Method and system for analyzing and controlling energy consumption of reboiler

Abstract

The invention provides a reboiler energy consumption analysis and control method and system, and relates to the technical field of intelligent control, and the method comprises the steps: constructing a reboiler energy consumption model; the reboiler energy consumption model is analyzed, and the total energy consumption of the reboiler in the operation process is determined; constructing a reboiler control module based on a PID neural network; optimizing a reboiler control module based on the PID neural network by taking reduction of the total energy consumption of the reboiler in the operation process and increase of the heat flux and the heat transfer coefficient of the evaporation area as targets; the target steam mass flow output by the reboiler is set; the actual steam mass flow output by the reboiler is obtained; and the reboiler is controlled through the optimized reboiler control module based on the PID neural network. According to the method, the limitation of setting control parameters depending on experience is overcome, the effect is better in the face of complex dynamic characteristics and nonlinear problems, and the change of the system can be fully coped with.

Description

Technical Field

[0001] The invention relates to the field of intelligent control technology, and in particular to a reboiler energy consumption analysis and control method and system. Background Art

[0002] With the continuous advancement of industrialization, the demand for efficient energy management and optimization in industries such as chemical, petroleum and energy is increasing. As a key heat exchange equipment in these industries, reboilers are widely used in heating, evaporation and separation processes. Since they consume a lot of energy in the production process, the energy management and optimization of reboilers directly affect production efficiency and operating costs. Therefore, how to reduce the total energy consumption of reboilers and improve their energy efficiency has become an important research direction in the industry.

[0003] In the existing technical solutions, the traditional reboiler energy consumption analysis and control methods usually rely on experience to set control parameters, which have limited effects when facing complex dynamic characteristics and nonlinear problems and cannot fully cope with system changes.

[0004] In addition, existing energy consumption management and optimization technologies often lack dynamic adjustment capabilities, making it difficult to achieve comprehensive optimization of reboiler energy efficiency. Summary of the invention

[0005] In order to solve the technical problems that traditional reboiler energy consumption analysis and control methods usually rely on experience to set control parameters, have limited effects when facing complex dynamic characteristics and nonlinear problems, cannot fully cope with system changes, and existing energy consumption management and optimization technologies often lack dynamic adjustment capabilities, making it difficult to achieve comprehensive optimization of reboiler energy efficiency, the present invention provides a reboiler energy consumption analysis and control method and system.

[0006] The technical solution provided by the embodiment of the present invention is as follows:

[0007] First aspect:

[0008] An embodiment of the present invention provides a reboiler energy consumption analysis and control method, comprising:

[0009] S1: Construct reboiler energy consumption model;

[0010] S2: Analyze the reboiler energy consumption model to determine the total energy consumption of the reboiler during operation;

[0011] S3: Building a reboiler control module based on PID neural network;

[0012] S4: The reboiler control module based on PID neural network is optimized with the goal of reducing the total energy consumption of the reboiler during operation and increasing the heat flux and heat transfer coefficient of the evaporation zone;

[0013] S5: setting the target steam mass flow rate output by the reboiler;

[0014] S6: Obtain the actual steam mass flow rate output by the reboiler;

[0015] S7: The reboiler is controlled by an optimized reboiler control module based on a PID neural network.

[0016] Second aspect:

[0017] An embodiment of the present invention provides a reboiler energy consumption analysis and control system, comprising: a memory and one or more processors;

[0018] One or more application programs are stored in the memory, and the one or more application programs are suitable for being executed by the one or more processors to implement the above-mentioned reboiler energy consumption analysis and control method.

[0019] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0020] In the present invention, a reboiler control module based on a PID neural network is constructed and a PID neural network control module is used to replace the traditional control method, thereby overcoming the limitation of relying on experience to set control parameters, achieving better results in the face of complex dynamic characteristics and nonlinear problems, and being able to fully cope with changes in the system. Furthermore, the reboiler control module based on the PID neural network is optimized with the goal of reducing the total energy consumption of the reboiler during operation and increasing the heat flux in the evaporation zone and the heat transfer coefficient in the evaporation zone. The reboiler is controlled by the optimized reboiler control module based on the PID neural network, thereby providing dynamic adjustment capabilities for energy consumption management and optimization technology, making it easier to achieve comprehensive optimization of the reboiler energy efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 A schematic flow chart of a reboiler energy consumption analysis and control method provided in an embodiment of the present invention;

[0023] Figure 2 A schematic structural diagram of a reboiler energy consumption analysis and control system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0025] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0026] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0027] Reference Manual Attached Figure 1 , showing a flow chart of a reboiler energy consumption analysis and control method provided in an embodiment of the present invention.

