Dynamic tubular continuous nitration process simulation device and simulation system

Abstract

The invention discloses a dynamic tubular continuous nitrification process simulation device and simulation system, and relates to the technical field of process simulation, and the method comprises the following steps: building a simulation nitrification training channel; acquiring a first nitrification control parameter, inputting the first nitrification control parameter into a simulated nitrification training channel, performing simulated nitrification simulation, outputting a first nitrification speed, first nitrification quality and first nitrification temperature distribution, and calculating to obtain a first nitrification fitness; when the first nitrification fitness does not meet the training requirement, calculating a score matching degree of nitrification training scores; optimizing nitrification training scores to obtain optimal nitrification training scores, displaying the optimal nitrification training scores to operators, continuing to perform nitrification process control training until training requirements are met, and completing nitrification process simulation training. The technical problems that in the prior art, nitration process parameters and fitness matching cannot be efficiently evaluated, and dynamic optimization is difficult to achieve are solved, and the technical effect of improving control adaptability is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of process simulation, and in particular to a dynamic tubular continuous nitration process simulation device and a simulation system. Background Art

[0002] With the continuous improvement of industrial production and environmental protection requirements, the dynamic tubular continuous nitrification process has been widely used in water treatment, chemical production and other fields. However, with the increase in process complexity and the expansion of production scale, the control and management of the nitrification process are facing severe challenges. Traditional nitrification process control methods usually use static or experience-driven means, which have problems such as low control accuracy, insufficient parameter matching, and poor dynamic response ability. It is difficult to adapt to the requirements of modern industrial production for efficiency, accuracy and real-time performance. Summary of the invention

[0003] The present application provides a dynamic tubular continuous nitrification process simulation device and a simulation system, which are used to solve the technical problem that the prior art cannot efficiently evaluate the matching between nitrification process parameters and fitness and is difficult to achieve dynamic optimization.

[0004] In view of the above problems, the present application provides a dynamic tubular continuous nitration process simulation device and simulation system.

[0005] In a first aspect of the present application, a dynamic tubular continuous nitration process simulation device is provided, the device comprising:

[0006] A training channel building module, wherein the training channel building module obtains basic process parameters of a tubular continuous nitrification process and builds a simulated nitrification training channel; a simulated nitrification simulation module, wherein the simulated nitrification simulation module obtains a first nitrification control parameter input by an operator, inputs the simulated nitrification training channel, performs a simulated nitrification simulation, outputs a first nitrification rate, a first nitrification mass and a first nitrification temperature distribution, and calculates and obtains a first nitrification fitness; a score matching degree calculation module, wherein when the first nitrification fitness does not meet the training requirements, the score matching degree calculation module randomly generates a nitrification training score, and calculates the score matching degree of the nitrification training score in combination with the first nitrification fitness; and a simulation training module, wherein the simulation training module optimizes the nitrification training score, obtains the optimal nitrification training score, displays it to the operator, and continues the nitrification process control training until the training requirements are met and the nitrification process simulation training is completed.

[0007] The second aspect of the present application provides a dynamic tubular continuous nitration process simulation system, the system comprising:

[0008] The memory is used to store executable instructions; the processor is used to implement a dynamic tubular continuous nitration process simulation device provided by the present application when executing the executable instructions stored in the memory.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] The present application obtains the basic process parameters of the tubular continuous nitrification process, builds a simulated nitrification training channel; obtains the first nitrification control parameter input by the operator, inputs the simulated nitrification training channel, performs simulated nitrification simulation, outputs the first nitrification speed, the first nitrification mass and the first nitrification temperature distribution, and calculates and obtains the first nitrification fitness; when the first nitrification fitness does not meet the training requirements, randomly generates a nitrification training score, and calculates the score matching degree of the nitrification training score in combination with the first nitrification fitness; optimizes the nitrification training score, obtains the optimal nitrification training score, displays it to the operator, and continues to perform nitrification process control training until the training requirements are met and the nitrification process simulation training is completed. The present invention solves the technical problem that the prior art cannot efficiently evaluate the matching of nitrification process parameters and fitness, and is difficult to achieve dynamic optimization. Through the simulated nitrification training channel, combined with the control parameters input by the operator, the nitrification process is dynamically simulated and the fitness is evaluated. For situations that do not meet the requirements, the training score is randomly generated and iteratively optimized to obtain the optimal training score until the training requirements are met, and the accurate simulation and dynamic optimization of nitrification process training are achieved, and the technical effect of improving control adaptability is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1 A schematic diagram of the structure of a dynamic tubular continuous nitration process simulation device provided in an embodiment of the present application;

[0013] Figure 2 This is a schematic diagram of the structure of a dynamic tubular continuous nitration process simulation system exemplified in this application.

