CN116841269B - Process adjustment method, system and storage medium based on coal tar production flow - Google Patents
Process adjustment method, system and storage medium based on coal tar production flow Download PDFInfo
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- C10C—WORKING-UP PITCH, ASPHALT, BITUMEN, TAR; PYROLIGNEOUS ACID
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Abstract
The invention discloses a process adjustment method, a system and a storage medium based on a coal tar production flow, which relate to the technical field of coal tar production and comprise the following steps: acquiring performance parameters of coal tar raw materials; determining a coal tar processing technology; training and determining a prediction model of a distillate product of a coal tar processing technology; carrying out coal tar production; monitoring the coal tar production process in real time to obtain real-time monitoring data; acquiring real-time process completion data; comprehensively analyzing the quality of each distilled product; and adjusting the subsequent refining processing technology of each distillate product based on the quality of each distillate product. The invention has the advantages that: by combining the operation of workers and comprehensive analysis of process parameters in the coal tar production process, the quality stability of the final product produced by the coal tar can be effectively ensured, and the production and processing of various coal tar with high quality, low energy consumption and high environmental protection are realized.
Description
Technical Field
The invention relates to the technical field of coal tar production, in particular to a process adjustment method, a process adjustment system and a storage medium based on a coal tar production flow.
Background
Coal tar is a black or blackish brown viscous liquid with pungent odor generated during carbonization of coal. The coal tar can be divided into low-temperature coal tar, medium-temperature coal tar and high-temperature coal tar according to carbonization temperature, and the coal tar obtained in coke production belongs to the high-temperature coal tar. It is one of coke oven gas purifying products condensed and separated in the course of cooling raw gas. Coal tar is generally used as a raw material for processing and refining to prepare various chemical products, can be directly used as a material component of a binder for industrial briquette, formed coke and coal activated carbon, and can also be used as a raw material of fuel oil, blast furnace injection fuel, wood preservative oil and carbon black.
Coal tar is a complex mixture of tens of thousands of components from which about 500 or more individual compounds have been isolated and identified. The main research directions of coal tar processing are increasing product variety, improving product quality grade, saving energy and protecting environment, in the process of coal tar fractionation, the operation steps of each procedure can affect the quality of the distilled product, the prior art lacks the comprehensive analysis of the quality of the distilled product aiming at each parameter in the coal tar production flow, and the follow-up refining processing technology is difficult to adjust aiming at the components of the distilled product, so that the quality of the product produced by the coal tar is unstable.
Disclosure of Invention
In order to solve the technical problems, the technical scheme provides a process adjustment method, a system and a storage medium based on a coal tar production flow, and solves the problems that the prior art lacks the quality of a distillation product aiming at comprehensive analysis of various parameters in the coal tar production flow, and is difficult to purposefully adjust a subsequent refining processing process aiming at components of the distillation product, so that the quality of the product produced by the coal tar is unstable.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a process adjustment method based on a coal tar production flow comprises the following steps:
performing performance detection on the coal tar raw material to obtain performance parameters of the coal tar raw material;
determining the grade of the coal tar raw material based on the performance parameters of the coal tar raw material, and determining the coal tar processing technology based on the grade of the coal tar raw material;
determining a prediction model of a distillate product of the coal tar processing technology based on historical processing parameter training of the coal tar processing technology, wherein the prediction model of the distillate product of the coal tar processing technology takes process completion degree data in a coal tar production flow as input and the quality of each distillate product as output;
carrying out coal tar production according to a determined coal tar processing technology;
monitoring the coal tar production process in real time to obtain real-time monitoring data;
comprehensively analyzing and calculating process completion data based on the real-time monitoring data to obtain real-time process completion data;
inputting real-time process completion data into a prediction model of a distillate product of the coal tar processing process, and comprehensively analyzing the quality of each distillate product;
and adjusting the subsequent refining processing technology of each distillate product based on the quality of each distillate product.
Preferably, the process completion degree data calculating method comprises the following steps:
comprehensively calculating the completion weight index of the process procedure based on the acquired real-time operation images of operators in each procedure;
substituting the acquired real-time process parameters of the process and the standard parameters of the process into a parameter fitting degree calculation formula to calculate parameter fitting indexes of the process;
comprehensively calculating the process completion degree of the process procedure according to a completion degree calculation formula based on the completion weight index of the process procedure and the parameter fitting index of the process procedure;
the parameter fitting degree calculation formula specifically comprises the following steps:
wherein C is the process completion of the process, n is the total number of parameters of the process, alpha i Is the important weight value of the ith parameter of the process procedure, x i Real-time process parameter, x, being the ith parameter of the process sequence i0 Is a standard parameter for the ith parameter of the process.
