CFD-based optimization method and system of fermentation tank for amino acid fermentation

Abstract

A CFD-based optimization method of a fermentation tank for amino acid fermentation according to one embodiment of the present invention comprises: a first step of setting a target variable, a constraint variable, and a design variable with respect to an optimization target of at least one impeller fermentation tank; a second step of generating a three-dimensional shape for a flow area of the impeller fermentation tank under each condition of the constraint variable and the design variable according to the target variable; a third step of calculating a primary value, which is at least one of an oxygen diffusion coefficient, a liquid density, a liquid viscosity, a gas retention rate, a bubble diameter, an energy dissipation rate, and a torque, by a computational fluid dynamic analysis of the flow region on the basis of information on the three-dimensional shape; a fourth step of calculating a secondary value using the primary value, the secondary value being an oxygen transfer coefficient and a power consumption ratio (power ratio); and a fifth step of repeating the second, third, and fourth steps while changing the design variable of the first step in various ways, and finding an optimal point on the basis of a comparison value obtained by comparing secondary values before and after the change.

Description

Technical Field

[0001] The present invention relates to a CFD-based optimization method and system for a fermentation tank for amino acid fermentation. Background Art

[0002] In the field of biology, methods to improve fermentation tank efficiency include biological microbial modification and microbial recycling. Microbial modification and microbial recycling methods increase microbial productivity and microbial concentration. In this process, the oxygen demand directly related to microbial amino acid production increases.

[0003] In this case, various variables such as high concentration of oxygen, aeration rate, rotation speed (RPM) and impeller type are considered to ensure rapid transfer of oxygen from the supply air to the culture medium.

[0004] There are also some methods, such as increasing the diameter or speed (RPM) of the impeller, to ensure rapid oxygen delivery, but these methods consume a lot of energy. Therefore, it is necessary to consider both oxygen delivery and energy consumption to find the operating conditions suitable for the current situation.

[0005] Therefore, many experiments are needed to analyze oxygen transfer and energy consumption under each of the various operating conditions. In the laboratory, experiments can be performed by making small-scale fermenters with different heights, diameters, and impeller sizes and shapes and based on different operating conditions.

[0006] However, since the fermenters used in production sites are large-scale, changing the structure of these fermenters or conducting experiments under different operating conditions requires stopping production and incurring a lot of costs. Therefore, there are limitations to conducting experiments with large-scale fermenters. Summary of the invention

[0007] Technical issues

[0008] An object of the present invention is to provide a CFD-based optimization method for a fermenter for amino acid fermentation, which simulates flow phenomena in the fermenter based on computational fluid dynamics.

[0009] Another object of the present invention is to provide a CFD-based optimization method for a fermenter for amino acid fermentation, which overcomes physical limitations and predicts oxygen transfer coefficient and power consumption for amino acid fermentation in a fermenter for amino acid fermentation.

[0010] Another object of the present invention is to provide a CFD-based optimization method for an amino acid fermentation tank, for finding the optimal conditions for effectively supplying oxygen to the fermentation medium in the amino acid fermentation tank.

[0011] Another object of the present invention is to provide a CFD-based optimization system for a fermenter for amino acid fermentation, which applies the above-mentioned CFD-based optimization method for a fermenter for amino acid fermentation.

[0012] Technical Solution

[0013] According to one embodiment of the present invention, a CFD-based optimization method for a fermenter for amino acid fermentation includes: setting target variables, constraint variables and design variables relative to an optimization target of a fermenter having at least one impeller; generating a three-dimensional shape of a flow area of ​​the impeller fermenter under each condition of the target variable to the constraint variable and the design variable; calculating primary values ​​by computational fluid dynamics analysis of the flow area based on information about the three-dimensional shape, the primary values ​​being at least one of an oxygen diffusion coefficient, a liquid density, a liquid viscosity, a gas retention rate, a bubble diameter, an energy dissipation rate and a torque; calculating secondary values ​​using the primary values, the secondary values ​​being an oxygen transfer coefficient and a power consumption ratio (power ratio); and repeatedly generating a three-dimensional shape, calculating the primary values ​​and calculating the secondary values ​​while changing the set design variables in various ways, and finding an optimal point based on a comparison value obtained by comparing the secondary values ​​before and after the change.

