Optimization method of small end difference thermal efficiency of series plate heat exchangers

Abstract

The present invention discloses a method for optimizing the thermal efficiency of a small end difference of a series plate heat exchanger, which is applied to a heat pump device and relates to the technical field of heat pumps. The method comprises: collecting the initial temperature information of the heating medium and the heat source, obtaining the heat exchange control parameter space, randomly generating a plurality of control parameter combinations for heat exchange prediction, respectively obtaining the head-end difference, the tail-end difference and the corresponding medium and heat source temperature information, and performing thermal efficiency analysis. By calculating the thermal efficiency fitness and the heat exchange fitness, the control parameters are optimized and adjusted to obtain the optimal head heat exchange control parameters and tail heat exchange control parameters. The present invention solves the technical problem in the prior art that the heat exchanger of a traditional heat pump device has a large end difference and low heat exchange efficiency during operation, which affects the energy efficiency of the device, and achieves the technical effect of improving the thermal efficiency and reducing the end difference by optimizing the control parameters of the series plate heat exchanger in the heat pump device, thereby improving the energy efficiency of the device.

Description

Technical Field

[0001] The invention relates to the technical field of heat pumps, and in particular to a method for optimizing the small end difference thermal efficiency of a series plate heat exchanger. Background Art

[0002] As an efficient energy conversion device, heat pump equipment is widely used in industrial systems. Its core principle is to absorb heat from a low-temperature environment and transfer it to a high-temperature environment, thereby achieving efficient use of energy. In order to improve the overall performance of the heat pump system, the heat exchanger is a key component, and its thermal efficiency directly affects the energy consumption and output efficiency of the system. However, the heat exchanger in traditional heat pump equipment often has problems such as large end difference and low heat exchange efficiency during operation, which limits the energy-saving effect of the equipment. Summary of the invention

[0003] The present application provides a method for optimizing the thermal efficiency of a series plate heat exchanger with a small end difference, which is used to solve the technical problem in the prior art that the heat exchanger of a traditional heat pump device has a large end difference and low heat exchange efficiency during operation, thereby affecting the energy efficiency of the device.

[0004] The present application provides a method for optimizing the small-end difference thermal efficiency of a series plate heat exchanger, the method comprising: collecting the medium initial temperature information of the heating medium to be heated in the heat pump device, and the heat source initial temperature information of the heat source in the heat pump device; obtaining the heat exchange control parameter space of the head plate heat exchanger and the tail plate heat exchanger, and randomly generating a plurality of first head heat exchange control parameters and a plurality of first tail heat exchange control parameters in the heat exchange control parameter space; performing heat exchange prediction of the head plate heat exchanger according to the plurality of first head heat exchange control parameters, in combination with the medium initial temperature information and the heat source initial temperature information, obtaining a plurality of first head-end difference information, a plurality of first medium head heat exchange temperature information and a plurality of first heat source head heat exchange temperature information, performing heat exchange prediction of the tail plate heat exchanger in combination with the plurality of first tail heat exchange control parameters, and obtaining a plurality of first A plurality of first end difference information, a plurality of first medium end heat exchange temperature information and a plurality of first heat source end heat exchange temperature information are obtained, and a thermal efficiency analysis is performed to obtain a plurality of first head thermal efficiency information and a plurality of first tail thermal efficiency information; a plurality of first thermal efficiency adaptability is calculated based on the plurality of first head end difference information, a plurality of first head thermal efficiency information, a plurality of first tail end difference information and a plurality of first tail thermal efficiency information, and a plurality of first heat exchange adaptability is calculated based on the plurality of first medium end heat exchange temperature information and a plurality of first heat source end heat exchange temperature information; a plurality of first head heat exchange control parameters and a plurality of first tail heat exchange control parameters are exchanged and adjusted based on the plurality of first thermal efficiency adaptability and the plurality of first heat exchange adaptability, and iterative optimization is performed to obtain the optimal head heat exchange control parameters and the optimal tail heat exchange control parameters as the optimization result of the series plate heat exchanger.

[0005] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0006] The small end difference thermal efficiency optimization method of a series plate heat exchanger provided in the present application relates to the field of heat pump technology. By collecting the initial temperature information of the medium and the heat source, a plurality of control parameters are randomly generated to perform heat exchange prediction and thermal efficiency analysis. The control parameters are exchanged and adjusted according to the thermal efficiency and heat exchange adaptability. After iterative optimization, the optimal control parameter combination is finally obtained to achieve the improvement of the performance of the heat exchanger. The technical problem that the heat exchanger of the traditional heat pump equipment in the prior art has a large end difference and low heat exchange efficiency during operation, which affects the energy efficiency of the equipment, is solved. The technical effect of improving the thermal efficiency and reducing the end difference, thereby improving the energy efficiency of the equipment, is achieved by optimizing the control parameters of the series plate heat exchangers in the heat pump equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] 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.

[0008] Figure 1 A schematic flow chart of a method for optimizing the small end difference thermal efficiency of a series plate heat exchanger provided in an embodiment of the present application;

[0009] Figure 2 A schematic diagram of a process for performing thermal efficiency analysis in a method for optimizing the small end difference thermal efficiency of a series plate heat exchanger provided in an embodiment of the present application;

[0010] Figure 3 A schematic diagram of a process for calculating and obtaining multiple first heat exchange adaptabilities in the small-end difference thermal efficiency optimization method for a series of plate heat exchangers provided in an embodiment of the present application. DETAILED DESCRIPTION

[0011] The present application provides a method for optimizing the thermal efficiency of a series plate heat exchanger with a small end difference, which is used to solve the technical problem in the prior art that the heat exchanger of a traditional heat pump device has a large end difference and low heat exchange efficiency during operation, thereby affecting the energy efficiency of the device.

