Method and system for monitoring temperature of gluten meal drying based on temperature sensor

Abstract

This invention discloses a method and system for monitoring the drying temperature of gluten powder based on temperature sensors, belonging to the field of drying technology. The method includes: deploying multiple temperature sensors to collect raw temperature data; calculating the correction amounts for heat conduction error and dust adhesion error based on a heat conduction correction model to obtain accurate temperature; inputting the accurate temperature, drying air velocity, material layer thickness, and hot air humidity into a temperature-moisture coupling model to dynamically calculate real-time moisture content; combining the temperature change rate and moisture content deviation, determining the normal, first-level warning, second-level alarm, or emergency interlock level through an abnormal early warning and interlock control model and outputting control parameters. The system includes a sensing layer, a dual-mode transmission layer, a control layer, a management layer, and an execution layer, achieving full-link automation from data acquisition to closed-loop control. It effectively improves temperature monitoring accuracy, establishes a coupling relationship between temperature and moisture content, realizes graded early warning and intelligent interlock, and ensures the stability, safety, and energy efficiency of the drying process.

Description

Technical Field

[0001] This invention relates to the field of drying technology, and more specifically, to a method and system for monitoring the drying temperature of gluten powder based on a temperature sensor. Background Technology

[0002] Wheat gluten powder requires a drying process during production to remove moisture and ensure product quality and storage stability. Precise control of the drying temperature is a key factor affecting the drying effect, energy consumption, and finished product yield. Currently, wheat gluten powder drying systems generally employ multi-point temperature monitoring schemes, such as deploying temperature sensors (e.g., PT100 RTDs, armored thermocouples, infrared non-contact sensors) at key points like the air inlet duct, the material layer inside the drying tower, the exhaust outlet, and the discharge outlet. Existing systems collect temperature data from each point using a programmable logic controller (PLC) or embedded controller, and then adjust the hot air temperature, fan speed, and steam valve opening using manually set thresholds or simple PID control strategies. Some systems also have data logging and alarm functions, enabling basic judgment of temperature anomalies and triggering audible and visual alerts or shutdown protection. Furthermore, to meet the demands of continuous production, existing technologies have also developed temperature calibration methods based on fixed cycles or human experience, attempting to reduce measurement deviations caused by sensor drift or environmental interference.

[0003] However, existing wheat gluten drying temperature monitoring technologies still have the following shortcomings in practical applications:

[0004] The accuracy of sensor measurements is significantly affected by operating conditions: the high temperature and high dust environment inside the drying tower can easily cause dust to adhere to the surface of the thermocouple sensor, resulting in thermal conduction errors and response lag; at the same time, factors such as sensor insertion depth and uneven temperature distribution of the material layer will further reduce the accuracy of the original temperature data, and the existing system lacks a dynamic compensation mechanism for thermal conduction errors and dust adhesion errors.

[0005] Temperature monitoring and moisture content control are disconnected: Existing methods only focus on the temperature value itself and fail to establish a coupling relationship between temperature and the moisture content of wheat gluten. In the actual drying process, the rate of change of moisture content is related to multiple factors such as temperature integral, air volume, material layer thickness, and hot air humidity. Relying solely on temperature threshold control makes it difficult to accurately determine the drying endpoint, which can easily lead to over-drying or under-drying problems.

[0006] Lack of systematic anomaly early warning and interlock control capabilities: Traditional systems mostly use fixed temperature threshold alarms, which cannot dynamically adjust the early warning threshold according to the rate of temperature change and moisture content deviation, resulting in frequent false alarms and missed alarms; and after an anomaly occurs, it often only triggers a shutdown, lacking a hierarchical control strategy (such as first-level early warning, second-level alarm, and emergency interlock), which affects production continuity and safety. Summary of the Invention

[0007] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a method and system for monitoring the drying temperature of gluten powder based on a temperature sensor, and solve the problems mentioned in the background art through the following solutions.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a method for monitoring the drying temperature of gluten powder based on a temperature sensor, comprising:

[0009] Step 1: Select and install temperature sensors according to the requirements of the gluten drying process, collect raw temperature data at each point, and perform signal processing and preprocessing on the raw temperature data to obtain effective temperature measurement data.

[0010] Step 2: Input the effective temperature measurement data obtained in Step 1 into the multi-point temperature compensation mathematical model based on heat conduction correction to calculate the accurate temperature measurement value;

[0011] Step 3: Input the accurate temperature measurement value and drying condition auxiliary parameters obtained in Step 2 into the drying temperature-moisture coupling mathematical model of gluten powder to calculate the moisture content W(t) of gluten powder at time t.

