Calibration control method for moisture content of oil in high-temperature heat pump and moisture sensor

Abstract

The invention discloses a method capable of being used in a high-temperature heat pump and performing oil moisture content calibration control and an intelligent moisture sensor, relates to the field of oil component quality detection, and is an intelligent sensing component. High-precision moisture collection in the mixed-flow heat pump drying box is achieved through the moisture sensor based on the microwave space wave method. The related sensor design work comprises the following steps of: calibrating a sensor in a laboratory; a laboratory temperature compensation method of the sensor; high-temperature-resistant design of the sensor; and designing a control algorithm of the intelligent sensor. The moisture sensor with related design has the characteristics of capability of working in a high-temperature environment (50-60 DEG C) for a long time, wide acquisition range (0-20%) and high precision (less than or equal to 0.1%). The related design lays a foundation for online detection based on quality and intelligent reconstruction of field drying equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent oil quality detection and control, and in particular to an intelligent moisture sensor that can be used in high-temperature and high-humidity environments and an oil moisture content control method. Background Art

[0002] In the field of oil drying, moisture content is often used as a key indicator of material quality and drying results. Traditionally, field drying equipment has relied on relatively common handheld moisture sensors for data collection. However, these general-purpose devices often suffer from poor data collection accuracy (errors >2-3%) and lack real-time data collection. Furthermore, measuring moisture content in field drying processes often requires direct contact with high-temperature materials and exposure to high-temperature working environments, making traditional sensors inadequate for these harsh environments. These technical bottlenecks have resulted in a low level of automation in oil field drying and quality evaluation equipment.

[0003] In summary, the study of an intelligent moisture sensor that can adapt to high temperature environment and has high precision plays an important role in the intelligent transformation of site heat pump drying with quality detection function. Summary of the Invention

[0004] The embodiments of the present invention provide a method for calibrating and controlling the moisture content of oil in a high-temperature heat pump and a moisture sensor, which are used to achieve effective full-automatic control of the heat pump and output material moisture content detection in a high-temperature environment.

[0005] In a first aspect, the present invention provides a method for calibrating and controlling the water content of oil in a high-temperature heat pump, comprising: Simulate the on-site working environment, produce rapeseed with different moisture contents, obtain the digital output values ​​of the moisture sensor under different moisture contents, and fit the digital output values ​​of the moisture sensor to achieve moisture content curve calibration; Using the calibrated moisture content value and the real-time temperature signal, the calibrated moisture content is corrected to perform temperature compensation for rapeseed at different temperatures; The moisture content value after temperature compensation is used as the input of fuzzy control. The moisture content value after temperature compensation is compared with the target moisture content to obtain the deviation value. Based on the deviation value, fuzzy logic rules are used to make intelligent decisions and finally output the control amount.

[0006] In some examples, the simulating on-site working conditions to produce rapeseed with different moisture contents includes: Design a sampling box, design a sampling cavity in the middle of the sampling box, fill it with rapeseed, and install a moisture sensor on the cavity; Rapeseeds with different moisture contents are produced by controlling the drying time.

[0007] In some examples, fitting the digital value output by the moisture sensor to achieve curve calibration of moisture content includes: , moisture content ; , moisture content ; , moisture content , , 1≤i≤N-1.

[0008] In some instances, Perform temperature compensation, , , where Mcomp is the compensated moisture content value, M_raw is the original sensor value, T_board is the board temperature, T_probe is the probe temperature, α and β are the weight factors of the probe temperature and the board temperature respectively. is the temperature compensation coefficient, and T_ref is the reference temperature.

[0009] In some instances, The output control quantity is obtained, where O is the conveyor belt speed value, K E is the moisture content error quantification factor, K C K is the quantitative factor of the moisture content error change rate, p is the output scaling factor, is the moisture content error, is the moisture content error change rate.

[0010] In some instances, , ,in, is the oil moisture content instruction, is the moisture content value after temperature compensation.