[0014] Explanation of the reference numerals: training channel building module 11, simulation nitration simulation module 12, score matching degree calculation module 13, simulation training module 14, bus 300, receiver 301, processor 302, transmitter 303, memory 304, bus interface 305. DETAILED DESCRIPTION

[0015] The present application provides a dynamic tubular continuous nitrification process simulation device and simulation system to solve the technical problem that the prior art cannot efficiently evaluate the matching between nitrification process parameters and fitness and is difficult to achieve dynamic optimization. By simulating the nitrification training channel and combining the control parameters input by the operator, the nitrification process is dynamically simulated and the fitness is evaluated. For situations that do not meet the requirements, training scores are randomly generated and iteratively optimized to obtain the optimal training score until the training requirements are met, thereby achieving accurate simulation and dynamic optimization of nitrification process training and achieving the technical effect of improving control adaptability.

[0016] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0017] It should be noted that any variations of the terms "include" and "have" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.

[0018] Embodiment 1, as Figure 1 As shown, the present application provides a dynamic tubular continuous nitration process simulation device, the device comprising:

[0019] The training channel building module 11 obtains the basic process parameters of the tubular continuous nitrification process and builds a simulated nitrification training channel.

[0020] Furthermore, in the device provided in the embodiment of the application, the training channel building module 11 is also used for:

[0021] The basic process parameters of the tubular continuous nitrification process are obtained; according to the basic process parameters, a set of sample nitrification control parameters of the same tubular continuous nitrification process in the historical time is retrieved, as well as the speed, mass and temperature distribution of the nitrification process under the control of different sample nitrification control parameters, which are annotated as a sample nitrification speed set, a sample nitrification mass set and a sample nitrification temperature distribution set, wherein each sample nitrification temperature distribution includes the temperatures at multiple positions of the reaction pipeline; the sample nitrification control parameter set is used as simulation input data, and the sample nitrification speed set, the sample nitrification mass set and the sample nitrification temperature distribution set are used as simulation supervision data; the simulation input data and simulation supervision data are used to construct and train a nitrification training channel by using machine learning to obtain a nitrification training channel.

[0022] In an embodiment of the present application, the training channel construction module first extracts the basic process parameters of the tubular continuous nitration process from the historical database, including the geometric structure of the reaction pipeline (such as length, diameter), material properties, and operating conditions (such as temperature range, pressure range), etc.

[0023] Then, based on the basic process parameters obtained, relevant sample data of the same tubular continuous nitration process in the historical time are retrieved to construct a multidimensional sample set. Specifically, it includes a sample nitration control parameter set, which records the control parameter values ​​used in different historical operations, such as reactant concentration, reactant flow rate and input temperature; a sample nitration rate set, which reflects the nitration rate under the corresponding control parameter conditions, to reveal the kinetic characteristics of the nitration reaction; a sample nitration quality set, which is the quality data of the product under different process conditions, that is, the purity of the product; and a sample nitration temperature distribution set, which records the temperature distribution at multiple locations of the reaction pipeline to describe the thermodynamic characteristics and heat transfer characteristics of the reaction. The speed, mass and temperature distribution of the nitration process under the control of different sample nitration control parameters are marked as a sample nitration speed set, a sample nitration mass set and a sample nitration temperature distribution set.

[0024] After the data preparation is completed, these sample data are used to construct the input and supervision data sets of the simulation model. Specifically, the sample nitrification control parameter set is used as the simulation input data, and the sample nitrification rate set, sample nitrification mass set and sample nitrification temperature distribution set are used as the simulation supervision data. Then, machine learning techniques such as random forest are used to train these data. The model learns the complex mapping relationship between nitrification control parameters and nitrification rate, mass and temperature distribution to construct a simulation training channel that can accurately predict the reaction results. After the training is completed, the nitrification training channel is obtained.

