Optimization of batch fermentation processes. II. Optimum temperature profiles for batch penicillin fermentations

Optimization methods based on the continuous maximum principle and the calculus of variations were used to calculate optimum temperature profiles for batch penicillin fermentations. These methods were first applied to several general models to develop effective techniques for the numerical solution of the equations. Subsequently, these methods were applied to two particular models, derived from experimental data, and the optimum temperature profiles were determined. The results indicated that an improvement, in penicillin yield of about 15% was possible if the optimum temperature profiles were followed.     

Optimization of batch fermentation processes. I. Development of mathematical models for batch penicillin fermentations

Two kinds of mathematical models have been developed for batch penicillin fermentations: (1) general models, based on averaged, nondimensionalized cell and penicillin synthesis curves from plant, scale fermentors and (2) particular models developed from specific sets of experimental data from two sources. Parameter-temperature functions used with the general models were assumed to have general shapes which could apply to many fermentations, i.e., they were based on the familiar temperature response of enzyme-catalyzed reactions. Parameter-temperature functions for the particular models were determined from experimental data for batch runs at various temperatures.     

Optimization of batch fermentation conditions for dextran production

The nutrient medium (containing sucrose, yeast extract and K₂HPO₄), temperature and initial pH conditions were optimised for batch dextran production in shake flask fermentations using a strain of Leuconostoc mesenteroides NRRL B 512 (F). A 2⁵⁻¹ fractional factorial central composite experimental design was attempted. Multistage Monte Carlo optimization program was used to maximize the multiple regression equation obtained. The optimal values of tested variables for maximal dextran production were found to be: sucrose, 300 g/l; yeast extract, 10 g/l; K₂HPO₄, 30 g/l; temperature, 23°C and initial pH 8.3 with a predicted dextran yield of 154 g/l.     

Optimization of bacteriocin production by batch fermentation of Lactobacillus plantarum LPCO10

Optimization of bacteriocin production by Lactobacillus plantarum LPCO10 was explored by an integral statistical approach. In a prospective series of experiments, glucose and NaCl concentrations in the culture medium, inoculum size, aeration of the culture, and growth temperature were statistically combined using an experimental 2(3)(5-2) fractional factorial two-level design and tested for their influence on maximal bacteriocin production by L. plantarum LPCO10. After the values for the less-influential variables were fixed, NaCl concentration, inoculum size, and temperature were selected to study their optimal relationship for maximal bacteriocin production. This was achieved by a new experimental 3(2)(3-1) fractional factorial three-level design which was subsequently used to build response surfaces and analyzed for both linear and quadratic effects. Results obtained indicated that the best conditions for bacteriocin production were shown with temperatures ranging from 22 to 27 degrees C, salt concentration from 2.3 to 2.5%, and L. plantarum LPCO10 inoculum size ranging from 10(7.3) to 10(7.4) CFU/ml, fixing the initial glucose concentration at 2%, with no aeration of the culture. Under these optimal conditions, about 3.2 x 10(4) times more bacteriocin per liter of culture medium was obtained than that used to initially purify plantaricin S from L. plantarum LPCO10 to homogeneity. These results indicated the importance of this study in obtaining maximal production of bacteriocins from L. plantarum LPCO10 so that bacteriocins can be used as preservatives in canned foods.     

Optimization of batch alcoholic fermentation of glucose syrup substrate

The quantitative effects of substrate concentration, yeast concentration, and nutrient supplementation on ethanol content, fermentation time, and ethanol productivity were investigated in a Box–Wilson central composite design experiment, consisting of five levels of each variable, High substrate concentration, up to 30° Brix, resulted in higher ethanol content (i.e., up to 15.7% w/v or 19.6% v/v) but longer fermentation time and hence lower ethanol productivity. Increasing yeast concentration, on the other hand, resulted in shorter fermentation time and higher productivity. The highest ethanol productivity of about 21 g EiOH/L h was obtained at low substrate concentration (i.e., 12° Brix), low alcohol content (i.e., 6% by weight), high yeast concentration (i.e., 4.4%), and high supplementation of yeast extract (i.e., 2.8). Productivity of this magnitude is substantially higher that that of the traditional batch fermentation of fed-batch fermentation. It is comparable to the results of continuous fermentation but lower than those of vacuum fermentation but lower than those of vacuum fermentation. Optimal conditions for maximal ethanol productivity can be established by a multiple regression analysis technique and by plotting the contours of constant response to conform to the constraints of individual operations.     

