A method and apparatus for controlling the outlet temperature of the medium in a distillation tower top air cooling system

Abstract

This invention discloses a method and apparatus for controlling the outlet temperature of a medium in a distillation tower top air-cooled system. The method includes: acquiring real-time operating data and real-time environmental data of the distillation tower top air-cooled system; determining whether fluctuations exist in the air-cooled system based on the real-time operating data, the real-time environmental data, and a preset temperature change range; when system fluctuations exist, calculating a motor control quantity based on preset control rules and preset control parameters; and performing frequency conversion control on the motor based on the motor control quantity and a gradient optimization algorithm to achieve temperature control of the medium outlet of the distillation tower top air-cooled system. By using real-time operating data to perform frequency conversion control on the motor, the temperature control of the medium outlet of the distillation tower top air-cooled system is achieved, improving the efficiency and adaptability of the temperature control at the medium outlet of the distillation tower top air-cooled system.

Description

Technical Field

[0001] This invention relates to the field of temperature control technology, and in particular to a method and apparatus for controlling the outlet temperature of a medium in a distillation tower top air cooling system. Background Technology

[0002] The overhead air-cooling system of the fractionation tower includes inlet and outlet pipes, packing material, and an axial flow fan. The axial flow fan draws air from above the packing material, cooling it through forced convection heat exchange with the material flowing down the packing. The fan is driven by a motor. To change the fan speed and thus its cooling capacity, a frequency converter is known to be used, allowing for easy adjustment of the motor speed. A frequency converter is a sensitive electronic component.

[0003] The air-cooling system, as a power unit for cooling materials, uses a fan to exchange heat between flowing air and the medium entering the tower, thereby lowering the medium's temperature. However, when the ambient temperature and humidity change, or when fluctuations in the unit's operating conditions cause changes in the medium's flow rate and inlet temperature, the cooling capacity provided by the axial flow fan operating at industrial frequency may be higher or lower than the cooling capacity required to cool the medium to the target temperature. In this case, the air-cooling system's outlet temperature will deviate from the design temperature, and the fan speed will not be at its optimal value.

[0004] Existing fractionation tower air-cooling systems optimize fractionation tower parameters based on fractionation tower data and computer programs to control the overall temperature of the fractionation tower. However, the frequency conversion of the fans in the fractionation tower air-cooling system requires manual adjustment by operators, which cannot be adjusted in a timely manner. Furthermore, the fans in the fractionation tower air-cooling system cannot achieve stepless speed regulation and can only perform fixed-speed frequency conversion, which cannot effectively cope with various changes and accurately control the medium outlet temperature of the fractionation tower top air-cooling system. Summary of the Invention

[0005] This invention provides a method for controlling the outlet temperature of a medium in a distillation tower top air-cooling system, which controls the outlet temperature of the medium in the distillation tower top air-cooling system according to real-time scenarios, thereby improving the efficiency and adaptability of the temperature control of the medium outlet temperature in the distillation tower top air-cooling system.

[0006] To address the aforementioned technical problems, embodiments of the present invention provide a method for controlling the medium outlet temperature of a distillation column top air-cooling system, comprising:

[0007] Acquire real-time operating data and real-time environmental data of the distillation tower top air cooling system;

[0008] Based on the real-time operating data, the real-time environmental data, and the preset temperature change range, it is determined whether there are fluctuations in the air-cooling system;

[0009] When system fluctuations occur, the motor control quantity is calculated based on preset control rules and preset control parameters; and the motor is frequency-controlled based on the motor control quantity and gradient optimization algorithm to complete the temperature control of the medium outlet of the distillation tower top air-cooling system.

[0010] This invention determines whether there are abnormal fluctuations in the fractionation tower top air-cooling system by collecting real-time operating data and a preset temperature change range. When abnormal fluctuations occur, the motor control quantity is calculated based on preset control rules and parameters. The motor is then frequency-controlled using this control quantity and a gradient optimization algorithm. The optimal motor control quantity is found through the gradient optimization algorithm, achieving efficient temperature control. By using real-time operating data to perform frequency-controlled motor control, the temperature at the medium outlet of the fractionation tower top air-cooling system is controlled, improving the efficiency and adaptability of the medium outlet temperature control.

[0011] Furthermore, the real-time operating data includes the medium feed temperature and the medium feed flow rate; the real-time environmental data includes the ambient temperature and the medium outlet temperature; the step of determining whether there are abnormal fluctuations in the fractionation tower top air cooling system based on the real-time operating data, the real-time environmental data, and a preset temperature change range includes:

[0012] When the outlet temperature of the medium is within the temperature variation range, if the sum of the rate of change of the medium feed temperature, the medium feed flow rate and the ambient temperature is greater than a preset change threshold, then the air-cooling system has abnormal fluctuations.

[0013] When the outlet temperature of the medium is within the temperature variation range, the present invention determines whether there is fluctuation in the system based on the sum of the change rates of the medium feed temperature, the medium feed flow rate, and the ambient temperature, thereby suppressing the trend of abnormal fluctuations in the air-cooled system.

