Search Results (80)

The increasing demand for recombinant proteins has driven innovation in bioprocessing strategies using Komagataella phaffii as a host organism. Conventional fed-batch cultivation with pure methanol induction remains widely used but presents challenges including high methanol consumption, extended downtime, and elevated operational costs. This study evaluates alternative strategies combining mixed induction (methanol/sorbitol) with continuous cultivation to enhance productivity, sustainability, and improved economic outcome. Using KEX2 protease as a model industrial recombinant protein, we compared four cultivation modes: fed-batch with methanol (benchmark), fed-batch with mixed induction, continuous with methanol, and continuous with mixed induction. Cell growth, volumetric yield, and specific productivity were evaluated at 5L scale and then modelled to simulate industrial scales (40 L and 400 L). Results demonstrate that continuous cultivation with mixed induction significantly improves yield up to 9-fold compared to conventional fed-batch and reduces methanol usage and oxygen demand. Techno-economic simulations reveal that a 40 L continuous process can match or exceed the output of two 400 L fed-batch runs, while lowering capital and operating costs and minimizing environmental footprint. This integrated strategy offers a scalable, low-cost, and safer method for recombinant protein production, supporting compact and sustainable manufacturing solutions. Full article

Due to the increasing demand for greater safety and ease of scale bioprocessing, fault detection and diagnosis (FDD) is becoming an effective method to avoid breakdowns and disasters. Therefore, this work focuses on developing a dedicated observer-based fault diagnosis for nonlinear systems. To solve this, the FDD scheme is needed to make it perform satisfactorily even in a faulty situation. A case study on bioethanol production is proposed to illustrate and demonstrate the proposed techniques in real time. Single faults and different sensor faults are considered. The effectiveness of the proposed model is proved by comparing its performance obtained by simulation with the experimental data. In order to supervise the change of the possible faulty parameter, robust adaptive full-order observers that focus not only on the state estimation but also on the parameter change are applied to the considered bioreactor. In order to achieve the desired outcome of sensor fault detection, we propose a residual evaluation function, given by the root-mean-square (RMS) value of the residual and a practical threshold for the bioreactor. Experimental results show that sensor faults can be well diagnosed by the proposed observer-based FDD method. The precision, recall rate, and overall accuracy of three diagnostic metrics for abrupt failures were compared. The diagnostic approach was successful, achieving an overall accuracy rate of over $90\%$ for each of the three abrupt failure scenarios in every sensor. Finally, even if the biomass or $C{O}_2$ sensors fail, the FDD system can reconstruct the substrate and ethanol dynamics that are typically quantified offline in bioprocesses in real time. Full article

This manuscript provides a comprehensive review of the enzymatic hydrolysis of lignocellulosic biomass, emphasizing how chemical composition, structural features, inhibitory compounds, and process configurations collectively influence the conversion of structural polysaccharides into fermentable sugars. Variability among herbaceous, woody, and residual biomasses results in differences in cellulose, hemicellulose, lignin content, and crystallinity, which strongly affect enzyme accessibility. The review discusses key inhibitory mechanisms, including nonproductive cellulase adsorption onto lignin, interference from phenolic derivatives and pretreatment by-products, and inhibition caused by accumulating mono- and oligosaccharides. Process configurations such as SHF, SSF, PSSF, and consolidated bioprocessing are compared, with SSF often achieving superior performance by mitigating end-product inhibition. The manuscript also highlights the growing relevance of computational modeling and simulation tools, which support kinetic prediction, the evaluation of transport limitations, and the optimization of operating conditions in high-solids systems. Additionally, recent advances in artificial intelligence are presented as powerful approaches for modeling nonlinear hydrolysis behavior, estimating kinetic parameters, identifying rate-limiting steps, and improving predictive accuracy in complex bioprocesses. Overall, the integration of experimental insights with advanced modeling, simulation, and AI-based strategies is essential for overcoming current limitations and enhancing the technical feasibility and industrial competitiveness of lignocellulosic bioconversion. Full article

