A liquid distribution state design method and system for a horizontal tube falling film evaporator

Abstract

The present invention relates to the technical field of evaporator heat transfer, and in particular to a method and system for designing the liquid distribution state of a horizontal tube falling film evaporator. The method comprises: acquiring spray characteristic information, and generating a plurality of liquid distribution schemes according to a single transformation combination of parameters contained in the spray characteristic information; constructing a virtual physical operation domain, and obtaining spray experiment simulation results; establishing a spray analysis database, and storing and managing the spray characteristic information, the liquid distribution schemes, and the spray experiment simulation results; calling the spray analysis database, and analyzing the sensitive effect of the spray experiment simulation results according to a single parameter in the spray characteristic information, and obtaining the single sensitivity of the single parameter to the spray experiment simulation results; constructing a liquid distribution state analysis model, and analyzing the uniformity of a plurality of liquid distribution schemes through single sensitivity, and determining a liquid distribution execution scheme. The present invention effectively solves the problem of uneven liquid spraying in a horizontal tube falling film evaporator.

Description

Technical Field

[0001] The invention relates to the technical field of evaporator heat transfer, and in particular to a liquid distribution state design method and system for a horizontal tube falling film evaporator. Background Art

[0002] As one of the key technologies to solve the global shortage of fresh water resources, seawater desalination technology has developed a variety of methods, including evaporation, membrane, electrodialysis and freezing. Among them, low-temperature multi-effect evaporation technology is widely used because it can effectively use low-quality steam for seawater desalination. Especially in China's seawater desalination industry, reverse osmosis and low-temperature multi-effect evaporation technology have become the mainstream. Among various types of evaporator equipment, horizontal tube falling film evaporator is the most widely used in actual low-temperature multi-effect evaporation desalination projects due to its compact structure, small footprint and high heat transfer coefficient.

[0003] The liquid distribution state of the horizontal tube falling film evaporator is affected by many factors. The distribution state of the raw liquid will directly affect the heat transfer coefficient of the evaporation side of the heat exchange tube, and then affect the overall performance of the horizontal falling film evaporator. The currently used horizontal tube falling film evaporator has the problem of uneven liquid spraying, which leads to uneven liquid distribution on the surface of the evaporator, affecting the evaporation efficiency and product quality. In addition, the existing control systems often lack sufficient data analysis capabilities, cannot accurately control the spray parameters, and cannot effectively predict the working state of the simulated evaporator.

[0004] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present disclosure, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art known to those skilled in the art. Summary of the invention

[0005] The present invention provides an evaporator heat transfer information management method and system, which can effectively solve the problems in the background technology.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] Acquire spray characteristic information, the spray characteristic information including spray liquid pressure parameter, spray height position parameter, spray droplet size parameter and spray angle parameter, and generate a plurality of liquid distribution schemes according to a single transformation combination of parameters included in the spray characteristic information;

[0008] Constructing a virtual physical operation domain, wherein the virtual physical operation domain performs a spray simulation experiment on the liquid distribution scheme to obtain a spray experiment simulation result;

[0009] Establishing a spray analysis database for storing and managing the spray characteristic information, liquid distribution scheme and the spray experiment simulation results;

[0010] Calling the spray analysis database, analyzing the sensitive effect of a single parameter in the spray characteristic information on the spray experiment simulation result, and obtaining the single sensitivity of the spray liquid pressure parameter, the spray height position parameter, the spray droplet size parameter, and the spray angle parameter to the spray experiment simulation result;

[0011] A liquid distribution state analysis model is constructed, the uniformity of several liquid distribution schemes is analyzed through the single sensitivity, and the liquid distribution execution scheme is determined according to the uniformity analysis results.

[0012] Furthermore, a liquid distribution state analysis model is constructed, and the uniformity of several liquid distribution schemes is analyzed through the single sensitivity to determine the liquid distribution execution scheme, including:

[0013] Constructing a spray characteristic mapping space according to the spray characteristic information, the spray characteristic mapping space performs deep learning on the sensitive effect of a single parameter of the spray characteristic information in the spray analysis database on the spray experiment simulation result, and performs uniformity prediction on the liquid distribution scheme without performing a spray simulation experiment;

[0014] Setting an input end and an output end based on the spray characteristic mapping space to generate the liquid distribution state analysis model;

[0015] Several liquid distribution plans are input into the liquid distribution state analysis model to obtain uniformity prediction results, and the liquid distribution execution plan is obtained according to the uniformity prediction result output.

[0016] Further, the liquid distribution execution plan is obtained according to the uniformity prediction result output, including:

[0017] Obtaining the spray characteristic information in the liquid distribution scheme corresponding to the uniformity prediction result, and analyzing the effects between single parameters in the spray characteristic information to obtain an effect analysis result;

[0018] The effects between the single parameters in the spray characteristic information include the effects of the spray liquid pressure parameter and the spray droplet diameter on the spray angle parameter and the effects of the spray liquid pressure parameter and the spray height position parameter on the spray angle parameter;

[0019] According to the uniformity prediction result and the effect analysis result, one of the liquid distribution methods is selected as the liquid distribution execution plan output.