[0014] Furthermore, the step of determining whether there are abnormal fluctuations in the distillation tower top air-cooling system based on the real-time operating data, the real-time environmental data, and the preset temperature change range includes:

[0015] When the outlet temperature of the medium is outside the temperature variation range, the air-cooled system experiences abnormal fluctuations.

[0016] This invention monitors real-time operating data and environmental data to determine whether the medium outlet temperature of the distillation tower top air cooling system exceeds a preset temperature change range, thereby identifying abnormal fluctuations in the system and adjusting the motor accordingly.

[0017] Furthermore, the step of calculating the motor control quantity based on the real-time operating data, the preset target temperature, and the preset fuzzy neural network includes:

[0018] Calculate the control coefficient and frequency conversion range based on the real-time operating data and the real-time environmental data;

[0019] Calculate the temperature error and error change rate based on the real-time operating data and the target temperature;

[0020] PID parameters are calculated based on the temperature error, the rate of change of error, and the fuzzy neural network.

[0021] The motor control quantity is calculated based on the control coefficient, the frequency range, and the PID parameters.

[0022] This invention utilizes real-time operating data, preset target temperatures, and fuzzy neural networks to calculate precise motor control quantities, thereby achieving accurate control of the medium outlet temperature of the distillation tower top air-cooling system. This helps improve temperature control accuracy and enhances the stability and operating efficiency of the air-cooling system, ensuring that the medium outlet temperature of the air-cooling system remains within the set range under various operating conditions.

[0023] Furthermore, the variable frequency control of the motor based on the motor control quantity and gradient optimization algorithm includes:

[0024] The parameters of the fuzzy neural network are updated based on the preset error cost function and the gradient optimization algorithm.

[0025] The motor control input is dynamically adjusted based on the updated fuzzy neural network to perform frequency conversion control on the motor.

[0026] This invention uses a gradient optimization algorithm and an error cost function to adaptively adjust the parameters of a fuzzy neural network, optimize the motor control input, minimize the system's control error, and thus achieve more precise control and efficient regulation of the motor's frequency conversion control.

[0027] In a second aspect, the present invention provides a medium outlet temperature control device for a distillation tower top air cooling system, comprising: a data acquisition module, a monitoring module, and a control module;

[0028] The data acquisition module is used to acquire real-time operating data and real-time environmental data of the distillation tower top air cooling system;

[0029] The monitoring module is used to determine whether there are abnormal fluctuations in the top air cooling system of the distillation tower based on the real-time operating data, the real-time environmental data and the preset temperature change range.

[0030] The control module is used to calculate the motor control quantity based on the real-time operating data, the preset target temperature, and the preset fuzzy neural network when there are abnormal fluctuations in the air-cooling system at the top of the distillation tower; and to perform frequency conversion control on the motor based on the motor control quantity and the gradient optimization algorithm to complete the temperature control of the medium outlet of the air-cooling system at the top of the distillation tower.

[0031] This invention determines whether there are abnormal fluctuations in the fractionation tower top air-cooling system by collecting real-time operating data and a preset temperature change range. When abnormal fluctuations occur, the motor control quantity is calculated based on preset control rules and parameters. The motor is then frequency-controlled using this control quantity and a gradient optimization algorithm. The optimal motor control quantity is found through the gradient optimization algorithm, achieving efficient temperature control. By using real-time operating data to perform frequency-controlled motor control, the temperature at the medium outlet of the fractionation tower top air-cooling system is controlled, improving the efficiency and adaptability of the medium outlet temperature control.

[0032] Furthermore, the real-time operating data includes the medium feed temperature and the medium feed flow rate; the real-time environmental data includes the ambient temperature and the medium outlet temperature; the monitoring module is used for:

[0033] When the outlet temperature of the medium is within the temperature variation range, if the sum of the rate of change of the medium feed temperature, the medium feed flow rate and the ambient temperature is greater than a preset change threshold, then the air-cooling system has abnormal fluctuations.

[0034] Furthermore, the monitoring module is used for:

[0035] When the outlet temperature of the medium is outside the temperature variation range, the air-cooled system experiences abnormal fluctuations.

[0036] Furthermore, the control module is used for:

[0037] Calculate the control coefficient and frequency conversion range based on the real-time operating data and the real-time environmental data;

[0038] Calculate the temperature error and error change rate based on the real-time operating data and the target temperature;

[0039] PID parameters are calculated based on the temperature error, the rate of change of error, and the fuzzy neural network.

[0040] The motor control quantity is calculated based on the control coefficient, the frequency range, and the PID parameters.

[0041] Furthermore, the control module is used for:

[0042] The parameters of the fuzzy neural network are updated based on the preset error cost function and the gradient optimization algorithm.