[0025] The simulated nitrification simulation module 12 obtains the first nitrification control parameter input by the operator, inputs the simulated nitrification training channel, performs simulated nitrification simulation, outputs the first nitrification speed, the first nitrification quality and the first nitrification temperature distribution, and calculates and obtains the first nitrification fitness.

[0026] Furthermore, in the device provided in the embodiment of the application, the simulated nitrification simulation module 12 is also used for:

[0027] The first nitrification control parameter input by the operator who is performing operation training is obtained; the first nitrification control parameter is input into the simulated nitrification training channel, simulated nitrification is simulated, and a first nitrification rate, a first nitrification mass and a first nitrification temperature distribution are output; and a first nitrification fitness is calculated according to the first nitrification rate, the first nitrification mass and the first nitrification temperature distribution.

[0028] In the embodiment of the present application, the simulated nitration simulation module first obtains the first nitration control parameters input by the operator according to the training target, including reactant flow rate, reactant concentration, reaction temperature, etc. Then, the first nitration control parameters are sent as input to the simulated nitration training channel to perform simulated nitration simulation. The simulated nitration training channel calculates according to the input control parameters and outputs the first nitration rate, the first nitration mass and the first nitration temperature distribution.

[0029] Finally, based on the pre-introduced formula, the first nitrification rate, the first nitrification quality and the first nitrification temperature distribution are calculated to obtain the first nitrification fitness.

[0030] Furthermore, in the device provided in the embodiment of the application, the simulated nitrification simulation module 12 is also used for:

[0031] The average of the temperatures at multiple locations within the first nitrification temperature distribution is calculated to obtain a first average temperature; and the first nitrification fitness is calculated according to the first nitrification rate, the first nitrification quality and the first average temperature, as shown in the following formula:

[0032] ;

[0033] Among them, XHF is the nitrification fitness, , and is the weight, is the nitrification rate, is the preset nitrification rate, Q is the nitrification quality, is the average temperature, is the preset temperature.

[0034] In the embodiment of the present application, firstly, a statistical analysis is performed on the temperatures at multiple locations within the first nitration temperature distribution, and the mean thereof is calculated to obtain a first average temperature.

[0035] Next, the first nitrification rate, the first nitrification mass and the first average temperature are substituted into the formula to calculate and determine the first nitrification fitness. The formula is ; Among them, XHF is the nitrification fitness, , and The weights are pre-set by technical experts according to their importance. is the nitrification rate, is the preset nitrification rate, Q is the nitrification quality, is the average temperature, is the preset temperature. and Also set by technical experts.

[0036] The score matching degree calculation module 13 randomly generates a nitrification training score when the first nitrification fitness does not meet the training requirement, and calculates the score matching degree of the nitrification training score in combination with the first nitrification fitness.

[0037] In the embodiment of the present application, the score matching calculation module first compares the first nitrification fitness with the preset nitrification fitness threshold to determine whether the first nitrification fitness is greater than the preset nitrification fitness threshold. If it is less than the preset nitrification fitness threshold, it is considered that the first nitrification fitness does not meet the training requirements. At this time, a nitrification training score is randomly generated, where the nitrification training score is 1 to 100.

[0038] Then, the ratio of the nitrification training score to the maximum training score in the training score interval is calculated, that is, the ratio of the nitrification training score to 100 is calculated to obtain the score distribution coefficient.

[0039] Then, the nitrification control data in the historical time in the historical database is obtained, and the nitrification fitness interval is obtained by calculation and processing. Then, the ratio of the first nitrification fitness to the maximum nitrification fitness in the nitrification fitness interval is calculated to obtain the fitness distribution coefficient.

[0040] Finally, the difference between the score distribution coefficient and the fitness distribution coefficient is calculated to obtain the deviation percentage, and the deviation percentage is subtracted from 1 to obtain the score matching degree. For example, the difference between the score distribution coefficient and the fitness distribution coefficient can also be calculated, and then the fitness distribution coefficient is removed to obtain the deviation percentage, and finally the deviation percentage is subtracted from 1 to obtain the score matching degree.

