Allocation of biomass from production sites to bioenergy plants in Flanders: The role of multimodal transportation networks

Allocation of biomass from production sites to bioenergy plants in Flanders

The role of multimodal transportation networks

By Junhui Jiang, Annelies De Meyer, Jos Van Orshoven

To reduce human dependency on limited fossil fuel and to mitigate climate change, increasing attention is attributed to the development of the bioenergy sector. Besides, the development of the bioenergy sector also favours social and economic objectives by among others creating jobs and increasing farmers’ incomes. Although a variety of policy initiatives from global, national and local levels are launched to promote the development of the bioenergy sector, a sustainable development is hampered by high logistics costs, particularly related to the handling and transportation of biomass to the conversion facility. Therefore, increasing research attention must go to the optimisation of the biomass-for-bioenergy supply chain. This master thesis research frames in an overall research effort regarding the “Spatio-temporal location-allocation modelling for the energetic valorisation of biomass”, which comprises an intelligent use of a complete biomass supply network to maximise the energy output, to maximise the economic profit and/or to minimise the total greenhouse gas emissions of the chain.

This master thesis intended to answer three research questions (related to the three research objectives). Firstly, the research investigated how to build a consistent multimodal transportation network based on different unimodal transportation networks considering restrictions and attributes related to energy consumption, economic costs and CO2-emissions during the delivery of biomass to the conversion facilities. Secondly, the research explored whether and how this developed multimodal transportation network can be used to optimise the allocation of biomass to the conversion facilities in the biomass-for-bioenergy supply chain considering single and multiple objectives. Finally, a sensitivity analysis was performed to study how the allocation of biomass to the conversion facilities changes by varying the definition of the multimodal transportation network or the parameters of the location-allocation analysis. To perform this sensitivity analysis, relevant data were collected for Flanders (case study area) from existing datasets and literature research, and were processed using ArcGIS software as well as MATLAB software.

Eventually, a multimodal transportation network in Flanders was successfully built based on three unimodal transportation networks (road network, railway network and navigable waterway network). The key attributes (energy consumption, economic costs and CO2-emissions) were calculated from published data and the restrictions (drive direction and vehicle type) were also attached through Visual Basic scripting. This multimodal transportation network was the basis for location-allocation analysis in which the allocation of biomass from the biomass production sites to the conversion facilities was optimised considering single (primarily for minimum energy consumption) and multiple criteria (i.e., minimise a combination value of energy consumption, economic cost and CO2-emissions). Based on an analysis of the sensitivity of a number of characteristics of the multimodal transportation network, it was found that in all scenarios road transport is the major transportation mode. When transshipment costs were not considered, minimal energy was consumed for transport. However, when transshipment costs were incorporated, rail and waterway transport were no longer included in the result. Therefore, scenarios were analysed for a derived network in which the transportation segment lengths were artificially magnified. Analysis of these scenarios indicated that the contribution of waterway transportation was gradually increasing with the scale factor but nevertheless remained low while the contribution of railway transportation remained limited to 2%. Furthermore, the scenario analysis showed that the energy consumption, CO2-emissions, time use and total cost were not proportional to the distance travelled since the share of the three transportation modes was important. From scenarios using a network scaled with factor 10 for distance and assuming that the capacity of the conversion plants for processing the considered biomass ranges from 100% to 1.5%, it could be concluded that the lower the available capacity, the more energy must be consumed to transport the biomass to the most appropriate conversion plants. Furthermore, the tri-modal network always led to the lowest amount of energy consumed in comparison with a unimodal road network or with bimodal networks (road-rail or road-water). Finally the scenario analysis indicated that when the three criteria were optimised simultaneously rather than the single energy consumption criterion, energy consumption for transport approached very closely the minimal value and was lower than in the cases in which CO2-emissions or economic costs were minimised.

Two hints are provided for further research. The first one is to study the allocation of biomass to bioenergy conversion facilities taking the whole supply chain into account, including e.g., collection, pre-processing and storage. The other is to further examine the factors that affect the performance of multi-modal transportation with a view to make the best possible use of available multimodal infrastructures.