Bioreactor Model Research Articles

Interpretation of possible biogas production capacity by investigating the effects of anaerobic digester tank geometry and angular velocity on flow characteristics.

Mixing performance in reactors producing biogas through anaerobic digestion is one of the parameters that directly affect biogas yield. The most commonly used mixing model for bioreactors in biogas-production processes is mechanical mixing. In the present study, we focus on the geometry of the tank, where the mechanical mixing actually takes place. In this context, by using the six-blade standard Rushton impeller in two different types of tank, flow patterns involving velocity, dead zone volume, turbulent kinetic energy, and turbulent eddy dissipation rate in the angular velocity range of 25-100rpm were observed, and the possible effects of the results on biogas production were interpreted. A new impeller design was proposed that maximizes the interface between the fluid inside the reactor tank and the impeller, which has the potential to reduce the dead zone volume to significantly lower levels. Our results showed that the lowest dead zone volume was achieved for a 60° slope reactor tank compared to the conventional 90° slope reactor tank at an angular velocity of 100rpm. The dead zone volume decreased to 0.000094 m3 at 100rpm in the 60° slope reactor tank with a total volume of 0.0305 m3, which by comparison was 0.000374 m3 in the 90° slope reactor tank. The magnitudes of both maximum turbulent kinetic energy and maximum turbulent eddy dissipation were higher in the 60° slope reactor tank at all angular velocities examined, which would be expected to enhance mixing performance. It is hoped that the reader will benefit from the results of this study; however, further studies should be conducted on the use of actual biowaste as the working fluid instead of water.

Gastrointestinal-inert prebiotic micro-composites improve the growth and community diversity of mucosal-associated bacteria.

The process of microencapsulation and the development of microparticle-based drug formulations have gained increased pharmaceutical interest, particularly for drug delivery and bacterial-encapsulation purposes for probiotic delivery. Existing studies have examined microcomposite (MC) responses to gastrointestinal (GI) conditions with the aim of controlling disintegration, and thus release, across the small and large bowel. However, the delivery of MCs which remain intact, without degrading, could act as bacterial growth scaffolds or materials providing a prebiotic support, conferring potentially beneficial GI health properties. This present study employs prilling as a method to produce a portfolio of MCs using a variety of biopolymers (alginate, chitosan, pectin and gellan gum) with a range of MC diameters and density compositions. Fluorescent probes are co-encapsulated within each MC to enable flow-cytometry directed release profile assessments following exposure to chemical simulated gastric and intestinal digestion conditions. We observe that MC size, gel-strength, density, and biopolymer material all influence response to gastric and intestinal conditions. Gellan gum (GG) MCs demonstrated complete resistance to disintegration throughout GI-simulation in the stomach and small intestine. Considering these MCs could reach the colon intact, we then examined how such MCs, doped with prebiotic growth supporting carboxymethyl cellulose (CMC) polymers, could impact microbial communities using a bioreactor model of the colonic microbiome. Following supplementation with GGCMC MCs, mucosal bacterial diversity (using 16 s rRNA sequencing and Shannon entropy and observed feature diversity metrics) and taxonomic composition changes were observed. Concentrations of short chain fatty acid (SCFA) metabolites were also found to be altered. This is the first study to comprehensivelyexamine how MC physicochemistry can be manipulated to tailor MCs to have the desired GI release performance and subsequently, how GI-resistant MCs could have influential microbial altering properties and be adopted in novel prebiotic strategies.

Novel pectin-carboxymethylcellulose-based double-layered mucin/chitosan microcomposites successfully protect the next-generation probiotic Akkermansia muciniphila through simulated gastrointestinal transit and alter microbial communities within colonic ex vivo bioreactors

The rapid acceleration of microbiome research has identified many potential Next Generation Probiotics (NGPs). Conventional formulation processing methods are non-compatible, leading to reduced viability and unconfirmed incorporation into intestinal microbial communities; consequently, demand for more bespoke formulation strategies of such NGPs is apparent. In this study, Akkermansia muciniphila (A.muciniphila) as a candidate NGP was investigated for its growth and metabolism properties, based on which a novel microcomposite-based oral formulation was formed. Initially, a chitosan-based microcomposite was coated with mucin to establish a surface culture of A.muciniphila. This was followed by ‘double encapsulation’ with pectin (PEC) using a novel Entrapment Deposition by Prilling method to create core–shell double-encapsulated microcapsules. The formulation of A.muciniphila was verified to require no oxygen-restriction properties, and additionally, biopolymers were selected, including carboxymethylcellulose (CMC), that support and enhance its growth; consequently, a high viability (6 log CFU/g) of A.muciniphila microencapsulated in PEC-CMC double-encapsulates was obtained. Subsequently, the high stability of the PEC-CMC double-encapsulates was verified in simulated gastric fluid, successfully protecting and then releasing the A.muciniphila under intestinal conditions. Finally, employing a model of gastrointestinal transit and faecal-inoculated colonic bioreactors, significant alterations in microbial communities following administration and successful establishment of A.muciniphila were demonstrated.