[0012] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0013] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices.

[0014] Embodiment 1

[0015] like Figure 1 As shown, the present application provides a method for optimizing the small end difference thermal efficiency of a series plate heat exchanger, the method comprising:

[0016] P10: Collecting the initial temperature information of the heating medium to be heated in the heat pump device and the initial temperature information of the heat source in the heat pump device.

[0017] Specifically, key temperature information of the heat pump device is collected from the heat pump device, including the initial temperature information of the heating medium and the initial temperature information of the heat source. The heating medium refers to the substance that needs to be heated during the heat exchange process, usually including fluids such as water and gas. Collecting the initial temperature information of the medium can accurately understand the actual temperature of the medium before heating. This information is an important input parameter for subsequent heat exchange prediction and control, and can directly affect the heat exchange efficiency and end difference performance of the heat exchanger.

[0018] At the same time, the initial temperature information of the heat source in the heat pump device is collected. The heat source refers to a substance that provides heat for heating, such as hot water or steam. The initial temperature of the heat source is directly related to the energy transfer efficiency, because the higher the temperature of the heat source, the more heat can be provided in theory. However, in the series plate heat exchanger system, the reasonable control of the heat source temperature also needs to be optimized according to the specific heat exchange requirements to achieve the optimal distribution of energy.

[0019] Necessarily, in order to achieve accurate data collection, temperature sensors can be installed at key locations in the heat pump equipment to monitor the temperature of the heating medium and the heat source respectively. These sensors need to have sufficient accuracy and response speed to ensure that the collected temperature information can reflect the actual status of the equipment in real time. The collected initial temperature information is not only the basic data for system operation, but also plays a key role in the subsequent heat load distribution and thermal efficiency optimization process.

[0020] P20: Acquire the heat exchange control parameter space of the head plate heat exchanger and the tail plate heat exchanger, and randomly generate a plurality of first head heat exchange control parameters and a plurality of first tail heat exchange control parameters in the heat exchange control parameter space.

[0021] Furthermore, step P20 of the embodiment of the present application further includes:

[0022] P21: Obtain the heat exchange control parameter intervals of the head plate heat exchanger and the tail plate heat exchanger, and construct a heat exchange control parameter space; P22: In the heat exchange control parameter space, randomly generate a plurality of first head heat exchange control parameters and a plurality of first tail heat exchange control parameters for heat exchange control of the head plate heat exchanger and the tail plate heat exchanger.

[0023] Optionally, it is necessary to first obtain the heat exchange control parameter space of the head plate heat exchanger and the tail plate heat exchanger, and the heat exchange control parameter space includes the value range of all control parameters of the head plate heat exchanger and the tail plate heat exchanger. Heat exchange control parameters usually refer to multiple variables that affect heat exchange efficiency and end difference, including but not limited to the flow rate of the heating medium, the flow rate of the heat source, the flow direction and pressure of the fluid channel, etc. By clarifying the range of these parameters, a basis can be provided for the subsequent optimization process.

[0024] To further refine the construction process of the heat exchange control parameter space, it is first necessary to obtain the heat exchange control parameter interval. This interval contains the upper and lower limits of each control parameter to ensure that the generated control parameters are within a reasonable range. For example, the intervals of the heating medium flow rate and the heat source flow rate will be set according to the design specifications of the heat exchanger and the actual operation requirements of the heat pump system. The data sources of these intervals can include historical operation data of the equipment, design manuals, and related experimental data.

[0025] Furthermore, based on the obtained heat exchange control parameter interval, a heat exchange control parameter space is constructed. The heat exchange control parameter space is essentially a multi-dimensional coordinate system, and each dimension represents a control parameter. In this space, each point corresponds to a set of specific control parameter combinations, that is, a set of settings that can be used to control the heat exchange process.

[0026] Furthermore, in the heat exchange control parameter space, a plurality of first head heat exchange control parameters and first tail heat exchange control parameters, i.e., initial head heat exchange control parameters and initial tail heat exchange control parameters, are randomly generated. These parameter combinations will be used as the initial solution set for subsequent heat exchange prediction and thermal efficiency analysis. The purpose of random generation is to find the best configuration to optimize heat exchange efficiency and reduce end difference through different parameter combinations. In this process, the control parameters of the head plate heat exchanger and the tail plate heat exchanger can be randomly generated independently of each other to control their respective heat exchange loads. The head plate heat exchanger usually bears a larger heat load, so its control parameter range may be wider, while the tail plate heat exchanger is mainly used for waste heat recovery, and its control parameters may be relatively small.

[0027] P30: Based on the multiple first head heat exchange control parameters, combined with the medium initial temperature information and the heat source initial temperature information, the heat exchange prediction of the head plate type heat exchanger is performed to obtain multiple first head end difference information, multiple first medium head heat exchange temperature information and multiple first heat source head heat exchange temperature information; the heat exchange prediction of the tail plate type heat exchanger is performed in combination with the multiple first tail heat exchange control parameters to obtain multiple first tail end difference information, multiple first medium tail heat exchange temperature information and multiple first heat source tail heat exchange temperature information; and a thermal efficiency analysis is performed to obtain multiple first head thermal efficiency information and multiple first tail thermal efficiency information.

