Control Of Fermentation Process Research Articles

Multipoint monitor of beer fermentation

This short communication is devoted to a fully-mechanized flow analysis system for the control of beer fermentation process. The developed system is based on microsolenoid flow controlling devices (valves and pumps) and a flow-through optoelectronic detector. All these components are powered and controlled by a Adruino-compatible microprocessor platform that creates an integrated, compact, and robust analytical tool. Multiplication of sample aspiration ports of the analytical system allows for simultaneous monitoring of several independently performed fermentation processes, as well as a single process at the different places of fermentation tank. To demonstrate its practical utility, the developed system has been applied for online and real-time monitoring of yeast propagation and distribution in beer worts in the course of various fermentation processes. Potentially, this flow analysis system can be easily expanded to the form of multianalyte monitor equipped with optoelectronic sensors and biosensors for the determination of other parameters and analytes.

Global Stabilizing Control of a Continuous Ethanol Fermentation Process Starting from Batch Mode Production

Global Stabilizing Control of a Continuous Ethanol Fermentation Process Starting from Batch Mode Production

Enhancing β-farnesene production in engineered Yarrowia lipolytica: A new process control strategy

β-Farnesene, a sesquiterpene with diverse applications, is applied in fields ranging from bioenergy and agriculture to medicine. In this study, a series of fermentation conditions and process control strategies were employed to enhance the accumulation of β-farnesene. Through the optimization of medium components, a suitable environment was created for β-farnesene biosynthesis in Yarrowia lipolytica, using only three inorganic salts as the initial medium. On this basis, cell growth and citrate supply were enhanced by adjusting the feeding time point, which begins when dissolved oxygen level below 40%. Additionally, initial cell density and glucose concentration during fermentation were separately optimized. Ultimately, nitrogen-limited conditions were applied to maximize carbon flux towards β-farnesene synthesis and the ideal condition was found to be C/N ratio of 100. Under the optimal condition, the titer of β-farnesene was increased by 227%, reaching 22.04 g/L in a 2 L bioreactor. This study optimizes terpenoid biosynthesis fermentation control and establishes a platform for the potential utilizing of waste biomass in Y. lipolytica.

Elevated abundance of Komagataeibacter results in a lower pH in kombucha production; insights from microbiomic and chemical analyses

Kombucha consumption has grown rapidly worldwide in the last decade, with production at both small- and large scales. The complex fermentation process involves both bacterial and yeast species, but little is known regarding the progression of microbial development during production. We explored the microbial diversity of multiple batches across two kombucha types, i. e commercial scale versus laboratory-made (hereafter “home”) kombucha brew using metabarcoding to characterize both fungal and bacterial communities. We found the microbial community of the commercial kombucha brew to be more complex than that of the home brew. Furthermore, PERMANOVA uncovered significant compositional differences between the bacterial (F = 2.68, R2 = 0.23, p = 00.001) and fungal (F = 3.18, R2 = 0.26, p = 00.006) communities between batches. For the home brew, both alpha and beta diversity analyses revealed no significant differences between all batches and replicates. When the microbial diversity of the home and commercial kombucha types were directly compared, the former had higher proportions of Ammoniphilus and Komagataeibacter. The commercial kombucha on the other hand were high in Anoxybacillus, Methylobacterium and Sphingomonas. For the fungal communities, the most dominant fungal genera detected in both kombucha types were similar. Linear model revealed significant correlations between some microorganisms and the sugars and organic acids assayed in this study. For example, rising glucose levels correlated with an increase in the relative abundance of Komagataeibacter (F = 7.115, Adj. R2 = 0.44, p = 00.0003). We believe these results contribute towards achieving a better control of the kombucha fermentation process and may assist in targeted product diversification.

Bioengineering, waste processing and fermentation process control for biogas production

The article discusses the analysis of the state of control of the processes of biogas production from animal waste by methane digestion. The article discusses the problems of synthesis of automated control systems of biotechnological processes under conditions of information uncertainty. The analysis of the current state of control of fermentation stage processes shows that insufficient attention is paid to the problem of synthesis of second-tier banks under conditions of information uncertainty. Construction of mathematical modeling of biosynthetic processes is a kinetic model, where experimental and analytical methods are used due to the difficulty of identifying patterns in microbiological processes. The article discusses the application of methods and algorithms for intellectualization of problem solving in ACS for the synthesis of complex biotechnological objects in conditions of lack of information, that they should be attributed to priority tasks. The results of research on the application of a neuro-fuzzy system for controlling fermentation processes under conditions of uncertainty and multimode of processes, as well as a forecasting algorithm using nonlinear sets and neural networks are presented.

