Rotary kiln temperature intelligent control method and Internet of Things system

Abstract

The invention discloses a rotary kiln temperature control method which comprises the following steps: taking outputs of a plurality of temperature sensors as inputs of a temperature detection module, collecting a plurality of temperature data of a rotary kiln, and preprocessing the temperature data; the material weight sensor is arranged in a feeding conveying device of the rotary kiln and used for detecting the weight of materials, and the rotary kiln is heated through the combustor. Constructing a temperature control system of the rotary kiln, and controlling a combustor according to the detected temperature data and the temperature target value; the rotary kiln temperature control system further dynamically controls the combustion progress of the combustor in real time according to the detected feeding weight and the real-time temperature of the rotary kiln at the current moment in combination with a set temperature target value and a set material weight target value, and the temperature in the rotary kiln is adjusted in real time so that the temperature can meet the temperature target value set by the rotary kiln temperature control system. And the system meets the material weight target value through the feeding conveying device, and the accuracy, the dynamic nature and the robustness of temperature control of the rotary kiln can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control automation equipment for rotary kiln temperature, and in particular to an intelligent control method for rotary kiln temperature and an Internet of Things system. Background Art

[0002] The existing rotary kiln temperature control method mainly realizes the temperature control of the rotary kiln during the heating process through simple means such as PID controller and PI controller. Since the rotary kiln temperature has the characteristics of nonlinearity, large hysteresis and uncertain change, it leads to the problems of low stability and low accuracy of rotary kiln temperature control, and it is difficult to control the rotary kiln temperature at the target value and control it smoothly. In addition, adding materials to the rotary kiln will inevitably affect the temperature in the rotary kiln. Adding materials will inevitably reduce the temperature and combustion temperature in the rotary kiln, thereby affecting the working efficiency and quality of the rotary kiln. How to dynamically and real-time adjust the temperature of the rotary kiln is a technical problem that technicians in this field urgently need to solve. With the development of intelligent control technology, intelligent control of the rotary kiln temperature has become an inevitable trend. Summary of the invention

[0003] Purpose of the invention: In view of the problems pointed out in the background technology, the present invention discloses a rotary kiln temperature intelligent control method and an Internet of Things system, the rotary kiln temperature can be more accurately and stably controlled to ensure the heating effect and benefit of the rotary kiln, and provides an Internet of Things system for intelligent control of the rotary kiln temperature.

[0004] Technical solution: The present invention discloses a rotary kiln temperature control method, comprising the following steps:

[0005] Step 1: multiple temperature sensor outputs are used as inputs to a temperature detection module, multiple temperature data of the rotary kiln are collected through the temperature detection module, and the temperature data are preprocessed;

[0006] Step 2: A feed transmission device is provided at the front end of the rotary kiln, and a material weight sensor is arranged in the feed transmission device to detect the weight of the material, and the rotary kiln is heated by a burner;

[0007] Step 3: Construct a rotary kiln temperature control system, which controls the burner according to the temperature data detected by the temperature detection module and the temperature target value set by the rotary kiln temperature control system; the rotary kiln temperature control system also dynamically controls the burner combustion progress in real time according to the feed weight detected by the material weight sensor and the real-time temperature detected by the temperature detection module at the current moment, combined with the temperature target value and the material weight target value set by the rotary kiln temperature control system, adjusts the temperature in the rotary kiln in real time to meet the temperature target value set by the rotary kiln temperature control system, and enables the system to meet the material weight target value through the feed transmission device.

[0008] Further, the rotary kiln temperature control system includes a NARX neural network model 3-5 of WOA, an ESN neural network model-PID controller 1-3 of WOA, a fuzzy recursive wavelet neural network model 3-5 of WOA, a differential unit 1-3, and a fuzzy self-adjusting controller;