[0028] The embodiment of the present invention provides a reboiler energy consumption analysis and control method, which can be implemented by a reboiler energy consumption analysis and control device, and the reboiler energy consumption analysis and control device can be a terminal or a server. The processing flow of the reboiler energy consumption analysis and control method can include the following steps:

[0029] S1: Construct a reboiler energy consumption model.

[0030] It should be noted that the reboiler is an important heat exchange equipment, usually installed at the bottom of a distillation tower or a separation tower. Its main function is to generate steam by heating a liquid mixture, thereby maintaining the distillation or separation process in the tower. The reboiler heats the bottom liquid to evaporate the volatile components therein, forming rising steam, which is returned to the tower for further separation or purification.

[0031] Furthermore, by constructing a reboiler energy consumption model, we can fully understand the various energy consumptions of the reboiler during operation, such as liquid thermal energy, steam energy consumption, heat loss, and auxiliary equipment energy consumption.

[0032] Among them, liquid thermal energy refers to the heat required for the liquid entering the reboiler, which is used to heat the liquid to a certain temperature or partially evaporate it.

[0033] Steam energy consumption refers to the amount of steam consumed by the reboiler during operation to provide enough heat for the liquid to generate steam.

[0034] Among them, heat loss refers to the part of heat that cannot be effectively utilized during the operation of the reboiler.

[0035] Among them, auxiliary equipment energy consumption includes the energy consumed by auxiliary equipment such as circulating pumps and control systems during the operation of the reboiler. Although this part of energy consumption is relatively small, it is still part of the overall energy consumption, especially in the case of long-term operation, it will accumulate into significant energy consumption.

[0036] S2: Analyze the reboiler energy consumption model to determine the total energy consumption of the reboiler during operation.

[0037] Optionally, the total energy consumption includes: liquid thermal energy, steam energy consumption, heat loss and auxiliary equipment energy consumption.

[0038] The total energy consumption is calculated as follows:

[0039] Q total =Q liquid +Q steam +Q loss +Q aux

[0040] Among them, Q total represents the total energy consumption, Q liquid represents the heat energy of the liquid, Q steam represents steam energy consumption, Q loss represents heat loss, Q aux Indicates the energy consumption of auxiliary equipment.

[0041] The calculation method of liquid thermal energy is as follows:

[0042] Q liquid =m C ·c p ·(T pinch -T in )

[0043] Among them, m C represents the mass of liquid entering the reboiler, c p represents the specific heat capacity of the liquid, T pinch represents the pinch point temperature, T in Indicates the temperature of the liquid when it enters the reboiler.

[0044] The pinch temperature refers to the intersection temperature of the two linear temperature trends of the heating zone and the evaporation zone during the heat exchange process.

[0045] The calculation method of steam energy consumption is as follows:

[0046]

[0047] in, represents the mass flow rate of steam generated by the reboiler, H steam Indicates the enthalpy of steam.

[0048] The heat loss is calculated as follows:

[0049] Q loss =U surface ·A surface ΔT surface

[0050] Among them, U surface represents the overall heat transfer coefficient of the reboiler surface, A surface represents the external surface area of ​​the reboiler, ΔT surface Represents the temperature difference between the reboiler surface and the environment.

[0051] The calculation method of auxiliary equipment energy consumption is as follows:

[0052]

[0053] in, and Represent the power of the circulation pump and the control system respectively, t pump and t control Respectively represent the running time of the circulation pump and the control system.

[0054] In the present invention, by decomposing the total energy consumption into liquid thermal energy, steam energy consumption, heat loss and auxiliary equipment energy consumption, the energy consumption of the reboiler in each link can be understood in detail. This helps to identify which parts are the main sources of energy consumption and which parts may have room for optimization, thereby laying the foundation for improving the overall system efficiency. After clarifying the energy consumption of each part, the operating parameters of the reboiler can be optimized more specifically. For example, by reducing heat loss or optimizing steam energy consumption, the total energy consumption can be significantly reduced. Accurate energy consumption analysis provides a scientific basis for subsequent optimization of control strategies and avoids waste of resources caused by blind adjustments.

[0055] S3: Construct a reboiler control module based on PID neural network.

