With the ongoing advances in generative artificial intelligence (GenAI), the initial skepticism surrounding its tools is gradually diminishing. In fact, tools such as ChatGPT, Copilot and similar, are often used in everyday tasks, both in our personal lives and in educational contexts. Educators may use them for content creation, grading exams, or automating repetitive tasks. Students resort to them to better understand a topic, get feedback on an assignment and brainstorm ideas. Research has shown that, if used correctly, these tools can spur and support both teaching and learning. However, these continuous advancements and the increasing number of available tools also require more research to benchmark all these models and, if possible, provide quantifiable indications of which tool is better to use for which specific subtopic. As such, we created FermBench, a dataset of fermentation knowledge, which can be used to benchmark various large language models (LLMs). The models selected f... [more]

Real-time monitoring of metabolite concentrations is critical for optimising bioprocess performance. While Raman spectroscopy offers a non-invasive solution, translating spectra into metabolite concentration estimates requires robust machine learning models. Foundation models such as TabPFN demonstrate exceptional predictive performance but suffer from high inference complexity when trained on large calibration datasets, hindering their use in real-time laboratory settings. This study proposes a batch Active Learning (AL) strategy to efficiently calibrate TabPFN using a minimal subset of data. We employ a weighted K-means clustering strategy that balances model uncertainty and dataset diversity to select the most informative calibration samples. We evaluated this method on a dataset of nearly 7, 000 Raman spectra covering eight substances. Our AL strategy achieved a mean R² score greater than 0.95 with approximately 1, 000 samples, significantly outperforming random sampling. Notably,... [more]

Batch manufacturing is central to fine chemicals, pharmaceuticals, and bioprocessing. Its operation evolves across phases and recipes, which yields high-dimensional trajectories and strong batch-to-batch variability. Meanwhile, key quality-indicative variables are often measured offline and cannot be used as online model inputs. This work presents an integrated deep learning framework that unifies soft sensing and process monitoring in a single module using only process variables as inputs. A multi-scale Temporal Convolutional Network with multiple kernel sizes extracts complementary dynamic features from sliding windows. These features are concatenated and pooled into a compact representation that feeds two task branches. A variational autoencoder branch reconstructs the input window and provides fault monitoring signals via reconstruction deviation while regularizing the latent space through KL divergence. In parallel, a prediction branch estimates the quality-indicative variable dir... [more]

Single-cell protein (SCP) fermentation efficiently converts carbohydrates into high-protein food products but typically relies on purified glucose. Better understanding of SCP growth on mixed sugar substrates could allow for use of lower-cost, less processed sugars and waste-derived feedstocks. Glucose-fructose mixtures are particularly relevant, as these are the main sugars in sucrose hydrolysates and are also common in many food and beverage waste streams. In this study, the growth of Fusarium venenatum on glucose-fructose mixtures was investigated experimentally and modelled using a lagged dual-substrate Monod framework incorporating inhibition of fructose growth by glucose. Batch fermentations were conducted at a fixed total sugar concentration (15 g/L) with four different initial substrate compositions. Model parameters were estimated using both single-experiment and multi-experiment fitting strategies using differential evolution and wild bootstrap uncertainty analysis. A shared... [more]

Recombinant protein production in Escherichia coli is a widely used system in industry for biopharmaceuticals, enzymes or other proteins. For protein expression, lactose poses as a more favorable and cost-effective induction agent over the common IPTG trigger. It imposes less stress on the cells and is fully metabolizable by the strain used. Therefore, lactose serves as an additional substrate source and adds a degree of freedom through tunable induction levels. To harness this opportunity, a physiological bioprocess model was created, describing the growth and production dynamics of this 2-feed system. Green fluorescent protein is expressed as a model protein in a fed-batch process using glucose as the main substrate and lactose as the digestible inducer. A suitable production kinetic is chosen by fitting a number of models to a collected dataset. The resulting model is used to highlight opportunities for improved process design and control of a 2-stage fed-batch process. It emphasize... [more]

Bioprocesses are inherently multiscale, spanning intracellular metabolism to production-scale reactors. Simulation models that integrate these scales offer potential strategies to study the effect of changing metabolic states and enable efficient integration of biological knowledge gathered from lab-scale experiments. In this study, we demonstrate the potential of such simulation model towards the production of mevalonate, an important pharmaceutical drug compound produced through fermentation of a fungal species Aspergillus terreus. We integrate a genome-scale metabolic model of the organism with a plant-wide simulation model for the bioprocess that encompasses several upstream and downstream unit operations. Through this integration, we identify potential targets for metabolic engineering towards increased product flux and simultaneously estimate the associated oxygen requirements. This framework serves as a foundation for developing digital twins of bioprocesses that bridges strain... [more]

