Automatic control system for soybean milk separation

Abstract

The invention provides an automatic control system for soybean milk separation, which is applied to soybean product processing equipment, belongs to the technical field of soybean product processing, and is characterized in that a main controller is a core processing unit of the automatic control system and is internally provided with pre-configured soybean milk separation multi-stage control logic; the sensor group is electrically connected with an input port of the main controller and used for collecting environmental parameters in the soybean milk separation process, and the man-machine interaction interface is in communication connection with the main controller and used for receiving process parameters set by a user and displaying the working state of the automatic control system in real time; and the main controller is used for analyzing the environmental parameters to obtain an initial control instruction set, adjusting the initial control instruction set according to the process parameters and the working state, and issuing the initial control instruction set to the actuator group to execute corresponding actions. The problems of parameter fragmentation control, much manual intervention and long production changing debugging in traditional soybean milk separation are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of bean product processing, in particular to an automatic control system for soy milk separation. Background Art

[0002] Soy milk separation is a key link in determining product quality, and its technical level directly affects the protein extraction rate, product stability and production efficiency. However, this technology still has significant limitations in terms of automated control and parameter coordination. For example, vibration screening equipment is the mainstream choice for small and medium-sized soy product companies. It achieves pulp-residue separation through three-dimensional vibration at a fixed frequency, but its core parameters such as vibration frequency and amplitude need to be manually preset and fixed throughout the process. In actual production, it was found that this fixed parameter mode cannot adapt to the differences in the characteristics of raw materials of different bean varieties and different soaking degrees. When processing soybean varieties with high protein content, the screen is often clogged due to insufficient vibration intensity, and for low-moisture raw materials, excessive vibration can easily cause protein denaturation. In addition, the temperature control of this type of equipment mostly relies on independent heating devices and lacks linkage with the screening process. When the soy milk temperature deviates from the optimal separation range, the separation efficiency is easily reduced or the moisture content of the soy dregs exceeds the standard.

[0003] The core defects of the existing technology are concentrated in three aspects: first, parameter control is fragmented. Key parameters such as temperature, liquid level, and vibration intensity are mostly controlled by independent closed-loop control, and lack a coordinated adjustment mechanism. For example, the amplitude adjustment of the vibrating screen is unrelated to the concentration change; second, the adaptive ability is insufficient, and the process parameters cannot be automatically corrected according to the differences in bean varieties and batch changes, resulting in the need to shut down and re-debugging during production changes; third, the degree of automation is limited. Most equipment still relies on manual judgment of separation effects and parameter corrections, which is not only labor-intensive, but also causes excessive fluctuations in product qualification rates due to human errors. That is, with consumers' increasing requirements for consistency in soy milk quality and the pursuit of efficiency in industrial production, existing technologies have become difficult to meet the production needs of high precision, high stability, and low human intervention.

[0004] Therefore, the present invention proposes an automatic control system for soy milk separation. Summary of the Invention

[0005] The present invention provides an automated control system for soymilk separation, which is applied to soy product processing equipment and comprises:

[0006] The main controller is the core processing unit of the automated control system and has a pre-configured multi-stage control logic for soymilk separation. The multi-stage control logic includes: a concentration adaptive control stage and a temperature-liquid level coordinated control stage.

[0007] A sensor group, electrically connected to the input port of the main controller, for collecting environmental parameters during the soymilk separation process, the sensor group comprising at least: a liquid level sensor for detecting the soymilk liquid level in the separation chamber; a temperature sensor for detecting the real-time temperature of the soymilk; a viscosity detection unit for indirectly inferring the concentration of the soymilk; and a photoelectric turbidity sensor for collecting the turbidity of the soymilk before separation, wherein the viscosity detection unit and the photoelectric turbidity sensor collect turbidity synchronously.

[0008] A human-computer interaction interface, connected to the main controller for receiving process parameters set by the user and displaying the working status of the automation control system in real time;

[0009] The main controller is configured to analyze environmental parameters to obtain an initial control instruction set, wherein the initial control instruction set includes: an initial motor control instruction for the vibration motor driver, an initial heating control instruction for the heating control circuit, and an initial trigger opening instruction or an initial closing instruction for the water inlet solenoid valve of the vibration motor driver;

[0010] The initial control instruction set is adjusted according to the process parameters and working status, and sent to the actuator group to perform corresponding actions, wherein the actuator group at least includes:

[0011] Vibration motor driver, used to drive and control the vibration frequency and amplitude of the separation screen;

[0012] A heating control circuit is used to adjust the power of the heating tube according to temperature feedback;

[0013] The water inlet solenoid valve is used to control the on and off of water supply to the separation chamber.

[0014] Preferably, the main controller is configured to receive the current signal collected by the viscosity detection unit as a first feedback value representing the concentration of the soymilk and receive the current signal collected by the photoelectric turbidity sensor as a second feedback value representing the turbidity of the soymilk, and determine the initial vibration frequency of the vibration motor driver;

[0015] ,and ;

[0016] in, is the initial vibration frequency of the vibration motor driver; is the reference vibration frequency of the vibration motor driver; is the first feedback value; is the reference viscosity current value corresponding to the standard concentration; is the second feedback value; is the reference turbidity current value corresponding to the standard turbidity; is the viscosity influence weight coefficient; is the turbidity influence weight coefficient; is the correction factor for the separation stage; is the bean variety correction factor; is the frequency analysis function;

[0017] The main controller is configured to perform a first comparison between the first feedback values ​​obtained at different times and a preset concentration-vibration parameter mapping table, and to perform a second comparison between the second feedback values ​​obtained at different times and a preset turbidity-vibration parameter mapping table, to construct a two-dimensional array;

[0018] The main controller is used to perform fitting analysis on two rows of arrays in the two-dimensional array respectively, and perform step-by-step analysis on the concentration fitting function and the turbidity fitting function to obtain an offset set;

[0019] The main controller is configured to expand the range of the initial vibration frequency based on the offset set, and select a middle value of the expanded range as a target vibration frequency;

[0020] The main controller is configured to: Obtaining a target amplitude of the vibration motor driver;

[0021] ;

[0022] in, is the initial amplitude of the vibration motor driver; is the reference amplitude of the vibration motor driver; is the influence coefficient of turbidity on amplitude; is the frequency correction term; is the maximum vibration frequency in the two-dimensional array; is the sieve aperture; is the wheelbase length of the vibration motor;

[0023] The main controller is used to obtain an initial motor control instruction for controlling the operation of the vibration motor driver based on the target vibration frequency and the target amplitude.

