Evaporation energy-saving self-adaptive regulation and control method and system under multi-parameter analysis

Abstract

The invention provides an evaporation energy-saving self-adaptive regulation and control method and system under multi-parameter analysis, and relates to the technical field of data processing, and the method comprises the steps: obtaining an evaporation control parameter space of a single-effect evaporator, and randomly selecting a plurality of initial evaporation control parameters meeting a uniform distribution constraint; based on the solution attributes, the environmental parameters and the expected concentration indexes of the to-be-evaporated saline water, evaporation efficiency prediction of the multiple initial evaporation control parameters under multiple nodes of a preset time zone is executed, multiple predicted evaporation efficiency sets are obtained, and multiple evaporation fitness degrees are obtained through evaluation; and by taking the evaporation control parameter space as a constraint, carrying out evaporation control parameter optimization according to the plurality of evaporation fitness, outputting an optimal evaporation control parameter, and controlling the single-effect evaporator to carry out evaporation operation. The technical problems that in the prior art, due to the fact that a multi-parameter collaborative optimization method is lacked, energy consumption is high in the brine evaporation process, and adaptability to environment changes is poor are solved, and the technical effects of improving evaporation efficiency and reducing evaporation energy consumption are achieved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to an evaporation energy-saving adaptive control method and system under multi-parameter analysis. Background Art

[0002] Brine evaporation is a key link in the chlor-alkali chemical industry. Chloride ions and hydroxide ions in brine are migrated and combined through a single-effect evaporator to produce chlorine, hydrogen and sodium hydroxide.

[0003] At present, the energy-saving control method of brine evaporation is usually based on a simple analysis of the operating conditions of the evaporator, and empirical parameter adjustments or single variable optimization, such as fixed temperature, pressure or flow control. However, this control method based on a single parameter or empirical rules has significant limitations. On the one hand, it is impossible to effectively tap the synergistic effect between multiple evaporation control parameters, resulting in insufficient overall optimization capabilities; on the other hand, it has poor adaptability to changes in environmental conditions (such as initial solution concentration, changes in external temperature and humidity, etc.), and it is impossible to accurately adjust the evaporation process according to actual working conditions, making it difficult to achieve optimal evaporation efficiency, high energy consumption, and difficult to ensure the stability of the production process, which is not conducive to the sustainable development of the chlor-alkali chemical industry. Summary of the invention

[0004] The present application provides an evaporation energy-saving adaptive control method and system under multi-parameter analysis, which solves the technical problems in the prior art that the energy consumption in the brine evaporation process is high and the adaptability to environmental changes is poor due to the lack of a multi-parameter collaborative optimization method, and achieves the technical effect of improving evaporation efficiency and reducing evaporation energy consumption.

[0005] In view of the above problems, on the one hand, the present application provides an evaporation energy-saving adaptive control method under multi-parameter analysis, the method comprising: obtaining the evaporation control parameter space of a single-effect evaporator, and randomly selecting a plurality of initial evaporation control parameters that satisfy uniform distribution constraints; based on the solution properties, environmental parameters and expected concentration index of the brine to be evaporated, performing evaporation efficiency prediction of the plurality of initial evaporation control parameters at a plurality of nodes in a predetermined time zone, obtaining a plurality of predicted evaporation efficiency sets, and evaluating to obtain a plurality of evaporation fitnesses; taking the evaporation control parameter space as a constraint, optimizing the evaporation control parameters according to the plurality of evaporation fitnesses, and outputting the optimal evaporation control parameters, wherein parameter optimization is performed according to a dynamic optimization direction and an adaptive optimization step length, and the dynamic optimization direction is an optimal adjustment or an inferior adjustment; within the predetermined time zone, controlling the single-effect evaporator to perform evaporation operations according to the optimal evaporation control parameters.

[0006] On the other hand, the present application also provides an evaporation energy-saving adaptive control system under multi-parameter analysis, the system comprising: an initial evaporation control parameter selection module, used to obtain the evaporation control parameter space of the single-effect evaporator, and randomly select multiple initial evaporation control parameters that meet the uniform distribution constraint; an evaporation efficiency prediction and evaluation module, used to perform evaporation efficiency prediction of the multiple initial evaporation control parameters at multiple nodes in a predetermined time zone based on the solution properties, environmental parameters and expected concentration indicators of the brine to be evaporated, obtain multiple predicted evaporation efficiency sets, and evaluate to obtain multiple evaporation fitnesses; an evaporation control parameter optimization module, used to optimize the evaporation control parameters according to the multiple evaporation fitnesses with the evaporation control parameter space as a constraint, and output the optimal evaporation control parameters, wherein the parameter optimization is performed according to the dynamic optimization direction and the adaptive optimization step length, and the dynamic optimization direction is optimization adjustment or avoidance adjustment; an evaporation control module, used to control the single-effect evaporator to perform evaporation operations according to the optimal evaporation control parameters within the predetermined time zone.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: The evaporation control parameter space of the single-effect evaporator is obtained to determine the scope of subsequent optimization. By randomly selecting the parameters in a way that satisfies the uniform distribution constraint, the possible parameter combinations can be covered more comprehensively, avoiding the one-sidedness of parameter selection and providing more possibilities for finding the global optimal solution. The evaporation efficiency is predicted by comprehensively considering the solution properties, environmental parameters and expected concentration index of the brine to be evaporated, which can more realistically reflect the complex situation in the actual evaporation process. The predicted evaporation efficiency is evaluated to obtain multiple evaporation fitnesses, which quantify the pros and cons of different parameter combinations in the actual evaporation scenario and provide guidance for subsequent parameter optimization. Based on the evaporation fitness obtained in the previous step, parameter optimization is performed under the constraints of the parameter space to find the optimal evaporation control parameters. The introduction of dynamic optimization direction and adaptive optimization step length can more flexibly respond to different environmental and condition changes, and find the optimal evaporation control parameters more efficiently and accurately. Within the predetermined time zone, the single-effect evaporator is controlled to perform evaporation operations according to the optimal evaporation control parameters, and the obtained optimal evaporation control parameters are applied to the actual evaporation operation to ensure that the evaporator operates according to the optimal parameter combination, thereby achieving an energy-saving and efficient evaporation process.

[0008] In summary, the present application can comprehensively consider the evaporation control parameters, comprehensively consider multiple factors to make accurate efficiency prediction and fitness evaluation, and find the optimal parameters through dynamic optimization, thereby improving the evaporation efficiency of the single-effect evaporator, achieving the goal of energy saving and consumption reduction, and being able to better adapt to various changes in the chemical production process, thereby improving the stability of the production process and ensuring the stability of product quality.

[0009] 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

[0010] Figure 1 A schematic flow chart of the evaporation energy-saving adaptive control method under multi-parameter analysis provided in an embodiment of the present application.

[0011] Figure 2 A schematic diagram of a process for obtaining multiple predicted evaporation efficiency sets in the evaporation energy-saving adaptive control method under multi-parameter analysis provided in an embodiment of the present application.

[0012] Figure 3 A schematic diagram of a process for obtaining optimal evaporation control parameters in the evaporation energy-saving adaptive control method under multi-parameter analysis provided in an embodiment of the present application.

[0013] Figure 4 A schematic diagram of the structure of an evaporation energy-saving adaptive control system under multi-parameter analysis provided in an embodiment of the present application.

[0014] Description of reference numerals: initial evaporation control parameter selection module 10 , evaporation efficiency prediction and evaluation module 20 , evaporation control parameter optimization module 30 , evaporation control module 40 . DETAILED DESCRIPTION

[0015] The embodiment of the present application provides an evaporation energy-saving adaptive control method and system under multi-parameter analysis, thereby solving the technical problems in the prior art that the energy consumption in the brine evaporation process is high and the adaptability to environmental changes is poor due to the lack of a multi-parameter collaborative optimization method, thereby achieving the technical effect of improving evaporation efficiency and reducing evaporation energy consumption.