Aerobic Fermentation Drum Temperature Field Study for Cattle Mattress Regeneration

Aerobic fermentation is a key link in the regeneration system of cattle mattress material. In this paper, the three-dimensional structure of the aerobic fermentation drum was designed using SolidWorks software, and the discrete element model of the aerobic fermentation temperature field was established using Fluent software. The simulation analysis shows that the temperature inside the aerobic fermentation drum can be maintained stably between 60°C and 75°C by reasonably adjusting the feed rate, ventilation volume and drum speed, and the fermentation time of the regenerated bedding material can be adjusted from 8 to 22 hours, which can achieve continuous and stable feed and discharge production, indicating the feasibility and effectiveness of the aerobic fermentation process. When the feed rate is 2 m3/h, the ventilation rate is 180 m3/h and the drum speed is 0.3 r/min, a rated production rate of 48 m3/day can be met. The research has important reference value for aerobic fermentation process control and system design of cattle mattress material regeneration system.

Development of a continuous fermentation process for the production of recombinant uricase enzyme by Pichia pastoris.

Recent studies in the biopharmaceutical industry have shown an increase in the productivity and production efficiency of recombinant proteins by continuous culture. In this research, a new upstream fermentation process was developed for the production of recombinant uricase in the methylotrophic yeast Pichia pastoris. Expression of recombinant protein in this system is under the control of the AOX1 promoter and therefore requires methanol as an inducing agent and carbon/energy source. Considering the biphasic growth characteristics of conventional fed-batch fermentation, physical separation of the growth and induction stages for better control of the continuous fermentation process resulted in higher dry-cell weight (DCW) and enhanced recombinant urate oxidase activity. The DCW and recombinant uricase activity enzyme for fed-batch fermentation were 79g/L and 6.8u/mL. During the continuous process, in the growth fermenter at a constant dilution rate of 0.025h-1 , DCW increased to 88.39g/L. In the induction fermenter, at methanol feeding rates of 30, 60, and 80mL/h, a recombinant uricase activity was 4.13, 7.2, and 0u/mL, respectively. The optimum methanol feeding regime in continuous fermentation resulted in a 4.5-fold improvement in productivity compared with fed-batch fermentation from 0.04u/mL/h (0.0017mg/mL/h) to 0.18u/mL/h (0.0078mg/mL/h).

Application of water-soluble conjugated polymers in intelligent measurement and control of food microbial fermentation process.

In order to reduce the difficulty of measurement and control (MAC) during food fermentation, this paper applies water-soluble conjugated polymers to sensors, conducts data modeling and prediction, and integrates the sensors into intelligent MAC systems. This article uses fermentation rate, product yield, and energy consumption efficiency as evaluation indicators to analyze the effectiveness of smart MAC. By comparing and analyzing the MAC method based on water-soluble conjugated polymers with the traditional MAC method, this article found that the MAC method based on water-soluble conjugated polymers can improve product yield, fermentation efficiency and energy utilization compared with traditional MAC methods. The MAC accuracy, timeliness, stability, speed and security of the MAC system based on water-soluble conjugated polymers are higher than those of traditional MAC systems. Among them, the average test stability of the traditional MAC system is 19.93% lower than that of the smart MAC system based on water-soluble conjugated polymers. Research shows that the intelligent MAC method based on water-soluble conjugated polymers can effectively improve the fermentation environment of food microorganisms and improve product quality, and is worthy of further promotion.

Improving statins production: From non-genetic strategies to genetic strategies.