[0014] In the setting, considering the current situation, at least one of the oxygen transfer coefficient and the power ratio may be selected as the target variable.

[0015] In the arrangement, the constraint variables may include at least one of a height of the fermentor, a diameter of the fermentor, a shape of the sparger, an aeration rate, and an operating volume of the fermentor.

[0016] In the setup, the design variables can be set to the CFD simulation conditions within the range that the fermenter may vary.

[0017] In the arrangement, the design variables may include at least one of a rotation speed (rpm) of the impeller, a shape of the impeller, a number of impellers, a diameter of the impeller, and a mounting height of the impeller.

[0018] In the arrangement, the maximum height of the impeller can be set within the operating volume height of the fermenter.

[0019] Calculating the secondary value may include calculating a tertiary value using the oxygen transfer coefficient and the power ratio, the tertiary value being a ratio of the oxygen transfer coefficient to the power ratio (kLa / power ratio).

[0020] Finding the optimal point may include finding the optimal point based on a comparison value obtained by comparing tertiary values ​​before and after the change.

[0021] According to one embodiment of the present invention, a CFD-based optimization system for a fermenter for amino acid fermentation includes a simulation unit, which is configured to perform simulation through a CFD-based optimization method for a fermenter for amino acid fermentation; a fermenter, which is optimized by the simulation unit and applied in the field; a monitoring sensor, which is configured to monitor error data that deviates from the optimal state of the fermenter during operation; and a control unit, which is configured to compare whether the error data monitored by the monitoring sensor exceeds an allowable error range, and notify an administrator of the comparison result.

[0022] The fermentation tank may include a first-stage impeller, a second-stage impeller and a third-stage impeller, which are installed on a shaft driven by a motor and are spaced apart at predetermined intervals from the lower end upward; a first baffle and a second baffle, which are arranged on both sides of the radial inner wall; and an air jet, which is arranged below the first-stage impeller to blow air.

[0023] In this manner, embodiments of the present invention may adjust the optimal impeller height according to the amino acid type and the operating conditions of the fermentor based on the three-dimensional shape information by computational fluid dynamics analysis of the flow area of ​​the fermentor, wherein the fermentor employs at least one impeller.

[0024] The embodiments of the present invention can produce various amino acids according to the demand for various products, and in a production plant that needs to adjust the production volume, the productivity can be improved and the power consumption can be reduced by adjusting the impeller height of the fermenter.

[0025] Embodiments of the present invention can calculate the fluid phenomena in the fermenter and objectively verify the oxygen transfer coefficient and power consumption through computational fluid dynamics analysis, thereby being able to calculate the impact of increasing the oxygen transfer coefficient relative to power consumption.

[0026] Therefore, economic benefits can be expected. That is, the embodiments of the present invention can reflect the structural characteristics of the fermenter used in the industrial field through computational fluid dynamics analysis, and achieve optimization by predicting the oxygen transfer coefficient and power consumption of the fermenter. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 is a flow chart of a CFD-based optimization method of a fermenter for amino acid fermentation according to one embodiment of the present invention.

[0028] Figure 2 1 is a basic structural diagram of a fermenter for amino acid fermentation according to one embodiment of the present invention.

[0029] Figure 3 is a photograph of an impeller used in the first experimental example of the CFD-based optimization method of a fermenter for amino acid fermentation according to one embodiment of the present invention.

[0030] Figures 4 to 6 : is a perspective view showing the relationship of the impeller in the fermenter generated in the simulation of the CFD-based optimization method of the fermenter for amino acid fermentation according to one embodiment of the present invention.

[0031] Figure 7 is a block diagram of a CFD-based optimization system for a fermenter for amino acid fermentation according to one embodiment of the present invention. DETAILED DESCRIPTION

[0032] Best Mode for Carrying Out the Invention

[0033] The present invention will be described more fully below with reference to the accompanying drawings, in which embodiments of the present invention are shown. As will be appreciated by those skilled in the art, the described embodiments may be modified in a variety of ways without departing from the spirit or scope of the present invention. The accompanying drawings and descriptions are to be considered illustrative in nature, rather than restrictive. Throughout the specification, the same reference numerals designate the same elements.

