Engineering strategies for improving the CO2 fixation and carbohydrate productivity of Scenedesmus obliquus CNW-N used for bioethanol fermentation
Introduction
There is an urgent need to develop effective and sustainable technologies for CO2 capture, storage, and sequestration, in order to alleviate the adverse effects of CO2 emissions on the environment, and especially on the climate (Ho et al., 2012a). Biological CO2 fixation, which is achieved by microalgae through photosynthesis, has emerged as one of the most effective and environmental-friendly methods to mitigate CO2 emissions (de Morais and Costa, 2007a). Theoretically, photosynthesis can be used to transport atmospheric carbon into a cycle in which no additional CO2 is created (Kumar et al., 2010). Microalgae can grow about 10–50 times faster than terrestrial plants, and thus achieving a much higher CO2 fixation rate (Chen et al., 2011). Therefore, there is considerable potential with regard to using microalgae to develop technically- and commercially-feasible processes for the mitigation of CO2 emissions (Ho et al., 2010a). Moreover, the microalgal biomass produced through photosynthesis can be converted into a wide range of valuable products, such as biofuels, food additives, health-care products, and so on, which represent additional benefits of any microalgae-based CO2 reduction system (Ho et al., 2011).
Due to the fast growing global demand for fuel energy, alternative biofuels have attracted great attention because they are made from non-toxic, biodegradable, and renewable resources, and their use can lead to a decrease in the emission of harmful pollutants, such as CO2. Biomass is an excellent renewable resource for the generation of biofuels, such as biodiesel (Ho et al., 2010b) and bioethanol (John et al., 2011). To date, bioethanol has been mostly produced from food crops (e.g., corn and sugarcane) and lignocellulosic materials (e.g. rice straw and switchgrass) (Nigam and Singh, 2011), but this raises the issue of competition with the arable lands needed to produce food, in addition to the high cost associated with conversion of lignocellulosic materials into ethanol (Sun and Cheng, 2002). As a result, more attention has recently been paid to evaluating the feasibility of using microalgae as a so-called “third generation feedstock” for biofuel production. In addition to their favorable characteristics of a fast growth rate and a high CO2 fixation rate, some microalgae species contain high carbohydrate contents (in the form of starch in the chloroplast and cellulose on inner cell walls), which are suitable for ethanol fermentation (Ho et al., 2013a). In particular, the absence of lignin in microalgae-based cellulose also makes the saccharification process much easier when compared with that of lignocellulosic biomass (Harun et al., 2010). There are also some other advantages associated with using microalgae as potential feedstock, as they can be cultivated on non-arable land, with non-potable water, without seasonal culture limitations, and can be harvested daily (Siaut et al., 2011).
Although microalgae seem to have high potential to become feedstock for ethanol production, there are still some limitations, mainly due to the cost issues, that may hinder their commercial applications. To enhance the economic feasibility of microalgae-based bioethanol production, it is necessary to raise both the biomass productivity and carbohydrates (mainly glucose) content (John et al., 2011). Strategies that achieve a combination of the highest carbohydrate (glucose) content along with the shortest cultivation time can thus lead to an optimization of both carbohydrate productivity and CO2 removal ability.
A microalga isolate, Scenedesmus obliquus CNW-N, was used in this study to fix CO2 and also to accumulate cellular carbohydrates for subsequent use in making bioethanol (Ho et al., 2012a). The feasibility of the S. obliquus CNW-N strain with regard to serving as a feedstock for bioethanol production was first evaluated on batch mode in terms of its carbohydrate accumulation and biomass productivity. Moreover, various engineering strategies (namely, semi-batch and continuous operations) were applied to optimize biomass and carbohydrate productivity, and then the related bioethanol production yield was estimated. These processes are reported in detail in the following sections.
Section snippets
The microalgal strain and growth medium
The microalga used in this study was S. obliquus CNW-N, which was isolated from freshwater of Niaosung Wetland located in southern Taiwan (Ho et al., 2010a). A modified version of Detmer’s Medium (DM) was used to grow the pure culture of S. obliquus CNW-N. The medium consisted of (g L−1): Ca(NO3)2.4H2O, 1.00; KH2PO4, 0.26; MgSO4·7H2O, 0.55; KCl, 0.25; FeSO4·7H2O, 0.02; EDTA.2Na, 0.2; H3BO3, 0.0029; ZnCl2 0.00011; MnCl2·4H2O, 0.00181; (NH4)6Mo7O24·4H2O, 0.000018; CuSO4·5H2O, 0.00008. The S.
Effect of nitrogen starvation on the cell growth, carbohydrate content, and CO2 fixation ability of S. obliquus CNW-N under batch operation
To enhance the economic feasibility of microalgae-based bioethanol, the synthesis of carbohydrates in microalgae should be triggered to increase the cellular carbohydrate content. In addition, the cell growth rate should also be maintained at a satisfactorily high level to ensure high carbohydrate productivity. Previous studies have demonstrated that microalgae cells tend to allocated their carbon content to energy-rich compounds, such as lipids and carbohydrates when they encounter
Conclusions
This work demonstrates that the CO2 fixation ability and carbohydrate production of S. obliquus CNW-N were significantly enhanced by using different engineering strategies (i.e., continuous and semi-batch operations). The highest CO2 fixation rate and carbohydrate productivity obtained in this work were 1988.6, and 467.6 mg L−1 d−1, respectively, which are better than most of the previously reported values. The main carbohydrate composition was glucose (up to 80% of the total sugars), which is
Acknowledgements
The authors gratefully acknowledge financial support from Taiwan’s National Science Council (under grant number NSC 101-2221-E-006-094-MY3, 102-3113-P-006-016 and 101-3113-P-110-003) and by the National Natural Science Foundation of China (Grant No.: 51136007). This research was also received funding from the Headquarters of University Advancement at the National Cheng Kung University, which is sponsored by the Ministry of Education, Taiwan.
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