Understanding the scale-up of fermentation processes from the viewpoint of the flow field in bioreactors and the physiological response of strains

The production capability of a fermentation process is predominately determined by individual strains,which ultimately affected ultimately by interactions between the scale-dependent flow field developed within bioreactors and the physiological response of these strains.Interpreting these complicated interactions is key for better understanding the scale-up of the fermentation process.We review these two aspects and address progress in strategies for scaling up fermentation processes.A perspective on how to incorporate the multiomics big data into the scale-up strategy is presented to improve the design and operation of industrial fermentation processes.

Unscented Transformation Based Robust Kalman Filter and Its Applications in Fermentation Process

State estimation is the precondition and foundation of a bioprocess monitoring and optimal control. However,there are many difficulties in dealing with a non-linear system,such as the instability of process, un-modeled dynamics,parameter sensitivity,etc.This paper discusses the principles and characteristics of three different approaches,extended Kalman filters,strong tracking filters and unscented transformation based Kalman filters.By introducing the unscented transformation method and a sub-optimal fading factor to correct the prediction error covariance,an improved Kalman filter,unscented transformation based robust Kalman filter,is proposed. The performance of the algorithm is compared with the strong tracking filter and unscented transformation based Kalman filter and illustrated in a typical case study for glutathione fermentation process.The results show that the proposed algorithm presents better accuracy and stability on the state estimation in numerical calculations.

Optimization of Fermentation Process for Human-like Collagen Production of Recombinant Escherichia coli Using Response Surface Methodology

In order to improve the production of human-like collagen III(HLC III)by fed-batch culture of recombinant Escherichia coli BL21,the Plackett-Burman and Box-Behnken design were applied to optimize the fermentation process parameters.Three variables(induction time,inoculum age and pH),which have significant effects on HLC III production,were selected from eight variables by Plackett-Burman design.With the regression coefficient analysis in the Box-Behnken design,a relationship between HLC III production and three significant factors was obtained,and the optimum levels of the three variables were as follows:induction time 3.2h,inoculum age 12.6 h and pH 6.7.The 3D response surface plots and 2D contour plots created by the Box-Behnken design showed that the interaction between induction time and pH and that between innoculum age and pH were significant.An average 9.68 g·L1HLC III production was attained in the validation experiment under optimized condition,which was 80%higher than the yield of 5.36 g·L1before optimization.

An Improved Adaptive Multi-way Principal Component Analysis for Monitoring Streptomycin Fermentation Process

Multi-way principal component analysis (MPCA) had been successfully applied to monitoring the batch and semi-batch process in most chemical industry. An improved MPCA approach, step-by-step adaptive MPCA (SAMPCA), using the process variable trajectories to monitoring the batch process is presented in this paper. It does not need to estimate or fill in the unknown part of the process variable trajectory deviation from the current time until the end. The approach is based on a MPCA method that processes the data in a sequential and adaptive manner. The adaptive rate is easily controlled through a forgetting factor that controls the weight of past data in a summation. This algorithm is used to evaluate the industrial streptomycin fermentation process data and is compared with the traditional MPCA. The results show that the method is more advantageous than MPCA, especially when monitoring multi-stage batch process where the latent vector structure can change at several points during the batch.

Upgrading the Fermentation Process of Zhejiang Rosy Vinegar by Purebred Microorganisms

Purebred microorganisms were employed to upgrade the fermentation process of Zhejiang rosy vinegar. The fermentation cycle was greatly shorten from 5 months to 72 d. The transformation rate of raw materials was increased from 1:4.5 in the traditional fermentation to 1:5 or more in the upgraded fermentation. The content of organic acids in the traditional vinegar (TRV), the upgraded vinegar (UPV) and the submerged fermentation vinegar (SFV) were also investigated by HPLC. No significant difference was found regarding the proportion of phenylethanol to the total volatile components in UPV (7.47% ± 0.00324%) and TRV (7.23% ± 0.00329%), but it was significantly higher than that in SFV (2.26% ± 0.00143%). This study provides deep insight into upgrading the fermentation process of Zhejiang rosy vinegar by purebred microorganisms.

