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Optimal Design and Operation of Microbial Ecosyste.. (DOP-ECOS)
Optimal Design and Operation of Microbial Ecosystems for Bioenergy Production and Waste Treatment
(DOP-ECOS)
Date du début: 1 sept. 2011,
Date de fin: 31 août 2015
PROJET
TERMINÉ
Many microbial ecosystems, as part of their normal activity, have the potential to provide services to society and improve environmental quality. Some can degrade contaminants that pollute water, air or soil. Others can transform waste materials into valuable renewable resources, including bioenergy, biomaterials and high-value products. This generic capability opens the possibility for combining several microbial ecosystems in integrated bioprocesses or biorefineries, where various types of bioenergy or biomaterials are produced and multiple sources of pollution are treated, all at the same time.The focus in the DOP-ECOS project is on bioprocesses that couple a photobioreactor, where microalgae capture sunlight to produce new biomass and lipids, and an anaerobic digester, where bacteria convert biomass into biogas and recover nutrients. The general objective is twofold: (i) optimize the design, operation and control of integrated microalgal/bacterial processes; and (ii) develop the supporting methods and tools for their reliable analysis and optimization.While experimental research and demonstration programs are carried out worldwide to identify suitable algae strains and expand algal biofuel production from a craft to a major industrial process, the DOP-ECOS project is the first of its kind to apply a systematic, model-based methodology in order to determine optimal design and operation strategies. The need to account for operational issues at an early design stage is particularly pressing for integrated microalgal/bacterial systems, where the operation is expected to have a profound influence on the process design, due to the intricacy of the biological and physical processes involved. Because of the large uncertainty and variability inherent to microbial ecosystems, optimization under uncertainty provides a systematic framework to design and operation these processes.
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