Maintaining sufficient amounts of dissolved oxygen throughout a microbial cultivation is a classic control task in bioprocess engineering to avoid negative effects onto cell physiology and productivity. But traditional PID-based algorithms struggle when faced with pulsed substrate additions and the resulting sudden surge of oxygen uptake. In this work a nonlinear MPC is employed and compared to a PID setup for the cultivation of an E. coli strain exposed to intermittent feeding. Both controllers are tuned for a fast pulse response combined with efficient and robust control action. Their performance was tested in-silico with isolated feed pulses, as well as throughout a full cultivation run. Further, the effects of parameter uncertainty were investigated to assess the impact of a model-plant mismatch. The results showed that the predictive nature of the MPC is well suited for maintaining the dissolved oxygen levels above a threshold and outperforms the PID in almost all investigated sim... [more]

Maintaining sufficient amounts of dissolved oxygen throughout a microbial cultivation is a classic control task in bioprocess engineering to avoid negative effects onto cell physiology and productivity. But traditional PID-based algorithms struggle when faced with pulsed substrate additions and the resulting sudden surge of oxygen uptake. In this work a nonlinear MPC is employed and compared to a PID setup for the cultivation of an E. coli strain exposed to intermittent feeding. Both controllers are tuned for a fast pulse response combined with efficient and robust control action. Their performance was tested in-silico with isolated feed pulses, as well as throughout a full cultivation run. Further, the effects of parameter uncertainty were investigated to assess the impact of a model-plant mismatch. The results showed that the predictive nature of the MPC is well suited for maintaining the dissolved oxygen levels above a threshold and outperforms the PID in almost all investigated sim... [more]

The scale-up of processes with complex fluid flow presents significant challenges in process engineering, particularly in fermentation. Computational fluid dynamics (CFD) is a crucial tool for accurately modelling the hydrodynamic environment in bioreactors and understanding the effects of scale-up. This study utilizes Higher Order SVD (HOSVD), which is the multidimensional extension of Singular Value Decomposition (SVD), to identify the dominant structures (modes) of fluid flow in CFD data of fermentation process simulations. Similarly to Proper Orthogonal Decomposition (POD), also based on SVD, this method can be used to identify the dominant structures of fluid flow, and additionally explore the scale parameter space. As a first test case, we examined five scales of a reciprocally shaken flask bioreactor, from 125 mL to 10 L, specified using basic empirical scale-up rules. Results indicate a common set of spatial modes across all scales, thus confirming that the scale-up method assu... [more]

Polyhydroxyalkanoates (PHAs) are microbial bioplastics that are fully biodegradable, biocompatible and can be produced by renewable feedstocks through fermentation. These are all desirable attributes for the replacement of current fossil-based plastics. Strong mathematical models describing bioprocesses are invaluable tools that can be used for enhancing bioprocess understanding as well as optimization. In this study, polyhydroxybutyrate (PHB), by Cupriavidus necator DSM 545 was produced using glycerol and ammonium sulphate (AS) as the sole carbon and nitrogen sources, respectively. In addition, a kinetic bioprocess model was developed. The kinetic parameters of the model were calibrated with five fermentation experiments with different initial conditions (e.g. variable glycerol and AS concentrations) in order to properly establish the inhibition regions and provide a generalized model as much as possible. The model was successfully validated by three independent experiments, two with... [more]

Most industrial fermentations in food and drink use a single, high purity sugar as a substrate. These pure substrates are more expensive and less sustainable than mixed substrates, that can be derived from agricultural byproducts such as straw. However, use of mixed substrates in fermentation leads to challenging modelling and parameter estimation problems, particularly when much academic research, intended to inform industrial applications, uses batch fermentations, while large-scale fermentation is usually continuous, thanks to its cost and productivity advantages. Our findings highlight key challenges in using batch-derived experimental data to inform models of the continuous fermentation processes used at industrial scale. Extrapolating from data obtained in batch to continuous fermentation is risky, as models with near-equivalent data-fit and predictions in a batch context give very different predictions for continuous culture. For continuous fermentations to switch to mixed subst... [more]

Due to the importance of unsaturated fatty acids for human health and the increasing global demand in the food and food crop area, oleaginous yeasts are promising alternative microorganisms for commercial lipid production due to the high volumetric productivity, with Metchnikowia pulcherrima being an underexplored oleaginous yeast with potential as a lipid producer. Critical to achieving high productivity lipid production are nutrient factors. A sensitivity test identified carbon and nitrogen sources as important factors in nitrogen limited broth (NLB) for lipid production in M. pulcherrima i.e. glucose, yeast extract and Ammonium sulphate. Response Surface Methodology (RSM) involving sets of 15 experimental runs of three-factor three-level Box-Behnken Design (BBD) was implemented for exploring the design of the carbon and nitrogen source in the growth media composition. Quadratic surfaces were least-square fitted and used to identify regions of optimal lipid yield. Multiple sets of ru... [more]

