Design Optimization of a Tray Bioreactor for Solid-State Fermentation: Study of Process Parameters through Protein Modification of By-Products

Abstract

This study investigated the design of a tray bioreactor for solid-state fermentation, applying Rhizopus oryzae to oilseed meals as the substrate. Two process variables were continuously monitored in the bioreactor to ensure precise control of the environmental conditions: temperature and relative humidity (RH). The comprehensive analysis covered the effects of different fermentation conditions on the protein content, technological properties, and molecular distribution of the samples. The study revealed that the configuration factors suffered a stratification within the three trays of the bioreactor. Notably, the upper tray registered the largest dispersion, with a range of 1.5 °C. When analyzing the differences between sensors within each tray, the largest difference was found in the lower tray (10.9%). Furthermore, higher EAI (Emulsifying Activity Index) values were observed in the upper tray (T3) for rapeseed. As for ESI (Emulsion Stability Index) values, no differences were observed between the trays or fermentation periods. Using the changes induced by Rhizopus oryzae fungal enzymes, the study quantitatively examined the changes in the by-product valorization. While the bioreactor factors did not affect the protein quantity itself, they had significant impacts on specific changes within the molecular weight protein fraction. The findings of this study offer significant insights into the complex dynamics of solid-state fermentation processes. The outcomes of this study not only advance understanding of solid-state fermentation but also offer practical guidance for the design and operation of fermenters in industrial applications.

1. Introduction

Solid-state fermentation (SSF) is emerging as an attractive and alternative process for the valorization of by-products due to its numerous benefits, such as increased productivity, reduced wastewater consumption, lower risk of substrate contamination, and decreased energy requirements [1,2]. The lower water activity requirement (a_w) of SSF for fungal growth (0.5–0.6), compared to that needed by bacteria (around 0.8–0.9), makes SSF more suitable for mimicking the natural growth environment [1]. Currently, SSF has demonstrated its efficacy in the production mainly of enzymes and other products such as biopolymers, biosurfactants, organic acids, and pigments [3,4]. In the current context of protein demand, there is a growing interest to investigate the potential of fungal SSF to produce enriched protein ingredients using solid by-products as substrates.

In the context of SSF, the design of the bioreactor is crucial for creating a controlled environment for microorganism growth and also facilitates the transfer of nutrients and oxygen to the substrate. While many processes have been investigated in the laboratory, conducted in conical flasks, reagent bottles, and plastic bags, there are still relatively few examples of the successful implementation of large-scale SSF processes to produce these new products [5,6,7]. Selecting a suitable bioreactor and optimizing operating conditions becomes crucial when upscaling the production process [8]. One of the limiting factors has been the relatively poor knowledge base regarding the design and operation of large-scale SSF bioreactors. Among several critical challenges, moisture and the nature of the solid substrate play a significant role; the reason for this is because the thermal properties of a continuous aqueous phase, for example, the thermal conductivity and heat capacity of liquid water, are superior to those of a bed of wet solids with air between particles [9]. Therefore, providing a unique environment for the growth of the microorganisms is essential and requires the standard design of the bioreactor.

When analyzing various types of bioreactors for SSF, it is important to note that different designs have been explored, each with unique characteristics and advantages. Two of the most studied designs are packed-bed bioreactors and tray bioreactors; both are simple configurations because they keep the substrate static. In the packed-bed design, the substrate is placed in a column and has forced aeration through the fermentation bed. In the tray design, the substrate is placed in individual trays with layers of 1 to 5 cm thick, and there is no forced aeration through the sample [10]. There are also other more complex configurations, such as the fluidized bed bioreactor design, which employs a continuous flow of gas or liquid to suspend and mix the solid substrate, promoting better aeration and heat transfer. Finally, another option is the rotating drum bioreactor, where the substrate is placed in a rotating cylindrical drum to achieve a homogenous mixture [11].

