A precise stirring control method in a liquor brewing process

Abstract

This invention discloses a precision stirring control method in the brewing process of alcoholic beverages. The method involves receiving brewing process parameters through a main controller; controlling a multi-level sensor array to collect real-time temperature and density at multiple depth levels within the fermentation tank, and calculating the fluid stratification index; applying periodic speed micro-perturbations to the stirring motor, simultaneously reading the actual speed and real-time torque, online back-calculating the real-time rheological index, and calculating the dynamic viscosity; then using a dynamic coupling model to calculate the target stirring speed, and obtaining a safe speed limit based on a non-Newtonian fluid shear model and a motor torque model; outputting a control signal to drive the stirring motor, correcting the speed through closed-loop feedback, and triggering an alarm when parameters exceed limits; and entering a low-speed maintenance mode when the fluid stratification index is below the uniformity threshold.

Description

Technical Field

[0001] This invention relates to a method for precise stirring control in the brewing process of alcoholic beverages. Background Technology

[0002] In the winemaking industry, the stirring process within the fermentation tank directly determines fermentation efficiency and wine quality. During fermentation, uneven temperature and density distribution of the liquid within the tank can easily lead to fluid stratification, resulting in uneven yeast distribution, significant temperature differences between layers, and even localized temperatures reaching the yeast inactivation threshold. Therefore, real-time monitoring of the fluid state within the fermentation tank and precise stirring control are crucial for ensuring fermentation uniformity and yeast activity.

[0003] Existing fermentation stirring control technologies mainly employ constant rotation speed operation or rely on manual experience for timed start-stop. These methods cannot adaptively adjust to the real-time fluid physical state inside the fermenter, making it difficult to cope with the continuously changing operating conditions during fermentation.

[0004] Furthermore, the fermentation broth is a typical non-Newtonian fluid, and its viscosity changes significantly during the fermentation process. Traditional control methods do not take fluid viscosity into account when making stirring decisions, resulting in insufficient stirring kinetic energy and incomplete mixing at high viscosity, while causing unnecessary energy waste at low viscosity.

[0005] More importantly, intense mechanical agitation generates enormous fluid shear forces, which can lead to yeast inactivation when they exceed the tolerance limits of the yeast cell walls. Existing systems lack real-time monitoring and local alarm avoidance mechanisms for excessive shear forces, making it difficult to achieve a balance between thorough mixing and protecting yeast activity. Furthermore, operators often cannot directly monitor the real-time status of the fluid inside the tank.

[0006] In summary, existing fermentation stirring control technologies have significant shortcomings in terms of adaptive adjustment, viscosity response, shear force safety protection, and human-machine interaction. There is an urgent need for a stirring control scheme that can precisely adjust the stirring based on the stratification state and dynamic viscosity of the fluid inside the tank, while also protecting yeast activity. Summary of the Invention

[0007] The present invention provides a precise stirring control method in the wine brewing process to solve the problems existing in the prior art.

[0008] The technical solutions adopted in this invention are as follows:

[0009] A method for precise stirring control in the brewing process of alcoholic beverages includes the following steps:

[0010] S1: The main controller receives brewing process parameters, which include reference temperature, maximum allowable temperature range, maximum allowable density range, uniformity threshold, safe threshold for yeast shear force, critical speed value, and critical torque value.

[0011] S2: The main controller controls the multi-level sensor array to collect data, obtain the real-time temperature and density of multiple depth levels in the fermenter, and calculate the fluid stratification index based on the real-time temperature, real-time density, maximum allowable temperature range and maximum allowable density range of the process.

[0012] S3: The main controller applies periodic speed micro-perturbations to the stirring motor, synchronously reads the actual speed and real-time torque of the stirring motor before and after the micro-perturbation, reverse-engineers the real-time rheological index of the fermentation broth online, calculates the apparent consistency coefficient of the current fluid, and calculates the dynamic viscosity of the current fluid based on temperature compensation.

[0013] S4: The main controller calculates the target stirring speed required to eliminate fluid stratification based on the fluid stratification index and dynamic viscosity using a dynamic coupling model. It also solves the biological limit speed based on the non-Newtonian fluid shear model and the safety threshold of yeast shear force, and solves the mechanical limit speed based on the motor torque model. The minimum value among the target stirring speed, biological limit speed and mechanical limit speed is taken as the safe limit speed.

[0014] S5: The main controller outputs a control signal to drive the stirring motor to run at the safe limit speed, and corrects the speed through closed-loop feedback control; at the same time, the operating status is displayed in real time, and an over-limit alarm is triggered when the actual speed reaches the speed threshold or the real-time torque reaches the torque threshold.

