations, biorefining locations and customers for different production pathways according to the associated transport as well as capital and operational costs. The scene was set in Scotland, UK, with two source streams, logged softwood versus the chipped by-products from the sawmill industry. The biorefinery was based on a technology developed by Bio-Sep Ltd which converts the lignocellulosic feedstock into three product streams, cellulose, hemicellulose and lignin. The results demonstrate the significant effect of moisture content on drying and transportation costs. Overall, it was demonstrated that decision making tools for biomass processing must allow for the consideration of different pre-processing stages with respect to overall costs and/or profit