A food additive production management and control method and system based on data processing

Abstract

The present application belongs to the technical field of data processing and industrial control, and relates to a food additive production management and control method and system based on data processing. The method comprises the following steps: firstly, collecting multi-source rheological data such as pipeline outer wall temperature, metering pump operating frequency and inlet and outlet pressure difference and carrying out filtering pretreatment; then, calculating the equivalent temperature of the fluid center by using the heat conduction hysteresis characteristic, and accurately correcting the heat hysteresis error caused by the environmental temperature fluctuation; based on the equivalent temperature and the operating parameters, constructing the engineering index containing the temperature sensing sensitivity and the shear thinning factor, calculating the dynamic flow resistance characteristic value, and real-time decoupling the combined influence of temperature and shear rate on the fluid viscosity; finally, according to the deviation ratio of the flow resistance characteristic value and the flow resistance reference value, and combining the flow state instability reflecting the pipeline aging state, generating an adaptive flow compensation instruction to adjust the metering pump. The present application effectively improves the feeding accuracy of trace additives under complex working conditions.

Description

Technical Field

[0001] This invention belongs to the field of data processing and industrial control technology, and in particular relates to a method and system for the production control of food additives based on data processing. Background Technology

[0002] In modern dairy processing and biochemical production, the precise metering of highly active food additives is a key factor determining the quality and stability of the final product. In actual production scenarios, such as dairy product formulation or nutrient solution fortification, it is often necessary to precisely inject high-concentration additives such as multivitamins, enzyme preparations, or amino acid nutrient solutions into the main pipeline for mixing. Because these additives typically possess high viscosity, non-Newtonian fluid properties, and are highly sensitive to temperature changes, controlling the flow rate during micro-addition processes is extremely challenging.

[0003] Although PID control based on flowmeter feedback is widely used in existing industrial control systems, significant technical limitations remain when handling complex fluids. First, fluctuations in ambient temperature lead to substantial measurement errors, primarily due to the hysteresis effect of heat conduction. Because the heat transfer efficiency of high-viscosity concentrates is far lower than that of metal transport pipes, when the ambient temperature changes, temperature sensors mounted on the outer wall of the pipe quickly detect and report the temperature change, while the temperature change at the fluid center inside the pipe is relatively slow. This temperature asynchrony between the pipe wall and the fluid center causes the viscosity compensation parameters used by the control system to deviate from the actual physical state of the fluid, severely impacting measurement accuracy.

[0004] Secondly, traditional control models often fail to effectively decouple the shear-thinning effect unique to non-Newtonian fluids. The viscosity of such additives is not only affected by temperature but also significantly influenced by the shear rate, i.e., the operating frequency of the metering pump. Existing technologies typically only perform temperature compensation in a single dimension, neglecting the nonlinear changes in fluid viscosity caused by pump speed variations. During variable-speed feeding, the fluid viscosity decreases significantly with increasing pumping frequency. If the control algorithm cannot detect and compensate for this physical characteristic change in real time, the actual flow rate will deviate significantly from the theoretical setpoint, resulting in severe distortion of control accuracy.

[0005] Furthermore, pipelines inevitably undergo physical aging during long-term operation. As production cycles extend, high-viscosity fluids tend to form a deposit on the inner wall of the pipeline, which not only reduces the effective flow cross-section but also causes turbulence and instability in the flow pattern. However, existing fixed-parameter control models cannot perceive these gradual changes in the physical environment and lack the ability to adaptively adjust to changes in the microstructure of the pipeline, such as deposits or wear, making it difficult to maintain constant control accuracy throughout the entire lifespan of the equipment.

