Batch Fermentation Process Research Articles

Bioplastic Compounds of Succinic Acid from Agriculture Waste; Date Palm Syrup And Date Palm Fronds

Excessive use of petrochemical-based plastics lead to severe plastic pollution by accumulation in soil, drainage system blockage, also each year trillions of plastic residues released into the ocean. In some cases, burning plastic waste releases toxic gases and may cause acid rain. Bioplastics, biodegradable, are eco-friendly alternatives to petroleum-based plastics, reduce environmental impacts and utilize non-edible waste products for decomposition by microorganisms, making them a significant topic in the twenty-first century. Succinic acid has wide medical and industrial application, in addition to being a solution to the problem of plastic pollution for use in the manufacture of bioplastics. One of the most important biomass wastes in the Iraqi environment that are candidates for biorefinery is palm waste due to its environmental friendliness and high sugar content. In this study, succinic acid was investigated in the fermentation product of poor-quality date palms Zahid and palm fronds. The residues of Zahid dates with palm fronds and the addition of soybean were used for the production of succinic through the process of batch fermentation and solid-state fermentation using a mixture of yeasts Saccharomyces cerevisiae and P. stipitis ATCC 58785 and fungi Trichoderma reesei and Aspergillus niger after pretreatment. The succinic acid was estimated using GC mass, and the quantities of acid produced from date juice from Zuhdi dates syrup were 20.6g/L, while the optimal ratio of acid produced from palm fronds with soybeans was estimated at 2.28g/L and production improved to 3.51 g/L by fed batch fermentation. Biotonic acid, oxalic acid malic acid were detected in significant amount as 10.82,23.68 and 70.40 g/L respectively from date syrup. This research opens the horizons for the use of the biomass of the dates palm in the production of bioplastics. Consolidated bioprocessing of lignocellulose to succinic acid through a microbial cocultivation system consolidated bioprocessing.

2G-lactic acid from olive oil supply chain waste: olive leaves upcycling via Lactobacillus casei fermentation

The transition towards a sustainable model, particularly the circular economy, emphasizes the importance of redefining waste as a valuable resource, paving the way for innovative upcycling strategies. The olive oil industry, with its significant output of agricultural waste, offers a promising avenue for high-value biomass conversion into useful products through microbial processes. This study focuses on exploring new, high-value applications for olive leaves waste, utilizing a biotechnological approach with Lactobacillus casei for the production of second-generation lactic acid. Contrary to initial expectations, the inherent high polyphenol content and low fermentable glucose levels in olive leaves posed challenges for fermentation. Addressing this, an enzymatic hydrolysis step, following a preliminary extraction process, was implemented to increase glucose availability. Subsequent small-scale fermentation tests were conducted with and without nutrient supplements, identifying the medium that yielded the highest lactic acid production for scale-up. The scaled-up batch fermentation process achieved an enhanced conversion rate (83.58%) and specific productivity (0.26 g/L·h). This research confirms the feasibility of repurposing olive waste leaves for the production of lactic acid, contributing to the advancement of a greener economy through the valorization of agricultural waste.Key points• Olive leaves slurry as it did not allow L. casei to ferment.• High concentrations of polyphenols inhibit fermentation of L. casei.• Enzymatic hydrolysis combined to organosolv extraction is the best pretreatment for lactic acid production starting from leaves and olive pruning waste.Graphical

Sorbitol production from mixtures of molasses and sugarcane bagasse hydrolysate using the thermally adapted Zymomonas mobilis ZM AD41

Byproducts from the sugarcane manufacturing process, specifically sugarcane molasses (SM) and sugarcane bagasse (SB), can be used as alternative raw materials for sorbitol production via the biological fermentation process. This study investigated the production of sorbitol from SM and sugarcane bagasse hydrolysate (SBH) using a thermally adapted Zymomonas mobilis ZM AD41. Various combinations of SM and SBH on sorbitol production using batch fermentation process were tested. The results revealed that SM alone (FM1) or a mixture of SM and SBH at a ratio of 3:1 (FM2) based on the sugar mass in the raw material proved to be the best condition for sorbitol production by ZM AD41 at 37 °C. Further optimization conditions for sorbitol production revealed that a sugar concentration of 200 g/L and a CaCl2 concentration of 5.0 g/L yielded the highest sorbitol content. The maximum sorbitol concentrations produced by ZM AD41 in the fermentation medium containing SM (FM1) or a mixture of SM and SBH (FM2) were 31.23 and 30.45 g/L, respectively, comparable to those reported in the literature using sucrose or a mixture of sucrose and maltose as feedstock. These results suggested that SBH could be used as an alternative feedstock to supplement or blend with SM for sustainable sorbitol production. In addition, the fermentation conditions established in this study could also be applied to large-scale sorbitol production. Moreover, the thermally adapted Z. mobilis ZM AD41 is also a promising sorbitol-producing bacterium for large-scale production at a relatively high fermentation temperature using agricultural byproducts, specifically SM and SB, as feedstock, which could reduce the operating cost due to minimizing the energy required for the cooling system.

