This study explores key success factors for ethanol production via fermentation of gas streams, by assessing the effects of eight process variables driving the fermentation performance on the production costs and greenhouse gas emissions. Three fermentation feedstocks are assessed: off-gases from the steel industry, lignocellulosic biomass-derived syngas and a mixture of H₂ and CO₂. The analysis is done through a sequence of (i) sensitivity analyses based on stochastic simulations and (ii) multi-objective optimizations. In economic terms, the use of steel off-gas leads to the best performance and the highest robustness to low mass transfer coefficients, low microbial tolerance to ethanol, acetic-acid co-production and to dilution of the gas feed with CO₂, due to the relatively high temperature at which the gas feedstock is available. The ethanol produced from the three feedstocks lead to lower greenhouse gas emissions than fossil-based gasoline and compete with first and second generation ethanol.

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A novel fermentation process was developed in which renewable electricity is indirectly used as an energy source in fermentation, synergistically decreasing both the consumption of sugar as a first generation carbon source and emission of the greenhouse gas CO₂. As an illustration, a glucose-based process is co-fed with formic acid, which can be generated by capturing CO₂ from fermentation offgas followed by electrochemical reduction with renewable electricity. This “closed carbon loop” concept is demonstrated by a case study in which cofeeding formic acid is shown to significantly increase the yield of biomass on glucose of the industrially relevant yeast species Yarrowia lipolytica. First, the optimal feed ratio of formic acid to glucose is established using chemostat cultivations. Subsequently, guided by a dynamic fermentation process model, a fed-batch protocol is developed and demonstrated on laboratory scale. Finally, the developed fed-batch process is tested and proven to be scalable at pilot scale. Extensions of the concept are discussed to apply the concept to anaerobic fermentations, and to recycle the O₂ that is co-generated with the formic acid to aerobic fermentation processes for intensification purposes.

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In fed-batch operated industrial bioreactors, glucose-limited feeding is commonly applied for optimal control of cell growth and product formation. Still, microbial cells such as yeasts and bacteria are frequently exposed to glucose starvation conditions in poorly mixed zones or far away from the feedstock inlet point. Despite its commonness, studies mimicking related stimuli are still underrepresented in scale-up/scale-down considerations. This may surprise as the transition from glucose limitation to starvation has the potential to provoke regulatory responses with negative consequences for production performance. In order to shed more light, we performed gene-expression analysis of Saccharomyces cerevisiae grown in intermittently fed chemostat cultures to study the effect of limitation-starvation transitions. The resulting glucose concentration gradient was representative for the commercial scale and compelled cells to tolerate about 76 s with sub-optimal substrate supply. Special attention was paid to the adaptation status of the population by discriminating between first time and repeated entry into the starvation regime. Unprepared cells reacted with a transiently reduced growth rate governed by the general stress response. Yeasts adapted to the dynamic environment by increasing internal growth capacities at the cost of rising maintenance demands by 2.7%. Evidence was found that multiple protein kinase A (PKA) and Snf1-mediated regulatory circuits were initiated and ramped down still keeping the cells in an adapted trade-off between growth optimization and down-regulation of stress response. From this finding, primary engineering guidelines are deduced to optimize both the production host's genetic background and the design of scale-down experiments.

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Scaling up bioprocesses is one of the most crucial steps in the commercialization of bioproducts. While it is known that concentration and shear rate gradients occur at larger scales, it is often too risky, if feasible at all, to conduct validation experiments at such scales. Using computational fluid dynamics equipped with mechanistic biochemical engineering knowledge of the process, it is possible to simulate such gradients. In this work, concentration profiles for the by-products of baker’s yeast production are investigated. By applying a mechanistic black-box model, concentration heterogeneities for oxygen, glucose, ethanol, and carbon dioxide are evaluated. The results suggest that, although at low concentrations, ethanol is consumed in more than 90% of the tank volume, which prevents cell starvation, even when glucose is virtually depleted. Moreover, long exposure to high dissolved carbon dioxide levels is predicted. Two biomass concentrations, i.e., 10 and 25 g/L, are considered where, in the former, ethanol production is solely because of overflow metabolism while, in the latter, 10% of the ethanol formation is due to dissolved oxygen limitation. This method facilitates the prediction of the living conditions of the microorganism and its utilization to address the limitations via change of strain or bioreactor design or operation conditions. The outcome can also be of value to design a representative scale-down reactor to facilitate strain studies.