[0009] The temperature target value set by the rotary kiln temperature control system is obtained, and the cumulative sum of the temperature target value and the fuzzy recursive wavelet neural network model 5 of WOA and the error and error change rate of the temperature detection module output are used as the input of the ESN neural network model-PID controller 3 of WOA; the error and error change rate of the temperature target value and the output of the NARX neural network model 5 of WOA are used as the input of the fuzzy self-adjusting controller; the temperature target value is used as the input of the NARX neural network model 3 and the differential unit 1 of WOA, the output of the differential unit 1 is used as the input of the NARX neural network model 4 of WOA, the output of the fuzzy recursive wavelet neural network model 5 of WOA is used as the input of the NARX neural network model 3 and the differential unit 3 of WOA, the output of the differential unit 3 is used as the input of the NARX neural network model 4 of WOA, and the difference between the output of the NARX neural network model 3 of WOA and the output of the temperature detection module and the change rate of the difference As the input of the ESN neural network model-PID controller 1 of WOA, the difference between the output of the NARX neural network model 4 of WOA and the output of the differential unit 2 and the rate of change of the difference are used as the input of the ESN neural network model-PID controller 2 of WOA, the outputs of the ESN neural network model-PID controller 1 of WOA and the ESN neural network model-PID controller 2 of WOA are used as the input of the fuzzy recursive wavelet neural network model 3 of WOA, and the difference between the cumulative sum of the output of the fuzzy recursive wavelet neural network model 3 of WOA and the output of the ESN neural network model-PID controller 3 of WOA and the fuzzy recursive wavelet neural network model 4 of WOA is used as the input of the burner and the fuzzy recursive wavelet neural network model 4 of WOA; the output of the temperature detection module is used as the input of the differential unit 2, the NARX neural network model 5 of WOA and the fuzzy recursive wavelet neural network model 4 of WOA;

[0010] The error and error change between the material weight target value set by the rotary kiln temperature control system and the material weight sensor output are used as the input of the fuzzy recursive wavelet neural network model 5 of WOA, and the material weight sensor output is used as the input of the fuzzy recursive wavelet neural network model 4 of WOA. Figure 2 .

[0011] Further, the temperature detection module includes the NARX neural network model 1-2 of WOA, the fuzzy recursive wavelet neural network model 1-2 of WOA and the ESN neural network model of WOA; the structure of the temperature detection module is shown in Figure 1shown.

[0012] The outputs of multiple temperature sensors are respectively used as inputs of NARX neural network model 1 of WOA, ESN neural network model of WOA and fuzzy recursive wavelet neural network model 1 of WOA, the outputs of NARX neural network model 1 of WOA and ESN neural network model of WOA are used as inputs of fuzzy recursive wavelet neural network model 1 of WOA, the output of fuzzy recursive wavelet neural network model 1 of WOA is used as inputs of NARX neural network model 2 of WOA and fuzzy recursive wavelet neural network model 2 of WOA, the output of NARX neural network model 2 of WOA is used as input of fuzzy recursive wavelet neural network model 2 of WOA, and the output of fuzzy recursive wavelet neural network model 2 of WOA is used as the predicted values ​​of the output temperatures of multiple temperature sensors.

[0013] Furthermore, the NARX neural network model 1 of WOA and the NARX neural network model 2 of WOA have the same structure, are both NARX neural network models of WOA, and both use the whale optimization algorithm to optimize the NARX neural network model parameters;

[0014] The structures of WOA's fuzzy recursive wavelet neural network models 1-2 are the same, both of which are WOA's fuzzy recursive wavelet neural network models, and the whale optimization algorithm is used to optimize the fuzzy recursive wavelet neural network model parameters;

[0015] WOA's ESN neural network model uses the whale optimization algorithm to optimize the ESN neural network model parameters.

[0016] Furthermore, the NARX neural network models 3-5 of WOA have the same structure, are all NARX neural network models of WOA, and all use the whale optimization algorithm to optimize the NARX neural network model parameters;

[0017] The WOA ESN neural network model-PID controllers 1-3 have the same structure, and are all WOA ESN neural network model-PID controllers, and the WOA ESN neural network model outputs the parameters of the PID controller;

[0018] The fuzzy recursive wavelet neural network models 3-5 of WOA have the same structure, are all fuzzy recursive wavelet neural network models of WOA, and all use the whale optimization algorithm to optimize the fuzzy recursive wavelet neural network model parameters.

[0019] Furthermore, the fuzzy self-adjusting controller is composed of two parts, fuzzy control and integral action, in parallel. The specific fuzzy control rules are as follows:

[0020] U f =k0×[a×e+(1-a)×e']

[0021] Among them, U f is the output of the fuzzy controller, k0 is the output coefficient, a is the adaptive correction factor, and the size of a reflects the influence of the rotary kiln temperature error e and the rotary kiln temperature error change rate e' on the output of the fuzzy self-adjusting controller.