[0056] Among them, PID Neural Network (PID-NN) is an intelligent control system that combines traditional PID controller and neural network technology. It uses the self-learning ability of neural network to optimize the parameters of PID controller, thereby improving the control performance of the system. PID neural network can better adapt to the dynamic characteristics of the system and external disturbances by adjusting the proportional, integral and differential parameters of PID controller in real time, achieving more precise control effect.

[0057] The working principle of the reboiler control module based on PID neural network is introduced. The PID controller is a classic feedback controller that controls the output of the system by adjusting three parameters: proportional, integral and derivative. The goal of the PID controller is to make the system output (such as steam mass flow) close to the set value (target value). The performance of traditional PID controllers in complex and nonlinear systems may not be ideal because its parameters are usually fixed, while the reboiler system has dynamic and nonlinear characteristics. In order to enhance the adaptability of the system, the PID neural network combines the self-learning function of the neural network so that the PID parameters can be dynamically adjusted according to the operating status of the system. The PID neural network usually consists of an input layer, a hidden layer and an output layer. The input layer receives the error signal of the system (such as the difference between the target steam mass flow and the actual steam mass flow), the hidden layer learns the relationship between the error and the control signal, and the output layer generates a new control signal.

[0058] In a possible implementation, the reboiler control module based on the PID neural network is specifically used for:

[0059] Get the input of each neuron in the input layer:

[0060] x1(k)=r(k)

[0061] x2(k)=y(k)

[0062] Among them, x1(k) represents the input of the first neuron in the input layer at sampling time k, r(k) represents the target steam mass flow rate of the reboiler output set at sampling time k, x2(k) represents the input of the second neuron in the input layer at sampling time k, and y(k) represents the actual steam mass flow rate of the reboiler output at sampling time k.

[0063] Calculate the input of each neuron in the hidden layer:

[0064]

[0065] Among them, n i (k) represents the input of the i-th neuron in the hidden layer at sampling time k, w ij represents the connection weight between the jth neuron in the input layer and the ith neuron in the hidden layer, x j (k) represents the input of the jth neuron in the input layer at sampling time k.

[0066] According to the input of each neuron in the hidden layer, the output of each neuron in the hidden layer is calculated:

[0067] u1(k)=n1(k)

[0068] u2(k)=n2(k)+u2(k-1)

[0069] u3(k)=n3(k)-n3(k-1)

[0070] Among them, u1(k) represents the output of the hidden layer proportional neuron at the k sampling time, n1(k) represents the input of the hidden layer proportional neuron at the k sampling time, u2(k) represents the output of the hidden layer integral neuron at the k sampling time, n2(k) represents the input of the hidden layer integral neuron at the k sampling time, u2(k-1) represents the output of the hidden layer integral neuron at the k-1 sampling time, u3(k) represents the output of the hidden layer differential neuron at the k sampling time, n3(k) represents the input of the hidden layer differential neuron at the k sampling time, and n3(k-1) represents the input of the hidden layer differential neuron at the k-1 sampling time.

[0071] According to the output of each neuron in the hidden layer, the output of the output layer is calculated:

[0072]

[0073] Among them, u(k) represents the control signal output by the output layer at sampling time k, and w i represents the connection weight between the i-th neuron in the hidden layer and the output layer, u i (k) represents the output of the i-th neuron in the hidden layer at sampling time k.

[0074] In the present invention, the control module based on the PID neural network combines the advantages of the fast response of the PID controller with the characteristics of learning and memory of the neural network. This combination can enhance its adaptability to nonlinear and complex dynamic characteristics while maintaining the fast response of the control system, thereby ensuring the stability of the system. Through more precise control, the PID neural network can optimize the operating efficiency of the reboiler, reduce unnecessary energy consumption, and thus achieve more efficient energy consumption management. This is of great significance for saving energy and reducing operating costs.

[0075] S4: With the goal of reducing the total energy consumption of the reboiler during operation and increasing the heat flux and heat transfer coefficient of the evaporation zone, the reboiler control module based on the PID neural network is optimized.

[0076] It should be noted that the heat flux and heat transfer coefficient in the evaporation zone have a direct impact on the efficiency and performance of the reboiler.

[0077] The heat flux in the evaporation zone refers to the heat transferred per unit area in the evaporation zone per unit time. It reflects the efficiency and intensity of the evaporation process and directly affects the evaporation capacity and production efficiency of the reboiler.

[0078] Among them, the evaporation zone heat transfer coefficient is an important parameter to measure the heat transfer capacity of the reboiler evaporation zone, which indicates the efficiency of heat transfer from the heating medium to the liquid through the reboiler wall.

