Fed-batch Fermentation Process Research Articles

Different fermentation strategies by Schizochytrium mangrovei strain pq6 to produce feedstock for exploitation of squalene and omega-3 fatty acids.

Schizochytrium mangrovei strain PQ6 was investigated for coproduction of docosahexaenoic acid (C22: 6ω-3, DHA) and squalene using a 30-L bioreactor with a working volume of 15L under various batch and fed-batch fermentation process regimes. The fed-batch process was a more efficient cultivation strategy for achieving higher biomass production rich in DHA and squalene. The final biomass, total lipid, unsaponifiable lipid content, and DHA productivity were 105.25g·L-1 , 43.40% of dry cell weight, 8.58% total lipid, and 61.66mg·g-1 ·L-1 , respectively, after a 96h fed-batch fermentation. The squalene content was highest at 48h after feeding glucose (98.07mg·g-1 of lipid). Differences in lipid accumulation during fermentation were correlated with changes in ultrastructure using transmission electron microscopy and Nile Red staining of cells. The results may be of relevance to industrial-scale coproduction of DHA and squalene in heterotrophic marine microalgae such as Schizochytrium.

Knowledge-data-integrated sparse modeling for batch process monitoring

Traditional data-driven modeling methods are unable to build easily interpretable process monitoring models because they ignore the useful process knowledge. This deficiency may decrease the fault detection and diagnosis capability. To correct this deficiency, a novel knowledge-data-integrated sparse modeling method is proposed for batch process monitoring. This method builds a knowledge-data-integrated sparse (KDIS) monitoring model by integrating process data with fundamental process knowledge. The KDIS model is well suited for fault detection and diagnosis due to its sparsity and good interpretability. Based on the KDIS model, two new monitoring indices are proposed for fault detection, and two-level contribution plots are developed for fault diagnosis. Two-level contribution plots can not only identify faulty variables but also faulty variable groups corresponding to control loops or physical/chemical links in the process. The effectiveness and advantages of the proposed methods are illustrated with a case study on an industrial-scale fed-batch fermentation process.

Substrate sustained release-based high efficacy biosynthesis of GABA by Lactobacillus brevis NCL912

BackgroundGamma-aminobutyric acid (GABA) plays a significant role in the food and drug industries. Our previous study established an efficient fed-batch fermentation process for Lactobacillus brevis NCL912 production of GABA from monosodium l-glutamate; however, monosodium l-glutamate may not be an ideal substrate, as it can result in the rapid increase of pH due to decarboxylation. Thus, in this study, l-glutamic acid was proposed as a substrate. To evaluate its potential, key components of the fermentation medium affecting GABA synthesis were re-screened and re-optimized to enhance GABA production from L. brevis NCL912.ResultsThe initial fermentation medium (pH 3.3) used for optimization was: 50 g/L glucose, 25 g/L yeast extract, 10 mg/L manganese sulfate (MnSO4·H2O), 2 g/L Tween-80, and 220 g/L l-glutamic acid. Glucose, a nitrogen source, magnesium, and Tween-80 had notable effects on GABA production from the l-glutamic acid-based process; other factors showed no or marginal effects. The optimized levels of the four key components in the fermentation medium were 25 g/L glucose, 25 g/L yeast extract FM408, 25 mg/L MnSO4·H2O, and 2 g/L Tween-80. A simple and efficient fermentation process for the bioconversion of GABA by L. brevis NCL912 was subsequently developed in a 10 L fermenter as follows: fermentation medium, 5 L; glutamic acid, 295 g/L; inoculum, 10% (v/v); incubation temperature, 32 °C; and agitation, 100 rpm. After 48 h of fermentation, the final GABA concentration increased up to 205.8 ± 8.0 g/L.Conclusionsl-Glutamic acid was superior to monosodium l-glutamate as a substrate in the bioproduction of GABA. Thus, a high efficacy bioprocess with 205 g/L GABA for L. brevis NCL912 was established. This strategy may provide an alternative for increasing the bioconversion of GABA.