[0028] Specifically, based on the previously randomly generated multiple first head heat exchange control parameters and first tail heat exchange control parameters, combined with the known medium initial temperature information and heat source initial temperature information, heat exchange predictions are performed on the head plate heat exchanger and the tail plate heat exchanger respectively.

[0029] First, for the head plate heat exchanger, according to each first head heat exchange control parameter combination, combined with the initial temperature information of the medium and the initial temperature information of the heat source, the heat exchange prediction is performed using the physical model of the heat exchanger and the heat transfer principle. This prediction process takes into account the influence of the heat exchanger's structural parameters (such as plate material, spacing, etc.), the physical properties of the fluid (such as specific heat capacity, thermal conductivity, etc.) and the control parameters (such as flow rate, flow distribution, etc.) on the heat exchange effect. Through calculation, multiple first head end difference information (i.e., the temperature difference between the medium and the heat source before and after the heat exchange), multiple first medium head heat exchange temperature information (the temperature of the medium after the heat exchange) and multiple first heat source head heat exchange temperature information (the temperature of the heat source after the heat exchange) are obtained.

[0030] Next, using the medium temperature and heat source temperature after heat exchange in the head plate heat exchanger as input, combined with each first tail heat exchange control parameter combination, a similar heat exchange prediction is performed on the tail plate heat exchanger. This process also takes into account the physical characteristics of the tail plate heat exchanger and the influence of the control parameters on the heat exchange effect, and finally obtains multiple first tail end difference information, multiple first medium tail heat exchange temperature information and multiple first heat source tail heat exchange temperature information.

[0031] After obtaining these heat exchange prediction results, thermal efficiency analysis is performed. Thermal efficiency is an important indicator to measure the performance of the heat exchanger, reflecting the ability of the heat exchanger to transfer the heat from the heat source to the heating medium. By comparing the temperature changes before and after the heat exchange and the heat input of the heat source, multiple first-end thermal efficiency information and multiple first-end thermal efficiency information are calculated. This thermal efficiency information will be used in the subsequent optimization iteration process to find the heat exchange control parameter combination that can maximize the thermal efficiency and minimize the end difference.

[0032] Furthermore, the heat exchange prediction of the head plate heat exchanger is performed, and step P30 of the embodiment of the present application further includes:

[0033] P31: Based on the historical heat exchange record data of the head plate heat exchanger, collect the sample head heat exchange control parameter set, the sample medium initial temperature information set and the sample heat source initial temperature information set, and mark the head end difference, medium head heat exchange temperature and heat source head heat exchange temperature after heat exchange, obtain the sample head end difference information set, the sample medium head heat exchange temperature information and the sample heat source head heat exchange temperature information set, and obtain the head heat exchange prediction training data set; P32: Use the head heat exchange prediction training data set to train the head heat exchange predictor, and perform heat exchange prediction on the multiple first head heat exchange control parameters in combination with the medium initial temperature information and the heat source initial temperature information, and obtain multiple first head end difference information, multiple first medium head heat exchange temperature information and multiple first heat source head heat exchange temperature information.

[0034] It should be understood that the specific process of performing the heat exchange prediction of the head plate heat exchanger can be, first, to construct a head heat exchange prediction training data set based on historical heat exchange record data. This step involves collecting a sample head heat exchange control parameter set, a medium initial temperature information set, and a heat source initial temperature information set from historical data, and marking the head end difference after heat exchange, the medium head heat exchange temperature, and the heat source head heat exchange temperature to form a sample set. Through these sample data, a complete set of training data sets can be obtained for subsequent heat exchange prediction model training.

[0035] Furthermore, the head heat exchange prediction training data set is used to train a head heat exchange predictor. Exemplarily, a suitable machine learning model is selected according to the complexity of the problem and the characteristics of the data. For regression problems such as heat exchange prediction involving multiple input variables and output variables, models with strong nonlinear fitting capabilities such as neural network models (such as multi-layer perceptrons, convolutional neural networks, etc.) can be selected. Further, the selected model is trained using the training data set. The model learns the complex relationship between the input variables (heat exchange control parameters, medium initial temperature and heat source initial temperature) and the output variables (head end difference after heat exchange, medium head heat exchange temperature and heat source head heat exchange temperature) to obtain the head heat exchange predictor.

[0036] Furthermore, the trained head heat exchange predictor is used to predict multiple first head heat exchange control parameters, and the heat exchange effect of the head plate heat exchanger is accurately predicted by combining the initial temperature information of the medium and the heat source. In this way, the prediction results can provide multiple first head end difference information, medium head heat exchange temperature information, and heat source head heat exchange temperature information. These prediction results will be used for subsequent thermal efficiency analysis and optimization iteration process.