[0024] Preferably, the main controller is used to calculate the output power duty cycle of the heating control circuit;

[0025] ;

[0026] in, is the real-time output power duty cycle of the heating control circuit; is the proportionality coefficient; is the temperature deviation; is the temperature segmentation coefficient, and ; is the integration time constant; is the integral correction coefficient, and ; is the differential time constant; is the feedforward compensation coefficient; The target temperature at the current moment in the preset temperature curve; The real-time temperature of soy milk detected by the temperature sensor; The real-time density of soy milk at the current temperature; is the real-time specific heat capacity of soy milk at the current temperature; It is the standard density of soy milk at 25℃; is the standard specific heat capacity of soy milk at 25°C;

[0027] The main controller is further configured to: Get the initial heating control instruction for controlling the heating tube.

[0028] Preferably, the main controller is used to receive the soy milk liquid level collected by the liquid level sensor in real time, and generate an initial trigger start instruction when the soy milk liquid level is lower than the lower limit of the liquid level safety threshold range;

[0029] Determine the current opening instruction of the water inlet solenoid valve based on the historical actual opening instruction and the historical actual opening degree of the water inlet solenoid valve, and control the water inlet solenoid valve to open and replenish water;

[0030] When the liquid level data reaches the upper limit of the threshold range, an initial closing instruction is generated to control the water inlet solenoid valve to close.

[0031] Preferably, the main controller is used to determine the upper limit and lower limit of the liquid level safety threshold range required for the current production batch according to the planned output set by the human-computer interaction interface;

[0032] , ;

[0033] in, is the upper limit value; is the planned output set based on the human-computer interaction interface; k2 is the second empirical coefficient; is the centrifugal force correction factor, and , To separate the real-time speed of the actuator, The maximum speed of the separation actuator; is the cross-sectional area of ​​the separation chamber; is the feed flow correction coefficient, and , It is the real-time opening of the electromagnetic flow valve; is the maximum opening of the electromagnetic flow valve; is the full height of the separation bin; To compare the height values;

[0034] ;

[0035] in, is the lower limit; is the hysteresis interval.

[0036] Preferably, the main controller includes:

[0037] a calculation unit, configured to calculate a difference function between the concentration fitting function and the turbidity fitting function, and perform cluster analysis on the concentration differences at all acquisition moments according to the difference function to obtain a standard deviation, a mean, and a cluster weight of each cluster analysis result;

[0038] a first deviation unit, configured to obtain a first sub-deviation set based on a standard deviation, a mean, and a cluster weight of each cluster analysis result and in combination with a first constant of the concentration fitting function and a second constant of the turbidity fitting function;

[0039] a dividing unit, configured to divide the function curve of the difference function according to preset continuous acquisition moments to determine a single function of each divided curve, and obtain a single constant of the single function;

[0040] a second deviation unit, configured to perform a discrete analysis on the single constant to determine whether a discrete constant exists, and obtain a second sub-deviation set based on the acquisition time of the discrete constant and the average value of the non-discrete constant;

[0041] A moment group association unit is used to sort the concentrations at all acquisition moments in the concentration fitting function and the turbidity fitting function, determine the acquisition moment groups with the same concentration sorting sequence number, and analyze the two-dimensional correlation relationship between the concentration fitting function and the turbidity fitting function based on all acquisition moment groups;

[0042] The expansion unit is configured to determine an optimal offset based on the first sub-deviation set and the second sub-deviation set and in combination with a two-dimensional correlation relationship, and to expand the range of the initial vibration frequency based on the current soy milk level.

[0043] Preferably, the main controller further includes:

[0044] Sequence acquisition unit, used to obtain the historical actual opening instruction sequence of the water inlet solenoid valve And the corresponding historical actual opening sequence , and perform feature extraction to generate the opening time series feature vector Vh, where 、 The j2th historical actual opening instruction and the historical actual opening degree, indicates opening, =0 indicates closing; mo is the total number of instructions in the sequence;

[0045] Constructing a real-time parameter vector , wherein, is the real-time liquid level value of the separation tank, is the real-time temperature of the soybean milk, is the target liquid level value;

[0046] A prediction unit is configured to input Vh and into a pre-trained opening degree prediction model to obtain the current opening degree instruction of the water inlet electromagnetic valve.

[0047] Preferably, the main controller is further configured to obtain a standard state of the automation system based on process parameters, and perform difference analysis on the working state of the automation system to obtain a difference vector;

[0048] The main controller is further configured to input the difference vector into a pre-trained vector analysis model to obtain a first adjustment factor of each initial motor control instruction and a second adjustment factor based on an initial heating control instruction, and obtain an adjusted motor control instruction and an adjusted heating control instruction to control the corresponding vibration motor driver and heating control circuit to work.

[0049] Compared with the prior art, the application has the following beneficial effects:

[0050] Through the main controller integrating the concentration self-adaptation, temperature-liquid level coordination and multi-stage control logic, combined with multi-sensor synchronous acquisition and actuator precise control, the pain points of parameter fragmentation control, manual intervention and long production change debugging in traditional soybean milk separation are completely solved.

[0051] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application can be achieved and obtained by the structure particularly pointed out in the written description and the accompanying drawings.

[0052] The technical solutions of the present application will be further described in detail below with the help of the accompanying drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0053] The accompanying drawings are used to provide a further understanding of the present application, and constitute a part of the specification, together with the embodiments of the present application, to explain the present application, and do not constitute a limitation on the present application. In the drawings:

[0054] Figure 1This is a structural diagram of an automated control system for soy milk separation in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0056] The present invention provides an automatic control system for soy milk separation, which is applied to soy product processing equipment, such as Figure 1 Shown, including:

[0057] The main controller is the core processing unit of the automated control system and has a pre-configured multi-stage control logic for soymilk separation. The multi-stage control logic includes: a concentration adaptive control stage and a temperature-liquid level coordinated control stage.

