Method and system for controlling the temperature of an internal heating fluidized bed dryer

Abstract

The application discloses a kind of internal heating type fluidized bed drying temperature control method and system, by mechanism deduction and step response modeling, obtain the transfer function of first-order inertia plus pure lag link, the coupling between two heating pipes is regarded as interference, observer is added in control loop to eliminate dynamic coupling, cooperate with the control strategy of cascade control, effectively reduce the overshoot of internal heating type fluidized bed temperature (T1, T2), reduce steady-state error, improve the stability of production, significantly reduce the labor intensity of operator.The method solves the problem of separate isolated adjustment of each section temperature of the fluidized bed, poor drying effect;Solve the problem that the internal heating type fluidized bed drying process cannot be put into automatic control under full working condition;Solve the problem of dynamically adjusting each section bed temperature according to the demand of moisture content of drying material and accurately controlling;Solve the problem that when winter temperature drops, the steam pipeline of system, the condensate of tube is large, and the bed temperature is out of control and drops.

Description

Technical Field

[0001] This application relates to the field of temperature control technology for internally heated fluidized bed drying, and in particular to a method and system for controlling the temperature of internally heated fluidized bed drying. Background Technology

[0002] The drying process in an internally heated fluidized bed is a nonlinear process with high inertia, pure time delay, multivariable coupling, and time-varying parameters. Temperature control is primarily based on simple loop control, supplemented by manual control, making the entire control process heavily reliant on human intervention and resulting in poor timeliness of temperature regulation. Currently, both domestically and internationally, traditional PID controllers are mainly used to control the drying process. While this effectively controls the fluidized bed temperature, it fails to consider multivariable coupling factors. In actual internally heated fluidized beds, there are two sections with more than a dozen sets of heating tubes, and these tubes influence each other. Therefore, the temperature at any point on the fluidized bed is the result of the superposition of the thermal effects of multiple heating tubes. In other words, there is coupling between the heating tubes during heat transfer, making the entire system a multivariable, dynamically coupled system. This coupling alters the system characteristics, making PID controller parameter tuning difficult. When the coupling factors are strong, it can lead to unstable fluidized bed temperature control, compromising operational safety and stability. Because bed temperature control requires frequent manual operation and intervention, and because adjustments to constantly changing operating conditions are not timely, the operation of internally heated fluidized beds is not very stable. For example, when the load fluctuates, the temperature cannot keep up, causing large fluctuations in the moisture content of the dried product. These problems will have varying degrees of impact on the safe and economical operation of the entire fluidized bed equipment.

[0003] like Figure 2 As shown, the existing internally heated fluidized bed temperature (T1, T2) control mainly relies on controlling the main steam inlet regulating valve 1 and the secondary steam inlet regulating valve 2 when the material entering the bed or the steam source changes. Current technologies mostly involve independent control of bed temperatures T1 and T2, primarily using single-loop control with manual intervention.

[0004] When multiple input factors change, such as variations in the production load of the feed material front-end device, adjustments to the thermal response load, changes in steam pressure, and changes in the physical properties of the material, the bed temperature response is relatively lagging. Due to the single-loop PID algorithm control, its anti-interference capability is very poor, and the control has completely deviated from the scope of conventional automated control. This results in large fluctuations in bed temperature, which can even trigger interlocking alarms in severe cases. Upon discovery, operators switch from single-loop automatic to manual intervention, frequently adjusting the feed steam main regulating valve 1 and the feed second-stage steam regulating valve 2. This not only increases the workload for operators but also, if improperly operated, can sometimes cause frequent system oscillations. Consequently, the stability and uniformity of the control cannot be reliably guaranteed in the long term. Summary of the Invention

[0005] Based on this, in order to address the above-mentioned technical problems, a method and system for controlling the temperature of an internally heated fluidized bed dryer is provided to solve the problems that the existing methods have a relatively slow bed temperature response, resulting in high labor intensity for operators, and improper operation can sometimes lead to frequent system oscillations and fluctuations. The stability and uniformity of the existing control cannot be reliably guaranteed in the long term.

[0006] In a first aspect, a method for controlling the temperature of an internally heated fluidized bed dryer, the method comprising:

[0007] Using the material drying moisture content as a given target and the fluidized bed temperature as a follow-up target, a cascade process closed-loop control is performed. The dynamic coupling between the multiple heating tubes of the fluidized bed is regarded as interference, and an observer is added to the closed-loop control loop to eliminate the dynamic coupling.