Some works have studied and compared multiple bioreactor configurations, from small-scale laboratory [12] to mid-sized pilot-scale experiments [13]. These studies focused primarily on enzyme production and compared the two simpler configurations [5,14]. Some studies concluded that the tray configuration was the best in their case for many reasons, such as the rapid growth of the filamentous fungi due to the greater exposed surface area in the air, minimal mechanical stress, and simple operation and construction [13]. In addition, it is already the preferred type of SSF bioreactor for producing many industrial enzymes [4].

A tray bioreactor configuration comprises a chamber that houses individual trays, each crafted from a variety of materials, including metal or plastic. The trays exhibit open upper sections and, in some cases, are perforated. When stacked in layers with interspaces to improve airflow, these trays facilitate efficient air circulation. Operating as static beds, these trays require minimal or no agitation. The chamber is designed to introduce controlled levels of air, which circulates amongst the trays, maintaining optimal humidity and temperature conditions [15,16].

Therefore, despite certain limitations common to all SSF bioreactors, such as the desiccation and rising temperatures due to the inadequate removal of the internal heating and O₂ supply, the tray bioreactor represents a scalable and promising option, making it the primary focus of investigation [8,16,17].

Rhizopus oryzae is a common filamentous fungus in SSF studies, and it has already been tested in the production of enzymes like proteases using by-products of the bakery sector as a substrate [18]. One of the more explored groups of by-products are oilseed meals from the oil industry, due to the high amount of the product produced worldwide [19]. SSF has already been applied in oilseed meals, especially for the improvement of their nutritional fraction [20,21,22]. One of the fractions that is affected in the SSF process is the protein fraction, and although Rhizopus oryzae does not make significant changes in protein content [23,24,25,26], there are changes in the protein fraction and its properties due to the action of the fungal proteases [27,28].

The aim of this study is to explore the potential of SSF, focusing particularly on its implementation within a tray bioreactor. The main objective is to investigate the influence of key process variables, specifically relative humidity and temperature, within a laboratory-scale tray bioreactor configuration. The study focuses on improving fungal proliferation in a multi-tray bioreactor through the changes the microorganism Rhizopus oryzae makes in the substrate—rapeseed meal in this case. This investigation includes a comprehensive analysis of protein fraction and aims to elucidate how fermentation process variables impact the functional properties of the resulting products. By achieving these objectives, this study will contribute significantly to the advancement of SSF as a sustainable bioprocess and provide valuable insights into the practicality and potential of tray bioreactors for various fungal fermentation applications, drawing the pathway to a pilot-scale bioreactor.

2. Materials and Methods

2.1. Laboratory Tray Bioreactor Design Optimisation

Solid-state fermentation was carried out using a custom-designed tray bioreactor configuration with a size of 33.5 × 37.5 × 37.5 cm (Figure 1). A set of 3 trays was placed inside a forced air incubator (AXI-6000, ICT, SL, Erandio, Spain) to maintain them at the desired temperature during fermentation. Each tray had a size of 24.5 × 33 × 1 cm, and they were spaced 11.5 cm apart.

In each tray, 4 DHT22 sensors (AM2302) were placed, integrating a capacitive humidity sensor and a thermistor to measure the surrounding air. The sensors were connected to an Arduino Mega 2560 (Somerville, MA, USA) microcontroller board for logging temperature and relative humidity values using an Arduino Genuino 1.8.15 (Somerville, MA, USA) connected to Microsoft Excel (Redmond, WA, USA) to obtain real-time values.

2.2. By-Products

Rapeseed meal and sunflower meal were provided by two oil refining companies and obtained through solvent extraction. The by-products were characterized with moisture by heating to 105 °C, and the crude protein content by Kjeldahl combustion in a Bloc Digest and distilled in a PRO-NITRO A distiller (Selecta, Barcelona, Spain).

The pretreatment applied to the by-products was as follows: they were ground with an M20 Universal mill (IKA, Staufen, Germany), sieved at 0.5 mm, and sterilized by autoclaving at 121 °C for 20 min. Following sterilization, the samples were moistened to achieve humidity levels of 60% with sterilized distilled water.