[0012] Step 4: Input the accurate temperature measurement value obtained in Step 2, the moisture content W(t) obtained in Step 3, and the preset temperature safety range parameter into the temperature anomaly early warning and interlocking control mathematical model, determine the anomaly level, and output control instructions;

[0013] Step 5: Store the accurate temperature measurement value obtained in Step 2, the moisture content W(t) obtained in Step 3, the abnormality level and control instructions determined in Step 4 together with the drying batch information to establish a full-process traceability system.

[0014] A temperature sensor-based gluten powder drying temperature monitoring system includes:

[0015] The sensing layer consists of a distributed temperature sensor array and auxiliary sensors, used to collect temperature data and auxiliary operating parameters;

[0016] The transmission layer adopts a dual-mode transmission mode of wired and wireless transmission, and is equipped with a data breakpoint resume buffer module to achieve stable data transmission between the perception layer and the control layer.

[0017] The control layer, consisting of a PLC control cabinet and an embedded computing module, is used to perform the linkage calculations of the multi-point temperature compensation mathematical model based on heat conduction correction, the temperature-moisture coupling mathematical model for gluten drying, and the temperature anomaly warning and interlocking control mathematical model, and output control commands.

[0018] The management layer consists of a local industrial control computer and a cloud monitoring platform, used to realize data visualization, storage, and full-process traceability;

[0019] The execution layer is used to respond to the control commands output by the control layer and adjust the drying conditions. It includes a hot air blower, a steam regulating valve, a heating module, a unloading device, and an explosion-proof pressure relief device.

[0020] The technical effects and advantages of this invention are as follows:

[0021] 1. This invention constructs a multi-point temperature compensation mathematical model based on thermal conduction correction, and introduces dynamic correction terms for sensor insertion depth, average material layer temperature, and dust thickness. It can calculate and eliminate thermal conduction error and dust adhesion error in real time, so that the error of the final output temperature is controlled within ±0.2℃, which is better than the traditional direct measurement method.

[0022] 2. This invention establishes a coupled mathematical model of drying temperature and moisture content for gluten powder. By using temperature integral, actual moisture evaporation coefficient and initial moisture content, the real-time moisture content is dynamically calculated. This model can directly convert temperature monitoring results into quality indicators, providing a basis for automatic unloading and graded output.

[0023] 3. This invention designs a mathematical model for temperature anomaly early warning and interlock control, taking into account both temperature deviation and temperature change rate, and dynamically adjusts the early warning threshold according to the moisture content deviation. The system can distinguish four levels: normal operating conditions, first-level early warning, second-level alarm and emergency interlock, and output corresponding control quantities to achieve refined, hierarchical closed-loop control, which ensures safety and reduces unnecessary downtime.

[0024] 4. Based on a distributed sensor array, dual-mode transmission, embedded control module, local industrial control computer and cloud platform, and multiple actuators, this invention realizes full-link automation and intelligence from data acquisition, model calculation, control output to effect feedback, thereby improving the stability, safety and energy efficiency of the gluten drying process. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of the overall method flow of the present invention;

[0026] Figure 2 This is a flowchart of the calculation process of the multi-point temperature compensation mathematical model based on heat conduction correction of the present invention.

[0027] Figure 3 This is a flowchart of the combined control of drying temperature and moisture content and temperature anomaly early warning for gluten powder according to the present invention.

[0028] Figure 4 This is a block diagram of the overall system structure of the present invention. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] As attached Figures 1 to 3 The method for monitoring the drying temperature of gluten powder based on a temperature sensor, as shown, includes:

[0031] Step 1: Sensor selection: Based on the requirements of the gluten drying process, select suitable sensors and deploy them at multiple points to build the hardware foundation for temperature measurement, providing reliable raw temperature data for subsequent model calculations.

[0032] Sensor selection criteria:

[0033] Inlet air duct temperature measurement: A resistance temperature detector is selected, preferably a PT100 resistance temperature sensor, which has high precision, fast response and high protection characteristics, and is suitable for normal temperature measurement scenarios in air ducts.

[0034] Temperature measurement of the material layer inside the drying tower: Armored thermocouple temperature sensors are selected, preferably K-type thermocouple sensors, which have the characteristics of high temperature resistance, dust resistance and fast response speed, and are suitable for the harsh working conditions of high temperature and high dust inside the drying tower.

[0035] Exhaust gas and finished product temperature measurement: A non-contact infrared temperature sensor is selected, which can measure temperature without contact and has a fast response speed, and can be adapted to non-contact temperature measurement scenarios for exhaust gas and finished products.