Inner-phase and inter-phase analysis based operating performance assessment and nonoptimal cause identification for multiphase batch processes

Batch processes play a significant role in modern industrial processes. Nevertheless, the process operating performance may degrade from optimal level, which cancels the economic profits of the plant, and effective techniques for operating performance assessment are essential. Although multimodel approaches are proposed to fit its multiphase characteristic, the effect of combined action of multiple phases on the operating performance of the overall batch, which is very important for operating performance assessment, is neglected. In this study, a novel inner-phase and inter-phase analysis based operating performance assessment and nonoptimal cause identification strategy is proposed to overcome it. The key characteristic of the proposed method is that the inter-phase assessment models are developed based on the inner-phase assessment models of each phase, which takes the correlations and interactions between phases into consideration and reveals the combined effect of multiple phases on the operating performance of the overall batch. Furthermore, online local and global assessments are performed to master the operating performance from different perspectives and improve the algorithm performance. Possible cause variables can be determined by variable contributions under nonoptimal level. The effectiveness of the proposed methodology is demonstrated through a fed-batch penicillin fermentation process and a injection molding process.

Multiphase batch process with transitions monitoring based on global preserving statistics slow feature analysis

Abstract Most previous studies have shown that the multiphase characteristics of batch processes are critical for process monitoring; however, revealing and utilizing the information of multiplicity in multiphase batch process monitoring remains challenging. In this paper, statistics slow feature analysis (SSFA) is developed to extract slowly varying information at the same sampling time among different batches based on various statistics of the original variables. Then, a new index called the phase recognition factor (PRF), which is based on SSFA, is introduced to automatically achieve phase division. The proposed PRF takes advantage of the time sequence of process phases and does not require predefined parameters. After phase division, global preserving SSFA (GSSFA) is proposed not only to explore the time-varying dynamic information of batch processes but also to consider the mining of global data structure information. Furthermore, a novel process monitoring strategy based on the GSSFA model is developed to monitor batch processes with transitions. In each steady phase, a representative GSSFA model is built for process monitoring; in the transition period, different local GSSFA models are constructed online to monitor the test samples based on the just-in-time learning method. Two case studies, one simple numerical multiphase system and a benchmark fed-batch penicillin fermentation process, are used to demonstrate the effectiveness and superiority of the proposed method.

Research and application of biological potency soft sensor modeling method in the industrial fed-batch chlortetracycline fermentation process

The potency of fermentation broth is one of the key parameters reflect the yield and quality of fermentation products of chlortetracycline (CTC). But so far, there is no instrument available on-line detection of CTC potency. All the tests are done by manual off-line testing, it takes several hours to sample and analyze the results from a fermentation tank (the production process usually has dozens of small, large fermenters). The use of analytical results to control the amount of operation will lead to severe lag. This paper combines self-organizing feature map (SOM) neural network with accurate classification of data and least squares support vector machine (LSSVM) algorithm with strong described the nonlinear characteristics. The SOM–LSSVM global modeling method of forecasting CTC fermentation potency is establish in this paper. According to the characteristics of nonlinear CTC fermentation process, just-in-time learning-recursive least squares support vector regression (JITL–RLSSVR) is used to perform local real-time modeling and 10-folding cross validation, and a hybrid soft sensor modeling method (JITL–RLSSVR + SOM–LSSVM) for online prediction of CTC fermentation potency is proposed in this paper. Field experiments show that this method can obtain more accurate potency prediction value, and it can meet the requirements of the production process.

Sequential local-based Gaussian mixture model for monitoring multiphase batch processes

To address the incapability of using a single model to monitor multiphase batch processes with varying characteristics in different phases, a sequential local-based Gaussian mixture model (GMM) building method is proposed in this paper to improve monitoring performance. A multiphase process is divided into stable phases and transition phases in terms of the time sequence for sampling. Samples in local regions are used to partition each phase via two types of models, initial model and mixture model. Meanwhile, an adaptive iteration strategy is developed to properly determine stable phases including the in-between transition phases. Based on the partitioned phases, a localized probability index is introduced for process monitoring. A numerical example and a fed-batch penicillin fermentation process are used to demonstrate the effectiveness and merit of the proposed method.

