Planning strategies to address operational and price uncertainty in biodiesel production

The use of low-cost feedstocks such as waste cooking oils has gained prominence in biodiesel production due to its potential economic and environmental advantages. Since these feedstocks are derived from multiple sources, its compositional variability has led to quality concerns that may significantly limit its utilization. One potential strategy to address this concern is to use stochastic blending models to optimize the mixing of secondary and primary oils (e.g., palm, canola, or soya). In this paper, we present a stochastic blending model that embeds a second, key source of uncertainty: the future price of feedstocks, a topic of tremendous concern for producers. The stochastic blending model embeds a chance-constrained formulation to account for compositional variability and uses time-series methods to address feedstock price uncertainty. The model was developed to support production-planning decisions to minimize cost and cost variation in biodiesel production. We demonstrate that the proposed approach is useful for determining an optimal planning of feedstock acquisition, blending and storage in order to minimize the risks associated with feedstock price fluctuations. Results show that addressing the compositional uncertainty via the chance-constrained formulation will allow for the use of waste cooking oil in biodiesel blends without compromising their technical performance.