alt="Graph depicts simulation of sophorolipid production using the Gompertz model during the SSF."/>
Figure 2.12 Simulation of sophorolipid production using the Gompertz model during the SSF. Symbols are the experimental and data solid line is the data calculated by the model.
Figure 2.13 The plot of the equation design of ideal bioreactors for the production of sophorolipids using the Gompertz model.
In a similar way to the methodology applied in Figure 2.11, the analysis of Figure 2.13 was carried out. Thus, in the same way, Levenspiel [103] indicates that from the kinetics of the formation of the product of interest (sophorolipid), it can be obtained. The design equations of the ideal reactors are obtained plotting the inverse of the product formation rate against the sophorolipid concentration (Figure 2.13. From this graph, the size of the reactor per batch will be proportional to the area under the curve of the inverse of the reaction rate, evaluated between the limits of the formation of product (~ 0–100 g/kgdry mass). Compared to Figure 2.11, in this case, the size of the intermittent reactor increases when the level of BS is less than 40 (g/kg dry mass), this is due to the change in concavity modeled by the Gompertz equation, which simulates more precisely the production of BS.
On the other hand, in the case of continuous culture in SSF, the size of the mixed flow bioreactor will be proportional to a rectangle whose base is the increase in product formation (0–100 g/Kgdry mass) and its height is the value of the inverse of the reaction rate (1/rSL) evaluated at the point of discharge (~ 100 g/kg dry mass). In this case, there is no significant change in reactor size, either using the first‐order model or the Gompertz model. The comparison of these two types of bioreactor systems indicates that the batch reactor is smaller than the continuous culture reactor. Therefore, up to this point, the batch bioreactor is the most suitable to produce BS by SSF. The analysis of the areas indicates that the continuous reactor is almost 40 times larger than the batch reactor to obtain the same sophorolipid concentration (~ 100 g/kg dry mass). On the other hand, continuous reactors are very attractive due to their high productivity; however, these have the risk of contamination, especially in a relatively long‐time process. This possibility should not be ruled out for the future. To make it possible, it is essential to go deeper into the study of SSF bioreactors.
2.6 Conclusions and Perspectives
BS production seems to be the right choice that will change in the future the way the chemical industry works. BS can attend applications in a wide range of sectors as bioremediation, water treatment, food processing, health, sanitizers, cosmetics, and pharmaceuticals. All these compounds are produced by archaea, bacteria, yeasts, and molds. Isolation, screening, and selection of non‐pathogenic microorganisms are a critical task for the safety production of BS, as well as culture media design, modeling, and optimization of culture conditions and scale up are indispensable to achieve an economic feasible process. Other tools, such as genetic modification, CRISPR‐Cas, and metabolic engineering, are emerging as different strategies to improve the yield and productivity of fermentations. Glycolipids seem to be the species with the highest potential to be developed at larger scales, particularly in the case of BS, which are probably the most promising species because these are produced through non‐pathogenic yeasts with the best observable yields for a BS. SSF comes out as an excellent alternative for BS production because it troubleshoots specific problems of traditional liquid fermentations. However, there is still a long way to go to make this emerging technique the preferred one for producing BS at large scale (e.g. improvements of mass and heat transfer, avoiding the accumulation of metabolic heat and carbon dioxide, inter alia). Fortunately, some trends appear to be beneficial to the SSF process, such as the use of single‐cell microorganisms tolerant to agitated bioreactors. We have given some examples of our own experience producing BS in SSF. The critical variables are (i) sophorolipid production, (ii) substrates consumption, (iii) respiratory kinetic parameters (CO2 production, O2 uptake, respiratory quotient), and (iv) pH. For the simulation of sophorolipid production, it is preferred to use the Gompertz model. Accurate kinetic description is important for estimating the size of the reactors. In our case, it seems that the most convenient regime for BS production is in batch cultures, reducing the risks of contaminations. On the other hand, the analysis of the rate of carbon dioxide formation is a very relevant process monitoring tool, which allows determining the moment in which the maximum production of BS occurs, on‐line, in real‐time, and without disturbing the fermentation. The progress of the disciplines in the interface of chemistry, engineering, and biology shows an extremely attractive opportunity for the research, manufacturing, and application of BS. BS’ potential in the chemical industry is immeasurable because BS cover and exceed the scope of synthetic surfactants, due to more innovative and sustainable chemistry, as well as a strong character of a circular economy.
References
1 1 Sar, P., Ghosh, A., Scarso, A., and Saha, B. (2019). Surfactant for better tomorrow: applied aspect of surfactant aggregates from laboratory to industry. Research on Chemical Intermediates 45: 6021–6041.
2 2 Santos, D.K.F., Rufino, R.D., Luna, J.M. et al. (2016). Biosurfactants: multifunctional biomolecules of the 21st century. International Journal of Molecular Sciences 17: 401.
3 3 Ontiveros, J.F., Pierlot, C.M., Catté, M. et al. (2014). A simple method to assess the hydrophilic and lipophilic balance of food and cosmetic surfactants using the phase inversion temperature of C10E4/n‐octane/water emulsions. Colloids and Surfaces, A: Physicochemical and Engineering Aspects 458: 32–39.
4 4 Henkel, M. and Hausmann, R. (2019). Diversity and classification of microbial surfactants. In: Biobased Surfactants, 2e (eds. D. Hayes, D. Solaiman and R. Ashby), 41–63. Elsevier.
5 5 Vijayakumar, S. and Saravanan, V. (2015). Biosurfactants‐types, sources, and applications. Research Journal of Microbiology 10: 181–192.
6 6 Dhanarajan, G. and Sen, R. (2014). Cost analysis of biosurfactant production from a scientist’s perspective. In: Biosurfactants: Production and Utilization‐Processes, Technologies, and Economics, Surfactant Science, vol. 159 (eds. N. Kosaric and F. Vardar‐Sujan), 153–160. CRC Press.
7 7 Konishi, M., Yoshida, Y., and Horiuchi, J.‐i. (2015). Efficient production of sophorolipids by Starmerella bombicola using a corncob hydrolysate medium. Journal of Bioscience and Bioengineering 119: 317–322.
8 8 Funston, S.J., Tsaousi, K., Rudden, M. et al. (2016). Characterising rhamnolipids production in Burkholderia thailandesis E264, a non‐pathogenic producer. Applied Microbiology and Biotechnology 100: 7945–7956.
9 9 Delbeke, E.I.P., Movsisyan, M., Van Geem, K.M., and Stevens, C.V. (2016). Chemical and enzymatic modification of sophorolipids. Green Chemistry 18: 76–104.
10 10 Sharma, M., Patel, S.N., Lata, K. et al. (2016). A novel approach of integrated bioprocessing of cane molasses for the production of prebiotic and functional bioproducts. Bioresource Technology 219: 311–318.
11 11 Lata, K., Sharma, M., Patel, S.N. et al. (2018). An integrated bio‐process for production of functional biomolecules utilizing raw and by‐products from dairy and sugarcane