Fed-Batch Fermentation of Yarrowia Lipolytica Using Defatted Silkworm Pupae Hydrolysate: A Dynamic Model-Based Approach for High Yield of Lipid Production

Lipid production by Yarrowia lipolytica W29 in fed-batch mode was investigated by using low-cost substitutable defatted silkworm pupae hydrolysate (DSWPH) as a feedstock. Based on the optimized lipid fermentation conditions, three media (i.e. yeast extract, DSWPH, yeast extract-DSWPH as N sources) were investigated in a batch fermentation process. The DSWPH medium displayed the optimal lipid accumulation ability with lipid yield raised by 16.13%, ratio of unsaturated fatty acids/saturated fatty acids (UFAs/SFAs) improved by 95.70%, and ratio of unsaturated fatty acids in total fatty acids (UFAs/TFAs) increased to 87.23%. The mathematical equations based on experimental data provided a good description of temporal variations such as dry cell weight, glucose consumption, and product formation in the fermentation process. The results showed that the Luedeking–Piret type equation successfully described glucose consumption and lipid accumulation in the batch culture process. A fed-batch fermentation process was then designed based on the model prediction. In the lag phase, rapid biomass growth and lipid accumulation were sequentially achieved with the adjustment of temperature, pH, and dissolved oxygen. Finally, the maximum biomass and lipid productivity were 24.01 g/L and 2.76 g/L/d, respectively. Thus, the DSWPH is a nice and substitutable N source for lipid production by Y. lipolytica W29 in the fed-batch mode.

Jet aeration as alternative to overcome mass transfer limitation of stirred bioreactors

In industrial biotechnology increasing reactor volumes have the potential to reduce production costs. Whenever the achievable space time yield is determined by the mass transfer performance of the reactor, energy efficiency plays an important role to meet the requirements regarding low investment and operating costs. Based on theoretical calculations, compared to bubble column, airlift reactor, and aerated stirred tank, the jet loop reactor shows the potential for an enhanced energetic efficiency at high mass transfer rates. Interestingly, its technical application in standard biotechnological production processes has not yet been realized. Compared to a stirred tank reactor powered by Rushton turbines, maximum oxygen transfer rates about 200% higher were achieved in a jet loop reactor at identical power input in a fed batch fermentation process. Moreover, a model-based analysis of yield coefficients and growth kinetics showed that E. coli can be cultivated in jet loop reactors without significant differences in biomass growth. Based on an aerobic fermentation process, the assessment of energetic oxygen transfer efficiency [kgO2 kW-1 h-1] for a jet loop reactor yielded an improvement of almost 100%. The jet loop reactor could be operated at mass transfer rates 67% higher compared to a stirred tank. Thus, an increase of 40% in maximum space time yield [kg m-3 h-1] could be observed.

Pilot-scale process development for low-cost production of a thermostable biodiesel refining enzyme in Escherichia coli.

Biodiesels produced from vegetable oils have a major quality problem due to the presence of steryl glucosides (SGs), which form precipitates that clog filters and cause engine failures. Recently, we described an enzymatic process for removing SGs from biodiesel. However, industrial adoption of this technology was hindered by the cost of the steryl glucosidase (SGase) enzyme used. Here we report the development and validation at the pilot scale of a cost-efficient process for manufacturing the SGase. First, we tested various low-cost carbon sources for the Escherichia coli producing strain, ultimately developing a fed-batch fermentation process that utilizes crude glycerol as a feedstock. Next, we designed an efficient process for isolating the SGase. That process uses a novel thermolysis approach in the presence of a non-ionic detergent, centrifugation to separate the solids, and ultrafiltration to concentrate and formulate the final product. Our cost analysis indicates that on a large scale, the dose of enzyme required to eliminate SGs from each ton of biodiesel will have a manufacturing cost below $1. The new process for manufacturing the SGase, which will lead to biodiesels of a higher quality, should contribute to facilitate the global adoption of this renewable fuel. Our technology could also be used to manufacture other thermostable proteins in E. coli.