[0022] The present invention also discloses an Internet of Things system for intelligent control of rotary kiln temperature, including a rotary kiln parameter detection node, a rotary kiln temperature control node, a rotary kiln parameter communication node, a rotary kiln cloud platform server and a rotary kiln monitoring mobile phone APP. Two-way communication is achieved between the rotary kiln parameter detection node, the rotary kiln temperature control node, the rotary kiln parameter communication node, the rotary kiln cloud platform server and the rotary kiln monitoring mobile phone APP. The rotary kiln parameter detection node collects rotary kiln parameters, the rotary kiln temperature control node controls the temperature of the rotary kiln, the rotary kiln parameter communication node collects the rotary kiln parameters of the rotary kiln parameter detection node and the rotary kiln temperature control node and uploads them to the rotary kiln cloud platform server, and the rotary kiln cloud platform server stores and manages the parameters of the rotary kiln; the rotary kiln parameter detection node and the rotary kiln temperature control node execute the above-mentioned rotary kiln temperature control method. The structure of the Internet of Things system for intelligent control of rotary kiln temperature is shown in FIG. Figure 3 shown.

[0023] Compared with the prior art, the present invention has the following obvious advantages:

[0024] 1. The reserve pool of the ESN neural network model used in the present invention contains a large number of randomly sparsely connected neurons, which has the function of short-term memory of the rotary kiln temperature parameters. The reserve pool of the ESN neural network model can be regarded as a nonlinear time kernel, which maps the temperature dynamic time input to a high-dimensional static space, fully mines the timing information of the dynamic change of the rotary kiln temperature, and improves the dynamic performance, accuracy and robustness of processing the rotary kiln temperature.

[0025] 2. The NARX neural network model 3 of WOA, the fuzzy recursive wavelet neural network model 5 of WOA, the fuzzy self-adjusting controller, the material weight target value and the material weight sensor used in the present invention constitute a compensation control loop for the dynamic change of the rotary kiln temperature. The output of the fuzzy recursive wavelet neural network model 5 of WOA is used to perform real-time dynamic compensation control on the dynamic change of the rotary kiln temperature, thereby effectively reducing the influence and error of the sudden addition of materials and the temperature change of the rotary kiln on the temperature tracking performance of the rotary kiln, and reducing the influence of the uncertain disturbance of the material weight and the temperature change of the rotary kiln on the dynamic tracking control process of the rotary kiln temperature, thereby improving the accuracy, dynamics and robustness of the temperature control of the rotary kiln.

[0026] 3. The NARX neural network model of WOA in the present invention uses the output data of the previous rotary kiln temperature detection and control process in the next step of calculation by adding a delay link, so that the NARX neural network model used to detect and control the rotary kiln temperature process not only has dynamic characteristics but also the information of the rotary kiln temperature detection and control process is more complete, which improves the dynamics, robustness and accuracy of predicting and controlling the rotary kiln temperature process, and conforms to the dynamic mechanism of the change of rotary kiln temperature parameters.

[0027] 4. The temperature detection and control process of the rotary kiln is a complex system with nonlinearity and large hysteresis. Aiming at the problem that the traditional PID control method has poor control effect in it, the present invention designs a WOA ESN neural network model-PID controller, and utilizes the self-learning dynamic adaptive characteristics of the WOA ESN neural network model to improve the dynamics, accuracy and robustness of the rotary kiln temperature control process.

[0028] 5. The temperature target value, temperature detection module and WOA's ESN neural network model-PID controller 3 constitute the real-time control loop of the rotary kiln temperature. WOA's NARX neural network model 3, WOA's ESN neural network model-PID controller 3 and WOA's fuzzy recursive wavelet neural network model 3 constitute the steady-state control loop of the rotary kiln temperature control, realizing the interaction, mutual influence and mutual complementation of the real-time control and steady-state control of the rotary kiln temperature to improve the accuracy, dynamics and robustness of the rotary kiln temperature control.