[0016] Embodiment 1, as Figure 1 As shown, the embodiment of the present application provides an evaporation energy-saving adaptive control method under multi-parameter analysis, and the method includes: Step S1: Obtain the evaporation control parameter space of the single-effect evaporator, and randomly select a plurality of initial evaporation control parameters that satisfy the uniform distribution constraint.

[0017] Specifically, the evaporation control parameter space refers to the set of value ranges of all possible control parameters that affect the evaporation process of the single-effect evaporator. The uniform distribution constraint is a restriction condition when randomly selecting the initial evaporation control parameters, which means that when selecting the initial parameters, these parameters are uniformly distributed in the entire parameter space to ensure that all possible parameter combinations are covered.

[0018] The control system of the interactive single-effect evaporation controller collects historical control records and extracts all control parameters that may affect the evaporation process, such as steam supply, liquid inlet flow, liquid outlet flow, steam temperature and steam pressure. At the same time, according to historical data and equipment manuals, the value range of each parameter is determined to obtain the evaporation control parameter space. Next, a random number generator is used to randomly select multiple initial parameter combinations in this parameter space to ensure that these parameter combinations are evenly distributed throughout the space, so that the impact of different parameters on evaporation efficiency can be fully evaluated.

[0019] By randomly selecting initial evaporation control parameters that satisfy uniform distribution constraints in the evaporation control parameter space, possible parameter combinations are fully covered, avoiding the limitations of parameter selection caused by human experience or traditional fixed value methods, and providing rich basic data for subsequently finding the global optimal evaporation control parameters.

[0020] Step S2: Based on the solution properties, environmental parameters and expected concentration index of the brine to be evaporated, the evaporation efficiency prediction of the multiple initial evaporation control parameters at multiple nodes in a predetermined time zone is performed to obtain multiple predicted evaporation efficiency sets, and multiple evaporation fitness levels are evaluated.

[0021] Specifically, solution properties refer to the chemical and physical properties of the brine to be evaporated, such as salt concentration, viscosity, etc. Environmental parameters include the temperature, humidity, atmospheric pressure, etc. of the operating environment. The expected concentration index refers to the degree of brine concentration that is expected to be achieved during the evaporation process. Using a computational model or simulation software, the evaporation efficiency of each initial evaporation control parameter combination is predicted based on the solution properties, environmental parameters, and expected concentration index of the brine to be evaporated. The evaporation efficiency prediction will be performed at multiple nodes in a predetermined time zone (such as taking a node every hour in a day, a total of 24 nodes) to simulate the dynamic changes of the evaporation process. Then, the performance of each control parameter combination in the actual evaporation process is evaluated based on the prediction results, and the evaporation fitness corresponding to multiple initial evaporation control parameters is obtained. This evaporation fitness is a comprehensive evaluation index of different initial evaporation control parameters after considering the solution properties, environmental parameters, and expected concentration index, reflecting the pros and cons of each parameter combination in the actual evaporation process.

[0022] By comprehensively predicting and evaluating the evaporation efficiency of various factors, we can more accurately evaluate the advantages and disadvantages of different initial evaporation control parameters and obtain evaluation results that are more in line with the actual needs of the evaporation process, providing a reliable basis for subsequent parameter optimization and improving the accuracy and practicality of the entire evaporation control scheme.

[0023] Step S3: taking the evaporation control parameter space as a constraint, optimizing the evaporation control parameters according to the multiple evaporation fitnesses, and outputting the optimal evaporation control parameters, wherein the parameter optimization is performed according to a dynamic optimization direction and an adaptive optimization step length, and the dynamic optimization direction is an optimal adjustment or an inferior adjustment.

[0024] Specifically, the dynamic optimization direction refers to the flexible adjustment of the optimization direction according to the current evaporation fitness during the optimization process, which can be an optimization adjustment or an avoidance adjustment. The optimization adjustment is to adjust the parameters in the direction of making the evaporation fitness better (such as higher evaporation efficiency, closer to the expected concentration index, etc.); the avoidance adjustment is to avoid adjusting the parameters in the direction of making the evaporation fitness worse. The adaptive optimization step size is the magnitude of each parameter adjustment during the optimization process. This step size is not fixed, but is adapted according to the specific situation (such as the current parameter position, the changing trend of evaporation fitness, etc.). For example, in an area close to the optimal solution, a smaller optimization step size may be required to find the optimal solution more accurately; in an area far from the optimal solution, a larger optimization step size can be used to speed up the optimization process.

[0025] The evaporation control parameter space obtained in step S1 is used as a constraint condition, that is, to ensure that the parameters in the optimization process are always within this legal value range. According to the multiple evaporation fitness obtained in step S2, an optimization algorithm is used to perform optimization and determine the optimal evaporation control parameters. This optimal evaporation control parameter is the optimal solution obtained through the optimization process, which can achieve the parameter setting with the highest efficiency and lowest energy consumption under specific working conditions. The optimization process can use genetic algorithms, particle swarm optimization algorithms, etc. In the optimization process, the parameters are adjusted according to the dynamic optimization direction and the adaptive optimization step length. Exemplarily, the dynamic optimization direction can be determined based on the comparison result of the current fitness and the previous round of fitness, and the adaptive optimization step length can be determined based on factors such as the estimated distance between the current parameter and the optimal parameter, the rate of change of fitness, etc.

[0026] The combination of dynamic optimization direction and adaptive optimization step size enables the optimization process to converge quickly to the optimal solution and to adjust parameters more finely when approaching the optimal solution, thereby improving the efficiency and accuracy of optimization and finding the optimal evaporation control parameters in the parameter space more efficiently and accurately, thus providing the optimal parameter combination for achieving efficient and energy-saving evaporation operations.

[0027] Step S4: within the predetermined time zone, controlling the single-effect evaporator to perform evaporation operation according to the optimal evaporation control parameters.

[0028] Specifically, the predetermined time zone refers to a time range pre-set in the evaporation operation. The optimal evaporation control parameters outputted in step S3 are inputted into the control system of the single-effect evaporator, which may be an automated control system based on a PLC (programmable logic controller) or a DCS (distributed control system). Within the predetermined time zone, the automated control system controls the operation of the evaporator according to these optimal evaporation control parameters, ensuring that the evaporator operates in the best working state, thereby achieving an efficient evaporation process, improving evaporation efficiency, reducing energy consumption, and being able to stably achieve the desired concentration index.

[0029] Further, step S1 includes: Step S11: obtaining the evaporation control parameter space of the single-effect evaporator, wherein the evaporation control parameters include steam supply, liquid inlet flow, liquid outlet flow, steam temperature and steam pressure.

[0030] Step S12: randomly selecting a first evaporation control parameter in the evaporation control parameter space and setting it as a first initial evaporation control parameter, and randomly selecting a second evaporation control parameter again, and if the Euclidean distance between the second evaporation control parameter and the first initial evaporation control parameter is greater than a predetermined distance threshold, setting the second evaporation control parameter as a second initial evaporation control parameter.

[0031] Step S13: Continue to randomly select a third evaporation control parameter. If the Euclidean distance between the third evaporation control parameter and the first initial evaporation control parameter is greater than the predetermined distance threshold and the Euclidean distance between the third evaporation control parameter and the second initial evaporation control parameter is greater than the predetermined distance threshold, then set the third evaporation control parameter as the third initial evaporation control parameter, iterate until the predetermined number is met, and output the multiple initial evaporation control parameters.