[0079] Optionally, the heat flux in the evaporation zone is calculated as follows:

[0080] Calculate the heat transfer area of ​​the evaporation zone of the reboiler:

[0081] A EZ =W·h EZ ·(2n s -2)

[0082] Among them, A EZ represents the heat transfer area of ​​the evaporation zone, W represents the width of the reboiler, and h EZ represents the height of the evaporation zone, n s Indicates the number of pillow plates in the reboiler.

[0083] Calculate the heat flow in the heating zone of the reboiler:

[0084]

[0085] in, represents the heat flow in the heating zone, represents the mass flow rate of liquid entering the reboiler, c p represents the specific heat capacity of the liquid, T pinch represents the pinch point temperature, T in Indicates the temperature of the liquid when it enters the reboiler.

[0086] Based on the steam mass flow rate generated by the reboiler, calculate the total heat flow of the reboiler:

[0087]

[0088] in, represents the total heat flow of the reboiler, represents the mass flow rate of steam generated by the reboiler, ΔH steam Indicates the vaporization potential of a liquid.

[0089] According to the heat flow of the heating zone and the total heat flow of the reboiler, the heat flow of the evaporation zone is calculated:

[0090]

[0091] in, represents the heat flow in the evaporation zone, Represents the total heat flow of the reboiler.

[0092] According to the heat flow rate in the evaporation zone and the heat transfer area in the evaporation zone, the heat flux in the evaporation zone is calculated:

[0093]

[0094] in, represents the heat flux in the evaporation zone.

[0095] Optionally, the heat transfer coefficient of the evaporation zone is calculated as follows:

[0096] Based on the pinch point temperature, calculate the average temperature difference in the evaporation zone:

[0097] ΔT EZ =T HS -(T pinch +T out ) / 2

[0098] Where, ΔT EZ represents the average temperature difference in the evaporation zone, T HS Indicates the temperature of the heating surface, T out Indicates the temperature of the steam when it leaves the reboiler.

[0099] According to the heat flux in the evaporation zone, the heat transfer area in the evaporation zone and the average temperature difference in the evaporation zone, the heat transfer coefficient in the evaporation zone is calculated:

[0100]

[0101] Among them, U EZ represents the heat transfer coefficient of the evaporation zone.

[0102] In the present invention, by optimizing the control strategy and reducing the total energy consumption of the reboiler, the energy efficiency of the equipment can be significantly improved. This not only helps to reduce energy consumption and reduce operating costs, but also improves the overall economic benefits of the reboiler and achieves greener and more sustainable production. Increasing the heat flux in the evaporation zone means that thermal energy can be converted into steam more effectively, improving the evaporation efficiency. Through the optimized control module, the reboiler can utilize the input thermal energy more efficiently and produce more steam to meet production needs. By increasing the heat transfer coefficient of the evaporation zone, the heat transfer effect from the heating surface to the liquid can be enhanced. This will help reduce heat losses during the evaporation process and ensure that more heat is effectively utilized, thereby improving the overall performance of the reboiler.

[0103] In a possible implementation, S4 specifically includes sub-steps S401 to S403:

[0104] S401: Construct an objective function with the goal of reducing the total energy consumption of the reboiler during operation and increasing the heat flux and heat transfer coefficient of the evaporation zone of the reboiler during operation.

[0105] Among them, the objective function is specifically:

[0106]

[0107] Where f represents the objective function, represents the heat flux in the evaporation zone, U EZ represents the heat transfer coefficient of the evaporation zone, Q total represents the total energy consumption, α1 represents the weight parameter of the heat flux in the evaporation zone, α2 represents the weight parameter of the heat transfer coefficient in the evaporation zone, and α3 represents the weight parameter of the total energy consumption.

[0108] Among them, those skilled in the art can set the weight parameter α1 of the evaporation zone heat flux, the weight parameter α2 of the evaporation zone heat transfer coefficient, and the weight parameter α3 of the total energy consumption according to actual conditions, and the present invention does not limit them.

[0109] In the present invention, the objective function constructed integrates the evaporation zone heat flux, the evaporation zone heat transfer coefficient and the total energy consumption to achieve multi-objective optimization. This method can minimize energy consumption while maintaining high efficiency, ensuring that the system achieves the best performance in multiple key performance indicators.

[0110] It should be noted that the training of PID neural networks usually adopts the gradient descent method. Although this method can adaptively update the weights and biases of the neural network, there is no fixed algorithm for the selection of the initial controller parameters, which are usually randomly selected. Since the initial controller parameters determine the convergence direction and the starting point of learning of the neural network, the randomly selected initial values ​​may cause the network to start training from an uncertain direction. This will increase the training time because more iterations and learning are required to find the correct convergence direction. In addition, the randomly selected initial parameters may also lead to the risk of weights falling into a local optimal solution, thereby reducing the overall convergence speed of the network.