Fault diagnosis based on counterfactual inference for the batch fermentation process

Fault diagnosis plays a pivotal role in identifying the root causes of a fault. Current fault diagnosis methods encounter the shortcomings being unable to assess the fault amplitude or having low efficiency for batch fermentation process. In order to solve the above problems, this paper proposes a fault detection model named convolutional neural network based on variational autoencoder (CNN-VAE) and a fault diagnosis based on counterfactual inference (FDCI). To begin with, quality-related process variables are selected using mutual information (MI). Next, a two-dimensional moving window is used to obtain input sequences from the process data. Then, two statistics from the latent and residual domains of the CNN-VAE model are constructed for fault detection. Additionally, once a fault occurs, FDCI is used to locate the root cause of a fault. Finally, a simulation process and a real-world L. plantarum batch fermentation process are provided to demonstrate the effectiveness of the proposed approache.

High-Level Extracellular Expression of Hyaluronate Lyase HylP in Bacillus subtilis for Hyaluronan Degradation.

Hyaluronate lyase (HA lyase) has potential in the industrial processing of hyaluronan. In this study, HylP, an HA lyase from Streptococcus pyogenes phage (SPB) was successfully expressed in Bacillus subtilis. To improve the extracellular enzyme activity of HylP in B. subtilis, signal peptide engineering systematic optimization was carried out, and cultured it from shake flasks and fermenters, followed by purification, characterization, and analysis of degradation products. The results showed that the replacement of the signal peptide increased the extracellular enzyme activity of HylP from 1.0 × 104 U/mL to 1.86 × 104 U/mL in the shake flask assay, and using a 20L fermenter in a batch fermentation process, the extracellular enzyme activity achieved the level of 1.07 × 105 U/mL. HylP exhibited significant thermal and pH stability in the temperature range of 40°C and pH range of 4-8, respectively. The enzyme showed optimum activity at 40°C and pH 6, with significant activity in the presence of Na+, Mg2+, and Co2+ ions. Degradation analysis showed that HylP efficiently degraded hyaluronan as an endonuclease, releasing unsaturated disaccharides. These comprehensive findings underscore the substantial industrial potential of HylP for hyaluronan processing applications, offering valuable insights into enzyme characterization and optimization of expression for potential industrial utilization.

Brewer’s spent grain as a self-sufficient feedstock for homofermentative production of optically pure L-lactic acid using Lactobacillus rhamnosus

Brewer’s spent grain (BSG), accounting for 85% of the total by-product from breweries, was used as a feedstock for L-lactic acid production in the present study. BSG was enzymatically hydrolysed and promoted to be a self-sufficient feedstock for L-lactic acid production by using it as a sole source of carbon, protein and minerals. Process parameters like glucose concentration (10–20 g/L), glucose-to-protein source ratio (1:1 – 5:1), source of protein and inoculum concentration (3 – 10% v/v) were selected to divert the carbon source and enhance L-lactic acid concentration, which would otherwise result in bacterial biomass production. Yeast extract and whey permeate resulted in high bacterial growth, whereas self-sustained protein (SSP) in BSG resulted in higher L-lactic acid production. Further, the glucose-to-protein source ratio was maintained at its lowest level for better glucose conversion to L-lactic acid. Glucose concentration strongly influenced L-lactic acid production. Therefore glucose concentration in the batch fermentation process was further increased from 60 to 120 g/L. A maximum L-lactic acid concentration of 114.4 g/L and productivity of 5.14 g/L·h was achieved with an initial glucose concentration of 120 g/L, and the rest of the process parameters such as glucose to protein source ratio of 1:1, inoculum concentration of 10% v/v and SSP in BSG were maintained at its optimum level. Finally, L-lactic acid in the fermentation broth was purified and analysed for its similarity with commercially available L-lactic acid using proton-NMR and FTIR spectroscopy. Thus, the present study valorised BSG by producing L-lactic acid under a biorefinery approach.