[0032] Specifically, the steam supply refers to the amount of steam supplied to the single-effect evaporator to heat the brine and cause it to evaporate. The inlet flow rate is the flow rate of the brine solution entering the single-effect evaporator, which affects the liquid level and residence time of the solution in the evaporator. The outlet flow rate is the flow rate of the solution flowing out of the single-effect evaporator, and the outlet flow rate is related to factors such as the inlet flow rate and the evaporation amount. The steam temperature is the temperature of the supplied steam, and different steam temperatures will affect the heat transfer efficiency in the evaporator. The steam pressure is the pressure of the steam, which affects the energy transfer efficiency of the steam in the evaporator.

[0033] Consult the equipment manual of the single-effect evaporator to obtain the evaporation control parameter space, including the value ranges of control parameters such as steam supply, inlet flow, outlet flow, steam temperature and steam pressure. For example, for a certain model of single-effect evaporator, the value range of steam supply is 5-20 kg / hour, the value range of inlet flow is 3-10 cubic meters / hour, the outlet flow will vary within a certain range according to factors such as inlet flow and evaporation, the value range of steam temperature is 100-150°C, and the value range of steam pressure is 100-500kPa. These value ranges together constitute the evaporation control parameter space, which provides a complete range framework for the subsequent selection of initial evaporation control parameters, helps to fully consider various possible parameter combinations and avoid missing important parameter value situations.

[0034] In the evaporation control parameter space, the first evaporation control parameter is randomly selected as the first initial evaporation control parameter, and then the second evaporation control parameter is randomly selected again, and the Euclidean distance between the first initial evaporation control parameter and the second evaporation control parameter is calculated. For the evaporation control parameter, each parameter can be regarded as a dimension of a vector. The Euclidean distance calculation formula is used to calculate the Euclidean distance of the vector corresponding to the first initial evaporation control parameter and the second evaporation control parameter, and compared with the predetermined distance threshold. This predetermined distance threshold: a pre-set Euclidean distance value is used to determine whether the randomly selected evaporation control parameter is sufficiently dispersed. If the Euclidean distance between the second evaporation control parameter and the first initial evaporation control parameter is greater than the predetermined distance threshold, the second evaporation control parameter is set as the second initial evaporation control parameter; conversely, if the Euclidean distance between the second evaporation control parameter and the first initial evaporation control parameter is less than or equal to the predetermined distance threshold, the second evaporation control parameter is discarded, and then a new evaporation control parameter is selected for judgment. By setting the judgment condition of the Euclidean distance, the selected initial evaporation control parameter has a certain dispersion, avoiding the selection of too close parameter combinations, so that the evaporation control parameter space can be more comprehensively covered, and the possibility of finding the global optimal solution is increased.

[0035] After determining the first initial evaporation control parameter and the second initial evaporation control parameter, continue to randomly select the third evaporation control parameter, and then calculate the Euclidean distance between the third evaporation control parameter and the first initial evaporation control parameter and the second initial evaporation control parameter. If the Euclidean distance between the third evaporation control parameter and the first initial evaporation control parameter and the second initial evaporation control parameter is greater than the predetermined distance threshold, it is set as the third initial evaporation control parameter. Otherwise, the third evaporation control parameter is discarded, and a new evaporation control parameter is reselected from the evaporation control parameter space for distance calculation and judgment. In this iterative manner, evaporation control parameters are continuously randomly selected and distance judgment is performed to determine new initial evaporation control parameters until the predetermined number is met. Through this iterative selection method, it is ensured that the selected multiple initial evaporation control parameters have good dispersion in the evaporation control parameter space, and the parameter space can be explored more widely, thereby providing more diverse and representative initial parameters for subsequent evaporation efficiency prediction and optimization operations, and improving the probability of finding the optimal evaporation control parameters for the entire technical solution.

[0036] Further, such as Figure 2 As shown, step S2 includes: Step S21: collecting solution properties and environmental parameters of the brine to be evaporated, wherein the solution properties include solution type, initial concentration and initial temperature, and the environmental parameters include ambient temperature and ambient humidity.

[0037] Step S22: Taking the solution properties, environmental parameters and expected concentration index as conditional constraints, taking the property characteristics of the single-effect evaporator as equipment constraints, and taking evaporation control as a guide, big data retrieval obtains a sample evaporation control parameter set and a sample evaporation time set, and annotates the evaporation efficiency under different sample evaporation control parameters and different sample evaporation times to obtain a sample evaporation efficiency set.

[0038] Step S23: training an integrated learning operator based on the sample evaporation control parameter set, the sample evaporation time set and the sample evaporation efficiency set until convergence to obtain an evaporation efficiency prediction plug-in, wherein the integrated learning operator includes at least a random forest, a BP neural network and a support vector machine.

[0039] Step S24: Determine multiple evaporation times according to the multiple nodes in the predetermined time zone, use the evaporation efficiency prediction plug-in to perform evaporation efficiency prediction of the multiple initial evaporation control parameters under the multiple evaporation times, and output multiple predicted evaporation efficiency sets, wherein the evaporation time is the time interval between the node and the start time.

[0040] Specifically, the interactive control system or production task obtains the solution properties and environmental parameters of the brine to be evaporated. Among them, the solution properties include solution type, initial concentration and initial temperature, and the environmental parameters include ambient temperature and ambient humidity. Solution type refers to the specific component type of the brine to be evaporated, such as different salt solution types such as sodium chloride brine and potassium chloride brine. Different solution types have different physical and chemical properties during the evaporation process, which will affect the evaporation efficiency. The initial concentration is the solute concentration of the brine to be evaporated before evaporation begins. The concentration will affect the mass transfer driving force during the evaporation process. The initial temperature is the temperature when the brine starts to evaporate. The higher the temperature, the greater the kinetic energy of the water molecules, which is conducive to the evaporation of water to a certain extent. The ambient temperature is the temperature of the environment around the evaporator. The ambient temperature is closely related to the heat transfer during the evaporation of the brine. For example, in a high temperature environment, the rate of heat dissipation to the environment when the brine evaporates is relatively slow, which may be conducive to evaporation. Ambient humidity is the content of water vapor in the ambient air, usually expressed as relative humidity. The higher the relative humidity, the closer the water vapor in the ambient air is to saturation, which is not conducive to the evaporation of water.

[0041] The sample evaporation control parameter set refers to a set of evaporation control parameters collected in historical data. The sample evaporation time set refers to a set of evaporation times corresponding to the sample evaporation control parameters. The sample evaporation efficiency set is a set of evaporation efficiencies obtained by annotation under different sample evaporation control parameters and sample evaporation times. The attribute characteristics of the single-effect evaporator include the volume, heating method, material and other characteristics of the evaporator. With the collected solution properties, environmental parameters and expected concentration indicators as conditional constraints, the attribute characteristics of the single-effect evaporator as equipment constraints, and evaporation control as a guide, retrieval is performed in the big data platform (such as the database of the chemical industry, a large amount of evaporation process data accumulated within the enterprise, etc.). For example, from the evaporation process data accumulated by chemical enterprises for many years, evaporation process data that meets the conditions such as the solution type is sodium chloride brine, the initial concentration is within a certain range, and the ambient temperature and humidity are within a specific range are screened out. The evaporation control parameters and evaporation time in these data are extracted to form a sample evaporation control parameter set and a sample evaporation time set. Then, the evaporation efficiency of these samples is annotated according to the actual production records or experimental data to obtain a sample evaporation efficiency set. By integrating various constraints to obtain relevant data from big data, we construct a sample evaporation control parameter set, a sample evaporation time set, and a sample evaporation efficiency set, which provides a rich data source for training the evaporation efficiency prediction model. This enables the model to learn the evaporation laws under different conditions and improves the accuracy and reliability of the model prediction.

