Rotary kiln temperature control method and system based on fuzzy control algorithm

Abstract

The invention relates to a rotary kiln temperature control method and system based on a fuzzy control algorithm, and the method comprises the steps: obtaining the pellet temperature, the flame center temperature, the kiln wall temperature, the coal conveying air flow, the combustion-supporting air flow, the gas inlet flow, the gas inlet pressure and the speed of a chain grate machine during the operation of a rotary kiln as input variables, and taking the adjustment amount of pulverized coal as an output variable; carrying out pretreatment; determining a value interval of the input variable and the output variable as a discourse domain range, and defining a plurality of fuzzy sets in the corresponding discourse domain; and determining a membership function corresponding to each input variable, mapping continuous input values into a fuzzy set according to the membership functions, obtaining a fuzzy rule table corresponding to the output variables in combination with expert experience and historical data, and realizing temperature control of the rotary kiln according to the obtained fuzzy rule table.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature control, and particularly to a rotary kiln temperature control method and system based on a fuzzy control algorithm. Background Technique

[0002] The statements in this part merely provide background technical information related to the present invention and do not necessarily constitute prior art.

[0003] The rotary kiln is a high-temperature heat treatment equipment widely used in multiple industries such as metallurgy, building materials, and chemical engineering, and is also a key equipment for pellet material production. The stability of its temperature field is an important factor affecting pellet quality and production energy consumption. Effectively controlling the pellet temperature field has always been a hot issue in the research of pellet production lines. Since the roasting process of the pellet rotary kiln is relatively complex, it is a control object with large lag, parameter distribution, multi-variables, time-variation, and multi-coupling, and it is difficult to establish an accurate mathematical model. It is also difficult to obtain ideal results using traditional control algorithms. Currently, most enterprises still use manual control combined with upper computer monitoring parameters for the calcination temperature control. This method has high energy consumption, low productivity, and unstable product quality.

[0004] The most commonly used technology in rotary kiln temperature control is the traditional PID control technology. Its advantages lie in being simple and easy to implement, and having good effects for linear systems and systems with good static characteristics. However, the traditional PID control technology also has the following disadvantages: poor control effects for non-linear, time-varying, and multi-variable systems, difficult parameter adjustment, requiring experienced engineers to manually adjust parameters, and being unable to handle large disturbances and uncertainties. Summary of the Invention

[0005] In order to solve the technical problems existing in the above background technique, the present invention provides a rotary kiln temperature control method and system based on a fuzzy control algorithm, which combines fuzzy control theory and expert experience to establish a parameter fuzzy rule table. Through the fuzzy synthesis inference algorithm, a fuzzy control decision table is generated, improving the control accuracy of the rotary kiln temperature, in order to achieve a satisfactory effect. The design of the fuzzy controller does not depend on the accurate mathematical model of the controlled object, is easy to be accepted by operators, is convenient to be implemented by computer software and hardware, and has good robustness and adaptability.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] The first aspect of the present invention provides a rotary kiln temperature control method based on a fuzzy control algorithm, including the following steps:

[0008] Obtain the pellet temperature, flame center temperature, kiln wall temperature, coal conveying air flow rate, combustion-supporting air flow rate, gas inlet flow rate, gas inlet pressure and the speed of the grate-kiln cooler during the operation of the rotary kiln as input variables, take the adjustment amount of pulverized coal as the output variable, and perform preprocessing;

[0009] Determine the value ranges of the input variables and the output variable as the domain ranges, and define multiple fuzzy sets within the corresponding domains;

[0010] Determine the membership functions corresponding to each input variable, map the continuous input values into the fuzzy sets according to the membership functions, combine expert experience and historical data to obtain the fuzzy rule table corresponding to the output variable, and realize the temperature control of the rotary kiln according to the obtained fuzzy rule table.

[0011] Furthermore, use infrared sensors to collect the pellet temperature, flame center temperature and kiln wall temperature in the rotary kiln, and extract the real-time consumption of pulverized coal, coal conveying air flow rate, combustion-supporting air flow rate, gas inlet flow rate, gas inlet pressure and the speed of the grate-kiln cooler from the equipment PLC of the rotary kiln.