Statins are lipid-lowering drugs that selectively inhibit 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase, effectively reducing cholesterol synthesis. With improved nutritional conditions, the demand for statins is increasing in the global market. The use of microbial cell factories for statin biosynthesis has become advantageous due to the rapid advancements in biotechnology. These approaches offer simple operation and easy separation of products. This review provides an overview the strategies for statins production via microbial cell factories, including both traditional fermentation culture (non-genetic) and modern synthetic biology manufacture (genetic). Firstly, the complex fermentation parameters and process control technology on submerged fermentation (SmF) and solid-state fermentation (SSF) are introduced in detail. The potential use of recoverable agricultural wastes/(biomass) as a fermentation substrate in SSF for statin production is emphasized. Additionally, metabolic engineering strategies for constructing robust engineering strains and directed evolution are also discussed. The review highlights the potential and challenges of using microbial cell factories for statin production, and aims to promote greener production modes for statins.

Advancements in multivariate analysis of variance

The Journal of Chemometrics is pleased to announce a special issue focused on multivariate analysis of data from designed experiments. ANOVA (Analysis of Variance) is the standard method for analyzing data from experimental designs. The classical ANOVA methods are however univariate and do not handle multiple collinear response variables. Designed experiments with multivariate outputs are prevalent across various scientific disciplines, necessitating methods that appropriately consider both the experimental design and the multivariate nature of the data. Several multivariate ANOVA techniques have been presented already. The most prevalent approaches involve combining ANOVA with PCA (principal component analysis) or other exploratory component-based techniques in different ways. Some commonly used methods in this context include ASCA, ANOVA-PCA, AComDim, and fifty-fifty MANOVA. These methods integrate ANOVA and PCA in different ways to extract meaningful information from multivariate data. Additionally, there are alternative methods that replace PCA with partial least squares (PLS) regression, which allows for the utilization of PLS-specific validation and variable importance routines. One major advantage of all these methods is that they not only offer interpretation and variable importance metrics from latent variable-based methods but also provide estimates of multivariate effect sizes accompanied by corresponding significance testing. Despite the progress made in recent years, the field of multivariate analysis of data from designed experiments is still young. Several open questions remain unanswered, and there is a need to make the methodology available to a broader audience. The aim of this special issue was therefore to stimulate and explore advances in methods, applications, and software for multivariate ANOVA. The collection of papers includes methodical improvements, practical applications, a tutorial, and a software demonstration. Application areas range from spectroscopic control of fermentation processes to metabolomics and gene expressions. Overall, this issue showcases the power and applicability of multivariate ANOVA methods in a wide range of domains.

Metaproteomics profiling of the microbial communities in fermentation starters (Daqu) during multi-round production of Chinese liquor.

The special flavor and fragrance of Chinese liquor are closely related to microorganisms in the fermentation starter Daqu. The changes of microbial community can affect the stability of liquor yield and quality. In this study, we used data-independent acquisition mass spectrometry (DIA-MS) for cohort study of the microbial communities of a total of 42 Daqu samples in six production cycles at different times of a year. The DIA MS data were searched against a protein database constructed by metagenomic sequencing. The microbial composition and its changes across production cycles were revealed. Functional analysis of the differential proteins was carried out and the metabolic pathways related to the differential proteins were explored. These metabolic pathways were related to the saccharification process in liquor fermentation and the synthesis of secondary metabolites to form the unique flavor and aroma in the Chinese liquor. We expect that the metaproteome profiling of Daqu from different production cycles will serve as a guide for the control of fermentation process of Chinese liquor in the future.

A Deep Learning Approach to Optimize Recombinant Protein Production in Escherichia coli Fermentations

Fermentation is a widely used process in the biotechnology industry, in which sugar-based substrates are transformed into a new product through chemical reactions carried out by microorganisms. Fermentation yields depend heavily on critical process parameter (CPP) values which need to be finely tuned throughout the process; this is usually performed by a biotech production expert relying on empirical rules and personal experience. Although developing a mathematical model to analytically describe how yields depend on CPP values is too challenging because the process involves living organisms, we demonstrate the benefits that can be reaped by using a black-box machine learning (ML) approach based on recurrent neural networks (RNN) and long short-term memory (LSTM) neural networks to predict real time OD600nm values from fermentation CPP time series. We tested both networks on an E. coli fermentation process (upstream) optimized to obtain inclusion bodies whose purification (downstream) in a later stage will yield a targeted neurotrophin recombinant protein. We achieved root mean squared error (RMSE) and relative error on final yield (REFY) performances which demonstrate that RNN and LSTM are indeed promising approaches for real-time, in-line process yield estimation, paving the way for machine learning-based fermentation process control algorithms.