[0043] The motor control input is dynamically adjusted based on the updated fuzzy neural network to perform frequency conversion control on the motor. Attached Figure Description

[0044] Figure 1 A schematic flowchart illustrating a method for controlling the outlet temperature of a medium in a distillation tower top air cooling system, provided in an embodiment of the present invention;

[0045] Figure 2 A schematic diagram of a fuzzy neural network structure provided in an embodiment of the present invention;

[0046] Figure 3 This is a schematic diagram of an experimental apparatus for controlling the outlet temperature of a medium in a distillation tower top air-cooling system, as provided in an embodiment of the present invention.

[0047] Figure 4 A schematic diagram illustrating the changes in outlet temperature and motor frequency provided in an embodiment of the present invention;

[0048] Figure 5 This is another schematic diagram showing the changes in outlet temperature and motor frequency provided in an embodiment of the present invention;

[0049] Figure 6 This is another schematic diagram showing the changes in outlet temperature and motor frequency provided in an embodiment of the present invention. Detailed Implementation

[0050] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0051] The terms "first" and "second," etc., in the specification, claims, and drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such processes, methods, products, or apparatus.

[0052] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0053] Example 1

[0054] See Figure 1 , Figure 1 This is a flowchart illustrating a method for controlling the outlet temperature of a medium in a distillation column top air-cooling system, provided by an embodiment of the present invention. The embodiment of the present invention provides a method for controlling the outlet temperature of a medium in a distillation column top air-cooling system, including steps 101 to 103, as detailed below:

[0055] Step 101: Obtain real-time operating data and real-time environmental data of the distillation tower top air cooling system;

[0056] In this embodiment, real-time operating data and real-time environmental data of the distillation tower top air cooling system and tower environment are acquired based on temperature sensors and flow sensors.

[0057] In this embodiment, the real-time operating data of the distillation tower overhead air cooling system includes the medium feed temperature T of the distillation tower overhead air cooling system. in Medium feed flow rate (m) flow and medium outlet temperature T out Real-time temperature data for the distillation tower environment includes the ambient temperature T of the air-cooled system at the top of the distillation tower. env Axial flow fan outlet temperature T windout .

[0058] Step 102: Based on the real-time operating data, the real-time environmental data, and the preset temperature change range, determine whether there are abnormal fluctuations in the top air cooling system of the distillation tower;

[0059] In this embodiment, the system determines whether to start frequency conversion control based on the real-time operating data and real-time environmental data. When the real-time operating data and real-time environmental data meet the preset frequency conversion control conditions, the system performs frequency conversion control on the motor to adjust the medium outlet temperature.

[0060] In this embodiment, determining whether there are abnormal fluctuations in the distillation tower top air cooling system based on the real-time operating data, the real-time environmental data, and the preset temperature change range includes:

[0061] When the outlet temperature of the medium is within the temperature variation range, if the sum of the rate of change of the medium feed temperature, the medium feed flow rate and the ambient temperature is greater than a preset change threshold, then the air-cooling system has abnormal fluctuations.

[0062] In this embodiment, when the outlet temperature of the medium is within the temperature variation range, the system is judged to have fluctuations based on the sum of the rate of change of the medium feed temperature, the medium feed flow rate, and the ambient temperature, thereby suppressing the trend of abnormal fluctuations in the air-cooled system.

[0063] In this embodiment, determining whether there are abnormal fluctuations in the distillation tower top air cooling system based on the real-time operating data, the real-time environmental data, and the preset temperature change range includes:

[0064] When the outlet temperature of the medium is outside the temperature variation range, the air-cooled system experiences abnormal fluctuations.

[0065] In this embodiment, by monitoring real-time operating data and environmental data, it is determined whether the medium outlet temperature of the distillation tower top air cooling system exceeds the preset temperature change range, thereby identifying whether there are abnormal fluctuations in the system and adjusting the motor accordingly.

[0066] In this embodiment, when the medium outlet temperature is not within the temperature variation range, it indicates that the system may be fluctuating. Or, when the medium outlet temperature is within the temperature variation range and the sum of the change rates of the medium feed temperature, the medium feed flow rate, and the ambient temperature is greater than the preset change threshold X%, it indicates that the real-time operating data and real-time environmental data meet the preset frequency conversion control conditions. At this time, the air-cooled system has abnormal fluctuations or an abnormal fluctuation trend, and the motor needs to be frequency-controlled to adjust the medium outlet temperature.

[0067] In this embodiment, when the medium outlet temperature of the distillation tower top air cooling system is within the tolerable temperature range set by the program, if the sum of the rate of change of the medium feed temperature, the medium feed flow rate and the ambient temperature does not exceed X%, it means that the system is in a steady state and no frequency conversion control is required, and the fan motor maintains a constant speed.

[0068] In this embodiment, the temperature variation range is set based on the target temperature.

[0069] As a specific example of an embodiment of the present invention, the target temperature is set to 40°C, and the temperature variation range is 40±0.2°C.

[0070] In this embodiment, since the water pump flow rate of the fractionation tower is not a constant single value, but may fluctuate, if the value of this change threshold is too small, it will cause the system to amplify noise, frequently enter the control loop, generate frequent control actions, and waste computing resources.

[0071] In this embodiment, the change threshold is 0.001. Based on this change threshold, noise signals generated by traffic can be effectively filtered while the potential impact of slow changes in system parameters can be effectively reduced.