Stability of Stochastic Logistic Model with Ornstein-Uhlenbeck Process for Cell Growth of Microorganism in Fermentation Process

Current research is concerned with the stability of stochastic logistic equation with Ornstein-Uhlenbeck process. First, this research proves that the stochastic logistic model with Ornstein-Uhlenbeck process has a positive solution. After that, it also introduces the sufficient conditions for stochastically stability of stochastic logistic model for cell growth of microorganism in fermentation process for positive equilibrium point by using Lyapunov function. In addition, this research establishes the sufficient conditions for zero solution as mentioned in Appendix A due to the cell growth of microorganism μmax, which cannot be negative in fermentation process. Furthermore, for numerical simulation, current research uses the 4-stage stochastic Runge-Kutta (SRK4) method to show the reality of the results.

Optimization of Acetic Acid Fermentation Process of Apple Cider Vinegar

This paper studied the fermentation rules of apple cider vinegar from fruit juice,to provide a theoretical guidance for the production of apple cider vinegar.Using Fuji apples as raw materials,the process parameters(fermentation temperature,fermentation time,stirring speed,and inoculation amount)of apple cider vinegar fermentation were optimized through single-factor experiments and response surface analysis.The results indicated that the fermentation temperature had no significant effect on the total acid content of apple cider vinegar fermentation,the fermentation time had an extremely significant effect on the total acid content of apple cider vinegar fermentation,and the stirring speed and inoculation amount had a significant effect on the total acid content of apple cider vinegar fermentation.Through process optimization,the optimal process parameters for apple cider vinegar fermentation are fermentation temperature of 33℃,fermentation time of 39 h,stirring speed of 1500 r/min,and acetic acid bacteria inoculation amount of 7%.Under such conditions,the total acid content of fermented apple cider vinegar is 62.22 g/L,very close to the predicted value of the model,indicating that the process parameters of acetic acid fermentation obtained by response surface methodology(RSM)optimization are reliable and can be used for actual production prediction.

An Approach to Continuous Approximation of Pareto Front Using Geometric Support Vector Regression for Multi-objective Optimization of Fermentation Process

The approaches to discrete approximation of Pareto front using multi-objective evolutionary algorithms have the problems of heavy computation burden, long running time and missing Pareto optimal points. In order to overcome these problems, an approach to continuous approximation of Pareto front using geometric support vector regression is presented. The regression model of the small size approximate discrete Pareto front is constructed by geometric support vector regression modeling and is described as the approximate continuous Pareto front. In the process of geometric support vector regression modeling, considering the distribution characteristic of Pareto optimal points, the separable augmented training sample sets are constructed by shifting original training sample points along multiple coordinated axes. Besides, an interactive decision-making(DM)procedure, in which the continuous approximation of Pareto front and decision-making is performed interactively, is designed for improving the accuracy of the preferred Pareto optimal point. The correctness of the continuous approximation of Pareto front is demonstrated with a typical multi-objective optimization problem. In addition,combined with the interactive decision-making procedure, the continuous approximation of Pareto front is applied in the multi-objective optimization for an industrial fed-batch yeast fermentation process. The experimental results show that the generated approximate continuous Pareto front has good accuracy and completeness. Compared with the multi-objective evolutionary algorithm with large size population, a more accurate preferred Pareto optimal point can be obtained from the approximate continuous Pareto front with less computation and shorter running time. The operation strategy corresponding to the final preferred Pareto optimal point generated by the interactive DM procedure can improve the production indexes of the fermentation process effectively.

Dynamic soft sensor development based on Gaussian mixture regression for fermentation processes

The dynamic soft sensor based on a single Gaussian process regression(GPR) model has been developed in fermentation processes.However,limitations of single regression models,for multiphase/multimode fermentation processes,may result in large prediction errors and complexity of the soft sensor.Therefore,a dynamic soft sensor based on Gaussian mixture regression(GMR) was proposed to overcome the problems.Two structure parameters,the number of Gaussian components and the order of the model,are crucial to the soft sensor model.To achieve a simple and effective soft sensor,an iterative strategy was proposed to optimize the two structure parameters synchronously.For the aim of comparisons,the proposed dynamic GMR soft sensor and the existing dynamic GPR soft sensor were both investigated to estimate biomass concentration in a Penicillin simulation process and an industrial Erythromycin fermentation process.Results show that the proposed dynamic GMR soft sensor has higher prediction accuracy and is more suitable for dynamic multiphase/multimode fermentation processes.