A parallel-stirred tank bioreactor system on a 10 mL-scale automated with a liquid handling station introduces significant benefits in bioprocess analysis and design regarding preserving time, cost, and workload, thereby enabling quick generation of bioprocess results that can be easily scaled up. Although up-to-date approaches enable the online analysis of individual reactors for pH, dissolved oxygen (DO), and optical density (OD), the automated calibration of a new online laser-based infrared OD sensor device and noise reduction are still required. Among the extensive research on the full-data smoothing tools, the Savitzky–Golay (Savgol) filter was determined as the most effective one. Scattered and transmitted online light values were successfully aligned with the reference at-line OD values measured at 600 nm by the liquid handler with a step time of a few hours. The growth of an engineered Gluconobacter oxydans designed for specific whole-cell oxidations has been investigated in two parallel batch process setups with varied sugar types at varying sugar concentrations, combinations of sugars, and altered concentrations of complex media. Simulation of real-time smoothing was applied with a Kalman filter. Rapid adaptation was observed within a few upcoming data points by altering the parameters for the estimation of the noise in the signal. For almost all tested reaction conditions, a successful alignment of the simulation of real-time smoothed online OD with at-line values was achieved. The best growth condition was determined in the presence of 120 g L⁻¹ glucose and 30 g L⁻¹ fructose with the tripled peptone concentration. Under these conditions, OD₆₀₀ increased by 109%, from 2.1 to 4.4, compared to the reference process. Full article

This review highlights the growing relevance of ion-exchange nanofibrous membranes (IEX-NFMs) in membrane chromatography (MC) for protein purification, emphasising their structural advantages such as high porosity, tunable surface functionality, and low-pressure drops. While the adsorption of IEX-NFMs in MC is expanding due to their potential for high throughput and rapid mass transfer, a critical limitation remains: the precise binding capacity of these membranes is not well understood. Traditional experimental methods to evaluate protein–membrane interactions and optimise binding capacities are labour-intensive, time-consuming, and costly. Therefore, this review underscores the importance of computational modelling as a viable predictive approach to guide membrane design and performance prediction. Yet major obstacles persist, including the challenge of accurate representation of the complex and often irregular pore structures, as well as limited and/or oversimplified adsorption models. Along with molecular-scale simulations such as molecular dynamics (MD) simulations and quantum simulations, meso-scale simulations can provide insight into protein–fibre and protein–protein interactions under varying physicochemical conditions for larger time scales and lower computational burden. These tools can help identify key parameters such as binding accessibility, ionic strength effects, and surface charge density, which are essential for the rational design and performance prediction of IEX-NFMs. Moreover, integrating simulations with experimental validation can accelerate optimisation process while reducing cost. This technical review sets the foundation for a computationally driven design framework for multifunctional IEX-NFMs, supporting their use in next-generation chromatographic separations and broadening their applications in bioprocessing and analytical biotechnology. Full article

The bio-conversion of agricultural biomass into value-added products via solid-state fermentation (SSF) represents a cost-effective and eco-friendly approach, though it is often limited by low efficiency and prolonged processing times. While low-intensity magnetic fields (LMFs) have shown potential to enhance microbial metabolism and improve mass and heat transfer during SSF, the effects of conventional inhomogeneous magnetic fields remain inconsistent and may even cause localized microbial damage due to uneven field distribution. In this study, we designed and optimized a Helmholtz coil system capable of generating a highly homogeneous low-intensity magnetic field to overcome this limitation. Through electromagnetic simulation and experimental validation, an optimized aluminum profile-supported coil configuration was developed, achieving an average magnetic field intensity of 142.77 G under 70% power load with high spatial homogeneity (maximum deviation: ±1.32%). Applied to the solid-state fermentation of peanut meal, the homogeneous LMF treatment (40 G, 4 h) significantly increased peptide content by 77.76% compared to non-treated samples, and by 42.95% over traditional inhomogeneous LMF treatment. This work establishes homogeneous magnetic-field-assisted SSF as a novel, efficient, and scalable bioprocessing strategy, providing both a robust technological framework and new insights into the role of field uniformity in the magneto-fermentation of agricultural biomass. Full article

