Challenges in industrial fermentation technology research.

Biotechnology Journal

Biotechnol. J. 2014, 9

DOI 10.1002/biot.201300236

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Perspective

Challenges in industrial fermentation technology research Luca Riccardo Formenti1, Anders Nørregaard1, Andrijana Bolic1, Daniela Quintanilla Hernandez1, Timo Hagemann1,2, Anna-Lena Heins3, Hilde Larsson1, Lisa Mears1, Miguel Mauricio-Iglesias1, Ulrich Krühne1and Krist V. Gernaey1 1 Department

of Chemical and Biochemical Engineering, Technical University of Denmark (DTU), Lyngby, Denmark A/S, Kalundborg, Denmark 3 Department of Systems Biology, Technical University of Denmark (DTU), Lyngby, Denmark 2 Novozymes

Industrial fermentation processes are increasingly popular, and are considered an important technological asset for reducing our dependence on chemicals and products produced from fossil fuels. However, despite their increasing popularity, fermentation processes have not yet reached the same maturity as traditional chemical processes, particularly when it comes to using engineering tools such as mathematical models and optimization techniques. This perspective starts with a brief overview of these engineering tools. However, the main focus is on a description of some of the most important engineering challenges: scaling up and scaling down fermentation processes, the influence of morphology on broth rheology and mass transfer, and establishing novel sensors to measure and control insightful process parameters. The greatest emphasis is on the challenges posed by filamentous fungi, because of their wide applications as cell factories and therefore their relevance in a White Biotechnology context. Computational fluid dynamics (CFD) is introduced as a promising tool that can be used to support the scaling up and scaling down of bioreactors, and for studying mixing and the potential occurrence of gradients in a tank.

Received 03 FEB 2014 Revised 01 APR 2014 Accepted 23 APR 2014

Keywords: Computational fluid dynamics · Fermentation process development · Fungal morphology · Modeling · Scale-down bioreactors

1 Introduction Fermentation processes have been used for production and conservation of food for thousands of years, and have been increasingly used for the industrial production of bulk chemicals, fine chemicals, and pharmaceuticals as well. Nowadays, industrial biotechnology – or “white biotechnology” – is well accepted and considered to be one of the important technologies that will help to reduce our

Correspondence: Prof. Krist V. Gernaey, Department of Chemical and Biochemical Engineering, Technical University of Denmark (DTU), Building 229, DK-2800 Lyngby, Denmark E-mail: [email protected] Abbreviations: CFD, computational fluid dynamics; CSTR, continuously stirred tank reactor; DO, dissolved oxygen; GMP, good manufacturing practices; OTR, oxygen transfer rate; PID, proportional integral derivative; STR, stirred tank reactor

© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

dependence on traditional products produced from fossil fuels. Well-known examples of fermentation products are: • Organic acids produced by filamentous fungi, for example citric acid production by Aspergillus niger [1]. • 1,3-propanediol produced by several species [2], where the most well known is the 1,3-propanediol production process established by DuPont, based on a genetically modified Escherichia coli strain [3]. • Antibiotics. Penicillin, the first antibiotic that was discovered, is produced at large scale by Penicillium chrysogenum [4]. • The production of recombinant proteins such as insulin by E. coli [5] and human interferon, the hepatitis B surface antigen and insulin by Saccharomyces cerevisiae [6]. The start of the commercial operation of industrial fermentation processes also meant that optimization of fermentation processes has been a priority ever since. Fermentation process optimization aims at reaching volumetric production rates (= units of product formed per

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unit reactor volume per time) that are as high as possible, combined with achieving very high product titers. The latter are crucial in achieving an economically feasible process since high product titers will reduce the cost of downstream processing. As a consequence of substrate inhibition, a phenomenon that is common with many production hosts, many commercial fermentation processes are operated in fed-batch mode, where a limiting carbon source substrate is dosed to the fermenter during the fedbatch phase [7]. Optimization of fermentation processes is achieved through a combination of strain improvements and process improvements. Nowadays diverse and well-characterized tools are available to achieve strain improvements for many popular production hosts (e.g. [6, 8, 9]). Process improvements can, for example, include changes of the feeding strategy during the fermentation process, manipulation of the media composition, or changes to the pH set point, etc. For example, the lactic acid production can be improved by selectively removing the product from the fermentation broth, thereby reducing the effects of product inhibition on the production strain at high titers [10]. Interestingly, despite the fact that some fermentation processes have been in operation for several decades, introducing a new or improved production host in a production process is still done in a rather empirical way, by performing experiments at benchscale and pilot-scale which yield a suitable production process recipe that is subsequently transferred to fullscale production fermenters, and further optimized there if needed. In this respect, the introduction of the process analytical technology (PAT) guidance [11] has in general put more focus on the development of a more detailed process understanding, also in the fermentation industry, but this has thus far not resulted in improved scaling-up strategies. The use of advanced analytical tools, such as near infrared (NIR) or Raman spectroscopy, and the validation of relevant chemometric models in industrial-scale fermentation is far from being fully developed, due to technical limitations of the measurement equipment (spectrum of water is dominating in NIR spectroscopy, and challenges with fluorescent compounds in Raman spectroscopy), due to the variability and heterogeneity of a fermentation broth (i.e. complex media, cell morphology, and medium rheology), due to the interference of gas bubbles and biomass particles with the liquid phase of aerobic fermentations, and due to the required investment for such expensive equipment compared to the low added value of many fermentation products [12–14]. The focus of this contribution is on some of the challenges related to achieving process improvements. First of all, the paper provides an overview of engineering tools which are available to the process scientist to facilitate the search for potential process improvements. After-

