Method and system for controlling moisture during air flow drying process

Abstract

The moisture control method for air flow drying process comprises the following steps: comparing the reference moisture of the product with the real-time moisture of the material to obtain a first error, wherein the reference moisture is the moisture index of the product; obtaining a temperature fine-tuning value according to the first error; predicting a compensation temperature according to the real-time detected inlet air temperature and outlet air temperature and the real-time moisture of the material, wherein the compensation temperature is the theoretical outlet air temperature predicted in advance based on the real-time working condition; superimposing the temperature fine-tuning value and the compensation temperature to obtain a reference temperature; adjusting the rotating speed of the feeding mechanism for feeding to adjust the real-time moisture according to the reference temperature; and repeating the above five steps. The control method can overcome the time lag existing in the air flow drying process and improve the precision of moisture control. A control system for executing the control method is also provided.

Description

Technical Field

[0001] This invention relates to control technology for airflow drying processes, and in particular to a method for controlling moisture content during airflow drying, and a control system for implementing the method. Background Technology

[0002] Airflow drying is a process in which a screw in a feeding mechanism propels an emulsion into a drying tower, where rapidly flowing hot air dries the emulsion. Upon entering the drying tower, the emulsion is carried away by the high-speed hot air stream and moves towards the tower's outlet. Through heat exchange between the emulsion and the hot air, the emulsion is dried into powder, while the hot air absorbs moisture, becoming humid air, and its temperature decreases with the heat exchange.

[0003] Airflow drying is commonly used to dry various starches such as corn starch, potato starch, sweet potato starch, or other products that need to be converted from emulsion to powder. The moisture content of powdered products has a significant impact on product quality; therefore, moisture control is crucial throughout the entire airflow drying process.

[0004] Moisture detection exhibits a time lag during the airflow drying process. On one hand, the online moisture detection module is typically installed near the product packaging, which is far from the main drying area, resulting in a pure lag. On the other hand, the drying tower has a large space and a large thermal time constant, leading to a lag in the heat transfer process.

[0005] In existing airflow drying processes, without an online moisture detection module, an exhaust temperature is typically set based on experience, and the final moisture content of the product is controlled by manually adjusting the screw speed or using empirical rules. This control method results in low precision in moisture control.

[0006] Some airflow drying processes are equipped with online moisture detection modules to monitor real-time moisture content and compare it with reference moisture content to control the final moisture content of the product. However, due to the time lag in the airflow drying process, even online moisture detection cannot effectively improve the accuracy of moisture control. Summary of the Invention

[0007] The purpose of this invention is to provide a method for controlling moisture during airflow drying, which can overcome the time lag in the airflow drying process and improve the accuracy of moisture control.

[0008] Another objective of this invention is to provide a control system for a method of controlling moisture during airflow drying, which can overcome the time lag in the airflow drying process and improve the accuracy of moisture control.

[0009] This invention provides a method for controlling moisture during airflow drying, comprising the following steps: comparing a reference moisture content of the product with the real-time moisture content of the material to obtain a first error, wherein the reference moisture content is the moisture index of the product; obtaining a temperature fine-tuning value based on the first error; predicting a compensation temperature based on the real-time detected inlet and outlet temperatures and the real-time moisture content of the material, wherein the compensation temperature is a theoretical outlet temperature predicted in advance based on real-time operating conditions; superimposing the temperature fine-tuning value and the compensation temperature to obtain a reference temperature; adjusting the rotational speed of the feeding mechanism for feeding according to the reference temperature to adjust the real-time moisture content; and repeating the above five steps.

[0010] This method for controlling moisture during airflow drying calculates a compensation temperature based on inlet air temperature, exhaust air temperature, and real-time moisture content to pre-adjust the temperature during the drying process. It then obtains a temperature fine-tuning value by feeding back the real-time moisture content and comparing it with a reference moisture level. The reference temperature obtained by superimposing the compensation temperature and the temperature fine-tuning value is used to adjust the rotation speed of the feeding mechanism to regulate the feed rate, thereby gradually reducing the initial error. This method effectively overcomes the effects of time lag and significantly improves the accuracy of moisture control.