[0072] Step 103: When there are abnormal fluctuations in the top air cooling system of the distillation tower, calculate the motor control quantity based on the real-time operating data, the preset target temperature and the preset fuzzy neural network; and perform frequency conversion control on the motor based on the motor control quantity and the gradient optimization algorithm to complete the temperature control of the medium outlet of the top air cooling system of the distillation tower.

[0073] In this embodiment, the step of calculating the motor control quantity based on the real-time operating data, the preset target temperature, and the preset fuzzy neural network includes:

[0074] Calculate the control coefficient and frequency conversion range based on the real-time operating data and the real-time environmental data;

[0075] Calculate the temperature error and error change rate based on the real-time operating data and the target temperature;

[0076] PID parameters are calculated based on the temperature error, the rate of change of error, and the fuzzy neural network.

[0077] The motor control quantity is calculated based on the control coefficient, the frequency range, and the PID parameters.

[0078] In this embodiment, the real-time operating data includes the medium feed temperature T. in Medium feed flow rate (m) flow and medium outlet temperature T out Real-time temperature data includes the ambient temperature T of the fractionation tower overhead air-cooling system. env Axial flow fan outlet temperature

[0079] In this embodiment, the control coefficient is used to indicate the functional relationship between temperature rotation speed and outlet temperature difference under ideal static conditions, and it is based on the medium feed flow rate m. flow Fan outlet temperature and medium outlet temperature T out The calculations yielded the following specific results:

[0080]

[0081] Among them, C flow C is the specific heat capacity of the medium. wind For the specific heat capacity of air, m flow The feed flow rate is m. windFor the mass of the air used in convective heat transfer, α is used to indicate the appropriate control coefficients to quickly return to the target temperature when the temperature is too high or too low.

[0082] In this embodiment, the frequency range is used to set the upper limit of the frequency conversion f. max and frequency conversion lower limit f min To prevent system fluctuations caused by over-adjustment, it is based on the medium feed flow rate m flow Target temperature T target and medium outlet temperature T out The calculations yielded the following specific results:

[0083]

[0084] Among them, C flow C is the specific heat capacity of the medium. wind For the specific heat capacity of air, m flow The feed flow rate is m. wind The mass of the air used for convective heat transfer.

[0085] In this embodiment, the temperature error and the rate of change of error are calculated based on the medium outlet temperature and the target temperature.

[0086] In this embodiment, the temperature error and the rate of change of error are used as inputs to a fuzzy neural network to calculate the PID (proportional-integral-derivative) parameters. The PID parameters include the proportional parameter K. p Integral parameter K i and differential parameter K d PID control is a commonly used feedback control algorithm. PID control adjusts the control quantity through three parts: proportional, integral, and derivative, so that the output of the controlled object reaches the desired value.

[0087] Please refer to Figure 2 , Figure 2 This is a schematic diagram of a fuzzy neural network structure provided in an embodiment of the present invention.

[0088] In this embodiment, the fuzzy neural network structure is initialized. The fuzzy neural network includes an input layer, a fuzzification layer, a fuzzy rule layer, a normalization layer, and a decryption layer. Before calculation, the fuzzy neural network parameters are initialized, including the center value c of each language description value. ij , Membership function parameter σ ij Initialize the fuzzy control rule matrix w ij Initialize the neural network learning rate β.

[0089] In this embodiment, during the fuzzy neural network calculation process:

[0090] In the input layer:

[0091] f i =x i =f 1 ,x=[e,ec](i=1,2) (4)

[0092] In the blurring layer:

[0093]

[0094] In the fuzzy rule layer:

[0095]

[0096] In the normalized layer:

[0097]

[0098] In the clarity layer:

[0099]

[0100] Based on the calculated PID parameters, K p =y1,K i =y2,K d =y3.

[0101] Among them, f i Let f be the i-th output of each layer of the neural network. In particular, for the fuzzing layer, f ij x is the j-th linguistic description value of the i-th variable; i f is the i-th input variable of the neural network; L σ is the output of the Lth layer of the neural network; ij c represents the membership degree of the i-th variable to the j-th linguistic description value. ij σ is the center value of the membership function for the j-th linguistic description value of the i-th variable; ij α is the mean square value of the membership function of the j-th linguistic description value of the i-th variable; j The degree of effectiveness of the j-th fuzzy rule; y represents the effectiveness of the j-th fuzzy rule after normalization; i Let be the i-th output of the neural network.

[0102] In this embodiment, the control increment Δu of the motor at the current moment is calculated based on the control coefficient, the frequency range, and the PID parameters, and the control quantity at the current moment is calculated based on the control increment Δu at the current moment and the control quantity u1 at the previous moment.

[0103] In this embodiment, the control increment at the current moment is:

[0104] Δu=η×[K p(e-e1)+K i e+K d (e-2e1+e2)] (9)

[0105] Where e1 is the temperature difference at the previous moment, and e2 is the temperature difference between the two previous moments. Therefore, the control quantity at the current moment is: u = u1 + Δu. u1 is the control quantity at the previous moment.