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wards, the paper aims at discussing some of the major issues that are standing in the way for a more general use of these engineering tools, which in many cases are considered standard tools in other industries such as the petrochemical industry. Note that we will purposely not address the detailed biological aspects of industrial fermentation, for example the issues related to genetic instability [15], population heterogeneity [16], or the modeling of metabolism at single cells level [17]. We assume that the environmental conditions put a selective pressure on the phenotype of the cells, and therefore, by controlling the macroscopic variables of a fermentation it is also assumed to be possible to reduce and control the variability in the cellular population.

2 Engineering tools for process improvements In the search for fermentation process improvements, the fermentation process scientist relies on the available process knowledge to carefully select which steps are to be taken or which modifications are to be made in order to maximize the chance of success. In addition to that process knowledge, the process engineer can rely on a number of engineering tools to assist in the process optimization task: statistical methods for design of experiments, mathematical models, data pretreatment and data mining tools, control algorithms, etc. Some of these tools are briefly highlighted in this section.

2.1 Statistical design of experiments Statistical design of experiments [18] plays an important role in supporting the fermentation scientist in collecting – in a structured way – a data set that is assumed to include the effect of the main variables of interest – usually called process parameters – on the fermentation process performance. Albæk et al. [19], for example, used a full-factorial design consisting of two levels for three process variables: Specific agitation power input (1.5 and 15 kW/m3, respectively), aeration rate (96 and 320 NL/min, respectively), and headspace pressure (0.1 and 1.3 bar, respectively). The resulting data set subsequently formed the basis for the development and validation of a mathematical model describing the production of cellulosic enzymes by the filamentous fungus Trichoderma reesei. Media composition can be optimized in a similar fashion using design of experiments, as for example illustrated for optimization of phytase production by Sporotrichum thermophile by Singh and Satyanarayana [20]. In general, the data resulting from a statistical design of experiments form an essential source of information feeding in to a typical process optimization task, but can, for example, also form the basis for the development of mathematical process models [19]. Finally, it is





also important to note that statistical design of experiments combined with parallel cultivation techniques, for example shake flasks or microtiter plates, can contribute significantly to speeding up process development.

2.2 Mathematical models Mathematical models of a fermentation process usually consist of a combination of mechanistic and empirical model elements. Mechanistic models increasingly replace empirical models when more detailed process knowledge becomes available. Empirical models as such are powerful for data exploration (e.g. Kennedy and Krouse [21]), since they can be used to extract relations in a data set – either historical process data or data resulting from a dedicated statistical design of experiments – without requiring detailed knowledge of any underlying mechanism. Especially in the case of process data, appropriate data pretreatment is essential before using the data in the frame of data-driven or mechanistic model building [22]. Once developed, a fermentation process model can ideally predict performance of future production facilities considering the scale of operation, the process design and process conditions. For more details on the state of the art within mathematical modeling of fermentation processes, the review of Gernaey et al. [23] can be consulted. A recent fermentation technology-related example illustrating the practical use of mathematical models demonstrated the prediction of enzyme production by the filamentous fungus Aspergillus oryzae at pilotscale at different operating conditions [24]. The model was developed in such a way that the central part of the model – the oxygen mass transfer model – can be substituted for other applications. This was illustrated in practice as well, by applying the model to Trichoderma reesei fermentations [19]. However, the limitation of the model lies in the fact that the oxygen mass transfer correlation is strain- and scale-specific; therefore, a practical limitation is that a separate design of experiments needs to be carried out every time a different process is introduced in order to characterize the oxygen mass transfer correlation.