[0011] In another illustrative embodiment of the moisture control method during airflow drying, the steps include: obtaining a compensation temperature based on the real-time detected inlet and outlet temperatures and the real-time moisture content of the material. The compensation temperature is a theoretical outlet temperature predicted in advance based on real-time operating conditions. Specifically, this includes the following steps: obtaining a predicted moisture content using a moisture prediction model based on the inlet and outlet temperatures; obtaining a delayed moisture content based on the predicted moisture content; comparing the delayed moisture content with the real-time moisture content to obtain a second error; updating the parameters of the moisture prediction model based on the second error; and obtaining the compensation temperature based on the updated parameters of the moisture prediction model. By converting the predicted moisture content into delayed moisture content and calculating the compensation temperature based on the second error, the influence of time lag in the airflow drying process is effectively overcome, thus improving the accuracy of moisture control.

[0012] In another illustrative embodiment of the moisture control method during airflow drying, the moisture prediction model is represented by the function shown in equation (1): Equation (1) in To predict moisture, The exhaust temperature, Intake air temperature, As the exhaust temperature weight, Intake temperature weighting, The intercept of the function shown in equation (1) is given. The functional form of this model is simple and easy to calculate for predicting moisture.

[0013] In another illustrative embodiment of the moisture control method during airflow drying, the parameters of the moisture prediction model include exhaust temperature weights. Intake temperature weight The intercept of the function shown in equation (1) Exhaust temperature weighting Intake temperature weight intercept of the function Updated using equations (2), (3), and (4) respectively: Equation (2) Equation (3) Equation (4) in, For the updated exhaust temperature weighting, For the updated intake air temperature weighting, The intercept of the updated function. For real-time moisture content, , and The learning rates are the exhaust temperature, intake temperature, and the intercept of the function, respectively, and are values ​​less than 1.

[0014] This facilitates easy and quick parameter updates and the acquisition of predicted moisture levels that approximate real-time moisture.

[0015] In another illustrative embodiment of the moisture control method during airflow drying, the compensation temperature is calculated using equation (5): Equation (5) in, As the exhaust temperature weight, Intake temperature weighting, The intercept of the function. To compensate for temperature, For reference moisture content.

[0016] This facilitates the easy calculation of compensation temperature using the current parameters of the moisture prediction model.

[0017] In another illustrative embodiment of the moisture control method during airflow drying, the steps include: adjusting the rotational speed of the feeding mechanism to regulate real-time moisture based on a reference temperature; obtaining a third error by comparing the reference temperature with the exhaust temperature; adjusting the rotational speed of the feeding mechanism to regulate the exhaust temperature and real-time moisture based on the third error; and repeating the above two steps. By using the third error as the control target, the influence of time lag during airflow drying is eliminated, and the detection accuracy is high. Simultaneously, the temperature detection module for detecting the exhaust temperature has a fast response speed, accelerating the cyclic update speed of the third error. This effectively overcomes temperature and moisture fluctuations caused by air intake and / or feeding, thereby effectively improving the accuracy of moisture control.

[0018] In another illustrative embodiment of the moisture control method during airflow drying, the first error is calculated by a primary loop calculation module, and the third error is calculated by a secondary loop calculation module. The parameters of both the primary and secondary loop calculation modules are obtained through fuzzy inference. Fuzzy inference overcomes the problem of inappropriate calculation module parameters caused by changes in real-time operating conditions, thereby improving the robustness of the control.

[0019] The present invention also provides a control system for executing the above-described method for controlling moisture content during airflow drying, comprising a primary loop calculation module and a prediction device. The primary loop calculation module receives and compares the reference moisture content of the product with the real-time moisture content of the material to obtain a first error, and then obtains a temperature fine-tuning value based on the first error. The prediction device includes a moisture prediction model and a delay queue module. The moisture prediction model receives the inlet air temperature and the exhaust air temperature to obtain a predicted moisture content. The delay queue module receives the predicted moisture content and obtains a delayed moisture content, and then obtains a second error based on the delayed moisture content and the real-time moisture content. The moisture prediction model updates its parameters based on the second error and also obtains a compensation temperature based on the updated parameters.

