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TU Berlin

Inhalt des Dokuments

Intelligente Produktion von Mikroalgen-Biomasse mit adaptiver Zusammensetzung für Multiprodukt-Bioraffinerien
Förderer
Bundesministerium für Bildung und Forschung
Kennzeichen
031B1204
Laufzeit
1.11.2021 bis 31.10.2024
Kurzfassung
Microalgae biomass is a new and rich source of metabolites, for which growing markets exist already or are expected to arise. Due to fast cell growth and high metabolite concentrations, microalgae outperform other sources. As with plants, sunlight provides the energy for growth but no nutrients from soil are required. Algal production systems can thus be considered as an important part of the bioeconomy if their efficiency can be increased and costs reduced. The common strategy to increase competitiveness of algae-based products are biorefineries, aiming to gain as many products as possible. However, optimal conditions for the formation of valuable metabolites are often contradictory and the product yield in outdoor photobioreactors (PBR) is tightly related to the given weather, what is reflected by the fact that standardized "golden batches" do not actually exist for microalgae cultivation. The compulsion to deal with varying reaction environments implies to adapt the cultivation process accordingly.
The project generates new knowledge for improving microalgae biorefineries. First, it aims to develop innovative, intelligent strategies for the outdoor cultivation of microalgae. More precisely, models shall enable decisions how to adapt cultivation parameters and the final biomass composition according to the best possible scenario under the weather conditions at the time of cultivation. Because the classical approach of equation-based modeling hits borders with increasing complexity of the metabolic reactions, the proposal relies on machine learning to predict cell growth and metabolite formation. Such models are based on real process data, what favors the model flexibility and applicability. Despite its huge potential for process optimization, the application of machine learning to increase the productivity of microalgae production systems was practically never applied before and is therefore highly innovative, especially against the background of the progressive digitalization of processingindustries. To investigate the potential and limits of the new approach, the production of selected metabolites from two algae species (H. pluvialis and A. platensis) will be investigated experimentally in two countries with different climate conditions.
A second goal is the investigation of new products from microalgae for the bioeconomy, specifically biostimulants for a sustainable agriculture. Biostimulants include various substances and microorganisms being used to improve the development of crops and their fruits. Phytohormones are the active compound of biostimulants and biostimulants from microalgae extracts can be a favorable alternative to synthetic products being commonly used in agriculture. Experimentally, it will be investigated which cultivation conditions favor the formation of phytohormones in the two species, how these substances can be extracted by means of supercritical fluid extraction and to which extend plant growth can be stimulated.
Mitwirkende Institution
  • Universitaria Agustiniana - UNIAGUSTINIANA
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