[0015] S6: When the main controller determines that the fluid stratification index is lower than the uniformity threshold, it controls the stirring motor to enter the low-speed maintenance mode; regardless of whether it is currently in the low-speed maintenance mode, the main controller will continuously return to step S2 with a preset control cycle.

[0016] Furthermore, the multi-level sensor array includes temperature sensors and density sensors that are equidistantly distributed at different heights of the fermenter.

[0017] Further, in S2, the calculation logic of the fluid stratification index is as follows: calculate the standard deviation of real-time temperature and real-time density for each depth level, and normalize the standard deviations using the maximum allowable temperature range and the maximum allowable density range to obtain the temperature stratification sub-index and the density stratification sub-index; introduce a density compensation coefficient when calculating the density stratification sub-index, and use adaptive weights to linearly fuse the temperature stratification sub-index and the density stratification sub-index to obtain the fluid stratification index.

[0018] Furthermore, in S3, the main controller applies a speed step to the stirring motor, records the torque change rate before and after the speed change, and calculates the real-time rheological index online based on the logarithmic ratio of the torque change rate to the speed change rate.

[0019] Furthermore, in S4, the dynamic coupling model uses the basic stirring speed as a reference, calculates the dynamic adjustment amount based on the fluid stratification index and dynamic viscosity, and superimposes the basic stirring speed and the dynamic adjustment amount to obtain the target stirring speed.

[0020] Furthermore, in S4, the biological limit speed is obtained by substituting the apparent consistency coefficient and the real-time rheological index into the Metzner-Otto shear model and solving it in reverse by combining the safety threshold of yeast shear force; the mechanical limit speed is obtained by substituting the apparent consistency coefficient and the real-time rheological index into the motor torque model and solving it in reverse.

[0021] Furthermore, when the target stirring speed exceeds the biological or mechanical limit speed and is forcibly reduced, an audible and visual alarm is triggered simultaneously.

[0022] Furthermore, in S5, the closed-loop feedback control uses an incremental PID algorithm to compensate for and adjust the error between the target stirring speed and the actual speed, and adaptively adjusts the PID parameters when the dynamic viscosity undergoes a step change.

[0023] Furthermore, in S6, the rotational speed setting in the low-speed maintenance mode is 10% to 20% of the target stirring speed.

[0024] Furthermore, the main controller uploads the real-time calculated fluid stratification index and dynamic viscosity to the host computer to generate a digital curve of the fermentation process.

[0025] The present invention has the following beneficial effects:

[0026] (1) By collecting temperature and density at different depths in real time through a multi-level sensor array and calculating the fluid stratification index, the physical gradient imbalance state inside the fermenter can be quantified, providing a direct basis for the dynamic adjustment of stirring speed, thereby solving the problem that the traditional constant speed or timed start-stop method cannot adapt to the real-time working conditions changes inside the tank and reducing the blindness of stirring.

[0027] (2) The main controller applies periodic speed micro-disturbance to the stirring motor through the frequency converter, and combines the motor torque feedback to back-calculate the real-time rheological index and dynamic viscosity of the fermentation liquid online. There is no need to install an expensive online viscometer in the fermenter to obtain the current viscosity characteristics of the fluid, which reduces hardware costs and system complexity.

[0028] (3) When calculating the target stirring speed, a dynamic coupling model is introduced to compensate for viscosity power, so that the stirring kinetic energy can increase accordingly with the increase of fluid viscosity, which helps to break the stratification of high viscosity liquid; at the same time, the Metzner-Otto shear model and the motor torque model are used for dual limiting, which clamps the actual operating speed within the safe range that simultaneously meets the mechanical safety of the motor and the safety threshold of yeast shear force, thus alleviating the conflict between mechanical overload and biological activity protection during high viscosity stirring.

[0029] (4) Closed-loop feedback control is used to correct the actual rotation speed in real time. When the fluid stratification index is lower than the uniformity threshold, it automatically switches to low-speed maintenance mode to reduce unnecessary power consumption while maintaining the yeast suspension state.

[0030] (5) By displaying the operating status in real time and triggering an alarm when parameters exceed the limit, operators can promptly learn about the fluid status inside the fermenter and the equipment operation, which facilitates on-site intervention and process adjustment. Attached Figure Description

[0031] Figure 1 This is a block diagram of the control hardware principle corresponding to the system of the present invention.

[0032] Figure 2 This is a schematic diagram of a multi-level sensor arrangement and fluid stratification detection. Detailed Implementation

[0033] The invention will now be further described with reference to the accompanying drawings.