[0020] This control system can calculate the compensation temperature based on the inlet air temperature, exhaust air temperature, and real-time moisture content to adjust the temperature of the airflow drying process in advance. It also obtains a temperature fine-tuning value by feeding back the real-time moisture content and comparing it with a reference moisture content. The reference temperature obtained by superimposing the compensation temperature and the temperature fine-tuning value is used to adjust the rotation speed of the feeding mechanism, effectively overcoming the influence of time lag and improving the accuracy of moisture control.

[0021] In another illustrative embodiment of the control system, a secondary loop calculation module is also included. The secondary loop calculation module can superimpose the temperature fine-tuning value and the compensation temperature to obtain a reference temperature, compare the reference temperature with the exhaust temperature to obtain a third error, and then obtain the rotational speed of the feeding mechanism based on the third error.

[0022] In another illustrative embodiment of the control system, the primary loop calculation module integrates a first fuzzy inference element to obtain the parameters of the primary loop calculation module through fuzzy inference; the secondary loop calculation module integrates a second fuzzy inference element to obtain the parameters of the secondary loop calculation module through fuzzy inference. Fuzzy inference overcomes the problem of inappropriate calculation module parameters caused by changes in real-time operating conditions, thereby improving the robustness of the control. Attached Figure Description

[0023] The following figures are for illustrative purposes only and do not limit the scope of the invention.

[0024] Figure 1 This is a flowchart illustrating one implementation of a method for controlling moisture during airflow drying.

[0025] Figure 2 This is a schematic diagram illustrating one embodiment of the control method.

[0026] Figure 3 for Figure 1 The diagram shows a partial flowchart of the control method.

[0027] Figure 4 This is a schematic diagram illustrating another embodiment of the control method.

[0028] Figure 5 for Figure 1 The diagram shows a partial flowchart of the control method.

[0029] Figure 6 This is a schematic diagram of the control system.

[0030] Label Explanation

[0031] 10. Primary Loop Calculation Module

[0032] 11 First Fuzzy Inference Element

[0033] 20 Prediction Devices

[0034] 21 Moisture Prediction Model

[0035] 22 Delay Queue Module

[0036] 30 Second-level loop calculation module

[0037] 31 Second Fuzzy Inference Element Detailed Implementation

[0038] To provide a clearer understanding of the technical features, objectives, and effects of the invention, specific embodiments of the invention are now described with reference to the accompanying drawings, in which the same reference numerals denote the same parts.

[0039] In this document, “illustrative” means “serving as an example, illustration or description”, and any illustration or implementation described herein as “illustrative” should not be construed as a more preferred or advantageous technical solution.

[0040] In this document, "first," "second," and "third" do not indicate their importance or order, but are only used to distinguish them from each other for the purpose of document description.

[0041] Figure 1 This is a flowchart illustrating one implementation of a method for controlling moisture during airflow drying. Figure 2 This is a schematic diagram illustrating one embodiment of the control method. See also: Figure 1 and Figure 2 The present invention provides a method for controlling moisture during airflow drying, comprising steps S10 to S60.

[0042] S10: Compare the product's reference moisture content with the material's real-time moisture content to obtain a first error, where the reference moisture content is the product's moisture index. The material's real-time moisture content can be obtained through a moisture detection module installed near the product packaging end of the drying tower; this module could be a moisture sensor. Because the product packaging end is far from the main drying area, the material's real-time moisture content lags behind the real-time operating conditions.