2.3. Solid-State Fermentation (SSF) Process

2.3.1. Microorganism, Culture Media, and Propagation

A commercial culture of Rhizopus oryzae culture (ATCC 56536) was acquired from ATCC (Manassas, VT, USA) in lyophilized form and stored at −20 °C until use. To rehydrate the lyophilized Rhizopus oryzae culture (ATCC 56536), 1.0 mL of sterile distilled water was added to the pellet, and the mixture was stirred to obtain a suspension. The suspension was then transferred to a test tube containing sterile distilled water and left undisturbed at room temperature (25 °C) for a minimum of 2 h to facilitate rehydration and revival of the fungus’s viability. After rehydration, the suspension of Rhizopus oryzae was mixed, and several drops were inoculated into Potato Dextrose Agar (PDA) (Sigma-Aldrich, Sant Louis, MO, USA) for 5 days at 25 °C.

2.3.2. Inoculation

Fermentation was initiated by inoculating prepared spores of Rhizopus oryzae. Specifically, 2 mL of spore suspension containing 10⁶ spores/mL, quantified using a Thoma Pattern counting chamber (Braubrand^®, Brand, Wertheim, Germany), was added to autoclaved and prehumidified by-product sources. Each preconditioned and inoculated by-product source, weighing 20 g, was placed in a 90 mm Petri dish (Fischer Scientific, Waltham, MA, USA) in the tray bioreactor, maintained at 25 °C with 70% air humidity for a fermentation period of 72 and 120 h to perform 3 days and 5 days of the fermentation process, respectively. Four Petri dishes were distributed in each tray, with two containing non-inoculated samples (control) and two containing inoculated samples. An oversaturated salt solution (52 g of sodium chloride in 100 mL of distilled water) was prepared and distributed in two beakers in each tray to maintain humidity levels during the fermentation period. After fermentation, the samples were stored in an oven at 50 °C for 72 h to inactivate the fungus. Then, the samples were stored in refrigeration before analysis.

2.4. Analytical Determinations

2.4.1. Crude Protein Content

Crude protein content was determined by Kjeldahl combustion in a Bloc Digest and PRO-NITRO A distiller (Selecta, Barcelona, Spain).

2.4.2. Molecular Weight Distribution

The molecular weight distribution was analyzed by high-performance liquid chromatography (Waters, Milford, MA, USA) as described by Sentís-Moré et al. [29]. Briefly, an XBridge BEH 125Å SEC 3.5 µm column (7.8 × 300 mm) (Waters, Milford, MA, USA) was used with acetonitrile 30% and TFA 0.1% as the mobile phase. Peak signals were detected at 280 nm with a Waters 2996 Photodiode Array Detector (Waters, Milford, MA, USA). Chymotrypsin (25,000 Da), ribonuclease A (13,700 Da), aprotinin (6511 Da), insulin (5733 Da), insulin chain B (3495 Da), angiotensin (1046 Da), and L-tryptophan (204 Da) were used as molecular weight standards. The calibration curve and data were obtained with Empower 3 software (Waters, Milford, MA, USA). The results of the MWD in all samples are expressed as percentages on groups of different molecular weights (MW): (i) >5000 Da, (ii) 5000 to 3000 Da, (iii) 3000 to 1000 Da, and (iv) 1000 to 500. All the samples were measured at least in duplicate.

2.4.3. Determination of Emulsion Activity and Stability Indexes

Emulsion properties were determined according to Pearce and Kinsella [30], with some modifications. In brief, 120 mg of the sample was solubilized to 12 mL of distilled water using a vortex mixer. Sunflower oil (4 mL) was added, and the mixture was emulsified with a homogenizer Polytron PT 10–35 (Kinematica, Malters, Switzerland) at 14,000 rpm. Then, 50 µL of the lower part of the emulsion was added to 10 mL of 0.1% (w/v) sodium dodecyl sulphate (SDS) and the absorbance was measured in the spectrophotometer at 500 nm, at times 0 and 10 min, to obtain Abs0 and Abs10, respectively. The Emulsifying Activity Index (EAI) and Emulsifying Stability Index (ESI) were calculated using Equations (1) and (2), respectively.

$$EAI\left({\mathrm{m}}^2/\mathrm{g}\right)=\frac{2\times 2.303\times {Abs}_0\times DF}{C\times \phi \times 10000}$$

(1)

$$ESI\left(\mathrm{m}\mathrm{i}\mathrm{n}\right)=\frac{{Abs}_0}{{Abs}_0-{Abs}_{10}}\times ({\mathsf{\Delta }t}_{10}-{\mathsf{\Delta }t}_0)$$

(2)

where DF is the dilution factor of the samples, C is the protein mass, and $\phi$ is the volume fraction of the oil in the emulsion.