[0036] Sensor deployment scheme: One PT100 sensor is deployed at the air inlet; three armored thermocouple sensors are deployed in the upper, middle and lower layers of the drying tower, for a total of nine; one infrared non-contact sensor is deployed at the outlet, discharge port and exhaust port of the cyclone separator, for a total of three; two PT100 sensors are deployed on the heat exchange tube wall; a total of 14 sensors are deployed to form a distributed temperature measurement array.

[0037] Step 2: Temperature data acquisition and preprocessing: Complete the acquisition, signal processing and preprocessing of raw temperature data to obtain effective temperature measurement data, which will serve as the initial input parameters for model calculation.

[0038] Temperature data acquisition: Sensors at each location collect temperature data at different sampling frequencies. The sampling frequency for the air inlet duct and exhaust outlet is 1 time / 1s, the sampling frequency for the material layer inside the drying tower is 1 time / 0.5s, and the sampling frequency for the discharge outlet is 1 time / 2s.

[0039] Temperature signal processing: The analog temperature signal is sequentially processed by the instrumentation amplifier module, the low-pass filter module, and the high-precision analog-to-digital converter module to complete signal amplification, noise filtering, and analog-to-digital conversion, and output standardized digital temperature data to ensure signal acquisition accuracy and stability.

[0040] Temperature data preprocessing: A temperature change threshold of 3℃ is set. When the difference between a single measurement and the previous measurement is greater than 3℃, it is identified as abnormal data jump. Linear interpolation is used to supplement the data, and extreme abnormal data that cannot be supplemented are removed, retaining valid data. The interpolation formula is as follows: Where T is the temperature value after supplementation. , These are the effective temperature values ​​before and after, , , For each corresponding acquisition time, if the difference between a single measurement and the previous measurement is greater than 3℃, it is considered an abnormal data jump. In this case, the system first checks the abnormal data count variable: if the current number of consecutive abnormal data is ≤2, linear interpolation is used to supplement it, based on the two most recent valid temperature values ​​before and after the abnormal data; if the number of consecutive abnormal data reaches 3, it is determined that the sensor may be faulty (such as open circuit, short circuit, or severe drift), interpolation calculation is stopped, a fault log is recorded, and a "sensor abnormality" alarm is triggered through the management layer to remind the operator to check. Before the sensor fault is resolved, the control layer maintains the last valid control command or switches to a safe mode.

[0041] Step 3: Linked Calculation and Temperature Control: This step uses three sets of models to perform linked calculations to complete temperature correction, moisture content prediction, anomaly detection and control, achieving closed-loop linkage throughout the entire process.

[0042] Calculation based on a multi-point temperature compensation mathematical model with heat conduction correction

[0043] The original temperature measurement value obtained in step 2 Input this model, and the accurate temperature measurement value will be obtained through calculation. This provides input data for the other two sets of models; raw temperature measurements. The data consists of uncorrected temperature data for each monitoring point retained after preprocessing in step 2, with the sampling frequency consistent with that in step 2.

[0044] During the calculation process, first use the formula Calculate the heat conduction error correction amount Where α is a dimensionless empirical coefficient (range 0.1~0.3, default 0.2, calibration method is as follows: In a laboratory drying tower, set different insertion depths d (10mm, 20mm, 30mm) and material layer thickness L (20mm, 30mm, 40mm), use a standard thermocouple (calibrated) and a test sensor to simultaneously measure the temperature of the same material layer, and record the difference. The formula was fitted using multiple linear regression. We obtain α=0.2 (R²=0.96)), d is the sensor insertion depth (15~25mm, 20mm for the middle layer, 18mm for the top and bottom layers), and L is the total thickness of the material layer (25~35mm, default 30mm). The average temperature of the material layer (obtained by averaging the original measurements from three armored thermocouple sensors in the same layer, i.e.) The thermal conductivity λ of the gluten powder layer is used as a physical reference and is not included in the calculation. Its value ranges from 0.20 to 0.25 W / (m·℃).

[0045] Then through the formula Calculate the correction amount for dust adhesion error Combined with formula Obtain accurate temperature measurement values Where δ is the thickness of dust on the sensor surface (0~1mm, acquisition accuracy 0.01mm). The ambient air temperature (acquired by a DS18B20 sensor, accuracy ±0.1℃). The thermal conductivity of the dust is dynamically adjusted. For accurate temperature measurements, the error can be controlled within ±0.2℃.