[0037] Furthermore, the heat exchange prediction of the tail plate heat exchanger is performed, and step P30 of the embodiment of the present application further includes:

[0038] P33: Based on the historical heat exchange record data of the tail plate heat exchanger, collect the sample medium head heat exchange temperature information set, the sample heat source head heat exchange temperature information set and the sample tail heat exchange control parameter set, and mark the tail end difference, medium tail heat exchange temperature and heat source tail heat exchange temperature after heat exchange, obtain the sample tail end difference information set, the sample medium tail heat exchange temperature information set and the sample heat source tail heat exchange temperature information set, and combine them to obtain the tail heat exchange prediction training data set; P34: Use the tail heat exchange prediction training data set to train the tail heat exchange predictor, and perform heat exchange prediction on the multiple first tail heat exchange control parameters in combination with the multiple first medium head heat exchange temperature information and the multiple first heat source head heat exchange temperature information, and obtain multiple first tail end difference information, multiple first medium tail heat exchange temperature information and multiple first heat source tail heat exchange temperature information.

[0039] Optionally, a machine learning prediction method based on historical data is also used for the tail plate heat exchanger. By constructing a tail heat exchange prediction training data set and training a tail heat exchange predictor, accurate prediction of the heat exchange performance of the tail plate heat exchanger can be achieved.

[0040] First, through the historical heat exchange record data of the tail plate heat exchanger, a series of key parameter sets are collected, including the sample medium head heat exchange temperature information set, the sample heat source head heat exchange temperature information set and the sample tail heat exchange control parameter set. These data reflect the medium and heat source temperature after the head plate heat exchanger is processed, as well as the control parameters of the tail plate heat exchanger, such as flow rate and pressure.

[0041] Next, the tail end difference after heat exchange, the medium tail heat exchange temperature and the heat source tail heat exchange temperature are annotated to obtain the sample tail end difference information set, the sample medium tail heat exchange temperature information set and the sample heat source tail heat exchange temperature information set. The end difference refers to the difference in medium temperature on both sides of the heat exchanger, which is one of the important indicators for measuring the heat exchange effect. Through these annotated sample data, the heat exchange effect of the tail plate heat exchanger under different control parameters can be fully described, thereby combining into a complete tail heat exchange prediction training data set.

[0042] Furthermore, the tail heat exchange predictor is trained using the constructed tail heat exchange prediction training data set. The main function of the tail heat exchange predictor is to predict the heat exchange effect of the tail plate heat exchanger based on multiple first tail heat exchange control parameters, combined with multiple first medium head heat exchange temperature information and multiple first heat source head heat exchange temperature information obtained from the head plate heat exchanger. In this process, the prediction of the heat exchange effect of the tail plate heat exchanger is closely related to the head plate heat exchanger, because the tail plate heat exchanger mainly handles the remaining heat load of the head plate heat exchanger. By combining the heat exchange results of the head plate heat exchanger for prediction, the tail heat exchange predictor can further optimize the control parameters of the tail plate heat exchanger based on the temperature output of the previous heat exchange step, thereby improving the overall heat exchange efficiency. Through these data, the system can accurately predict multiple first tail end difference information, multiple first medium tail heat exchange temperature information and multiple first heat source tail heat exchange temperature information of the tail plate heat exchanger under the current control parameters, providing an important reference for subsequent optimization.

[0043] Further, such as Figure 2 As shown, thermal efficiency analysis is performed, and step P30 of the embodiment of the present application further includes:

[0044] P35: Based on the thermal efficiency test data, a head thermal efficiency training data set and a tail thermal efficiency training data set are collected, wherein the head thermal efficiency training data set includes multiple groups of sample medium initial temperature information, sample heat source initial temperature information, sample head-end difference information and sample head thermal efficiency information; P36: The head thermal efficiency training data set and the tail thermal efficiency training data set are respectively used to train the head thermal efficiency analyzer and the tail thermal efficiency analyzer; P37: The head thermal efficiency analyzer and the tail thermal efficiency analyzer are respectively used to perform thermal efficiency analysis on the medium initial temperature information, the heat source initial temperature information and the multiple first head-end difference information to obtain multiple first head thermal efficiency information, and the multiple first medium head heat exchange temperature information, the multiple first heat source head heat exchange temperature information and the multiple first tail-end difference information to obtain multiple first tail thermal efficiency information.

[0045] Specifically, the specific process of thermal efficiency analysis may be that, first, a head thermal efficiency training data set and a tail thermal efficiency training data set need to be collected. Among them, the head thermal efficiency training data set includes multiple sets of key sample data, such as sample medium initial temperature information, sample heat source initial temperature information, sample head end difference information and sample head thermal efficiency information. These data are obtained through thermal efficiency testing equipment, reflecting the thermal efficiency performance of the head plate heat exchanger under different initial conditions. The tail thermal efficiency training data set also contains similar information, which is used to train the thermal efficiency analyzer of the tail plate heat exchanger.

[0046] Furthermore, the head thermal efficiency training data set and tail thermal efficiency training data set collected above are used as training data to train the head thermal efficiency analyzer and tail thermal efficiency analyzer. The function of these analyzers is to accurately predict the thermal efficiency performance of the head plate type and tail plate type heat exchangers under different heat exchange conditions through learning and analyzing a large amount of historical data. This training process usually involves the use of machine learning or regression models to correlate the operating performance of the heat exchanger with variables such as initial temperature and end difference, thereby improving the model's ability to predict thermal efficiency.

[0047] Next, the trained head thermal efficiency analyzer and tail thermal efficiency analyzer are used to analyze multiple parameters in the system. Specifically, the head thermal efficiency analyzer performs thermal efficiency analysis based on the medium initial temperature information, the heat source initial temperature information, and multiple first head end difference information, and outputs multiple first head thermal efficiency information. The multiple first head thermal efficiency information can reflect the actual thermal efficiency performance of the head plate heat exchanger under the current operating conditions.