Preferably, the step of performing real-time image comprehensive calculation on the basis of the collected completion weight index of each step specifically includes:
determining a standard operation image of an operator in each procedure, and extracting standard action skeleton points of the operator from the standard operation image;
extracting real-time action skeleton points of an operator from a real-time operation image of the operator;
based on a matching weight formula, calculating the matching weight between the real-time action skeleton points of the operators and the standard action skeleton points of the operators, namely, the completion weight index of the process procedure;
the matching weight formula specifically comprises the following steps:
wherein K is the completion weight index of the process, m is the total number of action skeleton points, and delta (P j ,P j0 ) The value range of the fitting function between the j-th real-time action skeleton point of the operator and the j-th standard action skeleton point of the operator is [0,1 ]]The j-th real-time action skeleton point P of the operator is described as the larger the function value of the fitting function j J standard action skeleton point P with operator j0 The greater the distance between them.
Preferably, the completion degree calculation formula specifically includes:
J＝K×C
wherein J is the process completion of the process.
Preferably, the prediction model for determining the distillate product of the coal tar processing technology based on the historical processing parameter training of the coal tar processing technology specifically comprises the following steps:
randomly retrieving a plurality of model training sample data from historical processing parameters of a coal tar processing technology;
respectively obtaining the components of the distilled products of a plurality of each process based on model training sample data, and the process completion degree of each process corresponding to the components;
training a neural network based on model training sample data, taking the process completion degree of each process procedure as a neural network input layer, taking a distillate product corresponding to each procedure as a neural network output layer, and training node mapping logic of an implicit layer of the neural network to obtain a preliminary training model;
randomly retrieving a plurality of model verification sample data from historical processing parameters of a coal tar processing technology;
and verifying the preliminary training model by using model verification sample data, judging whether the preliminary training model can meet the component quality prediction of the distilled product, if so, taking the preliminary training model as a prediction model of the distilled product of the coal tar processing technology, and if not, returning to randomly retrieving a plurality of model training sample data from the historical processing parameters of the coal tar processing technology, and retraining the preliminary training model.
Preferably, the number of model verification sample data and the number of model training sample data should satisfy:
wherein N is Verification Verifying the number of sample data for the model, N Training The number of sample data is trained for the model.
Preferably, the verifying the preliminary training model with model verification sample data, and judging whether the preliminary training model can meet the component quality prediction of the distillate product specifically includes:
calculating regression determination coefficients of the preliminary training model based on the model verification sample data;
judging whether the regression determination coefficient of the preliminary training model is larger than a preset determination coefficient threshold value, if so, judging that the preliminary training model can meet the component quality prediction of the distilled product, and if not, judging that the preliminary training model cannot meet the component quality prediction of the distilled product;
the calculation formula of the regression determination coefficient is as follows:
wherein R is 2 Determining coefficients for the regression; RSS is the sum of squares of residuals of the preliminary training model; TSS is the sum of the total squares of the preliminary training models.
Further, a process adjustment system based on a coal tar production process is provided, which is used for implementing the process adjustment method based on the coal tar production process, and the process adjustment system comprises:
the raw material detection module is used for detecting the performance of the coal tar raw material and obtaining the performance parameters of the coal tar raw material;
the process determining module is electrically connected with the raw material detecting module and is used for determining the grade of the coal tar raw material based on the performance parameter of the coal tar raw material and determining the coal tar processing process based on the grade of the coal tar raw material;
the storage module is used for storing historical processing parameters of the coal tar processing technology;
the model training module is electrically connected with the storage module and is used for training and determining a prediction model of a distillate product of the coal tar processing technology based on historical processing parameters of the coal tar processing technology;
the real-time monitoring module is used for monitoring the coal tar production flow in real time to obtain real-time monitoring data;
the data analysis module is electrically connected with the real-time monitoring module and is used for comprehensively analyzing and calculating process completion data based on the real-time monitoring data to obtain real-time process completion data;
the prediction module is electrically connected with the model training module and the data analysis module, and is used for inputting real-time process completion data into a prediction model of a distillate product of the coal tar processing process and comprehensively analyzing the quality of each distillate product.
Optionally, the data analysis module is integrated with:
the first calculation unit is used for comprehensively calculating the completion weight index of the process procedure based on the acquired real-time operation images of the operators in each procedure;
the second calculation unit is used for substituting a parameter fitting degree calculation formula to calculate a parameter fitting index of the process based on the acquired real-time process parameters of the process and the standard parameters of the process;
and the third calculation unit is used for comprehensively calculating the process completion degree of the process procedure according to a completion degree calculation formula based on the completion weight index of the process procedure and the parameter fitting index of the process procedure.