Comparative metabolomic analysis on industrial continuous and batch ethanol fermentation processes by GC-TOF-MS

The intracellular metabolic profile characterization of Saccharomyces cerevisiae throughout industrial ethanol fermentation was investigated using gas chromatography coupled to time-of-flight mass spectrometry. A total of 143 and 128 intracellular metabolites in S. cerevisiae were detected and quantified in continuous and batch fermentations, respectively. The two fermentation processes were both clearly distinguished into three main phases by principal components analysis. Furthermore, the levels of some metabolites involved in central carbon metabolism varied significantly throughout both processes. Glycerol and phosphoric acid were principally responsible for discriminating seed, main and final phases of continuous fermentation, while lactic acid and glycerol contributed mostly to telling different phases of batch fermentation. In addition, the levels of some amino acids such as glycine varied significantly during both processes. These findings provide new insights into the metabolomic characteristics during industrial ethanol fermentation processes. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Optimization of cellulase production in batch fermentation byTrichoderma reesei

Maximum cellulase production was sought by comparing the activities of the cellulases produced by differentTrichoderma reesei strains andAspergillus niger. Trichoderma reesei Rut-C30 showed higher cellulase activity than otherTrichoderma reesei strains andAspergillus niger that was isolated from soil. By optimizing the cultivation condition during shake flask culture, higher cellulase production could be achieved. The FP (filter paper) activity of 3.7 U/ml and CMCase (Carboxymethylcellulase) activity of 60 U/ml were obtained from shake flask culture. When it was grown in 2.5L fermentor, where pH and DO levels are controlled, the Enzyme activities were 133.35 U/ml (CMCase) and 11.67 U./ml (FP), respectively. Ammonium sulfate precipitation method was used to recover enzymes from fermentation broth. The dried cellulase powder showed 3074.9 U/g of CMCase activity and 166.7 U/g of FP activity with 83.5% CMCase recovery.     

Real time phase detection based online monitoring of batch fermentation processes

Industrial fermentations conducted in a batch or semi-batch mode demonstrate significant batch-to-batch variability. Current batch process monitoring strategies involve manual interpretation of highly informative but low frequency offline measurements such as concentrations of products, biomass and substrates. Fermentors are also fitted with computer interfaced instrumentation, enabling high frequency online measurements of several variables and automated techniques which can utilize this data would be desirable. Evolution of a batch fermentation, which typically uses complex medium, can be conceptualized as a sequence of several distinct metabolic phases. Monitoring of batch processes can then be achieved by detecting the phase change events, also termed as singular points (SP). In this work, we propose a novel moving window based real-time monitoring strategy for SP detection based only on online measurements. The key hypothesis of the strategy is that the statistical properties of the online data undergo a significant change around an SP. The strategy is easily implementable and does not require past data or prior knowledge of the number or time of occurrence of SPs. The efficacy of the proposed approach has been demonstrated to be superior compared to that of reported techniques for industrially relevant model organisms. The proposed approach can be used to decide offline sampling timings in real time.     

Optimization and scale up of industrial fermentation processes

To increase product yields and to ensure consistent product quality, key issues of industrial fermentations, process optimization and scale up are aimed at maintaining optimum and homogenous reaction conditions minimizing microbial stress exposure and enhancing metabolic accuracy. For each individual product, process and facility, suitable strategies have to be elaborated by a comprehensive and detailed process characterization, identification of the most relevant process parameters influencing product yield and quality and their establishment as scale-up parameters to be kept constant as far as possible. Physical variables, which can only be restrictedly kept constant as single parameters, may be combined with other pertinent parameters to appropriate mathematical groups or dimensionless terms. Process characterization is preferably based on real-time or near real-time data collected by in situ and on-line measurements and may be facilitated by supportive approaches and tools like neural network based chemometric data analysis and modelling, clarification of the mixing and stream conditions through computational fluid dynamics and scale-down simulations. However, as fermentation facilities usually are not strictly designed according to scale-up criteria and the process conditions in the culture vessels thus may differ significantly and since any strategy and model can only insufficiently consider and reflect the highly complex interdependence and mutual interaction of fermentation parameters, successful scale up in most cases is not the result of a conclusive and straight-lined experimental strategy, but rather will be the outcome of a separate process development and optimization on each scale. This article gives an overview on the problems typically coming along with fermentation process optimization and scale up, and presents currently applied scale-up strategies while considering future technologies, with emphasis on Escherichia coli as one of the most commonly fermented organisms.     