[0012] Furthermore, the preprocessing includes filtering, denoising and normalization processing.

[0013] Furthermore, the membership functions are trigonometric functions and Gaussian functions.

[0014] Furthermore, during the temperature control, realize three goals of temperature control, energy consumption and daily output by controlling the addition amount of pulverized coal, and take the cost optimization between pulverized coal and blast furnace gas as the goal.

[0015] Furthermore, when determining the fuzzy rule table, divide the temperature control of the rotary kiln into multiple levels, adopt different fuzzy control rules for each level, and adjust the membership functions and rule sets according to the goals of each level.

[0016] Furthermore, in the fuzzy rule table, take the change rate of the pellet temperature as the row, the deviation of the pellet temperature as the column, and the change rate of the pellet temperature and the deviation of the pellet temperature as the input variables.

[0017] The second aspect of the present invention provides a rotary kiln temperature control system based on a fuzzy control algorithm, including:

[0018] A data acquisition and preprocessing module, configured to: obtain the pellet temperature, flame center temperature, kiln wall temperature, coal conveying air flow rate, combustion-supporting air flow rate, gas inlet flow rate, gas inlet pressure and the speed of the grate-kiln cooler during the operation of the rotary kiln as input variables, take the adjustment amount of pulverized coal as the output variable, and perform preprocessing;

[0019] The fuzzification module is configured to: determine the value ranges of the input variable and the output variable as the universe of discourse, and define multiple fuzzy sets within the corresponding universe of discourse.

[0020] The fuzzy rule module is configured to: determine the membership function corresponding to each input variable, map the continuous input values into the fuzzy sets according to the membership function, combine expert experience and historical data to obtain a fuzzy rule table corresponding to the output variable, and implement the temperature control of the rotary kiln according to the obtained fuzzy rule table.

[0021] The third aspect of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the steps in the above-mentioned rotary kiln temperature control method based on the fuzzy control algorithm are implemented.

[0022] The fourth aspect of the present invention provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps in the above-mentioned rotary kiln temperature control method based on the fuzzy control algorithm are implemented.

[0023] Compared with the prior art, the above one or more technical solutions have the following beneficial effects:

[0024] Combined with the fuzzy control theory and expert experience, a parameter fuzzy rule table is established. Through the fuzzy synthesis inference algorithm, a fuzzy control decision table is generated, improving the control accuracy of the rotary kiln temperature. The design of the fuzzy controller does not depend on the accurate mathematical model of the controlled object, is easy to be accepted by operators, and is convenient to be implemented by computer software and hardware, with good robustness and adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The specification drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention.

[0026] Figure 1 It is a schematic diagram of the architecture of the rotary kiln temperature control method based on the fuzzy control algorithm provided by one or more embodiments of the present invention. DETAILED DESCRIPTION

[0027] The present invention will be further described below in conjunction with the drawings and embodiments.

[0028] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0029] As introduced in the background art, the most commonly used technology in the temperature control of rotary kilns is the traditional PID control technology. Its advantages lie in its simplicity and ease of implementation, and it has good effects on linear systems and systems with good static characteristics. However, the traditional PID control technology also has the following disadvantages: it has poor control effects on non-linear, time-varying and multivariable systems, parameter adjustment is relatively difficult, and it requires experienced engineers to manually adjust the parameters, and it cannot handle large disturbances and uncertainties.

[0030] Some existing temperature control methods indirectly control the temperature of the product by controlling the temperature inside the kiln, resulting in deviations in the temperature of the actual product. Or by discovering patterns and relationships in the data, but its effect largely depends on the accuracy and integrity of the data. However, if the data has noise or is missing, it may lead to inaccurate models being mined. Or a heat effect model is implemented based on a series of assumptions and simplifications, but it may not be able to fully and accurately reflect the complex heat process of the actual rotary kiln.

[0031] Therefore, the following embodiments propose a temperature control method and system for rotary kilns based on the fuzzy control algorithm, which combines the fuzzy control theory and expert experience to establish a parameter fuzzy rule table. Through the fuzzy synthesis inference algorithm, a fuzzy control decision table is generated, improving the control accuracy of the temperature of the rotary kiln, in order to achieve satisfactory results. Advantages of the fuzzy control algorithm: The design of the fuzzy controller does not depend on the accurate mathematical model of the controlled object; the fuzzy controller is easily accepted by the operators; it is convenient to be implemented by computer software and hardware; it has good robustness and adaptability.