[0106] In this embodiment, by accurately calculating real-time data and target temperature, and combining this with fuzzy neural network optimization of PID parameters, high-precision temperature control can be achieved, reducing overshoot and oscillation. By dynamically adjusting PID parameters and motor control inputs, the system can quickly respond to changes and maintain stability. Furthermore, the self-learning and optimization capabilities of the fuzzy neural network allow for continuous optimization of the control strategy, improving the system's adaptability and robustness.

[0107] This invention utilizes real-time operating data, preset target temperatures, and fuzzy neural networks to calculate precise motor control quantities, thereby achieving accurate control of the medium outlet temperature of the distillation tower top air-cooling system. This helps improve temperature control accuracy and enhances the stability and operating efficiency of the air-cooling system, ensuring that the medium outlet temperature of the air-cooling system remains within the set range under various operating conditions.

[0108] In this embodiment, the variable frequency control of the motor based on the motor control quantity and gradient optimization algorithm includes:

[0109] The parameters of the fuzzy neural network are updated based on the preset error cost function and the gradient optimization algorithm.

[0110] The motor control input is dynamically adjusted based on the updated fuzzy neural network to perform frequency conversion control on the motor.

[0111] In this embodiment, the error cost function is used to measure the error between the current motor control quantity and the target control quantity.

[0112] In this embodiment, the error cost function is:

[0113]

[0114] In this embodiment, the parameters of the fuzzy neural network are calculated based on the error cost function and gradient optimization algorithm. Specifically:

[0115]

[0116] Where, Δw k Δc represents the change in the fuzzy rule matrix. ij Δσ represents the change in the central value of the membership function. ijβ represents the change in the mean squared error of the membership function; β is the learning rate of the neural network. This is the partial derivative of the error cost function with respect to the medium outlet temperature; It is the partial derivative of the medium outlet temperature with respect to the control quantity; It is the partial derivative of the control quantity with respect to the output of the fifth layer of the neural network; It is the partial derivative of the output of the fifth layer of the neural network with respect to the fuzzy rule matrix; It is the partial derivative of the output of the fifth layer of the neural network with respect to the output of the fourth layer of the neural network; It is the partial derivative of the output of the fourth layer of the neural network with respect to the output of the third layer of the neural network.

[0117] In this embodiment, the change Δw is based on the fuzzy rule matrix. k The change in the central value of the membership function Δc ij The change in the mean square error of the membership function, Δσ ij The fuzzy neural network is updated, and based on the updated fuzzy neural network, a new motor control quantity is calculated, and the motor is controlled by frequency converter.

[0118] In this embodiment, the parameters of the fuzzy neural network are gradually adjusted by calculating the gradient of the error cost function (i.e., the direction of the fastest error change) to minimize the error cost. Through the gradient optimization algorithm, the system can adaptively adjust the parameters of the fuzzy neural network, optimize the motor control input, minimize the system's control error, and thus achieve more precise control.

[0119] In this embodiment, by employing a gradient optimization algorithm and an error cost function, the system can continuously optimize the parameters of the fuzzy neural network, improving the accuracy of motor control and reducing control errors. Dynamically adjusting the parameters of the fuzzy neural network and the motor control quantities allows the system to respond more quickly to changes in the external environment and fluctuations in the internal state. By optimizing the motor control quantities, the system can maintain stable operation, reducing system oscillations and instability caused by control errors.

[0120] This invention uses a gradient optimization algorithm and an error cost function to adaptively adjust the parameters of a fuzzy neural network, optimize the motor control input, minimize the system's control error, and thus achieve more precise control and efficient regulation of the motor's frequency conversion control.

[0121] In this embodiment, when the medium outlet temperature of the distillation tower top air cooling system is within the temperature variation range, and the sum of the absolute values ​​of the errors within ten seconds is less than or equal to a preset first threshold, the frequency conversion control is stopped.

[0122] In this embodiment, when the medium outlet temperature of the distillation tower top air cooling system is within the temperature variation range, and the sum of the absolute values ​​of the errors within ten seconds is less than or equal to 1, that is, the average error is within 0.1℃.

[0123] This invention also provides a medium outlet temperature control device for a distillation tower top air cooling system, comprising: a data acquisition module, a monitoring module, and a control module;

[0124] The data acquisition module is used to acquire real-time operating data and real-time environmental data of the distillation tower top air cooling system;

[0125] The monitoring module is used to determine whether there are abnormal fluctuations in the top air cooling system of the distillation tower based on the real-time operating data, the real-time environmental data and the preset temperature change range.

[0126] The control module is used to calculate the motor control quantity based on the real-time operating data, the preset target temperature, and the preset fuzzy neural network when there are abnormal fluctuations in the air-cooling system at the top of the distillation tower; and to perform frequency conversion control on the motor based on the motor control quantity and the gradient optimization algorithm to complete the temperature control of the medium outlet of the air-cooling system at the top of the distillation tower.