[0011] In some instances, ,in, is the moisture content error, is the last moisture content error.

[0012] In a second aspect, the present invention provides an intelligent moisture sensor, comprising: A data acquisition module is used to obtain the digital value output by the moisture sensor under different moisture contents; Moisture content calibration module, used to fit the digital value output by the moisture sensor to achieve moisture content curve calibration; A temperature compensation module is used to correct the calibrated moisture content using the calibrated moisture content value and the real-time temperature signal to perform temperature compensation for rapeseed at different temperatures; The speed control module is used to use the moisture content value after temperature compensation as the input of fuzzy control, compare the moisture content value after temperature compensation with the target moisture content to obtain a deviation value, make intelligent decisions based on the deviation value using fuzzy logic rules, and finally output the control amount.

[0013] In some examples, the MCU in the PCB components of the moisture sensor is industrial-grade, applicable to a temperature range of -60°C to 105°C, and the temperature resistance of key microwave components, RC components, microwave connectors, power supplies, and interface ICs meets the requirements and has a certain degree of redundancy; The metal in the moisture sensor that contacts the material is made of stainless steel 304, and the plastic is made of PTFE. The sealing and filling materials in the moisture sensor are made of silicone sealing ring and 704 sealant; The output cable between the moisture sensor and the controller is made of pure copper and silicone sheath.

[0014] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art: The present invention can operate in a high-temperature environment for a long time, and can perform pre-calibration and temperature compensation in a laboratory environment to avoid material loss caused by on-site compensation. In addition, the relevant intelligent sensor can directly control the moisture content of the heat pump based on the moisture collection value. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 It is a schematic diagram of the method flow provided by an embodiment of the present invention; Figure 2 is a schematic diagram of a homemade test kit provided by an embodiment of the present invention; Figure 3 is a graph of the M value collected by the sensor provided in an embodiment of the present invention; Figure 4 is a temperature data graph provided by an embodiment of the present invention; Figure 5 is a temperature compensation curve provided by an embodiment of the present invention; Figure 6 This is a schematic diagram of a high-temperature resistant intelligent sensor and an industrial installation environment provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0018] In the following description, specific embodiments of the present invention will be described with reference to steps and symbols performed by one or more computers, unless otherwise specified. Therefore, these steps and operations will be mentioned several times as being performed by a computer, and computer execution as referred to herein includes operations by a computer processing unit that represents electronic signals of data in a structured form. This operation converts the data or maintains it at a location in the computer's memory system, which can be reconfigured or otherwise change the operation of the computer in a manner familiar to testers in the field. The data structure in which the data is maintained is a physical location in the memory, which has specific characteristics defined by the data format. However, the principles of the present invention are described in the above text, which does not represent a limitation, and testers in the field will understand that the various steps and operations below can also be implemented in hardware.

[0019] As used herein, the terms "module" or "unit" may be considered software objects executed on the computing system. The various components, modules, engines, and services herein may be considered implementation objects on the computing system. While the devices and methods herein are preferably implemented in software, they may also be implemented in hardware and remain within the scope of protection of the present invention.

[0020] It will be understood by those skilled in the art that, unless expressly stated otherwise, the singular forms "a", "an", and "the" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, "connected" or "coupled" as used herein may include wireless connections or wireless couplings. The term "and / or" used herein includes all or any unit and all combinations of one or more associated listed items.

[0021] In the first embodiment of the present invention, a smart moisture sensor design and pre-calibration and compensation method for high temperature and high humidity environments are provided. The relevant sensor can operate in a high temperature environment of 70-80 degrees for a long time, and can be pre-calibrated and temperature compensated in a laboratory environment to avoid material loss caused by on-site compensation. In addition, the relevant smart sensor can also directly control the moisture content of the heat pump based on the moisture collection value, such as Figure 1 As shown, the embodiment of the present invention includes the following steps: S1: Design the test box according to the actual site conditions, simulate the on-site working environment and material thickness in the laboratory, and calibrate the moisture content of the sensor by producing rapeseed with different moisture content gradients; S2: Using a circulating oven, temperature compensation is performed on rapeseed at different temperatures under the premise of sensor moisture content calibration; S3: Based on the high temperature and high humidity environment in which the sensor operates, the sensor structure and chip are designed to be resistant to high temperatures; S4: Integrate relevant control algorithms to realize direct moisture content control of relevant heat pump drying by intelligent sensors.