Sustainable development goals have spurred advancements in bioprocess design, driven by improved process monitoring, data storage, and computational power. High-fidelity models are essential for advanced process system engineering, yet accurate parametric models for bioprocessing remain challenging due to overparameterization, often resulting in poor predictive accuracy. Hybrid modeling, combining parametric and non-parametric methods, offers a promising solution by enhancing accuracy while maintaining interpretability. This study explores hybrid models for succinic acid fermentation by Escherichia coli, a critical process for sustainable bio-based chemical production. The research presents a structured exploration of hybrid model architectures and their robustness under varying conditions. Experimental data were preprocessed to remove noise and outliers, and hybrid model structures were developed with differing levels of hybridization (from one to all reaction rates). Kinetic paramete... [more]

Batch chemical processes face significant challenges due to frequent operational shifts and varying conditions, requiring models to be retrained for each new scenario. This high retraining demand limits the scalability of traditional process monitoring systems, making them unsuitable for dynamic batch operations. To address this, we propose a transfer learning-based framework that enhances adaptability by reusing learned features across different batch conditions, reducing the need for extensive retraining. Proposed method integrates Temporal Convolutional Networks (TCNs) for capturing temporal dependencies in batch data and predicting Quality-Indicative Variables (QIVs) to identify deviations. The core innovation lies in transfer learning, enabling the model to adapt to new process variations with minimal updates. This approach ensures robust, accurate monitoring even under evolving conditions. This framework is validated using the IndPenSim penicillin fermentation dataset, which simu... [more]

The growing demand for sustainable alternatives to petroleum-based products drives the development of biomanufacturing using agriculture-based sugars. However, agricultural sugar production faces significant challenges due to limited production capacity and potential negative environmental impacts. This research examines chemical sugar synthesis as an alternative, assessing its environmental impact with conventional agricultural production methods through life cycle assessment. As formaldehyde serves as a primary substrate in chemical synthesis, four production cases were evaluated—comprising two pathways (conventional methods and CO2 capture and utilization (CCU) technologies), each implemented with either fossil fuels or renewable energy sources. The analysis revealed that semi-batch reactors in chemical synthesis substantially reduce environmental impacts compared to batch reactors. Chemical sugar synthesis demonstrated marked advantages in reducing eutrophication, land use change,... [more]

This document contains supplementary materials for full-paper submission to ESCAPE 35 - European Symposium on Computer Aided Process Engineering.

This model uses the kinetic model from Foglers textbook "Elements of Chemical Reaction Engineering" to describe the fermentation of glucose to ethanol.
It is used as a template in the course Green Processes at Berlin University of Applied Science (BHT), where students use it to regress measured data from lab experiments and to design an optimal process.

Kombucha is a traditional, fermented beverage made with an essential biomaterial known as SCOBY (symbiotic culture of bacteria and yeast). Three different tea types, namely black, green, and oolong, were compared in kombucha fermentation in terms of pH dynamics, the formation of SCOBY biomass, and the production of acetic acid. The rational, exponential, and polynomial models described pH dynamics with good fit, R2 > 0.98. The formation of SCOBY biomass and the production of acetic acid were modelled using sigmoidal functions, with three-parameter logistic and Gompertz models and four-parameter Boltzmann and Richards models. The F-test indicated that the three-parameter models were statistically adequate; thus, the Gompertz model was modified to present the biological meaning of the parameters. The SCOBY biomass formation rates ranged from 7.323 to 9.980 g/L-day, and the acetic acid production rates ranged from 0.047 to 0.049% acid (wt/vol)/day, with the highest values from the non-con... [more]

Hot air-assisted radio frequency (HARF) is considered a rapid heating process. In order to improve the circular economy of agricultural byproducts, this study used different proportions of HARF stabilized rice bran (R) from milling rice, HARF dried ginseng residue (G) from ultrasonic extraction, and peanut residue (P) from HARF roasting and oil extraction as the Hericium erinaceus solid-state fermented media. Then, the whitening effects of water extracts from media and fermented products were analyzed. First, the surface temperature of 1 kg rice bran exceeded 90 °C after 3 min of 5 kW HARF heating, effectively deactivating lipase. The combinations of 1 kg of rice bran with 0.5, 1, 1.5, and 2 kg of ginseng residue (85% moisture content) were dried using 5 kW HARF. Each of the drying rates was about 27 g/min, and the drying periods were 14, 30, 46, and 62 min, respectively, which were used to reduce the moisture content below 10%. Compared to traditional air drying for ginseng residue, H... [more]

