Introduction

Foreign gene expression in filamentous fungi as eukaryotic hosts has gained increasing popularity [1]. Their morphology can be classified into two main types of dispersed and pelleted. Characterization of mycelial morphology is essential for the design and scale-up of fungal fermentations. Understanding the relationship between mycelial morphology, oxygen transfer, and overall productivity is critical to effectively optimize the fermentation conditions [2].

The performance of stirred tank bioreactors, including cell metabolism and viability, is susceptible to the shear sensitivity of the cell culture medium. The elevated agitation intensity can lead to shear damage, reducing the efficacy of the fermentation process [3]. Nienow [4] suggested using the term “fluid dynamic stresses” instead of “shear” to describe the effect of the mixing hydrodynamics on cells within the bioreactor. Currently, there is ongoing debate regarding the influence of hydrodynamic stress on fermenter performance. For instance, animal cell cultures are often considered shear sensitive, because they do not possess a protective cell wall [3, 5, 6]. Consequently, bioreactors containing animal cells typically operate under suboptimal conditions, leading to significant inhomogeneities that act as a barrier to successful scale-up [7, 8]. On the other hand, some studies showed that the agitation intensity has no impact on the production of protein therapeutics, with the exception of mycelial broth, such as filamentous fungal cell cultures [4]. In fact, when the shear stress is stronger than the tensile strength of a hyphae, cell fragmentation happens [9]. Therefore, the impact of agitation-induced hydrodynamic stress on cell cultures varies depending on the cell type [10].

The growth of filamentous organisms can give rise to large and complex shapes, leading to the production of viscous, rheologically complex broths [11, 12]. Because of that, high agitation intensity is often necessary to meet the mass transfer requirements of the fermentation process. However, excessive agitation can result in morphological changes and hyphal fragmentations, which may adversely affect product expression [13]. As a result, agitation intensity in fungal fermentations is carefully adjusted to provide suitable oxygen transfer and mixing while preventing adverse effects on the microorganisms. Among various methods for quantifying fluid dynamic stresses, only the energy dissipation/circulation function (EDCF) has shown strong correlation with the morphology of the fermentation broth [14]. This function establishes a connection between the frequency at which mycelial pass through the impeller's swept volume and the rate of specific energy dissipation within that region [2].

In spite of the influence of agitation intensity and high EDCF values on mycelial morphology, some authors have raised questions regarding the direct influence of mycelial shape on fermentation productivity [1, 13]. Nienow [15] stated that an increased EDCF value results in reduced clump size and lower viscosity due to hyphae fragmentation. Indeed, the lower viscosity resulting from the increased EDCF value enhances mass transfer and homogeneities in the system. While the impact of high EDCF values on the productivity of filamentous fungi remains a subject of controversy, existing literature unanimously agrees that altering EDCF values can lead to changes in the shape and size of fungal clumps. On the other hand, mycelial morphology plays a critical role in determining fermentation performance, encompassing aspects, such as mass transfer, fluid viscosity, and mixing hydrodynamics [2]. As a result, based on our literature review, EDCF is one of the most crucial bioreactor characteristics that must be considered in fermenter design and scale-up.

Bioreactor scale-up introduces significant changes in the microorganism environment. Achieving a good mixing at larger scales can be challenging, leading to the formation of dissolved oxygen heterogeneities in aerobic fermenters [16, 17]. Such heterogeneities, commonly observed in poorly mixed large-scale bioreactors, can have substantial impacts on cells [18, 19]. The characteristics of these heterogeneities depend on factors such as fluid dynamics and nutrient dispersion [20]. In fact, homogeneity becomes increasingly important at large scales [21] and heterogeneities within the bioreactor can result in various metabolic responses and production of undesired byproducts [5, 22]. In addition, filamentous fungal fermentations pose particular difficulties in scale-up due to the complex rheology of the broth, significantly affecting mass transfer rate [13, 23].

Nowadays, mixing system retrofitting has emerged as a crucial practice to reduce process costs and enhance mixing efficiency, specifically during the scale-up process. Due to the rise in popularity of mixing system retrofitting caused by the development of single-use bioreactors [3, 24], careful consideration is required while switching to such bioreactors [7]. Retrofitting offers numerous advantages, including the reduction of mechanical instabilities and vibrations in the mixing system, minimizing shear rates, and enhancing mass transfer capabilities. Among the prevailing and easily implemented retrofitting approaches, changing the impeller type stands out as one of the most common methods. By selecting a suitable impeller type, the performance and functionality of the mixing system can be significantly improved [2, 25, 26].