Solid-state fermentation for gossypol detoxification and nutritive enrichment of cottonseed cake: A scale-up of batch fermentation process

Gossypol, a toxic polyphenolic compound, limits the use of cottonseed cake (CSC) in animal feed. Different approaches have been employed to detoxify gossypol and improve the nutritive properties of feed. Microbial fermentation improves the nutritive quality of CSC by increasing lysine content and reducing free and bound gossypol. In this study, microbial fermentation was scaled up under batch conditions using a prototype device at the capacity of 40 kg per day. The mixed fungal culture C. tropicalis + S. cerevisiae was used for fermentation. An industrial trial was taken to ascertain the gossypol detoxification efficiency. The fermented CSC obtained under scale-up process had 60 to 80% and 40 to 60% reduction of free and bound gossypol, respectively, compared with raw CSC. The fermented CSC demonstrated an increase in crude protein (4 to 12%) and lysine (0.3 to 0.4%) and decrease in crude fibre (3 to 11%). The fermented CSC met the standards of US Food and Drug Administration in terms of its nutritional property. Thus, the simple method described in this study could be adopted for the production of detoxified CSC for use in the animal feed industry.

Analyzing the occurrence of foaming in batch fermentation processes using multiway partial least square approaches

AbstractThis article presents an application of multiway partial least squares (MPLS) methods to develop interpretative correlation models to monitor the foaming occurrence and improve batch fermentation. We choose the exhaust differential pressure as a quality variable to quantify the foaming occurrence and consider three‐dimensional datasets of different batches, process variables, and measurements. We integrate batch‐wise unfolding (BWU) and observation‐wise unfolding (OWU) of plant datasets with standard, dynamic, and kernel PLS methods. We find that dynamic PLS (DPLS) with OWU and time‐lagged quality variables to be the most efficient, accurate, and easy to implement. The BWU approach is useful for analyzing the differences between batches and identifying abnormalities and outliers, while the OWU quantifies the variation within a given batch. With OWU, the DPLS method with one unit of time lag in the quality variable is the most effective, accurate, and easy to implement. With both BWU and OWU, we identify the quantitative effects of process variables on the quality variable and providence guidance to improve fermentation performance.

Spontaneous and Controlled Fermentation Tests in Industrial Table Olives Production

The process of debittering table olives, known as “natural”, represents the most studied method for its peculiarities. Searching among other studies, very little evidence was found about “outside the lab” tests; this encouraged our research, given the numerous external factors that could influence the whole process. In this study, we followed batch fermentation processes inside an industrial facility, testing both spontaneous and guided processes. The starter selected was a 1:1 ratio of Lactiplantibacillus plantarum B1 strain and Saccharomyces cerevisiae SC24 strain. (108 CFU/mL). Chemical-physical, microbiological, and phenolic parameters of five different barrels were analysed, with a periodic sampling (seven samplings). Results obtained confirmed logical correlations (p < 0.05 Pearson) between chemical-physical parameters (ashes vs. pulp/seed ratio, ashes vs. humidity, salt content vs. pulp/seed ratio, oil content vs. humidity) and showed a good discrimination between samples, especially regarding the phenolic profile of the different barrels (PCA and AHC). Microbiological analysis showed the evolution of microorganisms in the barrels, but the results have an unclear interpretation. No Enterobacter were found, so the safety of both process and product can be confirmed. These results highlight some criticalities of the industrial processes and represent useful data for technicians and production structures in order to improve the quality of the product and minimize losses.

Dissolved oxygen on xylanase production by Trichoderma reesei using Reutalis trisperma press cake as an additional substrate

Dissolved oxygen (DO) level in media plays a big role in the succession of the fermentation process by a microorganism. In this project, Trichoderma reesei was cultivated in two types of media, media A and B, both in 250 mL shake flask and 1.5 L fermenter at varying DO level. Media A and B contained the same carbon source and mineral salts, the difference between both media was the presence of press cake in Media B as urea and ammonium sulfate replacement, whereas the nitrogen sources in media A consists of urea and ammonium sulfate. The batch fermentation process was conducted in a 7.8 C/N ratio with an initial pH of 6. The purified enzyme was shown to convert xylan to xylose. Media B that contained Reutealis trisperma press cake gives a higher xylanase activity than media A without press cake, for both shake flask and fermenter cultivations. A xylanase activity of 101.07±1.65 U/mL and 39.30±0.64 U/mL was produced in a shake flask under 140 rpm for 96 hours of fermentation with ratio of 0.6 media B/flask volume and 0.6 media A/flask volume, respectively. A xylanase activity of 205.90±0.84 U/mL and 88.87±1.45 U/mL was produced in 72 hours at 0.8 L/min of aeration rate in a 1.5 L fermenter with media A and media B, respectively. Thus, this study identified the Reutealis trisperma cake can be used as a beneficial additional substrate and nitrogen sources. The increase in DO level, which indicated by increasing aeration rate, can generate a higher xylanase activity for both media in shake flask and fermenter.