[0020] Furthermore, the effects of the spray liquid pressure parameter and the spray droplet size on the spray angle parameter are analyzed, including:

[0021] A pressure-particle size-angle analysis component is constructed according to the spray liquid pressure parameter, the spray droplet particle size parameter and the spray angle parameter, wherein the pressure-particle size-angle analysis component is used to analyze the relationship between the spray liquid pressure parameter and the spray droplet particle size parameter and the spray angle parameter;

[0022] Establishing a first link relationship between the spray liquid pressure parameter and the spray liquid droplet size parameter, wherein the first link relationship is a corresponding relationship between each parameter of the spray liquid pressure parameter and the spray liquid droplet size parameter, and generating a first influencing link group according to the first link relationship;

[0023] The first impact link group is analyzed by the pressure-particle size-angle analysis component to generate a pressure-particle size-angle analysis report for obtaining the effect analysis result.

[0024] Furthermore, the effects of the spray liquid pressure parameter and the spray height position parameter on the spray angle parameter are analyzed, including:

[0025] Constructing a pressure-height-angle analysis component according to the spray liquid pressure parameter, the spray height position parameter and the spray angle parameter, wherein the pressure-height-angle analysis component is used to analyze the effect relationship between the spray liquid pressure parameter and the spray height position parameter on the spray angle parameter;

[0026] Establishing a second link relationship between the spray liquid pressure parameter and the spray angle parameter, wherein the second link relationship is a corresponding relationship between each parameter of the spray liquid pressure parameter and the spray angle parameter, and generating a second influencing link group according to the second link relationship;

[0027] Establishing a third link relationship between the spray height position parameter and the spray angle parameter, wherein the third link relationship is a correspondence relationship between each parameter between the spray height position parameter and the spray angle parameter, and generating a third influence link group according to the third link relationship;

[0028] The second impact link group and the third impact link group are analyzed separately by the pressure-height-angle analysis component to generate a pressure-height-angle analysis report for obtaining the effect analysis result.

[0029] Furthermore, a liquid distribution state analysis model is constructed, and the uniformity of several liquid distribution schemes is analyzed through the single sensitivity to determine the liquid distribution execution scheme, including:

[0030] According to the spray liquid pressure parameter, the spray height position parameter, the spray droplet size parameter and the spray angle parameter, a single-dimensional evaluation coordinate axis is established respectively, and the single-dimensional evaluation coordinate axis constitutes the liquid distribution state analysis model;

[0031] Construct two evaluation points respectively according to the single sensitivity and the experimental simulation results, map the evaluation points to the corresponding single-dimensional evaluation coordinate axes, and analyze and compare the relative position differences of the two evaluation points on the corresponding single-dimensional evaluation coordinate axes;

[0032] The relative position difference between the two evaluation points on each of the single-dimensional evaluation coordinate axes is integrated, and the uniformity of the current liquid distribution scheme is determined, and the liquid distribution scheme with the best uniformity is selected as the liquid distribution execution scheme.

[0033] Further, a virtual physical operation domain is constructed, and the virtual physical operation domain simulates the spraying experiment under different parameters through the spraying characteristic information to obtain the spraying experiment simulation result, including:

[0034] Equipment information of a horizontal tube falling film evaporator, and based on the equipment information, combining fluid mechanics principles and thermodynamics theory, constructing a virtual physical operation domain, wherein the virtual physical operation domain simulates fluid dynamics behavior based on virtual physical operations;

[0035] The virtual physical operation domain performs an operation experiment on the liquid distribution scheme to obtain a spraying experiment simulation result.

[0036] Furthermore, the spray analysis database includes:

[0037] Sorting the input spray characteristic information and liquid distribution scheme to generate a first sorting sequence and a second sorting sequence respectively, wherein the first sorting sequence is arranged according to the order in which the spray characteristic information is obtained, and the second sorting sequence is arranged according to the order in which the liquid distribution scheme is generated;

[0038] Matching the spray test simulation result according to the second sorting sequence, wherein the spray test simulation result corresponds one-to-one to each sub-item of the second sorting sequence;

[0039] A relationship chain is established for the spray test simulation result according to the first sorting sequence, wherein the relationship chain links the parameters of the spray characteristic information contained in the first sorting sequence with the spray test simulation result for data calling.

[0040] An evaporator heat transfer information management method and system, the system comprising:

[0041] A spray information acquisition module acquires spray characteristic information, which includes spray liquid pressure parameters, spray height position parameters, spray droplet size parameters and spray angle parameters, and generates several liquid distribution schemes according to a single transformation combination of parameters contained in the spray characteristic information;

[0042] The virtual physical experiment module constructs a virtual physical operation domain. The virtual physical operation domain conducts a spray simulation experiment on the liquid distribution scheme to obtain the spray experiment simulation results;

[0043] Spray data storage module, which establishes a spray analysis database to store and manage spray characteristic information, liquid distribution schemes and spray test simulation results;

[0044] The single sensitivity analysis module calls the spray analysis database, analyzes the sensitivity of the spray test simulation results based on the single parameter in the spray characteristic information, and obtains the single sensitivity of the spray liquid pressure parameter, spray height position parameter, spray droplet size parameter and spray angle parameter to the spray test simulation results;

[0045] The liquid distribution analysis module constructs a liquid distribution state analysis model, analyzes the uniformity of several liquid distribution schemes through single-item sensitivity, and determines the liquid distribution execution plan based on the uniformity analysis results.

[0046] Furthermore, the liquid distribution analysis module also includes:

[0047] A mapping prediction unit constructs a spray characteristic mapping space based on the spray characteristic information. The spray characteristic mapping space performs deep learning on the sensitive effect of a single parameter of the spray characteristic information in the spray analysis database on the spray test simulation results, and performs uniformity prediction on the liquid distribution scheme without the spray simulation experiment;

[0048] A model generation unit, which sets an input end and an output end based on a spray characteristic mapping space and generates a liquid distribution state analysis model;

[0049] The solution output unit inputs several liquid distribution solutions into the liquid distribution state analysis model to obtain uniformity prediction results, and obtains the liquid distribution execution solution according to the uniformity prediction result output.