Generally, impellers can be classified into two main types of the axial-flow and radial-flow impellers. Radial-flow impellers are characterized by their flow direction perpendicular to the impeller shaft. The Rushton turbine as a radial-flow impeller generates a staged-flow pattern with a well-mixed zone near the impeller's blade [26]. On the other hand, the axial-flow impellers have a flow direction parallel to the impeller shaft, offering improved top-to-bottom blending. Consequently, using a lower power number impeller that possesses good air handling capabilities instead of a higher power number impeller allows for cost effective retrofitting [26]. Moreover, impellers can be categorized as either low shear or high shear. It has been suggested that low shear impellers may cause less damage to filamentous organisms compared to high shear impellers when operated at similar specific power [27]. Nevertheless, further investigations have revealed that this presumption is not accurate. In fact, so-called low shear impellers can possess high-energy dissipation/circulation function (EDCF) values [28].

Recently, computational fluid dynamics (CFD) has emerged as a highly promising method for studying shear rate and transport phenomena occurring within bioreactors [29,30,31,32]. The adoption of CFD tool for defining a design space has gained popularity in pharmaceutical applications, offering assurance of product quality. CFD simulations can predict optimum mixing in bioreactors, proposing a design space that ensures consistent quality. By employing the CFD method, the impact of hydrodynamic stress, fluid flow patterns, and gas dispersion uniformity can be optimized. Deviations from this defined design space are considered changes and may trigger a regulatory post approval change process in pharmaceutical industry [33].

In addition to radial-flow and axial-flow impellers, there is another specific type known as tangential-flow impellers. These impellers are suitable for the bulk movement of a highly viscous fluid within a bioreactor. It has been demonstrated that the simultaneous use of a tangential-flow impeller, such as an anchor impeller, in conjunction with a central impeller (either axial-flow or radial-flow) can significantly improve the homogeneity of gas dispersion inside the bioreactor [34, 35]. This combined system, known as a coaxial bioreactor, enhances the mixing process while requiring a lower power input. The combined effect of both anchor and central impellers in a coaxial bioreactor leads to improved hydrodynamics and enhanced mixing efficiency [36,37,38].

The use of model fluids has become a common practice to replicate the viscous fermentation broth from a rheological perspective [39,40,41]. This approach allows for ease of operation and consistency in experimental conditions across different batches and experiments. For filamentous fungi fermentation, abiotic systems can mimic the fermentation broth using a non-Newtonian model solution [42] that exhibits shear-thinning behavior [43,44,45]. A comprehensive review of the literature highlights the importance of EDCF in the design and scale-up of filamentous fungus fermentation. The highly oxygen-demanding nature and complex rheology of the fermentation process make scale-up studies even more intricate. Consideration of the interplay between oxygen transfer and mechanical stress is crucial in optimizing and scaling up the bioreactor [25, 46]. However, there is a lack of published data addressing the effect of the shear environment and mass transfer rate inside a coaxial bioreactor containing a shear-thinning fluid, especially during the scale-up process. Coaxial mixers are recognized for their enhanced mass transfer coefficients at lower aeration rates. Therefore, this study focused on lower aeration values compared to those typically used in large-scale aerobic fermentations in industries.

The aim of this investigation was to assess the hydrodynamic stress and central impeller retrofitting influence on the performance of an abiotic system using a coaxial bioreactor during the scale-up process. To achieve this objective, we examined the effects of impeller type, aeration rate, and agitation rate on shear environment, mass transfer rate, bubble size distribution, and spatial inhomogeneities. The findings of this study hold significant value in optimizing the design, scale-up, and operation of fungal fermentations.