[0032] Embodiment 1:

[0033] A rotary kiln is a hollow horizontal cylindrical device. During the pellet production process, its working principle mainly involves key steps such as the feeding, preheating, roasting and cooling of raw materials. These steps are triggered and connected with each other through the actions of different parts of the rotary kiln, jointly completing the roasting process of the pellets.

[0034] Raw material feeding: The raw material pellets enter the inlet end of the rotary kiln through a grate-kiln. The design of the feeding device ensures that the raw materials can enter the kiln stably and evenly, laying a foundation for the subsequent steps.

[0035] Preheating area: The raw materials gradually move forward in the rotary kiln. Due to the inclination and slow rotation of the cylinder, the materials roll along the circumferential direction and move along the axial direction. In the preheating area, the raw materials are subjected to the high temperature of the combustion gas and gradually heat up to the preheating temperature before the roasting temperature. The main purpose of preheating is to increase the temperature of the materials and prepare for subsequent physical and chemical changes.

[0036] Roasting Area: The preheated raw materials enter the roasting area, which is the part with the highest temperature inside the rotary kiln. In the roasting area, the raw materials reach their highest temperature, usually between 1400°C and 1500°C (which may also vary depending on the material properties and process requirements). At high temperatures, the chemical substances in the raw materials start to undergo petrification reactions, such as the consolidation of iron ore particles to form finished pellets. At the same time, the combustion gases (such as blast furnace gas) in the calcination area provide fuel and maintain the high temperature in the calcination area.

[0037] Cooling Area: After roasting is completed, the finished pellets enter the cooling area. In this area, the cooling gas (such as cold air) rapidly cools the clinker to prevent it from overheating and caking. The cooled finished pellets are discharged outside the kiln through the discharging device, ready for further processing and transportation.

[0038] During the entire working process, the barrel of the rotary kiln is driven by the main drive motor. The power is transmitted to the open gear device through the reducer, thereby driving the kiln body to rotate around the longitudinal axis. This rotation ensures the uniform distribution and continuous movement of the raw materials inside the kiln, enabling steps such as preheating, roasting, and cooling to proceed in an orderly manner.

[0039] Based on the working process of the rotary kiln, this embodiment proposes a temperature control method for the rotary kiln based on the fuzzy control algorithm, including the following steps:

[0040] Data Acquisition: Install infrared sensors to collect the temperature of the pellets, the temperature of the flame center, and the temperature of the kiln wall in the rotary kiln. Collect the real-time consumption of pulverized coal, the flow rate of coal conveying air, the flow rate of combustion-supporting air, the flow rate of gas inlet, the gas inlet pressure, and the speed of the grate-kiln cooler from the device PLC through OPC technology (OLE for Process Control, that is, Object Linking and Embedding for Process Control, which is an industrial communication standard).

[0041] Determine Input and Output Variables: The input variables in this embodiment are the temperature of the pellets, the temperature of the flame center, the temperature of the kiln wall, the flow rate of coal conveying air, the flow rate of combustion-supporting air, the flow rate of gas inlet, the gas inlet pressure, and the speed of the grate-kiln cooler, and the output variable is the adjustment amount of pulverized coal.

[0042] Data Preprocessing: Filter, denoise, and normalize the data such as the gas flow rate and the temperature of the pellets collected.

[0043] Fuzzification: Clearly define the domain ranges of the input variables and output variables, that is, the intervals within which they may take values. In this embodiment, the domain of the temperature deviation of the pellets is (-40, 40, 1), the domain of the temperature change rate of the pellets is (-5, 5, 1), and the domain of the adjustment value of pulverized coal is (-200, 200, 1).