Optimization of Fermentation Process for Hyaluronic Acid Production by Streptococcus zooepidemicus

Hyaluronic acid （HA） is a high molecular weight glycosaminoglycan consisting of alternating D-glucuronic acid and N-acetylglueasamine and plays ex- tremely important roles in many biological processes. In this study, we optimized fermentation process for the production of HA by Streptococcus zooepidemicus ATCC35246, including fermentation broth composition and various fermentation parameters. The experimental results showed that the optimal fermentation broth composition was： glucose 45 g/L, yeast extract 10 g/L, tryptone 12 g/L, KH2PO4 2 g/L, K2HPO4 . 3H20 2 g/L, MgSO4 · 7H2O 2 g/L, and （NH4 ）2SO4 0.4 g/L. The optimal parameters involved in fermentation was： liquid volume 20%, pH 6. 0, rotation speed 180 r/min, fermentation temperature 35 ℃, fermentation duration 18 h, CTAB concentration 25 mg/L. Under the optimized conditions, the yield of HA was 0. 305 g/L, which was dramatically improved by 43.87% compared to that of 0. 212 g/L before optimization.

Optimization of Solid-state Fermentation Conditions of Sophora japonica cv.jinhuai by Response Surface Methodology

[Objectives]To optimize the solid-state fermentation process of Flos Sophorae Immaturus by Penicillium with Sophora japonica cv.jinhuai as raw material.[Methods]The fermentation conditions were optimized by single factor experiment and response surface methodology with quercetin content as the dependent variable.[Results]According to the established model,the optimal fermentation process of Flos Sophorae Immaturus was obtained as follows:temperature 29.97℃,time 6.88 d,rotation speed 180.86 rpm,inoculation amount 3.93 mL,and the expected content of quercetin was 34.8053 mg/g.Based on this,the fermentation parameters were adjusted,and the actual content was 33.67 mg/g,which was close to the predicted value.[Conclusions]The optimization of fermentation process of Flos Sophorae Immaturus by response surface methodology provides a reference for the development and utilization of this medicinal material.

Method of Soft-Sensor Modeling for Fermentation Process Based on Geometric Support Vector Regression

The soft-sensor modeling for fermentation process based on standard support vector regression(SVR) needs to solve the quadratic programming problem(QPP) which will often lead to large computational burdens, slow convergence rate, low solving efficiency, and etc. In order to overcome these problems, a method of soft-sensor modeling for fermentation process based on geometric SVR is presented. In the method, the problem of solving the SVR soft-sensor model is converted into the problem of finding the nearest points between two convex hulls (CHs) or reduced convex hulls (RCHs) in geometry. Then a geometric algorithm is adopted to generate soft-sensor models of fermentation process efficiently. Furthermore, a swarm energy conservation particle swarm optimization (SEC-PSO) algorithm is proposed to seek the optimal parameters of the augmented training sample sets, the RCH size, and the kernel function which are involved in geometric SVR modeling. The method is applied to the soft-sensor modeling for a penicillin fermentation process. The experimental results show that, compared with the method based on the standard SVR, the proposed method of soft-sensor modeling based on geometric SVR for fermentation process can generate accurate soft-sensor models and has much less amount of computation, faster convergence rate, and higher efficiency.