[0008] The prediction model in the observer is a transfer function model of a first-order inertial + pure time-delay element obtained through mechanism deduction and step response modeling. The model parameters of the transfer function model are obtained through mechanism deduction and step response modeling.

[0009] Secondly, a control system for the temperature of an internally heated fluidized bed dryer, the system comprising:

[0010] The system includes an advanced moisture content control master regulator G. C1 (s), Temperature Advanced Control Sub-Regulator G C2 (s), Execution object G v (s), heating element object G 02 (s), fluidized bed object G 01 (s), Observer G s (s), Drying moisture content analyzer transmitter G m1 (s) and temperature transmitter G m2 (s);

[0011] The advanced moisture content control master regulator G C1 The input terminal of (s) is used to input the set value of the material drying moisture content, and the advanced moisture content control main regulator G... C1 The output terminal of (s) is connected to the temperature advanced control sub-regulator G C2 The input terminal of (s) is connected to the temperature advanced control sub-regulator G. C2 The output of (s) is respectively connected to the execution object G v The input terminal of (s) and the observer G s The first input terminal of (s) is connected, and the execution object G is connected. v The output of (s) is respectively connected to the observer G s The second input terminal of (s) and the heating tube object G 02(s) input terminal connection, the heating tube object G 02 The output of (s) is respectively connected to the fluidized bed object G 01 (s) input terminal and the temperature transmitter G m2 The input terminal of (s) is connected, and the fluidized bed object G 01 The output terminal of (s) is connected to the transmitter G of the drying moisture content analyzer. m1 The input terminal of (s) is connected;

[0012] The observer G s The output of (s) is connected to the execution object G v The input terminal of (s) is connected to eliminate dynamic coupling between multiple heating tubes in the fluidized bed;

[0013] The temperature transmitter G m2 The output terminal of (s) is connected to the temperature advanced control sub-regulator G C2 The input terminal of (s) is connected to send the current temperature Y2(s) of the fluidized bed to the temperature advanced control sub-regulator G. C2 (s) in;

[0014] The dry moisture content analyzer transmitter G m1 The output of (s) is connected to the advanced moisture content control master regulator G. C1 The input terminal of (s) is connected to send the current material drying moisture content Y1(s) to the advanced moisture content control main regulator G. C1 (s)

[0015] In the above scheme, optionally, the system further includes a first interference signal F1(s) and a second interference signal F2(s);

[0016] The first interference signal F1(s) is input to the fluidized bed object G. 01 The second interference signal F2(s) is input to the heating tube object G. 02 The input terminal of (s).

[0017] In the above scheme, optionally, the advanced moisture content control master regulator G C1 (s) is used to input the fluidized bed temperature setpoint X2(s) to the temperature advanced control sub-regulator G. C2 (s) in;

[0018] Wherein, the fluidized bed temperature setpoint X2(s) = [T1, T2] T T1 and T2 are the bottom temperatures of the first and second stages of the fluidized bed, respectively.

[0019] In the above scheme, optionally, the temperature advanced control sub-regulator G C2 (s) is used to input the control quantity U(s) of the multi-input multi-output system to the execution object G respectively. v (s) and the observer G s (s) in;

[0020] Wherein, the control quantity U(s) of the multiple-input multiple-output system is [u1, u2]. T u1 and u2 are control signals for the two heating tubes of the fluidized bed, respectively.

[0021] In the above scheme, optionally, the observer G s (s) includes the actual controlled object λ and the prediction model. Signal filter μ and inverse of prediction model

[0022] The observer G s The input signal U of (s) is respectively input to the actual controlled object λ and the prediction model. In the process, the output y of the actual controlled object λ, the coupling term f, and the output noise θ of the execution object of the multi-input multi-output system are combined and then input into the prediction model. In the signal filter μ, the output of the signal filter μ is connected to the inverse of the prediction model. The input terminal is connected, and the inverse of the prediction model is... The output terminals are respectively connected to the input terminals of the actual controlled object λ and the prediction model. The input terminal is connected.

[0023] In the above scheme, it is further optional that, in the observer G s In (s), y = G u UG f fG θ θ;

[0024] in, This represents the closed-loop transfer function between the input U and the output y. This represents the transfer function between the coupling quantity f and the output y. This represents the transfer function between the noise θ and the output y.