Materials and methods

Experimental setup

Two flat-bottomed cylindrical bioreactors were fabricated. Air was introduced inside the vessel through a ring gas distributor. The liquid height inside the bioreactor was equal to the vessel diameter. A schematic diagram and dimensions of both apparatuses are shown in Fig. 1. Each mixing system was furnished with a central impeller and an anchor impeller mounted on two separate shafts. Both the anchor and central impeller were rotating in the same direction (co-rotating mode). The working volume of the small-scale and large-scale tanks were about 50 and 173 L, respectively. In this paper, a coaxial bioreactor furnished with a central impeller of an elephant ear, a Scaba, a pitched blade, and a Rushton turbine was used. It should be noted that all axial-flow impellers employed in this study functioned in up pumping flow mode.

Fig. 1
figure 1

Experimental setup

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Abiotic system

Instead of the fermentation broth, a suitable alternative is simulated medium that replicates the desired rheological characteristics to mimic the viscosity, shear rate, and other relevant rheological properties of the actual fermentation broth. Therefore, a model fluid exhibiting shear-thinning behavior was selected. The rheological properties of filamentous fungus fermentation broths are influenced by the formation of clumps or aggregates within the broth. As a consequence, the rheological behavior of these fermentation broths can encompass a diverse range of shear-thinning behavior. Hence, in this study, 0.5 wt% NaCMC (sodium carboxymethyl cellulose) solution was used as an abiotic system. The rheological properties of the liquid phase were analyzed using the Bohlin C-VOR Rheometer 150 (Malvern Instruments, UK) at \(22\pm 1^\circ{\rm C}\). It was found that the working fluid follows power-law non-Newtonian model with the power law of 0.65, and consistency index of 2.36 Pa sn.

The fluid density was 0.965 kg/m3 and the kinematic diffusion was approximately 1.71 e -9 m2/s [36]. Furthermore, the gas phase was the air at \(22\pm 1\mathrm{^\circ{\rm C} }\).

Power consumption

The total power consumption resulted from the combined power consumed by both the central impeller and the anchor. Therefore, in this research, the torque generated by each impeller was individually assessed using dedicated torque meters from S. Himmelstein Company (USA). Subsequently, the obtained torque measurements were adjusted by subtracting any residual torques attributable to friction. The net power consumption of each impeller was calculated using the following equation:

$$P=2\pi NM,$$
(1)

where M and N are the net torque and the impeller rotational speed, respectively.

Gas hold-up

To measure the gas phase retention within the mixing vessel, the electrical resistance tomography (ERT) method was applied. Each bioreactor was furnished with four sensor planes at different heights. Each plane was comprised of 16 stainless steel electrodes with a rectangular shape. The height of sensor planes and electrode dimensions are illustrated in Fig. 1. In this technique, the conductivity profile of the multiphase mixture was measured by an ERT system. By utilizing the simplified Maxwell equation, the obtained conductivity profile was converted to a gas phase volume fraction [38]. Hence, the gas hold-up distribution was obtained.

Mass transfer coefficient (\({{\varvec{k}}}_{{\varvec{L}}}{\varvec{a}}\))

The dynamic gassing-in method was employed to determine the volumetric mass transfer coefficient (\({k}_{L}a\)). In this method, the oxygen was stripped out of the filled mixing tank via aeration with nitrogen. Then, oxygen was introduced, and the oxygen concentration was recorded until the upper dissolved oxygen (DO) probe reached to its 90% of saturation condition.\({k}_{L}a\) was determined by employing the following equation [41]:

$${C}_{me}={C}^{*}+\frac{{C}^{*}-{C}_{0}}{1-{\tau }_{r}{k}_{{\text{L}} }a} \left[{\tau }_{r}{k}_{{\text{L}} }a{\text{exp}}\left(-\frac{t}{{\tau }_{r}}\right)-{\text{exp}}({k}_{{\text{L}} }a .t)\right],$$
(2)

where \({C}_{me}\), \({C}^{*}\), \({C}_{0}\) are oxygen concentration measured by the probe, oxygen concentration at saturation condition, and initial oxygen concentration, respectively. In addition, \({\tau }_{r}\) is the probe response. As shown in Fig. 1, each bioreactor was equipped with three different DO probes at various heights. The response time for Probe #1 was 49 s and for two other probes was 20 s. The overall mass transfer coefficient was calculated as the arithmetic mean of the results attained from each individual oxygen probe.