Reducing Efficiencies of the Commonly Used Heat Treatment Methods and Fermentation Processes on Aflatoxin M1 in Naturally Contaminated Fresh Cow Milk

The reducing efficiencies of the commonly used heat treatment methods and fermentation processes on aflatoxin M1 (AFM1) in Nigeria were investigated. Seventy samples of fresh cow milk from both conventional and traditional dairy cattle herds were collected and analyzed for the determination of AFM1 using Cobra-cell incorporated high performance liquid chromatography. Of these analyzed samples, 56 (80.0%) tested positive for AFM1 out of which 3 milk samples with high AFM1 concentrations were selectively pooled and subjected to varied conditions of heat treatments and fermentation processes using both indigenized and exotic strains of lactic acid bacteria (Lactobacillus bulgaricus + Streptococcus thermophilus and L. rhamnosus and L. plantarum) as starter cultures respectively. Both processes used either singly or combined, demonstrated high degrees of reducing effects on AFM1 levels. Sterilization of the milk at 121?C and 80?C under the same condition of time (15 - 20) min showed significant reduction of up to 58.8% (p 0.05) in the level of AFM1 when compared with the initial mean AFM1 concentration of the untreated fresh milk. The situation was however different around the boiling temperature of 100?C at which point the level of AFM1 reduction was found to be inconsistent. The indigenized combined strains showed some slight margins of AFM1 reduction in the proportions of (20.5, 30.8 and 43.9)% over and above that of the exotic strains (17.4, 30.0 and 41.1)% in 12 h, 48 h and 72 h of fermentation respectively. Generally, fermentation alone showed lower reduction of AFM1 in milk from 24.5% to 43.9% compared with the reducing activities of (35.4 to 58.8)% when heat-treated milk samples were subsequently subjected to varied fermentation conditions.

Forward heuristic breadth-first reasoning based on rule match for biomass hybrid soft-sensor modeling in fermentation process

Biomass is a key parameter in fermentation process, directly influencing the performance of the fermentation system as well as the quality and yield of the targeted product. Hybrid soft-sensor modeling is a good method for on-line estimation of biomass. Structure of hybrid soft-sensor model is a key to improve the estimating accuracy. In this paper, a forward heuristic breadth-first reasoning approach based on rule match is proposed for constructing structure of hybrid model. First, strategy of forward heuristic reasoning about facts is introduced, which can reason complex hybrid model structure in the event of few known facts. Second, rule match degree is defined to obtain higher esti- mating accuracy. The experiment results of Nosiheptide fermentation process show that the hybrid modeling process can estimate biomass with higher accuracy by adding transcendental knowledge and partial mechanism to the process.

Recent Progress of Commercially Available Biosensors in China and Their Applications in Fermentation Processes

Biosensors, which are the products of the biotechnology industry, are among the key projects of the 7th, 8th, and 9th Fiveyear Plans of China Science ＆ Technology Developing Programs, respectively, and they play an important role in developing and reforming traditional biotechnology. SBA series biosensor analyzer, as the only one commercial biosensor in China, has attracted lots of attention in the process of information gathering and measurement for biological industry with the development of technology and society. In this paper, we presented an overview of the most important contributions dealing with the monitoring of the biochemical analytes in fermentation processes using SBA series biosensor analyzers in China. Future trends of the biosensor analyzer in China were also mentioned in the last section.

Scale-up of rifamycin B fermentation with Amycolatoposis mediterranei

Study of the effect of dissolved oxygen and shear stress on rifamycin B fermentation with A. mediterranei XC 9-25 showed that rifamycin B fermentation with Amycolatoposis mediterranei XC 9-25 needs high dissolved oxygen and is not very sensitive to shearing stress. The scale-up ofrifamycin B fermentation withA, mediterranei XC 9-25 from a shaking flask to a 15 L fermentor was realized by controlling the dissolved oxygen to above 25% of saturation in the fermentation process, and the potency of rifamycin B fermentation in the 15 L fermentor reached 10 g/L after 6-day batch fermentation. By continuously feeding glucose and ammonia in the fermentation process, the potency of rifamycin B fermentaion in the 15 L fermentor reached 18.67 g/L, which was 86.65% higher than that of batch fermentation. Based on the scale-up principle of constantly aerated agitation power per unit volume, the scale-up of rifamycin B fed-batch fermentation with continuous feed from a 15 L fermentor to a 7 m^3 fermentor and further to a 60 m^3 fermentor was realized successfully. The potency of rifamycin B fermentation in the 7 m^3 fermentor and in the 60 m^3 fermentor reached 17.25 g/L and 19.11 g/L, respectively.