[0025] The present invention has at least the following beneficial effects:

[0026] This invention, based on further analysis and research into the problems of existing technologies, recognizes that existing methods suffer from relatively slow bed temperature response, resulting in high labor intensity for operators. Furthermore, improper operation can sometimes lead to frequent system oscillations and fluctuations, and the stability and uniformity of existing control systems cannot be reliably guaranteed in the long term. This application, through mechanism deduction and step response modeling, obtains the transfer function of a first-order inertial plus pure time-delay element. Treating the coupling between the two heating tubes as interference, an observer is added to the control loop to eliminate dynamic coupling. Combined with a cascade control strategy, this effectively reduces the overshoot of the (two-section) internally heated fluidized bed temperature (T1, T2), reduces steady-state error, improves production stability, and significantly reduces the labor intensity of operators. The method described in this application solves the problems of poor drying effect due to isolated temperature adjustment of each section of the fluidized bed; it solves the problem that the internal heating fluidized bed drying process cannot be automatically controlled under all operating conditions; it solves the problem of dynamically adjusting and precisely controlling the bed temperature of each section according to the moisture content requirements of the dried material; it solves the problem of uncontrolled bed temperature drop due to large condensate flow in the system's steam pipes and tubes when the winter temperature drops; and it solves the problem of excessive energy consumption during the operation of the internal heating fluidized bed dryer. Attached Figure Description

[0027] Figure 1 A schematic flowchart illustrating a method for controlling the temperature of an internally heated fluidized bed dryer according to an embodiment of the present invention;

[0028] Figure 2 A relational distribution diagram of an internally heated fluidized bed dryer provided in one embodiment of the present invention;

[0029] Figure 3 A flowchart illustrating the transfer function process of a heating tube according to an embodiment of the present invention;

[0030] Figure 4 This is a block diagram of a cascade control system provided in one embodiment of the present invention;

[0031] Figure 5 A block diagram of an observer system provided in one embodiment of the present invention;

[0032] Figure 6 This is a block diagram of the inverse equivalent processing of the prediction model provided in one embodiment of the present invention;

[0033] Figure 7 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0035] The method for controlling the temperature of the internally heated fluidized bed dryer provided in this application, such as Figure 1 As shown, the process includes the following steps: taking the material drying moisture content as a given target and the fluidized bed temperature as a follow-up target, performing cascade process closed-loop control, and treating the dynamic coupling between the multiple heating tubes of the fluidized bed as interference, adding an observer to the closed-loop control loop to eliminate the dynamic coupling;

[0036] The prediction model in the observer is a transfer function model of a first-order inertial + pure time-delay element obtained through mechanism deduction and step response modeling. The model parameters of the transfer function model are obtained through mechanism deduction and step response modeling.

[0037] In one embodiment, a first control signal and a second control signal are received from the two heating tubes of the internally heated fluidized bed, and the output temperature values ​​are a first temperature value and a second temperature value. The target transfer function is determined by the static gain K. ij With dynamic characteristics G ij The static relationship, expressed by the partial differential method, is as follows:

[0038]

[0039] Wherein, u1 and u2 are the first control signal and the second control signal, respectively, and y1 and y2 are the first temperature value and the second temperature value, respectively, K ij This represents the open-loop gain between the i-th controlled variable and the j-th control variable;

[0040] The internally heated fluidized bed dryer is controlled by a first-order inertial plus pure time-delay process. In this context, K represents the open-loop gain, and T represents the open-loop gain. P Let τ be the time constant, and τ be the pure time delay.

[0041] Through mechanism deduction and step response modeling, G is obtained. ij The model parameters, where G ij This represents the dynamic characteristic of the i-th controlled variable relative to the j-th control variable;

[0042] A target observer is added to the internally heated fluidized bed drying control loop to eliminate dynamic coupling, and the internally heated fluidized bed drying temperature is controlled in conjunction with a preset cascade control strategy.

[0043] In this embodiment, the preset cascade control process includes:

[0044] The material drying moisture content setpoint is connected to the advanced moisture content control main controller via an input signal. The main controller generates a corresponding control signal based on the difference between the fluidized bed temperature setpoint and the actual measured moisture content. The control signal of the main controller is connected to the execution objects of the multiple input multiple output system. The execution objects can be heating tubes and fluidized beds, which adjust the temperature and other operating parameters according to the control signals.

