Coal mill outlet temperature control method, device, system and coal mill equipment

Abstract

The present application relates to a method, device, system and coal mill equipment for controlling the outlet temperature of a coal mill, including: obtaining a reference trajectory and a predicted output of the outlet temperature of the coal mill air duct at the prediction moment; processing the reference trajectory and the predicted output according to a target proportional-integral implicit generalized predictive control model to obtain a target control increment; controlling the air inlet component of the coal mill according to the target control increment to adjust the real-time temperature at the outlet of the coal mill air duct to the target temperature, wherein the air inlet component includes a hot air regulating valve and a cold air regulating valve. The present application combines the feedback structure of proportional-integral control with the prediction function of implicit generalized predictive control, which can effectively overcome the influence of uncertain factors such as nonlinearity, time-varying and hysteresis of the coal mill outlet temperature control, and significantly improves the robustness and anti-interference ability of the system.

Description

Technical Field

[0001] The present application relates to the technical field of coal mill control optimization, and in particular to a coal mill outlet temperature control method, device, system and coal mill equipment. Background Art

[0002] The pulverizer outlet temperature directly affects the combustion efficiency of pulverized coal. An appropriate outlet temperature ensures full combustion of the pulverized coal within the combustion chamber, improving energy utilization and reducing energy waste caused by incomplete combustion. The pulverizer outlet temperature must be maintained within a certain range. Excessively low outlet temperatures can lead to high moisture content in the pulverized coal, hindering ignition and reducing boiler combustion efficiency. Excessively high outlet temperatures can cause pulverized coal to ignite, compromising safe pulverizer operation.

[0003] In the related art, the coal mill outlet temperature control scheme has poor control effect in a strongly nonlinear, time-varying and uncertain system and cannot stably adjust the coal mill outlet temperature. Summary of the Invention

[0004] Based on this, it is necessary to provide a coal mill outlet temperature control method, device, system and coal mill equipment that can effectively improve the robustness and anti-interference ability of coal mill outlet temperature control in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for controlling the temperature of a coal mill air duct outlet, the method comprising:

[0006] Obtaining a reference trajectory and a predicted output of the coal mill air duct outlet temperature at the prediction moment, wherein the reference trajectory is a reference value trend line of the coal mill air duct outlet temperature within a preset time period, and the predicted output is a predicted value of the coal mill air duct outlet temperature;

[0007] Processing the reference trajectory and the predicted output according to a target proportional-integral implicit generalized predictive control model to obtain a target control increment;

[0008] The air inlet assembly of the coal mill is controlled according to the target control increment to adjust the real-time temperature at the air duct outlet of the coal mill to the target temperature, wherein the air inlet assembly includes a hot air regulating valve and a cold air regulating valve.

[0009] In one embodiment, obtaining a reference trajectory of the outlet temperature of the coal mill at the prediction time includes:

[0010] Obtaining the current real-time outlet temperature and the set outlet temperature, wherein the real-time outlet temperature is the real-time temperature of the coal mill air duct outlet, and the set outlet temperature is the set temperature of the coal mill air duct outlet;

[0011] According to the softening factor at the prediction moment, the real-time outlet temperature at the current moment and the set outlet temperature, a reference trajectory of the outlet temperature of the coal mill at the prediction moment is calculated.

[0012] In one embodiment, obtaining the predicted output of the coal mill air duct outlet temperature at the predicted time includes:

[0013] The predicted output of the pulverizer air duct outlet temperature is calculated based on the control increment at the moment before the prediction moment, the gain coefficient, the real-time outlet temperature at the current moment, the state coefficient, the control increment at the moment before the current moment and the feedforward coefficient.

[0014] In one embodiment, processing the reference trajectory and the predicted output according to a target proportional-integral implicit generalized predictive control model to obtain a target control increment includes:

[0015] Calculate a difference between the reference trajectory and the predicted output to obtain a target error value;

[0016] Processing the target error value according to a preset control performance index function to obtain a prediction performance index;

[0017] The target control increment is calculated based on the optimal performance index and the preset memory item.

[0018] In one embodiment, the controlling the air inlet assembly of the coal mill according to the target control increment to adjust the real-time temperature at the air duct outlet of the coal mill to the target temperature includes:

[0019] Calculating a sum value based on the control amount at the previous moment and the target control increment to obtain the target control amount;

[0020] The air inlet assembly of the coal mill is controlled according to the target control amount to adjust the real-time temperature at the air duct outlet of the coal mill to the target temperature.