[0006] It is evident that existing food additive production control technologies are significantly inadequate in addressing thermal hysteresis interference, changes in the shear characteristics of non-Newtonian fluids, and the effects of pipeline aging. There is an urgent need for a control method and system that can integrate multi-dimensional data processing, accurately reflect the fluid rheological state, and possess adaptive compensation capabilities. Summary of the Invention

[0007] The purpose of this invention is to propose a data processing-based method and system for the production control of food additives, in order to solve the technical problem of inaccurate metering of high-viscosity food additives during the production process due to multiple factors such as thermal hysteresis, non-Newtonian fluid shear thinning, and pipeline aging.

[0008] To address the above problems, the technical solution of the data processing-based food additive production control method proposed in this invention is as follows: Data-driven methods for controlling the production of food additives include: Multi-source rheological state data are collected from the additive delivery pipeline, and the multi-source rheological state data is preprocessed; the multi-source rheological state data includes the instantaneous temperature of the pipeline outer wall, the operating frequency of the metering pump, and the pressure difference between the inlet and outlet of the metering pump. By utilizing the hysteresis characteristic of heat conduction, the equivalent temperature of the fluid center is calculated based on the instantaneous temperature of the outer wall of the pipe and its changing trend, so as to correct the thermal hysteresis error caused by the fluctuation of ambient temperature. Based on the pressure difference, the operating frequency of the metering pump, and the equivalent temperature of the fluid center, a rheological thermodynamic model including temperature sensitivity and shear thinning factor is constructed to calculate the dynamic flow resistance characteristic value of the non-Newtonian fluid, so as to decouple the influence of temperature and shear rate on fluid viscosity. The fluctuation of the dynamic flow resistance characteristic value within a historical time window is statistically analyzed to calculate the flow instability. Based on the relative deviation ratio between the dynamic flow resistance characteristic value and the flow resistance reference value under standard operating conditions, and in conjunction with the flow instability, a final adaptive flow compensation control command is generated to adjust the metering pump.

[0009] Beneficial effects: By collecting multi-dimensional rheological state data and combining it with a thermal hysteresis correction algorithm, this invention can dynamically compensate for the temperature difference between the outer wall of the pipe and the center of the fluid, solving the problem of temperature parameter distortion caused by neglecting thermal inertia in traditional methods. At the same time, by constructing a rheological thermodynamic model, the combined effects of temperature and shear rate on fluid characteristics are decoupled in real time, and flow instability is introduced as a compensation term, so that the system can still automatically adjust the control strategy to maintain flow stability under complex conditions such as pipe aging or wall adhesion, thus improving the feeding accuracy.

[0010] Furthermore, the formula for calculating the equivalent temperature of the fluid center is:

[0011] In the formula, For the current moment The measured instantaneous temperature of the outer wall of the pipe; The previous sampling time The instantaneous temperature of the outer wall of the pipe; The thermal hysteresis compensation coefficient is determined by the specific heat capacity of the fluid and the thermal conductivity of the pipe material. It is the natural logarithm function; As an indicator of temperature trends; This indicates the range of temperature change.

[0012] Beneficial effects: By introducing the natural logarithm function, abrupt changes in temperature sensor data can be smoothed, preventing drastic fluctuations in calculation results; at the same time, combined with the thermal hysteresis compensation coefficient, the true thermodynamic state of the fluid center can be restored based on the pipe wall temperature, solving the hysteresis problem caused by surface temperature measurement.

[0013] Furthermore, the method for determining the temperature trend indicator factor is as follows: when the previous sampling time... The instantaneous temperature of the outer wall of the pipe is greater than the current temperature. When the instantaneous temperature of the outer wall of the pipe is measured, it indicates a cooling process, and the temperature trend indicator factor is set to a positive value of 1. At the previous sampling time The instantaneous temperature of the outer wall of the pipe is lower than the current time. When the instantaneous temperature of the outer wall of the pipe is measured, it indicates the heating process, and the temperature trend indicator factor is set to a negative value of -1. At the previous sampling time The instantaneous temperature of the outer wall of the pipe is equal to the current moment. When the instantaneous temperature of the outer wall of the pipe is measured, the temperature trend indicator factor is set to 0.