[0043] In the illustrative embodiment, see Figure 2 The reference moisture content of the product and the real-time moisture content of the material are input into node 1, which is, for example, a PID controller. The difference between the reference moisture content of the product and the real-time moisture content of the material is compared, with the reference moisture content of the product as a positive value and the real-time moisture content of the material as a negative value, to calculate the first error. Figure 2 (Not shown in the image).

[0044] S20: A temperature fine-tuning value is obtained based on the first error. In an illustrative embodiment, at node 1, a temperature fine-tuning value is calculated based on the first error and input to node 2, which is, for example, a PID controller.

[0045] S30: Based on the real-time detected inlet and outlet temperatures and the real-time moisture content of the material, a compensation temperature is obtained. This compensation temperature is the theoretical outlet temperature predicted in advance based on real-time operating conditions, and is acquired through node 3. Node 3 can be a model capable of predicting moisture content and calculating the compensation temperature.

[0046] The inlet air temperature is obtained by a temperature detection module, such as a temperature sensor, installed at the bottom of the drying tower. The exhaust air temperature is obtained by a temperature detection module, such as a temperature sensor, installed in the main drying area of ​​the drying tower. Because the exhaust air temperature is collected within the main drying area, its time lag relative to the actual operating conditions is not significant. However, the real-time moisture content of the material is collected from a location far from the main drying area, thus lagging behind the real-time operating conditions. The purpose of predictive compensation temperature is to adjust the airflow drying process temperature in advance.

[0047] S40: Superimpose the temperature fine-tuning value and the compensation temperature to obtain a reference temperature. Input the temperature fine-tuning value and the compensation temperature into node 2 to obtain the reference temperature. Figure 2 (Not shown in the image). In other illustrative embodiments, it can also be obtained by other means, such as manual calculation.

[0048] S50: Adjust the rotational speed of the feeding mechanism to regulate the real-time moisture content based on the reference temperature. The rotational speed of the feeding mechanism can be determined using a calculation module commonly used in the art. In other illustrative embodiments, it can also be determined by other methods, such as manual calculation. The rotational speed of the feeding mechanism determines the amount of material fed into the drying tower, which in turn affects the temperature and moisture content inside the drying tower, thereby affecting the exhaust temperature and real-time moisture content.

[0049] S60: Repeat steps S10 to S50 above. As the amount of feed gradually stabilizes, the first error can be stabilized within a small fluctuation range, and the airflow drying process operates stably under this condition. During operation, if the air intake and / or feed suddenly fluctuates, the first error will fluctuate accordingly, but as steps S10 to S50 are repeated continuously, the first error will eventually tend to stabilize.

[0050] By inputting the exhaust temperature to node 3 and combining it with the intake temperature and real-time moisture content to predict the compensation temperature, a feedforward loop is formed to adjust the temperature of the airflow drying process in advance. The real-time moisture content is fed back to node 1 to calculate the temperature fine-tuning value. The compensation temperature and the temperature fine-tuning value are then superimposed to obtain a reference temperature. This reference temperature is then used to adjust the rotation speed of the feeding mechanism, thereby regulating the amount of material thrown and influencing the exhaust temperature and real-time moisture content. By cyclically repeating steps S10 to S50, the first error is stabilized within a small fluctuation range, forming a primary loop. The control objective of the primary loop is the difference between the real-time moisture content and the product's reference moisture content, i.e., the first error. The synergistic effect of the feedforward loop and the primary loop enables the drying process to overcome the effects of time lag, maintaining a stable real-time moisture content in the material.

[0051] This method for controlling moisture during airflow drying calculates a compensation temperature based on inlet air temperature, exhaust air temperature, and real-time moisture content to pre-adjust the temperature during the drying process. It then obtains a temperature fine-tuning value by feeding back the real-time moisture content and comparing it with a reference moisture level. The reference temperature obtained by superimposing the compensation temperature and the temperature fine-tuning value is used to adjust the rotation speed of the feeding mechanism to regulate the feed rate, thereby gradually reducing the initial error. This method effectively overcomes the effects of time lag and significantly improves the accuracy of moisture control.