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Background: Ethanol production through fermentation of gas mixtures containing CO, CO2 and H2 has just started operating at commercial scale. However, quantitative schemes for understanding and predicting productivities, yields, mass transfer rates, gas flow profiles and detailed energy requirements have been lacking in literature; such are invaluable tools for process improvements and better systems design. The present study describes the construction of a hybrid model for simulating ethanol production inside a 700 m3 bubble column bioreactor fed with gas of two possible compositions, i.e., pure CO and a 3:1 mixture of H2 and CO2. Results: Estimations made using the thermodynamics-based black-box model of microbial reactions on substrate threshold concentrations, biomass yields, as well as CO and H2 maximum specific uptake rates agreed reasonably well with data and observations reported in literature. According to the bioreactor simulation, there is a strong dependency of process performance on mass transfer rates. When mass transfer coefficients were estimated using a model developed from oxygen transfer to water, ethanol productivity reached 5.1 g L-1 h-1; when the H2/CO2 mixture is fed to the bioreactor, productivity of CO fermentation was 19% lower. Gas utilization reached 23 and 17% for H2/CO2 and CO fermentations, respectively. If mass transfer coefficients were 100% higher than those estimated, ethanol productivity and gas utilization may reach 9.4 g L-1 h-1 and 38% when feeding the H2/CO2 mixture at the same process conditions. The largest energetic requirements for a complete manufacturing plant were identified for gas compression and ethanol distillation, being higher for CO fermentation due to the production of CO2. Conclusions: The thermodynamics-based black-box model of microbial reactions may be used to quantitatively assess and consolidate the diversity of reported data on CO, CO2 and H2 threshold concentrations, biomass yields, maximum substrate uptake rates, and half-saturation constants for CO and H2 for syngas fermentations by acetogenic bacteria. The maximization of ethanol productivity in the bioreactor may come with a cost: low gas utilization. Exploiting the model flexibility, multi-objective optimizations of bioreactor performance might reveal how process conditions and configurations could be adjusted to guide further process development.@en

In this work, a model was developed to predict the performance of a bubble column reactor for syngas fermentation and the subsequent recovery of anhydrous ethanol. The model was embedded in an optimization framework which employs surrogate models (artificial neural networks) and multi-objective genetic algorithm to optimize different process conditions and design variables with objectives related to investment, minimum selling price, energy efficiency and bioreactor productivity. The results indicate the optimal trade-offs between these objectives while providing a range of solutions such that, if desired, a single solution can be picked, depending on the priority conferred to different process targets. The Pareto-optimal values of the decision variables were discussed for different case studies with and without the recovery unit. It was shown that enhancing the gas-liquid mass transfer coefficient is a key strategy toward sustainability improvement.@en

Syngas fermentation is one of the bets for the future sustainable biobased economies due to its potential as an intermediate step in the conversion of waste carbon to ethanol fuel and other chemicals. Integrated with gasification and suitable downstream processing, it may constitute an efficient and competitive route for the valorization of various waste materials, especially if systems engineering principles are employed targeting process optimization. In this study, a dynamic multi-response model is presented for syngas fermentation with acetogenic bacteria in a continuous stirred-tank reactor, accounting for gas–liquid mass transfer, substrate (CO, H₂) uptake, biomass growth and death, acetic acid reassimilation, and product selectivity. The unknown parameters were estimated from literature data using the maximum likelihood principle with a multi-response nonlinear modeling framework and metaheuristic optimization, and model adequacy was verified with statistical analysis via generation of confidence intervals as well as parameter significance tests. The model was then used to study the effects of process conditions (gas composition, dilution rate, gas flow rates, and cell recycle) as well as the sensitivity of kinetic parameters, and multiobjective genetic algorithm was used to maximize ethanol productivity and CO conversion. It was observed that these two objectives were clearly conflicting when CO-rich gas was used, but increasing the content of H₂ favored higher productivities while maintaining 100% CO conversion. The maximum productivity predicted with full conversion was 2 g·L⁻¹·hr⁻¹ with a feed gas composition of 54% CO and 46% H₂ and a dilution rate of 0.06 hr⁻¹ with roughly 90% of cell recycle.