[0127] This invention determines whether there are abnormal fluctuations in the fractionation tower top air-cooling system by collecting real-time operating data and a preset temperature change range. When abnormal fluctuations occur, the motor control quantity is calculated based on preset control rules and parameters. The motor is then frequency-controlled using this control quantity and a gradient optimization algorithm. The optimal motor control quantity is found through the gradient optimization algorithm, achieving efficient temperature control. By using real-time operating data to perform frequency-controlled motor control, the temperature at the medium outlet of the fractionation tower top air-cooling system is controlled, improving the efficiency and adaptability of the medium outlet temperature control.

[0128] In this embodiment, the real-time operating data includes the medium feed temperature and the medium feed flow rate; the real-time environmental data includes the ambient temperature and the medium outlet temperature; the monitoring module is used for:

[0129] When the outlet temperature of the medium is within the temperature variation range, if the sum of the rate of change of the medium feed temperature, the medium feed flow rate and the ambient temperature is greater than a preset change threshold, then the air-cooling system has abnormal fluctuations.

[0130] In this embodiment, the monitoring module is used for:

[0131] When the outlet temperature of the medium is outside the temperature variation range, the air-cooled system experiences abnormal fluctuations.

[0132] In this embodiment, the control module is used for:

[0133] Calculate the control coefficient and frequency conversion range based on the real-time operating data and the real-time environmental data;

[0134] Calculate the temperature error and error change rate based on the real-time operating data and the target temperature;

[0135] PID parameters are calculated based on the temperature error, the rate of change of error, and the fuzzy neural network.

[0136] The motor control quantity is calculated based on the control coefficient, the frequency range, and the PID parameters.

[0137] In this embodiment, the control module is used for:

[0138] The parameters of the fuzzy neural network are updated based on the preset error cost function and the gradient optimization algorithm.

[0139] The motor control input is dynamically adjusted based on the updated fuzzy neural network to perform frequency conversion control on the motor.

[0140] Please refer to Figure 3 , Figure 3 This is a schematic diagram of an experimental apparatus for controlling the outlet temperature of a medium in a distillation tower top air-cooling system, as provided in an embodiment of the present invention.

[0141] As a specific example of an embodiment of the present invention, the material in the air-cooled system is circulating pure water, and the circulating water circulation rate is 1.38 t / h. A simulated heat source heats the circulating water to 53°C before sending it into the circulating water pipeline. The initial frequency of the frequency converter is 0 Hz, and the initial speed of the motor is 0 RPM. At this time, the outlet material temperature of the cooling tower is 50.7°C. The host computer program is started, and the data sensed in real time by the sensors is used as the initial operating data, which is acquired by the PLC. Since the outlet material temperature of 50.7°C is greater than the target set temperature of 40°C, the monitoring module of the PLC determines that the motor speed needs to be controlled. After receiving the frequency conversion control command from the monitoring module, the control module obtains the real-time operating data and real-time environmental data sensed by the sensors of the cooling tower and its surrounding environment from the PLC at a polling rate of 2 Hz, and calculates the control coefficient η and the frequency conversion upper limit f at the current moment. max Variable frequency lower limit f min The error e and the rate of change of error ec are used as inputs to obtain the PID control parameters K at the current moment through fuzzy neural network calculations. p K i K d The control module calculates the change in control quantity Δu at the current moment based on the obtained PID control parameters and control coefficients, and transmits this control signal to the PLC to drive the frequency converter. Simultaneously, the control module performs error backpropagation based on the error cost function at this time, updating the module parameter c. ij σ ij With matrix w kTo optimize control performance, the control module will continuously cycle through the above data acquisition-processing-control process until the cooling tower outlet material temperature drops to within the temperature tolerance range and there is no significant temperature fluctuation. After the system reaches a steady state, the program process will return to the monitoring module.

[0142] Please refer to Figure 4 , Figure 4 This is a schematic diagram illustrating the changes in outlet temperature and motor frequency, provided as an embodiment of the present invention.

[0143] In this embodiment, after the host computer program is started, the program begins frequency conversion control of the motor at the 4th second. The circulating water outlet temperature of the cooling tower enters the temperature tolerance range and reaches a steady state at the 92nd second. The control process takes a total of 88 seconds, and the controlled temperature value is 0.03℃ away from the target set temperature value.