[0052] Figure 3 for Figure 1 The diagram shows a partial flowchart of the control method. Figure 4 This is a schematic diagram illustrating another illustrative embodiment of the control method. See also: Figure 3 and Figure 4 Step S30 specifically includes steps S31 to S35.

[0053] S31: Based on the intake and exhaust temperatures, a predicted moisture content is obtained using a moisture prediction model. The intake and exhaust temperatures obtained by the temperature detection module are input to node 3. In this illustrative embodiment, node 3 is the moisture prediction model. The moisture prediction model is represented by the function shown in equation (1): Equation (1) in To predict moisture, The exhaust temperature, Intake air temperature, As the exhaust temperature weight, Intake temperature weighting, Let be the intercept of the function shown in equation (1).

[0054] The moisture prediction model is y=kx+b It is a function of type [type], which has a simple function form and is easy to calculate and predict moisture.

[0055] S32: Based on the predicted moisture content, a delayed moisture content is obtained. See the illustrative embodiment below. Figure 4 The predicted moisture content will be obtained The input is sent to node 4, which calculates a delayed moisture level. Node 4 is the delayed queue module. The delayed moisture level lags behind the predicted moisture level only in time; its calculation method and numerical value are consistent with the predicted moisture level. Figure 4 The delayed moisture content is not shown; only the predicted moisture content is displayed.

[0056] S33: Compare the delayed moisture content with the real-time moisture content to obtain a second error. In the calculation of the second error, the delayed moisture content is used as a positive value and the real-time moisture content is used as a negative value input to node 4 for calculation.

[0057] S34: Update the parameters of the moisture prediction model based on the second error. The parameters of the moisture prediction model include the exhaust temperature weight. Intake temperature weight The intercept of the function shown in equation (1) Exhaust temperature weighting Intake temperature weight intercept of the function Updated using equations (2), (3), and (4) respectively: Equation (2) Equation (3) Equation (4) in, For the updated exhaust temperature weighting, For the updated intake air temperature weighting, The intercept of the updated function. Real-time moisture content; , and The learning rates are the exhaust temperature, intake temperature, and the intercept of the function, respectively, and are values ​​less than 1. , and You can input the corresponding values ​​based on the specific actual working conditions to perform the calculation.

[0058] Within this field, the updated parameters of the moisture prediction model , and , and .

[0059] Node 3 is based on the second error of the input, i.e. Update the parameters of the moisture prediction model , and Then calculate the updated parameters. and The value, and so on in a cycle, eventually makes... The water level tends to stabilize, at which point the predicted water level calculated by the water prediction model approximates the real-time water level.

[0060] S35: The compensation temperature is obtained based on the updated moisture prediction model parameters. The compensation temperature is expressed by equation (5): Equation (5) in, As the exhaust temperature weight, Intake temperature weighting, The intercept of the function shown in equation (1) is... To compensate for temperature, For reference moisture content.

[0061] use Parameters after stabilization , and The compensation temperature will be calculated using the moisture input node 3 as a reference, effectively overcoming the effect of time lag during the airflow drying process. It is important to note that during the prediction of the compensation temperature, the parameters... , and The update frequency is the same as or lower than the prediction frequency of the compensation temperature, that is, each updated group , and This method uses at least one compensation temperature to predict, thereby reducing the computational load on the model and facilitating faster results. Each compensation temperature prediction uses the latest set of values ​​from the current moisture prediction model. , and parameter.

[0062] By calculating the predicted moisture content and the delayed moisture content, the predicted moisture content is made closer to the real-time moisture content while updating the parameters of the moisture prediction model. Then, the reference moisture content is used to calculate the compensation temperature, so as to adjust the temperature of the airflow drying process in advance, effectively overcome the influence of time lag, and improve the accuracy of moisture control.

[0063] Figure 5 for Figure 1 A partial flowchart of the control method is shown. See also the illustrative embodiment. Figure 4 and Figure 5 Step S50 specifically includes steps S51 to S53.