[0044] Define multiple fuzzy sets within these universes of discourse. The fuzzy sets of the pellet temperature deviation are Negative Maximal (PBA), Negative Big (PB), Negative Medium-Big (PMB), Negative Medium (PM), Negative Small (PS), Negative Tiny (PSA), Negative Zero (PO), Zero (O), Positive Zero (NO), Positive Tiny (NSA), Positive Small (NS), Positive Medium (NM), Positive Medium-Big (NMB), Positive Big (NB), and Positive Maximal (NBA). The fuzzy sets of the pellet temperature change rate are Negative Big (PB), Negative Medium (PM), Negative Small (PS), Negative Zero (PO), Zero (O), Positive Zero (NO), Positive Small (NS), Positive Medium (NM), Positive Big (NB). The fuzzy sets of the pulverized coal adjustment value are Negative Maximal (PBA), Negative Big (PB), Negative Medium-Big (PMB), Negative Medium (PM), Negative Small (PS), Negative Tiny (PSA), Negative Zero (PO), Zero (O), Positive Zero (NO), Positive Tiny (NSA), Positive Small (NS), Positive Medium (NM), Positive Medium-Big (NMB), Positive Big (NB), and Positive Maximal (NBA).

[0045] Define the membership functions: Select appropriate membership functions for each input variable to map continuous input values to fuzzy sets. Triangular functions and Gaussian functions are used.

[0046] Rule evaluation: According to expert experience and historical data, formulate a fuzzy rule table. The fuzzy rule table of this embodiment is shown in Table 1.

[0047] When formulating the fuzzy rule table, follow the principles of consistency, completeness, accuracy, interpretability, and practicality. Historical data provides empirical support through statistical analysis, hypothesis verification, and parameter optimization, while expert experience is used for knowledge acquisition, constructing preliminary rules, and handling abnormal situations. The combination of the two ensures that the rules not only reflect reality but also have a theoretical basis, and they jointly act on the design and continuous optimization of the rules.

[0048] Table 1 Fuzzy Rule Table

[0049] PBPM PS PO 0 NO NS NM NB -40 PBA NBANBA NBA NBA NBA NBA NBA NB NMB -25 PB NBANBA NBA NBA NB NB NB NMB NM -15 PMB NBANBA NB NMB NMB NMB NMB NM NM -10 PM NBNMB NMB NM NM NM NM NS NSA -7 PS NMBNMB NM NM NS NS NSA NO NO -4 PSA NMM NM NSA NSA NSA NO NO 0 -2 PO NSNS NSA NSA NO NO 0 0 PO 0 0 NSANO 0 0 0 0 0 PO PSA 2 NO NO0 0 0 0 0 PO PO PSA 4 NSA 00 0 PO PO PO PSA PSA PS 7 NS 0PO PO PSA PSA PSA PS PM PM 10 NM PSAPSA PS PS PS PS PM PM PMB 15 NMB PSPS PM PM PM PM PMB PMB PB 25 NB PMPM PMB PMB PMB PMB PB PB PBA 40 NBA PMPMB PMB PB PB PB PB PBA PBA

[0050] In Table 1, the rows represent the temperature change rate (TCR) of the pellets. The columns represent the temperature deviation (TD) of the pellets. The temperature change rate (TCR) of the pellets is one of the input variables, indicating the change speed of the current pellet temperature relative to the target temperature. The temperature deviation (TD) of the pellets is another input variable, indicating the difference between the current pellet temperature and the target temperature. After obtaining the data, find the corresponding fuzzy sets and apply the fuzzy rules.

[0051] On-site debugging: After all the preparations are completed, enter the field test phase, load the above fuzzy control method into the corresponding controller, and observe whether the performance meets expectations based on the input and output variables. If problems are found, it is necessary to return to the previous steps for modification and improvement.

[0052] Determine whether the standard is met: After a series of tests, if the performance of the algorithm does not meet the predetermined standard, it is necessary to re-examine the design ideas and make corresponding adjustments; otherwise, it means that the task has been successfully completed.

[0053] The adaptive fuzzy PID controller developed using the above process can dynamically adjust the PID parameters according to the real-time operating status of the rotary kiln and improve the accuracy and stability of temperature control.