Grape Stalk Valorization: An Efficient Re-Use of Lignocellulosic Biomass through Hydrolysis and Fermentation to Produce Lactic Acid from Lactobacillus rhamnosus IMC501

Lactobacillus rhamnosus is a homofermentative probiotic strain that was previously demonstrated to grow on lignocellulosic-derived raw materials and to convert glucose into L-lactic acid (LA) with yields that vary between 0.38 and 0.97 g/g. Lactic acid is a key platform chemical, largely applied in different biotechnological fields (spanning from the pharmaceutical to the food sector) and also as a building block for the production of biodegradable polymers. In the present study, grape stalks were evaluated as sources of fermentable sugars for the growth of L. rhamnosus IMC501 and for the production of LA, since millions of hectoliters of wine are produced every year worldwide, generating a huge amount of waste. Although grape stalks are quite recalcitrant, the combination of a steam explosion pre-treatment with optimized two-step hydrolysis and commercial enzymes (Cellic-CTec2) allowed us to obtain a cellulose conversion efficiency of about 37% and to develop small-scale 2 L batch fermentation processes. Results successfully demonstrate that L. rhamnosus IMC501 can tolerate biomass-derived inhibitors and grow on grape stalk hydrolysate without the need for additional sources of nitrogen or other nutritional elements, and that the strain can convert all glucose present in the medium into LA, reaching the maximal theoretical yield.

Development of a Dynamic Gray‐Box Model of a Fermentation Process for Spore Production

AbstractFermentation processes are difficult to describe using purely mechanistic relations as the underlying biochemical phenomena are complex and often not fully understood. In order to cope with this challenge, we developed an approach to augment standard dynamic model equations by data‐based components that are fitted to data using machine learning techniques, which results in dynamic gray‐box models. This methodology is applied here to the batch fermentation process of the sporulating bacterium Bacillus subtilis, using experimental data from a lab‐scale fermenter. The key step in developing the model is the estimation of a training set for the machine learning submodels. The quality of the resulting model is analyzed, and the predictions are compared with real data.

Effect of pH and Different Fermentation Time Intervals on the Production of Single Cell Proteins (SCPs) from Potato Peels

Dried cells of microorganisms such as fungi, algae, and bacteria, known as Single-Cell Proteins (SCPs), are utilized as a source of protein supplements in animal feed or human food. These SCPs can be produced through the use of low-cost feedstocks and waste materials as sources of carbon and energy, which can be converted into biomass and concentrated proteins. Objective: To optimize the yield and growth of dry cell biomass through the manipulation of fermentation conditions. Methods: A batch fermentation process was used to produce dry cell biomass from a microorganism. Different pH values, fermentation times, and reactor configurations were tested, and the resulting biomass was analyzed for its protein content. Results: The maximum yield of dry cell biomass was achieved at pH 4.5, with a yield of 1.951 g/100 ml. The maximum dry biomass was achieved after 72 hours of fermentation, with a yield of 2.824 g/100 ml. The maximum yield of dry biomass was achieved with an Airlift fermenter at an aeration rate of 1.0 vvm and a temperature of 35°C for 72 hours, resulting in a yield of 5.452 g/L. The protein content of the dried cell biomass was found to be in the range of 45-55%. Conclusions: This study demonstrates that the yield and growth of dry cell biomass can be optimized by controlling the fermentation conditions, specifically pH, fermentation time, and reactor configuration. These findings may have implications for the industrial-scale production of dry cell biomass, as they offer insight into how to maximize yield and protein content.