2.5. Statistical Analysis

The data are presented as the mean of two replicates. The statistical analysis of the full factorial was carried out using JMP^® Pro 16.0.0 (SAS Institute, Cary, NC, USA). Appropriate least square means were compared using the t-test after the F-tests were performed. The difference between samples was significant when p < 0.01.

3. Results and Discussion

In a tray bioreactor, relative humidity (RH) and temperature play a crucial role in influencing fungal growth and, therefore, the results of fermentation. RH is a key factor affecting water activity and moisture content within the trays; the availability of moisture profoundly impacts the growth rate and metabolism of microorganisms and the overall productivity of the fermentation process [31]. By controlling RH, we can optimize growing conditions to meet specific fungal requirements and achieve optimal yields. Temperature, on the other hand, governs the kinetics of fungal growth and may influence the enzymatic activity and metabolic rates of the fungi within the trays [32]. Different fungal strains have unique temperature optima for growth. By carefully adjusting and maintaining the temperature within the desired range, it can enhance the growth rate and quality of the fungal biomass.

Our approach involves deploying sensors in the laboratory-scale tray bioreactor, where these critical fermentation factors can be motorized. This data is invaluable for designing a pilot-scale tray bioreactor that can efficiently replicate the conditions necessary for successful fungal growth, addressing both scalability and homogeneity concerns. The ability to maintain optimal conditions in these parameters significantly influences the success of the fermentation process so that fermentation can be optimized and yields and product quality improved.

3.1. Fermentation Monitorization Factors

Temperature and RH monitoring employed four sensors in each of the three trays of the bioreactor. No significant variation in deviation was observed; all sensors were consistently kept below 0.5 °C for temperature and 1% for relative humidity during the 3- and 5-day fermentation period. Accordingly, we calculated average values for each sensor to create a thermal and relative humidity map of the bioreactor and to further examine sensor differences (Figure 2).

The temperature and RH monitoring data reveal different patterns. The highest temperature was consistent in the upper tray, reaching a maximum of 26.2 °C, while the lowest temperature was detected in the lower tray, which measured a minimum of 22.6 °C. It is important to note that this temperature behavior remained consistent between 3 and 5 days and between the two substrates used. By looking deeper into the temperature data, variations could be observed within each tray. The upper tray registered the largest dispersion, with a range of 1.5 °C, followed by the lower tray at 1.0 °C, and finally, the central tray was 0.6 °C. Interestingly, a closer analysis reveals that the sensors on the extreme sides of each tray displayed greater temperature deviation compared to the two central sensors, which maintained a more consistent temperature profile.

Regarding the average RH values recorded by the sensors after 3 and 5 days (Figure 2), a contrasting pattern could be observed. The highest values were found in the lower tray (94.3%) and the lowest in the upper tray (63.2%). This RH behavior, with stratification in the temperature factor but in reverse, remained constant during the fermentation periods of 3 and 5 days and for both substrates. When analyzing the differences between sensors within each tray, the largest difference was found in the lower tray (10.9%) and the lowest in the central tray (2.8%). In this case, as previously observed with temperature, the sensors located on the extreme sides of each tray also exhibited greater RH deviation compared to the two central sensors. In fact, it is interesting to note that inside the lower tray (T1), the sensor located near to the air inlet consistently recorded the lowest values for RH.