[0022] Because a temperature and moisture content gradient needs to be established before the sensor is used for the first time in a new scenario, the sensor is installed at a predetermined location on the production line. Then, by continuously changing production line processes such as temperature or heating time, the moisture content of the material flowing through the sensor changes continuously within a certain range. When the digital value output by the sensor shows a significant gradient change (in this embodiment of the present invention, it can be >20), the digital value is recorded and the material is synchronously sampled. Finally, the actual moisture content of the sample is matched with the digital value output by the sensor to achieve calibration and high-temperature compensation. To reduce the pressure of on-site debugging work and save debugging costs, a partial 1:1 restoration of the installation structure environment is performed.

[0023] In an embodiment of the present invention, a test box is designed according to actual site installation conditions to simulate a field test object in a laboratory, and rapeseed with a gradient moisture content is produced in an oven, including: Microwave-based moisture sensors emit electromagnetic waves to collect moisture content, treating objects within a certain distance (5 cm) from the sensor probe as a whole. Therefore, calibration must be performed based on the actual installation environment each time the sensor's installation location changes. To simulate the internal installation of a mixed-flow hot air drying chamber, a stainless steel sampling box with a 1 mm thick wall and a 10 mm central sampling cavity was designed. Rapeseed seeds were then placed in the cavity, and the corresponding moisture sensor was installed.

[0024] The rapeseed was then dried in an oven to create specific moisture content gradients. Since the target moisture content range was expected to be 3% to 15%, the oven was set to 70°C and the drying time was controlled to 0, 30, and 60 minutes, respectively. Three samples were produced, with moisture contents of 12.20%, 9.51%, and 3.96%, respectively.

[0025] In the embodiment of the present invention, an intelligent moisture sensor can be used inside a high-temperature heat pump to detect and control the moisture content of oil. A moisture content calibration device is designed based on the moisture content calibration device. Figure 2 As shown, and the corresponding calibration method.

[0026] The digital values ​​of the sensor output under different moisture contents were further obtained, and the relevant data were fitted with a first-order curve to achieve curve calibration of the moisture content. The obtained data are shown in Table 1 below: Among them, the digital value output by the sensor is the collected value output by the sensor AD module, and its numerical value corresponds to the moisture content. Since moisture is a load relative to electromagnetic waves, the sensor compares the output with the changes in the amplitude and frequency of the electromagnetic waves after absorbing moisture, and compares the digital value obtained by the AD conversion circuit with the actual moisture content value (using a halogen moisture meter to calculate the actual moisture content of the material by the weight change of the material before and after complete drying), and performs relevant calibration.

[0027] Table 1

[0028] like Figure 3 The figure shows the M value curve collected by the sensor. The calibration formula is as follows: The calibration uses piecewise linear interpolation to calculate the moisture content. Assume that (N) calibration points ((M1,M2) have been obtained through experiments. C1 ) ) to ( (M N ,W CN ) ) (arranged in ascending order by (M) value), the calculation rule of the water content (W) corresponding to the compensated Mcomp is as follows: If Mcomp is lower than the minimum calibration point M1, the moisture value is calculated based on the linear relationship of the first interval: the first interval is the interval from the smallest M value to the second smallest M value among the M values ​​obtained during the calibration process. The extrapolation is to calculate the moisture value when it is less than the minimum M value based on the linear relationship of this interval. The minimum moisture value is 0. If Mcomp falls between two calibration points M i and M i+1 If the value is between , the slope of the interval is used for interpolation calculation; If Mcomp exceeds the maximum calibration point M N, then extrapolate according to the linear relationship of the last interval: the last interval is the interval from the largest M value to the second largest M value among the several M values ​​obtained during the calibration process. Extrapolation is to calculate the moisture value when it is greater than the largest M value according to the linear relationship of this interval. The maximum moisture value is 100.