Cancellation of Steady State Error Using Auxiliary Diophantine Equation for a Bio-Reactor Process -A case study

RST (Regulation, Sensitivity and Tracking) controller is very widely used in electrical engineering application. The controller provides both feed-forward and feedback actions. PID, Internal Model Controller (IMC) designs are mainly very effective in set point tracking but poor in disturbance rejection, however, the disturbance rejection can be obtained at the cost of reduced stability margins. The controller is based on the resolution of a Diophantine equation. A mathematical Model for Bioreactor was developed using MATLAB. Once the discrete model of the process is known and a model transfer function for the closed loop system has been chosen, the cancellation of steady state errors in response to reference signals has been solved for polynomial reference signals of any order by the introduction of an auxiliary Diophantine equation. The results are compared with the PID controller based on pole assignment method.

A 3D bioreactor model to study osteocyte differentiation and mechanobiology under perfusion and compressive mechanical loading

Osteocytes perceive and process mechanical stimuli in the lacuno-canalicular network in bone. As a result, they secrete signaling molecules that mediate bone formation and resorption. To date, few three-dimensional (3D) models exist to study the response of mature osteocytes to biophysical stimuli that mimic fluid shear stress and substrate strain in a mineralized, biomimetic bone-like environment. Here we established a biomimetic 3D bone model by utilizing a state-of-art perfusion bioreactor platform where immortomouse/Dmp1-GFP-derived osteoblastic IDG-SW3 cells were differentiated into mature osteocytes. We evaluated proliferation and differentiation properties of the cells on 3D microporous scaffolds of decellularized bone (dBone), poly(L-lactide-co-trimethylene carbonate) lactide (LTMC), and beta-tricalcium phosphate (β-TCP) under physiological fluid flow conditions over 21 days. Osteocyte viability and proliferation were similar on the scaffolds with equal distribution of IDG-SW3 cells on dBone and LTMC scaffolds. After seven days, the differentiation marker alkaline phosphatase (Alpl), dentin matrix acidic phosphoprotein 1 (Dmp1), and sclerostin (Sost) were significantly upregulated in IDG-SW3 cells (p = 0.05) on LTMC scaffolds under fluid flow conditions at 1.7 ml/min, indicating rapid and efficient maturation into osteocytes. Osteocytes responded by inducing the mechanoresponsive genes FBJ osteosarcoma oncogene (Fos) and prostaglandin-endoperoxide synthase 2 (Ptgs2) under perfusion and dynamic compressive loading at 1 Hz with 5 % strain. Together, we successfully created a 3D biomimetic platform as a robust tool to evaluate osteocyte differentiation and mechanobiology in vitro while recapitulating in vivo mechanical cues such as fluid flow within the lacuno-canalicular network. Statement of significanceThis study highlights the importance of creating a three-dimensional (3D) in vitro model to study osteocyte differentiation and mechanobiology, as cellular functions are limited in two-dimensional (2D) models lacking in vivo tissue organization. By using a perfusion bioreactor platform, physiological conditions of fluid flow and compressive loading were mimicked to which osteocytes are exposed in vivo. Microporous poly(L-lactide-co-trimethylene carbonate) lactide (LTMC) scaffolds in 3D are identified as a valuable tool to create a favorable environment for osteocyte differentiation and to enable mechanical stimulation of osteocytes by perfusion and compressive loading. The LTMC platform imitates the mechanical bone environment of osteocytes, allowing the analysis of the interaction with other cell types in bone under in vivo biophysical stimuli.

Design of a bioreactor as a vital biological support for testing microalgae cultures in the stratosphere.