[0029] 6. The differential units 1-3 of the present invention, the NARX neural network model 4 of WOA, and the ESN neural network model-PID controller 2 of WOA constitute the temperature target value change rate of the rotary kiln temperature, the temperature detection module output change rate, and the fuzzy recursive wavelet neural network model 5 of WOA output compensation control quantity change rate. The three joint control realizes dynamic real-time tracking of the temperature target value change rate of the rotary kiln, thereby improving the dynamic performance, accuracy and robustness of the rotary kiln temperature control. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 The temperature detection module of the present invention;

[0031] Figure 2 The rotary kiln temperature control system of the present invention;

[0032] Figure 3 The Internet of Things system for intelligent temperature control of a rotary kiln of the present invention;

[0033] Figure 4 It is a rotary kiln parameter detection node of the present invention;

[0034] Figure 5 It is the temperature control node of the rotary kiln of the present invention;

[0035] Figure 6 It is the rotary kiln parameter communication node of the present invention;

[0036] Figure 7 It is the software function of the rotary kiln parameter processing and analysis monitoring terminal of the present invention. DETAILED DESCRIPTION

[0037] Combined with Figure 1-7 , the technical solution of the present invention is further described:

[0038] The present invention discloses a rotary kiln temperature intelligent control method and an Internet of Things system, which specifically includes the following steps for intelligent temperature detection and control of a rotary kiln and an Internet of Things system:

[0039] 1. Design of temperature detection module

[0040] The temperature detection module includes WOA's NARX neural network model 1-2, WOA's fuzzy recursive wavelet neural network model 1-2 and WOA's ESN neural network model. The temperature detection module structure diagram is shown in Figure 1 .

[0041] 1. Design of ESN neural network model for WOA

[0042] The reserve pool of the ESN neural network model contains a large number of randomly sparsely connected neurons, which have the function of short-term memory of the rotary kiln temperature parameters. The reserve pool of the ESN neural network model can be regarded as a nonlinear time kernel, which maps the temperature dynamic time input to a high-dimensional static space, fully explores the time series information of the dynamic change of the rotary kiln temperature, and improves the dynamic performance and accuracy of the rotary kiln temperature. The number of neurons in the reserve pool is called the reserve pool scale, which is one of the important parameters of the ESN neural network model. The ESN neural network model consists of three parts: input layer, reserve pool and output layer, which contain K, R and L neurons respectively. Win is the connection weight matrix of the input layer and the reserve pool. The connection matrix of the neurons in the reserve pool is randomly generated during the network initialization stage. Wout is the readout connection weight matrix of the forward connection reserve pool and the output layer. The input temperature sequence value at time t is X(t), the output temperature process value of the reserve pool neuron is S(t), and the temperature prediction value output sequence y(t). Then the update model of the ESN neural network model during the training process is:

[0043] S(t)=f(W in X(t)+WS(t+1)) (1)

[0044] y(t)=f out (W out S(t)+α) (2)

[0045] Among them, f(·) is the nonlinear reservoir activation function, f out (·) is the activation function of the output layer, α is the bias vector at output, and W is the connection weight.

[0046] The WOA ESN neural network model uses the whale optimization algorithm to optimize the parameters of the ESN neural network model. The WOA algorithm optimizes the target value by simulating three predation behaviors of humpback whales: encircling prey, attacking prey with a spiral bubble net, and randomly searching for prey. The construction steps of the WOA ESN neural network model are as follows: (1) Initialize the number of whale populations and the location of whale groups, set the maximum number of iterations, and map the weights and hidden layer thresholds of the ESN neural network model to the position vectors of individual whale groups.

[0047] (2) Calculate the fitness value corresponding to the individual position of the whale and obtain the optimal individual position of the whale.

[0048] (3) By comparing the whale position update probability p and the coefficient vector A to see whether they meet the cutoff condition, the whale position is updated. When the maximum number of iterations is met, the position vector of the optimal whale individual is output, and the optimal weights and thresholds are obtained, which are input into the ESN neural network model for training and recognition.

[0049] (4) Construct the ESN neural network model of WOA.