Still further, a computer readable storage medium is provided, on which a computer readable program is stored, wherein the computer readable program when called performs the process adjustment method based on the coal tar production process as described above.
Compared with the prior art, the invention has the beneficial effects that:
according to the method, a process adjustment scheme based on a coal tar production flow is provided, process completion data are comprehensively analyzed by combining operation and process parameters of workers in the coal tar production process, a prediction model of a distilled product of the coal tar processing process is determined based on historical processing parameter training of the coal tar processing process, components of the distilled product are predicted by inputting the process completion data, and a data basis is provided for the subsequent refinement processing process of the distilled product in a targeted adjustment mode.
Drawings
FIG. 1 is a flow chart of a process adjustment method based on a coal tar production flow provided by the invention;
FIG. 2 is a flow chart of a method for calculating process completion data according to the present invention;
FIG. 3 is a flowchart of a method for calculating a completion weight index for a comprehensive calculation process in the present invention;
FIG. 4 is a flow chart of a method for training to determine a predictive model of a distillate product of a coal tar processing process in accordance with the present invention;
FIG. 5 is a flow chart of a method of determining whether a preliminary training model can meet the component quality prediction of a distillate product in the present invention;
fig. 6 is a block diagram of a process adjustment system based on a coal tar production process according to the present invention.
Detailed Description
The following description is presented to enable one of ordinary skill in the art to make and use the invention. The preferred embodiments in the following description are by way of example only and other obvious variations will occur to those skilled in the art.
Referring to fig. 1, a process adjustment method based on a coal tar production process includes:
performing performance detection on the coal tar raw material to obtain performance parameters of the coal tar raw material;
determining the grade of the coal tar raw material based on the performance parameters of the coal tar raw material, and determining the coal tar processing technology based on the grade of the coal tar raw material;
determining a prediction model of a distillate product of the coal tar processing technology based on historical processing parameter training of the coal tar processing technology, wherein the prediction model of the distillate product of the coal tar processing technology takes process completion degree data in a coal tar production flow as input and the quality of each distillate product as output;
carrying out coal tar production according to a determined coal tar processing technology;
monitoring the coal tar production process in real time to obtain real-time monitoring data;
comprehensively analyzing and calculating process completion data based on the real-time monitoring data to obtain real-time process completion data;
inputting real-time process completion data into a prediction model of a distillate product of the coal tar processing process, and comprehensively analyzing the quality of each distillate product;
and adjusting the subsequent refining processing technology of each distillate product based on the quality of each distillate product.
According to the scheme, the operation of workers and the process parameters in the coal tar production process are combined to comprehensively analyze process completion data, a prediction model of a distillation product of the coal tar processing process is determined based on historical processing parameter training of the coal tar processing process, components of the distillation product are predicted by inputting process completion data, and a data basis is provided for the subsequent refinement processing process of the distillation product in a targeted adjustment mode, so that the component stability of the distillation product in refinement processing can be effectively guaranteed.
Referring to fig. 2, the process completion data calculating method includes:
comprehensively calculating the completion weight index of the process procedure based on the acquired real-time operation images of operators in each procedure;
substituting the acquired real-time process parameters of the process and the standard parameters of the process into a parameter fitting degree calculation formula to calculate parameter fitting indexes of the process;
comprehensively calculating the process completion degree of the process procedure according to a completion degree calculation formula based on the completion weight index of the process procedure and the parameter fitting index of the process procedure;
the parameter fitting degree calculation formula specifically comprises the following steps:
wherein C is the process completion of the process, n is the total number of parameters of the process, alpha i Is the important weight value of the ith parameter of the process procedure, x i Real-time process parameter, x, being the ith parameter of the process sequence i0 Is a standard parameter for the ith parameter of the process.
Based on the vector distance between the real-time parameter and the standard parameter in the coal tar production process, comprehensively calculating the parameter fitting index of the process procedure, wherein the larger the parameter fitting index of the process procedure is, the closer the parameter representing the process procedure is to the standard parameter of the process procedure, and the higher the parameter completion degree of the process procedure is.
