A novel kinetic model to describe 1,3‐propanediol production fermentation by Clostridium butyricum

Kinetics-based development of two-stage continuous fermentation of 1,3-propanediol from crude glycerol by Clostridium butyricum

BACKGROUND

Glycerol, as a by-product, mainly derives from the conversion of many crops to biodiesel, ethanol, and fatty ester. Its bioconversion to 1,3-propanediol (1,3-PDO) is an environmentally friendly method. Continuous fermentation has many striking merits over fed-batch and batch fermentation, such as high product concentration with easy feeding operation, long-term high productivity without frequent seed culture, and energy-intensive sterilization. However, it is usually difficult to harvest high product concentrations.

RESULTS

In this study, a three-stage continuous fermentation was firstly designed to produce 1,3-PDO from crude glycerol by Clostridium butyricum, in which the first stage fermentation was responsible for providing the excellent cells in a robust growth state, the second stage focused on promoting 1,3-PDO production, and the third stage aimed to further boost the 1,3-PDO concentration and reduce the residual glycerol concentration as much as possible. Through the three-stage continuous fermentation, 80.05 g/L 1,3-PDO as the maximum concentration was produced while maintaining residual glycerol of 5.87 g/L, achieving a yield of 0.48 g/g and a productivity of 3.67 g/(L·h). Based on the 14 sets of experimental data from the first stage, a kinetic model was developed to describe the intricate relationships among the concentrations of 1,3-PDO, substrate, biomass, and butyrate. Subsequently, this kinetic model was used to optimize and predict the highest 1,3-PDO productivity of 11.26 g/(L·h) in the first stage fermentation, while the glycerol feeding concentration and dilution rate were determined to be 92 g/L and 0.341 h-1, separately. Additionally, to achieve a target 1,3-PDO production of 80 g/L without the third stage fermentation, the predicted minimum volume ratio of the second fermenter to the first one was 11.9. The kinetics-based two-stage continuous fermentation was experimentally verified well with the predicted results.

CONCLUSION

A novel three-stage continuous fermentation and a kinetic model were reported. Then a simpler two-stage continuous fermentation was developed based on the optimization of the kinetic model. This kinetics-based development of two-stage continuous fermentation could achieve high-level production of 1,3-PDO. Meanwhile, it provides a reference for other bio-chemicals production by applying kinetics to optimize multi-stage continuous fermentation.

Dynamic flux balance analysis of 1,3-propanediol production by clostridium butyricum fermentation

To study the relationship between the yield of 1,3-propanediol (1,3-PDO) and the flux change of the Clostridium butyricum metabolic pathway, an optimized calculation method based on dynamic flux balance analysis was used by combining genome-scale flux balance analysis with a kinetic model. A more comprehensive and extensive metabolic pathway was obtained by optimization calculations. The primary extended branches include: the dihydroxyacetone node, which enters the pentose phosphate pathway; the α-oxoglutarate node, which has synthetic metabolic pathways for glutamic acid and amino acids; and the serine and homocysteine nodes, which produce cystathionine before homocysteine enters the methionine cycle pathway. According to the expanded metabolic network, the flux distribution of key nodes in the metabolic pathway and the relationship between the flux distribution ratio of nodes and the yield of 1,3-PDO were analyzed. At the dihydroxyacetone node, the flux of dihydroxyacetone converted to dihydroxyacetone phosphate was positively correlated with the yield of 1,3-PDO. As an important intermediate product, the flux change in the metabolic pathway of α-oxoglutarate reacting with amino acids to produce glutamic acid is positively correlated with the yield. When pyruvate was used as the central node to convert into lactic acid and α-oxoglutarate, the proportion of branch flux was negatively correlated with the yield of 1,3-PDO. These studies provide a theoretical basis for the optimization and further study of the metabolic pathway of C. butyricum.

Bioconversion of Glycerol into Lactic Acid by a New Bacterial Strain from the Brazilian Cerrado Soil

A lactic-acid-producing strain was isolated from the Brazilian Cerrado soil (Brazilian savanna). Glycerol, a byproduct of the biodiesel industry, can be converted into various chemical intermediates of industrial value by biotechnological routes. Klebsiella pneumoniae can metabolize glycerol in environments with or without oxygen and bioconvert it into several chemicals with high value-added, such as lactic acid, 3-hydroxypropionic acid and 1,3 propanediol. The wild-type bacterial strain (2GPP) isolated from a soil sample from the Brazilian Cerrado was determined to be a K. pneumoniae complex that was capable of successfully metabolizing glycerol. Fermentations were performed with different temperatures, pH, and inoculum concentrations to evaluate the best lactic acid production. At first, 1,3-propanediol and L-(+)-lactic acid were produced in mini reactors. A lactic acid production of 3.8 g·L−1 and a decrease in 1,3-propanediol output were observed. Thus, by adjusting process variables such as pH and temperature during fermentation, it was possible to maximize the production of lactic acid and decrease the formation of 1,3-propanediol by utilizing experimental design strategies.