[0050] 2. Design of Fuzzy Recursive Wavelet Neural Network Model 1-2 of WOA

[0051] The fuzzy recursive wavelet neural network model has five layers, and its hidden layer has three layers, namely, the membership function layer, the rule layer and the recursive wavelet function layer. The fuzzy recursive wavelet neural network model combines fuzzy logic, wavelet processing and recursive structure to improve its temperature processing capability and accuracy of the rotary kiln and solve the shortcomings of static mapping. The first layer is the input layer. The two neuron nodes are both input nodes, which are equivalent to input variables. The second layer is the membership function layer. The output of each neuron in this layer corresponds to the three neurons in the membership function layer. The nonlinear transformation in the membership function layer uses Gaussian function. This transformation method adds fuzzy logic inference, which improves the inductive performance of the fuzzy recursive wavelet neural network model. The third layer is the rule layer. Each neuron in the rule layer is the antecedent part of a fuzzy logic rule. The neurons in this layer perform product operations on the input signals of this layer. The fourth layer is the recursive wavelet layer, which contains the post-processing of wavelet function operation, recursive operation and fuzzy logic rules. In order to increase the sensitivity of the rotary kiln temperature signal input by the wavelet function and post-processing part, and achieve the purpose of adjusting and controlling the instant rotary kiln temperature self-adjustment, recursive processing is added to meet the needs of dynamic processing of rotary kiln temperature. The fifth layer is the output layer, which defuzzifies all the input rotary kiln temperature signals of this layer. The output layer value is the product of the fourth layer output and the connection weight of this layer. The gradient descent algorithm is used to adjust the connection weight value and the parameters of the membership function and wavelet function in the fuzzy recursive wavelet neural network model.

[0052] The fuzzy recursive wavelet neural network model 1-2 of WOA refers to the design method of the ESN neural network model of WOA in this patent.

[0053] 3. Design of NARX neural network model 1-2 of WOA

[0054] The NARX neural network model introduces the input delay and output delay feedback values ​​of the NARX neural network model over the past period of time as the input of the NARX neural network model, simulating the autoregressive relationship between the input and output of the rotary kiln temperature detection and control process. Its training method is divided into two stages: forward propagation and back propagation.

[0055] (1) Forward propagation stage: The historical input and output data of the rotary kiln temperature detection and control process are passed to the NARX neural network model, and the current NARX neural network model prediction output is generated.

[0056] (2) Back propagation stage. The weights and biases of the NARX neural network model are updated according to the error of predicting and controlling the temperature process of the rotary kiln to improve the accuracy of the NARX neural network model. The NARX neural network model consists of an input layer, a hidden layer, and an output layer. The NARX neural network model uses the external input u(t) at the current time t, the m-order delay of the external input, and the n-order delay of the output predicted by the neural network model as the input of the NARX neural network model, where the relationship between input and output is:

[0057] y(t)=f(y(t-1),y(t-2),…,y(t-ny),u(t),u(t-1),…,u(t-mu)) (3)

[0058] In the formula: u is input; y is output; m and n are the number of input and output variables.

[0059] The NARX neural network model of WOA refers to the design method of the ESN neural network model of WOA in this patent.

[0060] 2. Design of rotary kiln temperature control system

[0061] The rotary kiln temperature control system includes WOA's NARX neural network model 3-5, WOA's ESN neural network model-PID controller 1-3, WOA's fuzzy recursive wavelet neural network model 3-5, differential unit 1-3, temperature detection module and fuzzy self-adjusting controller. The structure of the rotary kiln temperature control system is shown in Figure 2 shown.

[0062] 1. Design of ESN neural network model of WOA - PID controller 1-3

[0063] WOA's ESN neural network model - PID controller is the Kp, Ki and Kd parameters of the PID controller output by WOA's ESN neural network model. The input of WOA's ESN neural network model - PID controller is the error and error change rate between the given value of the controlled quantity and the feedback detection value, which can realize online adjustment of PID controller parameters and further optimize the dynamics, accuracy and robustness of the controlled parameters. The output value of WOA's ESN neural network model - PID controller is:

[0064]

[0065] Where: u(k) is the output value of the PID controller, e(k) is the error of the controlled object, and k is the sampling number.