[0048] Similarly, the tail heat efficiency analyzer is used to analyze the thermal efficiency of the tail plate heat exchanger by combining multiple first medium head heat exchange temperature information, multiple first heat source head heat exchange temperature information, and multiple first tail end difference information to obtain multiple first tail thermal efficiency information. The tail plate heat exchanger mainly processes residual heat, so the accurate prediction of its thermal efficiency is crucial to the overall efficiency of the system.

[0049] P40: Based on the multiple first head-end difference information, the multiple first head thermal efficiency information, the multiple first tail-end difference information and the multiple first tail thermal efficiency information, multiple first thermal efficiency adaptabilities are calculated and obtained; based on the multiple first medium tail heat exchange temperature information and the multiple first heat source tail heat exchange temperature information, multiple first heat exchange adaptabilities are calculated and obtained.

[0050] Further, such as Figure 3 As shown, step P40 of the embodiment of the present application also includes:

[0051] P41: Based on the ratio of the multiple first head-end difference information, the multiple first head thermal efficiency information, the multiple first tail-end difference information, the multiple first tail thermal efficiency information and the preset small end difference and the preset thermal efficiency, a weighted calculation is performed to obtain multiple first thermal efficiency adaptability; P42: Based on the multiple first medium tail heat exchange temperature information and the multiple first heat source tail heat exchange temperature information, combined with the preset medium heat exchange temperature and the preset heat source heat exchange temperature, a plurality of first heat exchange adaptability is calculated, as shown in the following formula: Among them, HEA is heat transfer adaptability, w1 and w2 are weights, J ws is the heat transfer temperature information of the medium tail, J wy is the preset medium heat exchange temperature, Y ws is the heat transfer temperature information at the heat source end, Ywy The preset heat source heat exchange temperature.

[0052] It should be understood that the overall performance of the heat pump system is evaluated by calculating a plurality of first thermal efficiency fitnesses and a plurality of first heat exchange fitnesses, taking into account the thermal efficiency and heat exchange performance of the head plate heat exchanger and the tail plate heat exchanger.

[0053] Specifically, it is necessary to first calculate multiple first thermal efficiency fitness according to multiple first head-end difference information, first head thermal efficiency information, first tail-end difference information and first tail thermal efficiency information. Fitness refers to the degree of conformity of the current heat exchanger configuration relative to the ideal state. The process of calculating fitness needs to be compared with the preset small end difference and thermal efficiency value to ensure that the heat exchange system has sufficient efficiency and performance in heat load transfer.

[0054] Exemplarily, a weighted calculation is performed based on the ratio of the multiple first head-end difference information, multiple first head thermal efficiency information, multiple first tail-end difference information, multiple first tail thermal efficiency information and the preset small end difference and preset thermal efficiency to obtain the first thermal efficiency fitness. Weighted calculation means that different end differences and thermal efficiencies will be given different weights according to their impact on the overall system performance to ensure that more important parameters are given greater priority during the optimization process. In this way, the pros and cons of the current heat exchanger control parameter combination can be effectively evaluated and adjusted during optimization.

[0055] At the same time, it is also necessary to calculate and obtain multiple first heat exchange fitnesses according to the heat exchange temperature information of the medium tail and the heat exchange temperature information of the heat source tail. The first heat exchange fitness is an important indicator for evaluating whether the current heat exchange process is efficient, and the calculation formula is as follows: Among them, HEA is heat transfer adaptability, w1 and w2 are weights, J ws is the heat transfer temperature information of the medium tail. The higher the heat transfer temperature of the medium tail, the more sufficient the heat transfer is, so the higher the adaptability is. wy is the preset medium heat exchange temperature, Y ws is the heat source tail heat exchange temperature information. The higher the tail heat exchange temperature of the heat source, the more residual heat there is, which means that less energy is needed to be supplemented later, thereby improving the fitness. wy The preset heat source heat exchange temperature.

[0056] Through the above calculation method, the heat transfer temperature at the heat source tail and the heat transfer fitness are positively correlated, that is, the higher the temperature at the heat source tail, the greater the fitness. The higher the heat transfer temperature at the medium tail, the more sufficient the medium is heat exchanged, thereby improving the overall fitness of the heat exchange process. The above analysis steps provide an important data basis for the subsequent control parameter optimization, which can help the system make more precise adjustments in thermal efficiency and end difference, and ultimately improve the overall performance of the heat exchanger.

[0057] P50: According to the multiple first thermal efficiency fitness degrees and the multiple first heat exchange fitness degrees, the multiple first head heat exchange control parameters and the multiple first tail heat exchange control parameters are exchanged and adjusted, and iterative optimization is performed to obtain the optimal head heat exchange control parameters and the optimal tail heat exchange control parameters as the optimization result of the series plate heat exchanger.