Dynamic hybrid modeling of fuel ethanol fermentation process by integrating biomass concentration XGBoost model and kinetic parameter artificial neural network model into mechanism model

Fuel ethanol has drawn extensive attention as renewable energy. However, modeling the fuel ethanol batch fermentation process is still a critical task. The unstructured kinetic model (UKM) is often utilized to model the process, but it encounters two problems due to the large differences in initial glucose concentrations. First, the UKM has poor predictions of the yeast growth, which is a crucial production index. Second, the kinetic parameters of the UKM are time-varying because of the changing environmental conditions. The constant manually set kinetic parameters affect the prediction accuracy. To tackle the problems, we propose a dynamic hybrid model of the fuel ethanol fermentation process. First, a biomass concentration prediction model based on extreme gradient boosting is developed. It predicts the values of biomass concentrations and mycelium growth rate as supplementary mechanism knowledge. Then, we present an artificial neural network-based model to determine the time-varying kinetic parameters. Our model can accurately predict the time series of biomass, ethanol, and glucose concentrations, with RMSEs reaching 0.3323, 1.9295, and 3.0540. Experimental results show that the dynamic hybrid model performs with satisfactory accuracy in modeling the fuel ethanol fermentation process.

Economic assessment of cellulase production in batch and semi-batch solid-state fermentation processes

AbstractThe vast range of applications for enzymes in the production of biofuels has led to a major increase in market demand in recent years. Cellulase's high cost and poor catalytic efficiency are two key barriers to the industrial usage of lignocellulosic biomass as feedstocks for fuels in biorefineries. In this study, the economic evaluation of cellulase production in solid-state fermentation was done by designing two process routes (batch and semi-batch) to optimize the process. SuperPro Designer Software has been used to create a process model that assessed the economics of fermentation operations while Monte Carlo Software was used to conduct the sensitivity analysis investigation. The impacts of cellulase yield, productivity, aeration rate and specific power input on the unit production cost (UPC) of cellulase enzyme were studied. The batch procedure requires more sterilized air for the aerobic fermentation process than the semi-batch approach, according to the results. In comparison to the batch fermentation process, the semi-batch approach had a payback period of less than 2 years, an internal rate of return of 39.14% and a net present value of $142 089 000. The semi-batch process approach has a higher economic feasibility when compared with the batch fermentation process since it is more certain to attain its base case value of UPC at the maximum. This research was key to developing a process pathway that reduces cellulase production costs in solid-state fermentation while improving market supply for its use in a biorefinery to produce sustainable fuel.

Synthesis of Lactic Acid from Molasses by Lactobacillus acidophilus Using a Batch Fermentation Process

Lactic acid is a chemical with widespread applications, mainly in pharmaceutical, cosmetic, chemical as well as food industries. One of the major uses of lactic acid is the polylactic acid (PLA) feedstock which is biodegradable and biocompatible as an alternative to plastic derived from fossil fuels. Efforts continue to be made to reduce the cost of producing PLA in order to compete with the conventional petrochemical-based plastics. This include the use of molasses as a raw material because it is cheap and contains high glucose. The purpose of this study was to obtain cell concentrations through the addition of starter volume that produces high concentrations of lactic acid and to obtain the growth kinetics of Lactobacillus acidophilus during the fermentation process. This study was conducted in several stages; the design of bioreactors, inoculation of Lactobacillus acidophilus, and fermentation of molasses. In a batch system, molasses substrate was directly inserted as much as 500 ml in the fermentor and the addition of starter volume of Lactobacillus acidophilus was 1; 3; 5 dan 10% v/v. The fermentation of molasses for 72 hours and the product was analysed every 8 hours. The highest concentration of lactic acid produced in batch fermented molasses was added to 5% (v/v) starter volume with 72 hours of fermentation time, which was 23.1 mg/L, with value the carrying-capacity coefficient (k) and the maximum net specific growth rate (µnet) were 0.2379 h-1 and 0.0160 h-1 respectively.

Just-In-Time Learning Based Functional Spectral Data Modeling for In-Situ Measurement of Slurry Component Concentrations via Infrared Spectroscopy