The observed temperature variation, particularly the higher temperatures in the upper tray (T3), can be attributed to fundamental heat dynamics within a confined space. Heat rises naturally, and in the upper tray, the right zone is closer to the heat source at the lower right of the bioreactor and tends to accumulate more heat [33]. In addition, restricted airflow at the upper compared to the lower trays may contribute to heat buildup, creating a thermal gradient across the trays. It is worth noting that RH can influence these temperature variations; RH reflects moisture content in the air, and moisture is a key factor in heat transfer and thermal regulation. Higher RH levels can enhance heat retention, which can exacerbate temperature differences between trays. This interaction between temperature and RH emphasizes the importance of comprehensive environmental control within the bioreactor.

As far as our knowledge, there are no works studying the temperature and HR distribution in SSF tray bioreactors with different sensors. The only study with information on temperature records is by Sala et al. [14], where there was also a stratification of the temperature during 11 days of fermentation, with the upper tray always having more temperature records than the lower tray. In another work by Demir and Tari [32], the differences in the production of polygalacturonase enzyme by Aspergillus sojae in different relative humidity were studied, ranging from 70% to 90%; in their case, no significant differences were observed. Therefore, achieving consistent and optimal conditions for microbial growth and product quality requires careful control of these variables, especially in large-scale bioprocesses.

3.2. Process Variable Impact on Fermented Products

The bioreactor configuration affects the fermentation parameters, including temperature and RH, and can potentially affect fungal growth and its action towards the substrates. This study used the growth of Rhizopus oryzae in rapeseed and sunflower meal to analyze the impact that the two bioreactor factors may have on fungal growth.

Results of the crude protein content and emulsifying properties of activity and stability of the two substrates are presented in

Table 1. Results of control and inoculation samples obtained from SSF using rapeseed meal and sunflower meal substrates. Results from the crude protein content (g/100 g dm), emulsifying activity index (m²/g), and emulsifying stability index (min) for each of the three different trays of bioreactor: T1 (lower tray), T2 (central tray), and T3 (lower tray). The results are presented for both 3-day and 5-day fermentation periods.

Determinations	3 Days						5 Days						SED
	Control			Inoculation			Control			Inoculation
	T1	T2	T3	T1	T2	T3	T1	T2	T3	T1	T2	T3
Rapeseed Meal
Crude Protein Content (g/100 g dm)	37.4 ab	38.9 a	38.0 ab	38.2 ab	38.0 ab	37.4 ab	36.8 ab	36.9 ab	35.9 b	36.8 ab	37.3 ab	36.6 ab	0.24
Emulsifying Activity Index (m2/g)	5.2 d	3.5 d	3.7 d	21.5 bc	25.1 ab	27.1 a	5.7 d	6.0 d	6.9 d	17.5 c	24.5 ab	26.4 a	0.47
Emulsifying Stability Index (min)	44.0 a	43.8 a	42.1 a	11.9 c	13.1 c	13.2 c	36.1 b	36.9 b	35.1 b	14.2 c	11.7 c	12.3 c	0.31
Sunflower Meal
Crude Protein Content (g/100 g dm)	33.9 b	33.9 b	33.1 b	33.5 b	31.8 b	36.6 a	29.5 c	28.9 cd	28.4 cd	27.6 cd	27.1 d	29.5 c	0.19
Emulsifying Activity Index (m2/g)	7.6 e	8.6 e	9.3 e	21.9 bc	26.3 a	24.0 ab	8.9 e	8.7 e	8.4 e	20.1 c	16.3 d	13.0 d	0.39
Emulsifying Stability Index (min)	45.9 a	44.4 a	42.9 a	13.8 b	15.4 b	13.5 b	44.0 a	44.2 a	43.6 a	16.4 b	14.5 b	14.9 b	0.48

The results are expressed as the mean of measurements. Results not connected by the same letter are significantly different (p < 0.01). Statistics made by row. SED = standard error of the difference.

. Based on the full factorial analysis performed (

Table 2. Statistical results of F ratio and p-value of full factorial analysis of fermentation (days), tray and inoculation effects from rapeseed meal and sunflower meal SSF.