[0058] Furthermore, step P50 of the embodiment of the present application also includes:

[0059] P51: Combine the multiple first head heat exchange control parameters and the multiple first tail heat exchange control parameters respectively to obtain multiple series heat exchange control schemes; P52: Perform weighted calculation on the multiple first thermal efficiency fitnesses and the multiple first heat exchange fitnesses to obtain multiple heat exchange control fitnesses; P53: According to the multiple heat exchange control fitnesses, allocate exchange adjustment probabilities for exchanging and adjusting the first head heat exchange control parameters and the first tail heat exchange control parameters in the multiple series heat exchange control schemes to obtain multiple exchange adjustment probabilities; P54: According to the multiple exchange adjustment probabilities, randomly select a series heat exchange control scheme with a preset exchange adjustment number, exchange the first head heat exchange control parameters and the first tail heat exchange control parameters in the selected series heat exchange control scheme to form a new series heat exchange control scheme, and perform heat exchange prediction; P55: Continue to iteratively optimize the series heat exchange control scheme in the heat exchange control parameter space until convergence, output the series heat exchange control scheme with the largest heat exchange control fitness, and obtain the optimal head heat exchange control parameters and the optimal tail heat exchange control parameters.

[0060] Optionally, the control parameters are optimized through multiple first thermal efficiency fitness and multiple first heat exchange fitness, and iterative adjustment is performed to finally obtain the optimal combination of head plate type and tail plate type heat exchange control parameters. The optimization process of heat exchange control parameters is based on fitness evaluation, and through multiple exchanges and adjustments, the overall performance of the heat exchanger gradually approaches the optimal state.

[0061] First, multiple first-end heat exchange control parameters and multiple first-end heat exchange control parameters are combined to generate multiple different series heat exchange control schemes. Each control scheme represents a possible heat exchange configuration, which will be evaluated and adjusted in the subsequent optimization process.

[0062] Next, for multiple control schemes, the heat exchange control fitness of each series heat exchange control scheme is obtained through weighted calculation using the multiple first thermal efficiency fitness and multiple first heat exchange fitness calculated previously. This fitness combines the considerations of thermal efficiency and heat exchange performance, and is an important indicator for measuring the pros and cons of control schemes. The higher the fitness, the better the heat exchange efficiency and performance of the scheme. The weighted calculation method ensures that different fitness parameters can reasonably reflect their impact on the overall performance.

[0063] Furthermore, in order to guide the subsequent iterative optimization process, the exchange adjustment probability is assigned to the first head heat exchange control parameter and the first tail heat exchange control parameter in each series heat exchange control scheme according to the size of the heat exchange control fitness. This probability is assigned according to the fitness. The lower the fitness of the control parameter combination, the higher the probability of exchange adjustment, because the low fitness indicates that the control parameter design of the scheme is not ideal and needs further adjustment to improve performance.

[0064] According to these exchange adjustment probabilities, a certain number of series heat exchange control schemes are randomly selected for parameter exchange. Specifically, the first head heat exchange control parameters and the first tail heat exchange control parameters in the selected control scheme are exchanged to form a new series heat exchange control scheme. Subsequently, the tail heat exchange predictor is used to predict the heat exchange of these new schemes to evaluate the performance after parameter exchange. This process increases the randomness of the optimization and avoids falling into the local optimal solution. At the same time, the effectiveness of the optimization is further verified by predicting the heat exchange effect of the new scheme.

[0065] The above exchange, prediction and evaluation process will be repeated in the heat exchange control parameter space to form an iterative optimization. In each iteration, the control scheme is updated according to the new heat exchange control fitness, and a better solution will continue to be found. This process will continue until the convergence condition is met (such as the heat exchange control fitness is no longer significantly improved or the preset number of iterations is reached). Finally, the series heat exchange control scheme with the largest heat exchange control fitness is output, and the optimal head heat exchange control parameters and the optimal tail heat exchange control parameters are extracted from it as the optimization result of the heat pump system. Ensure that this combination reaches the optimal state in both thermal efficiency and heat exchange performance, thereby improving the overall operating efficiency and stability of the system.

[0066] Furthermore, step P53 of the embodiment of the present application also includes:

[0067] P53-1: Calculate the inverse of the ratio of each heat exchange control fitness to the sum of the multiple heat exchange control fitnesses to obtain multiple fitness ratio inverses; P53-2: Based on the multiple fitness ratio inverses, calculate the ratio of each fitness ratio inverse to the sum of the multiple fitness ratio inverses as the exchange adjustment probability to obtain multiple exchange adjustment probabilities.

[0068] Specifically, the specific process of allocating the exchange adjustment probability may be to first calculate the inverse of the ratio of the heat exchange control fitness of each heat exchange control scheme to the sum of all heat exchange control fitness. Here, the inverse of the fitness ratio represents the exchange adjustment priority of each control scheme relative to other schemes. The smaller the heat exchange control fitness, the larger its inverse, which means that the scheme performs poorly and needs more adjustment opportunities. In this way, it can be ensured that the schemes that perform poorly in the overall fitness get more adjustment opportunities in the subsequent optimization process.

[0069] Next, the exchange adjustment probability is further calculated based on the inverse of the fitness ratio. Specifically, the inverse of each fitness ratio is calculated by the ratio of the sum of the inverses of all fitness ratios to obtain the exchange adjustment probability of each control scheme. This probability represents the possibility that the control scheme will be randomly selected to exchange the head heat exchange control parameters and the tail heat exchange control parameters in the subsequent steps.

[0070] By introducing the inverse of the fitness ratio, the optimization process can guide parameter adjustment more effectively. By assigning a higher adjustment probability to control schemes with poorer fitness, the system can converge to a better control scheme more quickly and avoid falling into a local optimal solution. Specifically, heat exchange control schemes with poorer fitness performance will have a greater exchange adjustment probability, so the system will give priority to these schemes for parameter exchange and adjustment, while schemes with better fitness performance will have a relatively low exchange adjustment probability. This mechanism can ensure the balance of the optimization process, that is, it can fully optimize in schemes with lower fitness and maintain stability in schemes with higher fitness.