[0058] A sensor group, electrically connected to the input port of the main controller, for collecting environmental parameters during the soymilk separation process, the sensor group comprising at least: a liquid level sensor for detecting the soymilk liquid level in the separation chamber; a temperature sensor for detecting the real-time temperature of the soymilk; a viscosity detection unit for indirectly inferring the concentration of the soymilk; and a photoelectric turbidity sensor for collecting the turbidity of the soymilk before separation, wherein the viscosity detection unit and the photoelectric turbidity sensor collect turbidity synchronously.

[0059] A human-computer interaction interface, connected to the main controller for receiving process parameters set by the user and displaying the working status of the automation control system in real time;

[0060] The main controller is configured to analyze environmental parameters to obtain an initial control instruction set, wherein the initial control instruction set includes: an initial motor control instruction for the vibration motor driver, an initial heating control instruction for the heating control circuit, and an initial trigger opening instruction or an initial closing instruction for the water inlet solenoid valve of the vibration motor driver;

[0061] The initial control instruction set is adjusted according to the process parameters and working status, and sent to the actuator group to perform corresponding actions, wherein the actuator group at least includes:

[0062] Vibration motor driver, used to drive and control the vibration frequency and amplitude of the separation screen;

[0063] A heating control circuit is used to adjust the power of the heating tube according to temperature feedback;

[0064] The water inlet solenoid valve is used to control the on and off of water supply to the separation chamber.

[0065] In this embodiment, the soy product processing equipment is an industrialized equipment for soy product production. The scheme focuses on the matching equipment of the soy milk separation section, i.e., the special equipment for realizing the separation of soy milk and soy residue, such as a vibrating screen separator, a horizontal screw centrifugal separator, a negative pressure filter separator, etc. in a soy milk production line.

[0066] The main controller is selected from a programmable logic controller such as a Siemens S7-1200 CPU or a microcontroller such as an STM32.

[0067] The soy milk separation multi-stage control logic is an algorithm logic built in the main controller for realizing the accurate control of soy milk separation in stages, which solves the limitations of traditional single parameter control. The concentration self-adaptive regulation stage is a stage for automatically adjusting the vibration parameters according to the real-time concentration of soy milk to ensure the separation efficiency of soy milk residue. The temperature-liquid level cooperative control stage is a stage for correlating the real-time temperature of soy milk and the liquid level of the separation tank to avoid the waste of efficiency or quality problems caused by single regulation.

[0068] The liquid level sensor is selected from a submersible liquid level transmitter with a model of JYB-KO-Y2, a measurement range of 0-100 cm, and an output of 4-20 mA analog signal. The sensor probe is vertically submersed into the separation tank away from stirring or vibrating components. The cable is fixed on the tank top, and the signal cable is connected to the analog input terminal of the main controller.

[0069] The temperature sensor is selected from a PT100 platinum resistance temperature sensor such as OMRONE52-CA1D, with a measurement range of 0-150℃. The sensor probe is packaged in a food-grade stainless steel sleeve and inserted into the separation tank. The depth of the probe is to the middle of the soy milk. The resistance signal is converted into a 4-20 mA signal by a temperature transmitter and connected to the main controller.

[0070] The viscosity detection unit is selected from an online rotary viscometer such as Brookfield DV2T, with a measurement range of 10-10000 cP and an output of 4-20 mA analog signal. The measurement rotor of the viscometer is installed on the feed pipe of the separation tank and fixed through the pipe flange. The signal cable is connected to the main controller and synchronously triggered with the turbidity sensor.

[0071] The photoelectric turbidity sensor is selected from an online photoelectric turbidimeter such as HACH 2100Q, with a measurement range of 0-1000 NTU and an output of 4-20 mA analog signal. The transmitting end and receiving end of the sensor are respectively installed on the two sides of the feed pipe and fixed through quick connectors. The signal is connected to the main controller and shares a trigger signal with the viscosity detection unit.

[0072] A timer is set in the main controller to output switch quantity trigger signals at a fixed time interval, which are connected to the external trigger interfaces of the viscometer and turbidimeter, respectively. At the same time, a synchronous data receiving logic is set in the main controller program to ensure that the data of the two sensors are stored in the same data frame.

[0073] The human-machine interaction interface is the interactive medium between the user and the automation control system, realizing the two-way function of process parameter input and system working status display. For example, a 7-inch or 10-inch industrial touch screen with a resolution of 1024×600 can be selected and connected to the main controller through the RS485 communication interface.

[0074] Environmental parameters are a set of data collected in real time by the sensor group that reflects the current status of the soy milk separation process, such as liquid level 75cm, temperature 82℃, viscosity 480cP, and turbidity 130NTU.

[0075] The initial control instruction set is a set of instructions for controlling each actuator that is initially generated by the main controller based on environmental parameters and preset basic logic. For example, if the viscosity is 480 cP and the turbidity is 130 NTU, the initial instructions are: vibration frequency 50 Hz and amplitude 2.0 mm.

[0076] Process parameters are set by the user through the human-computer interaction interface according to production requirements. They are the basis for the main controller to adjust the initial instructions. For example, the planned output is 1000L, the target temperature is 85℃, the reference vibration frequency of the vibration motor driver is 45Hz, the liquid level safety range is 60-90cm, and the bean variety correction coefficient is 1.1.

[0077] The working status refers to the real-time status data of the system, including the environmental parameters collected by the sensor and the current action status of the actuator, which is used by the main controller to determine whether the instruction needs to be adjusted.

[0078] The vibration motor driver uses a three-phase asynchronous vibration motor driver, such as Delta VFD022EL43A. The driver is installed in the electrical cabinet, with the input end connected to a 380V three-phase power supply and the output end connected to the vibration motor; the control end is connected to the main controller through a signal line to receive frequency and amplitude instructions; the vibration motor is connected to the separation screen through a flange to ensure vibration transmission efficiency.