The emergence of feedforward periodicity for the fed-batch penicillin fermentation process

Abstract In this paper, focus is on identifying the feedforward structure for the emergence of input periodicity at the optimal situation, and the fed-batch penicillin fermentation process is applied for case study. Through information of optimal control, the reversed system analysis methods are constructed, and the criterion for the emergency of optimal periodic control (OPC) is built, and possibly, analytical method to compute out the OPC problem would be proposed.

Transfer of Qualitative and Quantitative Knowledge for Similar Batch Process Monitoring

The operating conditions for industrial batch production often cover a wide range in order to produce different products. Inconsistent working conditions and recipes may change the data properties, but the generated batches may share similar mechanisms in terms of their qualitative and quantitative knowledge domains. In this paper, we propose a transfer learning framework for both domains to improve the efficiency of monitoring in similar batch scenarios. First, a statistical pattern clustering strategy is developed for assessing and separating similar conditions. Based on this strategy, the phase-based generalized Procrustes analysis and the ordinary Procrustes analysis are proposed to produce the nominal representations and also to transfer quantitative knowledge by accommodating batch-wise and recipe-wise discrepancies. Furthermore, a multiphase Bayesian network is constructed for qualitative knowledge transfer and statistical modeling with the nominal representations. Finally, a systematic monitoring flowchart is established for fault detection and isolation based on a just-in-time transfer strategy. Under this framework, the efforts required for similar process modeling can be reduced and the monitoring efficiency can be improved. The feasibility and effectiveness of the proposed diagram for industrial uses are validated on a fed-batch penicillin fermentation process.

Enhancement of ergosterol production by Saccharomyces cerevisiae in batch and fed-batch fermentation processes using n-dodecane as oxygen-vector

The effect of n-dodecane used as oxygen-vector in batch and fed-batch fermentations of S. cerevisiae for producing ergosterol has been comparatively investigated. Regardless of n-dodecane concentration, the results indicated the increase for about three times of ergosterol content inside the yeasts cells in fed-batch process. In both fermentation systems, by adding the oxygen-vector, the ergosterol concentration increased with over 50%. The oxygen-vector concentration corresponding to the maximum level of ergosterol depended mainly on biomass concentration, being 5% vol. for batch fermentation, and 10% vol. for fed-batch one. The increase of biomass concentration in the fed-batch process partially affected the positive influence of oxygen-vector, effect that became less significant only for hydrocarbon concentration over 10% vol. The inhibitory phenomenon induced at higher concentration of hydrocarbon also limits the positive influence of oxygen-vector, but this effect was partially counteracted in the fed-batch process due to the higher amount of yeasts biomass produced.

Multi-scale control scheme: Stabilization of a class of fourth-order integrating-unstable systems

Even when based on simple models, a fed-batch fermentation process can demonstrate several different forms of complex dynamic behaviours throughout the course of its operation. One of the most intriguing forms of dynamic behaviours that can occur at some points during the fed-batch operation is represented by a fourth-order integrating-unstable system with multiple RHP zeros. To deal with the stabilization of this complex dynamics, a new triple-loop multi-scale control (TL-MSC) scheme is proposed. This scheme aims to first decompose the complex dynamics into three simpler sub-systems which are then separately addressed in a multi-scale structure. This paper presents both the fundamental of TL-MSC scheme and the details of its design. A typical fed-batch alcoholic fermentation is shown as an example to demonstrate the applicability of the new scheme.