[0111] In order to improve the control effect of the PID neural network, appropriate initial controller parameters must be selected. The present invention innovatively proposes a method for optimizing the initial controller parameters through a cuckoo search algorithm.

[0112] S402: According to the objective function, the initial controller parameters of the reboiler control module based on the PID neural network are determined by using a cuckoo search algorithm.

[0113] It should be noted that the Cuckoo Search Algorithm is a heuristic optimization algorithm that imitates the parasitic reproduction behavior of cuckoos to solve optimization problems. Cuckoos lay eggs in the nests of other birds, and reproduce by randomly placing eggs and taking advantage of the brooding behavior of host birds. In the algorithm, the solution space is regarded as a bird's nest, and the solution to the optimization problem is regarded as the cuckoo's egg. The core steps of the algorithm include randomly generating an initial solution (nest), generating a new solution (egg) through the cuckoo's strategy, and evaluating and updating the nest position with a fitness function. The Cuckoo Search Algorithm is widely used in function optimization and complex problem solving due to its global search capability and simple structure.

[0114] In a possible implementation, S402 specifically includes S4021 to S4025:

[0115] S4021: Initialize the number of iterations t, the discovery probability Pa, the size of the bird's nest N, and use the objective function as the fitness function of the cuckoo search algorithm.

[0116] Optionally, the discovery probability is set to 0.25.

[0117] S4022: Calculate the fitness value of each initialized bird's nest according to the fitness function.

[0118] S4023: Select the location of the bird's nest with the highest fitness value as the location of the initial bird's nest.

[0119] S4024: Update the location of the bird's nest:

[0120]

[0121] Levy:μ=t (-λ) ,1≤λ≤3

[0122] in, represents the position of the i-th bird's nest at the t+1th iteration, represents the i-th bird's nest

[0123] At the position of the tth iteration, α represents the step size control amount, represents point-to-point multiplication, Levy(λ) represents the Levy random search path, μ represents the random perturbation value of the Levy flight, and λ represents the parameter controlling the step size distribution.

[0124] It should be noted that Levy Flight is a random process used to describe the non-uniform random movement of an object in space. Its characteristic is that the displacement step follows the Levy distribution, which has a long tail characteristic, meaning that most step lengths are short, but occasionally there are longer step lengths. The Levy flight model is often used to simulate the foraging behavior of organisms or other random processes with long-range dependence characteristics. Compared with standard random walks, Levy flight can more effectively explore a large range of solution spaces, thereby improving the global search capability of the optimization algorithm.

[0125] S4025: Calculate the fitness value of the updated bird's nest, and compare the fitness value of the updated bird's nest with the fitness value of the initial bird's nest, and select the bird's nest with the largest fitness value as the current bird's nest position.

[0126] S4026: Generate a random number R between 0 and 1 at the current nest position, and compare the random number with the discovery probability Pa. If the random number is greater than the discovery probability, update the nest position and calculate the fitness value of the updated nest. If the fitness value of the updated nest is greater than the fitness value of the current nest position, update the fitness value. If the random number is less than or equal to the discovery probability, keep the current nest position unchanged.

[0127] S4027: Determine whether the maximum number of iterations has been reached. If so, output the optimal fitness value as the initial controller parameter. Otherwise, return to S4024 until the maximum number of iterations has been reached.

[0128] It should be noted that by using the objective function as the fitness function of the cuckoo search algorithm, the selection of controller parameters can be directly linked to the performance optimization objectives of the reboiler (such as reducing total energy consumption, improving heat transfer efficiency, etc.). This allows the optimization process to more accurately meet specific process requirements. The Levy flight mechanism in the algorithm allows the use of random search paths, which explore and develop the solution space through a combination of long and short steps. Through this adaptive step size adjustment mechanism, it is possible to converge to the optimal solution faster and improve search efficiency.

[0129] In the present invention, the initial controller parameters of the reboiler control module based on the PID neural network are determined by the cuckoo search algorithm, and the convergence direction and the starting point of learning of the neural network are determined, so as to quickly find the correct convergence direction and significantly improve the training efficiency, convergence speed and final control performance of the network.