[0014] Beneficial effects: By comparing the temperature values ​​at different sampling times, the current heating or cooling state can be accurately determined, ensuring that the sign of the thermal hysteresis correction term is consistent with the actual heat conduction direction, thus avoiding erroneous compensation due to misjudgment of temperature change trends.

[0015] Furthermore, the formula for calculating the dynamic flow resistance characteristic value is as follows:

[0016] In the formula, For the current moment The dynamic flow resistance characteristic value, For the current moment The pressure difference between the inlet and outlet of the metering pump; For the current moment The operating frequency of the metering pump; This is the system safety constant; Standard reference temperature; Temperature sensitivity; It is the shear thinning factor; For the natural constant An exponential function with base 0.

[0017] Beneficial effects: The influence of temperature and shear effects on fluid properties is separated at the mathematical model level. In particular, the shear thinning characteristics are described by an exponential function, which ensures the accuracy of the flow resistance calculation during variable frequency pumping.

[0018] Furthermore, the preprocessing of the multi-source rheological state data includes: setting the system sampling period, performing moving average filtering on the instantaneous temperature of the outer wall of the pipe, the operating frequency of the metering pump, and the pressure difference between the inlet and outlet of the metering pump to remove high-frequency electromagnetic noise and form a time series data stream.

[0019] Furthermore, the calculation formula for the adaptive traffic compensation control command is as follows:

[0020] In the formula, The theoretical target flow rate required by the formulation; This is the reference value for the flow resistance of the new pipeline under standard operating conditions. This is the rheological compensation gain coefficient; This is the aging compensation coefficient; It is an index of flow instability.

[0021] Beneficial effects: This formula incorporates both flow resistance deviation and flow instability into the flow compensation strategy, achieving dual regulation of static resistance changes and dynamic flow fluctuations.

[0022] Furthermore, the formula for calculating the flow instability index is as follows:

[0023] In the formula, The number of sampling points within the historical time window; For the past Dynamic flow resistance characteristic values ​​at each sampling point; For this The arithmetic mean of the dynamic flow resistance characteristic values ​​of each sampling point.

[0024] Furthermore, the aging compensation coefficient is used to provide positive compensation to prevent insufficient material feeding when wall adhesion or wear is detected on the inner wall of the pipe, which leads to increased flow resistance fluctuations.

[0025] Furthermore, a wall-mounted thin-film platinum resistance thermometer is used to collect the instantaneous temperature of the outer wall of the pipeline, the operating frequency of the metering pump is read through the communication interface of the frequency converter, and the pressure difference between the inlet and outlet of the metering pump is collected through differential pressure transmitters installed at the inlet and outlet of the metering pump.

[0026] The technical solution of the data processing-based food additive production control system proposed in this invention is as follows: A data-processing-based food additive production control system includes a processor and a memory. The memory stores computer program instructions. When the computer program instructions are executed by the processor, the data-processing-based food additive production control method described in any of the above technical solutions is implemented.

[0027] The beneficial effects of this invention are as follows: By collecting multi-source rheological state data and utilizing thermal hysteresis characteristics, this invention effectively eliminates measurement errors caused by ambient temperature fluctuations and pipe wall heat conduction hysteresis, thus truly restoring the internal thermodynamic state of the fluid; by constructing a rheological thermodynamic model that includes a shear thinning factor, the combined effects of temperature and pump speed on the viscosity of non-Newtonian fluids are decoupled, solving the nonlinear metering problem during variable-speed feeding; in addition, by using the flow instability index to establish an adaptive aging compensation mechanism, the interference of resistance changes caused by pipe wall adhesion or wear is overcome, ensuring the stability of flow control during long-term operation. Attached Figure Description

[0028] Figure 1 This is a flowchart of the steps of the data processing-based food additive production control method according to an embodiment of the present invention; Figure 2 This is a three-dimensional mapping diagram of the rheological properties of a non-Newtonian fluid according to an embodiment of the present invention; Figure 3 This is a comparison chart of the data processing-based food additive production control method of the present invention and existing technologies in terms of flow stability. Detailed Implementation

