3 Results
3.1 The effect of oil feeding rate on SLs production during fed-batch
fermentation
During
5 L fed-batch fermentations, SLs were synthesized with different
rapeseed oil feeding rates (high, medium, and low modes) for 168 h.
There were no significant differences between the three modes with
respect to the titer of SLs, total glucose consumption, and total oil
consumption (Fig. 2A, Table 1), however, the phased synthesis rate of
SLs differed substantially during the fermentation process. The higher
the oil feeding rate, the faster the accumulation of SLs in the early
stages of fermentation, although the accumulation rate decreased during
the middle and late stages. We also found that the phased productivity
of SLs differed significantly between the three modes (Fig 2B). The
highest productivity of SLs was 3.53 g/L/h under the high mode, while
the highest productivity in the medium and low modes was 2.63 and 2.09
g/L/h, thus representing reductions of 25.5% and 40.8%, respectively.
Notably, after 48 h, the productivity of SLs declined rapidly under the
high mode until 110 h and was then maintained at 0.98 g/L/h to the end
of the fermentation process. In contrast, the productivity of SLs under
the low mode was maintained at an average of 2.04 g/L/h until 120 h and
then began to decrease. As shown in Fig 2C, the difference between the
high and low modes reached a maximum of 139.7 g at around 87 h, while
the maximum difference between the high and medium modes was 85.7 g at
73 h. Otherwise, there were significant differences in the OUR and CER
profiles when compared between the three modes during the fermentation
process. In high mode, the maximum OUR reached up to 64 mmol/L/h,
gradually decreased after 48 h, and was then maintained at 47 mmol/L/h
after 110 h. In contrast, the OUR in low mode was maintained at
approximately 54 mmol/L/h for 120 h. The CER showed a similar trend to
OUR (Fig 2D).
3.2 Cellular metabolic
characteristics in different stages of SLs production
The fed-batch fermentation process underlying the production of SLs
predominantly consisted of two phases: the primary metabolism of cell
growth and the secondary metabolism of SLs synthesis. We found that the
maximum productivity of SLs was related to the oil feeding rate and that
the highest value was 3.53 g/L/h. However, a further increase in the oil
feeding rate did not enhance SLs productivity, meaning that the maximum
specific productivity for SLs reached 0.15 g/gDCW/h due
to limitations imposed by the production capacity of the cells.
Moreover, we observed that the production of SLs in the shake flask
began to decrease significantly (by 15.6%) when the concentration of
SLs exceeded 75 g/L (non-normalized)
(Fig. 3A); at this point, the productivity of SLs in the fermenter under
the three modes also gradually decreased, thus inferring that product
inhibition may have occurred. Furthermore, the degree of inhibition
increased with an increase in the concentration of SLs. Interestingly,
although the OUR and CER decreased during this phase, the RQ remained
unchanged, thus showing that product inhibition only limited the
metabolic capacity of the cells, while the metabolic pathway remained
unchanged. Notably, when the concentration of SLs in the fermentation
broth exceeded 140 g/L (non-normalized), although the OUR and CER
continued to decrease with the decline of SLs productivity, the RQ began
to increase from approximately 0.70 to 0.76 (Fig. 3B). This result
demonstrated that the metabolic pathways of cells underwent significant
changes at this time. By further analyzing the viscosity of the
fermentation broth, we found that the viscosity of the fermentation
broth would rise sharply during the middle and late phases of
fermentation, and when the concentration of SLs exceeded 140 g/L
(non-normalized), the viscosity exceeded 20 cP (Fig. 3C).
The fermentation of SLs is a high oxygen consumption process. High
viscosity can exert serious effects on oxygen supply, thus resulting in
alterations in cell metabolism and the synthesis of SLs. Further
analysis of the RQ profiles demonstrated that when the average RQ was
approximately 0.70 (high mode: 24-96 h; medium mode: 24-110 h; low mode:
24-120 h), the average yields of SLs to glucose and rapeseed oil were
1.21 g/g and 1.39 g/g, respectively. However, when the concentration of
SLs exceeded 140 g/L (non-normalized) (high mode: after 96 h; medium
mode: after 110 h; low mode: after 120 h), the average yields of SLs to
glucose and rapeseed oil changed to 0.82 g/g and 1.46 g/g, respectively,
and the average RQ reached 0.76 (Table 2). This implied that once the
oxygen supply became limited, the yield of SLs to glucose was reduced,
while the yield to rapeseed oil was increased, thus leading to an
increase in RQ value.
In summary, the synthesis of SLs during fed-batch fermentation could be
divided into three phases (Fig. 4). During the initial phase, the
synthesis of SLs was mainly limited by the production capacity of the
cells as well as the oil supply. The maximum specific SLs productivity
could reach up to 0.15 g/gDCW/h but was affected by the
oil feeding rate. During the second phase, when the concentration of SLs
in the fermentation broth exceeded 75 g/L (non-normalized), the
production capacity of the cells began to be inhibited by high product
concentration. The higher the concentration of SLs, the stronger the
degree of inhibition. However, cellular metabolic pathways remained
unchanged during this phase. During the last phase, the concentration of
SLs reached 140 g/L (non-normalized) and the production capacity of the
cells became significantly restricted by oxygen supply. On the basis of
these analyses, the first and second phases were considered to represent
the main stages of SLs accumulation during fed-batch fermentation.