[0130] S403: Using the initial controller parameters as the starting parameters of the reboiler control module based on the PID neural network, iteratively updating the controller parameters of the reboiler control module based on the PID neural network through a gradient descent algorithm, and optimizing the reboiler control module based on the PID neural network.

[0131] It should be noted that the Gradient Descent Algorithm is an iterative algorithm for optimization problems, which aims to minimize the value of the objective function by gradually adjusting the parameters. The algorithm calculates the gradient of the objective function at the current point, indicating the direction in which the function rises fastest, and then adjusts the parameters in the opposite direction of the gradient, thereby gradually approaching the minimum value of the objective function. The magnitude of the adjustment is determined by the learning rate. The Gradient Descent Algorithm is widely used in the training of machine learning and deep learning models. It can effectively find the optimal parameters of the model, minimize the loss function, and thus improve the prediction performance of the model.

[0132] In a possible implementation, S403 specifically includes sub-steps S4031 and S4032:

[0133] S4031: Construct the loss function of the gradient descent algorithm:

[0134]

[0135] Where L represents the loss function, θ represents the controller parameter, r(k) represents the target steam mass flow rate of the reboiler output set at the k sampling time, y(k) represents the actual steam mass flow rate of the reboiler output at the k sampling time, and K represents the total observation time.

[0136] S4032: The specific method of updating the controller parameters is:

[0137]

[0138] Among them, θ t+1 represents the controller parameters at the t+1th iteration, θ t represents the controller parameters at the tth iteration, θ t-1 represents the controller parameters at the t-1th iteration, η t represents the adaptive learning rate at the tth iteration, Denotes the loss function L on the controller parameter θ t is the gradient of , and β represents the momentum coefficient.

[0139] In the present invention, momentum optimization can utilize the gradient accumulation effect of the previous iterations to speed up the parameter update on the long gradient path. Especially when approaching the convergence point, momentum can help the model jump out of the local optimal point faster and move towards the global optimal direction. The introduction of momentum terms can help the model reduce the oscillation phenomenon in the parameter update process when the gradient direction is unstable or the noise is large, thereby making the training process smoother and more stable.

[0140] Optionally, the adaptive learning rate is calculated as:

[0141]

[0142] Among them, η min Represents the minimum learning rate, η max Represents the maximum learning rate, T max represents the maximum number of iterations, and cos represents the cosine function.

[0143] In the present invention, as the training process proceeds, the learning rate is smoothly reduced from an initial larger value to a smaller value. This smooth decay method allows the model to quickly learn global features in the early stages of training, and refines the learning process in the later stages of training to avoid missing tiny optimal solutions. At the same time, due to the properties of the cosine function, the adjustment of the learning rate is very smooth, avoiding the training instability that may be caused by sudden changes in the learning rate. Compared with piecewise linear decay or exponential decay, cosine annealing can provide a more stable training process.

[0144] S5: Set the target steam mass flow rate output by the reboiler.

[0145] It should be noted that the target steam mass flow rate refers to the steam mass flow rate that the reboiler wants to achieve.

[0146] S6: Get the actual steam mass flow rate output by the reboiler.

[0147] It should be noted that the actual steam mass flow rate output by the reboiler can be monitored by a sensor.

[0148] S7: The reboiler is controlled by an optimized reboiler control module based on a PID neural network.

[0149] The working principle of the reboiler control module based on the PID neural network to control the reboiler according to the target steam mass flow rate and the actual steam mass flow rate has been described above. To avoid repetition, the present invention will not repeat it again.

[0150] In the present invention, by setting the target steam mass flow rate and comparing it with the actual steam mass flow rate, the optimized PID neural network control module can adjust the control parameters in time according to the error to achieve accurate control of the reboiler. This closed-loop control method ensures that the amount of steam output by the reboiler can strictly meet the set target value to avoid the problem of excess or deficiency. The reboiler is controlled by the optimized PID neural network. The control module can quickly respond to changes in steam demand by continuously learning and adjusting parameters. This dynamic response capability allows the reboiler to quickly adjust its working state, adapt to load fluctuations in the production process, and maintain efficient operation of the system.

[0151] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0152] In the present invention, a reboiler control module based on a PID neural network is constructed and a PID neural network control module is used to replace the traditional control method, thereby overcoming the limitation of relying on experience to set control parameters, achieving better results in the face of complex dynamic characteristics and nonlinear problems, and being able to fully cope with changes in the system. Furthermore, the reboiler control module based on the PID neural network is optimized with the goal of reducing the total energy consumption of the reboiler during operation and increasing the heat flux in the evaporation zone and the heat transfer coefficient in the evaporation zone. The reboiler is controlled by the optimized reboiler control module based on the PID neural network, thereby providing dynamic adjustment capabilities for energy consumption management and optimization technology, making it easier to achieve comprehensive optimization of the reboiler energy efficiency.

