Synthesis of multiple biomass corridor via decomposition approach: a P-graph application

•A decomposition approach which integrates both P-graph framework and mathematical modelling is developed.•The computational time and model's complexity are reduced.•About 82% of overall variables in the case study are removed.•The under-utilised biomass is integrated into the existing palm oil biomass supply chain.•The optimal number of hubs, location of each hub and biomass allocation design are determined.

The increase of the global population has a negative impact to the environment due to the direct correlation between the amount of solid waste generated and the population growth. In order to create a more sustainable future, an adequate waste management system is necessary. Utilisation of biomass that have potential to be converted into various forms of valuable products (i.e. energy, biochemical and value-added products) is a promising way to deal with the increasing amount of agriculture residues. Therefore, it is suggested to develop a multi-biomass corridor in order to promote a global sustainable development of renewable energy. However, this large-scale problem normally consists of a huge number of variables (large set of possible locations of processing hub, large set of operating units and large set of material involved). This will lead to a longer computational duration due to the high complexity of the network. In order to address this issue, a novel “Decomposition Approach” which integrates P-graph framework and conventional mathematical modelling is proposed in this paper. The presented approach is demonstrated via an actual case study in Johor. In this paper, the correlated cost function for the four main biomass in Johor (i.e. palm oil biomass, sugarcane bagasse, pineapple residue and paddy biomass) is formulated. On top of that, a multiple biomass supply chain which integrates three underutilised biomass into the palm oil biomass supply chain in Johor is synthesised successfully and efficiently as well. The result shows that about 82% of the overall variables in this case study are removed from the model after applying the proposed approach, causing a 50% reduction in the computational time. This indicates that the proposed approach is useful for real-world applications. However, regular revision on the model is recommended in order to assure the reliability of the result. In addition, this paper also outlines several potential extension works that can be conducted in future.