Abstract In the present, the proposal for the design of an autonomous, light and completely closed bioreactor that will orbit in the stratosphere is developed, capable of promoting the growth of microalgae, specifically of the species: Chlorella vulgaris. The document will address the development of the relevant background information related to this proposal, which will support the bases and certain decisions described in the work, as well as the concepts that will define the theoretical framework for the identification of primary and secondary sources that allow to know, design and develop the proposed. In order to achieve a system capable of performing mixing and recirculation operations, necessary to promote the transfer of mass and energy, as well as the adequate distribution of light, the different existing bioreactor models and their most significant differences will be analyzed. For this work, only five of the nine stages of the mechatronic design methodology are covered: identification of needs, analysis of the problem, preparation of specifications, generation of possible solutions, and selection of the appropriate solution. The missing stages; detailed design, production of working drawings, prototype construction, and testing are underway.

Rapid evolution of colistin resistance in a bioreactor model of infection of Klebsiella pneumoniae

Colistin remains an important antibiotic for the therapeutic management of drug-resistant Klebsiella pneumoniae. Despite the numerous reports of colistin resistance in clinical strains, it remains unclear exactly when and how different mutational events arise resulting in reduced colistin susceptibility. Using a bioreactor model of infection, we modelled the emergence of colistin resistance in a susceptible isolate of K. pneumoniae. Genotypic, phenotypic and mathematical analyses of the antibiotic-challenged and un-challenged population indicates that after an initial decline, the population recovers within 24 h due to a small number of “founder cells” which have single point mutations mainly in the regulatory genes encoding crrB and pmrB that when mutated results in up to 100-fold reduction in colistin susceptibility. Our work underlines the rapid development of colistin resistance during treatment or exposure of susceptible K. pneumoniae infections having implications for the use of cationic antimicrobial peptides as a monotherapy.

Reinforcement learning based temperature control of a fermentation bioreactor for ethanol production.

Ethanol production is a significant industrial bioprocess for energy. The primary objective of this study is to control the process reactor temperature to get the desired product, that is, ethanol. Advanced model-based control systems face challenges due to model-process mismatch, but Reinforcement Learning (RL) is a class of machine learning which can help by allowing agents to learn policies directly from the environment. Hence a RL algorithm called twin delayed deep deterministic policy gradient (TD3) is employed. The control of reactor temperature is categorized into two categories namely unconstrained and constrained control approaches. The TD3 with various reward functions are tested on a nonlinear bioreactor model. The results are compared with existing popular RL algorithm, namely, deep deterministic policy gradient (DDPG) algorithm with a performance measure such as mean squared error (MSE). In the unconstrained control of the bioreactor, the TD3 based controller designed with the integral absolute error (IAE) reward yields a lower MSE of 0.22, whereas the DDPG produces an MSE of 0.29. Similarly, in the case of constrained controller, TD3 based controller designed with the IAE reward yields a lower MSE of 0.38, whereas DDPG produces an MSE of 0.48. In addition, the TD3 trained agent successfully rejects the disturbances, namely, input flow rate and inlet temperature in addition to a setpoint change with better performance metrics.

Micropropagation of Vaccinium corymbosum L.‘Bluecrop’ in Rocker Temporary Immersion System (TIS) Bioreactor

Blueberries are high-value fruits. The traditional method of propagation by cuttings cannot supply the modern market with large quantities of seedlings. The method of micropropagation of plants in vitro makes it possible to bring the production of blueberry seedlings to the highest level. Blueberries have not been sufficiently studied in in vitro culture, so the search for the simplest and most cost-effective methods of micropropagation remains relevant. The problem of accelerated micropropagation of blueberries can be solved using rocker-type bioreactors, which differ from other models in terms of simplicity of design and low cost. A study was carried out to evaluate the effectiveness of micropropagation of Vaccinium corymbosum 'Bluecrop' in rocker bioreactors. Two types of bioreactors were compared: the bioreactor of the Platform system and the TIS rocker bioreactor modified by the author. As a control, blueberries were grown on a semi-solid medium. The effectiveness of blueberry micropropagation was evaluated by the following indicators: multiplication coefficient, shoot length, and proportion of vitrified shoots. Experiments were conducted on WPM medium, with zeatin supplementation at a concentration of 1.0 mg/l, resulting in optimal results. It is shown that the rocker bioreactor is slightly inferior to the plantform bioreactor in micropropagation but outperforms the method of micropropagation on semisolid media. The rocker bioreactor can be fully utilized for production purposes. In order to reduce costs and increase technical reliability, the working principle of the mechanical drive of the author's model of a rocker-type bioreactor was changed.