[0029] The calibration formula is in the form of a piecewise function (expressed using a piecewise function). If we use N points for calibration, the principle is as follows:

[0030]

[0031]

[0032]

[0033]

[0034]

[0035] M is the M value measured at the test point (non-calibrated point), To calculate the measured compensated M value; is the moisture content corresponding to the M value of the corresponding calibration point. Note that 1≤i≤N-1, that is, at least two points are used for calibration.

[0036] The moisture value collected by the intelligent moisture sensor is affected by the temperature of the material and circuit board. The sensor operates inside the heat pump cavity, which is much higher than room temperature. Without temperature compensation, the deviation will gradually increase with rising temperature. Therefore, a heat oven is used to simulate and collect corresponding values ​​for temperature compensation.

[0037] After the moisture content is calibrated, further temperature compensation is required. The relevant test is carried out in a temperature chamber. The test cavity and the sensor are placed in the temperature chamber, and the temperature chamber environment is adjusted to change evenly from 23.4°C to 85°C. The temperature data obtained is as follows: Figure 4 shown.

[0038] Temperature compensation is performed under high temperature environment based on the moisture content curve calibration. The relevant compensation curve is shown in Figure 5 shown.

[0039] Temperature compensation is divided into board temperature compensation and probe temperature compensation. Here we use a first-order polynomial compensation. The relevant formula is as follows:

[0040]

[0041]

[0042] If the influencing factors of temperature compensation and temperature detection are assumed to be equal, the commonly used formula can be obtained:

[0043] in, is the sensor value after compensation, is the raw sensor value, is the plate temperature (°C), is the temperature (℃), 、 are the weight factors of probe temperature and plate temperature respectively, is the temperature compensation coefficient (determined by experiment), is the reference temperature (e.g. taking the average value of experimental data).

[0044] In the embodiment of the present invention, the sensor adopts a high temperature resistant design for the high temperature environment in which the smart sensor works. Figure 6 , design a moisture sensor that can work continuously in a high temperature environment of 90 degrees. Related designs include: PCB component selection, contact material selection, sealing material selection, and output cable design: PCB component selection: These components affect the board temperature and the stability of the microwave unit. The MCU should be industrial grade (-60°C-105°C). Key microwave components / RC components / microwave connectors / power supplies / interface ICs, etc., should meet temperature requirements and have a certain degree of redundancy.

[0045] Materials in contact with materials: The sensing part uses metal and plastic, the metal is made of stainless steel 304, and the plastic is made of PTFE.

[0046] Sealing and filling materials: silicone sealing ring (temperature resistant to 290 degrees), 704 sealant (temperature resistant to 260 degrees.

[0047] Output cable: Material: pure copper + silicone sheath.

[0048] Furthermore, a fuzzy closed-loop control of moisture is implemented based on the moisture command value designed by the moisture sensor. A fuzzy control algorithm is designed and integrated into the moisture sensor. After the sensor collects the material moisture content value, it performs relevant fuzzy control and outputs conveyor belt instructions to the servo control component through the 485 communication interface. The material moisture content is adjusted by adjusting the running time of the material in the heat pump cavity.

[0049] Furthermore, the moisture content error ,in, is the oil moisture content instruction, The moisture content after temperature compensation collected by the sensor; Moisture content error change rate ,in, is the moisture content error, where is the last moisture content error.

[0050] Furthermore, the above data is used to calculate the speed value of the heat pump conveyor belt through the fuzzy control algorithm.