[0050] The technical solution of the present invention can achieve the following technical effects:

[0051] It effectively solves the problem of uneven liquid spraying in the horizontal tube falling film evaporator, which leads to uneven liquid distribution on the evaporator surface and affects the evaporation efficiency and product quality. In addition, it also solves the problem that the existing control system lacks sufficient data analysis capabilities, cannot accurately control the spray parameters, and cannot effectively predict the working status of the simulated evaporator.

[0052] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0054] Figure 1 It is a flow chart of the design method of liquid distribution state of horizontal tube falling film evaporator;

[0055] Figure 2 It is a schematic diagram of the structure of the liquid distribution state model;

[0056] Figure 3 It is a schematic diagram of the structure of pressure-particle size-angle;

[0057] Figure 4 It is a schematic diagram of the pressure-height-angle structure;

[0058] Figure 5 This is a schematic diagram of the structure of the spray analysis database. DETAILED DESCRIPTION

[0059] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0060] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0061] Embodiment 1

[0062] like Figure 1 As shown, the present application provides a method for designing liquid distribution state of a horizontal tube falling film evaporator, the method comprising:

[0063] S10: obtaining spray characteristic information, the spray characteristic information including spray liquid pressure parameters, spray height position parameters, spray droplet size parameters and spray angle parameters, and generating a plurality of liquid distribution schemes according to a single transformation combination of parameters included in the spray characteristic information;

[0064] S20: constructing a virtual physical operation domain, wherein the virtual physical operation domain performs a spray simulation experiment on a liquid distribution scheme to obtain a spray experiment simulation result;

[0065] S30: Establish a spray analysis database for storing and managing spray characteristic information, liquid distribution schemes and spray experiment simulation results;

[0066] S40: calling a spray analysis database, analyzing the sensitivity of a single parameter in the spray characteristic information to the spray experiment simulation result, and obtaining the single sensitivity of the spray liquid pressure parameter, the spray height position parameter, the spray droplet size parameter, and the spray angle parameter to the spray experiment simulation result;

[0067] S50: construct a liquid distribution state analysis model, analyze the uniformity of several liquid distribution schemes through single sensitivity, and determine the liquid distribution execution plan according to the uniformity analysis results.

[0068] Specifically, various characteristic information of the spray system is obtained, including the pressure of the spray liquid, the installation height of the nozzle, the particle size of the spray droplets, and the spray angle. The acquisition of these parameters needs to be carried out through experiments and measurements to ensure the accuracy of the data. After obtaining these parameters, a single transformation combination is performed for each parameter to generate several liquid distribution schemes. For example, other parameters can be kept unchanged, and only the pressure of the spray liquid can be changed, and then distribution schemes under multiple pressure conditions can be generated. Similarly, for the spray height, a single transformation combination can be performed separately to generate a variety of possible liquid distribution schemes, each of which corresponds to a different liquid distribution state. After obtaining and generating several liquid distribution schemes, appropriate computational fluid dynamics software can be selected to construct a virtual physical operating domain to accurately simulate the actual physical conditions of the horizontal tube falling film evaporator. In the simulation environment, for each generated liquid distribution scheme Generate corresponding simulation experiments, record the distribution state of liquid in the spraying process, pay special attention to liquid uniformity and droplet distribution. In order to effectively manage and analyze spray characteristic information, liquid distribution schemes and simulation results, establish a dedicated spray analysis database. After the database is established, call the spray characteristic information and corresponding simulation results in the database, apply statistical or data analysis tools, study the sensitivity of each single parameter to the spraying effect, clarify which parameters have the greatest impact on liquid distribution, establish a liquid distribution state analysis model, evaluate and compare the uniformity of different liquid distribution schemes, output the quantitative evaluation results of liquid distribution uniformity after running, use the model to perform uniformity analysis on all generated liquid distribution schemes, and select the best liquid distribution execution plan according to the analysis results. The optimization process may include adjusting the spray height, changing the pressure setting, adjusting the nozzle angle, or modifying the droplet size to achieve the best liquid distribution state.

[0069] The technical solution of the present invention effectively solves the problem of uneven liquid spraying in a horizontal tube falling film evaporator, which leads to uneven liquid distribution on the surface of the evaporator and affects the evaporation efficiency and product quality. In addition, it also solves the problem that the existing control system lacks sufficient data analysis capabilities, cannot accurately control spray parameters, and cannot effectively predict the working state of the simulated evaporator.

[0070] Further, if Figure 2 As shown in the figure, a liquid distribution state analysis model is constructed, and the uniformity of several liquid distribution schemes is analyzed through single sensitivity to determine the liquid distribution execution scheme, including:

[0071] A spray characteristic mapping space is constructed based on the spray characteristic information. The spray characteristic mapping space performs deep learning on the sensitive effect of a single parameter of the spray characteristic information in the spray analysis database on the spray test simulation results, and predicts the uniformity of the liquid distribution scheme without a spray simulation experiment.

[0072] Setting input and output ends based on the spray characteristic mapping space to generate a liquid distribution state analysis model;

[0073] Several liquid distribution schemes are input into the liquid distribution state analysis model to obtain uniformity prediction results, and a liquid distribution execution plan is obtained according to the output of the uniformity prediction results.