The development of efficient strategies for optimizing and controlling nonlinear bioprocesses remains a significant challenge due to their complex dynamics and sensitivity to operating conditions. This work addresses the problem by proposing a two-step methodology applied to a laboratory-scale fed-batch bioethanol process. The first step employs a dynamic optimization approach based on Fourier parameterization and orthonormal polynomials, which generates smooth and continuous substrate-feed profiles using only three parameters instead of the ten required by piecewise approaches. The second step introduces a controller formulated through basic linear algebra operations, which ensures accurate trajectory tracking of the optimized state variables. Simulation results demonstrate a $3.65\%$ increase in ethanol concentration at the end of the process, together with an accumulated tracking error of only $0.0189$ under nominal conditions. In addition, the closed-loop strategy outperforms open-loop implementation when the initial conditions deviate from their nominal values. These findings highlight that the proposed methodology reduces mathematical complexity and computational effort while producing continuous control profiles suitable for practical application. The combination of optimization and algebraic control thus provides a promising alternative for improving the efficiency of bioethanol-production processes. Full article

Industrial applications of xylanases in high-temperature settings are limited by enzyme instability. This study evaluated glycerol and phenolic compounds as modulators of the catalytic and structural properties of a recombinant Myceliophthora heterothallica endoxylanase (rMhXyn) expressed in Komagataella phaffii. Glycerol (20% v/v) significantly improved thermostability (5-fold increase in half-life at 55 °C), decreased the activation energy for catalysis, and enhanced structural rigidity as evidenced by molecular dynamics simulations (reduced RMSD and Rg). In contrast, phenolic acids provided only short-term stabilization at moderate temperatures and did not confer structural benefits. Enzyme kinetics revealed that glycerol enhanced catalytic turnover (↑V_max), while phenolic compounds modified both K′ and cooperativity (Hill coefficient). Thermodynamic analysis supported glycerol’s stabilizing effect, with increased ∆H_(D) and a positive shift in ∆S_(D). These results suggest glycerol as a superior stabilizer for rMhXyn in high-temperature bioprocesses such as lignocellulosic biomass hydrolysis. These findings highlight the potential of targeted additives to improve enzyme performance for biotechnological applications. Full article

Bioprocess simulation is a powerful tool for leveraging circular economy principles in the analysis of large-scale bioprocesses, enhancing decision-making for efficient and sustainable production. By simulating different process scenarios, researchers and engineers can evaluate the techno-economic feasibility of different approaches. This approach enables the identification of cost-effective and sustainable solutions, optimizing resource use and minimizing waste, thereby enhancing the overall efficiency and viability of bioprocesses within a circular economy framework. In this review, we provide an overview of circular economy concepts and trends before discussing design methodologies and challenges in large-scale bioprocesses. The analysis highlights the application and advantages of using process simulators like SuperPro Designer v.14 in bioprocess development. Process design methodologies have evolved to use specialized software that integrates chemical and biochemical processes, physical properties, and economic and environmental considerations. By embracing circular economy principles, these methodologies evaluate projects that transform waste into valuable products, aiming to reduce pollution and resources use, thereby shifting from a linear to a circular economy. In process engineering, exciting perspectives are emerging, particularly in large-scale bioprocess simulations, which are expected to contribute to the improvement of bioprocess technology and computer applications. Full article