Computational fluid dynamics (CFD)

In this study, the abiotic system inside the coaxial bioreactor was modeled using the Eulerian–Eulerian multiphase approach. The turbulence within the coaxial bioreactor was simulated using a standard k-ε model, complemented by an enhanced wall treatment function to improve accuracy near the boundaries. The enhanced wall treatment method combines the advantages of standard wall treatments for low Re numbers near the wall with wall functions for approximating the wall's effect on fluid flow near the wall for high Re numbers. The bubble-induced turbulence viscosity was considered using the Sato and Sekoguchi model [47]. The modified drag model proposed by Brucato et al. [48] was used to couple the gas and liquid momentum balance equations.

Population balance method (PBM)

To address the variation in air bubble sizes within the bioreactor, the population balance equation (PBE) was incorporated into the model via utilizing the quadrature method of moments (QMOM) approach. The QMOM method involved tracking six moments to accurately represent the distribution of bubble sizes [37]. This method stands out for its efficiency in terms of computational resources compared to other PBE-solving techniques. To simulate bubble breakage phenomena, the kernel introduced by Laakkonen et al. [49] was utilized, enabling a realistic representation of the breakage process. For bubble coalescence, the model suggested by Luo [50] was utilized.

Mass transfer coefficient and EDCF model

The liquid-side mass transfer coefficient was determined using the penetration model proposed by Higbie [51]

$${k}_{L}=\frac{2}{\sqrt{\pi }}\sqrt{{D}_{L}}{\left(\frac{\varepsilon }{\upsilon }\right)}^{1/4};$$
(3)

in this model, \(\varepsilon\) is the turbulent energy dissipation rate, \({D}_{L}\) corresponds the kinematic diffusion coefficient, and \(\upsilon\) refers liquid kinematic viscosity.

This study employed a method to quantify the shear environment by integrating the turbulence dissipation rate with the frequency of mycelium exposure to the high shear zone, leading to the establishment of the energy dissipation/circulation function (EDCF). The energy dissipation rate was calculated based on the volume averaged turbulence energy dissipation rate acquired from the CFD simulations within the central impeller swept volume. The frequency of the mycelium exposure or break-up frequency is \(1/{t}_{c}\). Here, \({t}_{c}\) is the circulation time and was calculated as follows [14]:

$${t}_{c}=\frac{V}{{Fl}_{g}N{D}^{3}};$$
(4)

in this equation, V represents the bioreactor volume. N and D correspond to the rotational speed and diameter of the central impeller, respectively. In addition, \({Fl}_{g}\) is the gas flow number of the central impeller indicating its pumping capability

$${Fl}_{g}=\frac{q}{N{D}^{3}};$$
(5)

in this equation, q is the fluid flow rate discharged from the central impeller swept volume and it was determined from the results obtained from the CFD simulations [52].

CFD solver

A commercial CFD package (ANSYS FLUENT 2022 R2) was used. The QUICK and the second-order upwind techniques were employed to discretize the volume fraction and momentum, respectively. The tetrahedral mesh was applied to model the mixing system geometry. The boundary conditions used in this study are provided in Fig. 2. It should be noted that no slip boundary condition is valid for both gas and liquid phases. In addition, the sliding mesh method was used to model the all moving zones [53].

Fig. 2
figure 2

Boundary conditions of the numerical model

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In this study, the minimum and maximum bubble size were set to 1 and 100 mm, respectively. The Miller [54] model was used to predict the bubble size on the sparger

$${d}_{{\text{b}}}={\left(\frac{6\sigma {d}_{0}}{g\left({\rho }_{l}-{\rho }_{g}\right)}\right)}^\frac{1}{3},$$
(5)

where \({d}_{0}\) is the diameters of the holes in the sparger, and \(\sigma\) is the surface tension of the fluid.

Simulations were conducted with a time step of 0.001 s. The SIMPLE algorithm was applied to perform the pressure–velocity coupling. The CFD simulations were solved for a minimum of 50 rotations of the central impeller. Also, a 120 partitioned parallel solver was applied to perform the simulations, utilizing the computational resources available in the Niagara Supercomputing Facilities. Each simulation required about 24 h of clock time.