[0074] As a preferred embodiment of the above, after obtaining and analyzing the spray characteristic information, all existing spray characteristic information and corresponding spray experiment simulation results are extracted from the spray analysis database to construct a spray characteristic mapping space, and deep learning technology is used to train these data. The nonlinear mapping relationship between the spray characteristic information and the spray experiment simulation results is captured by the training model. During the training process, cross-validation, regularization and other technologies can be used to prevent model overfitting, ensure the generalization ability of the model, and further improve the prediction accuracy. The constructed spray characteristic mapping space is verified using some experimental data that are not involved in the training. The verification standard is that the prediction accuracy of the mapping space for these data can accurately predict the experimental results. After the spray characteristic mapping space is constructed, an input end and an output end are set based on this space, and the input end receives data from the spray characteristic mapping space. The output end generates the corresponding liquid distribution uniformity prediction result. In the analysis model, multiple input nodes are set to receive different spray characteristic parameters, and the output nodes correspond to the liquid distribution uniformity prediction results. The liquid distribution state analysis model is further trained and tuned so that it can output accurate uniformity prediction results after inputting various spray characteristic information, thereby improving the prediction accuracy and computational efficiency of the model. After the liquid distribution state analysis model is generated, several generated liquid distribution schemes are input into the liquid distribution state analysis model. The uniformity of each input liquid distribution scheme is predicted through the analysis model. The model will output the liquid distribution uniformity prediction result of each scheme based on the deep learning results of the spray characteristic mapping space, and select the optimal liquid distribution execution scheme according to the uniformity prediction result.

[0075] Furthermore, the liquid distribution execution plan is obtained according to the uniformity prediction result output, including:

[0076] Obtaining spray characteristic information in a liquid distribution scheme corresponding to a uniformity prediction result, and analyzing the effects between single parameters in the spray characteristic information to obtain an effect analysis result;

[0077] The effects between the single parameters in the spray characteristic information include the effects of the spray liquid pressure parameter and the spray droplet diameter on the spray angle parameter and the effects of the spray liquid pressure parameter and the spray height position parameter on the spray angle parameter;

[0078] According to the uniformity prediction results and the effect analysis results, one of several liquid distribution methods is selected as the liquid distribution execution plan output.

[0079] As a preferred embodiment of the above, after completing the uniformity prediction results, it is first necessary to deeply analyze the interaction between the single parameters of each spray characteristic information in the liquid distribution scheme, and extract the corresponding spray characteristic information from the obtained uniformity prediction results. The statistical analysis method or machine learning model can be used to conduct a detailed analysis of the interaction between the single parameters in the spray characteristic information, analyze the effect of the spray liquid pressure parameter and the spray droplet particle size on the spray angle, that is, analyze the droplet size distribution formed by the spray liquid under different pressure conditions, and the movement trajectory of these droplets at the spray angle, and also analyze the effect of the spray liquid pressure parameter and the spray height position on the spray angle, that is, the spray liquid pressure and the nozzle height. How they jointly affect the spray angle. Pressure changes may affect the range of the liquid, while height affects the coverage of the liquid. The combined effect of the two has an important impact on the final effect of the spray angle. Through analysis, the effect analysis results between each single parameter are generated. After obtaining the parameter effect analysis results, it is necessary to combine these results with the uniformity prediction results, and select the best solution from several liquid distribution solutions as the final liquid distribution execution solution. The uniformity prediction results and the effect analysis results between single parameters are comprehensively analyzed to evaluate the feasibility and expected effect of each liquid distribution solution under actual operating conditions. According to the results of the comprehensive analysis, one of the multiple liquid distribution solutions is selected as the final execution solution.

[0080] Further, if Figure 3 As shown, the effects of the spray liquid pressure parameters and the spray droplet size on the spray angle parameters are analyzed, including:

[0081] A pressure-particle size-angle analysis component is constructed according to the spray liquid pressure parameter, the spray droplet particle size parameter and the spray angle parameter. The pressure-particle size-angle analysis component is used to analyze the relationship between the spray liquid pressure parameter and the spray droplet particle size parameter on the spray angle parameter.

[0082] Establishing a first link relationship between the spray liquid pressure parameter and the spray liquid droplet size parameter, the first link relationship being a corresponding relationship between each parameter between the spray liquid pressure parameter and the spray liquid droplet size parameter, and generating a first influencing link group according to the first link relationship;

[0083] The first impact link group is analyzed by the pressure-particle size-angle analysis component to generate a pressure-particle size-angle analysis report for obtaining the effect analysis result.

[0084] As a preferred embodiment of the above, a large number of spray liquid pressures, spray droplet sizes and spray angles under different conditions are extracted from the spray analysis database, and a multidimensional data analysis system is designed to process and analyze the complex relationship between pressure, size and angle. The component is constructed using machine learning algorithms, such as multivariate regression analysis, support vector machine, etc. to analyze the interaction between these three parameters. After the pressure-size-angle analysis component is constructed, the correlation analysis of different spray liquid pressure parameters and spray droplet size parameters is performed, that is, the change law of the spray droplet size under different pressure conditions is analyzed, and the effect of these changes on the spray angle is further analyzed. Potential impact, based on the results of association analysis, is described by function fitting, association rule mining or other mathematical methods to establish the first link relationship between the spray liquid pressure parameters and the spray droplet particle size parameters. Based on the constructed first link relationship, a first impact link group is generated, and the first impact link group is input into the pressure-particle size-angle analysis component to analyze and evaluate the actual impact of different pressure conditions and droplet particle sizes on the spray angle. A pressure-particle size-angle analysis report is generated, including: the influence curve of pressure parameters on particle size, and a summary of the interaction between the three. Based on the analysis report, the analysis results of the effects on the spray liquid pressure, particle size and angle are extracted.