[0064] S51: A third error is obtained by comparing the reference temperature with the exhaust temperature. See also Figure 4 The reference temperature is obtained by adding the temperature fine-tuning value and the compensation temperature together. Figure 4 (Not shown in the diagram), the third error is calculated in node 2 using the temperature fine-tuning value and compensation temperature as positive values ​​and the exhaust temperature as a negative value. Figure 4 (Not shown in the image).

[0065] S52: Based on the third error, the feeding mechanism adjusts the rotational speed of the feeding mechanism for feeding. The feeding mechanism includes, for example, a variable frequency screw. The frequency of the variable frequency screw is obtained based on the third error to adjust the rotational speed of the feeding mechanism, thereby adjusting the amount of feed to affect the temperature and moisture content inside the drying tower, and thus affecting the exhaust temperature and real-time moisture content.

[0066] S53: Repeat steps S51 and S52 to adjust the exhaust temperature and real-time moisture content. It should be noted that after executing step S53, on the one hand, the temperature detection module feeds back the real-time detected exhaust temperature to step S51 to calculate the third error; on the other hand, the moisture detection module feeds back the real-time detected moisture content to step S10 to calculate the first error.

[0067] Strong interference exists during the airflow drying process: on the one hand, the flow rate and concentration of the emulsion and the tonnage of the crushing disc fluctuate greatly due to the influence of the upstream production process; on the other hand, the temperature and moisture of the heated air fluctuate and are uncertain due to the changes in the temperature and flow rate of the waste heat steam from the thermal power plant and the changes in weather temperature and moisture.

[0068] If the intake air and / or feed are stable, the third error can stabilize within a small fluctuation range after several repetitions of steps S51 to S53, and the airflow drying process operates stably under this condition. During operation, if the intake air and / or feed suddenly fluctuates, the third error will respond quickly and fluctuate accordingly. The temperature detection module for detecting exhaust temperature has a fast response speed, which helps to accelerate the cyclical update of the third error; that is, as steps S51 to S52 are repeated continuously, the third error will quickly tend to stabilize. At the same time, since the temperature detection module for detecting exhaust temperature is installed in the main drying area, the effect of exhaust temperature on time lag is not significant and the detection accuracy is high, thereby improving the accuracy of moisture control.

[0069] In summary, see Figure 4The process involves several steps: First, the exhaust temperature is input to node 3 and combined with the intake temperature and real-time moisture content from node 4 to predict the compensation temperature. This forms a feedforward loop to adjust the temperature of the airflow drying process in advance. Second, the exhaust temperature is fed back to node 2 and compared with a reference temperature obtained by superimposing the compensation temperature and the temperature fine-tuning value. This results in a third error, which is used to adjust the rotation speed of the feeding mechanism, thereby affecting the exhaust temperature and real-time moisture content. By cyclically repeating steps S51 to S52, the third error is stabilized within a small fluctuation range, forming a second-level loop. The second-level loop uses the third error as the control target. Third, the real-time moisture content is fed back to node 1 to calculate the temperature fine-tuning value. The compensation temperature and the temperature fine-tuning value are then superimposed to obtain a reference temperature. The difference between the reference temperature and the exhaust temperature, i.e., the third error, is used to adjust the rotation speed of the feeding mechanism to regulate the amount of material thrown, thereby affecting the exhaust temperature and real-time moisture content. By cyclically repeating steps S10 to S50, the first error is stabilized within a small fluctuation range, forming a first-level loop. The first-level loop uses the first error as the control target.

[0070] It should be noted that the secondary loop operates at a higher speed than the primary loop, and therefore its response speed to fluctuations in air intake and / or feed is higher than that of the primary loop. Thus, the secondary loop can quickly eliminate the fluctuations in the third error caused by fluctuations in air intake and / or feed, ensuring the stable operation of the entire airflow drying process.