Mesh independence test and model validation

To determine the most suitable mesh size, a test for grid-independence was conducted. Three distinct mesh sizes were developed for the large-scale bioreactor model with 2,224,953, 4,673,962, and 6,228,801 cells. A comparison between the results obtained from the second and third mesh sizes revealed that the difference in volume average velocity at the impeller region for the large-scale Pitched blade-anchor mixer operating at 200 rpm was less than 2.5%. Additionally, the disparity in global gas hold-up obtained through CFD was below 6.3 percent. Considering the computational efficiency in terms of computational cost, the CFD model with 4,673,962 cells was selected for the large-scale mixer. Furthermore, the grid-independence test for the small-scale bioreactor indicated that 1,673,962 cells were sufficient for simulations with adequate precision. The computational grid is shown in Fig. 3.

Fig. 3
figure 3

Computational grid

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The accuracy of CFD simulations was checked using the spatial gas hold-up findings. As indicated in Table 1, the results obtained from the CFD model demonstrated reasonable agreement with the experimental data.

Table 1 Comparison of the measured and calculated gas hold-up of the Elephant ear-anchor bioreactor (an anchor impeller speed of 10 rpm, a central impeller speed of 400 rpm, an aeration rate of 0.12 vvm, and 0.5 wt% NaCMC)
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Results and discussion

Hydrodynamic stress and mass transfer

It is important to investigate the performance of the gas dispersion system under different operating conditions while maintaining a consistent specific power consumption. To achieve this objective, the specific power consumption of the small-scale coaxial bioreactor remained constant at 1.2 kW/m3 for all operating conditions and bioreactor configurations. This was due to the fact that most agitated bioreactors containing filamentous fungi are operated under similar specific power consumption [10]. It is crucial to note that increasing the aeration rate (Q) leads to a reduction in aerated power consumption at a constant impeller speed. To prevent such a reduction in aerated power consumption during aeration, the central impeller speed (Nc) was increased by increasing the aeration rate. In fact, by increasing Nc, the negative effect of the aeration rate on the total aerated power consumption obtained by the small-scale bioreactor was eliminated. Because of that, all the data reported in Fig. 4 were obtained at the same aerated power consumption. To achieve this objective, the specific power consumption of the small-scale coaxial bioreactor remained constant at 1.2 kW/m3 for all operating conditions and bioreactor configurations. In this regard, the aeration rates and central impeller speeds used for the small-scale coaxial bioreactor are listed in Table 2.

Fig. 4
figure 4

Shear environment and \({k}_{L}a\) of the small-scale coaxial bioreactor as a function of the aeration rate: a Pitched blade-anchor, b Elephant ear-anchor, c Rushton turbine-anchor, and d Scaba-anchor (the deviation for \({k}_{L}a\) was less than \(\pm\) 1.54%)

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Table 2 Operating conditions for the small-scale coaxial bioreactor
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The shear environment obtained by the small-scale bioreactor as a function of the aeration rate is presented in Fig. 4. As shown in this figure, by increasing Q, the shear environment created inside the bioreactor increased for all coaxial bioreactor configurations. Because by increasing Q, the total turbulence intensity generated inside the bioreactor was increased. In fact, as per the discussion in "Mass transfer coefficient and EDCF model" section, the EDCF has a direct relationship with turbulence intensity. Increasing both the impeller speed and aeration rate increased the energy dissipation rate, and as a result, the mixing fluid encountered higher shear rates. Moreover, increasing the aeration rate enhanced \({k}_{L}a\) obtained by the coaxial mixer. \({k}_{L}a\) comprises the liquid-side mass transfer coefficient and the interfacial area. The liquid-side mass transfer coefficient increased by increasing the level of turbulence intensity, and on the other hand, the interfacial area is function of the gas hold-up. Since gas hold-up increased by increasing the aeration rate in this study, the \({k}_{L}a\) obtained by the small-scale mixer improved by increasing the aeration rate.

The results attained by the coaxial bioreactor furnished with an axial-flow central impeller are shown in Fig. 4a and b. It was observed that the overall EDCF values acquired with the Pitched blade-anchor mixer exceeded those achieved with the Elephant ear-anchor bioreactor. Meanwhile, the Pitched blade-anchor bioreactor exhibited greater \({k}_{L}a\) compared to the Elephant ear-anchor bioreactor.