Stabilising PID tuning for a class of fourth-order integrating nonminimum-phase systems

ABSTRACTFed-batch fermentation processes are commonly used in bioprocessing industry. A fed-batch fermentation process often exhibits integrating/unstable type of dynamics with multiple right-half plane zeros. A class of fourth-order integrating model can be used to adequately represent such a complex dynamics of the fed-batch fermentation process. In this paper, rigorous stability analysis of proportional-integral-derivative (PID) controller based on the Routh-Hurwitz criteria for the fourth-order integrating system is presented. A set of all stabilising PID controller parameter regions is established. Based on these stabilising regions, a general PID controller tuning procedure is proposed for the fourth-order integrating system with two right-half plane zeros. Numerical study shows that based on the proposed tuning procedure, a low-order PID controller can outperform a fifth-order optimal LQG controller in terms of servo and regulatory controls.

Dynamic hypersphere based support vector data description for batch process monitoring

Support Vector Data Description (SVDD) is an efficient monitoring method that captures the spherically shaped boundary around the normal batch data and sets the control limit related to support vectors (SVs) for online monitoring. Using nonlinear transformation functions, SVDD constructs an irregular hypersphere in high dimensional space. When the batch process is complicated, the accuracy of monitoring will decrease with traditional control limit of SVDD. In this paper, dynamic hypersphere based support vector data description (DH-SVDD) is proposed for batch process monitoring. In training process, static hypersphere is built by the important SVs of training dataset. In testing process, dynamic hypersphere is built by the important SVs of combined dataset with current test sample and training dataset. If there is a significant change between these two hyperspheres, it means that the current test sample is an outlier. Thus, DH-SVDD has a relatively high monitoring accuracy because it fully considers relationship between the current test sample and the historical training dataset in high dimensional space. Comparison is conducted between the proposed DH-SVDD and traditional methods such as K-chart-SVDD, max limit SVDD and validation limit SVDD. The effectiveness of the DH-SVDD is also verified by a semiconductor etch process and a fed-batch penicillin fermentation process.

Phosphoenolpyruvate:glucose phosphotransferase system modification increases the conversion rate during L-tryptophan production in Escherichia coli.

Escherichia coli FB-04(pta1), a recombinant L-tryptophan production strain, was constructed in our laboratory. However, the conversion rate (L-tryptophan yield per glucose) of this strain is somewhat low. In this study, additional genes have been deleted in an effort to increase the conversion rate of E. coli FB-04(pta1). Initially, the pykF gene, which encodes pyruvate kinase I (PYKI), was inactivated to increase the accumulation of phosphoenolpyruvate, a key L-tryptophan precursor. The resulting strain, E. coli FB-04(pta1)ΔpykF, showed a slightly higher L-tryptophan yield and a higher conversion rate in fermentation processes. To further improve the conversion rate, the phosphoenolpyruvate:glucose phosphotransferase system (PTS) was disrupted by deleting the ptsH gene, which encodes the phosphocarrier protein (HPr). The levels of biomass, L-tryptophan yield, and conversion rate of this strain, E. coli FB-04(pta1)ΔpykF/ptsH, were especially low during fed-batch fermentation process, even though it achieved a significant increase in conversion rate during shake-flask fermentation. To resolve this issue, four HPr mutations (N12S, N12A, S46A, and S46N) were introduced into the genomic background of E. coli FB-04(pta1)ΔpykF/ptsH, respectively. Among them, the strain harboring the N12S mutation (E. coli FB-04(pta1)ΔpykF-ptsHN12S) showed a prominently increased conversion rate of 0.178gg-1 during fed-batch fermentation; an increase of 38.0% compared with parent strain E. coli FB-04(pta1). Thus, mutation of the genomic of ptsH gene provided an alternative method to weaken the PTS and improve the efficiency of carbon source utilization.