[0029] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0030] Specific embodiments of the data processing-based food additive production control method proposed in this invention: like Figure 1 As shown, the data processing-based method for controlling the production of food additives includes the following steps: S1. Collect multi-source rheological state data on the additive delivery pipeline and preprocess the multi-source rheological state data; the multi-source rheological state data includes the instantaneous temperature of the outer wall of the pipeline, the operating frequency of the metering pump, and the pressure difference between the inlet and outlet of the metering pump.

[0031] Specifically, a high-frequency data acquisition system is constructed on the delivery pipeline connecting the additive storage tank to the main mixer. For temperature data acquisition, a wall-mounted thin-film platinum resistance temperature sensor, such as the PT100 model, is selected and tightly fitted to the outer wall surface of the delivery pipeline. Thermal grease is applied to reduce contact thermal resistance, thereby acquiring the instantaneous temperature of the pipeline's outer wall in real time. The communication interface of the frequency converter is connected via shielded twisted-pair cable, and standard industrial fieldbus communication protocols, such as Modbus, are used to read the operating frequency data of the metering pump motor in real time. For pressure data acquisition, high-precision differential pressure transmitters are installed at the inlet and outlet of the metering pump, preferably using a sanitary flat diaphragm structure to accommodate high-viscosity fluids, to measure and calculate the pressure difference between the pump inlet and outlet in real time.

[0032] The system is configured with a fixed high-frequency sampling period, for example, a sampling interval of 100 milliseconds, to ensure the capture of transient changes in fluid flow. For the acquired raw data stream, the system employs a moving average filtering algorithm for digital signal processing to eliminate high-frequency electromagnetic noise interference from the on-site frequency converter. Specifically, a first-in-first-out (FIFO) data queue of length N is established (e.g., N equals 5). The values ​​from the five most recent sampling times are stored in the queue, and the arithmetic mean of the data in the queue is calculated as the valid measurement value for the current moment. This mechanism of high-frequency acquisition combined with digital filtering can sensitively capture instantaneous pressure jumps caused by changes in pump speed or pipe resistance, while effectively smoothing out meaningless random signal spikes. This provides real, accurate, and strictly time-synchronized fundamental data support for subsequent rheological thermodynamic model calculations.

[0033] S2. Utilizing the hysteresis characteristic of heat conduction, the equivalent temperature of the fluid center is calculated based on the instantaneous temperature of the outer wall of the pipe and its changing trend, in order to correct the thermal hysteresis error caused by fluctuations in ambient temperature.

[0034] To address the measurement bias caused by directly using pipe wall temperature to represent fluid temperature in existing technologies, this step utilizes the physical hysteresis characteristics of heat conduction for compensation and correction. Considering the significant thermal inertia of high-viscosity fluids, the fluid temperature change at the center of the pipe always lags behind the temperature change of the sensor on the outer wall of the pipe. The system analyzes the rate of change of pipe wall temperature between the current sampling time and the previous sampling time to construct a thermal hysteresis compensation model to inversely calculate the equivalent temperature at the fluid center.

[0035] In this embodiment, the formula for calculating the equivalent temperature at the fluid center is:

[0036] In the formula, For the current moment The measured instantaneous temperature of the outer wall of the pipe; The previous sampling time The instantaneous temperature of the outer wall of the pipe; The value representing the temperature change has been normalized before being substituted into the logarithmic function, i.e., implicitly divided by 1℃, resulting in a dimensionless pure numerical value.

[0037] The thermal hysteresis compensation coefficient, in °C, is determined by the specific heat capacity of the fluid and the thermal conductivity of the pipe material. The larger the value, the more obvious the thermal hysteresis effect of the system. In the specific application of this embodiment, the coefficient is taken as 0.2.