[0079] The heating control circuit consists of a 220V AC power supply → fuse → solid-state relay → heating tube → thermal relay → neutral wire. The solid-state relay control end is connected to the main controller. The circuit is installed on the insulating mounting plate in the electrical cabinet, and the heating tube is installed on the bottom or side wall of the separation chamber; the thermal relay is set with an overload protection current to prevent the heating tube from burning.

[0080] The water inlet solenoid valve should be a two-position, two-way, food-grade solenoid valve, such as the SMCVX212, with a DN25 connection. The solenoid valve should be connected in series to the water supply line, with the inlet connected to the tap water or purified water line and the outlet connected to the top of the separation chamber, secured with pipe clamps. The solenoid valve coil wiring should be connected to the main controller's digital output terminals, ensuring a secure connection to prevent loosening.

[0081] The beneficial effects of the above technical solution are: through the main controller integrating concentration adaptation, temperature-liquid level coordinated multi-stage control logic, combined with multi-sensor synchronous acquisition and actuator precise control, it completely solves the pain points of fragmented parameter control, frequent manual intervention, and long production changeover and debugging in traditional soy milk separation; in actual application, the soy milk separation efficiency can be increased by 5% to 8%, the product qualification rate fluctuation can be reduced from ±8% to ±2%, the production changeover and debugging time can be shortened from 1 to 2 hours to within 10 minutes, and the manual operation volume can be reduced by 30%, meeting the efficient and stable needs of industrial continuous production of soy products.

[0082] The present invention provides an automated control system for soymilk separation, wherein the main controller is configured to receive the current signal collected by the viscosity detection unit as a first feedback value representing the soymilk concentration and receive the current signal collected by the photoelectric turbidity sensor as a second feedback value representing the soymilk turbidity, and determine the initial vibration frequency of the vibration motor driver;

[0083] ,and ;

[0084] in, is the initial vibration frequency of the vibration motor driver; is the reference vibration frequency of the vibration motor driver; is the first feedback value; is the reference viscosity current value corresponding to the standard concentration; is the second feedback value; is the reference turbidity current value corresponding to the standard turbidity; is the viscosity influence weight coefficient; is the turbidity influence weight coefficient; is the correction factor for the separation stage; is the bean variety correction factor; is the frequency analysis function;

[0085] The main controller is configured to perform a first comparison between the first feedback values ​​obtained at different times and a preset concentration-vibration parameter mapping table, and to perform a second comparison between the second feedback values ​​obtained at different times and a preset turbidity-vibration parameter mapping table, to construct a two-dimensional array;

[0086] The main controller is used to perform fitting analysis on two rows of arrays in the two-dimensional array respectively, and perform step-by-step analysis on the concentration fitting function and the turbidity fitting function to obtain an offset set;

[0087] The main controller is configured to expand the range of the initial vibration frequency based on the offset set, and select a middle value of the expanded range as a target vibration frequency;

[0088] The main controller is configured to: Obtaining a target amplitude of the vibration motor driver;

[0089] ;

[0090] in, is the initial amplitude of the vibration motor driver; is the reference amplitude of the vibration motor driver; is the influence coefficient of turbidity on amplitude; is the frequency correction term; is the maximum vibration frequency in the two-dimensional array; is the sieve aperture; is the wheelbase length of the vibration motor;

[0091] The main controller is used to obtain an initial motor control instruction for controlling the operation of the vibration motor driver based on the target vibration frequency and the target amplitude.

[0092] In this embodiment, the optimal frequencies under different operating conditions are tested in the laboratory, and the standard operating condition frequencies are stored in the main controller parameter table.

[0093] In this embodiment, for example, the concentration of soybean milk is 8%, When the current is 10mA and the turbidity is 100NTU, 8mA; It is a coefficient that measures the degree of influence of viscosity on vibration frequency. The larger the value, the more significant the influence. Among them, the influence of viscosity on frequency is greater than that of turbidity. It is obtained based on a large number of experimental fittings. The value is 0.8, and + . According to the coefficients adjusted in the separation stage, i.e. the initial separation stage, the middle separation stage and the late separation stage, the variation rules of the amount and concentration of dregs are different in different stages. In general, at the beginning of separation, i.e. the first 10 minutes, The value is 1, 2, high frequency is required to prevent clogging, separation of the mid-term The value is 1.0, the late separation The value of is 0.9.

[0094] The correction coefficient of soybean varieties varies depending on the type of soybean, red bean, or black bean. For example, The value is 1.0, the black bean particles are hard, which is 0.1, and the red bean particles are small, which is 0.9.

[0095] In this embodiment, the concentration-vibration parameter mapping table is a soymilk concentration-optimal vibration frequency parameter comparison table established in advance through experiments. For example, a viscosity current of 10 mA corresponds to a concentration of 8%, and at this time, the frequency is 50 Hz.

[0096] In this embodiment, the turbidity-vibration parameter mapping table is a soymilk turbidity-optimal vibration frequency parameter comparison table established in advance through experiments. For example, a turbidity current of 8 means a turbidity of 100 NTU, and at this time, the frequency is 50 Hz.

[0097] The two-dimensional array is a two-dimensional data structure of the time series of the concentration comparison results and turbidity comparison results at different times. For example, the concentration at time t1 corresponds to a frequency of 55Hz and the turbidity corresponds to 53Hz. At this time, the two-dimensional data is: .

[0098] In this embodiment, the fitting analysis is to use a mathematical function to approximately describe the variation trend of the time-vibration parameters in the two-dimensional array, and obtain a concentration and turbidity fitting function, which is a linear function: y=kx+b.

[0099] For example, the concentration fitting function is: y1=k01t+b1, where k01 is 0.5 Hz / min and b1 is 50 Hz, and the turbidity fitting function is: y2=k02t+b2, where k02 is 0.3 Hz / min and b2 is 52 Hz.

[0100] In this embodiment, the stepwise analysis is to divide the difference function of the fitting function according to the preset continuous acquisition moments, and the preset continuous acquisition moments may be M0 consecutive moments, thereby obtaining an offset set and achieving range expansion.