As can be noted in Fig. 4c and d for the coaxial bioreactor equipped with a radial-flow central impeller, the shear environment achieved with the Rushton turbine-anchor bioreactor was more intense than those attained with the Scaba-anchor bioreactor. In contrast, the Scaba-anchor bioreactor demonstrated a higher mass transfer coefficient. This observation suggests that the Scaba-anchor mixer's efficiency surpasses that of the Rushton turbine-anchor bioreactor. Because, for the Scaba-anchor bioreactor, a higher \({k}_{L}a\) was achieved at the reduced shear environment.

Upon a comparison of the outcomes illustrated in Fig. 4a and b with those presented in Fig. 4c and d, it became evident that the shear environment established by the coaxial bioreactor featuring a radial-flow impeller was less intense than those achieved by the axial-flow impeller. Additionally, the axial-flow impeller exhibited a superior mass transfer coefficient when contrasted with the radial-flow impeller. The effect of the impeller type on the shear environment and \({k}_{L}a\) will be discussed in detail later in this paper.

The gas hold-up profile inside the bioreactor equipped with either a radial-flow or an axial-flow central impeller was investigated by both numerical and experimental methods. For the Scaba-anchor mixer, by increasing Q from 0.12 to 0.15 vvm, there was a noticeable augmentation in the gas hold-up profile (Fig. 5a and b). The conductivity profiles obtained from the ERT technique are illustrated in Fig. 5c and d. By adding the gas phase inside the bioreactor, the conductivity of the abiotic system decreased. Therefore, areas characterized by lower conductivity correspond to regions with higher gas retention. As presented in these figures, the gas hold-up uniformity acquired by the Scaba-anchor mixer at Q = 0.15 vvm was significantly better than that attained at a lower aeration rate. In fact, the conductivity profile obtained from the ERT method showed that by increasing Q, the air was dispersed more evenly within the bioreactor. This finding holds particular significance for highly oxygen-demanding microorganisms. Furthermore, the gas hold-up detected in the region surrounding the central impeller was higher than that observed elsewhere within the bioreactor. Hence, the majority of oxygen transfer took place in the vicinity of the central impeller region.

Fig. 5
figure 5

Gas hold-up profile of the small-scale coaxial bioreactor determined using both CFD and ERT methods: a, b, c, and d Scaba-anchor, e, f, g, and h Elephant ear-anchor

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For the bioreactor equipped with the elephant ear central impeller as an axial-flow impeller, almost similar behavior was observed as a Scaba-anchor mixer. However, the Elephant ear-anchor bioreactor generated a greater gas hold-up compared to the Scaba-anchor bioreactor when operating at the identical aeration rates and specific power consumption. This observation confirmed the findings presented in Fig. 4, which showed the superior \({k}_{L}a\) of the Elephant ear-anchor mixer. Indeed, the primary factor contributing to the higher mass transfer coefficient in these bioreactors, as opposed to those equipped with a radial-flow central impeller, was the greater gas hold-up achieved by the axial-flow impeller.

Importantly, by comparing the results depicted in Fig. 5g and h with those depicted in Fig. 5c and d, it was concluded that the gas hold-up homogeneity achieved with the Elephant ear-anchor bioreactor was significantly better than that accomplished with the Scaba-anchor bioreactor. Therefore, the microorganisms inside the coaxial bioreactor with an axial-flow impeller would encounter a uniform environment within the bioreactor.

Scale-up with complete geometrical similarity

In this section, the scale-up study was carried out with complete geometrical similarity. The scale-up was executed utilizing the constant \({k}_{L}a\) approach, which ensured the preservation of the mass transfer coefficient at the same level as that observed in the small-scale bioreactor, for the large-scale counterpart. To preserve \({k}_{L}a\) of the large-scale bioreactor at the same level as its small-scale counterpart under similar aeration rates per working fluid volume, the central impeller agitation rate was increased. The operating conditions of the large-scale system upon scale-up is reported in Table 3.

Table 3 Operating conditions for the large-scale coaxial bioreactor
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The shear environment obtained by the bioreactor at various scales and operating conditions is presented in Fig. 6. It was observed that for all bioreactor configurations whether equipped with an axial-flow or radial-flow impeller, the EDCF value achieved by the large-scale bioreactor exceeded that obtained by the small-scale. Indeed, for the large-scale bioreactor, the energy dissipation rate in the central impeller swept volume was increased to improve \({k}_{L}a\) during the scale-up with complete geometrical similarity. In other words, the key factor behind the increased shear environment in the large-scale bioreactor, in contrast to its small-scale counterpart, was primarily the heightened turbulence energy dissipation rate.