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The present study describes the procedure followed to construct, to quantitatively validate and the utilization of a mathematical model to simulate ethanol production inside a large-scale bubble column bioreactor fed by syngas of two possible compositions i.e., pure CO and a 3:1 mixture of H2 and CO2. The model is structured by i) a black-box model for catabolic ethanol production and biomass growth and ii) a mass transfer model of the bioreactor; both parts are combined through hyperbolic uptake kinetics for CO and H2. Thermodynamics are used to study the production of energy through catabolism and to estimate biomass yields and maximum specific CO and H2 uptake rates. The simulation identifies a strong dependence of bioreactor performance and mass transfer rate of CO and H2. When mass transfer rate is above the 90 % of the maximum estimated values, ethanol productivity would reach 4.7 g/L/h, gas utilization would be 22 % and, the fermentation process plus the distillation of the product would require 7 MW/kg of ethanol produced. H2/CO2 fermentation achieved 20 and 30 % higher productivity and gas utilization than CO fermentation, respectively. Moreover, gas compression and distillation of ethanol are the largest contributors to energetic costs.@en

We assess the effect of substrate heterogeneity on the metabolic response of P. chrysogenum in industrial bioreactors via the coupling of a 9-pool metabolic model with Euler-Lagrange CFD simulations. In this work, we outline how this coupled hydrodynamic-metabolic modeling can be utilized in 5 steps. (1) A model response study with a fixed spatial extra-cellular glucose concentration gradient, which reveals a drop in penicillin production rate q_p of 18–50% for the simulated reactor, depending on model setup. (2) CFD-based scale-down design, where we design a 1-vessel scale down simulator based on the organism lifelines. (3) Scale-down verification, numerically comparing the model response in the proposed scale-down simulator with large-scale CFD response. (4) Reactor design optimization, reducing the drop in penicillin production by a change of feed location. (5) Long-term fed-batch simulation, where we verify model predictions against experimental data, and discuss population heterogeneity. Overall, these steps present a coupled hydrodynamic-metabolic approach towards bioreactor evaluation, scale-down and optimization.

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Euler–Lagrange computational fluid dynamics simulations offer great potential for the integration of transport dynamics and metabolic dynamics in fermentation systems. Since the seminal work of Lapin et al. [1,2], progress has been made, mainly in the analysis of CFD data and translation to laboratory setup designs. Different large-scale processes require different analysis methods; in this paper we discuss which analysis methods are best suited for given reactor types, by reviewing prior simulation cases as well as introducing new test cases. Furthermore, we address challenges in the translation from Euler–Lagrange simulations to laboratory scale systems, and propose methods to work around these shortcomings. Based on the current state of the art, we propose guidelines for the selection of data analysis methods, and we discuss the design of rational scale-down simulators. We conclude with a brief discussion regarding the requirements and possibilities of next-generation scale-down simulators, such as microfluidic single-cell analysis, and possible ways to approximate cellular lifelines from invasive intra-cellular measurements.

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In the present work, by performing chemostat experiments at 400 and 600 RPM, two typical power inputs representative of industrial penicillin fermentation (P/V, 1.00 kW/m³ in more remote zones and 3.83 kW/m³ in the vicinity of the impellers, respectively) were scaled-down to bench-scale bioreactors. It was found that at 400 RPM applied in prolonged glucose-limited chemostat cultures, the previously reported degeneration of penicillin production using an industrial Penicillium chrysogenum strain was virtually absent. To investigate this, the cellular response was studied at flux (stoichiometry), residual glucose, intracellular metabolite and transcript levels. At 600 RPM, 20% more cell lysis was observed and the increased degeneration of penicillin production was accompanied by a 22% larger ATP gap and an unexpected 20-fold decrease in the residual glucose concentration (C_s,out). At the same time, the biomass specific glucose consumption rate (q_s) did not change but the intracellular glucose concentration was about sixfold higher, which indicates a change to a higher affinity glucose transporter at 600 RPM. In addition, power input differences cause differences in the diffusion rates of glucose and the calculated Batchelor diffusion length scale suggests the presence of a glucose diffusion layer at the glucose transporting parts of the hyphae, which was further substantiated by a simple proposed glucose diffusion-uptake model. By analysis of calculated mass action ratios (MARs) and energy consumption, it indicated that at 600 RPM glucose sensing and signal transduction in response to the low C_s,out appear to trigger a gluconeogenic type of metabolic flux rearrangement, a futile cycle through the pentose phosphate pathway (PPP) and a declining redox state of the cytosol. In support of the change in glucose transport and degeneration of penicillin production at 600 RPM, the transcript levels of the putative high-affinity glucose/hexose transporter genes Pc12g02880 and Pc06g01340 increased 3.5- and 3.3-fold, respectively, and those of the pcbC gene encoding isopenicillin N-synthetase (IPNS) were more than twofold lower in the time range of 100–200 hr of the chemostat cultures. Summarizing, changes at power input have unexpected effects on degeneration and glucose transport, and result in significant metabolic rearrangements. These findings are relevant for the industrial production of penicillin, and other fermentations with filamentous microorganisms.