[0101] In this embodiment, the target vibration frequency is calculated by calculating the upper and lower limits of the expansion range and obtaining an average value.

[0102] In this embodiment, the reference amplitude is the reference amplitude of the vibration motor under standard bean varieties and standard concentration / turbidity, and the influence coefficient of turbidity on the amplitude is obtained based on a large number of fitting calculations.

[0103] In this embodiment, the mesh aperture is directly obtained according to the screening model, and the wheelbase length of the vibration motor is determined when the equipment leaves the factory and can be used directly.

[0104] The beneficial effects of the above technical solution are: through the synchronous feedback of viscosity and turbidity sensors, combined with the formula calculation of multiple correction coefficients such as bean species and separation stage and the dynamic adjustment of two-dimensional array fitting and step analysis, precise coordinated control of the frequency and amplitude of the vibration motor can be achieved; the pulp-residue separation efficiency is improved, the probability of screen clogging is reduced, and the continuity and stability of the soymilk separation process are guaranteed.

[0105] The present invention provides an automated control system for soymilk separation, wherein the main controller is used to calculate the output power duty cycle of the heating control circuit;

[0106] ;

[0107] in, is the real-time output power duty cycle of the heating control circuit; is the proportionality coefficient; is the temperature deviation; is the temperature segmentation coefficient, and ; is the integration time constant; is the integral correction coefficient, and ; is the differential time constant; is the feedforward compensation coefficient; The target temperature at the current moment in the preset temperature curve; The real-time temperature of soy milk detected by the temperature sensor; The real-time density of soy milk at the current temperature; is the real-time specific heat capacity of soy milk at the current temperature; It is the standard density of soy milk at 25℃; is the standard specific heat capacity of soy milk at 25°C;

[0108] The main controller is further configured to: Get the initial heating control instruction for controlling the heating tube.

[0109] In this embodiment, the preset temperature curve is a preset target temperature trajectory that changes with time, which guides the temperature change pattern of soy milk heating.

[0110] In this embodiment, the feedforward compensation coefficient is used to compensate for the temperature change trend in advance, for example, is 1.5, when =2℃ / minute, the output of the feedforward link is 1.5×2=3.

[0111] Soy milk real-time density is based on Obtained from the temperature-density comparison table, for example, is 80℃, at this time, for .

[0112] In this embodiment, the main controller is based on The generated instructions for controlling the on / off or power adjustment of the heating tube, for example, When the output period is 1 second and the high level is 0.6 seconds, the PWM signal is controlled to operate at 60% power.

[0113] The beneficial effects of the above technical solution are: using an improved PID control algorithm, combining the temperature segmentation coefficient and the integral correction coefficient to adapt to the control characteristics of different temperature segments, and responding to the temperature change trend in advance through feedforward compensation, while at the same time performing range constraints on the power duty cycle. Compared with traditional heating control, the temperature control accuracy of soy milk heating is improved, the heating efficiency is improved, and damage to the heating tube or temperature out of control caused by excessive power is avoided, thereby ensuring the stability of the soy milk separation temperature conditions.

[0114] The present invention provides an automated control system for soymilk separation, wherein the main controller is configured to receive in real time the soymilk liquid level acquired by the liquid level sensor, and to generate an initial trigger start instruction when the soymilk liquid level is lower than the lower limit of the liquid level safety threshold range;

[0115] Determine the current opening instruction of the water inlet solenoid valve based on the historical actual opening instruction and the historical actual opening degree of the water inlet solenoid valve, and control the water inlet solenoid valve to open and replenish water;

[0116] When the liquid level data reaches the upper limit of the threshold range, an initial closing instruction is generated to control the water inlet solenoid valve to close.

[0117] Preferably, the main controller is used to determine the upper limit and lower limit of the liquid level safety threshold range required for the current production batch according to the planned output set by the human-computer interaction interface;

[0118] , ;

[0119] in, is the upper limit value; is the planned output set based on the human-computer interaction interface; k2 is the second empirical coefficient; is the centrifugal force correction factor, and , To separate the real-time speed of the actuator, The maximum speed of the separation actuator; is the cross-sectional area of ​​the separation chamber; is the feed flow correction coefficient, and , It is the real-time opening of the electromagnetic flow valve; is the maximum opening of the electromagnetic flow valve; is the full height of the separation bin; To compare the height values;

[0120] ;

[0121] in, is the lower limit; is the hysteresis interval.

[0122] In this embodiment, for example, n=1500r / min, is 2000r / min, then =1.0375, =50%, is 100%, then It is 1.015. For example, the maximum accommodating height of the separation bin is 100 cm.

[0123] This is the hysteresis range to prevent the valve from frequently starting and stopping, for example, =10cm.

[0124] Preferably, the main controller further includes:

[0125] Sequence acquisition unit, used to obtain the historical actual opening instruction sequence of the water inlet solenoid valve And the corresponding historical actual opening sequence , and perform feature extraction to generate the opening time series feature vector Vh, where 、 They are the j2th historical actual opening instruction and historical actual opening degree, Indicates opening, =0 means off; mo is the total number of instructions in the sequence;

[0126] Constructing real-time parameter vectors ,in, is the real-time liquid level value of the separation tank, The real-time temperature of soy milk. is the target liquid level value;

[0127] Prediction unit, used to compare Vh with Input into the pre-trained opening prediction model to obtain the current opening instruction of the output water inlet solenoid valve.

[0128] In this embodiment, the initial trigger start instruction is that when the liquid level is lower than the lower limit, the main controller generates an initial signal to start water replenishment.

[0129] The historical actual opening instructions of the water inlet solenoid valve are the sequence of open (1) / close (0) instructions sent by the main controller in the past, such as {1, 0, 1}, which means the first opening, the second closing, and the third opening. The historical actual opening degree is the actual opening degree of the solenoid valve corresponding to the opening instruction, such as {60%, 0%, 70%}.

[0130] In this embodiment, the current opening instruction is that the main controller combines the historical instructions and the opening to calculate the current opening degree. For example, if the opening is 65% when the historical liquid level is 45 cm, the current instruction of 65% is generated.