[0046] When δ > 0.8 mm, the calibration formula is used. Calibrate δ and recalculate. Ultimately Output to the other two sets of models.

[0047] Dust thermal conductivity The method for obtaining the white dust layer is as follows: Under dust thicknesses δ=0, 0.2, 0.4, 0.6, 0.8, and 1.0 mm, the temperature difference and heat flux density on both sides of the attached dust layer are measured respectively. The equivalent thermal conductivity is calculated according to Fourier's law, and the result is obtained by fitting the data. ,in , δ is in mm, and when δ > 1.0 Take 0.10.

[0048] Calculation of drying temperature-moisture coupling mathematical model for gluten powder

[0049] Will and auxiliary parameters for drying conditions (v, h, φ, Inputting the data into this model, the moisture content W(t) of the gluten powder at time t is calculated, providing a quality basis for the output of control commands; the auxiliary parameters are collected by the FS4000 wind speed sensor, the material layer thickness sensor, the SHT30 humidity sensor, and the MS3500 pre-moisture sensor, respectively.

[0050] During the calculation process, first use the formula Calibration of basic moisture evaporation coefficient The actual water evaporation coefficient k is obtained; where k is the actual water evaporation coefficient (8.0 × 10⁻⁶). -6 ~9.0×10 -6 ℃ -1 ·s -1 ), The baseline moisture evaporation coefficient (2.5 × 10⁻⁶) was used to calibrate the experiment. -5 ℃ -1 ·s -1 The calibration method is as follows: A constant temperature and humidity drying oven was used, with the temperature set at 60℃, wind speed at 1m / s, material layer thickness at 30mm, and hot air humidity at 10%. A wheat gluten powder sample with an initial moisture content of 35% was taken and weighed every 30 seconds to calculate the water loss rate. The result was obtained using a nonlinear fitting formula. Reverse push (The average value is taken from multiple experiments), v is the drying wind speed (10~15m / s, accuracy 0.1m / s), h is the material layer thickness (25~35mm, consistent with L), and φ is the relative humidity of hot air (10%~20%, accuracy ±2%RH).

[0051] Then through the formula Calculate the temperature integral from 0 to t, and combine it with the formula. The water content W(t) at time t is obtained; where The integral of temperature (°C·s) is calculated using the trapezoidal rule, where n is the number of samples. Let $T(τ)$ be the i-th accurate temperature value, $Δt$ be the sampling interval, and $T(τ)$ be the average accurate temperature of the middle layer of the drying tower (composed of the three middle layers). (The average value is taken), W(t) is the moisture content at time t (target value ≤8% / grade 1, ≤10% / grade 2, error is usually ≤0.5%). The initial moisture content is 30%~40%, with a default of 35% (which can be adjusted within the range of 30%~40% according to actual working conditions), with an accuracy of ±0.1%, and e is the natural constant (2.71828). For the drying rate correction function, when hour ,when hour , The critical temperature for protein denaturation in wheat gluten (range of values) ), Attenuation coefficient (range of values) ).

[0052] It should be further noted that the glutenin and gliadin in wheat gluten begin to denature at temperatures exceeding approximately 75°C, forming a network structure that hinders moisture diffusion and leads to a decrease in the evaporation rate. Through isothermal drying experiments (temperatures set at 60°C, 70°C, 80°C, and 90°C), the weight loss curves were measured and the drying rate constant was fitted. It was found that the drying rate below 70°C follows an exponential law. At temperatures above 80℃, the drying rate decreases by 15% to 25%. Based on this, the setting... , This modified model can more accurately predict changes in moisture content in the high-temperature range.

[0053] The formula for the dynamic adjustment of the thermal conductivity λ of the gluten powder layer with the moisture content W is as follows: ,in , , , .

[0054] Connect W(t) with The data are input into the temperature anomaly early warning and interlocking control model.

[0055] Mathematical Model Calculation for Temperature Anomaly Early Warning and Interlocking Control

[0056] Will The parameters W(t) and the preset temperature safety range are input into this model. The abnormal level is determined by calculation and the control command ΔP is output. After control, the operating parameters are fed back to the first two sets of models to realize closed-loop control. The temperature safety range is: material temperature T∈[50,70]℃, hot air temperature T∈[140,160]℃, and exhaust gas temperature T∈[55,65]℃.

[0057] During the calculation process, first use the formula , Calculate the rate of temperature change separately Temperature deviation ΔT; where The rate of temperature change is (-5~5℃ / s). , These are the precise temperature values ​​at time t and time t-Δt, respectively, where ΔT is the temperature deviation. Set the temperature value for each point.