[0038] The natural logarithm function is introduced to utilize its saturation properties to suppress abnormal temperature spikes.

[0039] As a temperature trend indicator, its determination method is as follows: when the previous sampling time... The instantaneous temperature of the outer wall of the pipe is greater than the current temperature. When the instantaneous temperature of the outer wall of the pipe is measured, it indicates a cooling process, and the temperature trend indicator factor is set to a positive value of 1; when the previous sampling time... The instantaneous temperature of the outer wall of the pipe is lower than the current time. When the instantaneous temperature of the outer wall of the pipe is measured, it indicates a heating process, and the temperature trend indicator factor is set to a negative value of -1; when the previous sampling time... The instantaneous temperature of the outer wall of the pipe is equal to the current moment. When the instantaneous temperature of the outer wall of the pipe is measured, the temperature trend indicator factor is set to 0.

[0040] As an optional interpretation of this embodiment, the present invention couples fluid thermodynamics with a non-Newtonian fluid rheology model. The physical basis for introducing a logarithmic function in the correction of the fluid's equivalent temperature lies in the fact that the temperature gradient change between the fluid center and the pipe wall during heat conduction conforms to the evolution characteristics of classical Newton's law of cooling, exhibiting nonlinear hysteresis and asymptotic saturation effects. The logarithmic curve can perfectly fit this thermal damping decay process.

[0041] By introducing a logarithmic function and a temperature trend indicator to construct a thermal hysteresis compensation model, temperature sensor data can be smoothly corrected, and the thermodynamic state inside the fluid can be accurately reproduced.

[0042] S3. Based on the pressure difference, the operating frequency of the metering pump, and the equivalent temperature of the fluid center, a rheological thermodynamic model including temperature sensitivity and shear thinning factor is constructed to calculate the dynamic flow resistance characteristic value of the non-Newtonian fluid, so as to decouple the influence of temperature and shear rate on fluid viscosity.

[0043] This step establishes a rheological thermodynamic model for the shear-thinning physical properties unique to non-Newtonian fluids. The model's design logic is first based on fundamental fluid mechanics principles, using the ratio of pressure difference to pumping frequency as the basic flow resistance framework to reflect the physical resistance during fluid transport. Secondly, a temperature correction term is introduced, using a power function relationship to describe the thermosensitive characteristic of high-viscosity fluids where viscosity decreases with increasing temperature, thus normalizing flow resistance data at different temperatures. More importantly, an exponential decay term is introduced to characterize the nonlinear effect of shear rate on viscosity; that is, as the metering pump's operating frequency increases, the fluid molecular chains are stretched and oriented, leading to an exponential decrease in apparent viscosity. Through this component-based modeling approach, the system can decompose single flow resistance data into basic physical resistance, thermodynamic influence components, and rheological influence components, thereby accurately extracting the true flow resistance characteristics of the fluid under variable frequency and speed operating conditions.

[0044] Specifically, the rheological thermodynamic model is as follows:

[0045] In the formula, For the current moment The dynamic flow resistance characteristic value, For the current moment The pressure difference between the inlet and outlet of the metering pump; For the current moment The operating frequency of the metering pump; The system safety constant is set to 0.01 in this embodiment. Its purpose is to prevent the calculation error of zero denominator when the frequency is zero due to the metering pump stopping. It is the standard reference temperature, set as the reference temperature for calibrating fluid properties. In this embodiment, it is taken as 25°C.

[0046] This is a temperature correction term; This represents the temperature sensitivity, with a value greater than 1. The larger the value, the more sensitive the fluid viscosity is to temperature changes.

[0047] For the natural constant An exponential function with base 0. It is an exponentially decaying term; The shear thinning factor is a key parameter characterizing the non-Newtonian properties of fluids. Its physical dimension is time, and its value is typically greater than 0, reflecting the rate of viscosity decay under shear stress. This exponential term is constructed using the classic Cross shear thinning model in rheology, mathematically characterizing the microscopic physical law governing the exponential decrease in apparent viscosity of polymeric food additives caused by the deentanglement of molecular chain segments under the high-frequency shear force of a metering pump.