Referring to fig. 3, the method specifically includes the steps of calculating the completion weight index of the process procedure based on the collected real-time operation image of the operator in each procedure:
determining a standard operation image of an operator in each procedure, and extracting standard action skeleton points of the operator from the standard operation image;
extracting real-time action skeleton points of an operator from a real-time operation image of the operator;
based on a matching weight formula, calculating the matching weight between the real-time action skeleton points of the operators and the standard action skeleton points of the operators, namely, the completion weight index of the process procedure;
the matching weight formula specifically comprises the following steps:
wherein K is the completion weight index of the process, m is the total number of action skeleton points, and delta (P j ,P j0 ) For the fitting function between the j-th real-time action skeleton point of the operator and the j-th standard action skeleton point of the operator, the value range of the fitting function is [0,1]The j-th real-time action skeleton point P of the operator is described as the function value of the fitting function is larger j J standard action skeleton point P with operator j0 The greater the distance between them.
The completion degree calculation formula is specifically as follows:
J＝K×C
wherein J is the process completion of the process.
It can be understood that during the coal tar production process, the operation of the staff can also affect the quality of each distilled product, when the staff performs the operation in the coal tar production process, the more the action flow approaches to the standard action flow, the less the impurities in the distilled products, and the higher the quality of the distilled products, so in the scheme, the matching weight between the real-time action skeleton point of the staff and the standard action skeleton point of the staff is calculated by adopting a skeleton point matching mode, and specifically, the skeleton point can select one or more of the left ear, the left eye, the left shoulder, the left elbow, the left head, the left waist, the left hip, the left knee, the left foot head, the right ear, the right eye, the right shoulder, the right elbow, the right head, the right waist, the right hip, the left knee and the right foot head according to the actual operation action.
Referring to fig. 4, the prediction model for determining the distillate product of the coal tar processing technology based on the historical processing parameter training of the coal tar processing technology specifically includes:
randomly retrieving a plurality of model training sample data from historical processing parameters of a coal tar processing technology;
respectively obtaining the components of the distilled products of a plurality of each process based on model training sample data, and the process completion degree of each process corresponding to the components;
training a neural network based on model training sample data, taking the process completion degree of each process procedure as a neural network input layer, taking a distillate product corresponding to each procedure as a neural network output layer, and training node mapping logic of an implicit layer of the neural network to obtain a preliminary training model;
randomly retrieving a plurality of model verification sample data from historical processing parameters of a coal tar processing technology;
and verifying the preliminary training model by using model verification sample data, judging whether the preliminary training model can meet the component quality prediction of the distilled product, if so, taking the preliminary training model as a prediction model of the distilled product of the coal tar processing technology, and if not, returning to randomly retrieving a plurality of model training sample data from the historical processing parameters of the coal tar processing technology, and retraining the preliminary training model.
In the embodiment, a prediction Model of a distillate product of the coal tar processing technology is trained by adopting a training neural computing network Model, and an artificial neural network (Artificial Neural Networks, abbreviated as ANNs) is also called a Neural Network (NNs) or a Connection Model (Connection Model) for short, which is an algorithm mathematical Model for simulating the behavior characteristics of an animal neural network and carrying out distributed parallel information processing; according to the scheme, the prediction model of the distillate products of the coal tar processing technology is obtained through training by comprehensively analyzing the process completion degree data and the internal correlation of the distillate products of each process through the operation of statistical staff and the process parameters, and a calculation basis is provided for the components of the distillate products subsequently.
It will be appreciated that in other embodiments, other ways of training predictive models of distillate products of coal tar processing may be selected.
The number of model verification sample data and the number of model training sample data should satisfy:
wherein N is Verification Verifying the number of sample data for the model, N Training The number of sample data is trained for the model.
Referring to fig. 5, verifying the preliminary training model with model verification sample data, determining whether the preliminary training model can satisfy component quality prediction of the distillate product specifically includes:
calculating regression determination coefficients of the preliminary training model based on the model verification sample data;
judging whether the regression determination coefficient of the preliminary training model is larger than a preset determination coefficient threshold value, if so, judging that the preliminary training model can meet the component quality prediction of the distilled product, and if not, judging that the preliminary training model cannot meet the component quality prediction of the distilled product;
the calculation formula of the regression determination coefficient is as follows:
wherein R is 2 Determining coefficients for the regression; RSS is the sum of squares of residuals of the preliminary training model; TSS is a preliminary training modelSum of total squares of the patterns.
Regression determination coefficient R 2 As a relative measure of interpretation variance, a larger value represents a closer relationship between the cross-linking represented by the preliminary training model and the cross-linking in reality, in particular, if R 2 =0.7, then 70% of the representative model validation sample data satisfies the preliminary training model, and in some embodiments, the determination coefficient threshold is set to 0.85.