[0042] The evaporation efficiency prediction plug-in is a tool that can be used to predict evaporation efficiency after training with an integrated learning operator. It can output evaporation efficiency according to input evaporation control parameters and evaporation time and other information. Integrated learning combines multiple machine learning algorithms to improve prediction performance, each of which is an integrated learning operator. In the embodiment of the present application, the integrated learning operator includes at least random forest, BP neural network and support vector machine. The sample evaporation control parameter set, sample evaporation time set and sample evaporation efficiency set are subjected to data preprocessing, such as normalization, to improve the training effect of the model. Then, the preprocessed sample evaporation control parameter set, sample evaporation time set and sample evaporation efficiency set are respectively input into integrated learning operators such as random forest, BP neural network and support vector machine for training using a machine learning framework (such as the Scikit-learn library in Python). During the training process, the parameters of the model (such as the number of decision trees in the random forest, the weights and biases in the BP neural network, etc.) are continuously adjusted, and the model convergence is judged by calculating the error (such as the mean square error) of the model on the validation set. When the model converges, that is, the parameters of the model gradually stabilize during the iteration process, and the prediction results of the model no longer change significantly, these ensemble learning operators are combined to obtain the evaporation efficiency prediction plug-in. The combination process of the ensemble learning operator first needs to set the trust weight of each ensemble learning operator. For example, the mean square error can be used as an evaluation indicator to evaluate the training accuracy of each ensemble learning operator on the validation set, and the weight is calculated according to the inverse of the mean square error. The ratio of the inverse of the mean square error of each ensemble learning operator to the sum of the inverses of the mean square errors of all ensemble learning operators is used as the trust weight of each ensemble learning operator. When using the evaporation efficiency prediction plug-in for prediction, the evaporation control parameters and evaporation time are input into multiple ensemble learning operators respectively to obtain the corresponding multiple predicted evaporation efficiencies, and then the multiple predicted evaporation efficiencies are weighted and summed according to the trust weights to obtain the final predicted evaporation efficiency. The evaporation efficiency prediction plug-in integrates the advantages of multiple machine learning algorithms and has higher prediction accuracy and stability. Compared with a single machine learning model, it can better adapt to different data distributions and complex evaporation process relationships, thereby providing a strong guarantee for the accurate prediction of evaporation efficiency.

[0043] Evaporation time refers to the time interval from the start time to a certain node in a predetermined time zone. It is an important factor affecting evaporation efficiency. Under different evaporation times, the evaporation efficiency will also be different due to the different progress of the evaporation process. Determine multiple nodes according to the predetermined time zone. For example, if the predetermined time zone is 24 hours a day, take a node every hour, so that 24 nodes are obtained. For each node, calculate the time interval between it and the start time as the evaporation time. Then, multiple initial evaporation control parameters and these evaporation times are input into the evaporation efficiency prediction plug-in, and the evaporation efficiency is predicted using the integrated learning algorithm in the plug-in to obtain the evaporation efficiency of each initial evaporation control parameter at each evaporation time, and finally output multiple predicted evaporation efficiency sets, where each predicted evaporation efficiency set corresponds to an initial evaporation control parameter, which includes the evaporation efficiency of the initial evaporation control parameter at different evaporation times. By predicting the evaporation efficiency of the initial evaporation control parameter at different evaporation times in the predetermined time zone, the evaporation efficiency of different parameter combinations at different time points can be fully understood, which provides a rich data basis for the subsequent evaporation fitness evaluation and evaporation control parameter optimization, and helps to find the optimal evaporation control parameter.

[0044] Furthermore, in step S2, multiple evaporation fitness levels are evaluated, including: Step S25: randomly selecting a first predicted evaporation efficiency set, performing mean calculation and variance calculation on the first predicted evaporation efficiency set, and determining a first predicted evaporation efficiency mean and a first predicted evaporation efficiency fluctuation coefficient.

[0045] Step S26: A first evaporation fitness is obtained by weighted calculation according to the first predicted evaporation efficiency mean and the first predicted evaporation efficiency fluctuation coefficient, and added to the multiple evaporation fitnesses, wherein the evaporation fitness is positively correlated with the predicted evaporation efficiency mean and negatively correlated with the predicted evaporation efficiency fluctuation coefficient.

[0046] Specifically, a predicted evaporation efficiency set is randomly selected from multiple predicted evaporation efficiency sets output by the evaporation efficiency prediction plug-in, and is recorded as the first predicted evaporation efficiency set. The arithmetic mean and variance of all predicted evaporation efficiencies in the first predicted evaporation efficiency set are calculated to obtain the first predicted evaporation efficiency mean and the first predicted evaporation efficiency fluctuation coefficient. The predicted evaporation efficiency mean reflects the average level of evaporation efficiency under a set of evaporation control parameters. Variance is a statistic used to measure the degree of discreteness of a set of data, reflecting the degree of fluctuation of the predicted evaporation efficiency. For the predicted evaporation efficiency set, the larger the variance, the greater the fluctuation of the evaporation efficiency at different time nodes or conditions. By calculating the predicted evaporation efficiency mean and fluctuation coefficient, the overall level and stability of the evaporation efficiency under a set of evaporation control parameters can be fully understood. The mean gives an average reference value, while the fluctuation coefficient reflects the change in evaporation efficiency. These two indicators provide an important basis for the subsequent evaluation of evaporation fitness.

[0047] The purpose of evaporation energy-saving regulation is to improve the evaporation efficiency and the stability of the evaporation process. According to this goal, a weighted calculation formula is set to perform weighted calculation on the predicted evaporation efficiency mean and the predicted evaporation efficiency fluctuation coefficient to obtain the evaporation fitness. In this weighted calculation formula, the evaporation fitness is positively correlated with the predicted evaporation efficiency mean and negatively correlated with the predicted evaporation efficiency fluctuation coefficient. Exemplarily, the calculation formula can be set to F=a×Eb×V, where F is the evaporation fitness, E is the predicted evaporation efficiency mean, V is the predicted evaporation efficiency fluctuation coefficient, and a and b are weight coefficients, whose values ​​can be customized according to actual conditions. Substitute the first predicted evaporation efficiency mean and the first predicted evaporation efficiency fluctuation coefficient calculated above into the formula to obtain the first evaporation fitness, and add it to multiple evaporation fitnesses.

[0048] According to the above calculation process, the evaporation fitness corresponding to each set of multiple predicted evaporation efficiency sets is determined to obtain multiple evaporation fitnesses. This weighted calculation method of evaporation fitness can comprehensively consider the average level and stability of evaporation efficiency, avoiding the situation of only focusing on the efficiency mean and ignoring fluctuations, or only focusing on fluctuations and ignoring efficiency levels, providing a more comprehensive and reasonable evaluation basis for subsequent optimization of evaporation control parameters, and helping to find evaporation control parameters that perform well in both efficiency and stability.

[0049] Further, such as Figure 3 As shown, step S3 includes: Step S31: setting the initial evaporation control parameters as the initial solution, arranging the multiple initial evaporation control parameters from large to small according to the multiple evaporation fitnesses, and determining the initial solution sequence.

[0050] Step S32: Set the first P solutions of the initial solution sequence as optimal solutions, and set the last Q solutions as inferior solutions, to obtain P optimal solutions and Q inferior solutions, where Q is N times of P, P and Q are both integers, and N is an integer greater than 5.

[0051] Step S33: Randomly cluster the Q inferior solutions according to the P superior solutions to obtain P solution sets, wherein the number of inferior solutions in each solution set is the same.

[0052] Step S34: For the P solution sets, the inferior solution with the smallest fitness in the solution set is set as a differential solution, the fitness of P differential solutions is obtained, and P dynamic optimization directions are set according to the fitness of the P superior solutions and the fitness of the P differential solutions, wherein the dynamic optimization direction is an adjustment toward the optimal solution or an adjustment away from the inferior solution.

[0053] Step S35: According to the P dynamic optimization directions, the inferior solutions in the P solution sets are adjusted according to the adaptive optimization step length to obtain P updated solution sets, and the P updated solution sets are identified. If the fitness of the inferior solution in the updated solution set is greater than the fitness of the superior solution, the superior solution in the solution set is replaced by the inferior solution, wherein if the adjusted inferior solution does not satisfy the evaporation control parameter space, this adjustment is not performed.