[0048] It should be noted that the key parameter in the above rheological thermodynamic model is temperature sensitivity. and shear thinning factor The calibration method needs to be determined in advance based on the specific characteristics of the food additive fluid being transported. The specific calibration method is as follows: before the system is officially put into production, extract fluid samples and test them in a laboratory rheometer, or during the production line commissioning phase, control the system at different temperatures (e.g., ...). ) and different pump operating frequencies (e.g. The system operates under these conditions, recording the corresponding stable pressure difference data. A nonlinear regression analysis is then performed on the above formula using the least squares method to obtain the optimal fit for this specific fluid. and The value is then stored in the memory of the control system.

[0049] S4. Statistically analyze the fluctuation of the dynamic flow resistance characteristic value within the historical time window to calculate the flow instability. Based on the relative deviation ratio between the dynamic flow resistance characteristic value and the flow resistance reference value under standard operating conditions, and in conjunction with the flow instability, generate the final adaptive flow compensation control command to adjust the metering pump.

[0050] To address the drift in flow resistance characteristics caused by internal wall fouling, scaling, or wear during long-term pipeline operation, this step designs an adaptive compensation model based on data volatility. The model's construction logic includes two dimensions: static resistance compensation and dynamic stability compensation. First, the system calculates the relative deviation between the current dynamic flow resistance characteristic value and the baseline flow resistance value of the new pipeline under standard operating conditions. This determines the current physical resistance level of the conveying system. When resistance increases, the flow rate setpoint is automatically increased to overcome the obstruction. Second, the system introduces a flow instability index as a probe to detect the degree of pipeline aging. Based on fluid mechanics principles, when the roughness of the pipeline inner wall increases or uneven fouling occurs, the turbulence and resistance fluctuations of the fluid flow significantly increase. Therefore, the system quantifies this fluctuation as a compensation signal. When flow instability is detected, a positive flow safety redundancy is actively applied to prevent instantaneous flow interruption or insufficient feed caused by sudden changes in resistance.

[0051] Specifically, the calculation formula for the adaptive flow compensation control instruction ultimately issued to the PLC for execution is as follows:

[0052] In the formula, This represents the theoretical target flow rate required by the formulation. The reference value for the flow resistance of a new pipeline under standard operating conditions is the inherent flow resistance parameter measured at standard temperature and pressure after the initial installation or cleaning of the system. It is also used for normalization to eliminate the influence of dimensions. is the rheological compensation gain coefficient, a dimensionless parameter used to adjust the system's response speed to relative changes in viscosity. The aging compensation coefficient is a dimensionless parameter used to set the system's sensitivity to the relative proportion of flow instability. When a significant increase in flow resistance fluctuation relative to the flow resistance reference value is detected, positive flow compensation is provided through this coefficient.

[0053] and For adjustable PID control parameters, technicians can determine specific values ​​based on the actual flow fluctuation amplitude and response speed requirements on site using conventional engineering tuning methods (such as trial and error). The determination of these parameters is common knowledge and does not require creative work.

[0054] The flow instability index is calculated using the standard deviation algorithm within a time sliding window. Specifically, it first calculates the arithmetic mean of the dynamic flow resistance characteristic values ​​of all sampling points within a time window of length N. Then, it calculates the sum of squares of the differences between the dynamic flow resistance characteristic values ​​of the past N sampling points and this arithmetic mean. Finally, it divides this sum of squares by the number of sampling points N and takes the square root. The specific calculation formula is as follows:

[0055] In the formula, The number of sampling points within the historical time window; For the past Dynamic flow resistance characteristic values ​​at each sampling point; For this The arithmetic mean of the dynamic flow resistance characteristic values ​​of each sampling point.