[0054] The above method forms a multi-level fuzzy control strategy, which divides the temperature control of the rotary kiln into multiple levels. Each level adopts different fuzzy control rules to achieve refined control of the temperature in different areas. The first level of control is mainly responsible for the stability of the global temperature, the second level of control is responsible for the fine adjustment of the local temperature, and the third level of control is used to deal with sudden temperature fluctuations. Through the multi-level control strategy, the response speed and control accuracy of the overall system are improved.

[0055] The multi-level fuzzy control strategy achieves refined management of the rotary kiln temperature through hierarchical design. When formulating the fuzzy rule table, the membership function and rule set are adjusted according to the objectives of each level, and these rules are applied through real-time monitoring and dynamic switching mechanisms, thereby improving the system's response speed and control accuracy. This method enhances the system's adaptability and stability to different working conditions.

[0056] By collecting and analyzing operating data and continuously optimizing the fuzzy control model, a closed-loop feedback system is formed, which allows the control performance to gradually improve over time and achieves data-driven continuous improvement.

[0057] In combination with the experience of industry experts, a detailed fuzzy control rule table was established, and the fuzzy control algorithm was optimized through expert knowledge to improve the accuracy and robustness of rotary kiln temperature control.

[0058] Multi-objective optimization control is achieved, which not only focuses on temperature control accuracy, but also comprehensively considers multiple factors such as energy consumption, production efficiency and product quality. Through the fuzzy logic algorithm, the system can find the best balance between multiple goals and optimize the overall performance. The goal in this embodiment is to achieve the three goals of temperature control, energy consumption, and indirect control of daily output by controlling the amount of coal powder added. When achieving temperature control, the cost optimization between coal powder and blast furnace gas should be considered, and the speed of the chain grate, that is, the daily output, and the relationship between the three should also be considered.

[0059] By adopting multi-sensor fusion technology, combining data from various sensors such as temperature, pressure, and flow rate, and conducting comprehensive processing through fuzzy logic, the measurement accuracy and reliability are improved. At the same time, the system has a redundant design to ensure the accuracy and security of key data.

[0060] In this embodiment, through the adaptive fuzzy controller and multi-level fuzzy control strategy, the accuracy of the control system has been significantly improved, the temperature fluctuation has been reduced, and both the control error and the standard deviation have decreased.

[0061] This embodiment can maintain stable temperature control under different working conditions. Even in the case of gas pressure fluctuations and gas quality changes, the temperature control remains stable.

[0062] This embodiment can more effectively cope with sudden temperature fluctuations and external environment changes. For example, in the case of sudden material dropping in the rotary kiln, the temperature recovery time is significantly shortened.

[0063] In this embodiment, through the multi-level control strategy, the response speed of the system to temperature changes is significantly accelerated, and the response time is greatly shortened.

[0064] This embodiment has the ability of self-learning, can automatically optimize the control model according to the actual operation data, adapt to new working condition changes, and reduces the frequency and difficulty of manual parameter adjustment.

[0065] This embodiment can improve the temperature uniformity of the rotary kiln, enhance the thermal efficiency, and further reduce the energy consumption.

[0066] Through the data acquisition module, data such as temperature, pressure, and flow rate are collected in real time and displayed through a graphical interface. Operators can intuitively see the change trends of each parameter and adjust the control strategy in a timely manner.

[0067] Embodiment 2:

[0068] The rotary kiln temperature control system based on the fuzzy control algorithm includes:

[0069] The data acquisition and preprocessing module is configured to: obtain the pellet temperature, flame center temperature, kiln wall temperature, coal conveying air flow rate, combustion-supporting air flow rate, gas inlet flow rate, gas inlet pressure during the operation of the rotary kiln, and the speed of the grate cooler as input variables, and take the adjustment amount of pulverized coal as the output variable, and conduct preprocessing;

[0070] The fuzzyfication module is configured to: determine the value range of the input variables and the output variable as the domain of discourse, and define multiple fuzzy sets within the corresponding domain of discourse;

[0071] The fuzzy rule module is configured to: determine the membership function corresponding to each input variable, map continuous input values into fuzzy sets according to the membership function, combine expert experience and historical data to obtain a fuzzy rule table corresponding to the output variable, and implement the temperature control of the rotary kiln according to the obtained fuzzy rule table.