[0054] Step S36: Continue iterative optimization based on the dynamic optimization direction and the adaptive optimization step size until a predetermined number of iterations is reached, output P current solution sets, select the current solution set with the largest sum of fitness among the P current solution sets as the optimal solution set, and output the optimal solution of the optimal solution set as the optimal evaporation control parameter.

[0055] Specifically, multiple initial evaporation control parameters and corresponding evaporation fitness are obtained. The initial evaporation control parameters are set as the initial solution, and then these initial evaporation control parameters are arranged from large to small according to the evaporation fitness using a sorting algorithm (such as bubble sort, quick sort, etc.) to obtain an initial solution sequence. The initial evaporation control parameters with better evaporation fitness can be placed in front, which provides a basis for distinguishing between good solutions and bad solutions in the future, helps to quickly locate possible better solution areas, and improves the optimization efficiency.

[0056] The first P solutions of the initial solution sequence are set as optimal solutions. These solutions have relatively high evaporation fitness and are solutions that perform better among all current initial solutions. The last Q solutions are set as inferior solutions. These solutions have relatively low evaporation fitness. P optimal solutions and Q inferior solutions are determined, where Q is N times P, P and Q are both integers, and N is an integer greater than 5. By distinguishing between optimal solutions and inferior solutions, the solution space can be further analyzed and processed, and the information of optimal solutions can be used to improve inferior solutions, so as to gradually find better evaporation control parameters.

[0057] A random number generator is used to randomly cluster the Q inferior solutions, and the Q inferior solutions are randomly divided into P groups. Each group corresponds to a superior solution, and the superior solutions and the corresponding inferior solutions are integrated to obtain P solution sets. Each solution set contains one superior solution and N inferior solutions (N=Q / P). Random clustering can make the inferior solutions evenly distributed in different solution sets, avoiding the excessive concentration or dispersion of inferior solutions in some solution sets, and providing a reasonable grouping basis for the subsequent adjustment of inferior solutions in different solution sets based on superior solutions.

[0058] For each solution set, traverse the inferior solutions and find the inferior solution with the smallest fitness as the differential solution. Then compare the fitness of the differential solution and the fitness of the optimal solution of each solution set, and set P dynamic optimization directions according to the fitness of P optimal solutions and P differential solutions. The dynamic optimization direction can be to adjust the inferior solution in the direction of the optimal solution (i.e., optimal adjustment), or to avoid adjusting the inferior solution in the direction of the differential solution (i.e., avoidance adjustment). By determining the differential solution and setting the dynamic optimization direction, a reasonable optimization strategy can be formulated according to the specific situation of each solution set, making the optimization process more targeted and improving the efficiency and accuracy of the optimization.

[0059] According to the determined dynamic optimization direction, for each inferior solution in the solution set, it is adjusted according to the adaptive optimization step length to form an updated solution set. If the fitness of the inferior solution in the updated solution set is greater than the fitness of the superior solution, the superior solution is replaced by the inferior solution. During the adjustment process, it is necessary to check the parameter range before and after the adjustment. If the adjusted inferior solution does not meet the evaporation control parameter space (for example, the steam supply exceeds the maximum value allowed by the equipment), this adjustment will not be performed. By adjusting the inferior solution according to the dynamic optimization direction and the adaptive optimization step length, the inferior solution can be gradually improved in the solution space and developed in a better direction. At the same time, by limiting the adjusted inferior solution within the evaporation control parameter space, the effectiveness and feasibility of the parameter adjustment are guaranteed.

[0060] The predetermined number of iterations is the number of iterations in the optimization process that is pre-set. Iterative optimization is performed based on the dynamic optimization direction and the adaptive optimization step length. Each iteration is based on the determined dynamic optimization direction. For the inferior solutions in each solution set, adjustments are made according to the adaptive optimization step length to form an updated solution set. After reaching the predetermined number of iterations, the sum of the fitness of all solutions in each solution set in the current P solution sets is calculated. The solution set with the largest sum of fitness is selected as the optimal solution set, and the optimal solution in the optimal solution set is output as the optimal evaporation control parameter. Through multiple iterative optimization searches, the optimal solution can be searched more comprehensively in the solution space, and finally the optimal solution in the optimal solution set with the largest sum of fitness is selected as the optimal evaporation control parameter, which increases the probability of finding the global optimal evaporation control parameter, thereby optimizing the evaporation operation of the single-effect evaporator.

[0061] Furthermore, in step S34, P dynamic optimization directions are set according to the fitness of the P optimal solutions and the fitness of the P poor solutions, including: Step S341: randomly selecting a first solution set from the P solution sets, and obtaining a first optimal solution fitness, a first poor solution fitness, and a plurality of first inferior solution fitnesses of the first solution set.

[0062] Step S342: Calculate the first inferior solution fitness mean of the multiple first inferior solution fitnesses, respectively calculate the absolute values ​​of the deviations between the first superior solution fitness, the first differential solution fitness and the first inferior solution fitness mean, and determine the first superior solution deviation value and the first inferior solution deviation value.

[0063] Step S343: Set a first dynamic optimization direction according to the first optimal solution deviation value and the first inferior solution deviation value, and add it to the P dynamic optimization directions, wherein if the first optimal solution deviation value is greater than or equal to the first inferior solution deviation value, the first dynamic optimization direction is an optimization-oriented adjustment; if the first optimal solution deviation value is less than the first inferior solution deviation value, the first dynamic optimization direction is an avoidance-oriented adjustment.

[0064] Specifically, the specific process of determining the dynamic optimization direction includes: using a random number generator to randomly select a first solution set from P solution sets, and obtaining a first optimal solution fitness, a first differential solution fitness, and multiple first inferior solution fitnesses from the first solution set. The first optimal solution fitness is the fitness corresponding to the optimal solution in the first solution set, that is, the maximum fitness, the first differential solution fitness is the fitness corresponding to the differential solution in the first solution set, that is, the minimum fitness, and the first inferior solution fitness is the fitness corresponding to other solutions except the optimal solution and the differential solution.

[0065] Add up the fitness of all inferior solutions in the first solution set, and then divide it by the number of inferior solutions to obtain the mean fitness of the first inferior solution. This mean fitness of the first inferior solution reflects the average fitness level of the inferior solutions in the first solution set. Then, calculate the absolute value of the deviation between the fitness of the first superior solution and the mean fitness of the first inferior solution, recorded as the first superior solution deviation value, and calculate the absolute value of the deviation between the fitness of the first inferior solution and the mean fitness of the first inferior solution, recorded as the first inferior solution deviation value. By calculating the mean fitness of the first inferior solution and the absolute value of the deviation from the fitness of the superior solution and the inferior solution, the degree of deviation of the superior solution and the inferior solution relative to the average level of the inferior solution can be quantified, providing a quantitative basis for accurately setting the dynamic optimization direction.

[0066] The calculated deviation value of the first optimal solution is compared with the deviation value of the first inferior solution. If the deviation value of the first optimal solution is greater than or equal to the deviation value of the first inferior solution, the first dynamic optimization direction is set to the optimal adjustment; if the deviation value of the first optimal solution is less than the deviation value of the first inferior solution, the first dynamic optimization direction is set to the inferior adjustment. By setting the first dynamic optimization direction in this way, a reasonable optimization strategy can be formulated according to the deviation of the optimal solutions and inferior solutions in the first solution set relative to the average level of inferior solutions, thereby improving the efficiency of optimization and moving more quickly toward the optimal solution or away from the worst solution.

[0067] The operations of step S341 to step S343 are performed for each solution set in the P solution sets, and a corresponding dynamic optimization direction is set for each solution set.