[0034] Figure 1 is a flow chart of a CFD-based optimization method for a fermenter for amino acid fermentation according to one embodiment of the present invention. Figure 1 , a CFD-based optimization method of one embodiment includes a first step (ST1), a second step (ST2), a third step (ST3), a fourth step (ST4) and a fifth step (ST5).

[0035] The first step (ST1) includes setting target variables, constraint variables and design variables with respect to an optimization target of a fermenter having at least one impeller. The first step (ST1) may include selecting at least one of an oxygen transfer coefficient (kLa) and a power consumption ratio (power ratio) as a target variable in consideration of a current situation.

[0036] The first step (ST1) may include at least one of a height of the fermenter, a diameter of the fermenter, a shape of the sparger, an aeration rate, and an operating volume of the fermenter as a constraining variable.

[0037] The first step (ST1) may include setting the design variables to computational fluid dynamics simulation conditions within a range that the fermenter may vary. The first step (ST1) may include at least one of the speed (rpm) of the impeller, the shape of the impeller, the number of impellers, the diameter of the impeller, and the installation height of the impeller as the design variable. The first step (ST1) may include setting the maximum height of the impeller within the operating volume height of the fermenter.

[0038] Figure 2 : is a basic structural diagram of a fermentation tank for amino acid fermentation according to one embodiment of the present invention. Figure 2, a fermentation tank 1 for amino acid fermentation, comprising a first-stage impeller IP1, a second-stage impeller IP2 and a third-stage impeller IP3, which are mounted on a single shaft 2 and spaced apart at predetermined intervals from the lower end to the upper end; a first baffle BF1 and a second baffle BF2, which are arranged on both sides of the radial inner wall; and an air jet 3, which is arranged below the first-stage impeller IP1 to blow air. The shaft 2 is rotated by the torque of the motor (M, see Figure 7 ).

[0039] Figure 3 is a photograph of an impeller used in the first experimental example of the CFD-based optimization method of a fermenter for amino acid fermentation according to one embodiment of the present invention. Figure 3 , the first-stage impeller IP1 , the second-stage impeller IP2 , and the third-stage impeller IP3 are configured on a single shaft 2 .

[0040] Figures 4 to 6 : is a perspective view showing the relationship of the impeller in the fermenter generated in the simulation of the CFD-based optimization method of the fermenter for amino acid fermentation according to one embodiment of the present invention.

[0041] Reference again Figure 1 , the second step (ST2) includes generating a three-dimensional shape of the flow area of ​​the impeller fermenter under each condition of the target variable to the constraint variable and the design variable. Figures 4 to 6 The three-dimensional shape generated for the flow area of ​​an impeller fermenter is shown.

[0042] The third step (ST3) includes calculating a primary value, which is at least one of oxygen diffusion coefficient, liquid density, liquid viscosity, gas retention rate, bubble diameter, energy dissipation rate and torque, by computational fluid dynamics analysis of the flow area based on the information about the three-dimensional shape.

[0043] Liquid is filled into the fermenter, and gas retention and bubbles are set by air blown in through the sparger 3. Torque refers to the rotational force of the motor (M) applied to the shaft 2 through the first stage impeller IP1, the second stage impeller IP2 and the third stage impeller IP3 due to the rotation of the shaft 2.

[0044] The fourth step (ST4) includes calculating secondary values ​​using the primary values, the secondary values ​​being the oxygen transfer coefficient (kLa) and the power consumption ratio (power ratio). The oxygen transfer coefficient (kLa) is calculated using Mathematical Formula 1.

[0045] (Mathematical formula 1)

[0046] k L aOxygen transfer coefficient

[0047]

[0048] DL = oxygen diffusion coefficient (kg / m 3 ),

[0049] ∈=turbulent dissipation rate (CFD, m 2 / s 3 )

[0050] ρ = liquid density (kg / m 3 ), μ = liquid viscosity (kg / ms)

[0051]

[0052] d b = average diameter (m), α g = Gas retention rate (-)

[0053] Torque-based power consumption (Pg) is calculated using mathematical formula 2, which is the number of revolutions per second (N) and torque.