[0085] Further, if Figure 4 As shown, the effects of the spray liquid pressure parameters and the spray height position parameters on the spray angle parameters are analyzed, including:

[0086] A pressure-height-angle analysis component is constructed according to the spray liquid pressure parameter, the spray height position parameter and the spray angle parameter, and the pressure-height-angle analysis component is used to analyze the effect relationship between the spray liquid pressure parameter and the spray height position parameter on the spray angle parameter;

[0087] Establishing a second link relationship between the spray liquid pressure parameter and the spray angle parameter, the second link relationship being a corresponding relationship between each parameter between the spray liquid pressure parameter and the spray angle parameter, and generating a second influence link group according to the second link relationship;

[0088] A third link relationship is established between the spray height position parameter and the spray angle parameter, where the third link relationship is a correspondence relationship between each parameter between the spray height position parameter and the spray angle parameter, and a third influence link group is generated according to the third link relationship;

[0089] The second impact link group and the third impact link group are analyzed separately by the pressure-height-angle analysis component to generate a pressure-height-angle analysis report for obtaining the effect analysis result.

[0090] As a preferred embodiment of the above embodiment, experimental data of spray liquid pressure, spray height position and spray angle under different spray conditions are extracted from the spray analysis database, and a multidimensional data analysis system is designed to analyze and process the interaction relationship between spray liquid pressure, spray height position and spray angle. The complex relationship between the three is accurately captured based on regression analysis, neural network or other suitable machine learning algorithms. The input of spray liquid pressure parameters and spray height position parameters is accepted, and their comprehensive influence on the spray angle is analyzed, and the corresponding analysis results are output. After constructing the pressure-height-angle analysis component, the pressure-height-angle analysis component is first used to analyze how different spray liquid pressure parameters affect the spray angle. Through experimental data and model calculation, the change law of the spray angle under different pressure conditions is determined. Based on the change law, a second link relationship between the spray liquid pressure parameter and the spray angle parameter is established. According to the established second link relationship, a second influence link group is generated. The pressure-height-angle analysis component is also used to conduct an in-depth analysis on how different spray height position parameters affect the spray angle. Based on the analysis results, a third link relationship between the spray height position parameters and the spray angle parameters is constructed. According to the constructed third link relationship, a third influence link group is generated. The second influence link group and the third influence link group are respectively input into the pressure-height-angle analysis component to analyze the comprehensive impact of parameter changes in each link group on the spray angle, focusing on analyzing the synergistic effect of different spray liquid pressures and height positions on the spray angle. After completing the analysis, a detailed pressure-height-angle analysis report is generated, including: a correlation diagram between pressure and angle, a correlation diagram between height and angle, and a summary of the comprehensive impact of spray liquid pressure and height position on the spray angle. Based on the analysis report, the analysis results of the effect of spray liquid pressure and height position on the spray angle are extracted.

[0091] Furthermore, a liquid distribution state analysis model is constructed to analyze the uniformity of several liquid distribution schemes through single sensitivity, and determine the liquid distribution execution scheme, including:

[0092] According to the spraying liquid pressure parameter, the spraying height position parameter, the spraying liquid droplet size parameter and the spraying angle parameter, a single-dimensional evaluation coordinate axis is established respectively, and the single-dimensional evaluation coordinate axis constitutes a liquid distribution state analysis model;

[0093] According to the single sensitivity and experimental simulation results, two evaluation points are constructed respectively, and the evaluation points are mapped to the corresponding single-dimensional evaluation coordinate axis, and the relative position differences of the two evaluation points on the corresponding single-dimensional evaluation coordinate axis are analyzed and compared;

[0094] Integrate the relative position difference of the two evaluation points on each single-dimensional evaluation coordinate axis, determine the uniformity of the current liquid distribution scheme, and select the liquid distribution scheme with the best uniformity as the liquid distribution execution scheme.

[0095] In another embodiment, single-dimensional evaluation coordinate axes are established according to key characteristic parameters of the spray liquid, and four key parameters are extracted from the spray analysis database: spray liquid pressure parameter, spray height position parameter, spray droplet size parameter and spray angle parameter. Each parameter is standardized and its numerical range is mapped to a common scale (for example, 0 to 1) for comparison in a unified coordinate system. A separate evaluation coordinate axis is established for each standardized parameter, and each coordinate axis represents a dimension of influence of a parameter in the liquid distribution state. These coordinate axes together constitute a multi-dimensional space of a liquid distribution state analysis model, and each dimension corresponds to a specific spray characteristic parameter. Based on the experimental simulation results, a first evaluation point is constructed, which reflects the degree of influence of each spray characteristic parameter on the uniformity of liquid distribution under the current liquid distribution scheme. Based on the results of the single sensitivity analysis, a second evaluation point is constructed, which reflects the influence of each parameter in the sensitivity analysis. Relative importance and impact on uniformity. The two evaluation points are mapped to each single-dimensional evaluation coordinate axis respectively. On each coordinate axis, the coordinate value of the evaluation point represents the performance or impact of the corresponding parameter under the current liquid distribution scheme. Through mapping, the relative positions of the two evaluation points in each dimension can be intuitively seen. On each single-dimensional evaluation coordinate axis, the relative position difference of the two evaluation points is calculated, and the relative position difference of each dimension is quantitatively analyzed. For example, the Euclidean distance or other similarity index of the two points is calculated to evaluate the effectiveness of the current parameter setting. The relative position differences on all single-dimensional evaluation coordinate axes are integrated to form a comprehensive uniformity evaluation index. The smaller the comprehensive index is, the better the coordination between the parameters is and the higher the uniformity of the liquid distribution is. All candidate liquid distribution schemes are evaluated in the same way, and their respective uniformity evaluation indicators are compared. The scheme with the best uniformity evaluation index is selected as the final liquid distribution execution scheme.

