Bio- and renewable fuels have a remarkable potential of being one of the central elements in the decarbonisation of the transportation section. In the European Union, bio- and renewable diesel accounts for approximately 93 % of all renewable energy used for transportation in 2018. The renewable fuel supply chain (RFSC) consists of discrete processes from harvesting to the arrival of biomass at the conversion facility. It essentially consists of the biomass suppliers, storage sites, and the refinery sites along with pre-treatment, blending, and transportation operations. One of the key differences between RFSC and traditional supply chains is that RFSCs have their economic viability more strongly tied with the biomass supply uncertainty (i.e., availability, quality, and so forth), whereas the supply chain for traditional goods deals most prominently with demand-side uncertainty. In this project, we develop optimization frameworks than can be employed to support decision making in RFSC management.
Despite being a promising key player in the decarbonisation pathway laid out in the Paris agreement, the production of renewable diesel still faces several challenges that curb its wider spread development. The first great challenge is associated with production costs. The main factor determining the current cost of renewable diesel production is the feedstock cost, which can be as high as 88 % of the total production cost. Biomass transportation costs are high because they are bulky in nature and have a low ratio of energy per unit of mass. This also suggests that the carbon footprint of renewable fuels can be high if not properly managed.
The proposed methodological framework will tackle the production cost challenge by providing analytical tools for identifying cost-efficient renewable fuel supply chain designs that are optimised focusing on minimising operational costs while complying with carbon footprint requirements. For example, the total production cost can be greatly reduced by lowering the cost of feedstock with the use of more economical alternatives such as waste fats or oils. However, the conversion technology utilising raw material with varying physical and chemical characteristics present major technical challenges. How to optimally manage these trade-offs is precisely one of the numerous potential uses of the framework being proposed.
The second major challenge RFSC management faces is uncertainty (e.g., weather uncertainty, seasonality, local transportation and distribution infrastructure reliability, etc) and the consequent exposition to risk it causes. Refineries producing renewable fuels (e.g., Neste) are typically continuously operating plants that require a continuous, all-year-around, cost-efficient and reliable supply of desired quality biomass feedstocks. However, RFSC typically comprise of numerous and widely scattered biomass suppliers with highly variable feedstock composition. This supply uncertainty exposes the supply chain to severe risks in terms of sustaining the required streamlined supply required by refineries, which inevitably lead to constant replanning and, therefore, increased costs.
We will develop state-of-the-art mathematical modelling methods to develop a data-driven robust optimisation model that can consider uncertainty in RFSC. This model will be capable of identifying cost-efficient RFSC designs that are optimised focusing on minimising operational costs and satisfy robustness requirements in terms of biomass supply security risks.
Our vision is that these models would become instrumental in the decision-making process of RFSC management, supporting analytical-driven decision making that is capable of meeting robustness requirements and can efficiently adapt to the most recent data available. We believe that this would ultimately support refineries producing renewable fuels (e.g., Neste) in achieving their overarching goal of increasing their own efficiency and playing an even more prominent role in decarbonising the transportation sector.