[0058] Combined The abnormality level is determined by ΔT and W(t). When W(t) deviates from the target value, the abnormality level is determined by the formula. Adjustment ;in For the adjusted threshold, The preset temperature deviation threshold is initially set at ±5℃. The target moisture content is (8% for Grade 1, 10% for Grade 2); four operating conditions are specifically determined: normal operating condition (|ΔT|≤ And| |≤0.5℃ / s), Level I warning ( <|ΔT|≤2 And 0.5℃ / s < | |≤1℃ / s), Level 2 alarm (2 <|ΔT|≤3 And 1℃ / s < | |≤2℃ / s), emergency interlock (|ΔT|>3) or | |>2℃ / s).

[0059] The control amount ΔP is calculated based on the anomaly level. For a Level 1 warning, the formula is used. Level 2 alarms use a formula ; where ΔP is the control amount (%, used to adjust the hot air power). The proportionality coefficient (0.8) was used to calibrate the experiment. The differential coefficient of regulation (0.5, ) was calibrated for the experiment. and The calibration method is as follows: Set up a small drying test bench and set the target temperature value. =60℃. Apply step disturbances (such as a sudden drop of 5℃ in hot air temperature) and record the temperature response curves. Use the Ziegler-Nichols critical proportional method to tune the PID coefficients: first use only proportional control, increase... Until the system oscillates with constant amplitude, the critical gain is measured. Critical period According to the PID empirical formula: = =0.96, However, considering the temperature sensitivity of wheat gluten, the drying process was fine-tuned through actual drying experiments. , And verify that the overshoot is ≤5% and the adjustment time is ≤30s); the emergency interlock condition directly triggers the shutdown command.

[0060] After adjustment, the operating parameters are fed back to the first two sets of models, and the values ​​of λ and k are adjusted to achieve closed-loop linkage.

[0061] Step 4: Data Storage and End-to-End Traceability: The output of the temperature compensation model... The temperature-moisture coupling model output W(t), the abnormality level and ΔP of the temperature anomaly early warning and interlocking control model, and the drying batch information are all stored in a local database and a cloud platform to establish a batch traceability system.

[0062] The stored data includes: W(t), anomaly level, ΔP, drying batch information (batch number, drying time, ... W) and intermediate data for each model operation ( , k , , ΔT).

[0063] The specific implementation method is as follows: data is stored in an industrial local database and a cloud-based IoT platform, with a storage period of no less than 1 year, and the sampling frequency is consistent with the temperature acquisition; each drying batch corresponds to a unique batch number, and the batch information includes sensor parameters, temperature time-series curves, calculation data, and control records.

[0064] As attached Figure 4 The temperature monitoring system for drying gluten powder based on a temperature sensor shown includes:

[0065] The perception layer, transmission layer, control layer, management layer, and execution layer work together to ensure the accuracy and reliability of monitoring and control.

[0066] The sensing layer consists of a distributed temperature sensor array and auxiliary sensors, used to collect temperature data and operating condition auxiliary parameters, providing basic input for model calculation.

[0067] The distributed temperature sensor array contains 14 temperature sensors, specifically 4 PT100 sensors (WZP-230), 9 armored thermocouple sensors (K-type WRNK-191), and 3 infrared non-contact sensors (IR2000). They are deployed according to the deployment scheme in step 1, using threaded, embedded, and bracket fixing methods respectively. They are connected to the transmission layer through RVVP2×0.75mm² shielded cables and output 4~20mA analog signals.

[0068] Auxiliary sensors include an FS4000 wind speed sensor, an SHT30 humidity sensor, an LVDT material layer thickness sensor, an OPT101 dust thickness sensor, an MS3500 pre-sensor moisture sensor, and a DS18B20 auxiliary temperature sensor, which respectively collect v, φ, h, δ, , Parameters: All sensors have a protection rating of IP65 or higher, making them suitable for high-temperature and high-dust environments in drying scenarios.

[0069] Under normal operating conditions (dust concentration ≤50mg / m³, ambient temperature ≤60℃), it is recommended that the temperature sensor be calibrated every 6 months; the dust thickness sensor (OPT101) lens be cleaned every 3 months; and the infrared sensor be calibrated using a blackbody furnace every 500 hours. In this embodiment, no sensor drift was observed after 8 hours of continuous operation, meeting the calibration cycle requirements.

[0070] The transmission layer adopts a dual-mode transmission method of wired and wireless transmission to achieve stable data transmission between the perception layer and the control layer, ensure the continuity of model operation, and avoid data loss.