[0021] In one embodiment, the method further comprises:

[0022] Using a gradually fading memory recursive least squares algorithm to estimate the control parameters of the air inlet component;

[0023] The memory factor of the preset memory item is adjusted according to the control parameter.

[0024] In one embodiment, obtaining the real-time outlet temperature at the current moment includes:

[0025] The real-time temperature of the coal mill air duct outlet is collected by an infrared monitoring module to obtain the real-time outlet temperature.

[0026] In a second aspect, the present application further provides a coal mill air duct outlet temperature control device, the device comprising:

[0027] An acquisition module, used to obtain the reference trajectory and prediction output of the coal mill air duct outlet temperature at the prediction moment;

[0028] a calculation module, configured to process the reference trajectory and the predicted output according to a target proportional-integral implicit generalized predictive control model to obtain a target control increment;

[0029] A control module is used to control the air inlet component of the coal mill according to the target control increment to adjust the real-time temperature at the air duct outlet of the coal mill to the target temperature, wherein the air inlet component includes a hot air regulating valve and a cold air regulating valve.

[0030] In a third aspect, the present application further provides a coal mill air duct outlet temperature control system, comprising a control module, an infrared monitoring module and an air inlet assembly, wherein the control module is connected to the infrared monitoring module and the air inlet assembly respectively, and the air inlet assembly includes a hot air regulating valve and a cold air regulating valve;

[0031] The infrared monitoring module is used to obtain the real-time temperature of the coal mill air duct outlet and send the real-time temperature to the control module;

[0032] The control module includes a proportional-integral implicit generalized predictive controller; the control module is used to execute the coal mill air duct outlet temperature control method described in the first aspect.

[0033] In a fourth aspect, the present application further provides a coal mill device, comprising the coal mill air duct outlet temperature control system described in the third aspect.

[0034] In summary, the present application proposes a method, device, system and coal mill equipment for controlling the outlet temperature of a coal mill, including: obtaining a reference trajectory and a predicted output of the outlet temperature of the coal mill air duct at the prediction moment; processing the reference trajectory and the predicted output according to a target proportional-integral implicit generalized predictive control model to obtain a target control increment; controlling the air inlet component of the coal mill according to the target control increment to adjust the real-time temperature at the outlet of the coal mill air duct to the target temperature, wherein the air inlet component includes a hot air regulating valve and a cold air regulating valve. The present application combines the feedback structure of proportional-integral control with the prediction function of implicit generalized predictive control, which can effectively overcome the influence of uncertain factors such as nonlinearity, time-varying and hysteresis of the coal mill outlet temperature control, and significantly improves the robustness and anti-interference ability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a structural block diagram of a coal mill air duct outlet temperature control system in one embodiment;

[0036] Figure 2 Schematic diagram of an algorithm of a target proportional-integral implicit generalized predictive control model in one embodiment;

[0037] Figure 3 Schematic diagram of a flow chart of a method for controlling the air duct outlet temperature of a coal mill in one embodiment;

[0038] Figure 4 Schematic diagram of the principle of a method for controlling the air duct outlet temperature of a coal mill in one embodiment;

[0039] Figure 5 1. A structural block diagram of a coal mill air duct outlet temperature control device according to an embodiment;

[0040] Figure 6 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment.

[0041] Summary of reference numerals:

[0042] Control module-110; infrared monitoring module-120; air inlet assembly-130; hot air regulating valve-131; cold air regulating valve-132. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0044] In one embodiment, Figure 1 As shown, a coal mill air duct outlet temperature control system is provided, including a control module 110, an infrared monitoring module 120 and an air intake assembly 130. The control module 110 is connected to the infrared monitoring module 120 and the air intake assembly 130 respectively. The air intake assembly 130 includes a hot air regulating valve 131 and a cold air regulating valve 132.

[0045] The infrared monitoring module 120 is used to obtain the real-time temperature of the coal mill air duct outlet and send the real-time temperature to the control module 110 .