For using the infrared spectroscopy to conduct in-situ measurement of slurry component concentrations during batch crystallization or fermentation process in engineering practice, a novel just-in-time learning (JITL) based functional modelling method is proposed for spectral data analysis in this paper to improve on-line measurement accuracy, by addressing engineering issues of very limited assay samples, spectral absorption nonlinearity and high-dimensional spectral variables. The traditional univariate or multivariate partial least-squares (PLS) model is subtly extended to the corresponding functional form for describing the relationship between the measured spectra and slurry component concentrations, such that the salient problem of only a small number of slurry component concentration samples available in practice for spectral data modeling could be effectively solved. The wavelet functions are adopted to properly approximate the measured spectral curves owing to their multi-scale and orthogonal properties. Accordingly, a functional spectral data model is established by wavelet functional PLS for on-line measurement of single and multiple component concentrations, respectively. Meanwhile, a JITL strategy is introduced to select the most representative samples for such modeling, which could efficiently handle the disparity issue involved with real-time samples and guarantee the model validity. Experimental results show that root mean square error of model prediction on the solution concentration during L-glutamic acid cooling crystallization process and multiple componts of ethanol fermentation process, could be evidently reduced over 40% and 50%, respectively, compared with the traditional PLS and support vector regression methods.

Investigation and Scaling of Hydrogen Production by Klebsiella sp. ABZ11 for Optimal Yield and the Kinetics of Batch Fermentation Process

The slow metabolism of bacteria at low temperatures affects the catalytic efficiency of enzymes and productivity. This article investigates the use of a psychrotolerant bacteria (Klebsiella sp. ABZ11) for biohydrogen production, yield and scaling at optimal temperature, pH and glucose in a batch fermentation process within a 2-liter bioreactor using Response Surface Methodology (RSM) to report the process performance. The results show 33.5°C, pH 6.75 and 9.15 g glucose as the optimal conditions. Scale-up yielded 137.56 mol/L biohydrogen, 22.13% more than production under optimized conditions. Biomass grew at 0.081/h and doubled in 17 h with 0.71 g cells to reach maximum production. Compared to 0.062/h, 22 h with 0.87 g cells in optimal condition to achieve maximum biohydrogen production. This result shows the potential of biohydrogen production using Antarctic psychrotolerant bacteria at mesophilic temperature.

Yeni İzole Edilen Trametes versicolor'un Kesikli, Tekrarlı-Kesikli ve Katı-Faz Fermentasyon Süreçlerinde Lakkaz Üretimi

In this study, the laccase production ability of the newly isolated Trametes versicolor strain was investigated in three different fermentation processes. In all three fermentation processes, the fungus was able to produce the laccase enzyme. During the solid-state fermentation process 13.21 U/mL laccase activity was detected on the 20th day in the 10 mM copper-containing medium, while this value reached to 27.30 U/mL in the medium containing 0.5 mM ABTS+10 mM copper. During the liquid batch fermentation process, laccase activity was significantly induced in the medium containing 1 mM copper and the laccase activities reached 2.25, 19.83 and 24.57 U/mL compared to the medium without copper on the 3rd, 6th, and 9th days, respectively. ABTS and xylidine induced the laccase production of this strain at a much lower level than copper. The liquid repeated-batch process also significantly induced the laccase production. While low level of enzyme activities were detected in a copper-free medium, laccase activities were induced in the copper-containing medium and the activity increased from 0.66 U/mL to 9.87 U/mL at the 6th use of the pellets. Copper was detected as an effective inducer for laccase production in all fermentation processes and activity staining after native polyacrylamide gel electrophoresis clearly showed the active laccase bands. The results revealed that this strain is a good laccase producer and the laccase production yield varies depending on the fermentation process, production time, and inducer used.

A Comprehensive Mechanistic Yeast Model Able to Switch Metabolism According to Growth Conditions

This paper proposes a general approach for building a mechanistic yeast model able to predict the shift of metabolic pathways. The mechanistic model accounts for the coexistence of several metabolic pathways (aerobic fermentation, glucose respiration, anaerobic fermentation and ethanol respiration) whose activation depends on growth conditions. This general approach is applied to a commercial strain of Saccharomyces cerevisiae. Stoichiometry and yeast kinetics were mostly determined from aerobic and completely anaerobic experiments. Known parameters were taken from the literature, and the remaining parameters were estimated by inverse analysis using the particle swarm optimization method. The optimized set of parameters allows the concentrations to be accurately determined over time, reporting global mean relative errors for all variables of less than 7 and 11% under completely anaerobic and aerobic conditions, respectively. Different affinities of yeast for glucose and ethanol tolerance under aerobic and anaerobic conditions were obtained. Finally, the model was successfully validated by simulating a different experiment, a batch fermentation process without gas injection, with an overall mean relative error of 7%. This model represents a useful tool for the control and optimization of yeast fermentation systems. More generally, the modeling framework proposed here is intended to be used as a building block of a digital twin of any bioproduction process.