[0034] like Figure 1 and Figure 2 This invention discloses a precision stirring control method in the wine brewing process, which is applicable to precision control scenarios in wine brewing fermentation tanks where high fluid uniformity is required and strict protection of the activity of fermentation microorganisms is necessary. Figure 1 The hardware system corresponding to the method of this invention is implemented using an STM32 microcontroller as the main controller, along with a frequency converter, an asynchronous motor with torque feedback, a multi-level sensor array, a display module, an audible and visual alarm module, and a wireless communication module. The main controller handles algorithm parsing and command issuance; the multi-level sensor array monitors the spatial gradients of temperature and density; the motor and frequency converter constitute the stirring and torque acquisition mechanism; and the display and alarm modules provide human-machine interaction and safety protection. The specific control and operation process of this system is described in detail below.

[0035] After system power-on initialization, the main controller establishes a handshake connection with the host computer in the production workshop via the wireless communication module, and simultaneously illuminates the local display module for interface initialization. When the fermenter is ready to start a new batch of brewing tasks, the main controller needs to receive brewing process parameters from the host computer. These brewing process parameters are not simple instructions, but rather multi-dimensional matrix data based on the current alcohol concentration and fermentation cycle, including the baseline temperature for the current brewing stage and the yeast shear force safety threshold. Upon receiving the relevant parameters, the main controller establishes a process feature library in its internal memory as the reference coordinate system for subsequent dynamic calculations and displays the current threshold on the display module's setting interface.

[0036] Combination Figure 2 During fermentation, the main controller controls a multi-level sensor array to collect data. Unlike traditional single-point temperature measurement, this invention arranges temperature sensors and density sensors at the bottom, lower middle, upper middle, and top of the fermenter.

[0037] The main controller will read the temperature of each level in real time. and density To address the issue of temperature and density having different dimensions and vastly different fluctuation ranges, the main controller utilizes a dimensionless normalization algorithm to calculate the temperature stratification sub-indices separately. and density stratification sub-index :

[0038] ,

[0039] During fermentation, a large amount of carbon dioxide bubbles are generated, which causes interference from random changes in gas holdup to the sensor, resulting in a falsely high standard deviation of the density measured online. Therefore, when calculating the density molecular layer index... When introducing density compensation coefficient To reduce the interference of gas content on online monitoring, the calibration method is as follows: During the equipment commissioning phase, in standard fermentation batches, the measured density range of online aerated fermentation broth and the true density range of offline deaerated fermentation broth samples are simultaneously acquired. The ratio of these two values ​​is fitted as a function of the corresponding fermentation time or sugar content decrease rate, generating a dynamic calibration curve which is then stored in the process feature library. The denser the bubbles, the smaller this coefficient, thereby suppressing and compensating for the artificially high density measurement caused by bubble entrainment.

[0040] ,

[0041] in, and This is the arithmetic mean of temperature and density across all levels; This represents the maximum allowable temperature range for the process. This represents the maximum density range allowed by the process. and These are brewing process parameters, stored in the process feature library; This represents the number of sensor layers.

[0042] Subsequently, a weight combination dynamically allocated according to the fermentation stage was used to calculate the comprehensive fluid stratification index. The formula for calculating the comprehensive fluid stratification index is as follows:

[0043] ,

[0044] in, and The fusion weights of the temperature and density stratification sub-indices for the current fermentation stage are stored in the process feature library.

[0045] While monitoring the spatial state, the main controller also needs to capture changes in the fluid's physical properties in real time. This is achieved by reading the current rotational speed from the inverter side via the industrial bus. and motor output torque .

[0046] Since the fermentation broth of alcoholic beverages is a typical non-Newtonian fluid, its apparent consistency coefficient is nonlinearly affected by the shear rate and is extremely sensitive to temperature fluctuations. To avoid the shortcomings of offline calibration in reflecting actual operating conditions, this invention introduces an online micro-perturbation rheological model. The main controller periodically applies an instantaneous speed step (i.e., micro-perturbation) to the motor through the frequency converter, from the steady-state speed... Mutation The disturbance amplitude of the speed step is determined through calibration experiments based on the rated speed of the stirring motor and the viscosity range of the fermentation broth, and is usually taken as 3% to 10% of the rated speed. The corresponding effective working torque is extracted simultaneously. and .