[0054] (Mathematical formula 2)

[0055] P g =2πNτ

[0056] N = rps (1 / s),

[0057] τ = torque

[0058] Torque is calculated during CFD operation. Power ratio is calculated as power consumption (Pg) based on torque.

[0059] The fifth step (ST5) includes sequentially repeating the second step (ST2), the third step (ST3) and the fourth step (ST4) while changing the design variables of the first step (ST1) in various ways, and finding the optimal point based on a comparison value obtained by comparing the secondary values ​​before and after the changes produced at each repetition.

[0060] Furthermore, the fourth step (ST4) may include further calculating a tertiary value using the secondary value, the tertiary value being a ratio of the oxygen transfer coefficient to the power ratio (kLa / power ratio).

[0061] In this case, the fifth step (ST5) may include sequentially repeating the second step (ST2), the third step (ST3), and the fourth step (ST4) while changing the design variables of the first step (ST1) in various ways, and finding the optimal point based on a comparison value obtained by comparing the tertiary values ​​before and after the changes produced at each repetition.

[0062] In the following, actual experimental embodiments and simulations are described as examples.

[0063] Experimental Example 1 and Simulation 1

[0064] refer to Figures 3 to 6 , Experimental Example 1 and Simulation 1 of the CFD-based optimization method according to the embodiment will be described. Figures 4 to 6 is a graph obtained in the second step (ST2). The second step (ST2) includes generating a three-dimensional shape of the flow area of ​​the impeller fermenter under each condition of the first step (ST1).

[0065] The third step (ST3) includes calculating a primary value, which is at least one of oxygen diffusion coefficient, liquid density, liquid viscosity, gas retention rate, bubble diameter, energy dissipation rate and torque, by computational fluid dynamics analysis of the flow area based on the information about the three-dimensional shape.

[0066] Table 1 shows the oxygen transfer coefficient (kLa), power ratio, and ratio of oxygen transfer coefficient to power ratio (kLa / power ratio) according to impeller height in a small-scale fermenter. As an example, the volume of the small-scale fermenter is greater than 1L and less than 100L.

[0067] [Table 1]

[0068]

[0069]

[0070] A small-scale fermenter was used for simulation. First, the size of the fermenter, the size of the impeller, and the speed (RPM) of the impeller were fixed as the first variable, and the height of the second-stage impeller IP2 was varied as the second variable (cases 1, 2, and 3), and the fermenter was simulated using CFD.

[0071] In cases 1, 2 and 3, the first stage impeller IP1, the second stage impeller IP2 and the third stage impeller IP3 are arranged from the lower side to the upper side, and the position of the second stage impeller IP2 located in the middle is set to 75mm as the reference, 55mm lowered from the reference, and 95mm raised from the reference.

[0072] Through this simulation, the oxygen transfer coefficient (kLa) and the power ratio are calculated as the third variable and the secondary value in the fourth step (ST4).

[0073] The simulation results are shown in Table 1. The first-stage impeller IP1, the second-stage impeller IP2 and the third-stage impeller IP3 are arranged from the lower side to the upper side, and in the fifth step (ST5), it is confirmed that as the position of the second-stage impeller IP2 located in the middle is reduced to 95, 75 and 55 mm (Case 3, Case 1 and Case 2), the oxygen transfer coefficient (kLa) is increased to 0.0441, 0.0452 and 0.0530, and the optimal point is found.

[0074] The reason for this result is that as the first-stage impeller IP1 and the second-stage impeller IP2 are close to each other, the two impellers of the first-stage impeller IP1 and the second-stage impeller IP2 work like one large impeller.

[0075] Based on the simulation results, the height of the second-stage impeller IP2 was changed as shown in Table 1, and the actual fermenter was applied in the field and then cultured.

[0076] As a result, as shown in Table 2, it was confirmed that the fermentation time ratio decreased with the increase of the oxygen transfer coefficient (kLa) as a secondary value, and the productivity ratio increased with the decrease of the fermentation time ratio. That is, it was confirmed that the fermentation closely related to oxygen resulted in a decrease in the culture time ratio with the increase of the oxygen transfer coefficient (kLa).