2.3 Optimization algorithms A mathematical optimization (mathematical programming) algorithm is a useful add-on to a mathematical model, and will attempt to select the best alternative from a set of available alternatives. It usually relies on minimization (or maximization) of an objective function. Important applications of optimization algorithms in industrial fermentation are parameter estimation and process optimization. Parameter estimation is the task of finding the values of model parameters so that the model predictions best fit the available experimental data – usually expressed as minimizing the sum of squared dif-


ferences between model prediction and experimental data [25]. Since the fermentation models are usually nonlinear, parameter estimation is carried out by nonlinear programming solvers (NLP). Popular solvers for leastsquares estimation are included in the softwares Matlab (lsqnonlin), SAS (PROC NLP), Octave (sqp), etc. The goal of process optimization is to change one or several decision variables related to the design or the operation of the fermentation in order to optimize a given criterion (in general to maximize profit). In particular, the research literature has focused on optimizing the feed rate and composition in fed-batch fermentation as a means to overcome substrate and product inhibition while maximizing the conversion and production of the fermenter, as reviewed by Smets et al. [26].

2.4 Uncertainty and sensitivity analysis Uncertainty and sensitivity analysis are useful as modelanalysis tools [27]. In an uncertainty analysis, propagation of the various sources of uncertainty – e.g. in the model parameters – to the model outputs is studied. The sensitivity analysis, on the other hand, aims at identifying and quantifying the individual contributions of the uncertain inputs to the output uncertainty. Local and global sensitivity analysis methods are distinguished [28], where the local methods aim at ranking the importance of the individual parameters in a single specific combination of parameters (one operating point), whereas the global methods consider a much wider parameter space. Results of a sensitivity analysis can be coupled back to experiments and parameter estimation: the analysis can on the one hand point toward which variables should be measured, for example when designing experiments which are suited to generate data that allow estimation of specific parameters. On the other hand, the sensitivity analysis can result in a simplified parameter estimation problem by pointing toward one or several parameters that are not influential on the model output and should thus not be estimated [29, 30].

2.5 Process control algorithms In general, control is implemented to maintain a process at the desired operating conditions safely and efficiently, while satisfying environmental, economic and product quality requirements. Control of fermentations deals with complex dynamic behavior of microorganisms, significant model mismatch, nonlinear and even inherently unstable dynamics, scarce online measurements of a number of representative variables, etc. [31]. Hence, to date, the control systems applied to industrial fermenters aim mainly at the regulation of the process, that is they aim at keeping a certain control variable at a given set point or trajectory despite disturbances. This is frequently done by simple single input–single output (SISO) con-

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trollers using the ubiquitous proportional integral derivative (PID) (most often only PI) algorithm [32].

2.6 Computational fluid dynamics Computational fluid dynamics (CFD) is a powerful tool for reactor design and process understanding, since it can be used to model and predict important process parameters such as mixing behavior, mass transfer, and energy input requirements for different fermentation setups. CFD has been employed for simulating fermentation systems on many different scales, for example in order to model gas–liquid mass transfer in shake flasks [33], gas-liquid dispersion in a 5 L bioreactor [34] and gas–liquid flow and local mass transfer in reactors with volumes up to 200 L [35]. In [36], interfacial oxygen mass transfer and energy dissipation is simulated and compared to experimental data for a 10 mL cylindrical-shaped and paddle-stirred reactor, designated for cultivation of filamentous microorganisms. The mixing characteristics of the same system have also been investigated and results are presented in [37].

3 Major challenges Despite the availability of an extended set of engineering tools which was introduced in the previous section, a large part of the engineering tools is not frequently used in industry, or has not been exploited to its full potential. In this section, we discuss a number of fermentation process-related challenges that make it difficult for the fermentation scientist to use specific engineering tools.

3.1 Scaling up Scaling up a fermentation process serves the purpose of transforming optimal operating conditions found in laboratory- or pilot-scale bioreactors to the production-scale bioreactor in order to reach maximal volumetric productivity of the full-scale process. However, several other tasks need to be completed before scaling up a process: screening and selection of the production strain, strain improvements, manipulation of media composition, and various process parameter optimizations [38]. All these tasks are usually first performed as parallel microtiter plate cultivations for screening purposes. Later on, when the number of potential production strain candidates has been narrowed down sufficiently, experiments are performed in bench-scale cultivation equipment (stirred bioreactors with a typical volume from 0.5 to 20 L) for further strain characterization and a first round of process parameter optimization. Finally, further process parameter optimization is done in pilot-scale equipment. A major advantage of bench-scale cultivations is that they can be run with different types of feeding schemes,