[0056] The entire algorithm combines flow resistance deviation and flow regime fluctuation for final compensation. It is an engineering optimal control law that reduces and decouples complex nonlinear characteristics of fluid mechanics, effectively balancing environmental robustness and real-time computation.

[0057] The following combination Figure 2 and Figure 3 The solution and effects of the present invention will be further explained.

[0058] Figure 2A three-dimensional mapping diagram illustrating the rheological properties of a non-Newtonian fluid is presented. The diagram constructs a three-dimensional coordinate system, where the horizontal axis represents the shear rate (i.e., the operating frequency of the metering pump), the vertical axis represents the instantaneous temperature of the pipe's outer wall, and the apparent viscosity of the fluid. The diagram presents a smooth, curved spatial surface that intuitively reflects the changing physical properties of the fluid under varying operating conditions: along the vertical axis, as the instantaneous temperature of the pipe's outer wall increases, the fluid viscosity decreases; along the horizontal axis, as the pump's operating frequency increases, the fluid viscosity exhibits a significant non-linear decreasing trend, i.e., shear thinning. This three-dimensional surface strongly demonstrates the necessity of the multi-dimensional decoupling strategy employed in calculating the dynamic flow resistance characteristic value in this invention. That is, the thermosensitive effects of temperature and the rheological effects of shear rate must be considered simultaneously to accurately describe the true state of the fluid. In contrast, considering only temperature as a single variable only yields a two-dimensional slice of the surface at a specific frequency, failing to cover the dynamic changes of the entire rheological surface. This is the fundamental source of control errors in existing technologies.

[0059] Figure 3 This diagram illustrates a two-dimensional planar comparison of the data processing-based food additive production control method of this invention with existing technologies in terms of flow stability. The horizontal axis represents the time axis of the production batch, and the vertical axis represents the actual added flow rate. The diagram includes three curves of different shapes for comparison: a straight, constant horizontal line in the middle represents the ideal target setpoint required by the formulation; a dashed curve that fluctuates wildly around the target setpoint and shows a large deviation over time represents the flow rate change under existing control technologies; and a solid curve that closely follows the target setpoint with minimal fluctuation represents the flow rate change under the control method of this invention. The comparison shows that the adaptive control commands generated by this invention based on flow resistance deviation and flow instability have significant effects in suppressing environmental interference, compensating for shear thinning effects, and addressing pipeline aging, demonstrating that this system has high control stability throughout its entire lifecycle.

[0060] Specific embodiments of the data processing-based food additive production control system proposed in this invention are as follows: A data-processing-based food additive production control system includes a processor and a memory. The memory stores computer program instructions. When the computer program instructions are executed by the processor, the data-processing-based food additive production control method described in the above embodiments is implemented.

[0061] The data processing-based food additive production control system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces. Their setup and functions are known in the art and will not be described in detail here.

[0062] While various embodiments of the invention have been shown and described in this specification, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention.

Claims

1. A data-processing-based method for controlling the production of food additives, characterized in that, include: Multi-source rheological state data are collected from the additive delivery pipeline, and the multi-source rheological state data is preprocessed; the multi-source rheological state data includes the instantaneous temperature of the pipeline outer wall, the operating frequency of the metering pump, and the pressure difference between the inlet and outlet of the metering pump. By utilizing the hysteresis characteristic of heat conduction, the equivalent temperature of the fluid center is calculated based on the instantaneous temperature of the outer wall of the pipe and its changing trend, so as to correct the thermal hysteresis error caused by the fluctuation of ambient temperature. Based on the pressure difference, the operating frequency of the metering pump, and the equivalent temperature of the fluid center, a rheological thermodynamic model including temperature sensitivity and shear thinning factor is constructed to calculate the dynamic flow resistance characteristic value of the non-Newtonian fluid, so as to decouple the influence of temperature and shear rate on fluid viscosity. The fluctuation of the dynamic flow resistance characteristic value within a historical time window is statistically analyzed to calculate the flow instability. Based on the relative deviation ratio between the dynamic flow resistance characteristic value and the flow resistance reference value under standard operating conditions, and in conjunction with the flow instability, a final adaptive flow compensation control command is generated to adjust the metering pump.