[0077] Reference again Figure 1 , the fourth step (ST4) further includes a 41st (ST41) step of calculating a tertiary value, which is a ratio of the oxygen transfer coefficient (kLa) to the power ratio, by using the oxygen transfer coefficient (kLa) and the power ratio as the secondary values. In this case, the fifth step (ST5) further includes a 51st (ST51) step of finding the optimal point based on a comparison value obtained by comparing the tertiary values ​​before and after the change.

[0078] The 51st step (ST51) includes finding the optimal point based on the comparison value obtained by comparing the tertiary values ​​before and after the change when the design variable is changed. The 51st step (ST51) includes repeating the second step (ST2), the third step (ST3), and the fourth step and the 41st step (ST4 and ST41), and finding the optimal point based on the comparison value obtained by comparing the tertiary values ​​before and after the change.

[0079] Referring to Table 1, as a result of step 41 (ST41), when the position of the second-stage impeller IP2 in case 1 was at the middle 75 mm, the ratio of the oxygen transfer coefficient to the power ratio (kLa / power ratio) was 0.0452. In case 3, when the position of the second-stage impeller IP2 was lowered to 55 mm, the ratio of the oxygen transfer coefficient to the power ratio (kLa / power ratio) increased to 0.0482. In case 2, when the position of the second-stage impeller IP2 was raised to 95 mm, the ratio of the oxygen transfer coefficient to the power ratio (kLa / power ratio) was lowered to 0.0450.

[0080] That is, as a result of step 51 (ST51), as in case 3, when the position of the second-stage impeller IP2 is lowered, it can be seen that the ratio of the oxygen transfer coefficient to the power ratio (kLa / power ratio) as the third-stage value increases, which is confirmed in step 51 (ST51), and the optimal point is found.

[0081] As a result, as shown in Table 1, as the ratio of the oxygen transfer coefficient to the power ratio (kLa / power ratio) as the tertiary value increases, the fermentation time ratio decreases and the productivity ratio increases. That is, it can be confirmed that as the ratio of the oxygen transfer coefficient to the power ratio (kLa / power ratio) as the tertiary value increases, the fermentation time ratio that is closely related to oxygen decreases.

[0082] Figure 7 is a block diagram of a CFD-based optimization system for an amino acid fermentation tank according to one embodiment of the present invention. Figure 7 The CFD-based optimization system of the embodiment includes a simulation unit 10, a fermentation tank 1, a monitoring sensor 20 and a control unit 30.

[0083] The simulation unit 10 is configured to implement the CFD-based optimization method for the fermenter for amino acid fermentation. The CFD-based optimization method for the fermenter for amino acid fermentation of the embodiment is used to optimize the fermenter 1 and is applied in the field. The monitoring sensor 20 can be configured as a camera, an infrared sensor, etc., which can monitor error data that deviates from the optimal state of the fermenter 1 during operation.

[0084] The control unit 30 compares whether the error data monitored by the monitoring sensor 20 exceeds the allowable error range, and notifies the administrator of the comparison result. The control unit 30 controls the motor M of the fermenter 1 based on the data of the simulation unit 10 to drive the shaft 2 and the first-stage impeller IP1, the second-stage impeller IP2, and the third-stage impeller IP3. At the same time, the control unit 30 determines whether the fermenter 1 is out of its optimal state through the detection signal from the monitoring sensor 20, so that the optimal fermentation state of the fermenter 1 can be maintained.

[0085] In the following, various simulations and embodiments of field applications will be described.

[0086] Experimental Example 2 (Simulation 2 and Field Application)

[0087] Experimental Example 2 of the CFD-based optimization method according to the embodiment will be described. Table 2 shows the oxygen transfer coefficient (kLa) and productivity according to the impeller height in the large-scale fermenter. As an example, the volume of the large-scale fermenter is greater than 100,000L and less than 700,000L.

[0088] The experiment was conducted using a large-scale fermenter. First, the size of the fermenter, the size of the impeller, and the speed (RPM) of the impeller were kept as the first variable, the height of the second-stage impeller and the third-stage impeller were changed as the second variable, and the fermenter was simulated using CFD.

[0089] Through this simulation, the power ratio and the oxygen transfer coefficient (kLa) as the third variable and the secondary value, and the ratio of the oxygen transfer coefficient to the power ratio as the tertiary value are calculated in the fourth step and the 41st step (ST4 and ST41).