[0047] Considering the inherent mechanical friction interference of the system, the formula needs to subtract the no-load friction torque. The work done by pure fluid damping is obtained, and then the STM32 main controller uses the logarithmic derivative of the non-Newtonian pseudoplastic fluid to online inversely deduce the real-time rheological index of the fermentation broth. The formula for calculating the real-time rheological index is as follows:

[0048] ,

[0049] Obtain real-time rheological index Then, combined with the no-load friction torque and stirring geometry constants Calculate the current apparent consistency coefficient, and then introduce the real-time average temperature collected by multi-level sensors. Reference temperature and the viscosity-temperature decay coefficient of the fermentation broth Precise mathematical compensation is performed to obtain the real-time dynamic viscosity of the fluid. The real-time dynamic viscosity formula is:

[0050] ,

[0051] ,

[0052] Among them, the geometric constants of the stirring blades The torque-speed relationship of the specific blade in standard silicone oil needs to be determined in advance, along with the viscosity-temperature decay coefficient of the fermentation broth. The data was obtained through prior rheological calibration experiments using multiple temperature gradients on the fermentation broth samples.

[0053] By combining online micro-perturbation with exponential temperature compensation, the interference of temperature variation and shear thinning effect on the measurement is eliminated, making the calculated fluid rheological state characterization value closer to the real physical state.

[0054] When the fluid stratification index is extracted With dynamic viscosity After obtaining the two core characteristic data, the main controller will activate the dynamic coupling model to calculate the target stirring speed. The dynamic coupling model is as follows:

[0055] ,

[0056] Among them, the basic stirring speed Set to 25 rpm to ensure basic fluid circulation within the tank. Stratification sensitivity coefficient. Set to 12.5, i.e., the stratification index. For every 1 increase, the target speed increases by 12.5 rpm. This is the viscosity-power compensation factor. The initial reference viscosity of the fermentation broth, preset for the process, is determined by a rheometer; γ is the power balance factor, the value of which is related to the type of impeller, and is usually taken as 0.3 to 0.5 in the non-Newtonian fluid transition flow state.

[0057] To prevent the aforementioned strong compensation from causing mechanical overload or damaging biological activity, the main controller performs dual extreme value verification in parallel, utilizing the acquired online apparent consistency coefficient. and real-time rheological index Inverse solution for the two limiting speed thresholds:

[0058] First, based on the maximum safe torque of the motor Derived mechanical limiting speed :

[0059] ,

[0060] Secondly, based on the Metzner-Otto non-Newtonian fluid shear model and yeast survival threshold Derived biological limit speed :

[0061] ,

[0062] in The Metzner-Otto constant is pre-calibrated based on the geometry of the impeller, and its value can be obtained by referring to standard impeller literature charts.

[0063] Based on the "weakest link" principle, the main controller will ultimately send the safe rotation speed. Strictly confined within an absolutely secure domain:

[0064] ,

[0065] In the above dual-limiting verification, once the model's expected value is detected... Greater than or greater than The main controller will then limit the motor speed to [a certain value]. It immediately raises the level of the corresponding I / O port, triggering the audible and visual alarm module to issue an urgent alarm so that on-site personnel can intervene.

[0066] After completing the calculation and verification, the STM32 microcontroller uses its internal timer to output the final result. The signal is converted into a high-frequency PWM signal with an adjustable duty cycle and sent to the frequency converter. The frequency converter drives the stirring motor to approach the target speed. During this process, due to the nonlinearity of fluid damping, the actual speed often lags or overshoots. The main controller uses an incremental PID algorithm to perform closed-loop compensation for the error and monitors the viscosity. When a step occurs, the PID integral time constant is adaptively adjusted.

[0067] After stirring for a period of time, the fermentation broth gradually becomes more uniform. The value continues to decrease. When the main controller determines... If the uniformity falls below a preset threshold for several consecutive sampling periods, an energy-saving strategy is immediately triggered, controlling the stirring motor to exit dynamic high-speed adjustment and enter a low-speed maintenance mode. This is sufficient to keep the yeast in suspension and prevent sedimentation. The system continuously monitors the process in this mode. Changes. If caused by the continuous release of heat during fermentation... If the temperature rises again and exceeds the threshold, or if the host computer issues a new fermentation stage command, the main controller will exit the low-speed maintenance mode.

[0068] Throughout the control process, the main controller will , Data such as rotational speed and torque are packaged and transmitted back to the host computer in real time, forming a digital curve of the fermentation process. This provides process engineers with invaluable data support, enabling production to transform from experience-based black-box operations into data-driven precision manufacturing.

[0069] The above description is only a preferred embodiment of the present invention. It should be noted that those skilled in the art can make several improvements without departing from the principle of the present invention, and these improvements should also be considered within the scope of protection of the present invention.