Micro-electrochemical DO sensor with ultra-micropore matrix fabricated with femtosecond laser processing successfully applied in on-line DO monitoring for yeast culture.

Accurate monitoring of dissolved oxygen (DO) is vital for aerobic fermentation process control. This work presents an autoclavable Micro-Dissolved oxygen Sensor (MDS) that can monitor real time DO. The proposed sensor is much cheaper to be manufactured (< $35) and can be adapted to varying measurement environments. An ultra-micropore matrix was created using femtosecond laser processing technology to reduce flow dependency of probe signals. The validity of the proposed DO sensor was verified by testing it under different DO levels. The result revealed consistency between the new designed sensor and a commercial DO sensor. The obtained sensitivity was-7.93 μA·L·mg-1 (MDS with ultra-micropore matrix). Moreover, the MDS can function without an oxygen-permeable membrane and a solid electrolyte was used which reduced the response time (4.6s). For real-time monitoring, the stability of the MDS was validated during a yeast batch fermentation carried out until 18h.

Effect of stress factors on the production of biogenic amines by lactic acid bacteria isolated from fermented Mexican foods (cheese and beer)

The growth of lactic acid bacteria (LAB) in fermented foods, especially in ripened foods, could be associated with the production of biogenic amines (BAs). BAs are small, nitrogen-containing, thermostable, non-volatile organic compounds, which can induce toxic effects in food consumers. This work aimed to determine potential BAs-producer LAB strains isolated from ripened cheese and beer and to evaluate stress factors that induce the production of these metabolites. Determination of BAs-related genes in eighteen strains of LAB was done by polymerase chain reaction (PCR). The effect of pH, NaCl concentration and ethanol concentration on the LAB growth and BAs production was evaluated. The concentration of BAs (histamine, tyrosine, putrescine and cadaverine) was determined by high performance liquid chromatography (HPLC). Additionally, survival of selected LAB strains during beer making process was evaluated. All LAB strains could tolerate concentrations of 4 and 6 mL/100 mL of ethanol. More than one strain showed the ability to produce all the BAs studied and all beer strains were able to produce Tyramine. The pH level was the most influent factor in BAs production; pH 4 was optimal for the production of histamine (cheese and beer strains) and pH 5 for tyramine (beer strains). LAB population showed a 2-log reduction along the two initial days of fermentation. At the end of the beer making process 1.6 log of LAB were recovered. BAs production has been considered a mechanism that increases bacterial survival against acidic environment. Some LAB strains in fermented products may represent a health risk for consumers. Sanitary practices during food production and raw material quality should be complement measurements to a good control of fermentation process to minimize BAs presence in foods.

Fermentation Process Control and Optimization

AbstractProcess control and optimization is important in ensuring a process in sustaining profitable income while maintaining the required quality. The fermentation process has attracted attention from food processing, pharmaceutical energy, and waste treatment industries due to its lower impact on the environment and lower operating cost than the conventional chemical process. However, its control and monitoring are relatively complex as living cell activity is not easily comprehended and highly nonlinear. Various control techniques applied in the fermentation process are reviewed, including a summary of the control objective, manipulated and controlled variables chosen, and their strengths and limitations in determining the optimal fermentation process.

Batch-to-Batch Iterative Learning Control of a Fed-Batch Fermentation Process

In this work, Iterative Learning Control on a fed-batch fermentation process using linearised models has been studied. The repetitive nature of batch processes enables ILC to obtain information from a previous batch in order to improve the performance of the current batch such that the product quality converges asymptotically to the desired trajectory The basic batch to batch ILC law presents the control action of a current batch as a summation of the control action from the previous batch and the deviation of the output trajectory from the desired reference trajectory incorporation with a learning rate. In a bid to address the issue of the process non-linearity, the control policy and the output trajectory were linearised around their respective nominal trajectories. The linearised models were then identified using Multi Linear Regression (MLR), Principal Component Analysis (PCR) and Partial Least Squares (PLS). In order to curb the effects of plant-model mismatches and process variations, the linearised models were reidentified after each batch operation. This was done by selecting the immediate previous batch as the nominal batch and then adding the recently obtained process data into the historical data batch on completion of the current batch run. The weighting matrices in the objective function were carefully selected taking into consideration that they have a major influence on the robust performance of the process. In using PLS and PCR models the issue of process collinearity was effectively addressed. The proposed batch to batch ILC strategy was applied to a simulated fed-batch fermentation process for the production of secreted protein. The results of the optimal control policy were comparable to that obtained in using full mechanistic model. ILC, a simple but yet an effective optimal control strategy has demonstrated to be a viable option in complex processes such as batch processes where mechanistic models are difficult to develop. Keywords: Iterative Learning Control, batch process, fed-batch fermentation, batch to batch ILC, control policy. DOI: 10.7176/CMR/14-3-02 Publication date: August 31 st 2022

