Centralised vs. distributed supply chains
Biorefineries for the production of fuels, chemicals, or materials can be an important contributor to reduce the dependence of fossil fuels. The economic performance of biorefinery supply chains can be improved by different strategies, such as industrial integration in order to e.g. utilise excess heat and products, economy-of-scale benefits from increased plant sizes, and intermediate upgrading to reduce feedstock transport cost. The identification of cost-efficient supply chain configurations is crucial in order to enable large-scale introduction of biorefineries. Two different case studies are outlined to investigate industrially integrated lignocellulosic biorefinery concepts regarding the impact of different economic conditions on the preferred supply chain configurations.
Case 1: Pyrolysis and catalytic co-gasification with black liquor
Case 1 considered methanol production via black liquor gasification, with the option to also add pyrolysis liquids as a secondary feedstock in order to increase the production capacity (however leading to decreased overall biomass conversion efficiency). The analysis focused on trade-offs between high biomass conversion efficiency and economy-of-scale effects, as well as the selection of centralised vs. decentralised supply chain configurations.
The centralised supply chain was assessed using chemical (kraft) pulp mills as potential production locations. Distributed supply chain was assessed along two paths: pyrolysis at sawmills, CHP plants and stand-alone forest terminals and; upgrading for gasification at chemical pulp mills.
Larger sites are available when allowing all sites than when only considering mills with old recovery boilers. Therefore the total supply chain cost was lower when allowing all sites. This implies that economy-of-scale is favourable, as long as it does not influence the biomass resource efficiency. Co-gasification significantly increases production capacity when the black liquor is limited.
Case 2: HTL and hydro-processing
Case 2 considered biofuel production from forest biomass via conversion to biocrude through hydrothermal liquefaction (HTL). The biocrude was subsequently hydroprocessed to drop-in biofuels at refineries, LNG terminals or natural gas grid connections. The analysis focused on the impact of and interrelation between four cost reduction strategies for biofuel production: economies-of-scale, intermodal transport, integration with existing industries, and distributed supply chain configurations.
Simultaneous implementation of all cost reduction strategies yielded minimum biofuel production costs of 18.1–18.2 € per GJ at biofuel production levels between 10 and 75 PJ per year. Limiting the economies-of-scale was shown to cause the largest cost increase, followed by disabling integration benefits and allowing unimodal truck transport only. Distributed supply chain configurations were introduced once biomass supply became increasingly dispersed, but did not provide a significant cost benefit (<1%). Disabling the benefits of integration favours large-scale centralised production, while intermodal transport networks positively affect the benefits of economies-of-scale.
The results show a clear economic advantage for the supply chain configurations with high biomass efficiency, for the cases when the biorefinery was assumed to benefit from an alternative investment credit due to replacement of current capital intensive equipment at the host industry. Decentralised supply chain configurations were only favourable at very high biofuel production levels or under very high biomass competition. Under lower biomass competition conditions, site specific conditions were found to have a strong influence on the preference for either centralised or decentralised configurations. As biofuel production costs still exceed the price of fossil transport fuels in Sweden after implementation of the investigated cost reduction strategies, policy support and stimulation of further technological learning remains essential to achieve cost parity with fossil fuels for the studied feedstock/technology combinations in this spatial-temporal context.