[0071] The synergistic effect of the feedforward loop, primary loop, and secondary loop enables the airflow drying process to overcome the effects of time lag, maintaining a stable real-time moisture content in the product, which is close to the product's reference moisture content. Secondly, the control target of the secondary loop is the third error, namely the difference between the reference temperature and the exhaust temperature. The temperature detection module for detecting the exhaust temperature has a fast response speed, facilitating faster cyclic updates of the third error. The secondary loop can quickly eliminate fluctuations in the third error caused by fluctuations in intake air and / or feed, ensuring the smooth operation of the entire airflow drying process. Thirdly, since the exhaust temperature detection module is installed in the main drying area, the effect of exhaust temperature on time lag is minimal, and the detection accuracy is high. With the synergy of the primary loop and the feedforward loop, the accuracy of moisture control can be effectively improved.

[0072] In the illustrative implementation, the first error is calculated by a first-level loop calculation module, as described above (node ​​1), and the third error is calculated by a second-level loop calculation module, as described above (node ​​2). The first-level and second-level loop calculation modules are, for example, PID controllers. The parameters of both the first-level and second-level loop calculation modules are obtained through fuzzy inference. Fuzzy inference overcomes the problem of inappropriate parameters caused by changes in real-time operating conditions, thereby improving the robustness of the control.

[0073] Figure 6 This is a schematic diagram of the control system. See also... Figure 6The present invention also provides a control system 100 for executing the above-described method for controlling moisture content during airflow drying, comprising: a primary loop calculation module 10 and a prediction device 20. The primary loop calculation module 10 is, for example, a PID controller.

[0074] The primary loop calculation module 10 can receive the reference moisture content of the product and the real-time moisture content of the material and compare them to obtain the first error, and then obtain the temperature fine-tuning value based on the first error.

[0075] The prediction device 20 includes a moisture prediction model 21 and a delay queue module 22.

[0076] Moisture prediction model 21 can obtain a predicted moisture content based on the intake and exhaust temperatures. Delay queue module 22 can receive the predicted moisture content and obtain a delayed moisture content, then calculate a second error based on the delayed moisture content and the real-time moisture content. The moisture prediction model can update its parameters based on the second error and also obtain a compensation temperature based on the updated parameters.

[0077] In the illustrative embodiment, the control system further includes a secondary loop calculation module 30. The secondary loop calculation module 30 can superimpose the temperature fine-tuning value and the compensation temperature to obtain a reference temperature, compare the reference temperature with the exhaust temperature to obtain a third error, and then obtain the rotational speed of the feeding mechanism based on the third error. The secondary loop calculation module 30 is, for example, a PID controller.

[0078] In the illustrative embodiment, the primary loop calculation module 10 integrates a first fuzzy inference element 11 to obtain the parameters of the primary loop calculation module 10 through fuzzy inference. The secondary loop calculation module 30 integrates a second fuzzy inference element 31 to obtain the parameters of the secondary loop calculation module 30 through fuzzy inference. Fuzzy inference overcomes the problem of inappropriate calculation module parameters caused by changes in real-time operating conditions, thereby improving the robustness of control.

[0079] It should be understood that although this specification is described according to various embodiments, not every embodiment contains only one independent technical solution. This way of describing the specification is only for clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

[0080] The detailed descriptions listed above are merely specific descriptions of feasible embodiments of the present invention and are not intended to limit the scope of protection of the present invention. All equivalent implementation schemes or modifications made without departing from the spirit of the present invention, such as combinations, divisions or repetitions of features, should be included within the scope of protection of the present invention.

Claims

1. A method for controlling moisture content during airflow drying, characterized in that, Includes the following steps: The reference moisture content of the product is compared with the real-time moisture content of the material to obtain a first error, wherein the reference moisture content is the moisture index of the product. A temperature fine-tuning value is obtained based on the first error; A compensation temperature is obtained based on the real-time detected intake and exhaust temperatures and the real-time moisture content of the material. The compensation temperature is the theoretical exhaust temperature predicted in advance based on real-time operating conditions. A reference temperature is obtained by superimposing the temperature fine-tuning value and the compensation temperature; The real-time moisture content is adjusted by adjusting the rotation speed of the feeding mechanism based on the reference temperature. as well as Repeat the above five steps.

