|Title||Production of fungal lipids : kinetic modeling and process design|
|Source||University. Promotor(en): Hans Tramper, co-promotor(en): Arjen Rinzema. - [S.l.] : S.n. - ISBN 9789461730930 - 239|
|Publication type||Dissertation, internally prepared|
|Keyword(s)||mortierella - lipiden - bioproceskunde - afvalhergebruik - biodiesel - lipids - bioprocess engineering - waste utilization|
Finding alternatives for fossil fuels is currently urgent. One of the new processes in this field is the production of biodiesel from lipids accumulated by microorganisms. Some yeasts and fungi accumulate lipids when a component needed for growth, usually the N-source, is limiting while the C-source is in excess. These oleaginous yeasts and fungi were previously mainly used for unsaturated fatty acid production, but now also come into view for production of lipids as a source of biodiesel.
This thesis takes the first steps in the development of a new process to produce lipids with an oleaginous fungus in solid-state fermentation on agro-industrial waste. Solid-state fermentation is the cultivation on solid substrate particles without (free) flowing water, and has several advantages over submerged fermentation such as less waste water production, less energy use for oxygen transfer and lower production costs. In this thesis, we focused on growth and lipid production kinetics in submerged as well as solid-state fermentation. The models developed for these systems provide insight in the lipid production mechanism, needed to develop the new process based on solid-state fermentation.
The thesis starts with the selection of a model strain (Chapter 2). With this strain, the kinetics of growth and lipid accumulation were studied and modeled. We started with a steady-state model (Chapter 3 and 4) in submerged chemostat culture, and extended this to a dynamic model for submerged batch culture (Chapter 5). As the next step towards solid-state fermentation, we developed a model for growth and lipid accumulation on κ-carrageenan plates with monomers (Chapter 6). These three models were finally used to calculate potential lipid yield and energy use in a biodiesel production system (Chapter 7).
For the system we want to develop, we need a fungus that can utilize different substrates and can produce lipids. For this purpose, we tested two oleaginous fungi: Mortierella alpina and Umbelopsis isabellina, which is described in Chapter 2. We cultivated both fungi on agar plates containing glucose, xylose, starch, cellulose or pectin, and on sugar beet pulp in a packed bed. M. alpina did not utilize xylose, cellulose and pectin, utilized starch much slower than glucose and only consumed approximately 40% of the sugar beet pulp in 20 days. This shows that M. alpina is not a suitable organism for our production system. U. isabellina utilized pectin and xylose with the same rate as glucose, but used starch slower and (crystalline) cellulose not at all. It consumed approximately 75% of the sugar beet pulp after 8 days and approximately 100% after 20 days. Also, it accumulated some lipids (3% of remaining dry mass) in the culture on sugar beet pulp; optimization of this process by addition of enzymes increased the lipid content to 9% of remaining dry mass. This shows that U. isabellina is a promising strain for lipid production from agro-industrial waste, and is therefore a good strain to use in our research.
The lipid concentrations found in SSF culture were quite low; we therefore decided to look in more depth into the kinetics of lipid production in different model systems. The first model system was a submerged chemostat culture, because the substrate supply rates can be varied in this system by varying the dilution rate as well as the concentrations in the feed. Chapter 3 describes the development of a mathematical model that includes growth, lipid accumulation and substrate consumption of oleaginous fungi in submerged chemostat cultures. Key points of the model are: (1) If the C-source supply rate is limited, maintenance has a higher priority than growth, which has a higher priority than lipid production; (2) the maximum specific lipid production rate of the fungus is independent of the actual specific growth rate. This model was validated with chemostat cultures of U. isabellina grown on mineral media with glucose and NH4+. Because of practical problems at low dilution rates, the model could only be validated for D>0.04 h‑1. For further validation, published data sets for chemostat cultures of oleaginous yeasts and a published data set for a poly-hydroxyalkanoate accumulating bacterial species were used, which is described in Chapter 4. All data sets could be described well by the model. Analysis of all data showed that the maximum specific lipid production rate is in most cases very close to the specific production rate of membrane and other functional lipids for cells growing at their maximum specific growth rate. The limiting factor suggested by Ykema et al.(1986, Antonie van Leeuwenhoek 52: 491-506), i.e. the maximum glucose uptake rate, did not give good predictions of the maximum lipid production rate. The model shows that both the C/N-ratio of the feed as well as the dilution rate has a large influence on the lipid production rate. When these data are translated to SSF, it means that a low substrate supply rate can prevent lipid production, even when the C/N-ratio of the substrate is high.
The next step towards understanding lipid accumulation was a model that also describes changes in time. Therefore, we developed a model for growth, lipid production and lipid turnover in submerged batch fermentation, which is shown in Chapter 5. This model describes three subsequent phases: exponential growth when both a C-source and an N-source are available, carbohydrate and lipid production when the N-source is exhausted, and turnover of accumulated lipids when the C-source is exhausted. The model was validated with submerged batch cultures of U. isabellina with two different initial C/N-ratios. In batch culture, the specific lipid production rate was almost four times higher than in chemostat cultures and it decreased exponentially in time. This indicates that different mechanisms for lipid production are active in batch and chemostat cultures. The model could also describe several data sets from literature very well. Furthermore, the model shows that local limitation of C-source in SSF can cause lipid turnover before the average C-source concentration in the substrate is zero.
The next step towards an SSF system is the inclusion of diffusion in the batch model. We did this by developing a model that describes growth, lipid production and lipid turnover in a culture on κ-carrageenan plates containing the monomers glucose and alanine as C-source and N-source, respectively. This is described in Chapter 6. The model includes reaction kinetics and diffusion of glucose, alanine and oxygen. It was validated with U. isabellina and describes the different phases of the culture very well: exponential growth, linear growth because of oxygen limitation, accumulation of lipids and carbohydrates after local N-depletion and turnover of lipids after local C-depletion. Extending the model with an unidentified extracellular product improved the fit of the model to the data. The model shows that oxygen limitation is extremely important in solid-state cultures using monomers. Together with the low specific lipid production rate found in SSF, it explains the difference in production rate with submerged cultures.
In Chapter 7, we used the models from Chapter 3, 5 and 6 together with basic engineering principles to calculate lipid yield and energy use in the modeled systems. We evaluated a process including pretreatment, cultivation and down-stream processing with sugar beet pulp and wheat straw as substrate, described different reactor types, and considered both a yeast and a fungus as microorganisms. According to the models, lipid yields on substrate were between 5% w/w and 19% w/w, depending on the culture system. With the same models, improvement of the yield to 25-30% w/w was shown to be possible, for example by genetic modification of the microorganism. The net energy ratio of the non-optimized systems varied between 0.8 and 2.5 MJ produced per MJ used; energy use for pretreatment and for oxygen transfer were most important. For the optimized systems, the net energy ratio increased to 2.9 – 5.5 MJ produced per MJ used, which can compete very well with other biofuels such as bioethanol or algal biodiesel. So although there is still quite some work to be done, microbial lipids have the potential to be tomorrow’s source of biodiesel.