PRODIGIO will boost the efficiency of solar energy conversion into biogas by increasing the performance of i) microalgae production systems and ii) anaerobic digestion systems, thanks to the development of early-warning signals for improved systems monitoring and control.

The MAIN objective of PRODIGIO is to establish a knowledge base for the development of a system failure prediction technology that increases the performance of microalgae biomass production and anaerobic digestion systems and advance towards more favourable techno-economic, environmental and social performance to achieve more sustainable microalgae biogas.

Anaerobic Digestion Systems

Anaerobic digestion (AD) is probably the most economically attractive process for the production of biofuel from microalgae since it does not require drying of biomass and it is a relatively simple procedure from an infrastructure and engineering perspective. AD is a natural biomass degradation process carried out by microorganisms, which very efficiently transform the organic matter firstly into intermediate bioproducts and finally into biogas under anaerobic conditions.

Microalgae Production Systems

Microalgae are some of nature’s finest examples of solar energy conversion systems, transforming carbon dioxide into complex organic molecules through photosynthesis. They are capable of achieving solar energy to biomass conversion efficiencies up to one order of magnitude higher than oleaginous crops, and there is biotechnology potential to further increase conversion efficiency4. Due to their outstanding photosynthetic yields and ability to grow in non-arable lands, non-potable water sources (e.g. wastewater, seawater), and a wide range of environmental conditions, there is much interest in the use of microalgae biomass as a source of truly sustainable bioenergy feedstock.

Early-warning Signal

PRODIGIO will develop an innovative and versatile methodology for the identification of early warning signals that combines causal detection methods with the analysis of interaction networks. The method sequentially calculates the interaction network over time (its topology and strength of the interactions) and, at each time step, it calculates a measure of the stability of the network. When stability approaches a critical value (or tipping point), the method evaluates the dominant eigenvalue of the interaction network, providing the best warning/s for system failure. By combining ‘big data’ acquisition from thoroughly designed perturbation experiments in bioreactor systems, advanced metaOmics and chemical fingerprint technologies, state-of-the-art bioinformatic tools, and novel methods for the analysis of causal interaction networks, PRODIGIO will decode triggers, identify early-warnings, define threshold values, and calculate warning times for critical state transitions in bioreactor systems.

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