[0090] The simulation results are shown in Table 2, and when the second-stage impeller and the third-stage impeller were installed at positions 10 cm and 50 cm higher than the standard (STD), respectively, as in fermenter 1, the highest oxygen transfer coefficient was confirmed in fermenter 1. Considering the heights of the second-stage impeller and the third-stage impeller in fermenters 1 to 4, the oxygen transfer coefficient was high when the second-stage impeller and the third-stage impeller were in appropriate positions.

[0091] The proportional relationship between the oxygen transfer coefficient and the productivity was obtained as a result of comparing the productivity based on the accumulated data from fermenters 1 to 4. Then, fermenter 4 was changed so that the second-stage impeller and the third-stage impeller were located at the same height as in fermenter 1, and the productivity was improved by 1% to 2%.

[0092] [Table 2]

[0093]

[0094]

[0095] Experimental Example 3 (Simulation 3)

[0096] Experimental Example 3 of the CFD-based optimization method according to the embodiment will be described. The target fermenter is a fermenter in which oxygen is insufficient, so the feed is reduced to prevent oxygen depletion and abnormal fermentation, which ultimately leads to reduced fermentation productivity. The corresponding fermenter in Table 3 is a large-scale fermenter with a volume greater than 100,000L and less than 700,000L.

[0097] A simulation was performed using a large-scale fermenter. In this simulation, the size of the fermenter, the size of the impeller, and the rotation speed (RPM) of the impeller were kept as the first variable, the height of the second-stage impeller was varied as the second variable, and the fermenter was simulated using CFD.

[0098] Through this simulation, the power ratio and the oxygen transfer coefficient (kLa) as the third variable and the secondary value, and the ratio of the oxygen transfer coefficient to the power ratio as the tertiary value are calculated in the fourth step and the 41st step (ST4 and ST41).

[0099] The simulation results are shown in Table 3, which confirm that the lower the installation height of the second-stage impeller, the higher the oxygen transfer coefficient in the fermenter.

[0100] [Table 3]

[0101]

[0102]

[0103] Experimental Example 4 (Simulation 4)

[0104] Experimental Example 4 of the CFD-based optimization method according to the embodiment will be described. The target fermenter is a fermenter in which the oxygen transfer coefficient needs to be increased due to oxygen depletion. The corresponding fermenter is a medium-scale fermenter with a volume greater than 100L and less than 100,000L.

[0105] The simulation was performed using a medium-sized fermenter. The height of the impeller and the speed of the impeller were varied for the corresponding fermenters in Table 4. In this simulation, the size of the fermenter and the size of the impeller were kept as the first variable, the height and speed (RPM) of the third-stage impeller were varied as the second variable, and the fermenter was simulated using CFD.

[0106] Through this simulation, the power ratio and the oxygen transfer coefficient (kLa) as third variables, and the ratio of the oxygen transfer coefficient to the power ratio as a tertiary value are calculated in the fourth step and the 41st step (ST4 and ST41).

[0107] As a result, simply changing the height of the impeller had little effect, and the oxygen transfer coefficient changed by about 1%. In addition, simulations were performed by changing the rotation speed (RPM) of the impeller, which was variable, and it was confirmed that the oxygen transfer coefficient increased by 6%.

[0108] [Table 4]

[0109]

[0110] Experimental Example 5 (Simulation 5)

[0111] Experimental Example 5 of the CFD-based optimization method according to the embodiment will be described. The corresponding fermenter in Table 5 is a large-scale fermenter having a volume greater than 100,000L and less than 700,000L.

[0112] The simulation was performed using a large-scale fermenter. First, under given conditions, the size of the fermenter, the size of the impeller, and the speed of the impeller were kept as the first variable, and the fermenter was simulated using CFD according to the amount of air supplied to the sparger as the second variable.

[0113] Through this simulation, the power ratio and the oxygen transfer coefficient (kLa) as the third variable and the secondary value, and the ratio of the oxygen transfer coefficient to the power ratio are calculated in the fourth step and the 41st step (ST4 and ST41).