[0046] In this embodiment, the infrared monitoring module 120 can use a high-resolution infrared sensor. The infrared monitoring module 120 can be set inside or outside the air duct of the coal mill to monitor the real-time temperature at the outlet of the coal mill air duct in real time. After collecting the temperature data, the temperature data is converted into a digital signal and transmitted to the control module 110. This embodiment does not specifically limit the specific types of infrared sensors and analog-to-digital conversion circuits in the infrared monitoring module 120. Suitable infrared sensors and analog-to-digital conversion circuits or chips can be selected according to the needs of actual application scenarios. The infrared monitoring module 120 in this embodiment can be installed inside the coal mill according to the needs of actual application scenarios, and can be installed at any position to realize the function of monitoring the temperature at the outlet of the coal mill air duct.

[0047] The control module 110 includes a proportional-integral implicit generalized predictive controller. In this embodiment, the model structure of the proportional-integral (PI) implicit generalized predictive controller (IGPC) is as follows: Figure 2 As shown, it at least includes a PI implicit generalized prediction module and a parameter estimation module, wherein the controlled object is the air inlet component 130, Represents the observed output value of the controlled object, that is, the temperature at the outlet of the coal mill air duct, Indicates the preset temperature value of the mill air duct outlet, that is, the reference temperature value.

[0048] The PI implicit generalized prediction module is used to calculate the target control variable for the controlled object based on the target proportional-integral implicit generalized predictive control model. The target control variable acts on the controlled object to open the air inlet component 130 according to the target opening value, and to open the hot air control valve 131 and the cold air control valve 132 according to the corresponding target opening values, thereby adjusting the air inlet parameters at the coal mill air duct inlet and, in turn, adjusting the temperature value at the coal mill air duct outlet. In this embodiment, the PI implicit generalized prediction module combines the prediction function of the IGPC with the feedback control function of the PI to adapt to various complex operating conditions, such as coal mill operating scenarios with uncertainties, time variations, large time lags, large inertia, and nonlinear characteristics.

[0049] In this embodiment, the PI controller uses the proportional term to quickly respond to error changes and the integral term to eliminate steady-state errors, directly addressing local nonlinearities in the system. Automatic adjustment of the proportional and integral gains allows the controller to adapt to time-varying operating conditions. The integral term in the PI controller continuously eliminates residual errors caused by hysteresis.

[0050] Rolling optimization and multi-step forecasting in GPC ensure that the control strategy adapts to nonlinearity, hysteresis, and time-varying properties.

[0051] The parameter estimation module is used to estimate control parameters using the Fading Memory Recursive Least Squares (FM-RLS) algorithm and adjust the memory factor of the preset memory items based on the control parameters. The memory factor of the FM-RLS algorithm is also known as the forgetting factor. By gradually reducing the weight of historical data, the parameter estimation becomes more dependent on the latest data, quickly tracking the time-varying characteristics of the system. In actual application scenarios, the FM-RLS algorithm, as the identification link of the PI feedback process, incorporates a forgetting factor into the recursive process. By reducing the weight of historical data and enhancing the correction effect of new data, the parameter estimation becomes more adaptable to time-varying systems, thereby effectively enhancing the robustness and adaptability of the PI implicit generalized prediction module.

[0052] In one embodiment, Figure 3 As shown, a method for controlling the temperature of a coal mill air duct outlet is provided, comprising:

[0053] S301, obtaining the reference trajectory and prediction output of the mill air duct outlet temperature at the prediction moment, wherein the reference trajectory is the reference value trend line of the mill air duct outlet temperature within a preset time period, and the prediction output is the predicted value of the mill air duct outlet temperature.

[0054] In this embodiment, the predicted time is any future time compared to the current time. For example, Time is the predicted time, where For the current moment, is the difference between the future time and the current time.

[0055] The predicted output of the coal mill air duct outlet temperature at the prediction time is the predicted value of the temperature at the coal mill air duct outlet at the prediction time.

[0056] The reference trajectory of the coal mill air duct outlet temperature at the prediction time is a trend line of the reference temperature value at the coal mill air duct outlet within the preset time period corresponding to the prediction time. The preset time period in this embodiment can be customized based on actual application needs. The reference value can be determined based on the actual application requirements of the coal mill, and the reference value of the coal mill will vary under different operating conditions.

[0057] S302 : Process the reference trajectory and the predicted output according to the target proportional-integral implicit generalized predictive control model to obtain a target control increment.