The content of differentially abundant metabolites in the fermentation broth of grapefruit peels fermented by Cordyceps militaris at different fermentation times was analyzed via LC−MS/MS. Small molecule metabolites and differential metabolic pathways were analyzed via multivariate analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. A total of 423 metabolites were identified at 0, 2, 6, and 10 days after fermentation. Among them, 169 metabolites showed differential abundance, with significant differences observed between the fermentation liquids of every two experimental groups, and the metabolite composition in the fermentation liquid changed over the fermentation time. In summary, the upregulation and downregulation of metabolites in cancer metabolic pathways collectively promote the remodeling of cancer cell metabolism, facilitating increased glycolysis, alterations in TCA cycle flux, and enhanced biosynthesis of the macromolecules required for rapid proliferatio... [more]

The microbial fermentation of plants is a promising approach for enhancing the yield of polysaccharides with increased activity. In this study, ten microbial strains, Lactiplantibacillus plantarum CCFM8661, Limosilactobacillus reuteri CCFM8631, Lactobacillus helveticus M10, Lacticaseibacillus rhamnosus CCFM237, Lactilactobacillus sakei GD17-9, Lacticaseibacillus casei CCFM1073, Bacillus subtilis CCFM1162, Bacteroides cellulosilyticus FTJSI-E-2, Bacteroides stercoris FNMHLBEIK-4, and Saccharomyces cerevisiae HN7-A5, were used to ferment Dendrobium officinale. The skin care activity of the resulting polysaccharides (F-DOP) was evaluated in cultured HaCaT and RAW 264.7 cells, and a mouse model. The results indicated that D. officinale medium promoted strain proliferation, and fermentation significantly enhanced polysaccharide yield (up to 1.42 g/L) compared to that without fermentation (0.76 g/L). Moreover, F-DOPs, especially after CCFM8631 fermentation, exhibited an excellent ability to... [more]

The rise in population, urbanization, and industrial developments have led to a substantial increase in waste generation and energy demand, posing significant challenges for waste management as well as energy conservation and production. Bioenergy conversions have been merged as advanced, sustainable, and integrated solutions for these issues, encompassing energy generation and waste upcycling of different types of organic waste. Municipal solid waste (MSW) and agricultural residues (AR) are two main resources for bioenergy conversions. Bioenergy production involves feedstock deconstruction and the conversion of platform chemicals to energy products. This review provides a detailed overview of waste sources, biofuel, and bioelectricity production from fermentation and microbial fuel cell (MFC) technology, and their economic and environmental perspectives. Fermentation plays a critical role in liquid biofuel production, while MFCs demonstrate promising potential for simultaneous product... [more]

Menaquinone-7 (MK-7) provides significant health gains due to its excellent pharmacokinetic properties. However, MK-7 occurs at low concentrations in mainstream foods, heightening the demand for nutritional supplements. MK-7 exists as geometric isomers, and only all-trans MK-7 is bioactive. Exposure to certain environments impacts the isomer profile. Knowledge of these factors and their influence on the isomer composition is important, as the efficacy of fermented MK-7 end products is solely determined by the all-trans isomer. This investigation aimed to evaluate the short- and long-term effect of atmospheric oxygen, common temperatures, and light on the isomer profile. From the short-term study, it was ascertained that MK-7 is moderately heat-stable but extremely light-sensitive. The stability of all-trans MK-7 was then examined during 8 weeks of storage at a low temperature with minimal oxygen exposure in the absence of light. Negligible change in the all-trans MK-7 concentration occ... [more]

The subject matter of this paper is the functioning of a highly effective waste management system. Assumptions of the Energy Recovery Waste Processing (ERWP) model, being a universal solution for towns and regions irrespective of their population, are presented here. The result of simulations illustrating the energetic potential of municipal waste stored and processed in biological and physicochemical processes are also presented. Calculations were performed for the municipality of Koszalin (Poland), with a population of 106,000. Mixed household and commercial waste, organic waste, waste from selective collection and sewage sludge from a municipal wastewater treatment plant were considered in the waste mass balance. Empirical equations and unit coefficients describing the energetic efficiency of particular processes originating from the author’s own research work as well as from the results available from the scientific literature were used in the calculations. The developed ERWP model... [more]

Five major operations for the conversion of lignocellulosic biomasses into bioethanol are pre-treatment, detoxification, hydrolysis, fermentation, and distillation. The fermentation process is a significant biological step to transform lignocellulose into biofuel. The interactions of biochemical networks and their uncertainty and nonlinearity that occur during fermentation processes are major problems for experts developing accurate bioprocess models. In this study, mechanical processing and pre-treatment on the palm trunk were done before fermentation. Analysis was performed on the fresh palm sap and the fermented sap to determine the composition. The analysis for total sugar content was done using high-performance liquid chromatography (HPLC) and the percentage of alcohols by volume was determined using gas chromatography (GC). A model was also developed for the fermentation process based on the Adaptive-Network-Fuzzy Inference System (ANFIS) combined with particle swarm optimization... [more]