[0071] In summary, the embodiments of the present application have at least the following technical effects:

[0072] This application acquires the heat exchange control parameter space by collecting the initial temperature information of the heating medium and the heat source, randomly generates multiple control parameter combinations for heat exchange prediction, obtains the head-end difference, tail-end difference and the corresponding medium and heat source temperature information, and performs thermal efficiency analysis. By calculating the thermal efficiency fitness and heat exchange fitness, the control parameters are optimized and adjusted to obtain the optimal head heat exchange control parameters and tail heat exchange control parameters, and the optimization of the series plate heat exchanger is achieved. The technical effect of improving the thermal efficiency and reducing the end difference, thereby improving the energy efficiency of the equipment, is achieved by optimizing the control parameters of the series plate heat exchangers in the heat pump equipment.

[0073] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0074] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0075] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for optimizing the small end difference thermal efficiency of a series plate heat exchanger, characterized in that: The method is applied to a heat pump device, wherein the heat pump device includes a head plate heat exchanger and a tail plate heat exchanger connected in series, including: Collecting the initial temperature information of the heating medium to be heated in the heat pump device and the initial temperature information of the heat source in the heat pump device; Acquire the heat exchange control parameter space of the head plate heat exchanger and the tail plate heat exchanger, and randomly generate a plurality of first head heat exchange control parameters and a plurality of first tail heat exchange control parameters in the heat exchange control parameter space; According to the multiple first head heat exchange control parameters, combined with the medium initial temperature information and the heat source initial temperature information, the heat exchange prediction of the head plate heat exchanger is performed to obtain multiple first head end difference information, multiple first medium head heat exchange temperature information and multiple first heat source head heat exchange temperature information; the heat exchange prediction of the tail plate heat exchanger is performed in combination with the multiple first tail heat exchange control parameters to obtain multiple first tail end difference information, multiple first medium tail heat exchange temperature information and multiple first heat source tail heat exchange temperature information; and thermal efficiency analysis is performed to obtain multiple first head thermal efficiency information and multiple first tail thermal efficiency information; Calculate and obtain a plurality of first thermal efficiency adaptability degrees according to the plurality of first head-end difference information, the plurality of first head thermal efficiency information, the plurality of first tail-end difference information and the plurality of first tail thermal efficiency information, and calculate and obtain a plurality of first heat exchange adaptability degrees according to the plurality of first medium tail heat exchange temperature information and the plurality of first heat source tail heat exchange temperature information; According to the multiple first thermal efficiency adaptabilities and the multiple first heat exchange adaptabilities, the multiple first head heat exchange control parameters and the multiple first tail heat exchange control parameters are exchanged and adjusted, and iterative optimization is performed to obtain optimal head heat exchange control parameters and optimal tail heat exchange control parameters as the optimization result of the series plate heat exchanger.

2. The method for optimizing the small end difference thermal efficiency of a series plate heat exchanger according to claim 1, characterized in that: Acquiring the heat exchange control parameter space of the head plate heat exchanger and the tail plate heat exchanger, and randomly generating a plurality of first head heat exchange control parameters and a plurality of first tail heat exchange control parameters in the heat exchange control parameter space, including: Obtaining heat exchange control parameter intervals of the head plate heat exchanger and the tail plate heat exchanger to construct a heat exchange control parameter space; In the heat exchange control parameter space, a plurality of first head heat exchange control parameters and a plurality of first tail heat exchange control parameters for heat exchange control of the head plate heat exchanger and the tail plate heat exchanger are randomly generated.

3. The method for optimizing the small end difference thermal efficiency of a series plate heat exchanger according to claim 1, characterized in that: According to the multiple first head heat exchange control parameters, combined with the medium initial temperature information and the heat source initial temperature information, the heat exchange prediction of the head plate heat exchanger is performed to obtain multiple first head end difference information, multiple first medium head heat exchange temperature information and multiple first heat source head heat exchange temperature information, including: According to the historical heat exchange record data of the head plate heat exchanger, the sample head heat exchange control parameter set, the sample medium initial temperature information set and the sample heat source initial temperature information set are collected, and the head end difference after heat exchange, the medium head heat exchange temperature and the heat source head heat exchange temperature are marked to obtain the sample head end difference information set, the sample medium head heat exchange temperature information and the sample heat source head heat exchange temperature information set, and obtain the head heat exchange prediction training data set; The head heat exchange prediction training data set is used to train a head heat exchange predictor, and heat exchange prediction is performed on the multiple first head heat exchange control parameters in combination with the medium initial temperature information and the heat source initial temperature information to obtain multiple first head end difference information, multiple first medium head heat exchange temperature information and multiple first heat source head heat exchange temperature information.