Effects	Crude Protein Content		Emulsifying ActivityIndex		Emulsifying StabilityIndex
	F	p-Value	F	p-Value	F	p-Value
Rapeseed Meal
Time (days)	13.69	0.0030	0.05	0.8282	71.24	<0.0001
Tray	1.95	0.1846	9.85	0.0004	1.48	0.2406
Inoculation	0.03	0.8584	782,05	<0.0001	3875.74	<0.0001
Time (days) Tray	0.14	0.8745	2.10	0.1376	0.89	0.4214
Time (days) Inoculation	0.84	0.3787	8.34	0.0065	71.34	<0.0001
Tray Inoculation	0.37	0.6955	11.72	0.0001	1.99	0.1518
Time (days) Tray Inoculation	0.99	0.4012	0.09	0.9117	2.92	0.0667
Sunflower Meal
Time (days)	370.87	<0.0001	46.37	<0.0001	0.15	0.7048
Tray	9.09	0.0039	1.92	0.1614	1.24	0.3016
Inoculation	0.94	0.3516	450.92	<0.0001	1861.73	<0.0001
Time (days) Tray	1.38	0.2889	10.19	0.0003	0.48	0.6239
Time (days) Inoculation	5.44	0.0379	49.22	<0.0001	1.25	0.2713
Tray Inoculation	22.95	<0.0001	3.40	0.0444	0.13	0.8826
Time (days) Tray Inoculation	2.01	0.1764	4.53	0.0176	1.37	0.2667

p-values in bold are significant (p < 0.01).

), all variables showed statistical differences (p < 0.01) in at least one determination unless the interaction was among time (days), tray, and inoculation. Consequently, in order to assess the impact of the solid-state fermentation process, the variables were studied together, considering the different trays of the bioreactor and fermentation period for both by-products.

Oilseed meals contain a substantial protein content; in the present study, rapeseed meal contained 38.3 g/100 g dm, and sunflower meal contained 37.0 g/100 g dm. This content positions them as a promising resource for fermentation, with the potential to recover valuable functional and nutritional benefits. If we analyze the crude protein content, no differences were observed between inoculated and control samples in the case of rapeseed for either of the two fermentation periods (3 and 5 days), nor between the trays. However, differences in protein content were observed for sunflower in the fermented samples from the upper tray (T3) of the 3-day fermentation, which could highlight the higher values for temperature and the lower values for RH. This majority of non-significant results of the crude protein content are in concordance with other works with the same fungal strain, Rhizopus oryzae, where no differences were stated in this determination in other by-products, even at more fermentation days [25,34].

In relation to the technological properties tested, it can be observed that there were differences in both activity and stability indexes between inoculated and control samples for both sources at 3- and 5-day fermentation (Table 1). Those values were higher in the emulsifying activity for the fermented samples and higher in the emulsifying stability for the control samples. Both were significantly higher (p < 0.01) (Table 2). When analyzing among trays, significantly (p < 0.01) higher EAI values were observed in the upper tray (T3) for rapeseed, both with 3- and 5-day fermentation, which was correlated with higher values of temperature. Conversely, no differences were identified between the central tray (T2) and the lower tray (T1). In the case of sunflower, a different trend was observed, as the values were higher in the lower tray (T1) and were only significant with the central tray (T2) and upper tray (T3) with the 5-day fermentation (p < 0.01). For ESI values, no differences were observed between trays or fermentation periods for either of the two sources studied.

Microorganisms involved in the fermentation process are known to produce enzymes as a secondary metabolite during their growth [35]. The changes in the technological properties of the inoculated samples can be attributed to the action of the protease enzymes produced by Rhizopus oryzae, which can result in the hydrolysis of the protein fraction [27,28]. Other studies have also corroborated that smaller proteins result in better results for emulsifying activity and bigger proteins in better emulsifying stability [36].

To verify the changes in the protein fraction, the molecular weight distribution of the protein fraction was analyzed from the soluble fraction of the samples. The results are presented in Figure 3 for rapeseed meal and in Figure 4 for sunflower meal. We can clearly see that the molecular weight distribution has changed in both sources and fermentation days when the by-products were inoculated. The action of the protease enzyme from Rhizopus oryzae broke down the bigger molecular weight groups, principally above 5000 Da, into middle and smaller molecular weight groups, from 3000 to 500 Da. In the case of the rapeseed meal, this effect was higher with 5 days of fermentation, but not in the case of sunflower meal. However, no differences were identified in the MWD within the same source among different trays. These results prove that the technological results are affected by the Rhizopus oryzae fermentation.