Technoeconomic Analysis of Intensified PEGylated Biopharmaceutical Recombinant Protein Production: Alpha Antitrypsin as a Model Case

Alpha-1 antitrypsin deficiency (AATD) is a genetic disorder characterized by the insufficient production of the AAT protein. Due to availability limitations, not all AATD patients receive protein therapy treatment. In this study, the technoeconomic analysis of different processes (conventional and intensified) producing 200 kg/year of PEGylated recombinant AAT (PEG-AAT) using a Chinese hamster ovary cell line was investigated. All bioprocesses consist of upstream, downstream, and PEGylation sections. A base-case model (process A) of the conventional fed-batch production bioreactor was developed using SuperPro Designer software (Version 13) to evaluate the economic feasibility of the process. The cost of goods (COG) was estimated to be approximately USD 387.6/g. Furthermore, an intensified process (B) was modeled and evaluated to reduce the COG. Process intensification was implemented in the process (N-1 perfusion bioreactor). The specific operating COG for process B was found to be 10% less than that of process A. Scenario analysis was performed to assess the impact of process capacity (100–1000 kg/year) and cell-specific productivity (30–90 pg/cell/day). With an increase in process capacity, the specific operating COG was reduced for all processes. Increasing cell-specific productivity decreases the specific operating COG at different rates for each process, depending on the titer level. Future investigations into the PEGylation section are required since it has the highest COG of all the sections.

A low-cost, easy-to-use prototype bioreactor model for the investigation of human gut microbiota: validation using a prebiotic treatment.

In vitro gut models allow for the study of the impact of molecules on human gut microbiota composition and function without the implication of the host. However, current models, such as the Simulator of Human Intestinal Microbial Ecosystem (SHIME®), are expensive, time-consuming, and require specialized personnel. Homemade in vitro models that lessen these issues have limited evidence of their humanlike functionality. In this study, we present the development of a low-cost and easy-to-use bioreactor with the proven functionality of human microbiota. In our model, we evaluated the capability of replicating human gut microbiota growth and the response of the human bacterial community to a prebiotic, resistant starch, particularly resistant starch type 2 (RS2). Our bioreactor produced an environment that was stable for pH, temperature, and anaerobic conditions. The bioreactor consistently cultivated bacterial communities over a 48 h time period, replicating the composition of the gut microbiota and the associated metabolite production response to RS2, in line with prior human studies. In response to the RS2 prebiotic, we observed an increase in Bifidobacterium adolescentis and Bifidobacterium faecale and an increase in the production of the short-chain fatty acids such as acetate, propionate, and isobutyrate. Taken together, these data demonstrate that our low-cost and user-friendly prototype bioreactor model provides a favorable environment for the growth of human gut microbiota and can mimic its response to a prebiotic.

Assessing phage-host population dynamics by reintroducing virulent viruses to synthetic microbiomes

Microbiomes feature complex interactions between diverse bacteria and bacteriophages. Synthetic microbiomes offer a powerful way to study these interactions; however, a major challenge is obtaining a representative bacteriophage population during the bacterial isolation process. We demonstrate that colony isolation reliably excludes virulent viruses from sample sources with low virion-to-bacteria ratios such as feces, creating "virulent virus-free" controls. When the virulent dsDNA virome is reintroduced to a 73-strain synthetic gut microbiome in a bioreactor model of the human colon, virulent viruses target susceptible strains without significantly altering community structure or metabolism. In addition, we detected signals of prophage induction that associate with virulent predation. Overall, our findings indicate that dilution-based isolation methods generate synthetic gut microbiomes that are heavily depleted, if not devoid, of virulent viruses and that such viruses, if reintroduced, have a targeted effect on community assembly, metabolism, and prophage replication.

Meta-analysis of the Microbial Diversity Cultured in Bioreactors Simulating the Gut Microbiome

Understanding the intricate ecological interactions within the gut microbiome and unravelling its impact on human health is a challenging task. Bioreactors are valuable tools that have contributed to our understanding of gut microbial ecology. However, there is a lack of studies describing and comparing the microbial diversity cultivated in these models. This knowledge is crucial for refining current models to reflect the gastrointestinal microbiome accurately. In this study, we analysed the microbial diversity of 1512 samples from 18 studies available in public repositories that employed cultures performed in batches and various bioreactor models to cultivate faecal microbiota. Community structure comparison between samples using t-distributed stochastic neighbour embedding and the Hellinger distance revealed a high variation between projects. The main driver of these differences was the inter-individual variation between the donor faecal inocula. Moreover, there was no overlap in the structure of the microbial communities between studies using the same bioreactor platform. In addition, α-diversity analysis using Hill numbers showed that highly complex bioreactors did not exhibit higher diversities than simpler designs. However, analyses of five projects in which the samples from the faecal inoculum were also provided revealed an amplicon sequence variants enrichment in bioreactors compared to the inoculum. Finally, a comparative analysis of the taxonomy of the families detected in the projects and the GMRepo database revealed bacterial families exclusively found in the bioreactor models. These findings highlight the potential of bioreactors to enrich low-abundance microorganisms from faecal samples, contributing to uncovering the gut microbial “dark matter”.