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and most types of standard sensor equipment can be inserted in the bioreactor without any problem. A major issue related to the use of bench-scale bioreactors as well as the larger pilot-scale bioreactors (20–2000 L) is that experiments at different scales are very difficult to compare to larger size industrial bioreactors. Efficient and fast scaling up of a process was described in detail by Schmidt [39], and is even today one of the major challenges in the fermentation industry. Indeed, scale-up is not done based on a mathematical process model or by using design of experiments, but is typically relying on a rather empirical approach (e.g. relying on oxygen transfer correlations), and is therefore still an important research topic. In practice, scaling up a process is often done iteratively, by comparison of results of pilot plant experiments with key data obtained from the industrial-scale processes. If pilot plant results can be reproduced at industrial scale, scale-up is considered successful. If not, an additional round of pilot plant experiments might be performed, for example to investigate the effect of additional process parameters on process performance. The fact that most industrial-scale data are proprietary is a severe limitation. As a consequence, typical data from process scale-up studies cannot be found as part of the publicly available scientific literature, and can thus not be used to extend the available knowledge about this important engineering task. One should of course not expect industrial partners to share their company secrets. But somehow it must be clear that significant progress with respect to understanding the scaling-up problem can only be made through a close collaboration between industry and academia: industry has the equipment and the knowhow about how to run a full-scale fermentation, whereas academia can contribute with expertise in, for example, CFD (see also below) and detailed knowledge related to the cellular behavior at large scale to develop a better understanding of the major scaling-up challenges. Due to the physical differences between scales, the flow patterns in the bioreactors will differ. In bench-scale bioreactors, perfect mixing of the broth can be assumed, while in a large bioreactor the mixing time for achieving 95% homogeneity (θ95) can be in the range of minutes [40]. This can lead to oxygen starvation in the medium if the degree of mixing is too low [41]. Furthermore, glucose, pH, and temperature gradients are all likely to emerge in production-scale fermentation [42, 43]. These heterogeneities in the environment of the organism will affect biomass growth and are one of the primary concerns when dealing with process scaling. The Reynolds number is another way of clarifying the differences in flow regime as it also changes between scales. The same fermentation broth (i.e. same rheological parameters) in bioreactors with equally scaled dimensions and with the same specific power input will give a Reynolds number in the turbulent flow regime in a production-scale fermenter while in a bench-scale fermenter





and explains why the mass transfer of filamentous broths is limited in large scale, which can be observed as the squares in Figure 1.

3.2 Scaling down

Figure 1. The volumetric power input (kW/m3) plotted for different scales of bioreactors while conserving oxygen transfer rate (mol/L/h) between the processes. Both plots are calculated examples based on conserving the OTR from large scale at smaller scales. Circles (䡩) represent water-like broth, while squares (▫) represent a highly viscous filamentous broth. Information on the host organism is not available for proprietary reasons. Reprinted with permission from Elsevier [44].

it will yield a Reynolds number in the transitional or laminar flow regime [40, 44]. These differences in mixing properties will lead to differences in mass and heat transfer in the processes [45]. Mass transfer of oxygen to the broth and carbon dioxide removal are important for the cellular response. The volumetric mass transfer coefficient of oxygen (kLa) is often used as a critical parameter for scaling purposes [46], meaning that scale-up then relies on maintaining kLa constant across scales. Other important scaling parameters include the oxygen transfer rate (OTR), the volumetric power input (P/V) and the superficial gas velocity (vg). It is important to mention that it is not possible to conserve all parameters across scales, and a suitable compromise has to be found. Figure 1 shows the specific power consumption at different process scales with a conserved OTR. As can be seen from Figure 1, the power requirement for agitation in large vessels is lower than that in small vessels, which is due to the effect of the higher back pressure and the increased pressure from the liquid column that will facilitate oxygen mass transfer. In order to achieve a high volumetric power input in small bioreactors, the agitation speed has to be very high, which leads to higher shear rates in the smaller vessels. Fermentation broths of filamentous organisms can exhibit non-Newtonian flow behavior. The viscosity of nonNewtonian broths is dependent on the shear rate and thus the difference in shear rate across scales will lead to differences in apparent viscosity [47]. This in turn affects kLa