[0045] The output signal of the temperature transmitter represents the deviation between the actual temperature and the temperature setpoint. This signal is processed by an algorithm inside the secondary controller to generate a corresponding adjustment signal. The adjustment signal is connected to the execution object of the multiple input multiple output system to adjust the temperature and other operating parameters. The primary and secondary disturbances of the cascade system are connected to the observer through external input signals.

[0046] In this embodiment, the observer is used to monitor and estimate interference signals and provide corresponding compensation signals; in the preset cascade control process, the main controller is used to control the moisture content of the dried material, and the secondary controller is used to control the temperature; the execution object and the observer are used for actual operation and interference compensation.

[0047] In this embodiment, adding a target observer to the internally heated fluidized bed drying control loop to eliminate dynamic coupling includes:

[0048] The observer input is connected to the actual controlled object, which includes two heating tubes and other components. The input signal represents the operating parameters to be controlled.

[0049] The actual controlled object transmits its output signal to the input of the observer system through a coupling term;

[0050] The predictive model is a core component of the observer system. It takes the input signal of the actual controlled object as input and generates the predictive model output.

[0051] The observer output is the output signal of the observer system, representing the estimated value of the actual output of the controlled object;

[0052] The output noise of the execution object in a multiple-input multiple-output system is connected to the input of the observer system through a signal filter, which is used to filter the noise.

[0053] The inverse of the prediction model is generated by taking the output of the prediction model as input to the signal filter.

[0054] In this embodiment, based on the elimination of dynamic coupling by adding a target observer to the process control loop of the internally heated fluidized bed dryer, Equation 1 is derived:

[0055]

[0056] Wherein, U is the observer input, and λ is the actual controlled object. Here, y is the prediction model, f is the observer output, θ is the coupling term, θ is the output noise of the executing object in the multi-input multi-output system, and μ is the signal filter. It is the inverse of the prediction model.

[0057] In this embodiment, Equation 2 is generated based on Equation 1: y = G u UG f fG θ θ where, The closed-loop transfer function represents the relationship between the observer input U and the observer output y. This represents the transfer function between the coupling quantity f and the observer output y. This represents the transfer function between the output noise θ and the observer output y.

[0058] In this embodiment, the and In the middle, μ = 1, G f =0, achieving the function of decoupling;

[0059] The filter order is either 1 or 2, τ θ Less than the system process time constant T P ;

[0060] Equivalent processing transfer function Z(s) is a first-order inertial element, τ Z It takes values ​​from 0.1τ to τ;

[0061]

[0062] K Z Take (0.2~0.5)K Zmax ;

[0063] By the G ij Model Known We obtain Z1(s) and Z2(s).

[0064] In one embodiment, such as Figure 2The diagram shows a schematic of an internally heated fluidized bed dryer. The temperature of the first bed is typically controlled at 50–56℃, and the temperature of the second bed is controlled below 68℃. Under stable hot air temperature conditions, the bed temperature is stabilized by controlling the steam pressure and temperature entering the internal heating tube bundle. Low-pressure steam below 0.2 MPa is generally used. Before entering the internal heating tubes, hot water is injected into the steam to reduce the steam pressure and temperature, maintaining the steam pressure at approximately 0.04 MPa and the temperature at approximately 105℃. Too low a bed temperature will not achieve the desired drying effect; too high a temperature will cause over-drying of the product, generating static electricity, which will create difficulties for subsequent transportation and packaging. In particular, it will cause scaling on the outer wall of the heating tube bundle, affecting heat exchange efficiency and posing a significant safety hazard to production operations.

[0065] In this embodiment, the system inputs are control signals u1 and u2 for two heating tubes, and the outputs are temperature values ​​y1 and y2. The transfer function of the process is determined by the static gain K. ij With dynamic characteristics G ij Composition. For simplicity, this discussion focuses on the static case, where the static relationship of the process can be expressed using partial differential equations as Equation 1-1.

[0066]

[0067] In this embodiment, K ij G represents the open-loop gain between the i-th controlled variable and the j-th control variable. ij This represents the dynamic characteristic of the i-th controlled variable relative to the j-th control variable.