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In a 54 m³ large-scale penicillin fermentor, the cells experience substrate gradient cycles at the timescales of global mixing time about 20–40 s. Here, we used an intermittent feeding regime (IFR) and a two-compartment reactor (TCR) to mimic these substrate gradients at laboratory-scale continuous cultures. The IFR was applied to simulate substrate dynamics experienced by the cells at full scale at timescales of tens of seconds to minutes (30 s, 3 min and 6 min), while the TCR was designed to simulate substrate gradients at an applied mean residence time ((Formula presented.)) of 6 min. A biological systems analysis of the response of an industrial high-yielding P. chrysogenum strain has been performed in these continuous cultures. Compared to an undisturbed continuous feeding regime in a single reactor, the penicillin productivity (q_PenG) was reduced in all scale-down simulators. The dynamic metabolomics data indicated that in the IFRs, the cells accumulated high levels of the central metabolites during the feast phase to actively cope with external substrate deprivation during the famine phase. In contrast, in the TCR system, the storage pool (e.g. mannitol and arabitol) constituted a large contribution of carbon supply in the non-feed compartment. Further, transcript analysis revealed that all scale-down simulators gave different expression levels of the glucose/hexose transporter genes and the penicillin gene clusters. The results showed that q_PenG did not correlate well with exposure to the substrate regimes (excess, limitation and starvation), but there was a clear inverse relation between q_PenG and the intracellular glucose level.

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The broth in industrial scale fermentors may contain significant gradients in, for example, substrate concentration, dissolved oxygen and shear rates. From the perspective of microbes in this fermentor, these gradients translate to temporal variations in their environment that may affect their metabolic response. As a result, there may be differences in process yield between laboratory scale fermentations and their industrial counterpart. Rather than scaling-up bioprocesses based on equivalence, it is recommended to scale-down: mimic the large-scale environment in lab scale setups, to account for hydrodynamic-metabolic interaction from the start. In this thesis, the use of Euler-Lagrange computational fluid dynamics to capture the large-scale fermentation environment is explored. Lagrangian simulations offer to study processes from the microbial perspective (so-called “lifelines”), and enable coupling of metabolic models describing the response to external variations. With this, it is possible to take the history of the trajectory of the microbe into account, as organisms may not adapt to their surroundings instantaneously. Guidelines for the setup of fermentor simulations are presented, and several means for processing the lifelines are discussed. The obtained information is used to design lab-scale fermentations that mimic large-scale conditions. It is furthermore shown how coupled hydrodynamic-metabolic simulations can be used to predict yield-loss, assess process improvements, and study the onset of population heterogeneity in large-scale fermentors. Additionally, a more fundamental towards the role of the turbulent Schmidt number in multi-impeller mixing is included.@en

Large substrate concentration gradients can exist in chemical or biochemical reactions, resulting from a large circulation time compared to the turnover time of substrates. The influence of such gradients on the microbial metabolism can significantly compromise optimal bioreactor performance. Lapin et al. (2004) proposed an Euler–Lagrange CFD method to study the impact of such gradients from the microbial point of view. The discrete representation of the biomass phase yields an advantageous perspective for studying the impact of extra-cellular variations on the metabolism, but at significant computational cost. In particular, the tracked number of particles, as well as the applied time resolution, have a large impact on both the accuracy of the simulation and the runtime of the simulation. In this work we study the influence of these parameters on both the simulation results and computation time, and provide guidelines for accurate Euler–Lagrange bioreactor simulations at minimal computational cost.