[0041] Furthermore, in the device provided in the embodiment of the application, the score matching degree calculation module 13 is also used for:

[0042] It is determined whether the first nitrification fitness is greater than or equal to a preset nitrification fitness threshold. If so, the training requirement is met; if not, the training requirement is not met; when the training requirement is not met, a nitrification training score is randomly generated within a training score interval; and a score matching degree of the nitrification training score is calculated in combination with the first nitrification fitness.

[0043] In the embodiment of the present application, the first nitrification fitness is first compared with the preset nitrification fitness threshold. If the first nitrification fitness is greater than or equal to the preset nitrification fitness threshold, it is determined that the current training requirements have been met, the nitrification process has reached the preset standard, and no further adjustment is required; if the first nitrification fitness is less than the threshold, it is determined that the current fitness does not meet the training requirements.

[0044] When the fitness does not meet the training requirements, a nitration training score is randomly generated within the training score range (usually 1 to 100).

[0045] Then, the score matching degree is calculated by combining the first nitrification temperature distribution and the first nitrification fitness. When calculating the score matching degree, the score distribution coefficient is first obtained by calculating the ratio of the randomly generated nitrification training score to the maximum value (100) in the training score interval. The score distribution coefficient represents the relative level of the current training score in the entire training range and is used to quantify the intensity of the training target. Then, the nitrification fitness interval (i.e., the historical fitness range) is calculated through the historical nitrification process data, and the first nitrification fitness is calculated by calculating the ratio of the first nitrification fitness to the maximum value of the fitness interval to obtain the fitness distribution coefficient, which is used to evaluate the historical performance of the current fitness.

[0046] Finally, by calculating the difference between the score distribution coefficient and the fitness distribution coefficient, the deviation percentage is obtained, which indicates the degree of difference between the current score and the fitness. The final score matching degree is calculated by "1 minus the deviation percentage".

[0047] Furthermore, in the device provided in the embodiment of the application, the score matching degree calculation module 13 is also used for:

[0048] The ratio of the nitrification training score to the maximum training score in the training score interval is calculated to obtain a score distribution coefficient; the nitrification fitness interval is obtained by calculation and processing according to the nitrification control data in the historical time; the ratio of the first nitrification fitness to the maximum nitrification fitness in the nitrification fitness interval is calculated to obtain a fitness distribution coefficient; the deviation percentage between the score distribution coefficient and the fitness distribution coefficient is calculated, and the score matching degree is obtained by subtracting the deviation percentage from 1.

[0049] In the embodiment of the present application, first, the score distribution coefficient is calculated, that is, the ratio of the randomly generated nitration training score to the maximum training score in the training score interval. The maximum value of the training score interval is usually 100, and the score distribution coefficient is obtained by dividing the nitration training score by the maximum training score in the training score interval.

[0050] Next, the nitrification fitness of each set of data is calculated based on the nitrification control data in the historical time. Specifically, the nitrification fitness interval is obtained by calculating in the same way as the above calculation to obtain the first nitrification fitness.

[0051] Then, the first nitrification fitness is calculated to have a ratio with the maximum fitness in the nitrification fitness interval to obtain a fitness distribution coefficient. Specifically, the first nitrification fitness is divided by the maximum fitness in the nitrification fitness interval to obtain a fitness distribution coefficient.

[0052] Then subtract the fitness distribution coefficient from the score distribution coefficient to get the deviation percentage. Finally, subtract the deviation percentage from 1 to get the score matching degree.

[0053] The simulation training module 14 optimizes the nitrification training score, obtains the optimal nitrification training score, displays it to the operator, and continues the nitrification process control training until the training requirements are met and the nitrification process simulation training is completed.

[0054] In the embodiment of the present application, the simulation training module first continues to randomly generate nitrification training scores, and calculates the matching degree of each generated score. Subsequently, iterative optimization is performed based on the score matching degree, and the training score is adjusted to gradually approach the optimal state until the score matching degree converges. Finally, the training score with the highest score matching degree is output as the optimal nitrification training score, and it is displayed to the operator.

[0055] After the operator views the optimal nitrification training score, the operator continues to receive new nitrification control parameters and repeats the training process in combination with the simulation results until the parameters input by the operator meet the training requirements and the nitrification process simulation training is completed.