The statistical observations of this study were supported by the principal component analysis (PCA) performed with all the data from the study. The findings provide valuable insights into the complex interplay of environmental factors (temperature and RH) and key variables (crude protein content, EAI, and ESI) in the SSF process carried out in both substrates (Figure 5). The PCA showed that there are no important differences between the samples of the two by-products tested, and also not with different fermentation times. The temperature and RH showed an inverse relationship in the PCA plot. As the temperature increased, RH tended to decrease. This suggests that these environmental factors are closely linked, and changes in one can impact the other, as we have seen in the values registered in the upper and lower trays (T3 and T1, respectively). The observed relationships highlight the importance of RH control for optimizing Rhizopus oryzae growth and protein content. Changes in RH appear to have more influence on protein content variation than changes in temperature, as we have observed for sunflower substrates after 5 days of fermentation, although the influence is not strong. Finally, PCA also highlights that technological properties mainly vary with the fungal inoculation, as EAI is correlated on the right side of the graph with the inoculated samples, and ESI is correlated with the control samples on the left side of the graph.

3.3. Design of Pilot Scale Tray Bioreactor

The design of a pilot-scale tray bioreactor should consider several critical factors that have emerged from this study. Temperature control is of importance, as evidenced by the significant temperature variations observed among the different trays. To address this, the bioreactor design should prioritize precise temperature control mechanisms that ensure uniformity across all trays. Improved heat distribution methods and adaptive temperature regulation can help maintain stable and consistent conditions.

In addition to temperature control, humidity regulation is another crucial consideration. The variations in RH levels observed between trays highlight the need for a comprehensive humidity control system. Achieving uniform RH levels throughout the bioreactor is essential for creating an environment conducive to optimal microbial growth. This may involve optimizing airflow patterns and moisture distribution mechanisms within the trays.

To facilitate precise control over the temperature and humidity, the incorporation of advanced environmental monitoring systems is recommended. Multiple sensors placed strategically within each tray can provide real-time data on temperature and RH, enabling operators to fine-tune environmental conditions and ensure homogeneity. These monitoring systems should also integrate adaptive control algorithms that can dynamically respond to changes in environmental parameters, maintaining stability and consistency in the fermentation process.

The design of the trays themselves should also be optimized to minimize temperature and humidity variations. Factors such as heat distribution and airflow within the trays should be carefully considered to create an environment where environmental conditions remain consistent. Furthermore, scalability should be a key consideration during the design process. The successful conditions achieved at the pilot scale should be replicable at a larger industrial scale, ensuring that the bioreactor can efficiently handle increased production volumes without compromising product quality. Incorporating data analytics and advanced modeling tools, often associated with artificial intelligence (AI), into the design can be invaluable. These AI-driven tools have the capability to analyze vast datasets and complex interactions, allowing for the prediction and optimization of the environmental conditions required for specific fermentation processes. By harnessing AI, operators can ensure not only the stability but also the adaptability of the system, enabling consistent product quality and yields even in the face of varying conditions. This forward-looking approach empowers the bioreactor to respond dynamically to changing environmental factors, further enhancing the efficiency and effectiveness of the fermentation process.

4. Conclusions

This study sheds light on the critical importance of controlling temperature and relative humidity in a tray bioreactor to optimize the fermentation process. It has observed how variations in temperature and RH can have a significant impact on microbial growth and, therefore, on the quality of fermentation products. These findings highlight the need for precise environmental control in pilot-scale biotechnological processes. Furthermore, the study has highlighted significant differences in the technological properties of fermented products, emphasizing the importance of carefully considering these variables in process design and control. Future research, focused on the molecular weight distribution of protein fraction, holds promise for providing deeper insights into a microorganism’s enzymatic activity and protein hydrolysis, which could drive further advancements in this field of study.