Large-scale bioprocesses often suffer from spatial heterogeneities, which impact microbial performance and often lead to phenotypic population heterogeneity. To better understand these effects at the single-cell level, this study applied, for the first time, automated real-time flow cytometry (ART-FCM) to monitor L-phenylalanine production with an Escherichia coli triple reporter strain in a fed-batch process with glycerol as the carbon source. The strain was cultivated in both a well-mixed stirred-tank bioreactor (STR) and a scale-down two-compartment bioreactor (TCB), consisting of an STR and a coiled flow inverter (CFI) in bypass, to simulate spatial heterogeneities. ART-FCM enabled autonomous, high-frequency sampling every 20 min, allowing for real-time tracking of fluorescence signals linked to growth (rrnB-mEmerald), oxygen availability (narGHIJ-CyOFP1), and product formation (aroFBL-mCardinal2). The STR exhibited uniform reporter expression and higher biomass accumulation, while the TCB showed delayed product formation and pronounced phenotypic diversification depending on the set mean residence time in the CFI. Single-cell fluorescence distributions revealed that the shorter mean residence time in the CFI resulted in pronounced subpopulation formation, whereas longer exposure attenuated heterogeneity, indicating transcriptional adaptation. This finding highlights a critical aspect of scale-down studies: increased exposure duration to perturbations can enhance population robustness. Overall, this study demonstrates the relevance of ART-FCM, in combination with a multi-reporter strain, as a pioneering tool for capturing dynamic cellular behavior and correlating it to process performance, providing deeper insights into microbial heterogeneity under fluctuating bioprocess conditions. Full article

The formulation of biosimilar products critically determines their stability, safety, immunogenicity, and market accessibility. This article presents a novel integrative framework for biosimilar formulation that balances scientific, regulatory, and intellectual property dimensions, offering a holistic perspective rarely unified in the literature. It highlights the growing trend toward buffer-free, high-concentration systems that leverage protein self-buffering to improve patient comfort and formulation stability. The article also addresses regulatory flexibility from the FDA and EMA, which allows scientifically justified deviations from reference formulations to ensure pharmaceutical equivalence and minimize immunogenicity. A novelty of this article is its comprehensive analysis of how digital innovations, such as Quality-by-Design, Process-Analytical-Technology, and AI-based in silico simulations, are transforming formulation design and bioprocess optimization to reduce immunogenic risks and enhance bioequivalence. Two important key takeaways emerge: (1) strategic innovation in formulation, especially using buffer-free and high concentration systems, improve product stability and patient tolerability while complying with regulatory standards; and (2) intellectual property challenges, including patent thickets, strongly influence formulation decisions, making early legal-strategic alignment essential for market entry. The article confirms that practical recommendations for the selection of recombinant therapeutic protein formulations can effectively guide developers and regulators toward safer, more efficient, and commercially viable biosimilar products. Full article

A mathematical model was developed for the dynamic and static simulation of a continuous ethanol production process in a tower bioreactor packed with yeast cells immobilized in citrus pectin gel. To avoid accumulation of CO₂ gas during the bioprocess, a vertical fixed bed bioreactor with a working volume of 0.245 L, divided into four stages and equipped with external gas–liquid separators was used. The performance of the bioreactor was evaluated through continuous fermentations using feed medium (sugarcane juice) with substrate concentrations of 161.4 and 312.5 g/L, temperature of 30 °C, pH 4.0 and hydraulic residence times of 5 and 6 h. The developed mathematical model takes into account mass flow by convection and dispersion axial, external and internal mass transfer to/within particle, Contois kinetics for cell growth with inhibition terms, cell death, and substrate consumption for cell maintenance. The partial differential equations regarding cell, substrate and product mass balances in the solid and fluid phase were solved by numerical methods. The calculated profiles of state variables in the fluid phase agreed satisfactorily with the experimental data. The diffusional resistances within particles concerning the substrate consumption rate were not significant, resulting in calculated values of the effectiveness factor close to one. Full article