Increasing the productivity of SLs as much as possible during these
phases via the fine control of substrate feeding strategy was of
great significance to the improvement of production efficiency. In terms
of the third phase, although it was difficult to improve production
efficiency without increasing oxygen supply during the conventional
fermentation mode, the semi-continuous fermentation mode, involving thein-situ separation of SLs, which was regulated by the
concentration of oil in the fermentation broth (Chen et al., 2021b),
could effectively alleviate product inhibition and significantly reduce
the viscosity of the fermentation broth to eliminate oxygen limitation
(Liu et al., 2019).
3.3 Construction of an intelligent feedback feeding model based on
multi-parameter process data
Next, we constructed and optimized
an efficient, stable, and rational fermentation process for SLs
production by regulating feeding during the first and second phases.
First, we established correlations between the process macroscopic
parameters and the production capacity of cells. All the on-line
parameters, including agitation, temperature, pH, DO, glucose
concentration, oil concentration, SLs concentration, OUR, CER, and RQ
were applied as independent variables. The oil consumption rate, glucose
consumption rate, and SLs productivity were set as dependent variables.
Then, six linear and non-linear mathematical equations were adopted to
establish the correlation between independent and dependent variables,
including unary linear regression equations (LR), multiple linear
regression equations (MLR), support vector machines (SVMs), partial
least squares (PLS), random forest algorithms (RFs) and
gradient
boosting regression algorithms (GBRs). Finally, the pros and cons of the
mathematical equations were compared by the correlation coefficient
(R2) between the predicted values and the real values
(Fig. 5). Approximately 110 sets of data were used as the training set,
and 20 sets of data were used as the testing set. Three dependent
variables relating to SLs (productivity, oil consumption rate, and
glucose consumption rate) were all strongly correlated with the actual
values when applying the six equations (R2> 0.96), thus showing that these six algorithms accurately
reflected the relationship between the consumption of substrates and the
synthesis of product and fermentation process parameters (Table 3).
Notably, the R2 for the RF and GBR algorithms
decreased slightly after the testing set was added, while the
R2 for the other four equations increased as the
number of parameters increased. It is possible that with the continuous
input of parameters, the database of equations also increased;
consequently, the accuracy of the predicted value would also be
improved. Non-linear equations might be associated with over-fitting
problems; therefore, when there is less data, the predicted values of
ULR, MLR, SVM, and PLS, were more reliable. In contrast, with a
continuous increase in sample number, the prediction values for RF and
GBR became more accurate.
When the model produced a predicted value (SLs productivity, glucose
consumption rate, and oil consumption rate) with an optimal
R2, then the model would add data to the database,
thus increasing the data sample. This feedback feeding model was
combined with the data provided by on-line sensors. This meant that the
model could read the on-line data, perform equation fitting, and output
the optimal predicted value by the computer. By integrating the working
volume, the required concentrations of glucose and oil, as well as the
amounts of consumed glucose and oil, the model could automatically guide
the precise feeding rate via a computer-controlled feedback
module. Simultaneously, the model performed data overlay and
self-learning based on the continuous accumulation of data, thereby
further improving the accuracy of the model.
The
feedback feeding model was validated in a 5 L fermenter. As shown in
Fig. 6A and B, the concentration of residual oil was well controlled at
2 g/L and 10 g/L respectively, and the concentration of residual glucose
was maintained at 40 g/L from 24 h until the RQ was increased to end the
fermentation process. In addition, by applying the feedback feeding
model, the SLs titer with a residual oil concentration at 2 g/L was
improved by 4.9% during the first and second phases of fed-batch
fermentation, when compared to the high oil feeding mode. More
importantly, the feedback feeding model shortened the feeding interval
from 6 to 12 h by conventional manual intervention to 1 h by automatic
feeding. The model also reduced the error related to feeding control
from more than 15% to less than 5%, thus providing significant savings
in terms of labor and time costs.
3.4 An intelligent feedback feeding model facilitated semi-continuous
fermentation for the efficient production of SLs
To alleviate oxygen limitation in
the late phase of fed-batch fermentation, we used gravity sedimentation
for the in-situ separation of SLs, thus permitting
semi-continuous fermentation. When the concentration of SLs reached 140
g/L (non-normalized), we began the in-situ separation process and
allowed this to proceed until the concentration of SLs fell below 60
g/L, thus alleviating the influence of a high SLs concentration on the
rheological properties, as well as relieving product inhibition. As
illustrated in Fig. 7A, the feedback feeding model could stably control
the concentrations of residual glucose and residual oil at appropriate
levels during the entire fermentation process, so as to ensure the
efficient in-situ separation of SLs (Chen et al., 2021b). After two
rounds of in-situ separation of product, the total production of
SLs reached 1348.3 g within 234 h. Moreover, the overall SLs
productivity and yield were 2.30 g/L/h and 0.57 g/g, respectively, thus
representing increases of 40.2% and 18.7% in comparison to the
fed-batch fermentation with a high oil feeding mode (Fig. 7B). Notably,
the productivity of SLs did not exhibit a downwards trend at the end of
semi-continuous fermentation (Fig. 7C). Therefore, it was reasonable to
speculate that the production efficiency could be further improved by
performing longer operations with the assistance of an intelligent
feedback feeding model.