[0068] In this embodiment, as Figure 3 The block diagram of the transfer function of the heating tube is shown. The two heating tubes are dynamically coupled to each other, which makes it difficult to tune the parameters of the traditional PID controller. It has poor adaptability to the continuous changes in process parameters, and the control process is sometimes good and sometimes bad, resulting in a very unsatisfactory effect.

[0069] This internally heated fluidized bed dryer is a high-order system in process control, and it can be approximated by a first-order inertial element plus a pure time-delay element. K is the open-loop gain, T P Let τ be the time constant, and τ be the pure time delay.

[0070] Through mechanism deduction and step response modeling, G can be obtained. ij Model parameters.

[0071] In this embodiment, the overall technical solution concept is as follows:

[0072] In practical engineering, when fluidized bed temperature coupling is very severe, and satisfactory control cannot be achieved regardless of the variable pairing, decoupling design is necessary. Decoupling design involves designing a decoupling device so that a change in any one control variable only affects the paired controlled variable and not the controlled variables in other control loops. This decomposes the multivariable dynamically coupled control system into several independent single-variable control systems.

[0073] This design comprises two main parts: first, the cascade control section, which selects a commonly used high-performance cascade process control system to achieve the desired control effect; second, the dynamic decoupling section, which uses an observer-based method to decouple the internal components of the system.

[0074] Control scheme: cascade control, such as Figure 4 The cascade process control block diagram shown is illustrated, where X1(s) is the setpoint for the moisture content of the material during drying, and X2(s) = [T1, T2]. T It is the fluidized bed temperature setpoint, G C1 (s) is the advanced moisture content control main regulator, G C2 (s) is the advanced temperature control secondary regulator, U(s) = [u1, u2] T G is the control variable for a multiple-input multiple-output (MIMO) system. v (s) = [o1, o1] C It is the execution object of a multiple-input multiple-output system, G o2 (s) = [a, b] is the heating tube object, G 01 Y(s) is the fluidized bed object, and Y1(s) and Y2(s) correspond to the system outputs of the main and auxiliary objects, respectively. m1 (s) is the transmitter of the dry moisture content analyzer, G m2 (s) = [T1, T2] T It is a temperature transmitter. F1(s) is the primary disturbance of the cascade system, F2(s) is the secondary disturbance of the cascade system, and G... s (s) is an observer for dynamically coupled systems.

[0075] In this embodiment, decoupling control:

[0076] A viewer-based method is used to dynamically decouple the two heating tubes. The viewer G S The transfer function flowchart of (s) is as follows: Figure 5 As shown.

[0077] U is the observer input, and λ is the actual controlled object. Here, y is the prediction model, f is the observer output, θ is the coupling term, θ is the output noise of the executing object in the multi-input multi-output system, and μ is the signal filter. It is the inverse of the prediction model, from Figure 5It can be known that:

[0078]

[0079] From equation (3-1), we can obtain:

[0080] y = G u UG f fG θ θ (3-2)

[0081] In the above formula This represents the closed-loop transfer function between the input U and the output y. This represents the transfer function between the coupling quantity f and the output y. This represents the transfer function between the noise θ and the output y.

[0082] When μ = 1, G f =0, then the decoupling function is achieved; G θ =1, noise still exists. In actual production process, It cannot be achieved through a physical mathematical model; an appropriate μ must be selected in the feedback loop before dynamic decoupling control can be achieved.

[0083] make The filter order is either 1 or 2, τ θ It should be less than the system process time constant T. P , generally τ θ for.

[0084] 0.01T P ~0.1T P The transfer function of the predicted model is for a first-order inertial pure time-delay system. e τs It is the observer's advance prediction of the system, but it cannot be derived from the traditional control process; it can be converted into something like... Figure 6 The method is to handle it.

[0085] Transfer function of equivalent processing block diagram Z(s) is a first-order inertial element, τ Z The value of K is generally between 0.1τ and τ. Z The value of G needs to be large enough to ensure Z The stability of (s) is such that 1+Z(s)e -τs =0, then: K Z It's impossible to take the extreme value; generally, it's taken as (0.2~0.5)K. Zmax .

[0086] By Gij Model Known Z1(s) and Z2(s) can then be obtained.