Ideally, performing dedicated fermentation experiments in large scale could avoid all problems related to scaling up a process, but is not done in practice since it is prohibitively expensive. This is not only due to the large consumption of substrate and energy, but especially due to the loss of production time, for example when an experiment would result in off-spec product. In fermentationbased production of pharmaceuticals, where good manufacturing practices (GMP) applies, performing experiments in large scale is usually out of the question. As a consequence, it is important to realize that scaling down is also a major challenge in industrial fermentation technology. The overall goal of performing experiments in small-scale equipment is to allow investigation of the effect of process parameter changes on the process performance under a set of conditions that are relevant for production-scale reactors. Most biotechnological processes are indeed designed for existing equipment, that is the full-scale fermentation tanks are installed and are then used for several decades. As a consequence, the available process equipment ranges are already known before a new process is designed, and the bench-scale development can thus be focused toward exploiting the potential for maximizing volumetric productivity on condition that a suitable down-scaled version of the full-scale reactor is available, such that one is sure that the results obtained in the down-scaled reactor are relevant for production scale. With requirements for cost-effective, rapid process development and optimization, good understanding of critical process parameters, successful technology transfer and fast time to the market, work at small scale became critical for gaining initial knowledge and also a potential bottleneck in further advancement. Hence, no wonder that small-scale systems – sometimes the term “ultra scale down” is even used – have received increased attention in the last few decades compared to well-established bench-scale stirred tank reactors (STR). The aim of detailed studies of small-scale fermentation systems is on the one hand to gain a better understanding of the engineering fundamentals of small shaken or stirred systems already in use while paying special attention to the possibility for implementing improvements (microtiter plates and shake flasks), and on the other hand to create completely new devices (microfluidic devices, microbioreactors, and milliliter-scale stirred systems) that are considered to be suitable scale-down versions of larger bioreactors. However, experience has shown that each smallscale system brings benefits as well as problems during process development across scales. Some of these systems are presented in Table 1.

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Table 1. Advantages and disadvantages of small-scale systems in correlation with scale-up/down [88–92]

Small-scale systems

Advantages

Disadvantages

Microtiter plates

Standardization and high-throughput (if automated robotic liquid-handling platform exist) Not labor intensive

Expensive (robotic hand) Defined culture conditions Process control – pH, DO Evaporation, sterility Shaking and surface aeration – two main reaction engineering differences compared to STR Limited sampling – end point measurement Feeding capability (e.g. fed-batch)

Shake flasks

Simple Easy handling Inexpensive Bigger volume compared to microtiter plate Easy to maintain temperature

Defined culture conditions Process control – pH, DO Shaking and surface aeration – two main reaction engineering differences compared to STR Limited sampling – end point measurement Feeding capability (e.g. fed-batch)

Microfluidic devices, microbioreactor

Low cost Potentially high-throughput Modification Not labor intensive – disposable reactors Different feeding strategies Better defined culture condition compared to shake systems

No standardization Small volume Evaporation Microfluidic connections Analytical methods are not sufficiently developed for such a small scale

Milliliter-scale bioreactors

Geometric similarity to STR Process control – pH, DO Feeding capability High-throughput (if automated robotic liquid-handling platform exist) Sampling possibility

Expensive Not standardized yet Complex

Despite the disadvantages, microtiter plates and shake flasks are still very frequently used systems. Their application allows to analyze different fermentation conditions, such as growth rate, and to test the influence of such a condition on the primary recovery of heterologous proteins, as for example demonstrated for antibody fragments expressed intracellularly in Escherichia coli [48]. Development of non-invasive optical sensing together with adequate knowledge of engineering fundamentals provided better process understanding and process monitoring in these systems. Isett et al. [49] demonstrated scalability from 24-well plate (4–6 mL) to laboratory-scale stirred tank (20 L) using S. cerevisiae, while Islam et al. [50] showed predictive scale-up from microwell plate(2 mL) to laboratory- (7.5 L) and pilot- (75 L) scale using E. coli. Both authors indicated that scale transfer from a shaken to a stirred system is feasible if proper scaling-up criteria are chosen [50]. Milliliter-scale stirred bioreactors are mostly developed with an idea to maintain geometrical similarities with bench-scale reactors which according to us gives them a leading position in application of existing scale-up methodologies based on gassed power per unit volume; agitator tip speed; constant dissolved oxygen tension

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(DOT); oxygen mass transfer capacity (kLa); or mixing time [51, 52]. A few commercial examples of milliliterscale stirred bioreactors are depicted in Table 2 together with commercial platforms that are employing orbital shaking as a way of mixing. An interesting and industrially very relevant trend within scale-down reactors is the development of those reactors consisting of two STRs connected to each other, or consisting of an STR connected to a plug flow reactor (PFR) [53]. The availability of two separate reactors with a recycle in between allows investigating the effect of gradients, for example of oxygen or substrate, in a well-controlled environment. Such gradients do exist in large scale, as for example documented in the study by Enfors et al. [54], and are known to have an influence on productivity of the host organism in large scale. Neubauer and Junne [55] have reviewed such multi-compartment scaledown reactors.