[0072] Embodiment III:

[0073] This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the steps in the method for controlling the temperature of a rotary kiln based on a fuzzy control algorithm as described in Embodiment II above.

[0074] Embodiment IV:

[0075] This embodiment provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the steps in the method for controlling the temperature of a rotary kiln based on a fuzzy control algorithm as described in Embodiment II above.

[0076] The steps involved in Embodiments II to IV above correspond to those in Embodiment I. For specific implementation manners, reference may be made to the relevant description part of Embodiment I. The term "computer-readable storage medium" should be understood to include a single medium or multiple media containing one or more instruction sets; it should also be understood to include any medium that can store, encode, or carry an instruction set for execution by a processor and cause the processor to execute any method in the present invention.

[0077] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A rotary kiln temperature control method based on fuzzy control algorithm, characterized in that: The following steps are involved: The pellet temperature, flame center temperature, kiln wall temperature, coal conveying air flow, combustion-supporting air flow, gas inlet flow, gas inlet pressure and chain grate speed during the operation of the rotary kiln are obtained as input variables, and the adjustment amount of pulverized coal is used as the output variable, and preprocessed; Determine the value interval of input variables and output variables as the domain range, and define multiple fuzzy sets in the corresponding domain; Determine the membership function corresponding to each input variable, and map the continuous input value to the fuzzy set according to the membership function. Combine expert experience and historical data to obtain the fuzzy rule table corresponding to the output variable. The temperature control of the rotary kiln is realized according to the obtained fuzzy rule table.

2. The rotary kiln temperature control method based on fuzzy control algorithm according to claim 1, characterized in that: Infrared sensors are used to collect the pellet temperature, flame center temperature and kiln wall temperature in the rotary kiln, and the real-time coal powder usage, coal conveying air flow, combustion-supporting air flow, gas intake flow, gas intake pressure and chain grate speed are extracted from the rotary kiln equipment PLC.

3. The rotary kiln temperature control method based on fuzzy control algorithm according to claim 1, characterized in that: Preprocessing includes filtering, denoising and normalization.

4. The rotary kiln temperature control method based on fuzzy control algorithm according to claim 1, characterized in that: The membership functions are trigonometric and Gaussian functions.

5. The rotary kiln temperature control method based on fuzzy control algorithm according to claim 1, characterized in that: During the temperature control period, the three goals of temperature control, energy consumption and daily output are achieved by controlling the amount of coal powder added, and the optimal cost between coal powder and blast furnace gas is the goal.

6. The rotary kiln temperature control method based on fuzzy control algorithm according to claim 1, characterized in that: When determining the fuzzy rule table, the temperature control of the rotary kiln is divided into multiple levels, each level adopts different fuzzy control rules, and the membership function and rule set are adjusted according to the objectives of each level.

7. The rotary kiln temperature control method based on fuzzy control algorithm according to claim 1, characterized in that: In the fuzzy rule table, the change rate of pellet temperature is the row, the deviation of pellet temperature is the column, and the change rate of pellet temperature and the deviation of pellet temperature are the input variables.

8. The rotary kiln temperature control system based on fuzzy control algorithm is characterized by: include: The data acquisition and preprocessing module is configured to: obtain the pellet temperature, flame center temperature, kiln wall temperature, coal conveying air flow rate, combustion-supporting air flow rate, coal gas inlet flow rate, coal gas inlet pressure and chain grate speed during the operation of the rotary kiln as input variables, and use the adjustment amount of coal powder as output variable, and perform preprocessing; The fuzzification module is configured to: determine the value intervals of the input variables and the output variables as the domain range, and define multiple fuzzy sets in the corresponding domain; The fuzzy rule module is configured to: determine the membership function corresponding to each input variable, and map the continuous input value to the fuzzy set according to the membership function, combine expert experience and historical data to obtain the fuzzy rule table corresponding to the output variable, and realize the temperature control of the rotary kiln according to the obtained fuzzy rule table.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the program is executed by a processor, the steps in the rotary kiln temperature control method based on a fuzzy control algorithm as described in any one of claims 1 to 7 are implemented.

10. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the rotary kiln temperature control method based on the fuzzy control algorithm as described in any one of claims 1 to 7 are implemented.

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