Design and modeling of dilute methane oxidation bioreactors to guide bioengineering

Design and modeling of dilute methane oxidation bioreactors to guide bioengineering

Validation of a CFD model for cell culture bioreactors at large scale and its application in scale-up

Among all the operating parameters that control the cell culture environment inside bioreactors, appropriate mixing and aeration are crucial to ensure sufficient oxygen supply, homogeneous mixing, and CO2 stripping. A model-based manufacturing facility fit approach was applied to define agitation and bottom air flow rates during the process scale-up from laboratory to manufacturing, of which computational fluid dynamics (CFD) was the core modeling tool. The realizable k-ε turbulent dispersed Eulerian gas-liquid flow model was established and validated using experimental values for the volumetric oxygen transfer coefficient (kLa). Model validation defined the process operating parameter ranges for application of the model, identified mixing issues (e.g., impeller flooding, dissolved oxygen gradients, etc.) and the impact of antifoam on kLa. Using the CFD simulation results as inputs to the models for oxygen demand, gas entrance velocity, and CO2 stripping aided in the design of the agitation and bottom air flow rates needed to meet cellular oxygen demand, control CO2 levels, mitigate risks for cell damage due to shear, foaming, as well as fire hazards due to high O2 levels in the bioreactor gas outlet. The recommended operating conditions led to the completion of five manufacturing runs with a 100% success rate. This model-based approach achieved a seamless scale-up and reduced the required number of at-scale development batches, resulting in cost and time savings of a cell culture commercialization process.

An in vitro validation system for chicken bioreactors using immortalized chicken oviductal epithelial cells

The utilization of chicken oviductal epithelial cells (OECs) as a bioreactor to produce therapeutic proteins has shown promise, but the time taken to obtain transgenic offspring impedes efficient validation of protein production. To overcome this barrier, we focused on the immortalization of chicken OECs (cOECs) using retroviral vector-mediated c-MYC oncogene expression to establish an in vitro pre-validation system for chicken bioreactors. The resulting immortalized cOECs exhibited sustained proliferation, maintained a normal diploid chicken karyotype, and expressed key oviduct-specific genes (OVA, OVM, LYZ, AVD, and ESR1). Notably, hormonal administration of diethylstilbestrol (DES) or progesterone (P4) upregulated oviduct-specific genes in these cells. To enhance the utility of these immortalized cOECs as an in vitro validation system for chicken bioreactors, clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) technology was employed to knock-in (KI) an enhanced green fluorescence protein (EGFP) gene at the ovalbumin (OVA) locus. The resulting OVA EGFP KI immortalized cOECs secreted both EGFP and OVA proteins into the culture medium, with secretion enhanced under DES treatment. This successful integration of an exogenous gene into cOECs enhances their potential as a versatile in vitro validation system for chicken bioreactors. The established immortalized cOECs overcome previous challenges associated with long-term culture and maintenance, providing a reliable platform for efficient protein production validation. This study presents a comprehensive characterization of the immortalized cOECs, addressing critical limitations associated with in vivo systems and laying a foundation for the development of a streamlined and effective chicken bioreactor model.

Data-driven and physics informed modeling of Chinese Hamster Ovary cell bioreactors

Fed-batch culture is an established operation mode for the production of biologics using mammalian cell cultures. Quantitative modeling integrates both kinetics for some key reaction steps and optimization-driven metabolic flux allocation, using flux balance analysis; this is known to lead to certain mathematical inconsistencies Here, we propose a physically-informed data-driven hybrid model (a “gray box”) to learn models of the dynamical evolution of Chinese Hamster Ovary (CHO) cell bioreactors from process data The approach incorporates physical laws (e.g. mass balances) as well as kinetic expressions for metabolic fluxes Machine learning (ML) is then used to (a) directly learn evolution equations (black-box modeling); (b) recover unknown physical parameters (“white-box” parameter fitting) or—importantly—(c) learn partially unknown kinetic expressions (gray-box modeling) We encode the convex optimization step of the overdetermined metabolic biophysical system as a differentiable, feed-forward layer into our architectures, connecting partial physical knowledge with data-driven machine learning

