A mixed-integer dynamic optimization approach for the optimal planning of distributed biorefineries

Abstract

The implementation of supply chains based on biomass conversion requires the exploration of various aspects, including the selection of processing technologies, configuration of the supply chain, portfolio of products as well as the feedstock selection. One important feature of this system is that the composition of the available biomass changes drastically through the year because this depends significantly on the climatic conditions; this way, the dynamic behavior of this process is an important issue that must be considered. This study presents a dynamic optimization model for the optimal planning of a distributed biorefinery system taking into account the time dependence of the involved variables and parameters. In addition, this paper incorporates a model predictive control methodology to obtain the behavior of the storages and orders of the supply chain; where the objective function is the difference between the required and satisfied demands in the markets. Therefore, this study considers relevant issues, which include the multiple available biomass feedstocks at various harvesting sites, the availability and seasonality of biomass resources, potential geographical locations for processing plants that produce multiple products using diverse production technologies, economies of scale for the production technologies, demands and prices of multiple products in each consumer, locations of storage facilities and a number of transportation modes between the supply chain components. The model was applied to a case study for a distributed biorefinery system in Mexico. Results show that is possible to get the configuration and the behavior of the supply chain considering its dynamic behavior in a rigorous way; furthermore, the solutions obtained by the model illustrate that the supply chains based on biomass conversion are seriously affected by the availability of bioresources over the time.