[0144] As a specific example of an embodiment of the present invention, the material in the air-cooled system is circulating pure water, and the circulating water circulation rate is 1.04 t / h. A simulated heat source heats the circulating water to 54°C before sending it into the circulating water pipeline. The initial frequency of the frequency converter is 19 Hz, and the initial speed of the motor is 1330 RPM. At this time, the outlet material temperature of the cooling tower is 40.53°C. The host computer program is started, and the data sensed by the sensors in real time is used as real-time operating data and real-time environmental data. Since the outlet material temperature of 40.53°C is greater than the target set temperature of 40°C, the monitoring module determines that the program needs to intervene to control the motor speed. After obtaining authorization from the monitoring module, the control module obtains the sensor sensing data of the cooling tower and its surrounding environment from the PLC at a polling rate of 2 Hz, and calculates the control coefficient η and the frequency converter upper limit f at the current moment. max Variable frequency lower limit f min The error e and the rate of change of error ec are used as inputs to obtain the PID control parameters K at the current moment through fuzzy neural network calculations. p K i K d The control module calculates the change in control quantity Δu at the current moment based on the obtained PID control parameters and control coefficients, and transmits this control signal to the PLC to drive the frequency converter. Simultaneously, the control module performs error backpropagation based on the error cost function at this time, updating the module parameter c. ij σ ij With matrix w k To optimize control performance, the control module will continuously cycle through the above data acquisition-processing-control process until the cooling tower outlet material temperature drops to within the temperature tolerance range and there is no significant temperature fluctuation. After the system reaches a steady state, the program process will return to the monitoring module.

[0145] Please refer to Figure 5 , Figure 5This is another schematic diagram showing the changes in outlet temperature and motor frequency provided in an embodiment of the present invention.

[0146] After the host computer program is started, it begins frequency conversion control of the motor at 6 seconds. The circulating water outlet temperature of the cooling tower enters the temperature tolerance range and reaches a steady state at 61 seconds. The control process takes a total of 55 seconds, and the controlled temperature value is -0.05℃ from the target set temperature value.

[0147] As a specific example of an embodiment of the present invention, the material in the air-cooled system is circulating pure water, with a circulation rate of 1.05 t / h. A simulated heat source heats the circulating water to 54°C before it is sent into the circulating water pipeline. The initial frequency of the frequency converter is 20.5 Hz, and the initial speed of the motor is 1435 RPM. At this time, the outlet material temperature of the cooling tower is 39.03°C. The host computer program is started, and the data sensed in real time by the sensors is transmitted as initial data to the program's data acquisition module via the PLC. Since the outlet material temperature of 39.03°C is less than the target set temperature of 40°C, the monitoring module determines that the program needs to intervene to control the motor speed. After obtaining authorization from the monitoring module, the control module obtains the sensor sensing data of the cooling tower and its surrounding environment from the PLC at a polling rate of 2 Hz, and calculates the control coefficient η and the frequency converter upper limit f at the current moment. max Variable frequency lower limit f min The error e and the rate of change of error ec are used as inputs to obtain the PID control parameters K at the current moment through fuzzy neural network calculations. p K i K d The control module calculates the change in control quantity Δu at the current moment based on the obtained PID control parameters and control coefficients, and transmits this control signal to the PLC to drive the frequency converter. Simultaneously, the control module performs error backpropagation based on the error cost function at this time, updating the module parameter c. ij σ ij With matrix w k To optimize control performance, the control module will continuously cycle through the above data acquisition-processing-control process until the cooling tower outlet material temperature drops to within the temperature tolerance range and there is no significant temperature fluctuation. After the system reaches a steady state, the program process will return to the monitoring module.

[0148] Please refer to Figure 6 , Figure 6 This is another schematic diagram showing the changes in outlet temperature and motor frequency provided in an embodiment of the present invention.

[0149] After the host computer program is started, it begins frequency conversion control of the motor at 11 seconds. The circulating water outlet temperature of the cooling tower enters the temperature tolerance range and reaches a steady state at 87 seconds. The control process takes a total of 76 seconds, and the controlled temperature value is 0.14℃ away from the target set temperature value.

[0150] This invention introduces control coefficients and specifies specific fuzzy control logic. It cleverly adopts control logic that significantly changes the control quantity to obtain a rapid response of the system temperature, thereby reducing the influence of the time delay characteristics of the temperature system and quickly approaching the target set temperature. It also adopts control logic that reduces the gain of the control quantity on the system response when the temperature approaches the set point to reduce the influence of the inertial characteristics of the temperature system and avoid system overshoot.

[0151] In this embodiment of the invention, a terminal device is also provided, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the above-described method for controlling the medium outlet temperature of the distillation tower top air-cooling system.

[0152] In this embodiment of the invention, a computer-readable storage medium is also provided, which includes a stored computer program, wherein, when the computer program is running, it controls the device where the computer-readable storage medium is located to execute the above-described method for controlling the medium outlet temperature of the distillation tower top air-cooling system.

[0153] For example, a computer program can be divided into one or more modules, one or more of which are stored in memory and executed by a processor to perform the present invention. The one or more modules can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in a terminal device.

[0154] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor, memory, and display. Those skilled in the art will understand that the above components are merely examples of terminal devices and do not constitute a limitation on the terminal device. It may include more or fewer components, or combinations of certain components, or different components. For example, the terminal device may also include input / output devices, network access devices, buses, etc.

[0155] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device through various interfaces and lines.

[0156] Memory can be used to store computer programs and / or modules. The processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory. Memory can mainly include a program storage area and a data storage area. The program storage area can store the operating system, application programs required for at least one function (such as sound playback, text conversion, etc.), etc.; the data storage area can store data created based on the use of the mobile phone (such as audio data, text message data, etc.). In addition, memory can include high-speed random access memory, and can also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital cards (SD cards), flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0157] The module controlling the outlet temperature of the medium in the distillation tower top air-cooling system, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. Those skilled in the art can understand and implement this without any inventive effort.