[0096] Furthermore, a virtual physical operation domain is constructed. The virtual physical operation domain simulates the spraying experiment under different parameters through the spraying characteristic information to obtain the spraying experiment simulation results, including:

[0097] The equipment information of the horizontal tube falling film evaporator is used to construct a virtual physical operation domain based on the equipment information and the principles of fluid mechanics and thermodynamics. The virtual physical operation domain simulates the fluid dynamics behavior based on virtual physical operations.

[0098] The virtual physical operation domain conducts operation experiments on the liquid distribution scheme and obtains the simulation results of the spray experiment.

[0099] As a preferred embodiment of the above embodiment, detailed equipment information of the horizontal tube falling film evaporator is obtained, including the geometric dimensions, material properties, operating conditions, liquid properties and specific structural layout of the equipment, which are used as basic data input for modeling the virtual physical operation domain. The flow, heat transfer and evaporation process of the liquid in the evaporator are modeled using the principles of fluid mechanics and thermodynamics. A virtual physical operation domain is constructed on the computer using computational fluid dynamics software, and different liquid distribution schemes previously obtained are input into the virtual physical operation domain as simulation conditions. For each liquid distribution scheme, corresponding spray conditions are set to ensure that each simulation experiment reflects the actual operation effect of the scheme. During the simulation process, parameters such as the distribution state, uniformity, and droplet distribution of the liquid are recorded. The dynamic process of liquid flow can also be observed through visualization tools to better understand the performance of each scheme. After each simulation experiment, the simulation results of the spray experiment are extracted and saved. The results include detailed data on the liquid distribution state such as temperature field distribution, flow velocity field distribution, etc. All simulation results are stored in the spray analysis database for subsequent sensitivity analysis and optimization selection of liquid distribution schemes.

[0100] Further, if Figure 5 As shown, the spray analysis database includes:

[0101] The input spray characteristic information and liquid distribution scheme are sorted to generate a first sorting sequence and a second sorting sequence respectively, wherein the first sorting sequence is arranged according to the order in which the spray characteristic information is obtained, and the second sorting sequence is arranged according to the order in which the liquid distribution scheme is generated;

[0102] Matching the spray test simulation results according to the second sorting sequence, the spray test simulation results corresponding to each sub-item of the second sorting sequence one by one;

[0103] A relationship chain is established for the spray test simulation results according to the first sorting sequence. The relationship chain links the parameters of the spray characteristic information contained in the first sorting sequence with the spray test simulation results for data calling.

[0104] As a preferred embodiment of the above embodiment, all input spray characteristic information is sorted to generate a first sorting sequence, which is marked with a timestamp sequence identifier to ensure that each spray characteristic information has a unique sorting identifier. Then, according to the generation order of each liquid distribution scheme, a second sorting sequence is created. The sequence reflects the generation order of the liquid distribution scheme and can be arranged based on liquid distribution characteristics or other scheme generation mechanisms. Each liquid distribution scheme will occupy a position in this sequence to ensure that each scheme can be uniquely identified according to the sequence number. After performing a spray experiment simulation in the virtual physical operation domain, the obtained simulation results are matched with the second sorting sequence, and each simulation result corresponds one-to-one to a liquid distribution scheme in the second sorting sequence. , the matched spray experiment simulation results are stored in the spray analysis database. According to the first sorting sequence, a relationship chain is established between the spray characteristic information and the spray experiment simulation results, which is used to store and manage data, and is also used to provide data support for subsequent sensitivity analysis, model building and parameter optimization. When calling data, all experimental simulation results related to a specific spray characteristic can be quickly retrieved according to the relationship chain. Through the first sorting sequence and the relationship chain, all simulation results related to specific spray characteristic information can be quickly retrieved, which provides a convenient tool for sensitivity analysis and parameter optimization. According to the second sorting sequence, users can easily match the simulation results of specific liquid distribution schemes, so as to compare and optimize different schemes.

[0105] Embodiment 2

[0106] Based on the same inventive concept as the method for designing the liquid distribution state of a horizontal tube falling film evaporator in the aforementioned embodiment, the present invention further provides a system for designing the liquid distribution state of a horizontal tube falling film evaporator, the system comprising:

[0107] A spray information acquisition module acquires spray characteristic information, which includes spray liquid pressure parameters, spray height position parameters, spray droplet size parameters and spray angle parameters, and generates several liquid distribution schemes according to a single transformation combination of parameters contained in the spray characteristic information;

[0108] The virtual physical experiment module constructs a virtual physical operation domain. The virtual physical operation domain conducts a spray simulation experiment on the liquid distribution scheme to obtain the spray experiment simulation results;

[0109] Spray data storage module, which establishes a spray analysis database to store and manage spray characteristic information, liquid distribution schemes and spray test simulation results;

[0110] The single sensitivity analysis module calls the spray analysis database, analyzes the sensitivity of the spray test simulation results based on the single parameter in the spray characteristic information, and obtains the single sensitivity of the spray liquid pressure parameter, spray height position parameter, spray droplet size parameter and spray angle parameter to the spray test simulation results;

[0111] Liquid distribution analysis module, which builds a liquid distribution state analysis model, analyzes the uniformity of several liquid distribution schemes through single sensitivity, and determines the liquid distribution execution plan based on the uniformity analysis results;

[0112] The above-mentioned adjustment system in the present invention can effectively realize the liquid distribution state design method of the horizontal tube falling film evaporator, and the technical effects that can be achieved are as described in the above-mentioned embodiments, which will not be repeated here.

