Staff PhD

Agricultural Biomass Supply Chain Management

Ana Sambra

Background

The raw materials for biomass conversion are produced over large geographical areas, have a limited availability window, and often are handled as very voluminous materials. As a result the transportation and logistics between the point of production and to the conversion facilities becomes a vital part of the overall operational, economic and energetic viability of the biomass conversion process. The necessary requirements include a reliable and optimised infrastructure capable of supplying biomass components to be fed into the conversion plant in the right quantity and at the right time. In this regard, the supply chain must comprise optimised steps of harvesting biomass crops, collecting biomass residues, storing and transporting of biomass resources. There is an urgent need for a comprehensive modelling approach for developing an operational, interactive and adaptive tool for planning and analysing the overall transport, pre-processing, logistics and its efficiency, economic viability and environmental impacts.

Aim

Optimize the harvest and logistics for the transport of oilseed crops and suitable agricultural residues to production facilities and return the process residues for agricultural use as a part of the overall biomass feedstock infrastructure.

Develop a modelling tool for analysing and optimising the harvesting handling and the supply chain management (SCM) of biomass to conversion facilities.

Research Outline

Based on the initial analysis, an assessment tool (in terms of system performance, energy requirement, costs, etc.) for evaluating different user-defined configurations of biomass supply chain systems will be developed. The following steps will be required: 1) determination of the scope of the model, e.g. which assumptions are used and which parameters are included in the model, 2) development of submodels for harvesting workability, harvesting, handling (loading unloading, etc.), transportation, and facility location, 3) theoretical solution methods will be developed and implemented to find optimal solutions for the logistics, 4) validation and verification of the modelling tool by comparing with independent system performance data and flow tracing. Finally, data analysis of the logistics found by the model will be analyzed to quantify the benefits in terms of economic and operational performance enhancement