3.3 Mass transfer, morphology, and rheology Mass transfer is one of the crucial criteria for high biomass systems as O2 and nutrients have to be distributed, and possible toxic compounds, for example CO2, have to be





Table 2. Examples of commercially available high-throughput platforms for microbial fermentation and cell cultures

High-throughput platforms

Working volume

Mode of operation

Agitation type

Parameters monitored

BioLector (m2p-labs) [93]

0.1–2 mL

Batch, fed-batch

Orbital shaking

2mag bioreactor 48 [94] Pall Micro – 24 Reactor System [95, 96] AMBR 24 Reactor System (TAP) [97]

8–15 mL 3–7 mL 10–15 mL

Batch, fed-batch Batch, fed-batch Batch, fed-batch

Stirred Orbital shaking Stirred

Biomass, NADH, GFP, pH, DO, T pH, DO, OD, T pH, DO, T pH, DO, T

removed. High biomass is, of course, a relative term and very much dependent on the specific cultivated organism. It is reported that mammalian cells can reach a concentration of about 10 g/L with 2 × 107 cells/mL [56], while at least 50 g/L [57] can be achieved with single cell organisms. Mass transfer is a phenomenon composed of different sub-processes, which are strongly dependent on the viscosity of the fermentation broth. The rheology of the broth is thereby at the same time dependent on the biomass concentration and the morphology of the cells used as expression system [58]. It is the complex relation between all these variables that makes scaling up/scaling down difficult. In order to have proper oxygen supply, stirring and aeration are a prerequisite for almost all types of cells and even for cell cultures [59]. However, agitation may also lead to changes in morphology which in turn will affect product formation [60, 61]. Therefore, with an aerated STR setup the question of shear sensitivity of the employed organisms arises, and there has been an ongoing discussion around the topic. Mammalian cells are supposed to be prone to higher shear rates because of their size and the lack of a cell wall. It is discussed that it is just a perception that originates from experiments employing cell cultures immobilized on cells on microcarriers – with cells dying when they were detached from them due to the hydrodynamic forces [56]. In fact, it is possible to run Chinese hamster ovary (CHO) cells in a 2-L aerated STR with agitation rates up to 776 RPM with just slightly reduced productivity for monoclonal antibodies. This experiment was designed to scale down a production process based on a local energy dissipation rate of up to 1.015 W/kg and a tip speed of 2 m/s [62]. Bacteria and yeast on the other hand appear to be organisms that are more resistant to mechanical stresses, and it has been suggested that their small size is the reason for their resistance to shear stress: they are smaller than the Kolmogorov micro-scale of turbulence [63]. Therefore, it is believed that the morphology is not affected by mechanical mixing. Nienow [64] reported no cell damage by agitation based on flow cytometry. Nevertheless, they reported a reduction in biomass concentration, but increase in viability when scaling up a process; this


Parallelization 48/96 48 24 24/48

was attributed to the poor homogeneities in industrialscale reactors [64]. The most challenging expression systems are the filamentous microorganisms in terms of mass transfer and rheology, since their morphology, both at the microscopic and macroscopic level, will have a major influence on mass transfer. At the microscopic level, the optimal morphology for a given bioprocess varies and cannot be generalized, and relies on the desired product [65]. Also, dependent on the fermentation and the employed strain, pellet formation can occur. The pellet formation process can start as early as aggregation of spores when inoculating the media and can be completed in a couple of minutes [66]. This makes the requirements for analysis methods to document the change of morphology rather demanding. A least, pelleted biomass is spherical and almost non-transparent which could enable the utilization of classic particle size analysis like laser diffraction [67] and focused beam reflectance [66]. For tracking biomass development, also for other organisms, different technologies like capacitance probes, multi-wavelength spectroscopy, scanning di-electric spectroscopy, turbidity measurement and, of course image analysis (semi- and automatic), are available [68–70]. However, their applicability remains limited due to the poor experience with the use of the models developed on such data across scales or processes. For example, Petersen et al. [71] developed a multivariate partial least square (PLS) model, using laser size distribution and biomass concentration to predict rheological properties of filamentous fermentation broth; nevertheless, their model was not applicable to different scales or strains. In some processes, a pellet-type morphology is preferred since it allows for simplified downstream processing and yields a Newtonian fluid behavior of the medium (macroscopic level), which results in low aeration and agitation power input. However, the pelleted morphology results in nutrient concentration gradients within the pellet. This situation is not observed in freely dispersed mycelia, although gradients can occur in such a system dependent on the mixing time of the reactor system, due to their high broth viscosity [72]. Thus, freely dispersed mycelia allow enhanced growth and production, which has been attributed to the morphology having an influence on the production kinetics at the microscopic level,