Fig. 6
figure 6

Specific power consumption and shear environment in coaxial bioreactors at various aeration rates during scale-up, without retrofitting of the central impeller: a EDCF and b specific power consumption

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Upon conducting the scale-up, the change in EDCF value achieved at a low aeration rate was minimal. However, at Q = 0.20 vvm, the EDCF value obtained by the large-scale bioreactor was considerably higher than that acquired by its small-scale counterpart. This was attributed to the necessity of significantly increasing the agitation rate of the large-scale system to enhance \({k}_{L}a\) when the aeration rate was raised.

To further elaborate on this finding, the specific power consumption (power consumption per working fluid volume) obtained by the large-scale bioreactor was examined, as shown in Fig. 6b. It was found that for the scale-up at a constant mass transfer coefficient method and with geometrical similarity, the specific power consumption of the large-scale bioreactor was lower than that of its small-scale counterpart. This difference could be attributed to the larger cavity size generated near the central impeller region in the large-scale bioreactor. As a result, the homogeneity of the mixing system decreased upon scale-up. Moreover, it can be observed that the amount of specific power consumption at a higher aeration rate was less than that discovered at a lower aeration rate. The detailed explanation for this phenomenon has been extensively discussed in the literature before [37]. To attain an equivalent level of specific power consumption (P/V) as the large-scale mixer, the total power consumption (P) had to be three times greater than that of the small-scale counterpart, because the working fluid volume of the large-scale bioreactor was three times greater than that of the small-scale. Indeed, during the scale-up process, despite an increase in power consumption and energy dissipation rate within the central impeller swept volume, the specific power consumption was reduced. This result was achieved by adopting a constant mass transfer approach and maintaining geometrical similarity between the small and large-scale setups.

The shear environment inside a bioreactor directly depends on the turbulent energy dissipation rate and hydrodynamic intensity. The turbulence energy dissipation rate and fluid flow pattern attained by the coaxial bioreactor are shown in Fig. 7. As discussed per the results illustrated in Fig. 6, for the large-scale bioreactor, the energy dissipation rate observed in the impeller swept volume was greater than that detected for the small-scale. As depicted in Fig. 7a and b, the CFD simulation results also validated this finding. Moreover, according to the CFD simulation results, the fluid flow pattern demonstrated that the fluid discharged from the central impeller blade of the large-scale bioreactor was considerably more intense than that of the small-scale bioreactor profiles for both bioreactors, equipped with either a radial-flow impeller or an axial-flow impeller.

Fig. 7
figure 7

CFD outcomes pertaining to the fluid velocity vector and the rate of energy dissipation in the coaxial bioreactor during the scale-up process: a Scaba-anchor, Q = 0.12 vvm, and b Elephant ear-anchor Q = 0.15 vvm

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Scale-up with central impeller retrofitting

In this section, the investigation focused on examining the influence of the central impeller type during the scale-up process of a coaxial bioreactor. In this study, the performance of different types of central impellers, including the radial-flow and the axial-flow impeller, was separately assessed to ensure a comprehensive and adequate comparison. As a matter of fact, a small-scale Rushton turbine-anchor bioreactor was retrofitted with a Scaba-anchor bioreactor. Under these circumstances, both bioreactors were equipped with a radial-flow central impeller. Likewise, for the bioreactor with an axial-flow central impeller, the Pitched blade-anchor mixer was retrofitted with an Elephant ear-anchor mixer upon conducting the scale-up study based on the constant \({k}_{L}a\) method.

EDCF as a function of aeration rate for the scale-up of a coaxial bioreactor is presented in Fig. 8. Interestingly, it was observed that the shear environment obtained by the large-scale bioreactor was almost the same as that attained by the small-scale bioreactor upon scale-up. Moreover, it was observed that, for both radial-flow and axial-flow bioreactors, the shear environment of the large-scale bioreactor was a little lower than that of the small-scale bioreactor upon the central impeller retrofitting.