[0051]

[0052] Where, O is the mixed flow heat pump conveyor belt speed value, K E is the moisture content error quantification factor, K C K is the quantitative factor of the moisture content error change rate, p is the output scaling factor, is the moisture content error, is the moisture content error change rate.

[0053] In the embodiments of the present invention, moisture content calibration (curve calibration) is a fundamental step. It converts the raw sensor signal into a preliminary moisture content estimate, resolving the fundamental nonlinear mapping problem and obtaining a physically meaningful preliminary actual moisture content value. Subsequent temperature compensation utilizes this calibration value (or its raw signal) and the real-time temperature signal to correct the preliminary moisture content according to a preset model. The core objective is to eliminate measurement deviations caused by temperature fluctuations and significantly improve the accuracy and reliability of the final output value, Mcomp. Mcomp is the high-precision actual moisture content obtained through dual calibration (moisture content curve + temperature compensation). The fuzzy control algorithm relies on this highly accurate and reliable Mcomp value as its core input. The fuzzy controller compares Mcomp with the target moisture content, calculates the deviation (and the rate of change of the deviation), and uses fuzzy logic rules to make intelligent decisions, ultimately outputting the control variable (the target conveyor speed). Therefore, calibration and compensation are prerequisites for ensuring the accuracy of the input signals to the fuzzy controller, providing a reliable basis for intelligent decision-making. Fuzzy control, in turn, utilizes this precise information to drive the actuator (conveyor belt) and achieve closed-loop control of the moisture content. The three form a complete chain of "precise perception (calibration compensation) - intelligent decision-making (fuzzy control) - precise execution (adjusting conveyor belt)".

[0054] In the embodiment of the present invention, the material running time is finally adjusted according to the conveyor belt speed: The material in the mixed flow heat pump cavity mainly moves from top to bottom in a free fall manner. The total running time t) of the material through the cavity is basically determined by the cavity effective height H and the average speed of the material falling. Decide( ). The speed of the inlet and outlet conveyor belts directly affects the flow state of the material. Adjusting the speed of the inlet conveyor belt mainly controls the initial delivery rate of the material into the cavity (filling rate). Adjusting the speed of the outlet conveyor belt mainly controls the removal rate of the material from the cavity (emptying rate). When the fuzzy controller outputs a command to reduce the speed of the outlet conveyor belt, the material removal speed slows down, resulting in an increase in the amount of material retained in the cavity, enhanced interaction between materials, and their average falling speed. Conversely, increasing the speed of the outlet conveyor will speed up the removal, reduce the retention volume, and increase , shorten t. Increasing the speed of the inlet conveyor belt (while the outlet speed remains unchanged) will increase the packing density of the material in the cavity and also slow down the average falling speed. By precisely controlling the speed of the inlet and outlet conveyor belts, the retention rate of the material in the heat pump and its average falling speed can be effectively adjusted. , and then directly control the running time t of the material undergoing the drying / humidification process, and ultimately achieve the goal of adjusting the moisture content of the outlet material.

[0055] In a second embodiment of the present invention, to facilitate better implementation of the method provided in the embodiment of the present invention, the embodiment of the present invention further provides a moisture sensor based on the above method. The meanings of the terms herein are the same as in the above method, and the specific implementation details can be referred to the description in the method embodiment.

[0056] The smart moisture sensor includes: A data acquisition module is used to obtain the digital value output by the moisture sensor under different moisture contents; Moisture content calibration module, used to fit the digital value output by the moisture sensor to achieve moisture content curve calibration; A temperature compensation module is used to correct the calibrated moisture content using the calibrated moisture content value and the real-time temperature signal to perform temperature compensation for rapeseed at different temperatures; The speed control module is used to use the moisture content value after temperature compensation as the input of fuzzy control, compare the moisture content value after temperature compensation with the target moisture content to obtain a deviation value, make intelligent decisions based on the deviation value using fuzzy logic rules, and finally output the control amount.