[0058] In this embodiment, a target proportional-integral implicit generalized predictive control model is used to realize the calculation of the target control increment. By combining the PI feedback structure with the prediction function of the IGPC, a control performance index function with a PI structure is constructed. Then, based on the optimal performance index function, the target control increment is solved and applied to the air inlet component 130 of the coal mill to adjust the opening of the coal mill air inlet component 130, thereby realizing dynamic control of the temperature at the coal mill air duct outlet.

[0059] In this embodiment, the specific structure of the target proportional integral implicit generalized predictive control model can refer to the above-mentioned embodiment. Figure 2 The structure of the proportional-integral implicit generalized predictive controller is shown.

[0060] In actual operation, Figure 4 As shown in Figure 2, the working principle of the target proportional-integral implicit generalized predictive control model can be:

[0061] First, by setting the temperature and temperature real-time values Perform smoothing to obtain the reference trajectory of the mill air duct outlet temperature at the predicted time The prediction output of the coal mill air duct outlet temperature at the prediction time is obtained by using the prediction function of the PI-IGPC controller. The infrared monitoring module 120 detects the actual output of the air duct outlet of the coal mill system, that is, the real-time temperature value. .

[0062] Second, combined with the reference trajectory and predicted output Perform rolling optimization, multi-step prediction and feedback correction processing to finally obtain the control quantity for the air inlet component 130 in the coal mill system. , the control amount Act on the coal mill system so that the coal mill outlet temperature is controlled within the preset range. During the rolling optimization process, the control quantity calculated in real time will be Multiple inputs to the PI-IGPC controller to update the predicted output , and then based on the updated prediction output and reference trajectory Multiple predictions are achieved within the control cycle (control length and prediction length), and ultimately dynamic control of the air inlet assembly 130 of the coal mill system is achieved.

[0063] S303 , controlling the air inlet assembly 130 of the coal mill according to the target control increment to adjust the real-time temperature at the coal mill air duct outlet to the target temperature, wherein the air inlet assembly 130 includes a hot air regulating valve 131 and a cold air regulating valve 132 .

[0064] In this embodiment, the current control variable is obtained by combining the target control increment with the control variable at the previous moment. Based on the current control variable, the coal mill air inlet assembly 130 is controlled to maintain the opening of the air inlet assembly 130 within a preset opening range, thereby adjusting the real-time temperature at the coal mill air duct outlet to the target temperature.

[0065] In this embodiment, the target control increment and the control amount are both control parameters for the corresponding control object, namely, the opening increment and opening value of the air inlet assembly 130. It should be noted that the air inlet assembly 130 includes a hot air control valve 131 and a cold air control valve 132. The control amount in this embodiment can indicate that the hot air control valve 131 is opened to a first opening degree and the cold air control valve 132 is opened to a second opening degree. The specific form of the control amount and control increment in this embodiment is determined by the number of control objects. When the number of control objects is two, the control amount and control increment each include two control parameters for different control objects.

[0066] Based on the above steps, this embodiment provides a method for controlling the air duct outlet temperature of a coal mill that combines PI feedback and IGPC predictive control functions. This method overcomes the problems of the conventional PID cascade control tuning method being complicated and having poor adaptability to operating conditions. Through multi-step prediction, rolling optimization, and feedback correction control strategies, it can effectively overcome the influence of uncertain factors such as the nonlinearity, time-varying, and hysteresis of the air duct outlet temperature control of the coal mill, significantly improving the robustness and anti-interference ability of the system.

[0067] In one embodiment, obtaining a reference trajectory of the outlet temperature of the coal mill at the prediction time includes:

[0068] Get the real-time outlet temperature and set outlet temperature at the current moment, where the real-time outlet temperature is the real-time temperature of the coal mill air duct outlet, and the set outlet temperature is the set temperature of the coal mill air duct outlet.

[0069] According to the softening factor at the prediction moment, the real-time outlet temperature at the current moment and the set outlet temperature, a reference trajectory of the outlet temperature of the coal mill at the prediction moment is calculated.

[0070] In this embodiment, the calculation formula of the reference trajectory includes:

[0071]

[0072] in, is the softening factor at the prediction moment, , With time step decreases with the increase of The weight of gradually decreases, The weight of gradually increases, is the real-time outlet temperature at the current moment, is the set outlet temperature at the current moment, is the reference trajectory.

[0073] In this embodiment, by adding softening processing, the prediction process of the outlet temperature of the coal mill can be made smoother, taking into account both the real-time temperature and the set temperature, and achieving smoother tracking.