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for example higher enzyme secretion from a more densely branched mutant of A. oryzae [73]. So, the morphology of filamentous fungi is double edged as the productivity as well as the fermentation conditions can be affected by the outer appearance of the fungus. The challenge is to separate the effects to be able to definitely connect gains in productivity to the correct setting: the balance between effects resulting from bulk gradients in the fermentation media compared to gradients inside the biomass, and between the effect of a higher OTR at lower viscosities versus better growth/secretion of proteins at higher biomass concentration. Inducing a shift in morphology usually requires a disturbance of the system/equilibrium in the form of adding particles, changing pH and salt composition to start pellet formation from a free mycelial growth or to employ N-starvation to achieve the reverse effect. All of these methods have the potential to harm productivity, making it at least difficult to compare different morphologies directly. Finally, one of the traditional problems when working with fermentation broths – non-Newtonian fluids especially for fungal fermentations – is at which shear rate the viscosity should be evaluated. Until now, it has not been possible to estimate a reliable shear rate in the fermentation tank itself. Considering a typical STR, it is well known that the shear rate is at its maximum at the agitator tip, and it decreases when approaching the vessels walls; therefore, the way of calculating shear rate can be expressed as the maximum or the average shear rate. However, hitherto it is not clear which shear rate is governing the mass transfer processes, and the way of calculating this shear rate is limited to the Metzner and Otto correlation [74]. It should not be forgotten that this empirical correlation was developed for Reynolds numbers in the laminar and transitional regime. Thus its applicability is limited to laboratory and pilot-scale fermenters, even though it has been typically employed for calculating shear rates in full-scale fermenters as well [44]. Therefore, it still remains challenging to estimate/calculate a reliable shear rate to evaluate the viscosity across scales. This problem however is not typically experienced in bacteria and yeast cultivation since they exhibit a Newtonian behavior due to the spherical shape of their morphology [75]. This assumption facilitates the work with these types of microorganisms and it is one of the reasons why many fermentation process optimization studies are focused on filamentous fungi.

3.4 Measurements for control The available sensors in industrial bioreactors are most often limited to pH, dissolved oxygen (DO) and temperature sensors at a single location in vessels with large volumes, often with concentration gradients [42]. These sensors display an average value for the entire process which can be correlated to the processes in the vessel, but the

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available information on the spatial heterogeneity in the vessel is very limited. A PID controller is the standard method used to keep a controlled variable at or close to its set point or set point trajectory. However, the downside of the PID controller is that it cannot guarantee that the fermentation is operated optimally. The set points (which in batch or fed-batch operation can be time-varying trajectories) which the controlled variables must follow only result in an optimal operation under certain nominal conditions. When disturbances such as changes in substrate quality or composition, moisture content, ambient temperature, etc. take place, the set points should be modified accordingly, which is often called supervision or optimization. In industrial fermentations, supervision is difficult for the following reasons: (i) The most common controlled variables, DO and pH, are only indirectly linked to the optimal operation of a fermenter, that is the operation that leads to the highest volumetric productivity. Important variables such as substrate, product, or by-product concentration are only sporadically monitored (and less frequently controlled). Biomass concentration is seldom measured online and it rarely plays a role in automatic control of industrial-scale processes. Accurate online measurement of biomass and substrate/product can be difficult in fermentations featuring a large number of compounds, making pH and DO the most common variables for automatic control. However, optimal operation and fulfillment of quality constraints cannot be guaranteed with pH and DO control only, thus increasing the importance of advanced monitoring. (ii) Models can be used to synthesize controllers that operate close to optimal conditions and to determine optimal trajectories for (fed-)batch operations. Despite the advances in fermentation modeling, many industrial operations lack sufficiently accurate models that can be used for this purpose. For instance, Smets et al. [26] reviewed optimal feed rate strategies and pointed out how this approach is hampered in reality by model and sensor uncertainty; nevertheless, they derived a number of near-optimal heuristic strategies, proving that models are useful to gather a major insight in the process behavior. Complementary approaches are investigated to overcome these limitations: robust control design with minimal modeling, and advanced modeling based either on first-principles or the so-called hybrid models (using both first-principles and data-driven models). It is also worth mentioning that one of the major limitations in online measurements and the introduction of new sensors for process monitoring originates from GMP aspects, especially in the pharmaceutical biotechnology industry. Fulfilling GMP regulations is not a trivial task, and is a major bottleneck in process development and optimization. A method to further improve data quality of





online sensors without violating the GMP regulations is to use a soft-sensor approach where already existing data is interpreted with simultaneous computational analysis in order to improve the quality and thus prediction of the sensors [76]. In such a soft-sensor robust online measurements, which are not subject to time delay, are used to calculate the expected value of new parameters which are desirable to control, such as biomass, specific growth rate, or oxygen transfer capacity [77, 78]. Soft sensing can also be a tool for industrial application as part of a framework for quality by design (QbD) and PAT principles since it provides a method for on line monitoring of the process [79, 80].