Fig. 8
figure 8

Shear environment in coaxial bioreactors at various aeration rates during scale-up with retrofitting of the central impeller

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As previously discussed in "Introduction" section, the morphology of filamentous fungi is well correlated with the EDCF value. Consequently, it is crucial to accurately predict and maintain the EDCF value during the scale-up process to ensure consistent results. Under these circumstances, fluid rheological behavior, mass transfer requirements, and hydrodynamic stress can be adjusted before conducting a scale-up experiment.

To further elaborate on the influence of the central impeller retrofitting on the shear environment generated by the large-scale bioreactor, the results obtained from the CFD simulations are provided in Fig. 9. The rate of energy dissipation near the central impeller blades was higher. However, the amount of energy dissipation rate obtained by the large-scale bioreactor was almost the same as that gained by the small-scale bioreactor. As delineated in Fig. 9a and b, by changing the central impeller type from the Pitched blade impeller to the Elephant ear impeller, mixing uniformity considerably increased. Furthermore, the fluid flow pattern generated by the Elephant ear-anchor bioreactor demonstrated better top-to-bottom blending compared to that created by the Pitched blade-anchor bioreactor. This can be attributed to the high pumping capacity of the elephant ear impeller compared to that of the Pitched blade impeller. A similar trend was also observed during the scale-up of the radial-flow bioreactor when the central impeller was retrofitted from a Rushton turbine to the Scaba impeller. These findings illustrated the significance of the appropriate impeller selection during the scale-up of a bioreactor containing a shear-thinning fluid.

Fig. 9
figure 9

CFD outcomes pertaining to the fluid velocity vector and the rate of energy dissipation in the coaxial bioreactor during the scale-up process via central impeller retrofitting (for all cases, aeration rate = 0.20 vvm): a Pitched blade-anchor, b Elephant ear-anchor, c Rushton turbine-anchor, and d Scaba-anchor

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The question may arise as to how a scale-up with a lower EDCF resulted in an improved mass transfer coefficient. To address this question, the mass transfer profile and air mean bubble diameter were investigated and are presented in Fig. 10 for the scale-up of a coaxial bioreactor with central impeller retrofitting. The observation clearly indicated that \({k}_{L}a\) obtained by the large-scale bioreactor was not only higher than that achieved by the small-scale bioreactor but also that the uniformity of the mass transfer profile was significantly improved. In fact, the hydrodynamic inhomogeneity was reduced for the scale-up with central impeller retrofitting. This was also confirmed as per the results depicted in Fig. 9. Therefore, the energy dissipated by the central impeller inside the abiotic system was uniformly distributed throughout the bioreactor. Figure 10 shows that the mean diameter of air bubbles achieved through the operation of the large-scale bioreactor was smaller in comparison to the outcomes observed with the small-scale bioreactor. Consequently, this difference in the bubble size led to a greater interfacial area being generated by the large-scale bioreactor, thereby resulting in a higher level of mass transfer when compared to the performance of the smaller scale bioreactor. The small bubble size achieved by the large-scale bioreactor was attributed to the effective hydrodynamics generated by the coaxial bioreactor, which successfully broke down the air into smaller sizes. Therefore, by retrofitting the central impeller of the large-scale coaxial bioreactor upon conducting a scale-up study based on the constant mass transfer technique, both oxygen transfer homogeneity and shear environment were enhanced. The low shear environment and enhanced oxygen transfer can provide a gentle gas dispersion environment for the filamentous fungus cell culture.

Fig. 10
figure 10

CFD outcomes pertaining to the mean diameter of air bubbles and \({k}_{L}a\) in the coaxial bioreactor during the scale-up process via central impeller retrofitting: a bioreactor furnished with an axial-flow impeller, and b bioreactor furnished with a radial-flow impeller

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Conclusion

This study investigated the performance and scalability of a coaxial bioreactor for filamentous fungus cell culture, focusing on fluid dynamics aspects. The system was successfully scaled up using central impeller retrofitting with both radial-flow and axial-flow impellers. Our findings revealed that the radial-flow impeller exhibited a lower shear environment compared to the axial-flow impeller. The shear environment of the system increased during the scale-up, because large-scale mixers required more power to keep the mass transfer rate constant. However, the scale-up study incorporating central impeller retrofitting demonstrated a promising approach for optimizing large-scale processes, achieving high mass transfer while reducing the shear environment.