[0153] Reference Manual Attached Figure 2 , showing a structural schematic diagram of a reboiler energy consumption analysis and control system provided by the present invention.

[0154] The present invention further provides a reboiler energy consumption analysis and control system 30 , comprising: a memory 303 and one or more processors 301 .

[0155] One or more application programs are stored in the memory 303 , and the one or more application programs are suitable for being executed by the one or more processors 301 to implement the reboiler energy consumption analysis and control method described in the method embodiment.

[0156] The reboiler energy consumption analysis and control system 30 comprises: a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302.

[0157] The structure of the reboiler energy consumption analysis and control system 30 does not constitute a limitation on the embodiment of the present invention.

[0158] Processor 301 may be a CPU, a general purpose processor, a DSP, an ASIC, an FPGA or other programmable logic device, a transistor logic device, a hardware component or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of the present invention. Processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0159] The bus 302 may include a path to transmit information between the above components. The bus 302 may be a PCI bus or an EISA bus, etc. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0160] The memory 303 can be a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, or an EEPROM, a CD-ROM or other optical disk storage, an optical disk storage (including a compressed optical disk, a laser disk, an optical disk, a digital versatile disk, a Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

[0161] It should be noted that the reboiler energy consumption analysis and control system 30 can implement the above-mentioned reboiler energy consumption analysis and control method, and can achieve the same or similar technical effects. To avoid repetition, the present invention will not go into details.

[0162] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0163] In the present invention, by constructing a reboiler control module based on a PID neural network, a PID neural network control module is used to replace the traditional control method, thereby overcoming the limitation of relying on experience to set control parameters, having better effects when facing complex dynamic characteristics and nonlinear problems, and being able to fully cope with changes in the system. Further, by taking reducing the total energy consumption of the reboiler during operation and increasing the heat flux in the evaporation zone and the heat transfer coefficient in the evaporation zone as the goal, the reboiler control module based on the PID neural network is optimized, and the reboiler is controlled by the optimized reboiler control module based on the PID neural network, thereby providing dynamic adjustment capabilities for energy consumption management and optimization technology, and more easily realizing comprehensive optimization of the reboiler energy efficiency. The present invention also provides a computer-readable storage medium having a computer program stored thereon, which can be loaded and executed by a processor in accordance with the reboiler energy consumption analysis and control method described in the first aspect.

[0164] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0165] There are a few points to note:

[0166] (1) The drawings of the embodiments of the present invention only relate to the structures related to the embodiments of the present invention, and other structures may refer to the general design.

[0167] (2) For the sake of clarity, in the drawings used to describe the embodiments of the present invention, the thickness of the layers or regions is exaggerated or reduced, that is, these drawings are not drawn according to the actual scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or there may be intermediate elements.

[0168] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to obtain new embodiments.

[0169] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A reboiler energy consumption analysis and control method, It is characterized in that include: S1: Construct reboiler energy consumption model; S2: Analyze the reboiler energy consumption model to determine the total energy consumption of the reboiler during operation; S3: Building a reboiler control module based on PID neural network; S4: The reboiler control module based on PID neural network is optimized with the goal of reducing the total energy consumption of the reboiler during operation and increasing the heat flux and heat transfer coefficient of the evaporation zone; S5: setting the target steam mass flow rate output by the reboiler; S6: Obtain the actual steam mass flow rate output by the reboiler; S7: The reboiler is controlled by an optimized reboiler control module based on a PID neural network.

2. The reboiler energy consumption analysis and control method according to claim 1, It is characterized in that The total energy consumption includes: liquid heat energy, steam energy consumption, heat loss and auxiliary equipment energy consumption; The total energy consumption is calculated as follows: Q total =Q liquid +Q steam +Q loss +Q aux Among them, Q total represents the total energy consumption, Q liquid represents the heat energy of the liquid, Q steam represents steam energy consumption, Q loss represents heat loss, Q aux Indicates the energy consumption of auxiliary equipment.

3. According to the reboiler energy consumption analysis and control method of claim 2, the liquid thermal energy is calculated in the following manner: Q liquid =m C ·c p ·(T pinch -T in ) in, Q liquid represents the heat energy of liquid, m C represents the mass of liquid entering the reboiler, c p represents the specific heat capacity of the liquid, T pinch represents the pinch point temperature, T in Indicates the temperature of the liquid when it enters the reboiler.