[0071] Wired transmission uses an industrial bus transmission module, which has strong anti-interference capabilities and stable transmission. It is used to connect sensors and main control equipment, transmit analog signals, and is equipped with shielded cable wiring to reduce electromagnetic interference in the industrial environment.

[0072] The wireless transmission uses a long-range industrial wireless communication module, which has strong penetration and long transmission distance, for the wireless transmission of system digital signals, ensuring the continuity of data transmission under complex working conditions.

[0073] The system is configured with a data breakpoint resume caching module, which automatically caches the collected data when the transmission is interrupted, and synchronizes it to the database in batches after communication is restored, preventing data loss and ensuring data integrity.

[0074] The control layer consists of a PLC control cabinet and an embedded computing module, which is used to perform the linkage calculation of three sets of core mathematical models and output control instructions to the execution layer.

[0075] The PLC control cabinet uses an industrial programmable logic controller, which has multiple digital and analog input / output interfaces, wide operating temperature range, and stable operation. It connects to the transmission layer and embedded computing module through industrial bus and Ethernet interfaces respectively.

[0076] The embedded computing module uses an STM32F407 microcontroller (168MHz main frequency, 1MB memory) to run the model calculation program, calculate the control quantity ΔP and output it to the PLC; it is equipped with a Kunlun Tongtai TPC1061Ti industrial touch screen (10.1 inches) which can display various data in real time, support manual intervention and control, and improve the convenience of operation.

[0077] The control process is as follows: The sensor collects data at a set frequency, and the data is sent to the STM32F407 via the transmission layer; the STM32 runs three sets of models to calculate ΔP, with a calculation cycle of ≤100ms; ΔP is sent to the S7-1200 PLC via Ethernet (ModbusTCP); the PLC drives the actuators (frequency converter, electric heating tube, etc.); if the communication between the PLC and the STM32 is interrupted, the PLC retains the most recent valid ΔP and continues to operate, while triggering a local alarm; if the communication is continuously interrupted for more than 5 seconds, the PLC automatically switches to the preset safety mode (hot air temperature drops to 120℃, speed drops to 80% of the rated value); the host computer software synchronizes with the STM32 every 1 second, and if data inconsistency is found (deviation >0.5℃ for 3 consecutive cycles), the local calibration value stored in the host computer is used as the standard and overwritten.

[0078] The management layer consists of a local industrial control computer and a cloud monitoring platform, which is used to realize data visualization, storage and full-process traceability, and meet the needs of production compliance and remote management.

[0079] The local industrial control computer adopts an industrial-grade computer configuration, is equipped with an industrial operating system, and has built-in self-developed temperature monitoring and control software. It supports real-time data display, data export, and parameter calibration functions, and bidirectionally connects to the PLC and local database to achieve full-process data synchronization and localized management.

[0080] Supplement to software technical solution:

[0081] The data acquisition module adopts the Modbus TCP protocol, and the sampling frequency is consistent with the temperature acquisition (1 time / 1s for air inlet duct and exhaust outlet, 1 time / 0.5s for material layer in drying tower, and 1 time / 2s for material outlet). The breakpoint resume processing uses the AT24C02 module to store cached data, which is automatically synchronized to the database after the connection is restored to avoid data loss.

[0082] The model calculation module encapsulates three sets of mathematical models into a .dll file. Input parameters include raw data collected by each sensor and auxiliary operating parameters, while output parameters include precise temperature values. The calculation cycle is ≤100ms for the moisture content W(t), anomaly level, and control amount ΔP, ensuring timely control response.

[0083] The human-machine interaction module can display the temperature time-series curves of each monitoring point, the temperature cloud map inside the drying tower, the real-time value of moisture content and the predicted trend in real time. It supports manual parameter calibration and manual intervention of control commands, and has an abnormal alarm sound and light prompt function to facilitate operators to deal with abnormalities in a timely manner.

[0084] The storage and traceability module employs a tiered strategy: high-frequency data (material layer inside the drying tower, 1 time / 0.5s) is retained in the local SQL Server for one month; after one month, it is automatically downsampled to 1 time / 2min and transferred to the archive table. Low-frequency data (1 time / 1s) from areas such as the air inlet duct and exhaust outlet is retained for one year. Cloud storage only stores downsampled data (1 time / 1min) and all abnormal alarm records (original frequency). During network outages, local data is temporarily stored in the AT24C02 (16KB) and the local hard drive queue. After network recovery, it is incrementally uploaded to the cloud, using FIFO sequential transmission to avoid backlog and overload. Recommended local industrial PC configuration: CPU no less than i5, memory no less than 8GB, and available hard drive space no less than 500GB.