[0056] Furthermore, in the device provided in the embodiment of the application, the simulation training module 14 is also used for:

[0057] Continue to randomly generate nitrification training scores, calculate score matching, perform iterative optimization of nitrification training scores until convergence, output the nitrification training score with the largest score matching, and obtain the optimal nitrification training score; display the optimal nitrification training score to the operator; continue to receive nitrification control parameters input by the operator until the training requirements are met and the nitrification process simulation training is completed.

[0058] In the embodiment of the present application, firstly, a nitration training score (ranging from 1 to 100) is randomly generated, and then the score matching degree is obtained by calculating using the same method as described above.

[0059] After that, new nitration training scores are continuously randomly generated, the score matching is calculated one by one, and the training scores and matching results of each round are recorded for optimization and screening. In each iteration, based on the changing trend of matching, the direction and range of generated training scores are adjusted to gradually approach the optimal result.

[0060] The key to the optimization process is convergence judgment. When the change in score matching in multiple consecutive iterations (such as 5 rounds) is less than the set threshold (such as 0.001), the optimization process is considered to have reached a convergence state and no new training scores will be randomly generated. At the same time, the training score with the highest matching degree is selected from all the recorded training scores as the optimal nitration training score under the current process conditions.

[0061] After that, the optimal nitrification training score is displayed to the operator in a visual form. Then, the new nitrification control parameters input by the operator are continuously received. The corresponding nitrification training score is recalculated according to the new nitrification control parameters and compared with the preset nitrification training score. If the calculated nitrification training score is greater than or equal to the preset nitrification training score, it is considered that the training requirements have been met and the nitrification process simulation training is completed; otherwise, the above process is repeated, and optimization and training are continued until the preset requirements are met.

[0062] In the embodiments of the present application, in summary, the embodiments of the present application have at least the following technical effects:

[0063] The present application obtains the basic process parameters of the tubular continuous nitrification process, builds a simulated nitrification training channel; obtains the first nitrification control parameter input by the operator, inputs the simulated nitrification training channel, performs simulated nitrification simulation, outputs the first nitrification speed, the first nitrification mass and the first nitrification temperature distribution, and calculates and obtains the first nitrification fitness; when the first nitrification fitness does not meet the training requirements, randomly generates a nitrification training score, and calculates the score matching degree of the nitrification training score in combination with the first nitrification fitness; optimizes the nitrification training score, obtains the optimal nitrification training score, displays it to the operator, and continues to perform nitrification process control training until the training requirements are met and the nitrification process simulation training is completed. The present invention solves the technical problem that the prior art cannot efficiently evaluate the matching of nitrification process parameters and fitness, and is difficult to achieve dynamic optimization. Through the simulated nitrification training channel, combined with the control parameters input by the operator, the nitrification process is dynamically simulated and the fitness is evaluated. For situations that do not meet the requirements, the training score is randomly generated and iteratively optimized to obtain the optimal training score until the training requirements are met, and the accurate simulation and dynamic optimization of nitrification process training are achieved, and the technical effect of improving control adaptability is achieved.

[0064] Embodiment 2, based on the same inventive concept as a dynamic tubular continuous nitration process simulation device in the aforementioned embodiment, the present application also provides a dynamic tubular continuous nitration process simulation system, comprising: at least one processor; a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the steps of any one of the devices described in the aforementioned embodiment 1.

[0065] Figure 2 This is a schematic diagram of a dynamic tubular continuous nitration process simulation system for this application. Figure 2 In the embodiment, the bus architecture is represented by bus 300, which may include any number of interconnected buses and bridges, and bus 300 connects various circuits including one or more processors represented by processor 302 and memory represented by memory 304. Bus 300 may also connect various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. Bus interface 305 provides an interface between bus 300 and receiver 301 and transmitter 303. Receiver 301 and transmitter 303 may be the same element, namely a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 302 is responsible for managing bus 300 and general processing, while memory 304 may be used to store data used by processor 302 when performing operations.