Influence of technological factors on the physical and chemical properties of rye dough and the parameters of its fermentation

Preparing rye dough, special attention should be paid to assessing its rheological behavior taking into account the semi-finished product titratable acidity. Water dosage determination for mixing the dough should be carried out taking into account its consistency. At the same time, dough effective viscosity should be determined after mixing. This indicator will exactly allow establishing the baking properties of the processed rye flour. The control of the rye dough fermentation process, which allows determining the optimal duration of dough first proofing and final proofing of dough pieces, should be carried out according to the rate of change in the carbon dioxide volume and the relative change in potentiometric characteristics.

A novel cascaded fractional fuzzy approach for control of fermentation process

In this work, a cascaded control strategy based on fractional-order fuzzy PD/PI (FOFPD/PI) is proposed for temperature control of the bioreactor. The FOFPI is used to control the ethanol concentration in the inner loop, while the FOFPD is used for temperature control of the bioreactor in the outer loop. The integer order fuzzy PD/PI (IOFPD/PI), 2DOF FOPID, 2DOF PID, and PID are also designed for comparison purposes. The design parameter of FOFPD/PI and IOFPD/PI are estimated using non-dominated sorting genetic algorithm II (NSGA-II). Results revealed that the proposed cascaded control scheme reduced the IAE by 33.5 %, 40.5%, 47%, and 64% compared to IOFPD/PI, 2DOF FOPID, 2DOF PID, and PID, respectively. Hence, it can be concluded that the proposed FOFPD/PI controller provides precise control of reactor temperature in different operating conditions compared to other controllers.

Multi-objective reinforcement learning for fed-batch fermentation process control

Many real-world control problems involve conflicting objectives. For different objectives, it is necessary to obtain Pareto optimal solution sets for each one. Over recent years, multi-objective reinforcement learning (MORL) has been extensively studied to solve this problem. However, the multi-objective optimization problem of complex continuous control processes still requires exploration. Soft proximal policy optimization algorithms have also been proposed to be combined with a hybrid weight-generation method for application to find the Pareto front approximation of the fed-batch fermentation process. This algorithm intends to initially find a single policy for the multi-objective reinforcement learning problem. A hybrid weight-generation method is then used to change the weights between different objectives to find a set of Pareto optimal solutions. In addition, we analyzed the mechanism of the fed-batch process, established the kinetic model, and designed the experimental environment based on the OpenAI Gym library. Experimental results showed that the proposed algorithm is effective and efficient in approaching the Pareto front of the fed-batch fermentation problem.

A multi-model predictive control method for the Pichia pastoris fermentation process based on relative error weighting algorithm

The Pichia pastoris fermentation process is with highly nonlinear and time-varying and strong coupling characteristics, traditional control methods such as classic PID cannot satisfy the actual needs of the fermentation process control. In order to effectively solve the control problem of the Pichia pastoris fermentation process, a multi-model predictive control method is presented based on the relative error weighting algorithm. First, the prior sample data are divided into multiple training sample sets (sample cluster) by fuzzy C-means clustering algorithm (FCM). Then, the corresponding sub prediction model is obtained by using least square support vector machine (LSSVM) and Improved particle swarm optimization (IPSO) algorithm for each sample cluster. Finally, the control strategy of predictive model is constructed based on multi model relative error weighting algorithm. The simulation in the Pichia pastoris fermentation process shows that the algorithm proposed in this paper could improve the transient response and perform good output tracking in a wide range. It improves the adaptive ability of the model and makes it more accurate to describe the actual state of the nonlinear system.