[0066] 2. Design of fuzzy self-tuning controller

[0067] The fuzzy self-tuning controller consists of two parts in parallel: fuzzy control and integral action. The fuzzy control rule is:

[0068] U f =k0×[a×e+(1-a)×e'] (5)

[0069] U f is the output of the fuzzy controller, k0 is the output coefficient, a is the adaptive correction factor, and the size of a reflects the influence of the rotary kiln temperature error e and the rotary kiln temperature error change rate e″ on the output of the fuzzy self-adjusting controller.

[0070] In the initial stage, the temperature error of the rotary kiln is relatively large. At this time, a larger a value should be selected to eliminate the existence of the rotary kiln temperature error as soon as possible, and the weight of the rotary kiln temperature error in the fuzzy self-adjusting controller should be increased; in the mid-term stage, the rotary kiln temperature error decreases and the rotary kiln temperature rises faster. In order to reduce the overshoot of the rotary kiln temperature, the control effect on the change of the rotary kiln temperature error should be highlighted. A smaller a value should be selected. When the rotary kiln temperature is close to the expected temperature value, since the rotary kiln temperature error and its change are both small at this time, the rotary kiln temperature error and the error change rate can take the same weight.

[0071] The design methods of other models refer to the design method of the temperature detection module of this patent.

[0072] 3. Design of IoT system for intelligent temperature control of rotary kiln

[0073] The Internet of Things system for intelligent control of rotary kiln temperature includes rotary kiln parameter detection node, rotary kiln temperature control node, rotary kiln parameter communication node, rotary kiln cloud platform server and rotary kiln monitoring mobile phone APP. Two-way communication is realized between rotary kiln parameter detection node, rotary kiln temperature control node, rotary kiln parameter communication node, rotary kiln cloud platform server and rotary kiln monitoring mobile phone APP. The rotary kiln parameter detection node collects rotary kiln parameters, the rotary kiln temperature control node controls the temperature of the rotary kiln, the rotary kiln parameter communication node collects the rotary kiln parameters of the rotary kiln parameter detection node and the rotary kiln temperature control node and uploads them to the rotary kiln cloud platform server. The rotary kiln cloud platform server stores and manages the parameters of the rotary kiln. The rotary kiln parameter processing and analysis monitoring terminal realizes the processing and analysis of the collected rotary kiln big data and the rotary kiln parameter monitoring function. Structure of the Internet of Things system for intelligent control of rotary kiln temperature Figure 3 .

[0074] 1. Design of rotary kiln parameter detection node

[0075] The present invention adopts a rotary kiln parameter detection node as a rotary kiln parameter sensing terminal to be detected. The rotary kiln parameter detection node and the rotary kiln parameter communication node realize two-way communication through a LoRa communication module. The rotary kiln parameter detection node includes temperature, crack, deformation, gas sensors and corresponding signal conditioning circuits, cameras, STM32 microprocessors, LoRa communication modules, and gas sensors include oxygen, carbon dioxide, sulfur dioxide and nitrogen oxides. The software of the rotary kiln parameter detection node mainly realizes LoRa communication module communication, rotary kiln parameter collection and preprocessing. The software is designed with C language programming, which has a high degree of compatibility, greatly improves the work efficiency of software design and development, and enhances the reliability, readability and portability of the program code. See the rotary kiln parameter detection node. Figure 4 shown.

[0076] 2. Design of rotary kiln temperature control nodes

[0077] The rotary kiln temperature control node includes an STM32 microprocessor, a camera, a LoRa communication module, a rotary kiln parameter processing and analysis monitoring terminal and a control device, which are used for real-time monitoring of the rotary kiln temperature. The camera obtains real-time images of the rotary kiln for video monitoring. The control equipment includes a feed transmission device, a discharge transmission device, a burner and a material weight sensor to realize intelligent control to ensure that the temperature parameters of the rotary kiln are within the appropriate heating range. The rotary kiln temperature control node exchanges information with the rotary kiln parameter communication node through the LoRa communication module. The rotary kiln parameter processing and analysis monitoring terminal is an industrial control computer, which is connected to the STM32 microprocessor through a USB interface. The rotary kiln parameter processing and analysis monitoring terminal mainly realizes rotary kiln communication parameter setting, rotary kiln data analysis, rotary kiln data management and rotary kiln temperature control system, etc. See the rotary kiln temperature control node structure diagram for details. Figure 5 , the software functions of rotary kiln parameter processing and analysis monitoring terminal are shown in Figure 6 .