[0068] Furthermore, the adaptive optimization step length is obtained, including: Step S3-1: determining a first optimization fitness of a first direction according to the first dynamic optimization direction, wherein the first optimization fitness is a first optimal solution fitness or a first differential solution fitness.

[0069] Step S3-2: Calculate the ratios of multiple first inferior solution fitnesses to the first optimal solution fitnesses respectively, and determine multiple compensation coefficients, wherein if the first optimal solution fitness is the first optimal solution fitness, then the compensation coefficient is the ratio of the first inferior solution fitness to the first optimal solution fitness; if the first optimal solution fitness is the first differential solution fitness, then the compensation coefficient is the ratio of the first differential solution fitness to the first inferior solution fitness.

[0070] Step S3-3: Optimize the initial optimization step length according to the multiple compensation coefficients, determine a first step length set, and add it to the adaptive optimization step length.

[0071] Specifically, for the first solution set selected above, the first optimization fitness of the first direction is determined according to the determined first dynamic optimization direction. If the first dynamic optimization direction is an adjustment towards optimality, then the first optimization fitness is the first optimal solution fitness; if the first dynamic optimization direction is an adjustment away from inferiority, the first optimization fitness is the first inferior solution fitness.

[0072] Obtain multiple first inferior solution fitnesses, calculate the ratios of multiple first inferior solution fitnesses to the first optimization fitnesses respectively, and determine multiple compensation coefficients, which reflect the proportional relationship between the inferior solution and the optimization reference solution (optimal solution or poor solution) in terms of fitness, and are used to optimize the initial optimization step length. When the first optimization fitness is the first optimal solution fitness, that is, the optimization reference solution is an optimal solution, the compensation coefficient is the ratio of the first inferior solution fitness to the first optimal solution fitness. When the first optimization fitness is the first poor solution fitness, that is, the optimization reference solution is a poor solution, the compensation coefficient is the ratio of the first poor solution fitness to the first inferior solution fitness. Calculating the compensation coefficient can quantify the relationship between the inferior solution and the optimization reference solution in terms of fitness, and provides a basis for adjusting the optimization step length according to different inferior solutions, thereby improving the adaptability and accuracy of the optimization step length setting.

[0073] The first step length set is an optimized step length set obtained by adjusting the initial optimization step length according to multiple compensation coefficients, and includes the optimized optimization step length for each inferior solution. The initial optimization step length is the basic parameter adjustment range set at the beginning of the optimization process, including the initial adjustment range of evaporation control parameters such as steam supply, liquid inlet flow, liquid outlet flow, steam temperature and steam pressure. For each solution set, the initial optimization step length is optimized using multiple compensation coefficients, that is, the compensation coefficient is multiplied by the initial optimization step length to determine multiple first step lengths, each of which corresponds to an inferior solution in the solution set. The first step length set is obtained by summarizing multiple first step lengths and added to the adaptive optimization step length to facilitate the subsequent adjustment and optimization of multiple inferior solutions. By optimizing the initial optimization step length according to the compensation coefficient, the optimization step length can be more adapted to different inferior solutions and the optimization accuracy can be improved. Different inferior solutions obtain different optimization step lengths according to their fitness relationship with the optimization reference solution, making the optimization process more reasonable and efficient.

[0074] In summary, the evaporation energy-saving adaptive control method under multi-parameter analysis provided in the embodiment of the present application has the following technical effects: The evaporation control parameter space of the single-effect evaporator is obtained to determine the scope of subsequent optimization. By randomly selecting the parameters in a way that satisfies the uniform distribution constraint, the possible parameter combinations can be covered more comprehensively, avoiding the one-sidedness of parameter selection and providing more possibilities for finding the global optimal solution. The evaporation efficiency is predicted by comprehensively considering the solution properties, environmental parameters and expected concentration index of the brine to be evaporated, which can more realistically reflect the complex situation in the actual evaporation process. The predicted evaporation efficiency is evaluated to obtain multiple evaporation fitnesses, which quantify the pros and cons of different parameter combinations in the actual evaporation scenario and provide guidance for subsequent parameter optimization. Based on the evaporation fitness obtained in the previous step, parameter optimization is performed under the constraints of the parameter space to find the optimal evaporation control parameters. The introduction of dynamic optimization direction and adaptive optimization step length can more flexibly respond to different environmental and condition changes, and find the optimal evaporation control parameters more efficiently and accurately. Within the predetermined time zone, the single-effect evaporator is controlled to perform evaporation operations according to the optimal evaporation control parameters, and the obtained optimal evaporation control parameters are applied to the actual evaporation operation to ensure that the evaporator operates according to the optimal parameter combination, thereby achieving an energy-saving and efficient evaporation process.

[0075] In general, the embodiments of the present application can comprehensively consider the evaporation control parameters, comprehensively consider multiple factors to perform accurate efficiency prediction and fitness evaluation, and find the optimal parameters through dynamic optimization, thereby improving the evaporation efficiency of the single-effect evaporator, achieving the goal of energy saving and consumption reduction, and being able to better adapt to various changes in the chemical production process, thereby improving the stability of the production process and ensuring the stability of product quality.

[0076] Embodiment 2, as Figure 4 As shown, the embodiment of the present application provides an evaporation energy-saving adaptive control system under multi-parameter analysis, and the system includes: The initial evaporation control parameter selection module 10 is used to obtain the evaporation control parameter space of the single-effect evaporator and randomly select a plurality of initial evaporation control parameters that meet the uniform distribution constraint.

[0077] The evaporation efficiency prediction and evaluation module 20 is used to perform evaporation efficiency prediction of the multiple initial evaporation control parameters at multiple nodes in a predetermined time zone based on the solution properties, environmental parameters and expected concentration index of the brine to be evaporated, obtain multiple predicted evaporation efficiency sets, and evaluate multiple evaporation fitnesses.

[0078] The evaporation control parameter optimization module 30 is used to optimize the evaporation control parameters according to the multiple evaporation fitnesses with the evaporation control parameter space as a constraint, and output the optimal evaporation control parameters, wherein the parameter optimization is performed according to a dynamic optimization direction and an adaptive optimization step length, and the dynamic optimization direction is an optimal adjustment or an inferior adjustment.

[0079] The evaporation control module 40 is used to control the single-effect evaporator to perform evaporation operation according to the optimal evaporation control parameters within the predetermined time zone.

[0080] Furthermore, the initial evaporation control parameter selection module 10 of the embodiment of the present application is used to perform the following steps: Acquire an evaporation control parameter space of a single-effect evaporator, wherein the evaporation control parameters include steam supply, liquid inlet flow, liquid outlet flow, steam temperature and steam pressure; randomly select a first evaporation control parameter in the evaporation control parameter space and set it as a first initial evaporation control parameter; randomly select a second evaporation control parameter again, and if the Euclidean distance between the second evaporation control parameter and the first initial evaporation control parameter is greater than a predetermined distance threshold, set the second evaporation control parameter as the second initial evaporation control parameter; continue to randomly select a third evaporation control parameter, and if the Euclidean distance between the third evaporation control parameter and the first initial evaporation control parameter is greater than the predetermined distance threshold and the Euclidean distance between the third evaporation control parameter and the second initial evaporation control parameter is greater than the predetermined distance threshold, set the third evaporation control parameter as the third initial evaporation control parameter, iterate the selection until a predetermined number is met, and output the multiple initial evaporation control parameters.