[0066] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0067] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0068] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A dynamic tubular continuous nitration process simulation device, characterized in that: The device comprises: A training channel building module, wherein the training channel building module obtains basic process parameters of a tubular continuous nitrification process and builds a simulated nitrification training channel; A simulated nitrification simulation module, wherein the simulated nitrification simulation module obtains a first nitrification control parameter input by an operator, inputs the simulated nitrification training channel, performs a simulated nitrification simulation, outputs a first nitrification speed, a first nitrification quality and a first nitrification temperature distribution, and calculates and obtains a first nitrification fitness; a score matching degree calculation module, wherein when the first nitrification fitness does not meet the training requirement, the score matching degree calculation module randomly generates a nitrification training score, and calculates the score matching degree of the nitrification training score in combination with the first nitrification fitness; A simulation training module is used to optimize the nitrification training score, obtain the optimal nitrification training score, display it to the operator, and continue the nitrification process control training until the training requirements are met and the nitrification process simulation training is completed.

2. The dynamic tubular continuous nitration process simulation device according to claim 1, characterized in that: Training channel building module, used for: Obtain the basic process parameters of tubular continuous nitration process; According to the basic process parameters, a set of sample nitration control parameters of the same tubular continuous nitration process in historical time is retrieved, as well as the speed, quality and temperature distribution of the nitration process under the control of different sample nitration control parameters, which are marked as a sample nitration speed set, a sample nitration quality set and a sample nitration temperature distribution set, wherein each sample nitration temperature distribution includes the temperatures at multiple positions of the reaction pipeline; The sample nitrification control parameter set is used as simulation input data, and the sample nitrification rate set, the sample nitrification quality set and the sample nitrification temperature distribution set are used as simulation supervision data; The simulation input data and the simulation supervision data are used, and machine learning is utilized to construct and train a nitrification training channel to obtain a nitrification training channel.

3. The dynamic tubular continuous nitration process simulation device according to claim 1, characterized in that: Nitration simulation module for: Acquire a first nitrification control parameter input by an operator undergoing operation training; Inputting the first nitrification control parameter into a simulated nitrification training channel, performing simulated nitrification simulation, and outputting a first nitrification rate, a first nitrification mass, and a first nitrification temperature distribution; A first nitrification fitness is obtained by calculation according to the first nitrification rate, the first nitrification quality and the first nitrification temperature distribution.

4. The dynamic tubular continuous nitration process simulation device according to claim 3, characterized in that: Nitration simulation module for: Calculating the average of the temperatures at a plurality of locations within the first nitrification temperature distribution to obtain a first average temperature; According to the first nitrification rate, the first nitrification mass and the first average temperature, the first nitrification fitness is calculated as follows: ； Among them, XHF is the nitrification fitness, , and is the weight, is the nitrification rate, is the preset nitrification rate, Q is the nitrification quality, is the average temperature, is the preset temperature.

5. The dynamic tubular continuous nitration process simulation device according to claim 1, characterized in that: Score matching calculation module, used for: Determine whether the first nitrification fitness is greater than or equal to a preset nitrification fitness threshold, if so, the training requirement is met, if not, the training requirement is not met; When the training requirements are not met, a nitration training score is randomly generated within the training score range; The score matching degree of the nitrification training score is calculated in combination with the first nitrification temperature distribution and the first nitrification fitness.

6. The dynamic tubular continuous nitration process simulation device according to claim 5, characterized in that: Score matching calculation module, used for: Calculating the ratio of the nitration training score to the maximum training score in the training score interval to obtain a score distribution coefficient; According to the nitrification control data in the historical period, the nitrification fitness range is obtained by calculation and processing; Calculating the ratio of the first nitrification fitness to the maximum nitrification fitness in the nitrification fitness interval to obtain a fitness distribution coefficient; The deviation percentage between the score distribution coefficient and the fitness distribution coefficient is calculated, and the score matching degree is obtained by subtracting the deviation percentage from 1.

7. The dynamic tubular continuous nitration process simulation device according to claim 1, characterized in that: Simulation training modules, including: Continue to randomly generate nitrification training scores, calculate the score matching degree, perform iterative optimization of the nitrification training scores until convergence, output the nitrification training score with the largest score matching degree, and obtain the optimal nitrification training score; Displaying the optimal nitrification training score to the operator; Continue to receive nitrification control parameters input by the operator until the training requirements are met and the nitrification process simulation training is completed.

8. A dynamic tubular continuous nitration process simulation system, characterized in that: The system comprises: A memory for storing executable instructions; The processor is used to implement a dynamic tubular continuous nitration process simulation device according to any one of claims 1 to 7 when executing the executable instructions stored in the memory.

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