Supply chain optimization of palm oil mill effluent to biocompressed natural gas for industrial usage

Virtual distribution of Biocompressed Natural Gas (BioCNG) is economically attractive to industries which are remotely located from natural gas pipeline. However, this concept poses some issues concerning logistics due to scattered spatial distribution of palm oil mills. Addressing these aspects requires an integrated spatial planning and optimization to synthesise location and allocate network of BioCNG virtual transportation to the respective industry. This study presented the development of integrated spatial planning and optimization of BioCNG supply and distribution network through virtual pipeline to meet on-site energy demand of specific industry. This study also aimed to investigate the contribution of optimized BioCNG supply chain towards systematic energy hub among other energy alternatives. The data from network analysis of aeronautical reconnaissance coverage geographic information system were coded into generalized algebraic modelling system and advanced interactive multidimensional modelling system modelling to generate supply cost curve for multiple source of energy carrier i.e. liquefied natural gas import, natural gas (NG) through pipeline network, and BioCNG supply chain through virtual pipeline. The results show that standardised optimum compression pressures of BioCNG without and with biogas upgrading are 53.8 bar and 215 bar respectively. Minimum total cost per energy of decentralised BioCNG supply chain is 3.57 USD/GJ while that of centralised BioCNG supply chain is 3.64 USD/GJ. Decentralised production pathway was found to be more economically effective compared to centralised production at the study area of Johor. To achieve a 20 % greenhouse gas (GHG) emission reduction, energy mix with a combination of NG from natural gas grid extension, BioCNG production with upgrading and coal is required for the demand locations considered. BioCNG production with upgrading is a cost effective mitigation method on GHG emission reduction. The optimum energy mix not only has lower emission level than baseline but also reduces the total energy supply cost by 19.1 %