4. According to the reboiler energy consumption analysis and control method of claim 2, the steam energy consumption is calculated in the following manner: in, Q steam represents the steam energy consumption, represents the mass flow rate of steam generated by the reboiler, H steam Indicates the enthalpy of steam.

5. According to the reboiler energy consumption analysis and control method of claim 2, the heat loss is calculated in the following manner: Q loss =U surface ·A surface ·ΔT surface in, Q loss represents heat loss, U surface represents the overall heat transfer coefficient of the reboiler surface, A surface represents the external surface area of ​​the reboiler, ΔT surface Represents the temperature difference between the reboiler surface and the environment.

6. According to the reboiler energy consumption analysis and control method of claim 2, the energy consumption of the auxiliary equipment is calculated as follows: in, Q aux Indicates the energy consumption of auxiliary equipment, and Represent the power of the circulation pump and the control system respectively, t pump and t control Respectively represent the running time of the circulation pump and the control system.

7. The reboiler energy consumption analysis and control method according to claim 1, It is characterized in that The reboiler control module based on PID neural network is specifically used for: Get the input of each neuron in the input layer: x 1 (k)=r(k) x 2 (k)=y(k) Among them, x 1 (k) represents the input of the first neuron in the input layer at sampling time k, r(k) represents the target steam mass flow rate of the reboiler output set at sampling time k, and x 2 (k) represents the input of the second neuron in the input layer at sampling time k, and y(k) represents the actual steam mass flow rate output by the reboiler at sampling time k; Calculate the input of each neuron in the hidden layer: Among them, n i (k) represents the input of the i-th neuron in the hidden layer at sampling time k, w ij represents the connection weight between the jth neuron in the input layer and the ith neuron in the hidden layer, x j (k) represents the input of the th neuron in the input layer at the k sampling time; According to the input of each neuron in the hidden layer, the output of each neuron in the hidden layer is calculated: u 1 (k)=n 1 (k) you 2 (k)=n 2 (k)+u 2 (k-1) u 3 (k)=n 3 (k)-n 3 (k-1) Among them, u 1 (k) represents the output of the hidden layer proportional neuron at the k sampling time, n 1 (k) represents the input of the hidden layer proportional neuron at sampling time k, u 2 (k) represents the output of the hidden layer integrating neuron at sampling time k, n 2 (k) represents the input of the hidden layer integrating neuron at sampling time k, u 2 (k-1) represents the output of the hidden layer integrating neuron at the k-1 sampling time, u 3 (k) represents the output of the hidden layer differential neuron at sampling time k, n 3 (k) represents the input of the hidden layer differential neuron at sampling time k, n 3 (k-1) represents the input of the hidden layer differential neuron at the k-1 sampling time; According to the output of each neuron in the hidden layer, the output of the output layer is calculated: Among them, u(k) represents the control signal output by the output layer at sampling time k, and w i represents the connection weight between the i-th neuron in the hidden layer and the output layer, u i (k) represents the output of the i-th neuron in the hidden layer at sampling time k.

8. The reboiler energy consumption analysis and control method according to claim 1, It is characterized in that The S4 specifically includes: S401: constructing an objective function with the goal of reducing the total energy consumption of the reboiler during operation and increasing the heat flux and heat transfer coefficient of the evaporation zone of the reboiler during operation; S402: According to the objective function, determining initial controller parameters of a reboiler control module based on a PID neural network through a cuckoo search algorithm; S403: Using the initial controller parameters as the starting parameters of the reboiler control module based on the PID neural network, iteratively updating the controller parameters of the reboiler control module based on the PID neural network through a gradient descent algorithm, and optimizing the reboiler control module based on the PID neural network.

9. The reboiler energy consumption analysis and control method according to claim 8, It is characterized in that The objective function is specifically: Where f represents the objective function, represents the heat flux in the evaporation zone, U EZ represents the heat transfer coefficient of the evaporation zone, Q total represents the total energy consumption, α 1 represents the weight parameter of the heat flux in the evaporation zone, α 2 Represents the weight parameter of the heat transfer coefficient in the evaporation zone, α 3 A weight parameter representing the total energy consumption.

10. A reboiler energy consumption analysis and control system, It is characterized in that include: memory and one or more processors; One or more application programs are stored in the memory, and the one or more application programs are suitable for being executed by the one or more processors to implement the reboiler energy consumption analysis and control method according to any one of claims 1 to 9.

Images