The capture and conversion of industrial off-gases into valuable biomass using microalgae represents a promising strategy for CO₂ mitigation and sustainable production of biofuels and biochemicals. In this study, fifteen (15) microalgal strains were screened and evaluated for their growth performance and the accumulation of macromolecules like polysaccharides and lipids under CO₂-enriched conditions, simulating the off-gas composition of an operational 2G biorefinery producing bioethanol from wastes. It was found that Stichococcus sp. exhibited the highest polysaccharides accumulation (33% w/w) in biomass, while Chlorella vulgaris demonstrated superior lipids content (34% w/w). Both strains (coded as wild-AUTH) displayed robust growth, each achieving biomass concentrations of 1.5 g/L of Dry Cell Weight (DCW), while maintaining tolerance to the gas feedstock. The protein contents of the strains further support their potential integration into a 3G biorefinery framework, where advanced biofuels could be one of multiple valorization pathways. These findings underline the feasibility of using microalgae as a retrofitting solution for bioethanol and other bioenergy plants, enhancing CO₂ capture while enabling biofuel production. The top-performing species provide a basis for optimizing bioprocess parameters and scaling up the cultivation in industrial photobioreactors (PBRs) to improve productivity and commercial applicability. Full article

In this research work, a new software tool concept and its application for the rapid and flexible development of mechanistic digital twin core models for bioprocesses in various reactor designs are presented. The newly developed software tool concept automatically combines user-selected submodels into an overall digital twin core model. The main part is a biokinetic submodel, of which three were designed for enzymatic, microbial and biocatalytic processes, which can be adapted to specific processes. Furthermore, the digital twin core model contains a physico-chemical submodel (e.g., calculating pH or oxygen transfer) and a reactor submodel. The basis of the reactor submodel is an ideally mixed stirred tank reactor. The biokinetic submodel is decoupled from the reactor submodels and enables an independent parameterisation of submodels. Connecting ideally mixed stirred tank reactor models allows for the simulation of different reactor designs. The implementation of an executable digital twin core model was accelerated, creating a new software tool concept. When the concept was applied, the development time and the computing time of digital twin core models for the cultivation of Saccharomyces cerevisiae in two coupled stirred tank reactors as well as for enzymatic hydrolysis processes in a packed-bed reactor were reduced by 90%. Full article

One of the key guidelines in the European waste management policy is the diversion of waste from landfills, preventing harmful effects on human health and the environment and ensuring that economically valuable waste materials are efficiently recycled and reused through proper management. The organic fraction of municipal waste is abundant and contains biodegradable ingredients such lignocellulose, starch, lipids, pectin, and proteins, making it suitable for biotechnological production. Taking into account that a large amount of organic waste is disposed of in landfills, within this work, the amount of organic waste disposed of in the landfill in Banja Luka was considered. Four simulation model scenarios of the integrated production of bioethanol and biogas are generated, and their process and economic aspects are discussed. In the first two modelled scenarios, the pretreatment conditions (1% sulfuric acid and a different neutralization agent) were varied, while in the other two, the share of the amount of raw material used for the production of bioethanol, i.e., biogas, was varied (split factor: 10–90%). The modelled plant, with a designed capacity of 6 tons/h of organic waste, is a significant bioethanol producer, generating 5,000,000 L/year. The profitability indicators, when examined, revealed that dedicating a portion of the organic municipal waste input exclusively to biogas production leads to decreased process efficiency. Based on the modeled process parameters, ethanol’s minimum feasible selling price is $0.6616 per liter, while regarding the composition of organic municipal waste, carbohydrates have the most significant impact on the viability of the process. The developed model represents an excellent basis for further development of this integrated bioprocess in such a way that it can be modified with new process parameters or economic or ecological indicators and used at all levels of bioprocess design. Additionally, the obtained sustainable integrated bioethanol and biogas production plant models could support forthcoming steps in municipal waste management by providing reliable data on the conditions under which the integrated process of bioethanol and biogas production would take place, as well as the technical feasibility and economic profitability of such organic municipal waste utilization. Full article