[0074] In one embodiment, obtaining the real-time outlet temperature at the current moment includes:

[0075] The real-time temperature of the coal mill air duct outlet is collected by the infrared monitoring module 120 to obtain the real-time outlet temperature.

[0076] In this embodiment, the real-time temperature at the outlet of the coal mill air duct is collected by a high-resolution infrared sensor, which can realize the real-time temperature collection more quickly, and construct a reference trajectory based on the real-time temperature and the reference temperature, which can realize smooth tracking while realizing more timely temperature tracking and avoiding the lag effect of the coal mill outlet temperature control.

[0077] In one embodiment, obtaining a predicted output of the coal mill air duct outlet temperature at a predicted time includes:

[0078] The predicted output of the mill air duct outlet temperature is calculated based on the control increment at the moment before the prediction moment, the gain coefficient, the real-time outlet temperature at the current moment, the state coefficient, the control increment at the moment before the current moment and the feedforward coefficient.

[0079] In this embodiment, the calculation formula for the predicted output includes:

[0080]

[0081] The above formula can be simplified by the following formula:

[0082]

[0083] The simplified calculation formula for the prediction output can be obtained as follows:

[0084]

[0085] in, For the future The predicted temperature, for The control increment of the moment before the moment, is the gain matrix, Indicates the influence weight of the current control input increment on the output.

[0086] For the current moment temperature, is the state coefficient, which indicates the impact of the current state on future output. is the feedforward coefficient, which indicates the influence of the previous step control input increment on the future output. For the current moment The control increment at the previous moment.

[0087] In this example, the PI-IGPC control module is used to calculate the predicted output of the mill duct outlet temperature. In actual applications, the PI-IGPC control module function is constructed using the Autoregressive Integrated Moving Average Model (CARIMA model). The CARIMA model is expressed as:

[0088]

[0089] in, is the disturbance sequence; is the backshift operator; represents the difference operator; is nth order Polynomials, is m-order Polynomial, For n-th order Polynomial.

[0090] Specifically in this embodiment, it can be embodied as follows:

[0091]

[0092] in, is nth order Polynomial. for Step Polynomial, where . is nth order Polynomial.

[0093] In actual application, the FR-RLS algorithm user updates the CARIMA model parameters online, for example 、 The coefficient of 、 and .

[0094] In one embodiment, processing the reference trajectory and the predicted output according to a target proportional-integral implicit generalized predictive control model to obtain a target control increment includes:

[0095] The difference between the reference trajectory and the predicted output is calculated to obtain the target error value.

[0096] The target error value is processed according to the preset control performance index function to obtain the predicted performance index.

[0097] The target control increment is calculated based on the optimal performance index and the preset memory item.

[0098] In this embodiment, the target error value is calculated as follows:

[0099]

[0100] in, is the target error value, To predict the time The reference trajectory, is the predicted output at the prediction time.

[0101] In this embodiment, the calculation formula of the preset control performance index function is:

[0102]

[0103] in, is the scale factor, is the integrating factor, , , is the predicted length, i.e. the predicted coal mill outlet temperature range, To control the length, that is, the opening control range of the air inlet assembly 130, To control the weighting coefficient, To predict the time The control increment, is the target error value, is the target error value increment, For the current moment, is the interval between the predicted time and the current time. Represents the calculation result of the preset control performance index function, that is, the predicted performance index Specifically, Represents the weighted square sum of the proportional error term and the integral error term. The proportional error term penalizes changes in the error, encouraging the system to respond quickly to changes in the setpoint. The integral error term penalizes the accumulation of errors, encouraging the system to eliminate steady-state errors. It represents the weighted sum of squares of the control increment terms, which is used to penalize changes in the control input and encourage the system to use a smooth control strategy.

[0104] In this embodiment, the prediction performance index It can be used to adjust the proportional and integral gains to optimize the dynamic response and steady-state accuracy of the system. In the IGPC prediction process, the prediction performance index It is used to optimize the control input in the future to minimize the performance index. In this embodiment, the optimal performance index is the minimum predicted performance index. .

[0105] In this embodiment, the calculation formula of the target control increment includes:

[0106]

[0107]

[0108]

[0109] in, For preset memory items, is the memory factor, is the identity matrix, is the proportional gain, for The transpose of is the integral gain, for The transpose of is the variation of the reference trajectory, The change in the predicted output, is the reference trajectory, is the predicted output, is the predicted length, To control the length.