Pilot scale repeated fed-batch fermentation processes of the wine yeast Dekkera bruxellensis for mass production of resveratrol from Polygonum cuspidatum

Resveratrol has long been used as an ingredient in functional foods. Currently, Polygonum cuspidatum extract is the greatest natural source for resveratrol because of high concentrations of glycosidic-linked resveratrol. Thus, developing a cost-effective procedure to hydrolyze glucoside could substantially enhance resveratrol production from P. cuspidatum. This study selected Dekkera bruxellensis from several microorganisms based on its bioconversion and enzyme-specific activities. We demonstrated that the cells could be reused at least nine times while maintaining an average of 180.67U/L β-glucosidase activity. The average resveratrol bioconversion efficiency within five rounds of repeated usage was 108.77±0.88%. This process worked effectively when the volume was increased to 1200L, a volume at which approximately 35mgL−1h−1 resveratrol per round was produced. This repeated fed-batch bioconversion process for resveratrol production is comparable to enzyme or cell immobilization strategies in terms of reusing cycles, but without incurring additional costs for immobilization.

Sequential phase division by constructing pseudo time-slice for nonlinear multiphase batch process monitoring with uneven lengths

Batch and semi-batch processes are often characterized with strong nonlinearity. Most of the existing kernel-based methods proposed for nonlinear batch process monitoring have not considered the nonlinear characteristic, sequential phase division and uneven problem simultaneously. In this article, a novel similarity index is first defined on the basis of the kernel technique to describe the similarity of nonlinear characteristic in the high feature space. Then, the pseudo time-slice is constructed for each sample by searching samples within a range resembling to each time using the k-nearest neighbor (kNN) rule, which can effectively tackle the uneven problem without trajectory synchronization. A novel automatic sequential phase division procedure is proposed by analysing the nonlinear similarity between the local models derived from the pseudo time-slice and the representative model of each phase. For online application, the affiliation of each new sample is real-time judged to determine the proper phase model and fault status can be distinguished from phase shift event. To illustrate the feasibility and effectiveness, the proposed algorithm is applied to fed-batch penicillin fermentation process.

Automatic feed phase identification in multivariate bioprocess profiles by sequential binary classification

In this paper, we propose a new strategy for retrospective identification of feed phases from online sensor-data enriched feed profiles of an Escherichia Coli (E. coli) fed-batch fermentation process. In contrast to conventional (static), data-driven multi-class machine learning (ML), we exploit process knowledge in order to constrain our classification system yielding more parsimonious models compared to static ML approaches. In particular, we enforce unidirectionality on a set of binary, multivariate classifiers trained to discriminate between adjacent feed phases by linking the classifiers through a one-way switch. The switch is activated when the actual classifier output changes. As a consequence, the next binary classifier in the classifier chain is used for the discrimination between the next feed phase pair etc. We allow activation of the switch only after a predefined number of consecutive predictions of a transition event in order to prevent premature activation of the switch and undertake a sensitivity analysis regarding the optimal choice of the (time) lag parameter. From a complexity/parsimony perspective the benefit of our approach is three-fold: i) The multi-class learning task is broken down into binary subproblems which usually have simpler decision surfaces and tend to be less susceptible to the class-imbalance problem. ii) We exploit the fact that the process follows a rigid feed cycle structure (i.e. batch-feed-batch-feed) which allows us to focus on the subproblems involving phase transitions as they occur during the process while discarding off-transition classifiers and iii) only one binary classifier is active at the time which keeps effective model complexity low. We further use a combination of logistic regression and Lasso (i.e. regularized logistic regression, RLR) as a wrapper to extract the most relevant features for individual subproblems from the whole set of high-dimensional sensor data. We train different soft computing classifiers, including decision trees (DT), k-nearest neighbors (k-NN), support vector machines (SVM) and an own developed fuzzy classifier and compare our method with conventional multi-class ML. Our results show a remarkable out-performance of the here proposed method over static ML approaches in terms of accuracy and robustness. We achieved close to error free feed phase classification while reducing the misclassification rates in 17 out of 20 investigated test cases in the range between 39% and 98.2% depending on feature set and classifier architecture. Models trained on features based on selection by RLR significantly outperformed those trained on features suggested by experts and their predictive performance was considerably less affected by the choice of the lag parameter.