(620h) OPTIMAL Planning and Scheduling of A BIOMASS Conversion SYSTEM Considering ECONOMIC and ENVIRONMENTAL Aspects

Currently, extensive research efforts have been made to achieve energy sustainability by replacing fossil fuels with renewable sources of energy. In this context, biomass is emerging as an option to phase out fossil fuel dependence, as well as reducing environmental impact through the reduction of greenhouse gas emissions; furthermore, the biomass is a renewable resource that can be guaranteed through time.

The available biomass mainly comes from the agro-industrial sector; such is the case of animal and vegetable residues. However, the production, cost and availability for the biomass is not constant through the year. This way, it is necessary to develop a new methodology for planning the production of a biomass conversion system considering the availability of the bioresources through the year to satisfy the demands at the maximum profit and at the same time at the lowest environmental impact.

This work presents a multiobjective optimization model for the optimal planning and scheduling of a biomass conversion system considering the selection of feedstocks, processing technologies, and products. It also takes into account seasonality aspects, since the availability and the demand of bioresources and products are different for each season, central supply zone, storage and market. It is important to note, that this paper uses network based models of states and tasks to make a good scheduling, in which the states are referred to different types of materials, for example bioresources, intermediate products and final products, on the other hand, the tasks are the activities for the treatment of each state and they are time dependent.

The proposed multiobjective problem considers simultaneously the maximization and minimization of the economic and environmental objectives, respectively. The economic objective function involves the availability of the bioresources, demand of products, processing limits as well as the costs associated to raw materials, products and processing, besides transport and storage costs. Whereas, the environmental objective function is measured through the life cycle analysis methodology by the assessment of the Eco-Indicator 99, in order to obtain a more objective measure of environmental impact. Since both objectives contradict each other, a multiobjetive optimization strategy is proposed to identify the Pareto curve that describes the set of solutions that simultaneously satisfies the economic and environmental objectives. The proposed methodology is applied to a case of study for a biomass conversion system in Mexico, which allows to identify the technologies and the location as well as the best feedstocks to yield economical and environmental attractive solutions. No numerical complications were observed in the solution of the model.