4. The method for optimizing the small end difference thermal efficiency of a series plate heat exchanger according to claim 1, characterized in that: Combining the plurality of first tail heat exchange control parameters to perform heat exchange prediction of the tail plate heat exchanger, obtaining a plurality of first tail end difference information, a plurality of first medium tail heat exchange temperature information and a plurality of first heat source tail heat exchange temperature information, including: According to the historical heat exchange record data of the tail plate heat exchanger, the sample medium head heat exchange temperature information set, the sample heat source head heat exchange temperature information set and the sample tail heat exchange control parameter set are collected, and the tail end difference, medium tail heat exchange temperature and heat source tail heat exchange temperature after heat exchange are marked to obtain the sample tail end difference information set, the sample medium tail heat exchange temperature information set and the sample heat source tail heat exchange temperature information set, and the tail heat exchange prediction training data set is obtained by combining them; The tail heat exchange prediction training data set is used to train a tail heat exchange predictor, and heat exchange prediction is performed on the multiple first tail heat exchange control parameters in combination with the multiple first medium head heat exchange temperature information and the multiple first heat source head heat exchange temperature information to obtain multiple first tail end difference information, multiple first medium tail heat exchange temperature information and multiple first heat source tail heat exchange temperature information.

5. The method for optimizing the small end difference thermal efficiency of a series plate heat exchanger according to claim 1, characterized in that: Conduct thermal efficiency analysis, including: According to the thermal efficiency test data, a head thermal efficiency training data set and a tail thermal efficiency training data set are collected, wherein the head thermal efficiency training data set includes multiple sets of sample medium initial temperature information, sample heat source initial temperature information, sample head end difference information and sample head thermal efficiency information; Using the head thermal efficiency training data set and the tail thermal efficiency training data set to train a head thermal efficiency analyzer and a tail thermal efficiency analyzer respectively; The head thermal efficiency analyzer and the tail thermal efficiency analyzer are respectively used to perform thermal efficiency analysis on the medium initial temperature information, the heat source initial temperature information and the multiple first head-end difference information to obtain the multiple first head thermal efficiency information, and the thermal efficiency analysis is performed on the multiple first medium head heat exchange temperature information, the multiple first heat source head heat exchange temperature information and the multiple first tail-end difference information to obtain the multiple first tail thermal efficiency information.

6. The method for optimizing the small end difference thermal efficiency of a series plate heat exchanger according to claim 1, characterized in that: The method comprises: calculating and obtaining a plurality of first thermal efficiency adaptability degrees according to the plurality of first head-end difference information, the plurality of first head thermal efficiency information, the plurality of first tail-end difference information, and the plurality of first tail thermal efficiency information; and calculating and obtaining a plurality of first heat exchange adaptability degrees according to the plurality of first medium tail heat exchange temperature information and the plurality of first heat source tail heat exchange temperature information, including: Obtaining multiple first thermal efficiency adaptability by weighted calculation according to the ratios of the multiple first head-end difference information, the multiple first head thermal efficiency information, the multiple first tail-end difference information, the multiple first tail thermal efficiency information and the preset small-end difference and the preset thermal efficiency; According to the plurality of first medium tail heat exchange temperature information and the plurality of first heat source tail heat exchange temperature information, combined with the preset medium heat exchange temperature and the preset heat source heat exchange temperature, a plurality of first heat exchange adaptability is calculated and obtained, as shown in the following formula: Among them, HEA is heat transfer adaptability, w1 and w2 are weights, J ws is the heat transfer temperature information of the medium tail, J wy is the preset medium heat exchange temperature, Y ws is the heat transfer temperature information at the heat source end, Y wy The preset heat source heat exchange temperature.

7. The method for optimizing the small end difference thermal efficiency of a series plate heat exchanger according to claim 1, characterized in that: According to the plurality of first thermal efficiency adaptabilities and the plurality of first heat exchange adaptabilities, the plurality of first head heat exchange control parameters and the plurality of first tail heat exchange control parameters are exchanged and adjusted, and iteratively optimized, including: Combining the plurality of first head heat exchange control parameters and the plurality of first tail heat exchange control parameters respectively to obtain a plurality of series heat exchange control schemes; Performing weighted calculation on the plurality of first thermal efficiency fitness degrees and the plurality of first heat exchange fitness degrees to obtain a plurality of heat exchange control fitness degrees; According to the multiple heat exchange control adaptabilities, assigning exchange adjustment probabilities for exchanging and adjusting the first head heat exchange control parameters and the first tail heat exchange control parameters in the multiple series heat exchange control schemes to obtain multiple exchange adjustment probabilities; According to the multiple exchange adjustment probabilities, randomly select a series heat exchange control scheme with a preset exchange adjustment number, exchange the first head heat exchange control parameter and the first tail heat exchange control parameter in the selected series heat exchange control scheme to form a new series heat exchange control scheme, and perform heat exchange prediction; Continue to iteratively optimize the series heat exchange control scheme in the heat exchange control parameter space until convergence, output the series heat exchange control scheme with the maximum heat exchange control fitness, and obtain the optimal head heat exchange control parameters and the optimal tail heat exchange control parameters.

8. The method for optimizing the small end difference thermal efficiency of a series plate heat exchanger according to claim 7, characterized in that: Allocating exchange adjustment probabilities for exchange adjustment of first head heat exchange control parameters and first tail heat exchange control parameters in the plurality of series heat exchange control schemes according to the plurality of heat exchange control adaptabilities, including: Calculating the inverse of the ratio of each heat exchange control fitness to the sum of the multiple heat exchange control fitnesses to obtain multiple fitness ratio inverses; According to the multiple fitness ratio inverses, the ratio of each fitness ratio inverse to the sum of the multiple fitness ratio inverses is calculated as the exchange adjustment probability, and multiple exchange adjustment probabilities are obtained.

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