Optimization of batch processes involving simultaneous enzymatic and microbial reactions

Two general models for batch simultaneous enzymatic and microbial reaction (SEMR) processes are presented, the second derived from and simpler than the first and accounting for enzyme denaturation. Using the second model and parameter values from the literature, simulation was used to examine a range of enzyme addition rate strategies (in which the rate was a linear function of time) for a relatively fast ethanol fermentation and for a longer duration citric acid fermentation, both using cellulose as the substrate. For the ethanol process it is optimal (for a specific objective function which accounts for product value and enzyme cost) to add all the enzyme at the beginning of the process. But for the citric acid process a linearly decreasing enzyme addition rate, coupled with the addition of a small fraction of the enzyme at time zero, is better than pure batch operation or operation with the best constant enzyme feed rate.     

Optimization of enterocin P production by batch fermentation of Enterococcus faecium P13 at constant pH

The influence of pH on growth, enterocin P production and glucose consumption by Enterococcus faecium P13 was studied during anaerobic batch fermentation in MRS broth at 32 degrees C in a fermentor. Growth and glucose consumption were maximal at pH 7.0. Enterocin P production displayed primary metabolite kinetics and was strongly dependent on pH. A maximum antimicrobial activity of 1,949 bacteriocin units (BU) ml(-1) was obtained at pH 6.0, which represented a four-fold increase compared with the antimicrobial activity obtained without pH regulation. The pH exerted a marked effect on the decrease in bacteriocin activity, with the decrease being maximal at pH 7.0. In this report, we propose models for the growth of E. faecium P13 as well as enterocin P production and inactivation. Enterocin P production decreased when potentially stress-inducing compounds (NaCl or ethanol) were included in the growth medium.     

Fed-batch versus batch fermentation

This article concerns the comparison between batch and fed-batch fermentation based on productivity and including the analysis of the implications on product manufacturing cost. The calculation method of productivity is based on the assumption that batch production rate data are applicable to fed-batch fermentation and that, within a certain range of concentrations, the percent rate of change of batch production rate with respect to time is proportional to the difference of product concentration at maximum production rate minus instant concentration. General correlations are developed and plotted in diagrams for easy practical application of the proposed method of calculation. Correlations so established may find application to design and to planning and control of operating conditions of fermentation plants. Example calculations refer to penicillin G production.     

Analysis of fermentation processes using flow microflourometry: Single-parameter observations of batch bacterial growth

The laser flow microfluorometer (FMF) can determine the amounts of certain components in single cells at sample rates of several thousand cells per second. This technique has been employed to characterize Bacillus subtilis populations in batch fermentations with different inocula. Protein and distributions obtained by FMF analyses at different times during the batch have been decomposed using an optimized fit of summed subpopulation distributions. The results of these decomposition calculations, some of which have been approximately confirmed by independent microscopic observations, indicate cells relative numbers of single rods, cell chains, spores, and swollen rounded cells change dramatically during the entire fermentation including the stationary phase. The dynamics of these subpopulations may be related to secondary metabolite production.     

Multivariate statistical monitoring of batch processes: an industrial case study of fermentation supervision

This article describes the development of Multivariate Statistical Process Control (MSPC) procedures for monitoring batch processes and demonstrates its application with respect to industrial tylosin biosynthesis. Currently, the main fermentation phase is monitored using univariate statistical process control principles implemented within the G2 real-time expert system package. This development addresses integrating various process stages into a monitoring system and observing interactions among individual variables through the use of multivariate projection methods. The benefits of this approach will be discussed from an industrial perspective.     

Analysis of fermentation processes using flow microfluorometry: Single-parameter observations of batch bacterial growth

The laser flow microfluorometer (FMF) can determine the amounts of certain components in single cells at sample rates of several thousand cells per second. This technique has been employed to characterize Bacillus subtilis populations in batch fermentations with different inocula. Protein and nucleic acid distributions obtained by FMF analyses at different times during the batch have been decomposed using an optimized fit of summed subpopulation distributions. The results of these decomposition calculations, some of which have been approximately confirmed by independent microscopic observations, indicate that the relative numbers of single rods, cell chains, spores, and swollen rounded cells change dramatically during the entire fermentation including the stationary phase. The dynamics of these subpopulations may be related to secondary metabolite production.     

Optimization of fermentation medium by a modified method of genetic algorithms

Summary During the study of mevinolin biosynthesis by Aspergillus terreus ATCC 20542, 10 different medium components were selected for medium optimization. A new optimization method based on genetic algorithms and inductive learning was used for experimental design. For better efficiency the method was supported by a model, constructed with machine learning method, to predict the productivity. In four generations of fermentation experiments the productivity increased nearly three times.