[0081] Furthermore, the evaporation efficiency prediction and evaluation module 20 of the embodiment of the present application is also used to perform the following steps: The solution properties and environmental parameters of the brine to be evaporated are collected, wherein the solution properties include solution type, initial concentration and initial temperature, and the environmental parameters include ambient temperature and ambient humidity; taking the solution properties, environmental parameters and expected concentration index as conditional constraints, taking the property characteristics of the single-effect evaporator as equipment constraints, and taking evaporation control as a guide, the big data retrieval obtains a sample evaporation control parameter set and a sample evaporation time set, and annotates the evaporation efficiency under different sample evaporation control parameters and different sample evaporation times to obtain a sample evaporation efficiency set; based on the sample evaporation control parameter set, the sample evaporation time set and the sample evaporation efficiency set, an integrated learning operator is trained until convergence to obtain an evaporation efficiency prediction plug-in, wherein the integrated learning operator at least includes a random forest, a BP neural network and a support vector machine; multiple evaporation times are determined according to multiple nodes in the predetermined time zone, and the evaporation efficiency prediction plug-in is used to perform the evaporation efficiency prediction of the multiple initial evaporation control parameters under the multiple evaporation times, and multiple predicted evaporation efficiency sets are output, wherein the evaporation time is the time interval between the node and the start time.

[0082] Furthermore, the evaporation efficiency prediction and evaluation module 20 of the embodiment of the present application is also used to perform the following steps: The solution properties and environmental parameters of the brine to be evaporated are collected, wherein the solution properties include solution type, initial concentration and initial temperature, and the environmental parameters include ambient temperature and ambient humidity; taking the solution properties, environmental parameters and expected concentration index as conditional constraints, taking the property characteristics of the single-effect evaporator as equipment constraints, and taking evaporation control as a guide, the big data retrieval obtains a sample evaporation control parameter set and a sample evaporation time set, and annotates the evaporation efficiency under different sample evaporation control parameters and different sample evaporation times to obtain a sample evaporation efficiency set; based on the sample evaporation control parameter set, the sample evaporation time set and the sample evaporation efficiency set, an integrated learning operator is trained until convergence to obtain an evaporation efficiency prediction plug-in, wherein the integrated learning operator at least includes a random forest, a BP neural network and a support vector machine; multiple evaporation times are determined according to multiple nodes in the predetermined time zone, and the evaporation efficiency prediction plug-in is used to perform the evaporation efficiency prediction of the multiple initial evaporation control parameters under the multiple evaporation times, and multiple predicted evaporation efficiency sets are output, wherein the evaporation time is the time interval between the node and the start time.

[0083] Furthermore, the evaporation control parameter optimization module 30 of the embodiment of the present application is also used to perform the following steps: The initial evaporation control parameter is set as the initial solution, and the multiple initial evaporation control parameters are arranged from large to small according to the multiple evaporation fitnesses to determine the initial solution sequence; the first P solutions of the initial solution sequence are set as optimal solutions, and the last Q solutions are set as inferior solutions, and P optimal solutions and Q inferior solutions are obtained, wherein Q is N times of P, P and Q are both integers, and N is an integer greater than 5; the Q inferior solutions are randomly clustered according to the P optimal solutions to obtain P solution sets, wherein the number of inferior solutions in each solution set is the same; for the P solution sets, the inferior solution with the smallest fitness in the solution set is set as a differential solution, and P differential solution fitnesses are obtained, and P dynamic optimization directions are set according to the P optimal solution fitnesses and the P differential solution fitnesses, wherein the dynamic optimization directions are The dynamic optimization direction is an adjustment toward the best or an adjustment away from the worst; according to the P dynamic optimization directions, the inferior solutions in the P solution sets are adjusted according to the adaptive optimization step length, P updated solution sets are obtained, and the P updated solution sets are identified. If the fitness of the inferior solution in the updated solution set is greater than the fitness of the superior solution, the superior solution in the solution set is replaced by the inferior solution, wherein if the adjusted inferior solution does not satisfy the evaporation control parameter space, no adjustment is performed; iterative optimization is continued based on the dynamic optimization direction and the adaptive optimization step length until a predetermined number of iterations is reached, P current solution sets are output, the current solution set with the largest sum of fitness among the P current solution sets is selected as the optimal solution set, and the superior solution of the optimal solution set is output as the optimal evaporation control parameter.

[0084] Further, according to the fitness of the P optimal solutions and the fitness of the P poor solutions, P dynamic optimization directions are set, and the execution steps include: A first solution set is randomly selected from the P solution sets, and a first optimal solution fitness, a first differential solution fitness, and multiple first inferior solution fitnesses of the first solution set are obtained; a first inferior solution fitness mean of the multiple first inferior solution fitnesses is calculated, and the absolute values ​​of the deviations between the first optimal solution fitness, the first differential solution fitness, and the first inferior solution fitness mean are calculated respectively, to determine a first optimal solution deviation value and a first inferior solution deviation value; a first dynamic optimization direction is set according to the first optimal solution deviation value and the first inferior solution deviation value, and is added to the P dynamic optimization directions, wherein if the first optimal solution deviation value is greater than or equal to the first inferior solution deviation value, the first dynamic optimization direction is an optimization-oriented adjustment, and if the first optimal solution deviation value is less than the first inferior solution deviation value, the first dynamic optimization direction is an inferior-avoidance adjustment.

[0085] Furthermore, the evaporation control parameter optimization module 30 of the embodiment of the present application is also used to perform the following steps: Determine the first optimization fitness of the first direction according to the first dynamic optimization direction, wherein the first optimization fitness is the first optimal solution fitness or the first differential solution fitness; calculate the ratios of multiple first inferior solution fitnesses to the first optimization fitness respectively, and determine multiple compensation coefficients, wherein if the first optimization fitness is the first optimal solution fitness, then the compensation coefficient is the ratio of the first inferior solution fitness to the first optimal solution fitness; if the first optimization fitness is the first differential solution fitness, then the compensation coefficient is the ratio of the first differential solution fitness to the first inferior solution fitness; optimize the initial optimization step length according to the multiple compensation coefficients, determine the first step length set, and add it to the adaptive optimization step length.

[0086] Through the above detailed description of the evaporation energy-saving adaptive control method under multi-parameter analysis, those skilled in the art can clearly understand the evaporation energy-saving adaptive control system under multi-parameter analysis in this embodiment. For the system disclosed in Example 2, since it corresponds to the method disclosed in Example 1, it has corresponding functional modules and beneficial effects. For the relevant parts, please refer to the description of the method part.

[0087] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An adaptive control method for evaporation energy saving under multi-parameter analysis, characterized in that the method include: Obtaining the evaporation control parameter space of the single-effect evaporator, and randomly selecting a plurality of initial evaporation control parameters that satisfy the uniform distribution constraint; Based on the solution properties, environmental parameters and expected concentration index of the brine to be evaporated, performing evaporation efficiency prediction of the multiple initial evaporation control parameters at multiple nodes in a predetermined time zone, obtaining multiple predicted evaporation efficiency sets, and evaluating and obtaining multiple evaporation fitnesses; Taking the evaporation control parameter space as a constraint, optimizing the evaporation control parameters according to the multiple evaporation fitnesses, and outputting the optimal evaporation control parameters, wherein the parameters are optimized according to a dynamic optimization direction and an adaptive optimization step length, and the dynamic optimization direction is an optimal adjustment or an inferior adjustment; In the predetermined time zone, the single-effect evaporator is controlled to perform evaporation operation according to the optimal evaporation control parameter.

2. The evaporation energy-saving adaptive control method under multi-parameter analysis according to claim 1 is characterized in that: The evaporation control parameter space of the single-effect evaporator is obtained, and multiple initial evaporation control parameters that meet the uniform distribution constraint are randomly selected, including: Obtaining an evaporation control parameter space of a single-effect evaporator, wherein the evaporation control parameters include steam supply, liquid inlet flow, liquid outlet flow, steam temperature and steam pressure; randomly selecting a first evaporation control parameter in the evaporation control parameter space and setting it as a first initial evaporation control parameter, and randomly selecting a second evaporation control parameter again, and if the Euclidean distance between the second evaporation control parameter and the first initial evaporation control parameter is greater than a predetermined distance threshold, setting the second evaporation control parameter as a second initial evaporation control parameter; Continue to randomly select a third evaporation control parameter. If the Euclidean distance between the third evaporation control parameter and the first initial evaporation control parameter is greater than the predetermined distance threshold and the Euclidean distance between the third evaporation control parameter and the second initial evaporation control parameter is greater than the predetermined distance threshold, set the third evaporation control parameter as the third initial evaporation control parameter, iterate the selection until a predetermined number is met, and output the multiple initial evaporation control parameters.

