Bioprospecting of Microorganism-Based Industrial Molecules. Группа авторов

5.92 × 10⁻¹⁴

Figure 2.11 Plot of the equation design of ideal bioreactors for the production of sophorolipids using a first‐order reaction rate.

To analyze the change in the concavity of the sophorolipid production curve already indicated, the Gompertz model was used. This model is a sigmoidal equation that has the characteristic of not being symmetric at the inflexion point; as is the case with the logistic equation, it is a very flexible model to simulate behavior such as that described for the production of BS in SSF. Equations (2.4) and (2.5) show the differential and integral form of the model. Equation (2.6) shows the solution of Equation (2.5) for the initial conditions of the fermentation:

(2.4)

(2.5)

(2.6)

Where P is the product concentration (sophorolipid), P_max and P₀, are the maximum (88.9) and initial (1.61) concentrations of BS in g/kg_{dry mass}; k is a specific first‐order constant (0.032 h⁻¹), and b (8.898) is a parameter associated with the initial concentration of BS. The mentioned parameters were estimated using Excel’s Solver subroutine. Figure 2.12 shows the result of the simulation of the experimental data by the Gompertz model. It is worth noticing the use of the Gompertz model, allows predicting the change in the concavity of the production rate of BS in SSF. The correlation coefficient between experimental and calculated data was 0.98.

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/kg_{dry 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/Kg_{dry mass}) and its height is the value of the inverse of the reaction rate (1/r_SL) 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’