Design and management of biomass-for-bioenergy supply chains: Towards a comprehensive spatio-temporal optimisation approach

The depletion of fossil fuel reserves and the negative environmental impacts associated with their use are the driving forces towards the biobased economy. In analogy to today's oil refineries, biorefineries can process renewable biological resources into a range of value-added bioproducts (e.g., bioplastics, paper, transport fuels, electricity, heat). However, the discontinuous (in time) and geographically fragmented (in space) availability of biomass and the relatively high maintenance and logistics costs still compromise the economic viability of biobased products for large scale production and commercialisation. Besides the economical side, also environmental sustainability, energy efficiency and social acceptance are important concerns for the development of a sustainable biobased sector. Poor planning can hurt the environment, damage the image of biobased products, and limit available resources. Comprehensive planning for the supply chain must start prior to or in combination with the expansion of the biobased sector. So, the role that biomass will play in the future will depend upon the extent to which the constraining factors inhibiting trade as well as a sustainable and efficient production of biobased products can be overcome. This dissertation presents the design of a generator (prototype) to create decision support systems to address strategic (e.g., design) and tactical (e.g., inventory and fleet management) decisions in all kinds of biomass-for-bioenergy supply chains with a view to optimise (a combination of) different objectives. This generator encompasses four modules: (1) a database module, (2) a query module, (3) the decision module and (4) a user interface. The database module holds a reference data model which can be used to specify all kinds of biomass-for-bioenergy supply chains. The roots of the reference data model in a generic cradle-to-gate analysis ensures that all kinds of biomass sources, all kinds of biomass destinations and all kinds of handling techniques can be classified into one of the 6 key object types of the data model: (1) biomass production, (2) harvest, (3) collection, (4) pre-treatment, (5) storage and (6) conversion. The database module is connected to the query module to organise and pre-process the initial spatial information and to visualise and post-process the optimisation results. The decision module encompasses the mixed integer linear programming models, OPTIMASS and t-OPTIMASS, to define the strategic and tactical decisions in the supply chain in order to meet the maximum energy output, the maximum profit and/or the minimum environmental impact. OPTIMASS takes into account the geographical fragmentation of biomass to define the optimal supply chain configuration considering one time period, the re-injection of by-products from the conversion process and the changes in biomass characteristics (e.g., moisture content, particle size) due to treatment operations. t-OPTIMASS, the spatio-temporal optimisation model, adds the consideration of temporal variations (e.g., seasonal, regrowth) in biomass availability and energy demand. t-OPTIMASS supports the use of biomass as a sustainable, renewable source by balancing the seasonal availability and regeneration of biomass and the required year-round constant supply of biomass at the conversion facilities. The development of the user interface and the automation of the linkages between the modules are not part of this dissertation. The generator has been implemented to create specific decision support systems for three case studies: (1) the biomass supply chain based on low input high diversity (LIHD) biomass systems in the province of Limburg (Belgium), (2) the municipal wastewater sludge processing chain in Flanders and (3) the Jatropha-to-electricity chain in Mali. The specific DSSs are used to address strategic questions (i.e. new dryer?) as well as tactical questions (i.e. allocation?). The specific DSSs are used to investigate the effect of changes in biomass availability, energy demand, etc. on the supply chain configuration. In general, the results highlight that the requirements imposed to the biomass mixture at the conversion facilities are the main drivers in the decision process. So, OPTIMASS and t-OPTIMASS define the harvesting moment and introduce treatment operations to make sure that biomass is delivered with characteristics that fit best these requirements. In addition, the analyses indicate that storage facilities are indispensable to deal with the temporal availability of biomass, the conflicting temporal (energy) demand and the required constant feeding of the conversion facilities. Additionally, the biomass is preferably grown in the areas where it is also consumed to ensure long term sustainability and to guarantee biomass supply. Directly, nearby biomass availability reduces the transport costs to the conversion facility. Indirectly, the proximity of biomass leads to the reduction of the negative side effects of an uncontrolled biomass demand on tropical forest cover, to the support of landscape functions such as biodiversity, recreation, hydrological and erosion buffering, etc. Unfortunately, the biomass to be converted in Flanders is often grown in areas with low marginal cost and shipped (often imported) for valorisation. We believe that the generator prototype is an inspiring tool for a variety of stakeholders working with or having a large impact on the biomass sector and mostly interested in a macro-analysis (e.g. governments, government institutions, consultancy agents, etc.). Stakeholders are able to get insight in evolutions of biomass flows and the development of the biomass network through the simulation of the consequences of e.g. political decisions, import restrictions, introducing toll, etc. The tool supports the evaluation of the biomass potential, the feasibility of new operation facilities and the definition of the optimal type and location of facilities from a set of potential facilities proposed by the user. Also, guidance can be provided to stakeholders (e.g. biomass suppliers, owners and operators of storage and conversion facilities) ones the supply chain is operational to evaluate the impact of e.g. the shortage in biomass supply, disappearance of a neighbouring facility, potential new biomass sites, etc. Furthermore, a range of scenarios can be run and serve as the basis for a dialogue between the various stakeholders working with biomass to help resolve bottlenecks hampering the optimal use of biomass. Being able to optimise (and somehow simulate) the biomass flows within the biomass network considering different kinds of criteria, this can be an attractive tool that can lead to successful networking between stakeholders in the biomass sector.nrpages: 264status: publishe