3. The evaporation energy-saving adaptive control method under multi-parameter analysis according to claim 1 is characterized in that: Based on the solution properties, environmental parameters and expected concentration index of the brine to be evaporated, the evaporation efficiency prediction of the multiple initial evaporation control parameters at multiple nodes in a predetermined time zone is performed to obtain multiple predicted evaporation efficiency sets, including: Collecting solution properties and environmental parameters of the brine to be evaporated, wherein the solution properties include solution type, initial concentration and initial temperature, and the environmental parameters include ambient temperature and ambient humidity; Taking the solution properties, environmental parameters and expected concentration index as conditional constraints, the property characteristics of the single-effect evaporator as equipment constraints, and evaporation control as a guide, the big data search obtains the sample evaporation control parameter set and the sample evaporation time set, and annotates the evaporation efficiency under different sample evaporation control parameters and different sample evaporation times to obtain the sample evaporation efficiency set; Based on the sample evaporation control parameter set, the sample evaporation time set and the sample evaporation efficiency set, an integrated learning operator is trained until convergence to obtain an evaporation efficiency prediction plug-in, wherein the integrated learning operator includes at least a random forest, a BP neural network and a support vector machine; Determine a plurality of evaporation times according to a plurality of nodes in the predetermined time zone, use the evaporation efficiency prediction plug-in to perform evaporation efficiency prediction of the plurality of initial evaporation control parameters under the plurality of evaporation times, and output a plurality of predicted evaporation efficiency sets, wherein the evaporation time is the time interval between a node and a start time.

4. The evaporation energy-saving adaptive control method under multi-parameter analysis according to claim 1 is characterized in that: Multiple evaporation fitness levels were evaluated, including: Randomly selecting a first predicted evaporation efficiency set, performing mean calculation and variance calculation on the first predicted evaporation efficiency set, and determining a first predicted evaporation efficiency mean and a first predicted evaporation efficiency fluctuation coefficient; A first evaporation fitness is obtained by weighted calculation according to the first predicted evaporation efficiency mean and the first predicted evaporation efficiency fluctuation coefficient, and is added to the multiple evaporation fitnesses, wherein the evaporation fitness is positively correlated with the predicted evaporation efficiency mean and negatively correlated with the predicted evaporation efficiency fluctuation coefficient.

5. The evaporation energy-saving adaptive control method under multi-parameter analysis according to claim 1 is characterized in that: Taking the evaporation control parameter space as a constraint, optimizing the evaporation control parameters according to the multiple evaporation fitnesses, and outputting the optimal evaporation control parameters, including: The initial evaporation control parameters are set as initial solutions, and the multiple initial evaporation control parameters are arranged from large to small according to the multiple evaporation fitnesses to determine an initial solution sequence; The first P solutions of the initial solution sequence are set as optimal solutions, and the last Q solutions are set as inferior solutions, to obtain P optimal solutions and Q inferior solutions, where Q is N times P, P and Q are both integers, and N is an integer greater than 5; Randomly clustering the Q inferior solutions according to the P superior solutions to obtain P solution sets, wherein the number of inferior solutions in each solution set is the same; For the P solution sets, the inferior solution with the smallest fitness in the solution set is set as a differential solution, the fitness of the P differential solutions is obtained, and P dynamic optimization directions are set according to the fitness of the P superior solutions and the fitness of the P differential solutions, wherein the dynamic optimization direction is an optimal adjustment or an inferior adjustment; According to the P dynamic optimization directions, the inferior solutions in the P solution sets are adjusted according to the adaptive optimization step length to obtain P updated solution sets, and the P updated solution sets are identified. If the fitness of the inferior solution in the updated solution set is greater than the fitness of the superior solution, the superior solution in the solution set is replaced by the inferior solution, wherein if the adjusted inferior solution does not satisfy the evaporation control parameter space, this adjustment is not performed; Based on the dynamic optimization direction and the adaptive optimization step size, iterative optimization is continued until a predetermined number of iterations is reached, P current solution sets are output, the current solution set with the largest sum of fitness among the P current solution sets is selected as the optimal solution set, and the optimal solution of the optimal solution set is output as the optimal evaporation control parameter.

6. The evaporation energy-saving adaptive control method under multi-parameter analysis according to claim 5 is characterized in that: According to the fitness of P optimal solutions and P inferior solutions, P dynamic optimization directions are set, including: Randomly select a first solution set from the P solution sets, and obtain a first optimal solution fitness, a first poor solution fitness, and a plurality of first inferior solution fitnesses of the first solution set; Calculate and obtain a first inferior solution fitness mean of the multiple first inferior solution fitnesses, respectively calculate the absolute values ​​of the deviations of the first superior solution fitness, the first differential solution fitness and the first inferior solution fitness mean, and determine a first superior solution deviation value and a first inferior solution deviation value; A first dynamic optimization direction is set according to the first optimal solution deviation value and the first inferior solution deviation value, and is added to the P dynamic optimization directions, wherein if the first optimal solution deviation value is greater than or equal to the first inferior solution deviation value, the first dynamic optimization direction is an optimization-oriented adjustment; if the first optimal solution deviation value is less than the first inferior solution deviation value, the first dynamic optimization direction is an avoidance-oriented adjustment.

7. The evaporation energy-saving adaptive control method under multi-parameter analysis according to claim 6 is characterized in that: Get the adaptation and search for Uber, including: Determining a first optimization fitness of a first direction according to the first dynamic optimization direction, wherein the first optimization fitness is a first optimal solution fitness or a first differential solution fitness; Respectively calculating the ratios of multiple first inferior solution fitnesses to the first optimal solution fitnesses, and determining multiple compensation coefficients, wherein if the first optimal solution fitness is the first optimal solution fitness, the compensation coefficient is the ratio of the first inferior solution fitness to the first optimal solution fitness, and if the first optimal solution fitness is the first differential solution fitness, the compensation coefficient is the ratio of the first differential solution fitness to the first inferior solution fitness; The initial optimization step length is optimized according to the multiple compensation coefficients, a first step length set is determined, and added to the adaptive optimization step length.

8. The evaporation energy-saving adaptive control system under multi-parameter analysis is characterized by: The system is used to execute the evaporation energy-saving adaptive control method under multi-parameter analysis according to any one of claims 1 to 7, comprising: An initial evaporation control parameter selection module is used to obtain the evaporation control parameter space of the single-effect evaporator and randomly select multiple initial evaporation control parameters that meet the uniform distribution constraint; An evaporation efficiency prediction and evaluation module is used to perform evaporation efficiency prediction of the multiple initial evaporation control parameters at multiple nodes in a predetermined time zone based on the solution properties, environmental parameters and expected concentration index of the brine to be evaporated, obtain multiple predicted evaporation efficiency sets, and evaluate and obtain multiple evaporation fitnesses; an evaporation control parameter optimization module, for optimizing the evaporation control parameters according to the plurality of evaporation fitnesses with the evaporation control parameter space as a constraint, and outputting the optimal evaporation control parameters, wherein the parameter optimization is performed according to a dynamic optimization direction and an adaptive optimization step length, and the dynamic optimization direction is an optimal adjustment or an inferior adjustment; The evaporation control module is used to control the single-effect evaporator to perform evaporation operation according to the optimal evaporation control parameters within the predetermined time zone.

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