A synthetic description of all Early Stage Researchers (ESR) Individual Research Projects is provided in the following table.
See also the playlist ESR Projects Presentations in our YouTube Channel.
|Assessment of light dynamics on growth performance and definition of model-based protocol for algae selection and genetic manipulation||University of Padova - contacts: Prof. Tomas Morosinotto, Prof. Fabrizio Bezzo||Biological assessment of the effects of light dynamics on microalgae growth. Identification of key biological parameters for manipulation (interaction with ESR13 to define priorities). Model-based quantification of potential benefits a given mutation on microalgae productivity. Prioritisation of potential targets for genetic manipulation (e.g. by deactivating heat dissipation boost performance in conditions of low illumination).|
|Experimental and numerical investigation of culture systems involving complementary species for N2 fixation and extended solar radiation absorption||Imperial College London - contacts: Prof. William Rutherford, Prof.|
|Measurement of photosynthetic parameters and ammonium levels in N2-fixing strains and newly discovered far-red chlorophyll f-containing strains; formulation/calibration of mathematical models describing light-limited growth for particular wavelengths, describing nitrogen fixation / nitrogen-limited growth; experimental investigation of multi-layered culture systems combining N2-fixing strains and far-red chlorophyll f-containing strains.|
|Development and experimental assessment of a modelling approach for nutrient-light interactions description and optimisation||Technical University of Denmark - contacts: Prof. Irini Angelidaki||The following objectives will be pursued: i) to elucidate interactions between light, nutrients in respect to microalgae growth via experimental investigations; ii) to formulate the interdependencies in growth kinetics; iii) to formulate usable model to describe these growth interdependencies; iv) to validate the model; v) assessment of wireless illumination technology for optimal low-energy intensity control.|
|Development and analysis of a digital twin for monitoring, control and optimisation applications in microalgae: the Microalgae Growth Model (MGM)||INRIA - contacts: Prof. Olivier Bernard||Integration of state-of-the-art photosynthesis models and metabolic flux modelling approaches (DRUM framework); numerical investigation of the dynamics of nitrogen and carbon storage under variable light (e.g. diurnal cycle) using dynamic flux balance analysis; derivation of reduced models for on-line monitoring and control applications; interaction with ESR11 to integrate MGM with deep learning techniques.|
|Advanced techniques for model-based experiment design under uncertainty||Process Systems Enterprise - contacts: Prof. Costas Pantelides, Prof. Benoit Chachuat.||Develop new techniques for designing experiments using first-principles models, with particular emphasis on (a) experiments leading to improved accuracy of both model parameters and specified key performance indicators; (b) experiments for model discrimination; (c) effective handling of model uncertainty in the context of model-based experiment design. Implement these techniques within the company process modelling environment.|
|Development of a continuous microphotobioreactor for rapid model identification||University of Padova - contacts: Prof. Fabrizio Bezzo, Dr. Eleonora Sforza||Develop a microphotobioreactor capable of: i) generation of multiple concentration gradients, ii) support of long-term culture of algae cells iii) setting different temperature levels and complex light dynamics; iv) high-throughput studies with large numbers of replicates, v) compatibility with on-line imaging and standard analytics, vi) capability to apply fast dynamic changes of environmental signals.
|Microalgal population dynamics related to changes of environmental conditions||TU Dresden - contacts: Prof. Thomas Walther, Dr. Felix Krujatz||Development of a population model to describe how environmental conditions (e.g. temperature, light, nutrients) may cause the formation of sub-populations with varying cellular and metabolic properties. Integration of population models with MGM model. Definition of control strategies for favouring the growth of desired high-product populations. Experimental assessment of flow cytometry single-cell analyses to monitor population dynamics.|
|Development of an online monitoring system of culture fitness and stress-induced intracellular metabolite accumulation in microalgae||“G. W. Leibniz” University of|
Hannover - contacs: Dr. Ivo Havlik, Prof. Thomas Scheper
|Development of an online optical sensor; development of an online culture colour measurement sensor (hardware, software); development of a software sensor using inputs from absorbance, colour, fluorescence, pO2 and other sensors for the microalgae culture fitness estimation; development of sensors for the estimation of accumulation of selected intracellular metabolites as lipids and/or pigments in selected microalgae strains.|
|Sensor technology fusion for on-line monitoring and control||Wageningen University - contacts: Dr. Marcel Janssen||Integration of state-of-the-art sensor technology: integration of on-line monitoring and mass balancing based on the measurement of CO2 consumption, O2 production, biomass production and nitrogen consumption; inclusion of PAM fluorescence and VIS absorption spectroscopy. Methods for integrating MGM approach to sensors for a generic model description of microalgal production processes.|
|Data mining approaches for monitoring and decision support of PBR processes.||Proviron - contacts: Dr. Luc Roef, Prof. Kris Laukens.||Key objectives will be: i) development of a platform for integrative data mining of heterogeneous monitoring data for the prediction of process outcomes;
ii) new methods for predictive analysis of the response to growth condition permutations; iii) development of artificial
intelligence-based predictive models for monitoring and optimisation of growth conditions.
|Development of a deep-learning platform for process monitoring and optimisation||TU Dresden - contacts: Prof. Leon Urbas||Development of deep learning algorithms combining process models and neural networks for a rapid indirect measurements of biological variables of interest; integration (data fusion) of sensor systems in different scales of industrial tubing PBRs (scale-effect); development of tools for automatic operation based on biological specific response (strain-dependent) and cultivation features (geometry-dependent).|
|Microalgae high rate production by optimising biofilm-based system||CentraleSupélec - contacts: Prof. Filipa Lopes, Prof.|
|Development of a model of biofilm growth dynamics and process productivity based on the generic MGM model; design of a control law aiming at maintaining optimal operating conditions that maximises the process productivity; the control law must be robust with respect to model uncertainties, estimation errors and disturbances (e.g. light variation); assessment of the benefits of a biofilm-based process over a suspended culture.|
|Modelling and control of the photoacclimation response in an industrial environment||TMCI Padovan - contacts: Dr. Diana Simionato, Prof.|
|Assessing photoacclimation dynamics and interactions with other photosynthetic processes; measurement and modelling in an industrial environment; assessment of photoacclimation and adaptation dynamics of selected strains and mutants; interactions between temperature and light dynamics; definition of operation strategies for optimising the response of different microalgae at varying light conditions.|
|Modelling and control of microalgae bacteria consortia for wastewater treatment||University of Almeria - contacts: Prof. Jose|
María Fernández Sevilla, Prof. Francisco Gabriel Acién Fernandez
|Experimental assessment of conditions affecting growth and activity of microalgae-bacteria consortia; identification of minimum measurements required to monitor the performance of wastewater treatment; development of a model-based approach for control and optimisation of wastewater treatment in large scale reactors using microalgae.|
|Robust control of microalgae processes accounting for future meteorology||INRIA - contacts: Dr. Madalena Chaves, prof. Olivier Bernard||Automatic model calibration from pre-treated on-line data; use future weather forecasts to optimise water and energy fluxes; from global optimisation of large scale models, develop an advanced framework within a commercial technological platform own by INRIA; assess the potential of this approach with pilot raceways; develop protocols allowing for real-time reconciliation of all sensor data resulting in real-time.|