[0057] The specific implementation of each module can refer to the description of the above method embodiment, and the embodiment of the present invention will not be repeated.

[0058] The above is a detailed introduction to a method for calibrating and controlling the moisture content of oil in a high-temperature heat pump and a moisture sensor provided in an embodiment of the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for calibrating and controlling the moisture content of oil in a high-temperature heat pump, characterized in that: include: Simulate the on-site working environment, produce rapeseed with different moisture contents, obtain the digital output values ​​of the moisture sensor under different moisture contents, and fit the digital output values ​​of the moisture sensor to achieve moisture content curve calibration; Using the calibrated moisture content value and the real-time temperature signal, the calibrated moisture content is corrected to perform temperature compensation for rapeseed at different temperatures; The moisture content value after temperature compensation is used as the input of fuzzy control. The moisture content value after temperature compensation is compared with the target moisture content to obtain the deviation value. Based on the deviation value, fuzzy logic rules are used to make intelligent decisions and finally output the control amount.

2. The method according to claim 1, characterized in that The method of simulating on-site working conditions to produce rapeseed with different moisture contents includes: Design a sampling box, design a sampling cavity in the middle of the sampling box, fill it with rapeseed, and install a moisture sensor on the cavity; Rapeseeds with different moisture contents are produced by controlling the drying time.

3. The method according to claim 1 or 2, characterized in that The step of fitting the digital value output by the moisture sensor to achieve moisture content curve calibration includes: , moisture content ; , moisture content ; , moisture content ,in is the M value of the calibration points arranged from large to small, 1≤i≤N-1, N represents the number of calibration data, It is the calibration point, corresponding to the nth voltage quantity corresponding to the phase and amplitude difference after microwave emission and output, 1≤n≤N.

4. The method according to claim 3, characterized in that Depend on Perform temperature compensation, , , where Mcomp is the compensated moisture content value, M_raw is the original sensor value, T_board is the board temperature, T_probe is the probe temperature, α and β are the weight factors of the probe temperature and the board temperature respectively. is the temperature compensation coefficient, and T_ref is the reference temperature.

5. The method according to claim 4, characterized in that Depend on The output control quantity is obtained, where O is the conveyor belt speed value, K E is the moisture content error quantification factor, K C K is the quantitative factor of the moisture content error change rate, p is the output scaling factor, is the moisture content error, is the moisture content error change rate.

6. The method according to claim 5, characterized in that , ,in, is the oil moisture content instruction, is the moisture content value after temperature compensation.

7. The method according to claim 6, characterized in that ,in, is the moisture content error, is the last moisture content error.

8. An intelligent moisture sensor, characterized in that: include: A data acquisition module is used to obtain the digital value output by the moisture sensor under different moisture contents; Moisture content calibration module, used to fit the digital value output by the moisture sensor to achieve moisture content curve calibration; A temperature compensation module is used to correct the calibrated moisture content using the calibrated moisture content value and the real-time temperature signal to perform temperature compensation for rapeseed at different temperatures; The speed control module is used to use the moisture content value after temperature compensation as the input of fuzzy control, compare the moisture content value after temperature compensation with the target moisture content to obtain a deviation value, make intelligent decisions based on the deviation value using fuzzy logic rules, and finally output the control amount.

9. The moisture sensor according to claim 8, characterized in that The MCU in the PCB components of the moisture sensor is industrial-grade and suitable for temperatures ranging from -60°C to 105°C. The temperature resistance of key microwave components, RC components, microwave connectors, power supplies, and interface ICs meets the requirements and has a certain degree of redundancy. The metal in the moisture sensor that contacts the material is made of stainless steel 304, and the plastic is made of PTFE. The sealing and filling materials in the moisture sensor are silicone sealing rings and 704 sealant.

10. The moisture sensor according to claim 9, characterized in that The output cable between the moisture sensor and the controller is made of pure copper and silicone sheath.

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