Claims

1. A method for precise stirring control in the brewing process of alcoholic beverages, characterized in that: Includes the following steps: S1: The main controller receives brewing process parameters, which include reference temperature, maximum allowable temperature range, maximum allowable density range, uniformity threshold, safe threshold for yeast shear force, critical speed value, and critical torque value. S2: The main controller controls the multi-level sensor array to collect data, obtain the real-time temperature and density of multiple depth levels in the fermenter, and calculate the fluid stratification index based on the real-time temperature, real-time density, maximum allowable temperature range and maximum allowable density range of the process. S3: The main controller applies periodic speed micro-perturbations to the stirring motor, synchronously reads the actual speed and real-time torque of the stirring motor before and after the micro-perturbation, reverse-engineers the real-time rheological index of the fermentation broth online, calculates the apparent consistency coefficient of the current fluid, and calculates the dynamic viscosity of the current fluid based on temperature compensation. S4: The main controller calculates the target stirring speed required to eliminate fluid stratification based on the fluid stratification index and dynamic viscosity using a dynamic coupling model. It also solves the biological limit speed based on the non-Newtonian fluid shear model and the safety threshold of yeast shear force, and solves the mechanical limit speed based on the motor torque model. The minimum value among the target stirring speed, biological limit speed and mechanical limit speed is taken as the safe limit speed. S5: The main controller outputs a control signal to drive the stirring motor to run at the safe limit speed, and corrects the speed through closed-loop feedback control; at the same time, the operating status is displayed in real time, and an over-limit alarm is triggered when the actual speed reaches the speed threshold or the real-time torque reaches the torque threshold. S6: When the main controller determines that the fluid stratification index is lower than the uniformity threshold, it controls the stirring motor to enter the low-speed maintenance mode; regardless of whether it is currently in the low-speed maintenance mode, the main controller will continuously return to step S2 with a preset control cycle.

2. The precision stirring control method in the wine brewing process as described in claim 1, characterized in that: The multi-level sensor array includes temperature sensors and density sensors that are equidistantly distributed at different heights of the fermenter.

3. The precision stirring control method in the wine brewing process as described in claim 1 or 2, characterized in that: In S2, the calculation logic of the fluid stratification index is as follows: calculate the standard deviation of real-time temperature and real-time density for each depth level, and normalize the standard deviation using the maximum allowable temperature range and the maximum allowable density range to obtain the temperature stratification sub-index and the density stratification sub-index; introduce a density compensation coefficient when calculating the density stratification sub-index, and use adaptive weights to linearly fuse the temperature stratification sub-index and the density stratification sub-index to obtain the fluid stratification index.

4. The precision stirring control method in the wine brewing process as described in claim 1, characterized in that: In S3, the main controller applies a speed step to the stirring motor, records the torque change rate before and after the speed change, and calculates the real-time rheological index online based on the logarithmic ratio of the torque change rate to the speed change rate.

5. The precision stirring control method in the wine brewing process as described in claim 1, characterized in that: In S4, the dynamic coupling model uses the basic stirring speed as a reference, calculates the dynamic adjustment amount based on the fluid stratification index and dynamic viscosity, and superimposes the basic stirring speed and the dynamic adjustment amount to obtain the target stirring speed.

6. The precision stirring control method in the wine brewing process as described in claim 1, characterized in that: In S4, The biological limiting speed is obtained by substituting the apparent consistency coefficient and real-time rheological index into the Metzner-Otto shear model and solving it in reverse, combined with the safety threshold of yeast shear force; the mechanical limiting speed is obtained by substituting the apparent consistency coefficient and real-time rheological index into the motor torque model and solving it in reverse.

7. The precision stirring control method in the wine brewing process as described in claim 6, characterized in that: When the target stirring speed exceeds the biological or mechanical limit speed and is forcibly reduced, an audible and visual alarm is triggered simultaneously.

8. The precision stirring control method in the wine brewing process as described in claim 1, characterized in that: In S5, the closed-loop feedback control uses an incremental PID algorithm to compensate for the error between the target stirring speed and the actual speed, and adaptively adjusts the PID parameters when the dynamic viscosity undergoes a step change.

9. The precision stirring control method in the wine brewing process as described in claim 1, characterized in that: In S6, the rotation speed setting in the low-speed maintenance mode is 10% to 20% of the target stirring speed.

10. The precision stirring control method in the wine brewing process as described in claim 1, characterized in that: The main controller uploads the real-time calculated fluid stratification index and dynamic viscosity to the host computer to generate a digital curve of the fermentation process.

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