On-line estimation of concentration parameters in fermentation processes

It has long been thought that bioprocess, with their inherent measurement difficulties and complex dynamics, posed almost insurmountable problems to engineers. A novel software sensor is proposed to make more effective use of those meas- urements that are already available, which enable improvement in fermentation process control. The proposed method is based on mixtures of Gaussian processes (GP) with expectation maximization (EM) algorithm employed for parameter estimation of mixture of models. The mixture model can alleviate computational complexity of GP and also accord with changes of operating condition in fermentation processes, i.e., it would certainly be able to examine what types of process-knowledge would be most relevant for local models’ specific operating points of the process and then combine them into a global one. Demonstrated by on-line estimate of yeast concentration in fermentation industry as an example, it is shown that soft sensor based state estimation is a powerful technique for both enhancing automatic control performance of biological systems and implementing on-line moni- toring and optimization.

First Development of a Biotechnological Ferment Based on a Consorsium of the Genus <i>Bacillus</i>for the Optimization of the Fermentation Process of Cassava Tubers

Due to its nutritional values, cassava has become an unavailable food and is one of the essential foods in the Republic of Congo. Fermentation of tubers is still traditional. Fiftyrod-shaped spore-forming bacteria were screened and carried out in batch mode for the fermentation abilities of cassava tubers in order to develop biotechnological starter. The Penetrometry Index (PI) has been used to screen bacteria and 16SrRNA as well as fibEone step multiplex PCR which were used to molecularly identify isolates. Emulsification Index, Proteolytic as well as amylolytic, and cellulolytic activities of some strains were quantitatively evaluated for prooving orgaleptic characterics. As results Bacillus subtilis (MT994787), Bacillus subtillis (MT994789), Bacillus tequilensis (MT994788), Bacillus safensis, and Bacillus subtilis have been identified. Single isolates were able to ferment tubers in 48 h and 72 hours meanwhile Bacillus consortia were able to shift fermentation of tubers from 48 hours to 24 hours. The consortium could be used as the major bacterial starters. Strains were associated with the ability to secrete biomolecules including biosurfactants, protease, amylase and cellulase.

Whole process reclamation and utilization of wastes produced in the biological fermentation industry

Wastes yielded in the vintage process and the biological fermentation of itaconic acid and sodium gluconate of a winery in Shandong, such as grain stillage, melon lees, cornstarch protein residues, itacanic acid mother liquid, itaconic acid mycelium and sodium gluconate mycelium, were studied. High-activity biological protein feed, foliar fertilizer and irrigation fertilizer were generated from these wastes by applying biological/microbial technologies. Meanwhile, a whole set of technological pathways was put forward. As a result, the optimal economical and social benefits can be obtained with low natural resource consumption and environmental costs by converting wastes into useful matters. In conclusion, through the utilization of limited resources in the whole process of reclamation and utilization of wastes, the harmony promotion can be achieved between the economic system and the natural ecosystem.

Learning control of fermentation process with an improved DHP algorithm

Control of the fed-batch ethanol fermentation processes to produce maximum product ethanol is one of the key issues in the bioreactor system.However,ethanol fermentation processes exhibit complex behavior and nonlinear dynamics with respect to the cell mass,substrate,feed-rate,etc.An improved dual heuristic programming algorithm based on the least squares temporal difference with gradient correction(LSTDC) algorithm(LSTDC-DHP) is proposed to solve the learning control problem of a fed-batch ethanol fermentation process.As a new algorithm of adaptive critic designs,LSTDC-DHP is used to realize online learning control of chemical dynamical plants,where LSTDC is commonly employed to approximate the value functions.Application of the LSTDC-DHP algorithm to ethanol fermentation process can realize efficient online learning control in continuous spaces.Simulation results demonstrate the effectiveness of LSTDC-DHP,and show that LSTDC-DHP can obtain the near-optimal feed rate trajectory faster than other-based algorithms.