(627f) Integrated Computational and Experimental Studies of Microalgal-Based FUEL Production

Authors: 
Theodoropoulos, C., The University of Manchester
Pittman, J., The University of Manchester
Bekirogullari, M., The University of MAnchester

In recent decades, it has become obvious that the world has been confronted with an energy crisis and the reliance on traditional fossil fuel resources is unsustainable, due to both the irreversible depletion of fossil fuels and the associated greenhouse gas emissions which cause global warming [1]. Production of biofuels from microalgae has the potential to overcome the major drawbacks of the fossil fuels and plant derived fuels, greenhouse gas emissions, energy security and competition for agricultural land [3]. Also, algal oil production is one of the most promising technological eco-innovations [2]. Nevertheless, the technology is in its early stage of development and it is not competitive with the traditional fossil fuels [3].

The current work aims to establish links between algal strains grown in large raceway open ponds and innovative fuel generation technologies in order to achieve positive energy balance and environmental sustainability. Specifically, novel multi-parameter quantification and predictive modelling will be investigated to describe algal growth and lipid accumulation in laboratory-scale batch systems. The purpose of modelling of such closed batch reactors is to identify key parameters, which lead to the microalgae lipid and starch accumulation. Subsequently, the key parameters defined in the batch system through modelling and optimisation studies will be used, in order to further develop novel harvesting techniques as well as oil and starch extraction methods for industrial scale pond use. Ultimately, the validated predictive closed batch system model will be used to construct open raceway pond models for maximization of the biomass production and as well as maximization of oil and starch production in such systems.

A multi-parameter quantification and predictive model is investigated [4] to describe algal growth and lipid and starch accumulation in laboratory-scale batch systems and it has been expanded to cover other growth parameters (temperature, light, pH) [5,6] to control productivity of microalgae cultivation technologies. Experiments have been conducted to analyse the effect of different input parameters and the results have been used to identify key parameters of the laboratory-scale batch systems by using a combination of deterministic and stochastic optimisation methods. 

References

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