[0087] In this embodiment, the following problems were solved: poor drying effect due to isolated temperature adjustment of each section of the fluidized bed; inability to implement automatic control of the internally heated fluidized bed drying process under full operating conditions; dynamic adjustment and precise control of the bed temperature of each section according to the moisture content requirements of the dried material; uncontrolled temperature drop due to large condensate flow in the system's steam pipes and tubes when the temperature drops in winter; and excessive energy consumption during the operation of the internally heated fluidized bed dryer.

[0088] In one embodiment, a temperature control system for an internally heated fluidized bed dryer is provided, the system comprising:

[0089] The system includes an advanced moisture content control master regulator G. C1 (s), Temperature Advanced Control Sub-Regulator G C2 (s), Execution object G v (s), heating element object G 02 (s), fluidized bed object G 01 (s), Observer G s (s), Drying moisture content analyzer transmitter G mm1 (s) and temperature transmitter G m2 (s);

[0090] The advanced moisture content control master regulator G C1 The input terminal of (s) is used to input the set value of the material drying moisture content, and the advanced moisture content control main regulator G... C1 The output terminal of (s) is connected to the temperature advanced control sub-regulator G C2 The input terminal of (s) is connected to the temperature advanced control sub-regulator G. C2 The output of (s) is respectively connected to the execution object G v The input terminal of (s) and the observer G s The first input terminal of (s) is connected, and the execution object G is connected. v The output of (s) is respectively connected to the observer G s The second input terminal of (s) and the heating tube object G 02 (s) input terminal connection, the heating tube object G 02 The output of (s) is respectively connected to the fluidized bed object G 01 (s) input terminal and the temperature transmitter G m2 The input terminal of (s) is connected, and the fluidized bed object G 01 The output terminal of (s) is connected to the transmitter G of the drying moisture content analyzer. m1The input terminal of (s) is connected;

[0091] The observer G s The output of (s) is connected to the execution object G v The input terminal of (s) is connected to eliminate dynamic coupling between multiple heating tubes in the fluidized bed;

[0092] The temperature transmitter G m2 The output terminal of (s) is connected to the temperature advanced control sub-regulator G C2 The input terminal of (s) is connected to send the current temperature Y2(s) of the fluidized bed to the temperature advanced control sub-regulator G. C2 (s) in;

[0093] The dry moisture content analyzer transmitter G m1 The output of (s) is connected to the advanced moisture content control master regulator G. C1 The input terminal of (s) is connected to send the current material drying moisture content Y1(s) to the advanced moisture content control main regulator G. C1 (s)

[0094] In this embodiment, the system further includes a first interference signal F1(s) and a second interference signal F2(s);

[0095] The first interference signal F1(s) is input to the fluidized bed object G. 01 The second interference signal F2(s) is input to the heating tube object G. 02 The input terminal of (s).

[0096] In this embodiment, the advanced moisture content control master regulator G C1 (s) is used to input the fluidized bed temperature setpoint X2(s) to the temperature advanced control sub-regulator G. C2 (s) in;

[0097] Wherein, the fluidized bed temperature setpoint X2(s) = [T1, T2] T T1 and T2 are the bottom temperatures of the first and second stages of the fluidized bed, respectively.

[0098] In this embodiment, the temperature advanced control sub-regulator G C2 (s) is used to input the control quantity U(s) of the multi-input multi-output system to the execution object G respectively. v (s) and the observer G s (s) in;

[0099] Wherein, the control quantity U(s) of the multiple-input multiple-output system is [u1, u1] Tu1 and u2 are control signals for the two heating tubes of the fluidized bed, respectively.

[0100] In this embodiment, the observer G s (s) includes the actual controlled object λ and the prediction model. Signal filter μ and inverse of prediction model

[0101] The observer G s The input signal U of (s) is respectively input to the actual controlled object λ and the prediction model. In the process, the output y of the actual controlled object λ, the coupling term f, and the output noise θ of the execution object of the multi-input multi-output system are combined and then input into the prediction model. In the signal filter μ, the output of the signal filter μ is connected to the inverse of the prediction model. The input terminal is connected, and the inverse of the prediction model is... The output terminals are respectively connected to the input terminals of the actual controlled object λ and the prediction model. The input terminal is connected.

[0102] In this embodiment, in the observer G s In (s), y = G u UG f fG θ θ;

[0103] in, This represents the closed-loop transfer function between the input U and the output y. This represents the transfer function between the coupling quantity f and the output y. This represents the transfer function between the noise θ and the output y.