[0078] 3. Design of rotary kiln parameter communication node

[0079] The rotary kiln parameter communication node includes an STM32 microprocessor, a LoRa communication module, a camera, and a wireless communication module. The LoRa communication module is used to build a self-organizing communication network to realize data interaction between the rotary kiln parameter detection node and the rotary kiln temperature control node and the rotary kiln parameter communication node. The rotary kiln parameter communication node realizes two-way data interaction with the rotary kiln cloud platform server through the wireless communication module. The detection parameters of the rotary kiln parameter detection node are uploaded to the rotary kiln cloud platform server through the rotary kiln parameter communication node to realize the storage and processing of the rotary kiln parameters. The rotary kiln parameter communication node is shown in Figure 7 .

[0080] The above embodiments are only for illustrating the technical concept and features of the present invention, and their purpose is to enable people familiar with the technology to understand the content of the present invention and implement it accordingly, and they cannot be used to limit the protection scope of the present invention. Any equivalent transformation or modification made according to the spirit of the present invention should be included in the protection scope of the present invention.

Claims

1. A rotary kiln temperature control method, characterized in that: The steps include: Step 1: multiple temperature sensor outputs are used as inputs of a temperature detection module, multiple temperature data of the rotary kiln are collected through the temperature detection module, and the temperature data are preprocessed; Step 2: A feed transmission device is provided at the front end of the rotary kiln, and a material weight sensor is arranged in the feed transmission device to detect the weight of the material, and the rotary kiln is heated by a burner; Step 3: Construct a rotary kiln temperature control system, which controls the burner according to the temperature data detected by the temperature detection module and the temperature target value set by the rotary kiln temperature control system; the rotary kiln temperature control system also dynamically controls the burner combustion progress in real time according to the feed weight detected by the material weight sensor and the real-time temperature detected by the temperature detection module at the current moment, combined with the temperature target value and the material weight target value set by the rotary kiln temperature control system, adjusts the temperature in the rotary kiln in real time to meet the temperature target value set by the rotary kiln temperature control system, and enables the system to meet the material weight target value through the feed transmission device.

2. A rotary kiln temperature control method according to claim 1, characterized in that: The rotary kiln temperature control system includes a NARX neural network model 3-5 of WOA, an ESN neural network model-PID controller 1-3 of WOA, a fuzzy recursive wavelet neural network model 3-5 of WOA, a differential unit 1-3, and a fuzzy self-adjusting controller; The temperature target value set by the rotary kiln temperature control system is obtained, and the cumulative sum of the temperature target value and the fuzzy recursive wavelet neural network model 5 of WOA and the error and error change rate of the temperature detection module output are used as the input of the ESN neural network model-PID controller 3 of WOA; the error and error change rate of the temperature target value and the output of the NARX neural network model 5 of WOA are used as the input of the fuzzy self-adjusting controller; the temperature target value is used as the input of the NARX neural network model 3 and the differential unit 1 of WOA, the output of the differential unit 1 is used as the input of the NARX neural network model 4 of WOA, the output of the fuzzy recursive wavelet neural network model 5 of WOA is used as the input of the NARX neural network model 3 and the differential unit 3 of WOA, the output of the differential unit 3 is used as the input of the NARX neural network model 4 of WOA, and the difference between the output of the NARX neural network model 3 of WOA and the output of the temperature detection module and the change rate of the difference As the input of the ESN neural network model-PID controller 1 of WOA, the difference between the output of the NARX neural network model 4 of WOA and the output of the differential unit 2 and the rate of change of the difference are used as the input of the ESN neural network model-PID controller 2 of WOA, the outputs of the ESN neural network model-PID controller 1 of WOA and the ESN neural network model-PID controller 2 of WOA are used as the input of the fuzzy recursive wavelet neural network model 3 of WOA, and the difference between the cumulative sum of the output of the fuzzy recursive wavelet neural network model 3 of WOA and the output of the ESN neural network model-PID controller 3 of WOA and the fuzzy recursive wavelet neural network model 4 of WOA is used as the input of the burner and the fuzzy recursive wavelet neural network model 4 of WOA; the output of the temperature detection module is used as the input of the differential unit 2, the NARX neural network model 5 of WOA and the fuzzy recursive wavelet neural network model 4 of WOA; The error and error change between the material weight target value set by the rotary kiln temperature control system and the material weight sensor output are used as the input of the fuzzy recursive wavelet neural network model 5 of WOA, and the material weight sensor output is used as the input of the fuzzy recursive wavelet neural network model 4 of WOA.