[0110] In the actual application process, by simplifying the calculation formula of the target control increment, let

[0111]

[0112]

[0113] The simplified calculation formula of the target control increment can be obtained:

[0114]

[0115] in,

[0116] Based on the above steps, the predictive performance indicators of IGPC control are integrated into the calculation process of the target control increment. By fully combining the PI feedback characteristics and the predictive characteristics of IGPC control, the dynamic changes of the control parameters of the target object can be accurately tracked, achieving high-precision control while greatly improving the system's anti-interference ability.

[0117] In one embodiment, the coal mill air duct outlet temperature control method further includes:

[0118] A gradually fading memory recursive least squares algorithm is used to estimate the control parameters of the air inlet assembly 130. The memory factor of the preset memory item is adjusted according to the control parameters.

[0119] In this embodiment, the FM-RLS algorithm serves as the identification link of the PI feedback process. A forgetting factor is added in the recursive process to reduce the weight of historical data and enhance the correction effect of new data, making parameter estimation more adaptable to time-varying systems.

[0120] In one embodiment, controlling the air inlet assembly 130 of the coal mill according to the target control increment to adjust the real-time temperature at the coal mill air duct outlet to the target temperature includes:

[0121] The target control amount is obtained by calculating the sum of the control amount at the previous moment and the target control increment.

[0122] The air inlet assembly 130 of the coal mill is controlled according to the target control amount to adjust the real-time temperature at the coal mill air duct outlet to the target temperature.

[0123] In this embodiment, the calculation formula of the target control amount includes:

[0124]

[0125] in, is the control amount of the air inlet assembly 130 at the moment before the current moment, Control increments for the target.

[0126] In summary, this embodiment provides a method for controlling the air duct outlet temperature of a coal mill. By effectively combining the PI feedback function and the IGPC predictive control function, it overcomes the problems of the conventional PID cascade control tuning method being complicated and having poor adaptability to operating conditions. Through multi-step prediction, rolling optimization and feedback correction control strategies, it can effectively overcome the influence of uncertain factors such as nonlinearity, time-varying and hysteresis of the air duct outlet temperature control of the coal mill, and significantly improve the robustness and anti-interference ability of the system.

[0127] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0128] Based on the same inventive concept, embodiments of the present application further provide a coal mill air duct outlet temperature control device for implementing the aforementioned coal mill air duct outlet temperature control method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the coal mill air duct outlet temperature control device provided below can be found in the above-described limitations of the coal mill air duct outlet temperature control method and are not further elaborated here.

[0129] In one embodiment, Figure 5 As shown, a coal mill air duct outlet temperature control device 500 is provided, comprising: an acquisition module 510, a calculation module 520 and a control module 530, wherein:

[0130] The acquisition module 510 is used to obtain the reference trajectory and prediction output of the coal mill air duct outlet temperature at the prediction moment.

[0131] The calculation module 520 is used to process the reference trajectory and the predicted output according to the target proportional-integral implicit generalized predictive control model to obtain the target control increment.

[0132] The control module 530 is used to control the air inlet assembly of the coal mill according to the target control increment to adjust the real-time temperature at the coal mill air duct outlet to the target temperature, wherein the air inlet assembly includes a hot air regulating valve and a cold air regulating valve.

[0133] In one embodiment, the acquisition module 510 is specifically used to obtain the real-time outlet temperature and the set outlet temperature at the current moment, wherein the real-time outlet temperature is the real-time temperature of the coal mill air duct outlet, and the set outlet temperature is the set temperature of the coal mill air duct outlet; based on the softening factor at the prediction moment, the real-time outlet temperature at the current moment and the set outlet temperature, the reference trajectory of the outlet temperature of the coal mill at the prediction moment is calculated.

[0134] In one embodiment, the acquisition module 510 is specifically used to calculate the predicted output of the mill air duct outlet temperature based on the control increment at the moment before the prediction moment, the gain coefficient, the real-time outlet temperature at the current moment, the state coefficient, the control increment at the moment before the current moment, and the feedforward coefficient.

[0135] In one embodiment, the calculation module 520 is specifically used to calculate the difference between the reference trajectory and the predicted output to obtain a target error value; process the target error value according to a preset control performance indicator function to obtain a predicted performance indicator; and calculate the target control increment based on the optimal performance indicator and a preset memory item.