[0158] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for controlling the outlet temperature of a medium in a distillation tower overhead air-cooling system, characterized in that, include: Acquire real-time operating data and real-time environmental data of the distillation tower top air cooling system; Based on the real-time operating data, the real-time environmental data, and the preset temperature change range, it is determined whether there are abnormal fluctuations in the top air cooling system of the distillation tower; When abnormal fluctuations occur in the air-cooled system at the top of the distillation tower, the motor control quantity is calculated based on the real-time operating data, the preset target temperature, and the preset fuzzy neural network. This includes: calculating the control coefficient and frequency range based on the real-time operating data and the real-time environmental data; calculating the temperature error and error change rate based on the real-time operating data and the target temperature; calculating PID parameters based on the temperature error, the error change rate, and the fuzzy neural network; calculating the motor control quantity based on the control coefficient, the frequency range, and the PID parameters, wherein the control coefficient is used to indicate the functional relationship between temperature and speed and the outlet temperature difference under ideal static conditions; and performing frequency conversion control on the motor based on the motor control quantity and the gradient optimization algorithm to complete the temperature control of the medium outlet of the air-cooled system at the top of the distillation tower.

2. The method for controlling the medium outlet temperature of a distillation tower top air-cooling system as described in claim 1, characterized in that, The real-time operating data includes the medium feed temperature and the medium feed flow rate; the real-time environmental data includes the ambient temperature and the medium outlet temperature. The step of determining whether there are abnormal fluctuations in the distillation tower top air-cooling system based on the real-time operating data, the real-time environmental data, and the preset temperature change range includes: When the outlet temperature of the medium is within the temperature variation range, if the sum of the rate of change of the medium feed temperature, the medium feed flow rate and the ambient temperature is greater than a preset change threshold, then the air-cooling system has abnormal fluctuations.

3. The method for controlling the medium outlet temperature of a distillation tower top air-cooling system as described in claim 2, characterized in that, The step of determining whether there are abnormal fluctuations in the distillation tower top air-cooling system based on the real-time operating data, the real-time environmental data, and the preset temperature change range includes: When the outlet temperature of the medium is outside the temperature variation range, the air-cooled system experiences abnormal fluctuations.

4. The method for controlling the medium outlet temperature of a distillation tower top air-cooling system as described in claim 3, characterized in that, The variable frequency control of the motor based on the motor control quantity and gradient optimization algorithm includes: The parameters of the fuzzy neural network are updated based on the preset error cost function and the gradient optimization algorithm. The motor control input is dynamically adjusted based on the updated fuzzy neural network to perform frequency conversion control on the motor.

5. A medium outlet temperature control device for a distillation tower top air cooling system, characterized in that, include: Data acquisition module, monitoring module, and control module; The data acquisition module is used to acquire real-time operating data and real-time environmental data of the distillation tower top air cooling system; The monitoring module is used to determine whether there are abnormal fluctuations in the top air cooling system of the distillation tower based on the real-time operating data, the real-time environmental data and the preset temperature change range. The control module is used to calculate motor control quantities based on the real-time operating data, preset target temperature, and preset fuzzy neural network when abnormal fluctuations occur in the fractionation tower top air-cooling system. This includes: calculating control coefficients and frequency range based on the real-time operating data and the real-time environmental data; calculating temperature error and error change rate based on the real-time operating data and the target temperature; calculating PID parameters based on the temperature error, error change rate, and the fuzzy neural network; calculating the motor control quantity based on the control coefficients, the frequency range, and the PID parameters, wherein the control coefficients indicate the functional relationship between temperature / speed and outlet temperature difference under ideal static conditions; and performing frequency conversion control on the motor based on the motor control quantity and a gradient optimization algorithm to achieve temperature control of the medium outlet of the fractionation tower top air-cooling system.

6. The medium outlet temperature control device for a distillation tower top air cooling system as described in claim 5, characterized in that, The real-time operating data includes the medium feed temperature and the medium feed flow rate; the real-time environmental data includes the ambient temperature and the medium outlet temperature; the monitoring module is used for: When the outlet temperature of the medium is within the temperature variation range, if the sum of the rate of change of the medium feed temperature, the medium feed flow rate and the ambient temperature is greater than a preset change threshold, then the air-cooling system has abnormal fluctuations.

7. The medium outlet temperature control device for a distillation tower top air cooling system as described in claim 6, characterized in that, The monitoring module is used for: When the outlet temperature of the medium is outside the temperature variation range, the air-cooled system experiences abnormal fluctuations.

8. The medium outlet temperature control device for a distillation tower top air cooling system as described in claim 7, characterized in that, The control module is used for: The parameters of the fuzzy neural network are updated based on the preset error cost function and the gradient optimization algorithm. The motor control input is dynamically adjusted based on the updated fuzzy neural network to perform frequency conversion control on the motor.

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