[0113] Furthermore, the liquid distribution analysis module also includes:

[0114] A mapping prediction unit constructs a spray characteristic mapping space based on the spray characteristic information. The spray characteristic mapping space performs deep learning on the sensitive effect of a single parameter of the spray characteristic information in the spray analysis database on the spray test simulation results, and performs uniformity prediction on the liquid distribution scheme without the spray simulation experiment;

[0115] A model generation unit, which sets an input end and an output end based on a spray characteristic mapping space and generates a liquid distribution state analysis model;

[0116] The solution output unit inputs several liquid distribution solutions into the liquid distribution state analysis model to obtain uniformity prediction results, and obtains the liquid distribution execution solution according to the uniformity prediction result output.

[0117] Similarly, the above-mentioned optimization schemes for the system can also respectively achieve the optimization effects corresponding to the method in Example 1, which will not be repeated here.

[0118] Although the present application has been described in conjunction with specific features and embodiments thereof, it is obvious that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the accompanying drawings are merely exemplary illustrations of the present application as defined therein, and are deemed to have covered 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 is intended to include these modifications and variations.

Claims

1. A method for designing liquid distribution state of a horizontal tube falling film evaporator, characterized in that: The method comprises: Acquire spray characteristic information, the spray characteristic information including spray liquid pressure parameter, spray height position parameter, spray droplet size parameter and spray angle parameter, and generate a plurality of liquid distribution schemes according to a single transformation combination of parameters included in the spray characteristic information; Constructing a virtual physical operation domain, wherein the virtual physical operation domain performs a spray simulation experiment on the liquid distribution scheme to obtain a spray experiment simulation result; Establishing a spray analysis database for storing and managing the spray characteristic information, liquid distribution scheme and the spray experiment simulation results; Calling the spray analysis database, analyzing the sensitive effect of a single parameter in the spray characteristic information on the spray experiment simulation result, and obtaining the single sensitivity of the spray liquid pressure parameter, the spray height position parameter, the spray droplet size parameter, and the spray angle parameter to the spray experiment simulation result; A liquid distribution state analysis model is constructed, the uniformity of several liquid distribution schemes is analyzed through the single sensitivity, and the liquid distribution execution scheme is determined according to the uniformity analysis results.

2. The method for designing liquid distribution state of a horizontal tube falling film evaporator according to claim 1, characterized in that: A liquid distribution state analysis model is constructed, and the uniformity of several liquid distribution schemes is analyzed through the single sensitivity, and a liquid distribution execution scheme is determined, including: Constructing a spray characteristic mapping space according to the spray characteristic information, the spray characteristic mapping space performs deep learning on the sensitive effect of a single parameter of the spray characteristic information in the spray analysis database on the spray experiment simulation result, and performs uniformity prediction on the liquid distribution scheme without performing a spray simulation experiment; Setting an input end and an output end based on the spray characteristic mapping space to generate the liquid distribution state analysis model; Several liquid distribution plans are input into the liquid distribution state analysis model to obtain uniformity prediction results, and the liquid distribution execution plan is obtained according to the uniformity prediction result output.

3. The method for designing liquid distribution state of a horizontal tube falling film evaporator according to claim 2, characterized in that: The liquid distribution execution plan is obtained according to the uniformity prediction result output, including: Obtaining the spray characteristic information in the liquid distribution scheme corresponding to the uniformity prediction result, and analyzing the effects between single parameters in the spray characteristic information to obtain an effect analysis result; The effects between the single parameters in the spray characteristic information include the effects of the spray liquid pressure parameter and the spray droplet diameter on the spray angle parameter and the effects of the spray liquid pressure parameter and the spray height position parameter on the spray angle parameter; According to the uniformity prediction result and the effect analysis result, one of the liquid distribution schemes is selected as the liquid distribution execution scheme output.

4. The method for designing liquid distribution state of a horizontal tube falling film evaporator according to claim 3, characterized in that: The effects of spray liquid pressure parameters and spray droplet size on spray angle parameters are analyzed, including: A pressure-particle size-angle analysis component is constructed according to the spray liquid pressure parameter, the spray droplet particle size parameter and the spray angle parameter, wherein the pressure-particle size-angle analysis component is used to analyze the relationship between the spray liquid pressure parameter and the spray droplet particle size parameter and the spray angle parameter; Establishing a first link relationship between the spray liquid pressure parameter and the spray liquid droplet size parameter, wherein the first link relationship is a corresponding relationship between each parameter of the spray liquid pressure parameter and the spray liquid droplet size parameter, and generating a first influencing link group according to the first link relationship; The first impact link group is analyzed by the pressure-particle size-angle analysis component to generate a pressure-particle size-angle analysis report for obtaining the effect analysis result.