[0136] In one embodiment, the control module 530 is specifically used to calculate and value based on the control quantity at the previous moment and the target control increment to obtain the target control quantity; and control the air inlet component of the coal mill according to the target control quantity to adjust the real-time temperature at the outlet of the coal mill air duct to the target temperature.

[0137] In one embodiment, the calculation module 520 is specifically configured to estimate the control parameters of the air inlet component using a gradually fading memory recursive least squares algorithm; and adjust the memory factor of the preset memory item according to the control parameters.

[0138] In one embodiment, the acquisition module 510 is specifically configured to collect the real-time temperature of the coal mill air duct outlet through an infrared monitoring module to obtain the real-time outlet temperature.

[0139] In summary, this embodiment provides a coal mill air duct outlet temperature control device, which effectively combines the PI feedback function and the IGPC predictive control function, overcomes the problems of the conventional PID cascade control tuning method being complicated and poor adaptability to operating conditions, and through multi-step prediction, rolling optimization and feedback correction control strategies, can effectively overcome the influence of uncertain factors such as nonlinearity, time-varying and hysteresis of the coal mill air duct outlet temperature control, and significantly improves the robustness and anti-interference ability of the system.

[0140] Each module in the aforementioned coal mill air duct outlet temperature control device may be implemented in whole or in part through software, hardware, or a combination thereof. Each module may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a computer device memory in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0141] In one embodiment, a coal mill device is provided, comprising the coal mill air duct outlet temperature control system in the aforementioned embodiment.

[0142] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 6As shown. The computer device includes a processor, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an 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 medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via wired or wireless communication, which can be achieved via Wi-Fi, mobile cellular networks, NFC (near-field communication), or other technologies. When executed by the processor, the computer program implements a method for controlling the air duct outlet temperature of a coal mill. The display unit of the computer device is used to produce a visual image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0143] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0144] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0145] Obtain the reference trajectory and prediction output of the mill air duct outlet temperature at the prediction time.

[0146] The reference trajectory and predicted output are processed according to the target proportional-integral implicit generalized predictive control model to obtain the target control increment.

[0147] The air inlet assembly of the coal mill is controlled according to the target control increment to adjust the real-time temperature at the coal mill air duct outlet to the target temperature, wherein the air inlet assembly includes a hot air regulating valve and a cold air regulating valve.

[0148] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0149] Obtain the reference trajectory and prediction output of the mill air duct outlet temperature at the prediction time.

[0150] The reference trajectory and predicted output are processed according to the target proportional-integral implicit generalized predictive control model to obtain the target control increment.

[0151] The air inlet assembly of the coal mill is controlled according to the target control increment to adjust the real-time temperature at the coal mill air duct outlet to the target temperature, wherein the air inlet assembly includes a hot air regulating valve and a cold air regulating valve.

[0152] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0153] Obtain the reference trajectory and prediction output of the mill air duct outlet temperature at the prediction time.

[0154] The reference trajectory and predicted output are processed according to the target proportional-integral implicit generalized predictive control model to obtain the target control increment.

[0155] The air inlet assembly of the coal mill is controlled according to the target control increment to adjust the real-time temperature at the coal mill air duct outlet to the target temperature, wherein the air inlet assembly includes a hot air regulating valve and a cold air regulating valve.

[0156] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, 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, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processors (GPUs), digital signal processors (DSPs), programmable logic devices (PLCs), and the like.

[0157] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, 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.

[0158] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for controlling the outlet temperature of a coal mill air duct, characterized in that: The method comprises: Obtaining a reference trajectory and a predicted output of the coal mill air duct outlet temperature at the prediction time, wherein the reference trajectory is a reference value trend line of the coal mill air duct outlet temperature within a preset time period, and the predicted output is a predicted value of the coal mill air duct outlet temperature; wherein the predicted output is calculated based on the CARIMA model; Processing the reference trajectory and the predicted output according to a target proportional-integral implicit generalized predictive control model to obtain a target control increment; controlling an air inlet assembly of the coal mill according to the target control increment to adjust the real-time temperature at the air duct outlet of the coal mill to the target temperature, wherein the air inlet assembly includes a hot air regulating valve and a cold air regulating valve; The step of processing the reference trajectory and the predicted output according to the target proportional-integral implicit generalized predictive control model to obtain a target control increment includes: Calculate a difference between the reference trajectory and the predicted output to obtain a target error value; Processing the target error value according to a preset control performance index function to obtain a prediction performance index; Calculating the target control increment based on the optimal performance index and the preset memory item; The calculation formula of the preset control performance index function includes: in, is the scale factor, is the integrating factor, , , is the predicted length, To control the length, To control the weighting coefficient, To predict the time The control increment, is the target error value, is the target error value increment, For the current moment, is the interval between the predicted time and the current time, Represents the calculation result of the preset control performance index function, specifically, represents the weighted sum of squares of the proportional error term and the integral error term, represents the weighted sum of squares of the control increment terms.