[0104] Specific limitations regarding the control system for the internally heated fluidized bed drying temperature can be found in the above description of the control method for the internally heated fluidized bed drying temperature, and will not be repeated here. Each module in the aforementioned control system for the internally heated fluidized bed drying temperature can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0105] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 7As shown, the computer device includes a processor, memory, communication interface, display screen, and input system connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a method for controlling the temperature of an internally heated fluidized bed dryer. The display screen can be an LCD screen or an e-ink screen. The input system can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0106] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0107] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program relating to all or part of the processes in the methods of the above embodiments.

[0108] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon relating to all or part of the processes in the methods of the above embodiments.

[0109] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0110] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0111] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for controlling the temperature of an internally heated fluidized bed dryer, characterized in that, A control system for the temperature of an internally heated fluidized bed dryer, the method comprising: Using the material drying moisture content as the given target and the fluidized bed temperature as the follow-up target, a cascade process closed-loop control is performed, and the dynamic coupling between the multiple heating tubes of the fluidized bed is regarded as interference. An observer is added to the closed-loop control loop to eliminate the dynamic coupling; wherein, the prediction model in the observer is a transfer function model of a first-order inertial + pure time delay element obtained by mechanism deduction and step response modeling, and the model parameters of the transfer function model are obtained by mechanism deduction and step response modeling. The system includes an advanced moisture content control master regulator. Advanced temperature control auxiliary regulator Execution object heating element object Fluidized bed objects Observer Drying moisture content analyzer transmitter and temperature transmitter ; The advanced moisture content control main regulator The input terminal inputs the set value of the material drying moisture content, which is then controlled by the advanced moisture content control main regulator. The output terminal is connected to the advanced temperature control auxiliary regulator. The input terminal is connected to the advanced temperature control sub-regulator. The output terminals are respectively connected to the execution object The input terminal and the observer The first input terminal is connected to the execution object. The output terminals are respectively connected to the observer The second input terminal and the heating tube object The input terminal is connected to the heating tube object. The output terminals are respectively connected to the fluidized bed object The input terminal and the temperature transmitter The input terminal is connected to the fluidized bed object. The output terminal is connected to the transmitter of the dry moisture content analyzer. The input terminal is connected; The observer The output terminal and the execution object The input terminal is connected to eliminate dynamic coupling between multiple heating tubes in the fluidized bed; The temperature transmitter The output terminal is connected to the advanced temperature control auxiliary regulator. The input terminal is connected to transmit the current temperature of the fluidized bed. The temperature advanced control auxiliary regulator is fed into it. middle; The dry moisture content analyzer transmitter The output terminal is connected to the advanced moisture content control main regulator. The input terminal is connected to display the current moisture content of the material being dried. The moisture content is fed into the advanced control main regulator. middle; The observer Including the actual controlled object Prediction Model Signal filters Inverse of the prediction model ; The observer The input signal U is respectively input to the actual controlled object. and prediction models In, the actual controlled object Output Coupling terms Output noise of the execution object in a multi-input multi-output system The combined data are then input into the prediction model. and the signal filter In the above, the signal filter The output terminal is the inverse of the prediction model. The input terminal is connected, and the inverse of the prediction model is... The output terminals are respectively connected to the actual controlled object. The input terminal and the prediction model The input terminal is connected; In the observer middle, ; in, This represents the closed-loop transfer function between the input U and the output y. This represents the transfer function between the coupling quantity f and the output y. , indicating noise The transfer function between the output y and the output y.

2. The control method according to claim 1, characterized in that, The system also includes a first interference signal. Second interference signal ; The first interference signal Input to the fluidized bed object The input terminal of the second interference signal Input to the heating tube object The input terminal.

3. The control method according to claim 1, characterized in that, The advanced moisture content control main regulator Used to set the fluidized bed temperature. Input to the advanced temperature control sub-regulator middle; Among them, the fluidized bed temperature setpoint , and These are the bottom temperatures of the first and second stages of the fluidized bed, respectively.

4. The control method according to claim 1, characterized in that, The advanced temperature control sub-regulator Used to control quantities of a multi-input multi-output system Input to the execution object respectively and the observer middle; Among them, the control quantity of the multiple input multiple output system , and These are the control signals for the two heating tubes of the fluidized bed.

Images