2. The control method according to claim 1, characterized in that, Steps: Based on the real-time detected inlet and outlet temperatures and the real-time moisture content of the material, a compensation temperature is obtained. This compensation temperature is a theoretical outlet temperature predicted in advance based on real-time operating conditions. Specifically, the steps include: Based on the intake air temperature and the exhaust air temperature, a predicted moisture content is calculated using a moisture prediction model. Based on the predicted moisture content, a delayed moisture content is obtained; By comparing the delayed moisture content with the real-time moisture content, a second error is obtained; Update the parameters of the moisture prediction model based on the second error; as well as The compensated temperature is obtained based on the parameters of the updated moisture prediction model.

3. The control method according to claim 2, characterized in that, The moisture prediction model is represented by the function shown in equation (1): Equation (1) in To predict moisture, The exhaust temperature, Intake air temperature, As the exhaust temperature weight, Intake temperature weighting, Let be the intercept of the function shown in equation (1).

4. The control method according to claim 3, characterized in that, The parameters of the moisture prediction model include exhaust temperature weights. Intake temperature weight The intercept of the function shown in equation (1) Exhaust temperature weighting Intake temperature weight intercept of the function Updated using equations (2), (3), and (4) respectively: Equation (2) Equation (3) Equation (4) in, For the updated exhaust temperature weighting, For the updated intake air temperature weighting, The intercept of the updated function. For real-time moisture content, , and The learning rates for the exhaust temperature, the intake temperature, and the intercept of the function are respectively, and are values ​​less than 1.

5. The control method according to claim 4, characterized in that, The compensation temperature is calculated using equation (5): Equation (5) in, As the exhaust temperature weight, Intake temperature weighting, The intercept of the function. The compensation temperature is... The reference moisture content is [value missing].

6. The control method according to claim 1, characterized in that, Steps: Based on the reference temperature, adjust the rotation speed of the feeding mechanism to regulate the real-time moisture content, specifically including the following steps: A third error is obtained by comparing the reference temperature with the exhaust temperature; Based on the third error, the rotational speed of the feeding mechanism is adjusted to regulate the exhaust temperature and the real-time moisture content; and Repeat the two steps above.

7. The control method according to claim 6, characterized in that, The first error is calculated by a first-level loop calculation module, and the third error is calculated by a second-level loop calculation module. The parameters of the first-level loop calculation module and the second-level loop calculation module are obtained through fuzzy inference.

8. A control system for performing a method for controlling moisture content during the airflow drying process according to any one of claims 1 to 7, characterized in that, include: A primary loop calculation module (10) is capable of receiving and comparing the reference moisture content of the product with the real-time moisture content of the material to obtain the first error, and then obtaining the temperature fine-tuning value based on the first error. as well as A prediction device (20) comprising: A moisture prediction model (21) is capable of receiving the intake air temperature and the exhaust air temperature and obtaining a predicted moisture content, and A delayed queue module (22) is able to receive the predicted moisture and obtain a delayed moisture, and then obtain a second error based on the delayed moisture and the real-time moisture. The moisture prediction model can update its parameters based on the second error, and can also obtain the compensation temperature based on the updated parameters.

9. The control system according to claim 8, characterized in that, It also includes a secondary loop calculation module (30), which can superimpose the temperature fine-tuning value and the compensation temperature to obtain a reference temperature, receive the feedback of the exhaust temperature and compare it with the reference temperature to obtain the third error, and then obtain the rotation speed of the feeding mechanism based on the third error.

10. The control system according to claim 9, characterized in that, The first-level loop calculation module (10) integrates a first fuzzy inference element (11) to obtain the parameters of the first-level loop calculation module (10) through fuzzy inference; the second-level loop calculation module (30) integrates a second fuzzy inference element (31) to obtain the parameters of the second-level loop calculation module (30) through fuzzy inference.

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