[0131] In this embodiment, the second empirical coefficient is a correction coefficient determined experimentally. For example, k2 of soybean milk is 1.2.

[0132] In this embodiment, the opening time series feature vector Vh is an ordered numerical group organized according to a fixed dimension, which converts the unstructured historical sequence into a structured input for the pre-trained opening prediction model to learn historical patterns and thus predict the current opening of the solenoid valve. For example, Vh can be expressed as:

[0133] .

[0134] In this embodiment, the pre-trained opening prediction model is a machine learning model trained based on more than 1000 sets of historical data, and the input of the historical data is Vh and , the output is the current opening instruction.

[0135] The beneficial effects of the above technical solution are: through the real-time collection of liquid level by the main controller and combined with historical data, the liquid level safety threshold is dynamically calculated, and the pre-trained model is used to accurately predict the opening of the solenoid valve, which solves the problems of large liquid level fluctuations and poor adaptability of the traditional fixed threshold. According to the output, rotation speed, feed flow rate, etc., the liquid level control accuracy is within ±2cm, the water replenishment response speed is accelerated by 40%, and manual debugging is reduced to ensure the stability of the soymilk separation liquid level, which indirectly improves the pulp-residue separation efficiency and product quality consistency.

[0136] The present invention provides an automated control system for soymilk separation, wherein the main controller comprises:

[0137] a calculation unit, configured to calculate a difference function between the concentration fitting function and the turbidity fitting function, and perform cluster analysis on the concentration differences at all acquisition moments according to the difference function to obtain a standard deviation, a mean, and a cluster weight of each cluster analysis result;

[0138] a first deviation unit, configured to obtain a first sub-deviation set based on a standard deviation, a mean, and a cluster weight of each cluster analysis result and in combination with a first constant of the concentration fitting function and a second constant of the turbidity fitting function;

[0139] a dividing unit, configured to divide the function curve of the difference function according to preset continuous acquisition moments to determine a single function of each divided curve, and obtain a single constant of the single function;

[0140] a second deviation unit, configured to perform a discrete analysis on the single constant to determine whether a discrete constant exists, and obtain a second sub-deviation set based on the acquisition time of the discrete constant and the average value of the non-discrete constant;

[0141] A moment group association unit is used to sort the concentrations at all acquisition moments in the concentration fitting function and the turbidity fitting function, determine the acquisition moment groups with the same concentration sorting sequence number, and analyze the two-dimensional correlation relationship between the concentration fitting function and the turbidity fitting function based on all acquisition moment groups;

[0142] The expansion unit is configured to determine an optimal offset based on the first sub-deviation set and the second sub-deviation set and in combination with a two-dimensional correlation relationship, and to expand the range of the initial vibration frequency based on the current soy milk level.

[0143] In this embodiment, the difference function is obtained by fitting the concentration difference between the concentration fitting function and the turbidity fitting function at the same acquisition time. At this time, all concentration differences are clustered and analyzed to obtain the standard deviation, mean, and clustering weight of each clustering result, wherein the clustering weight is the proportion of the data volume in the clustering result to the total data volume, and the clustering algorithm is implemented using the K-means algorithm. For example, the K-means algorithm clusters the differences into two groups: {0, 1}, {2, 3, 4}. At this time, the mean of the first group is 0.5, the standard deviation is 0.5, and the clustering weight is 0.4. The mean of the second group is 3, the standard deviation is 1, and the clustering weight is 0.6.

[0144] In this embodiment, the first constant and the second constant are intercepts of the corresponding functions.

[0145] At this time, the deviation under each clustering result is: (mean + standard deviation under the corresponding clustering result) × corresponding weight + (first constant - second constant), and the first sub-deviation set is obtained: {deviation of each clustering result}.

[0146] In this embodiment, the preset continuous acquisition moments are set in advance to be 3 units per segment, and the single function is also a fitted linear function, and the single constant is the intercept corresponding to the single function.

[0147] In this embodiment, the discrete analysis is performed using 3 The principle is to judge whether it is a discrete constant. For example, the first constant set is {1, 1, 5, 1}. The principle identifies that 5 is a discrete constant, the moment of the discrete constant is moment 3, the average value of the non-discrete constant is 1, and the second sub-deviation set {0, 0, 4, 0} obtained by calculating the deviation is 5-1=4, where the discrete constant deviation is 0.

[0148] In this embodiment, the collection time groups with the same concentration ranking sequence number are, for example, {the collection time corresponding to the first-ranked concentration and the first-ranked turbidity are grouped together}.

[0149] For example, the concentration fitting values ​​at time 1, 2, 3, 4, and 5 are 3, 5, 7, 9, and 11 respectively. At this time, after sorting, sequence number 1 corresponds to time 5, sequence number 2 corresponds to time 4, etc. The turbidity fitting values ​​at time 1, 2, 3, 4, and 5 correspond to 3, 4, 5, 6, and 7 respectively. After sorting, sequence number 1 corresponds to time 5, sequence number 2 corresponds to time 4, etc. The collection time groups with the same sequence number are (5, 5), (4, 4), etc. The two-dimensional correlation relationship is a positive correlation between concentration and turbidity, and the sorting sequence numbers are consistent. The two-dimensional correlation relationship is mainly determined by calculating the correlation coefficient.

[0150] In this embodiment, the Pearson correlation coefficient r based on the concentration fitting values ​​and the turbidity fitting values ​​in all collection time groups is calculated according to the Pearson correlation coefficient, which is a well-known attempt. If r is greater than 0, it indicates a positive correlation, and if r is less than 0, it indicates a negative correlation. The closer r is to 1 or -1, the stronger the correlation.

[0151] The sorting sequence number and the corresponding concentration value and turbidity value of each moment group are extracted, and the sequence number-concentration curve and sequence number-turbidity curve are drawn respectively. If the rising, falling or stable trends of the two curves are synchronized, the trends are judged to be consistent; otherwise, the trends are judged to be opposite.

