| dc.description.abstract | The purpose of this research is to analyse three different cultivation systems for microalgae cultivation. Microalgae have been considered as a promising alternative for the sustainable production of energy and materials, for their high possible photosynthetic efficiency, high lipid content (i.e., 30-70%), low land use, no competition for crop areas, high ability to fix nitrogen and phosphorus, and the consumption of CO2. One of the most important products of microalgae is biodiesel that is able to, with co-production of other products such as nutrients, replace fossil feedstocks.
Different systems are available for microalgae cultivation: open ponds, and tubular and flat panel photobioreactors. Open ponds are easy and cheap to exploit on large scale for mass production of microalgal biomass, but are less suitable for the production of specific strains or products due to little control of reaction conditions. Tubular and flat panel photobioreactors allow for much better control of these conditions, and allow for using higher concentrations resulting in a higher productivity.
Factors that influence microalgae growth are the temperature, solar irradiation intensity, reactor geometry, concentration, and the availability of CO2 and nutrients. To assess the growth rate and productivity of the algae a growth model was developed. This model assessed the microalgae growth affected by the amount of solar irradiance, the transmission through the wall, and the conversion efficiency of the algae which depends on the algae strain and environmental conditions such as pH and nutrient availability. Using Nannochloropsis sp. in Dutch conditions, the productivity was 64 g/m2/day, which is a slight overestimation when compared to literature. One of the weaknesses of the model is that it does not consider the reactor geometry, meaning no possibility to specify for open pond, tubular or flat panel, and that it is linear, therefore not considering feedback mechanisms or interconnections of the parameters and effects.
A second model was developed based on 6 sub models: light input, transmission, shading, light gradient inside the reactor, temperature and growth of the microalgae. Light input is influenced by the orientation of the reactor, the location and seasonal and daily changes. When multiple parallel reactors are used, the height and distance between them determine the shading. Transmission through the reactor wall is determined by the wall material being for example glass or plastic. Inside reactor the concentration and light path determine the amount of light the algae receive. Temperature fluctuates throughout the year and the day, but must be close to a optimal temperature and below the lethal temperature of the specific algae. The ability of the microalgae to utilize the light efficiency reflected in a maximum growth rate, respiration rate and the chlorofyll-carbon ratio in the cell, ultimately determines the growth rate of the microalgae.
The model was used to analyse the volumetric and areal productivity of open ponds, horizontal and vertically stacked tubular reactors and horizontal and vertical flat panels. This was done for two algae strains: Chlorella a fresh water algae, and Nannochloropsis a saline strain, and for two locations Rotterdam (Nl) and Narbonne (Fr). The analysis was done for one year and for one day every season to allow for comparison of seasonal differences and annual productivity. In terms of volumetric productivity the horizontal tubular reactors are the most advantageous (0.8-1.3 kg/m3/day). Per unit of area the flat panel is more beneficial (59-75 g/m2/day). The model reported a high performance in open ponds, that is not very plausible according to literature, probably due to the assumption of optimal conditions and too high concentrations. Working with the model showed that it is difficult to design one integrated model for all reactor configurations: the model was accurate for closed photobioreactors, but overestimated for open ponds. However, this models does allows for clear comparison of different locations or algae strains.
The next step was to use these results for a life cycle analysis of a base case for biodiesel production. The process consists of five steps: growth, harvesting of the algae (incrasing the mass percentage to 40%), pre treatment to disrupt the cells and further dry the slurry, extraction of the oil from the cells by using chloroform/methanol and transesterification of the lipids into oil and ultimately biodiesel.
The LCA was performed considering 1 kg of biodiesel produced. The non-renewable energy use and greenhouse gas emissions as well as land use are considered. The results were allocated according to mass and economic value. The life cycle analysis showed that a horizontal tubular reactor with Nannochloropsis in Rotterdam is the least energy and emissions intensive out of the assessed configurations. A large share of the energy consumption and greenhouse gas emissions is caused by the nutrients provided to the algae, therefore finding a different source for these such as waste water, could be a way of reducing the impacts of microalgae cultivation. This case was used for further analysis of the production of biodiesel.
The analysis of a base case for the production of biodiesel showed that the drying and purification processes are the most energy intensive steps. The total non-renewable energy use of the production of biodiesel from microalgae biomass is 3.29 times higher than the energy content of the biodiesel produced and 280 gram of CO2-eq was emitted. When allocated to mass or economic value, considering the fact that biodiesel is not the only product that is extracted from the algae, the energy consumption is 0.95 or 1.34 MJ/MJ biodiesel produced respectively and emissions are 81-114 g CO2-eq. This type of allocation shows that besides finding less energy intensive alternatives for the drying and purification it is also very important to consider co-production of multiple (high-value) products from microalgae in order to make the process more energy efficient and lower the emissions. | |