[0114] The simulation results are shown in Table 5. As the ventilation rate increases, the oxygen transfer coefficient increases, and the power ratio increases to a certain extent and then decreases. This confirms that the efficiency is highest when the maximum amount of air is supplied within the operable area.

[0115] [Table 5]

[0116] Classification Ventilation Oxygen transfer coefficient Power Ratio kLa / power ratio Case 1 1.00 0.1245 1.00 0.1245 Case 2 1.05 0.1296 1.08 0.1196 Case 3 1.10 0.1369 1.05 0.1305 Case 4 1.15 0.1476 0.98 0.1513

[0117] While the invention has been described in conjunction with what are presently considered to be practical embodiments, it is to be understood that the invention is not limited to the disclosed embodiments, but is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

[0118] (Explanation of Reference Numerals)

[0119] 1: Fermentation tank 2: Axis

[0120] 3: jet 10: simulation unit

[0121] 20: Monitoring sensor 30: Control unit

[0122] BF1: First baffle BF2: Second baffle

[0123] IP1: first stage impeller IP2: second stage impeller

[0124] IP3: third stage impeller M: motor

Claims

1. A CFD-based optimization method for an amino acid fermentation tank, comprising: setting objective variables, constraint variables, and design variables relative to an optimization goal for a fermenter having at least one impeller; generating a three-dimensional shape of the flow area of ​​the fermenter under each condition of the constraint variables and the design variables according to the target variable; calculating a primary value, based on the information about the three-dimensional shape, by computational fluid dynamics analysis of the flow region, the primary value being at least one of an oxygen diffusion coefficient, a liquid density, a liquid viscosity, a gas retention rate, a bubble diameter, an energy dissipation rate, and a torque; calculating secondary values ​​using the primary values, the secondary values ​​being oxygen transfer coefficient and power ratio; and While changing the set design variables in various ways, generating a three-dimensional shape, calculating a primary value, and calculating a secondary value are repeated, and an optimal point is found based on a comparison value obtained by comparing the secondary values ​​before and after the change.

2. The CFD-based optimization method according to claim 1, wherein: In the setting, considering the current situation, at least one of the oxygen transfer coefficient and the power ratio is selected as the target variable.

3. The CFD-based optimization method according to claim 1, wherein: In the arrangement, the constraint variables include at least one of a height of the fermentor, a diameter of the fermentor, a shape of the sparger, an aeration rate, and an operating volume of the fermentor.

4. The CFD-based optimization method according to claim 1, wherein: In the setup, the design variables are set to computational fluid dynamics simulation conditions within the range of possible variations of the fermenter.

5. The CFD-based optimization method according to claim 4, wherein: In the arrangement, the design variables include at least one of a rotation speed (rpm) of the impeller, a shape of the impeller, a number of impellers, a diameter of the impeller, and a mounting height of the impeller.

6. The CFD-based optimization method according to claim 4, wherein: In the arrangement, the maximum height of the impeller is set within the operating volume height of the fermenter.

7. The CFD-based optimization method according to claim 1, wherein: Calculating the secondary value includes using the oxygen transfer coefficient and the power ratio to calculate a tertiary value which is the ratio of the oxygen transfer coefficient to the power ratio (kLa / power ratio).

8. The CFD-based optimization method according to claim 7, wherein: Finding the optimal point includes finding the optimal point based on a comparison value obtained by comparing tertiary values ​​before and after the change.

9. A CFD-based optimization system for a fermentation tank for amino acid fermentation, comprising: a simulation unit configured to perform simulation by a CFD-based optimization method of a fermenter for amino acid fermentation; A fermenter optimized by the simulation unit and applied in the field; a monitoring sensor configured to monitor error data during operation that deviates from an optimal state of the fermenter; and The control unit is configured to compare whether the error data monitored by the monitoring sensor exceeds an allowable error range and notify a manager of the comparison result.

10. The CFD-based optimization system of claim 9, wherein: The fermentation tank comprises The first-stage impeller, the second-stage impeller and the third-stage impeller are mounted on a shaft driven by a motor and are spaced apart at predetermined intervals from the lower end upward. A first baffle and a second baffle are arranged on both sides of the radial inner wall, and An air ejector is disposed below the first-stage impeller to blow air.

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