[0152] Divide the concentration and turbidity values ​​into several intervals according to the equal intervals, and count the co-occurrence frequency of each concentration interval-turbidity interval:

[0153] If the co-occurrence frequency of high concentration interval and high turbidity interval is high, and the co-occurrence frequency of low concentration interval and low turbidity interval is high, it means that the distribution matching degree of the two is high;

[0154] If the high concentration interval and the low turbidity interval have a high co-occurrence frequency, it means that the distribution matching is low.

[0155] For example, high concentration {10%, 12%} and high turbidity {25, 30} co-occur twice, which is the main co-occurrence combination, indicating a high distribution matching degree.

[0156] Combined with the results of Pearson correlation coefficient, trend consistency, and distribution matching, the type of association relationship is comprehensively determined:

[0157] like , trend consistency ≥ 90%, and distribution matching is high. In this case, it is determined to be highly synchronously correlated;

[0158] like , trend consistency ≤ 30%, distribution matching is low, in this case, it is judged as weak correlation / no correlation;

[0159] If r<−0.8, trend consistency ≥90%, but the trend is opposite, high concentration corresponds to low turbidity and low concentration corresponds to high turbidity in the distribution, then it is judged as a high reverse correlation;

[0160] Otherwise, it is judged as a general association.

[0161] In this embodiment, the means of the first sub-deviation set and the second sub-deviation set are respectively obtained as D1 and D2, and combined with the weights wu1 and wu2 associated with the two dimensions, the comprehensive deviation impact f01=D1×wu1+D2×wu2 is calculated.

[0162] The empirical offset is set based on industrial control experience, and f02 is +2Hz.

[0163] According to the liquid level deviation, calculate the expansion base f03 = (ideal liquid level height - soy milk liquid level height) × 0.5Hz / cm;

[0164] The expanded vibration frequency range: [ft-f03+f01+f02,ft+f03+f01+f02].

[0165] The beneficial effects of the above technical solution are: through the collaboration of multiple units, difference analysis, clustering, deviation calculation, segmented fitting and two-dimensional correlation analysis are carried out on the fitting functions of concentration and turbidity, and finally the vibration frequency range is dynamically expanded in combination with the liquid level height, breaking through the limitation of poor adaptability of vibration parameters in traditional control, and can adapt to the dynamic changes of soy milk concentration, turbidity and liquid level, so that the vibration frequency can more accurately match the pulp-residue separation requirements, thereby improving the separation efficiency and soy milk quality stability.

[0166] The present invention provides an automated control system for soymilk separation, wherein the main controller is further configured to obtain a standard state of the automated system based on process parameters, and perform difference analysis with the working state of the automated system to obtain a difference vector;

[0167] The main controller is also used to input the difference vector into a pre-trained vector analysis model to obtain a first adjustment factor for each initial motor control instruction and a second adjustment factor based on the initial heating control instruction, and to obtain an adjusted motor control instruction and an adjusted heating control instruction to control the corresponding vibration motor driver and heating control circuit to operate.

[0168] In this embodiment, the standard state is the ideal operating state of the system when the process parameters are fully met, which is obtained from a pre-set process parameter-standard state mapping table. For example, the process parameters are 98°C, 45Hz, and the heating power duty cycle is 80%, the vibration frequency is stable at 45Hz, and the liquid level is 60cm in the standard state; the working state is the real-time state, for example, the actual temperature is 92°C, the vibration frequency is 42Hz, and the liquid level is 55cm. At this time, the difference vector is: {-6°C, -3Hz, -5cm}.

[0169] In this embodiment, the pre-trained vector analysis model is a machine learning model trained with a large amount of difference vector-adjustment factor data. For example, the output difference vector is {-6°C, -3Hz, -5cm}, the output first adjustment factor is +5Hz, and the second adjustment factor is 1.2 times. At this time, the coefficient of the adjusted initial heating instruction is: 70%×1.2, where 70% is the heating power ratio corresponding to the initial instruction.

[0170] The beneficial effect of the above technical solution is: through standard-actual state difference analysis and combined with pre-trained model intelligent adjustment, it is convenient to adapt to the fluctuations of soy milk separation conditions in real time and ensure separation efficiency and quality stability.

[0171] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. An automated control system for soymilk separation, applied to soy product processing equipment, characterized in that: include: The main controller is the core processing unit of the automated control system and has a pre-configured multi-stage control logic for soymilk separation. The multi-stage control logic includes: a concentration adaptive control stage and a temperature-liquid level coordinated control stage. A sensor group, electrically connected to the input port of the main controller, for collecting environmental parameters during the soymilk separation process, the sensor group comprising at least: a liquid level sensor for detecting the soymilk liquid level in the separation chamber; a temperature sensor for detecting the real-time temperature of the soymilk; a viscosity detection unit for indirectly inferring the concentration of the soymilk; and a photoelectric turbidity sensor for collecting the turbidity of the soymilk before separation, wherein the viscosity detection unit and the photoelectric turbidity sensor collect turbidity synchronously. A human-computer interaction interface, connected to the main controller for receiving process parameters set by the user and displaying the working status of the automation control system in real time; The main controller is configured to analyze environmental parameters to obtain an initial control instruction set, wherein the initial control instruction set includes: an initial motor control instruction for the vibration motor driver, an initial heating control instruction for the heating control circuit, and an initial trigger opening instruction or an initial closing instruction for the water inlet solenoid valve of the vibration motor driver; The initial control instruction set is adjusted according to the process parameters and working status, and sent to the actuator group to perform corresponding actions, wherein the actuator group at least includes: Vibration motor driver, used to drive and control the vibration frequency and amplitude of the separation screen; A heating control circuit is used to adjust the power of the heating tube according to temperature feedback; The water inlet solenoid valve is used to control the on and off of water supply to the separation chamber.