[0085] The alarm push module is developed based on a cloud-based IoT platform. When abnormal operating conditions such as Level 1 warning, Level 2 alarm, and emergency interlock occur, alarm information is pushed to the administrator in real time via mobile devices. This includes the abnormal location, abnormal parameters, abnormal level, and suggested handling measures, enabling rapid response and handling of abnormal operating conditions.

[0086] The software architecture is for illustrative purposes only; the actual software implementation shall prevail.

[0087] The cloud-based monitoring platform is built on an industrial cloud IoT platform, supports remote login and access, and has functions such as remote monitoring, anomaly push, historical data query, production batch traceability, and unified management and control of multiple devices. It can synchronize data with the local industrial control computer in real time to achieve remote intelligent management and control.

[0088] The execution layer responds to the control commands output by the control layer, adjusts the drying conditions, and ensures the drying quality and production safety of gluten powder. It consists of the following equipment:

[0089] Hot air blower: An industrial centrifugal hot air blower is selected, equipped with a frequency converter connected to a PLC, which can accurately adjust the blower speed and air volume, and realize dynamic control of drying air speed.

[0090] Steam regulating valve: An electric proportional regulating valve is selected, which has high regulation accuracy and strong controllability. It is used to accurately control the steam supply flow and indirectly realize the fine adjustment and regulation of hot air temperature.

[0091] Heating module: Industrial electric heating components are selected, which can directly control the hot air temperature through on / off and power adjustment, and achieve dual temperature control protection in conjunction with the regulating valve;

[0092] Unloading device: Screw unloader (LS-200), speed 60r / min, automatically unloads when W(t) reaches the target moisture content to ensure the quality of finished product;

[0093] Explosion-proof pressure relief device: An industrial explosion-proof pressure relief valve is selected, which can automatically open and relieve pressure in emergency interlocking conditions, effectively avoiding the risk of dust high-temperature explosion and ensuring production safety.

[0094] All actuators communicate bidirectionally with the PLC, enabling real-time operating parameters to be fed back to the sensing and control layers, providing feedback data for model calculation and regulation, and ensuring the stability of closed-loop linkage.

[0095] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other.

[0096] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for monitoring the drying temperature of gluten powder based on a temperature sensor, characterized in that, Includes the following steps: Step 1: Select and install temperature sensors according to the requirements of the gluten drying process, collect raw temperature data at each point, and perform signal processing and preprocessing on the raw temperature data to obtain effective temperature measurement data. Step 2: Input the effective temperature measurement data obtained in Step 1 into the multi-point temperature compensation mathematical model based on heat conduction correction to calculate the accurate temperature measurement value; Step 3: Input the accurate temperature measurement value and drying condition auxiliary parameters obtained in Step 2 into the drying temperature-moisture coupling mathematical model of gluten powder to calculate the moisture content W(t) of gluten powder at time t. Step 4: Input the accurate temperature measurement value obtained in Step 2, the moisture content W(t) obtained in Step 3, and the preset temperature safety range parameter into the temperature anomaly early warning and interlocking control mathematical model, determine the anomaly level, and output control instructions; Step 5: Store the accurate temperature measurement value obtained in Step 2, the moisture content W(t) obtained in Step 3, the abnormality level and control instructions determined in Step 4 together with the drying batch information to establish a full-process traceability system.

2. The method for monitoring the drying temperature of gluten powder based on a temperature sensor according to claim 1, characterized in that, The calculation method for the multi-point temperature compensation mathematical model based on heat conduction correction in step 2 is as follows: First, through the formula Calculate the heat conduction error correction amount ,in Here, d is a dimensionless empirical coefficient, d is the sensor insertion depth, and L is the total thickness of the material layer. The average temperature of the material layer. This is the original temperature measurement value; Then through the formula Calculate the correction amount for dust adhesion error ,in The thickness of dust on the sensor surface. The ambient air temperature The thermal conductivity of the dust is given; finally, the formula is used. Accurate temperature measurement value is calculated .

3. The method for monitoring the drying temperature of gluten powder based on a temperature sensor according to claim 1, characterized in that, The calculation method for the coupled mathematical model of wheat gluten drying temperature and moisture in step 3 is as follows: first, through the formula... Calibration of basic moisture evaporation coefficient The actual moisture evaporation coefficient k is obtained, where v is the drying air velocity and h is the material layer thickness. The relative humidity of the hot air; Then through the formula Calculate the temperature integral from time 0 to time t, and finally combine it with the formula. The water content W(t) at time t is calculated, where Let e ​​be the initial moisture content, and e be the natural constant. This is a drying rate correction function. This is the i-th accurate temperature value.

