Supply chain network design and operation: Systematic decision-making for centralized, distributed, and mobile biofuel production using mixed integer linear programming (MILP) under uncertainty - Sharifzadeh, Mahdi; Garcia, Marti Cortada; Shah, Nilay (Biomass & bioenergy, 2015)
Biomass resources are dispersed and subject to seasonal and geographical uncertainties. Therefore, supply chain network design and management can significantly influence the economic viability of a biofuel technology. Fast pyrolysis offers several advantages for biofuel production. It is a relatively cheap process and can be conducted in centralized, decentralizes, or even mobile configurations. Furthermore, it does not overlap with the human food supply chain, using wastes or lignocellulosic feedstocks. In this article, a mixed integer (piece-wise) linear program (MILP) was developed to determine the optimal supply chain design and operation, under uncertainty. Rigorous process modelling and detailed economic analysis were coupled with exhaustive search of potential production locations and biomass resources in order to enhance the fidelity of the solution. The optimisation results suggest that a combination of geographically centralized pyrolysis and upgrading centres would suffice for supply chain management under deterministic conditions. However, under uncertain scenarios, it is advantageous to deploy mobile pyrolyzers to add extra flexibility to the process operation. Further analysis suggested that as the mobile pyrolyzers are commercialized and their unit price is reduced, this technology has the potential to become a key member of the biofuel supply chain. [Display omitted] •Biofuel supply chain from hardwood biomass via fast pyrolysis in the UK was studied.•Positive net present value indicates investment opportunity in the UK market.•There is a trade-off between transportation of materials and economies of scale.•Large-scale centralised plants are optimal under deterministic conditions.•Small-scale mobile plants are more cost-effective under stochastic conditions.