3. A rotary kiln temperature control method according to claim 1, characterized in that: The temperature detection module includes a NARX neural network model 1-2 of WOA, a fuzzy recursive wavelet neural network model 1-2 of WOA and an ESN neural network model of WOA; The outputs of multiple temperature sensors are respectively used as inputs of NARX neural network model 1 of WOA, ESN neural network model of WOA and fuzzy recursive wavelet neural network model 1 of WOA, the outputs of NARX neural network model 1 of WOA and ESN neural network model of WOA are used as inputs of fuzzy recursive wavelet neural network model 1 of WOA, the output of fuzzy recursive wavelet neural network model 1 of WOA is used as inputs of NARX neural network model 2 of WOA and fuzzy recursive wavelet neural network model 2 of WOA, the output of NARX neural network model 2 of WOA is used as input of fuzzy recursive wavelet neural network model 2 of WOA, and the output of fuzzy recursive wavelet neural network model 2 of WOA is used as the predicted values ​​of the output temperatures of multiple temperature sensors.

4. A rotary kiln temperature control method according to claim 3, characterized in that: The NARX neural network model 1 of WOA and the NARX neural network model 2 of WOA have the same structure, both are NARX neural network models of WOA, and both use the whale optimization algorithm to optimize the NARX neural network model parameters; The structures of WOA's fuzzy recursive wavelet neural network models 1-2 are the same, both of which are WOA's fuzzy recursive wavelet neural network models, and the whale optimization algorithm is used to optimize the fuzzy recursive wavelet neural network model parameters; WOA's ESN neural network model uses the whale optimization algorithm to optimize the ESN neural network model parameters.

5. A rotary kiln temperature control method according to claim 2, characterized in that: The NARX neural network models 3-5 of WOA have the same structure, are all NARX neural network models of WOA, and all use the whale optimization algorithm to optimize the NARX neural network model parameters; The WOA ESN neural network model-PID controllers 1-3 have the same structure, and are all WOA ESN neural network model-PID controllers, and the WOA ESN neural network model outputs the parameters of the PID controller; The fuzzy recursive wavelet neural network models 3-5 of WOA have the same structure, are all fuzzy recursive wavelet neural network models of WOA, and all use the whale optimization algorithm to optimize the fuzzy recursive wavelet neural network model parameters.

6. A rotary kiln temperature control method according to claim 2, characterized in that: The fuzzy self-adjusting controller is composed of two parts in parallel: fuzzy control and integral action. The specific fuzzy control rules are as follows: U f =k0×[a×e+(1-a)×e'] Among them, U f is the output of the fuzzy controller, k0 is the output coefficient, a is the adaptive correction factor, and the size of a reflects the influence of the rotary kiln temperature error e and the rotary kiln temperature error change rate e' on the output of the fuzzy self-adjusting controller.

7. An Internet of Things system for intelligent temperature control of a rotary kiln, characterized in that: The invention comprises a rotary kiln parameter detection node, a rotary kiln temperature control node, a rotary kiln parameter communication node, a rotary kiln cloud platform server and a rotary kiln monitoring mobile phone APP. Two-way communication is realized among the rotary kiln parameter detection node, the rotary kiln temperature control node, the rotary kiln parameter communication node, the rotary kiln cloud platform server and the rotary kiln monitoring mobile phone APP. The rotary kiln parameter detection node collects rotary kiln parameters, the rotary kiln temperature control node controls the temperature of the rotary kiln, the rotary kiln parameter communication node collects the rotary kiln parameters of the rotary kiln parameter detection node and the rotary kiln temperature control node and uploads them to the rotary kiln cloud platform server, and the rotary kiln cloud platform server stores and manages the parameters of the rotary kiln; the rotary kiln parameter detection node and the rotary kiln temperature control node execute the rotary kiln temperature control method as described in any one of claims 1 to 6.

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