Planning strategies to address operational and price uncertainty in biodiesel production

Abstract

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.

A Robust Approach for Cooperation and Coopetition of Bio-Refineries Under Government Interventions by Considering Sustainability Factors

“…A two-stage stochastic linear mixed-integer programming was used to minimize predicted system cost while integrating yield uncertainty in strategic level decisions related to the production of biomass and investment in the biorefinery. Caldeira et al [48] developed a stochastic blending model by considering feedstock price uncertainty. The stochastic blending model embeds a chanceconstrained algorithm to compensate for variation in composition and uses time-series approaches to resolve volatility in feedstock prices.…”

Heterogeneously Chemo/Enzyme-Functionalized Porous Polymeric Catalysts of High-Performance for Efficient Biodiesel Production

“…Although the above-mentioned functionalized organic polymers and porous coordination polymers demonstrate excellent catalytic performance for biodiesel synthesis, they are nondegradable and relatively expensive. Therefore, envisaging the increasing future demands of local but available worldwide, inexpensive, and renewable materials that might serve as the basis for catalysts for large-scale applications. − In this regard, some existing biomass-based green catalysts, such as bamboo-based acid, rice husk char acid, and bagasse-derived acid, were synthesized through the high-temperature carbonization or incomplete carbonization of sucrose, cellulose, or starch followed by sulfonation . However, relatively harsh preparation conditions during the carbonization and sulfonation were usually employed.…”

Multistage Distributionally Robust Design of a Renewable Source Processing Network under Uncertainty

“…To remedy the feed disturbance because of different sources of waste cooking oils, the robust design of the biodiesel substitute production problem was modeled with chance-constrained programming, and the performance of optimal solutions was evaluated with technical and cost and cost variations. 45 A multiobjective robust possibilistic programming approach was also employed in optimal design of a lignocellulosic bioethanol production to incorporate market prices, biofuel demands, and environmental impact uncertainties, whose solutions outperformed the corresponding deterministic solutions. 46 It should be pointed out that the conservatism properties of the aforementioned robust optimization approaches are restricted by the uncertainty set representations or the approximated decision rules.…”

“…In detail, uncertain conversion rates were represented by uncertain scenarios, while the uncertain demand was described as an interval uncertainty set. To remedy the feed disturbance because of different sources of waste cooking oils, the robust design of the biodiesel substitute production problem was modeled with chance-constrained programming, and the performance of optimal solutions was evaluated with technical and cost and cost variations . A multiobjective robust possibilistic programming approach was also employed in optimal design of a lignocellulosic bioethanol production to incorporate market prices, biofuel demands, and environmental impact uncertainties, whose solutions outperformed the corresponding deterministic solutions .…”