2. The data processing-based food additive production control method according to claim 1, characterized in that, The formula for calculating the equivalent temperature at the fluid center is: In the formula, For the current moment The measured instantaneous temperature of the outer wall of the pipe; The previous sampling time The instantaneous temperature of the outer wall of the pipe; The thermal hysteresis compensation coefficient is determined by the specific heat capacity of the fluid and the thermal conductivity of the pipe material. It is the natural logarithm function; As an indicator of temperature trends; This indicates the range of temperature change.

3. The data processing-based food additive production control method according to claim 2, characterized in that, The method for determining the temperature trend indicator factor is as follows: when the previous sampling time... The instantaneous temperature of the outer wall of the pipe is greater than the current temperature. When the instantaneous temperature of the outer wall of the pipe is measured, it indicates a cooling process, and the temperature trend indicator factor is set to a positive value of 1. At the previous sampling time The instantaneous temperature of the outer wall of the pipe is lower than the current time. When the instantaneous temperature of the outer wall of the pipe is measured, it indicates the heating process, and the temperature trend indicator factor is set to a negative value of -1. At the previous sampling time The instantaneous temperature of the outer wall of the pipe is equal to the current moment. When the instantaneous temperature of the outer wall of the pipe is measured, the temperature trend indicator factor is set to 0.

4. The data processing-based food additive production control method according to claim 2, characterized in that, The formula for calculating the dynamic flow resistance characteristic value is as follows: In the formula, For the current moment The dynamic flow resistance characteristic value, For the current moment The pressure difference between the inlet and outlet of the metering pump; For the current moment The operating frequency of the metering pump; This is the system safety constant; Standard reference temperature; Temperature sensitivity; It is the shear thinning factor; For the natural constant An exponential function with base 0.

5. The data processing-based food additive production control method according to claim 1, characterized in that, The preprocessing of the multi-source rheological state data includes: setting the system sampling period, performing moving average filtering on the instantaneous temperature of the outer wall of the pipe, the operating frequency of the metering pump, and the pressure difference between the inlet and outlet of the metering pump to remove high-frequency electromagnetic noise and form a time series data stream.

6. The data processing-based food additive production control method according to claim 4, characterized in that, The calculation formula for the adaptive traffic compensation control command is as follows: In the formula, The theoretical target flow rate required by the formulation; This is the reference value for the flow resistance of the new pipeline under standard operating conditions. This is the rheological compensation gain coefficient; This is the aging compensation coefficient; It is an index of flow instability.

7. The method for controlling the production of food additives based on data processing according to claim 6, characterized in that, The formula for calculating the flow instability index is as follows: In the formula, The number of sampling points within the historical time window; For the past Dynamic flow resistance characteristic values ​​at each sampling point; For this The arithmetic mean of the dynamic flow resistance characteristic values ​​of each sampling point.

8. The method for controlling the production of food additives based on data processing according to claim 6, characterized in that, The aging compensation coefficient is used to provide positive compensation to prevent insufficient material feeding when wall adhesion or wear is detected on the inner wall of the pipeline, which leads to increased flow resistance fluctuations.

9. The data processing-based food additive production control method according to claim 1, characterized in that, The instantaneous temperature of the outer wall of the pipeline is collected using a wall-mounted thin-film platinum resistance thermometer. The operating frequency of the metering pump is read through the communication interface of the frequency converter. The pressure difference between the inlet and outlet of the metering pump is collected by differential pressure transmitters installed at the inlet and outlet of the metering pump.

10. A data processing-based food additive production control system, characterized in that, It includes a processor and a memory, the memory storing computer program instructions, which, when executed by the processor, implement the data processing-based food additive production control method according to any one of claims 1-9.

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