2. The method according to claim 1, characterized in that The step of obtaining a reference trajectory of the outlet temperature of the coal mill at the prediction moment includes: Obtaining the current real-time outlet temperature and the set outlet temperature, wherein the real-time outlet temperature is the real-time temperature of the coal mill air duct outlet, and the set outlet temperature is the set temperature of the coal mill air duct outlet; According to the softening factor at the prediction moment, the real-time outlet temperature at the current moment and the set outlet temperature, a reference trajectory of the outlet temperature of the coal mill at the prediction moment is calculated.

3. The method according to claim 1, characterized in that The method of obtaining the predicted output of the coal mill air duct outlet temperature at the predicted time includes: The predicted output of the pulverizer air duct outlet temperature is calculated based on the control increment at the moment before the prediction moment, the gain coefficient, the real-time outlet temperature at the current moment, the state coefficient, the control increment at the moment before the current moment and the feedforward coefficient.

4. The method according to claim 1, wherein The step of controlling the air inlet assembly of the coal mill according to the target control increment to adjust the real-time temperature at the air duct outlet of the coal mill to the target temperature includes: Calculating a sum value based on the control amount at the previous moment and the target control increment to obtain the target control amount; The air inlet assembly of the coal mill is controlled according to the target control amount to adjust the real-time temperature at the air duct outlet of the coal mill to the target temperature.

5. The method according to claim 4, characterized in that The method further comprises: Using a gradually fading memory recursive least squares algorithm to estimate the control parameters of the air inlet component; The memory factor of the preset memory item is adjusted according to the control parameter.

6. The method according to claim 2, characterized in that The step of obtaining the real-time outlet temperature at the current moment includes: The real-time temperature of the coal mill air duct outlet is collected by an infrared monitoring module to obtain the real-time outlet temperature.

7. A coal mill air duct outlet temperature control device, characterized in that: The device comprises: An acquisition module, configured to acquire a reference trajectory and a predicted output of the coal mill air duct outlet temperature at a prediction moment; wherein the predicted output is calculated based on a CARIMA model; a calculation module, configured to process the reference trajectory and the predicted output according to a target proportional-integral implicit generalized predictive control model to obtain a target control increment; a control module, configured to control an air inlet assembly of the coal mill according to the target control increment, so as to adjust the real-time temperature at the air duct outlet of the coal mill to the target temperature, wherein the air inlet assembly includes a hot air regulating valve and a cold air regulating valve; The calculation module is specifically configured to calculate a difference between the reference trajectory and the predicted output to obtain a target error value; process the target error value according to a preset control performance index function to obtain a predicted performance index; and calculate the target control increment according to the optimal performance index and a preset memory item; The calculation formula of the preset control performance index function includes: in, is the scale factor, is the integrating factor, , , is the predicted length, To control the length, To control the weighting coefficient, To predict the time The control increment, is the target error value, is the target error value increment, For the current moment, is the interval between the predicted time and the current time, Represents the calculation result of the preset control performance index function, specifically, represents the weighted sum of squares of the proportional error term and the integral error term, represents the weighted sum of squares of the control increment terms.

8. A coal mill air duct outlet temperature control system, characterized in that: It includes a control module, an infrared monitoring module and an air intake assembly, wherein the control module is connected to the infrared monitoring module and the air intake assembly respectively, and the air intake assembly includes a hot air regulating valve and a cold air regulating valve; The infrared monitoring module is used to obtain the real-time temperature of the coal mill air duct outlet and send the real-time temperature to the control module; The control module includes a proportional-integral implicit generalized predictive controller; the control module is used to execute the coal mill air duct outlet temperature control method according to any one of claims 1 to 6.

9. A coal mill equipment, characterized in that: Including the coal mill air duct outlet temperature control system as described in claim 8.

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