Forecasting regeneration parameters of free-floating biocarriers in an aeration tank

A mathematical model was developed to describe the efficiency of air regeneration used for immobilised biofilm on free-floating biocarriers “FB Bio-850”. For this purpose, a physical model of an aeration tank bioreactor was developed, forming the basis for this study. The use of biocarriers contributes to decreasing the concentration of water pollutants that pose a threat to humans and the environment when released into the surface or groundwater without adequate treatment. This increases the efficiency of the aeration tanks, accelerates the oxidation of pollutants and ensures high-quality water purification. In addition, biocarriers increase the performance of the biological treatment plant, promote the resilience of biological structures to abrupt discharges of pollutants and facilitate the nitrification process. Carrying out such studies on an industrial aerated facility poses significant technical challenges. The model aerated tank and the experimental setup provided the flexibility to vary the intensity of the air medium-bubble regeneration for free-floating biocarriers, regeneration time, and the specific gravity of the biocarriers across a broad range, along with controlling the concentration of free-floating sludge. The correlation between the efficiency of regeneration of the plastic carriers “FB Bio-850” and the following technical parameters was established, including the regeneration time of the biocarriers, the loading mass and the intensity of the fine bubble aeration of the water-sludge mixture. A mathematical model of the efficiency of air regeneration was developed, reflecting the influence of all the above parameters. The obtained predictive matrix for efficiency values was used to develop a geometric model of the efficiency surface. This model provides an optimal selection of technological parameters for air regeneration of immobilised biofilm in a biological wastewater treatment aeration tank bioreactor, ensuring a sufficiently high regeneration efficiency.

Spider silk enhanced tissue engineering of cartilage tissue: Approach of a novel bioreactor model using adipose derived stromal cells.

Human cartilage tissue remains a challenge for the development of therapeutic options due to its poor vascularization and reduced regenerative capacities. There are a variety of research approaches dealing with cartilage tissue engineering. In addition to different biomaterials, numerous cell populations have been investigated in bioreactor-supported experimental setups to improve cartilage tissue engineering. The concept of the present study was to investigate spider silk cocoons as scaffold seeded with adipose-derived stromal cells (ASC) in a custom-made bioreactor model using cyclic axial compression to engineer cartilage-like tissue. For chemical induction of differentiation, BMP-7 and TGF-β2 were added and changes in cell morphology and de-novo tissue formation were investigated using histological staining to verify chondrogenic differentiation. By seeding spider silk cocoons with ASC, a high colonization density and cell proliferation could be achieved. Mechanical induction of differentiation using a newly established bioreactor model led to a more roundish cell phenotype and new extracellular matrix formation, indicating a chondrogenic differentiation. The addition of BMP-7 and TGF-β2 enhanced the expression of cartilage specific markers in immunohistochemical staining. Overall, the present study can be seen as pilot study and valuable complementation to the published literature.

Modeling large-scale bioreactors with diffusion equations. Part II: Characterizing substrate, oxygen, temperature, carbon dioxide, and pH profiles.

Large-scale fermentation processes involve complex dynamic interactions between mixing, reaction, mass transfer, and the suspended biomass. Empirical correlations or case-specific computational simulations are usually used to predict and estimate the performance of large-scale bioreactors based on data acquired at bench scale. In this two-part-study, one-dimensional axial diffusion equations were studied as a general and predictive model of large-scale bioreactors. This second part focused on typical fed-batch operations where substrate gradients are known to occur, and characterized the profiles of substrate, pH, oxygen, carbon dioxide, and temperature. The physically grounded steady-state axial diffusion equations with first- and zeroth-order kinetics yielded analytical solutions to the relevant variables. The results were compared with large-scale Escherichia coli and Saccharomyces cerevisiae experiments and simulations from the literature, and good agreement was found in substrate profiles. The analytical profiles obtained for dissolved oxygen, temperature, pH, and were also consistent with the available data. Distribution functions for the substrate were defined, and efficiency factors for biomass growth and oxygen uptake rate were derived. In conclusion, this study demonstrated that axial diffusion equations can be used to model the effects of mixing and reaction on the relevant variables of typical large-scale fed-batch fermentations.