4. The method for monitoring the drying temperature of gluten powder based on a temperature sensor according to claim 1, characterized in that, The calculation method for the mathematical model of temperature anomaly early warning and interlocking control in step 4 is as follows: first, through the formula... Calculate the rate of temperature change Through formula Calculate temperature deviation ; Then, the judgment threshold is dynamically adjusted based on the moisture content W(t), and the formula is as follows: ,in For the adjusted threshold, The preset temperature deviation threshold is used; Finally, based on and Determine the anomaly level and calculate the control amount based on the anomaly level. The formula used during a Level 1 warning is as follows: The formula is used for level 2 alarms. An emergency interlock triggers a shutdown command, in which... This is the proportionality coefficient. To adjust the differential coefficient.

5. The method for monitoring the drying temperature of gluten powder based on a temperature sensor according to claim 1, characterized in that, In step 1, temperature data preprocessing includes: setting a temperature change threshold. When the difference between a consecutive single measurement and the previous measurement does not exceed When the difference exceeds a certain threshold, retain the data; when the difference exceeds a certain threshold, retain the data. If abnormal jump data occurs, it is identified as such and supplemented using linear interpolation. If three or more abnormal jump data occur consecutively, the sensor is deemed faulty, interpolation is stopped, and an alarm signal is triggered. The linear interpolation formula is: Where T is the temperature value after supplementation. , These are the effective temperature values ​​before and after, , , These represent the corresponding collection times.

6. A temperature monitoring system for drying gluten powder based on a temperature sensor, characterized in that, The method for monitoring the drying temperature of gluten powder based on a temperature sensor, as described in any one of claims 1 to 5, comprises: The sensing layer consists of a distributed temperature sensor array and auxiliary sensors, used to collect temperature data and auxiliary operating parameters; The transmission layer adopts a dual-mode transmission mode of wired and wireless transmission, and is equipped with a data breakpoint resume buffer module to achieve stable data transmission between the perception layer and the control layer. The control layer, consisting of a PLC control cabinet and an embedded computing module, is used to perform the linkage calculations of the multi-point temperature compensation mathematical model based on heat conduction correction, the temperature-moisture coupling mathematical model for gluten drying, and the temperature anomaly warning and interlocking control mathematical model, and output control commands. The management layer consists of a local industrial control computer and a cloud monitoring platform, used to realize data visualization, storage, and full-process traceability; The execution layer is used to respond to the control commands output by the control layer and adjust the drying conditions. It includes a hot air blower, a steam regulating valve, a heating module, a unloading device, and an explosion-proof pressure relief device.

7. The gluten powder drying temperature monitoring system based on a temperature sensor according to claim 6, characterized in that, The distributed temperature sensor array in the sensing layer includes: a PT100 resistance temperature detector (RTD) sensor installed at the air inlet, armored thermocouple temperature sensors installed in the upper, middle, and lower layers of the drying tower, and infrared non-contact sensors installed at the cyclone separator outlet, discharge port, and exhaust port.

8. The gluten powder drying temperature monitoring system based on a temperature sensor according to claim 6, characterized in that, In the control layer, the calculation cycle of the embedded computing module running the mathematical model is less than or equal to 100 milliseconds, and the PLC control cabinet is configured to: retain the most recent valid control command and maintain operation when communication with the embedded computing module is interrupted; if the communication is continuously interrupted for more than a preset time, it will automatically switch to a preset safety mode.

9. The gluten powder drying temperature monitoring system based on a temperature sensor according to claim 6, characterized in that, The local industrial computer in the management layer has built-in temperature monitoring and control software, which includes: The data acquisition module is used to acquire sensor data at a set frequency; The model calculation module encapsulates the three mathematical models into dynamic link library files, which are used to calculate accurate temperature values, moisture content, anomaly levels, and control amounts. The human-computer interaction module is used to display monitoring data, time-series curves and temperature cloud maps in real time, and supports manual parameter calibration and manual intervention of control commands; The storage and traceability module is used to store high-frequency and low-frequency data using a hierarchical strategy and to establish a batch traceability system.

10. The gluten powder drying temperature monitoring system based on a temperature sensor according to claim 6, characterized in that, The unloading device of the execution layer is configured to automatically perform unloading operation when the moisture content W(t) calculated by the temperature-moisture coupling mathematical model reaches the target moisture content, and W(t) is not higher than the target value for at least two consecutive sampling periods.

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