Quest for sources for chemicals, fuels, and food in a sustainable way is one of the great challenges facing humanity. The exploitation of photosynthesis can provide the key to make advances in this direction because it uses an inexhaustible source of energy (light) and a chemical component (CO2) that is abundant in the atmosphere and whose level shall be lowered to obtain a climate resilient environment. In this context, microalgae seem to be the most interesting organisms due to their high photosynthetic yields compared to terrestrial plants, due to their low need of agricultural land and drinkable water, and due to their valuable biochemical composition rich in proteins and lipid content.
Despite their enormous potential, industrial use of microalgae is still at an early stage of development. The capability of converting solar light energy into chemical energy, is about 10% and the light conversion efficiency is maximally 5% in lab-scale equipment and
is never beyond 2% in industrial facilities. The objective of DigitAlgaesation is to propose a digitalisation approach to optimise control and operation of microalgae cultivation processes and to maximise their light conversion efficiency.
To obtain this, three bottlenecks have to be overcome. The first one is the lack of suitable sensors and of effective monitoring techniques in industrial applications limiting the efficiency in the management of large-scale algae cultivation. The second is, despite the recent advances, the lack of robust control methodologies for microalgae cultivation which usually take into consideration only one parameter (pH, concentration, etc.) at a time and are not capable to represent the complexity that characterize microalgae cultivation systems.
The third bottleneck is related to the lack of specific skills and competencies to manage large scale cultivation.
The Digitalgaesation project therefore aims to:
- develop a sound modelling approach at the process scale valid for different microalgae of industrial interest capable to describe the dynamics of the photosynthetic phenomena (response to light and temperature fluctuations) and metabolic fluxes that are relevant to an effective automatic control strategy in an industrial environment;
- develop a reliable smart monitoring approach for measuring and/or estimating the biological key performance indicators (KPIs) required for model identification and to support an effective control system (i.e. biomass concentration, cell concentration and morphology, concentration of intracellular products, physiological state of microalgae, presence of competitors/grazers): new optical online in-situ sensors will be developed as well as AI and machine learning technology for soft sensors development;
- develop and implement automatic control strategies in real industrial systems characterized by non-ideal behaviour in the presence of uncertainty, to achieve high level of productivity, and reduce manpower and energy costs while maintaining a constant product quality;
- train new innovation leaders so that they can acquire the multidisciplinary skills required to make a difference in such a complex and challenging research environment, and to boost industrial potential towards excellence and widespread impact