2. The automated control system for soymilk separation according to claim 1, characterized in that: The main controller is configured to receive the current signal collected by the viscosity detection unit as a first feedback value representing the concentration of the soymilk and receive the current signal collected by the photoelectric turbidity sensor as a second feedback value representing the turbidity of the soymilk, and determine an initial vibration frequency of the vibration motor driver; ,and ; in, is the initial vibration frequency of the vibration motor driver; is the reference vibration frequency of the vibration motor driver; is the first feedback value; is the reference viscosity current value corresponding to the standard concentration; is the second feedback value; is the reference turbidity current value corresponding to the standard turbidity; is the viscosity influence weight coefficient; is the turbidity influence weight coefficient; is the correction factor for the separation stage; is the bean variety correction factor; is the frequency analysis function; The main controller is configured to perform a first comparison between the first feedback values ​​obtained at different times and a preset concentration-vibration parameter mapping table, and to perform a second comparison between the second feedback values ​​obtained at different times and a preset turbidity-vibration parameter mapping table, to construct a two-dimensional array; The main controller is used to perform fitting analysis on two rows of arrays in the two-dimensional array respectively, and perform step-by-step analysis on the concentration fitting function and the turbidity fitting function to obtain an offset set; The main controller is configured to expand the range of the initial vibration frequency based on the offset set, and select a middle value of the expanded range as a target vibration frequency; The main controller is configured to: Obtaining a target amplitude of the vibration motor driver; ； in, is the initial amplitude of the vibration motor driver; is the reference amplitude of the vibration motor driver; is the influence coefficient of turbidity on amplitude; is the frequency correction term; is the maximum vibration frequency in the two-dimensional array; is the sieve aperture; is the wheelbase length of the vibration motor; The main controller is used to obtain an initial motor control instruction for controlling the operation of the vibration motor driver based on the target vibration frequency and the target amplitude.

3. The automated control system for soymilk separation according to claim 1, characterized in that: The main controller is used to calculate the output power duty cycle of the heating control circuit; ； in, is the real-time output power duty cycle of the heating control circuit; is the proportionality coefficient; is the temperature deviation; is the temperature segmentation coefficient, and ; is the integration time constant; is the integral correction coefficient, and ; is the differential time constant; is the feedforward compensation coefficient; The target temperature at the current moment in the preset temperature curve; The real-time temperature of soy milk detected by the temperature sensor; The real-time density of soy milk at the current temperature; is the real-time specific heat capacity of soy milk at the current temperature; It is the standard density of soy milk at 25℃; is the standard specific heat capacity of soy milk at 25°C; The main controller is further configured to: Get the initial heating control instruction for controlling the heating tube.

4. The automated control system for soy milk separation according to claim 3, characterized in that: The main controller is configured to receive in real time the soy milk liquid level acquired by the liquid level sensor, and generate an initial trigger start instruction when the soy milk liquid level is lower than the lower limit of the liquid level safety threshold range; Determine the current opening instruction of the water inlet solenoid valve based on the historical actual opening instruction and the historical actual opening degree of the water inlet solenoid valve, and control the water inlet solenoid valve to open and replenish water; When the liquid level data reaches the upper limit of the threshold range, an initial closing instruction is generated to control the water inlet solenoid valve to close.

5. The automated control system for soy milk separation according to claim 4, characterized in that: The main controller is used to determine the upper limit and lower limit of the liquid level safety threshold range required for the current production batch according to the planned output set by the human-computer interaction interface; ， ； in, is the upper limit value; is the planned output set based on the human-computer interaction interface; k2 is the second empirical coefficient; is the centrifugal force correction factor, and , To separate the real-time speed of the actuator, The maximum speed of the separation actuator; is the cross-sectional area of ​​the separation chamber; is the feed flow correction coefficient, and , It is the real-time opening of the electromagnetic flow valve; is the maximum opening of the electromagnetic flow valve; is the full height of the separation bin; To compare the height values; ； in, is the lower limit; is the hysteresis interval.

6. The automated control system for soy milk separation according to claim 2, characterized in that: The main controller includes: a calculation unit, configured to calculate a difference function between the concentration fitting function and the turbidity fitting function, and perform cluster analysis on the concentration differences at all acquisition moments according to the difference function to obtain a standard deviation, a mean, and a cluster weight of each cluster analysis result; a first deviation unit, configured to obtain a first sub-deviation set based on a standard deviation, a mean, and a cluster weight of each cluster analysis result and in combination with a first constant of the concentration fitting function and a second constant of the turbidity fitting function; a dividing unit, configured to divide the function curve of the difference function according to preset continuous acquisition moments to determine a single function of each divided curve, and obtain a single constant of the single function; a second deviation unit, configured to perform a discrete analysis on the single constant to determine whether a discrete constant exists, and obtain a second sub-deviation set based on the acquisition time of the discrete constant and the average value of the non-discrete constant; A moment group association unit is used to sort the concentrations at all acquisition moments in the concentration fitting function and the turbidity fitting function, determine the acquisition moment groups with the same concentration sorting sequence number, and analyze the two-dimensional correlation relationship between the concentration fitting function and the turbidity fitting function based on all acquisition moment groups; The expansion unit is configured to determine an optimal offset based on the first sub-deviation set and the second sub-deviation set and in combination with a two-dimensional correlation relationship, and to expand the range of the initial vibration frequency based on the current soy milk level.

7. The automated control system for soy milk separation according to claim 4, characterized in that: The main controller further includes: Sequence acquisition unit, used to obtain the historical actual opening instruction sequence of the water inlet solenoid valve And the corresponding historical actual opening sequence , and perform feature extraction to generate the opening time series feature vector Vh, where 、 They are the j2th historical actual opening instruction and historical actual opening degree, Indicates opening, =0 means off; mo is the total number of instructions in the sequence; Constructing real-time parameter vectors ,in, is the real-time liquid level value of the separation tank, The real-time temperature of soy milk. is the target liquid level value; Prediction unit, used to compare Vh with Input into the pre-trained opening prediction model to obtain the current opening instruction of the output water inlet solenoid valve.

8. The automated control system for soymilk separation according to claim 1, characterized in that: The main controller is further configured to obtain a standard state of the automation control system based on process parameters, and perform difference analysis with the working state of the automation control system to obtain a difference vector; The main controller is also used to input the difference vector into a pre-trained vector analysis model to obtain a first adjustment factor for each initial motor control instruction and a second adjustment factor based on the initial heating control instruction, and to obtain an adjusted motor control instruction and an adjusted heating control instruction to control the corresponding vibration motor driver and heating control circuit to operate.

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