4 Discussion It is clear from the previous sections that the development and scale-up of fermentation processes is of a very complex nature. This is also an important reason explaining why after so many years of use of continuously stirred tank reactors (CSTR), this technology has so far not yet changed substantially. Experience with such CSTRs has been built up gradually, and gives the process scientist a kind of a “comfort zone” when operating such equipment where the change of a single parameter in the system might already have an impact on several output variables. A second factor that is standing in the way for introducing significant changes to the reactor design is the cost related to establishing a new full-scale fermenter, be it a CSTR or any other configuration. With the available knowledge on the occurrence of gradients in industrial-scale CSTRs [42], and considering that it is very difficult to access experimental data characterizing the gradients occurring inside a large industrial-scale bioreactor, it becomes increasingly appealing to model such systems. Until now, this has been especially attractive for miniaturized systems, where laminar flow conditions are often present, and therefore CFD methods have a high predictive ability since no turbulence models have to be applied. However, the prediction quality becomes more and more reliable for laboratory, pilot and full-scale fermentations as well, and hence we foresee that such fluid dynamic models become increasingly used as powerful tools for evaluation of mixing times, mass transfer, shear stress levels, and dead volumes, to name a few well-known challenges for the process scientist. Such numerical investigations can thereby increasingly contribute to the design of experiments, and will also help to reduce the requested amount of experiments, since some of the configurations evaluated with the CFD simulations can be excluded beforehand from the experimental planning on the basis of the simulation result. This includes obviously the geometric reactor design, but it is especially helpful when different operation patterns are investigated like, for example fed-batch operations or the analy-


Figure 2. Velocity vector profile of a full-scale reactor.

sis of inhomogeneity of the concentrations of important variables such as the biomass concentration, substrate or product concentration. In Figs. 2 and 3, the complex fluid dynamic characteristics resulting from applying CFD are illustrated for two different reactors: (i) a full-scale CSTR (>150 m3); and (ii) a miniaturized reactor (ca. 1 mL). Both numerically investigated mixing times have shown an impressive convergence with the experimental mixing time.

Figure 3. Streamlines and concentration plot for a milliliter-scale reactor.

9




Specifically with respect to filamentous fungi fermentations, CFD models are used in order to understand the impact of shear stress on the morphology of the organism as well as for the mass transfer from the bulk solution to the organisms [81–83]. But the real potential of CFD models is, according to us, the coupling of CFD models with biological kinetic reaction models. Such kinetic models are available for several commonly used production hosts for perfectly mixed reactors, for example at bench-scale, and can thereby be readily implemented in CFD models. One major advantage of CFD is that the model parameters identified at laboratory-scale in well-mixed reactors can be more readily translated to large scale, since the parameters do not have to be retuned in order to compensate for inadequate description of reactor hydrodynamics, as is the case when using a standard system of ordinary differential equations to describe biological phenomena in a large tank. In this way it is also possible to switch desired reactions on and off to investigate their impact on concentration dynamics, and even more complex models as, for example population balance models (PBM) have already been implemented [84, 85]. Such models might yield better insight into the impact of spatial inhomogeneity on the individual cell [17, 84] and on the fermentation performance, and could thereby contribute to improved understanding of the processes, also at industrial scale, and hence also to more efficient operation and improved design of new processes. Furthermore, theoretical numerical investigations can be implemented and yield new ideas for optimized reactor configurations. This was, for example demonstrated by Schäpper et al. [86] where a cultivation of surface adhered yeast cells was optimized in a microbioreactor setup to yield a considerably improved product formation rate. Furthermore, different model hypotheses can be implemented and tested in simulation before seeking experimentally validation, as shown by Krühne et al. [87], where the impact of shear stress on a tissue engineering cultivation in a micro-scaled scaffold has been studied.

5 Conclusions The use of common engineering tools, such as mathematical models and optimization techniques, in fermentation process development and optimization is seriously challenged by our lack of understanding about scaling up/scaling down fermentation processes and phenomena such as the influence of morphology on broth rheology and mass transfer. Furthermore, establishing suitable online measurements for control is not that straightforward either. CFD is now reaching a level of maturity such that CFD can start supporting the process scientist in addressing some of the engineering challenges, that is CFD can be increasingly used to test new ideas, both with respect to reactor operation and design, and to support scaling up and scaling down bioreactors.

10

The authors acknowledge financial support of the following organizations: The Danish Council for Strategic Research, project “Towards robust fermentation processes by targeting population heterogeneity at microscale” (project number 0603-00203B). The Novo Nordisk Foundation, project “Exploring biochemical process performance limits through topology optimization.” Region Zealand, the European Regional Development Fund (ERDF), CAPNOVA, CP Kelco, DONG Energy, Novo Nordisk, and Novozymes for funding the BIOPRO project (www.biopro.nu). The authors declare no financial or commercial conflict of interest.

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