5. The method for designing liquid distribution state of a horizontal tube falling film evaporator according to claim 3, characterized in that: The effect of spray liquid pressure parameters and spray height position parameters on spray angle parameters is analyzed, including: Constructing a pressure-height-angle analysis component according to the spray liquid pressure parameter, the spray height position parameter and the spray angle parameter, wherein the pressure-height-angle analysis component is used to analyze the effect relationship between the spray liquid pressure parameter and the spray height position parameter on the spray angle parameter; Establishing a second link relationship between the spray liquid pressure parameter and the spray angle parameter, wherein the second link relationship is a corresponding relationship between each parameter of the spray liquid pressure parameter and the spray angle parameter, and generating a second influence link group according to the second link relationship; Establishing a third link relationship between the spray height position parameter and the spray angle parameter, wherein the third link relationship is a correspondence relationship between each parameter between the spray height position parameter and the spray angle parameter, and generating a third influence link group according to the third link relationship; The second impact link group and the third impact link group are analyzed separately by the pressure-height-angle analysis component to generate a pressure-height-angle analysis report for obtaining the effect analysis result.

6. The method for designing liquid distribution state of a horizontal tube falling film evaporator according to claim 1, characterized in that: A liquid distribution state analysis model is constructed, and the uniformity of several liquid distribution schemes is analyzed through the single sensitivity, and a liquid distribution execution scheme is determined, including: According to the spray liquid pressure parameter, the spray height position parameter, the spray droplet size parameter and the spray angle parameter, a single-dimensional evaluation coordinate axis is respectively established, and the single-dimensional evaluation coordinate axis constitutes the liquid distribution state analysis model; Construct two evaluation points respectively according to the single sensitivity and the experimental simulation results, map the evaluation points to the corresponding single-dimensional evaluation coordinate axes, and analyze and compare the relative position differences of the two evaluation points on the corresponding single-dimensional evaluation coordinate axes; The relative position difference between the two evaluation points on each of the single-dimensional evaluation coordinate axes is integrated, and the uniformity of the current liquid distribution scheme is determined, and the liquid distribution scheme with the best uniformity is selected as the liquid distribution execution scheme.

7. The method for designing liquid distribution state of a horizontal tube falling film evaporator according to claim 1, characterized in that: Constructing a virtual physical operation domain, wherein the virtual physical operation domain simulates a spray experiment under different parameters through the spray characteristic information to obtain a spray experiment simulation result, including: Equipment information of a horizontal tube falling film evaporator, and based on the equipment information, combining fluid mechanics principles and thermodynamics theory, constructing a virtual physical operation domain, wherein the virtual physical operation domain simulates fluid dynamics behavior based on virtual physical operations; The virtual physical operation domain performs an operation experiment on the liquid distribution scheme to obtain a spraying experiment simulation result.

8. The method for designing liquid distribution state of a horizontal tube falling film evaporator according to claim 1, characterized in that: The spray analysis database includes: Sorting the input spray characteristic information and liquid distribution scheme to generate a first sorting sequence and a second sorting sequence respectively, wherein the first sorting sequence is arranged according to the order in which the spray characteristic information is obtained, and the second sorting sequence is arranged according to the order in which the liquid distribution scheme is generated; Matching the spray test simulation result according to the second sorting sequence, wherein the spray test simulation result corresponds one-to-one to each sub-item of the second sorting sequence; A relationship chain is established for the spray test simulation result according to the first sorting sequence, wherein the relationship chain links the parameters of the spray characteristic information contained in the first sorting sequence with the spray test simulation result for data calling.

9. A liquid distribution state design system for a horizontal tube falling film evaporator, characterized in that: The system comprises: A spray information acquisition module acquires spray characteristic information, which includes spray liquid pressure parameters, spray height position parameters, spray droplet size parameters and spray angle parameters, and generates several liquid distribution schemes according to a single transformation combination of parameters contained in the spray characteristic information; The virtual physical experiment module constructs a virtual physical operation domain. The virtual physical operation domain performs a spray simulation experiment on the liquid distribution scheme to obtain the spray experiment simulation results. Spray data storage module, which establishes a spray analysis database to store and manage spray characteristic information, liquid distribution schemes and spray test simulation results; The single sensitivity analysis module calls the spray analysis database, analyzes the sensitivity of the spray test simulation results based on the single parameter in the spray characteristic information, and obtains the single sensitivity of the spray liquid pressure parameter, spray height position parameter, spray droplet size parameter and spray angle parameter to the spray test simulation results; The liquid distribution analysis module constructs a liquid distribution state analysis model, analyzes the uniformity of several liquid distribution schemes through single-item sensitivity, and determines the liquid distribution execution plan based on the uniformity analysis results.

10. The liquid distribution state design system of the horizontal tube falling film evaporator according to claim 9, characterized in that: The liquid distribution analysis module also includes: A mapping prediction unit constructs a spray characteristic mapping space based on the spray characteristic information. The spray characteristic mapping space performs deep learning on the sensitive effect of a single parameter of the spray characteristic information in the spray analysis database on the spray test simulation results, and performs uniformity prediction on the liquid distribution scheme without the spray simulation experiment; A model generation unit, which sets an input end and an output end based on a spray characteristic mapping space to generate a liquid distribution state analysis model; The solution output unit inputs several liquid distribution solutions into the liquid distribution state analysis model to obtain uniformity prediction results, and obtains the liquid distribution execution solution according to the uniformity prediction result output.

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