Optimization and Scale-Up of Fermentation Processes Driven by Models

In the era of sustainable development, the use of cell factories to produce various compounds by fermentation has attracted extensive attention; however, industrial fermentation requires not only efficient production strains, but also suitable extracellular conditions and medium components, as well as scaling-up. In this regard, the use of biological models has received much attention, and this review will provide guidance for the rapid selection of biological models. This paper first introduces two mechanistic modeling methods, kinetic modeling and constraint-based modeling (CBM), and generalizes their applications in practice. Next, we review data-driven modeling based on machine learning (ML), and highlight the application scope of different learning algorithms. The combined use of ML and CBM for constructing hybrid models is further discussed. At the end, we also discuss the recent strategies for predicting bioreactor scale-up and culture behavior through a combination of biological models and computational fluid dynamics (CFD) models.

Optimization and feedback control system of dilution rate for 1,3-propanediol in two-stage fermentation: A theoretical study

In utilizing glycerol to produce 1,3-propanediol by microbial fermentation, the problems of low utilization rate and poor production performance need to be addressed. Based on the analysis of a mathematical model for 1,3-propanediol production from glycerol by Klebsiella pneumoniae, this study theoretically investigated the effects of the dilution rate and the initial glycerol concentration in a two-stage fermentation process and the feasibility of applying the feedback control methods. First, the optimal operation conditions of initial glycerol concentration and dilution rate were obtained. Through the use of feedback control theory, a control strategy for dilution rate was designed and optimized to shorten the settling time (time required for fermentation to reach stability) from 60.92 to 36.68 h for the first reactor, and from 53.66 to 22.68 h for the second reactor. In addition, the yield of 1,3-propanediol in both two reactors reached up to 0.5 g·g^-1 . The simulation results indicated that the feedback control strategy for dilution rate increased the product concentration, reduced the residual glycerol in the fermentation broth, and greatly improved the performance of the fermentation. A feeding strategy of automatic control for dilution rate has been established and will be applied as an effective guiding scheme in automatic continuous fermentations for production of 1,3-propanediol.

A novel downstream process for highly pure 1,3-propanediol from an efficient fed-batch fermentation of raw glycerol by Clostridium pasteurianum

An efficient downstream process without prior desalination was developed for recovering 1,3-propanediol (1,3-PDO) with high purity and yield from broth of a highly productive fed-batch fermentation of raw glycerol by Clostridium pasteurianum. After removal of biomass and proteins by ultrafiltration, and concentration by water evaporation, 1,3-PDO was directly recovered from the broth by vacuum distillation with continuous addition and regeneration of glycerol as a supporting agent. Inorganic salts in the fermentation broth were crystallized but well suspended by a continuous flow of glycerol during the distillation process, which prevented salt precipitation and decline of heat transfer. On the other hand, ammonium salt of organic acids were liberated as ammonia gas and free organic acids under vacuum heating. The latter ones formed four types of 1,3-PDO esters of acetic acid and butyric acid, which resulted in yield losses and low purity of 1,3-PDO (< 80%). In order to improve the efficiency of final 1,3-PDO rectification, we examined alkaline hydrolysis to eliminate the ester impurities. By the use of 20% (w/w) water and 2% (w/w) sodium hydroxide, > 99% reduction of 1,3-PDO esters was achieved. This step conveniently provided free 1,3-PDO and the sodium salt of organic acids from the corresponding esters, which increased the 1,3-PDO yield by 7% and prevented a renewed formation of esters. After a single stage distillation from the hydrolyzed broth and a followed active carbon treatment, 1,3-PDO with a purity of 99.63% and an overall recovery yield of 76% was obtained. No wastewater with high-salt content was produced during the whole downstream process. The results demonstrated that the monitoring and complete elimination of 1,3-PDO esters are crucial for the efficient separation of highly pure 1,3-PDO with acceptable yield from fermentation broth of raw glycerol.

A robust soft sensor to monitor 1,3-propanediol fermentation process by Clostridium butyricum based on artificial neural network

With the aggravation of environmental pollution and energy crisis, the sustainable microbial fermentation process of converting glycerol to 1,3-propanediol (1,3-PDO) has become an attractive alternative. However, the difficulty in the online measurement of glycerol and 1,3-PDO creates a barrier to the fermentation process and then leads to the residual glycerol and therefore, its wastage. Thus, in the present study, the four-input artificial neural network (ANN) model was developed successfully to predict the concentration of glycerol, 1,3-PDO, and biomass with high accuracy. Moreover, an ANN model combined with a kinetic model was also successfully developed to simulate the fed-batch fermentation process accurately. Hence, a soft sensor from the ANN model based on NaOH-related parameters has been successfully developed which cannot only be applied in software to solve the difficulty of glycerol and 1,3-PDO online measurement during the industrialization process, but also offer insight and reference for similar fermentation processes.