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This study presents the design and assessment of site-specific supply chains and related manufacturing processes for the production of bio-based chemicals from the syngas platform and via gasification of lignocellulosic biomass followed by syngas fermentation. The supply chains include feedstocks production and collection, biomass gasification, syngas fermentation, and downstream processing. For each of these stages, different alternatives were considered: four feedstocks (pine, corn stover, sugarcane bagasse, and eucalyptus), three products (ethanol, 2,3-butanediol and hexanoic acid), and three geographical locations (the Netherlands, the USA, and Brazil). Conceptual development and analysis of the supply chains were done through the combination of different design and assessment tools, namely biomass supply chains design, fermentation process design (based on thermodynamics and transport), process simulation, and economic and environmental assessments. The minimum selling price (MSP) and two environmental impact categories, i.e., global warming potential (GWP) and non-renewable energy use (NREU), were used as performance indicators. These were compared to data reported in scientific literature and commercial sources for similar processes and ­products. The best overall performance was obtained for the production of 2,3-butanediol from pine sourced in the USA. In the cases of ethanol and hexanoic acid, the syngas fermentation stage had significant contributions to MSP, GWP, and NREU, due mainly to its high energy requirements. Regarding the geographical location, the best economic performance was obtained for the USA followed by the Netherlands and Brazil respectively. Furthermore, operation in Brazil led to the lowest environmental impacts, followed by the Netherlands and the USA.

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Production of second generation ethanol can be accomplished with biomass gasification followed by syngas fermentation using acetogenic bacteria in a hybrid process. Using process simulation and financial analysis, this study evaluated the feasibility of producing hydrous ethanol from sugarcane bagasse in a conceptual hybrid plant designed to be energetically self-sufficient. The process was found to be competitive with other second generation routes, achieving an ethanol yield of 330 L per metric ton of dry biomass and an overall carbon conversion of 30%. The minimum ethanol selling price to achieve Net Present Value break-even with 10% Internal Rate of Return was estimated to be 706 US$/m³ after taxes in the base model. When accounting for uncertainties in the fixed capital investment and the cost of raw materials, the Net Present Value was found to be non-negative in 80% of the cases for a selling price of 783 US$/m³.

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Efficient optimization of microbial processes is a critical issue for achieving a number of sustainable development goals, considering the impact of microbial biotechnology in agrofood, environment, biopharmaceutical and chemical industries. Many of these applications require scale-up after proof of concept. However, the behaviour of microbial systems remains unpredictable (at least partially) when shifting from laboratory-scale to industrial conditions. The need for robust microbial systems is thus highly needed in this context, as well as a better understanding of the interactions between fluid mechanics and cell physiology. For that purpose, a full scale-up/down computational framework is already available. This framework links computational fluid dynamics (CFD), metabolic flux analysis and agent-based modelling (ABM) for a better understanding of the cell lifelines in a heterogeneous environment. Ultimately, this framework can be used for the design of scale-down simulators and/or metabolically engineered cells able to cope with environmental fluctuations typically found in large-scale bioreactors. However, this framework still needs some refinements, such as a better integration of gas–liquid flows in CFD, and taking into account intrinsic biological noise in ABM.

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The bio-economy is in transit from innovation to commercialization. The bioprocess industry is expected to increasingly deliver bio-products to the market, in large amounts, at high quality and at competitive cost levels. This requires flawless start-up of new large-scale bioprocesses and continuous improvement of running processes. Fermentation scale-up and operation can benefit from recent advances in three areas: 1. computation-driven design of scale-down simulators, 2. omics-driven metabolic engineering and 3. sensing and understanding of population heterogeneity. Integration of these fields requires a unified computational approach, linked to big data and simulated reality frameworks, of which the contours are becoming visible today.

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The trajectories, referred to as lifelines, of individual microorganisms in an industrial scale fermentor under substrate limiting conditions were studied using an Euler-Lagrange computational fluid dynamics approach. The metabolic response to substrate concentration variations along these lifelines provides deep insight in the dynamic environment inside a large-scale fermentor, from the point of view of the microorganisms themselves. We present a novel methodology to evaluate this metabolic response, based on transitions between metabolic “regimes” that can provide a comprehensive statistical insight in the environmental fluctuations experienced by microorganisms inside an industrial bioreactor. These statistics provide the groundwork for the design of representative scale-down simulators, mimicking substrate variations experimentally. To focus on the methodology we use an industrial fermentation of Penicillium chrysogenum in a simplified representation, dealing with only glucose gradients, single-phase hydrodynamics, and assuming no limitation in oxygen supply, but reasonably capturing the relevant timescales. Nevertheless, the methodology provides useful insight in the relation between flow and component fluctuation timescales that are expected to hold in physically more thorough simulations. Microorganisms experience substrate fluctuations at timescales of seconds, in the order of magnitude of the global circulation time. Such rapid fluctuations should be replicated in truly industrially representative scale-down simulators.

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