Further, referring to fig. 6, based on the same inventive concept as the process adjustment method based on the coal tar production process, the present disclosure further provides a process adjustment system based on the coal tar production process, including:
the raw material detection module is used for detecting the performance of the coal tar raw material and obtaining the performance parameters of the coal tar raw material;
the process determining module is electrically connected with the raw material detecting module and is used for determining the grade of the coal tar raw material based on the performance parameter of the coal tar raw material and determining the coal tar processing process based on the grade of the coal tar raw material;
the storage module is used for storing historical processing parameters of the coal tar processing technology;
the model training module is electrically connected with the storage module and is used for training and determining a prediction model of a distillate product of the coal tar processing technology based on historical processing parameters of the coal tar processing technology;
the real-time monitoring module is used for monitoring the coal tar production flow in real time to obtain real-time monitoring data;
the data analysis module is electrically connected with the real-time monitoring module and is used for comprehensively analyzing and calculating process completion data based on the real-time monitoring data to acquire real-time process completion data;
the prediction module is electrically connected with the model training module and the data analysis module, and is used for inputting real-time process completion data into a prediction model of a distillate product of the coal tar processing process and comprehensively analyzing the quality of each distillate product.
Wherein, the data analysis module is integrated with:
the first calculation unit is used for comprehensively calculating the completion weight index of the process procedure based on the acquired real-time operation images of the operators in each procedure;
the second calculation unit is used for substituting a parameter fitting degree calculation formula into the parameter fitting degree calculation formula based on the acquired real-time process parameters of the process and the standard parameters of the process, and calculating the parameter fitting index of the process;
and the third calculation unit is used for comprehensively calculating the process completion degree of the process procedure according to a completion degree calculation formula based on the completion weight index of the process procedure and the parameter fitting index of the process procedure.
The use process of the process adjustment system based on the coal tar production flow comprises the following steps:
step one: the raw material detection module detects the performance of the coal tar raw material to obtain the performance parameter of the coal tar raw material, and the process determination module determines the grade of the coal tar raw material based on the performance parameter of the coal tar raw material and determines the coal tar processing process based on the grade of the coal tar raw material;
step two: the model training module is used for calling historical processing parameters of the coal tar processing technology from the storage module and training and determining a prediction model of a distillate product of the coal tar processing technology based on the historical processing parameters of the coal tar processing technology;
step three: the real-time monitoring module monitors the coal tar production process in real time to obtain real-time monitoring data;
step four: the data analysis module comprehensively analyzes and calculates process completion data based on the real-time monitoring data to obtain the real-time process completion data, wherein the process completion data analysis and calculation specifically comprises the following steps: the first calculation unit calculates the completion weight index of the process procedure based on the collected real-time operation image of the operator of each procedure; the second calculation unit substitutes a parameter fitting degree calculation formula into the acquired real-time process parameters of the process and standard parameters of the process to calculate parameter fitting indexes of the process; the third calculation unit comprehensively calculates the process completion degree of the process procedure according to a completion degree calculation formula based on the completion weight index of the process procedure and the parameter fitting index of the process procedure;
step five: the prediction module inputs real-time process completion data into a prediction model of a distillate product of the coal tar processing process, and comprehensively analyzes components of each distillate product.
Still further, the present invention also provides a computer readable storage medium, on which a computer readable program is stored, which executes the above-mentioned process adjustment method based on the coal tar production process when being called;
it is understood that the computer readable storage medium may be a magnetic medium, e.g., floppy disk, hard disk, tape; optical media such as DVD; or a semiconductor medium such as a solid state disk SolidStateDisk, SSD, etc.
In summary, the invention has the advantages that: by combining the operation of workers and comprehensive analysis of process parameters in the coal tar production process, the quality stability of the final product produced by the coal tar can be effectively ensured, and the production and processing of various coal tar with high quality, low energy consumption and high environmental protection are realized.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made therein without departing from the spirit and scope of the invention, which is defined by the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (7)
1. The process adjustment method based on the coal tar production flow is characterized by comprising the following steps of:
performing performance detection on the coal tar raw material to obtain performance parameters of the coal tar raw material;
determining the grade of the coal tar raw material based on the performance parameters of the coal tar raw material, and determining the coal tar processing technology based on the grade of the coal tar raw material;
determining a prediction model of a distillate product of the coal tar processing technology based on historical processing parameter training of the coal tar processing technology, wherein the prediction model of the distillate product of the coal tar processing technology takes process completion degree data in a coal tar production flow as input and the quality of each distillate product as output;
carrying out coal tar production according to a determined coal tar processing technology;
monitoring the coal tar production process in real time to obtain real-time monitoring data;
comprehensively analyzing and calculating process completion data based on the real-time monitoring data to obtain real-time process completion data;
inputting real-time process completion data into a prediction model of a distillate product of the coal tar processing process, and comprehensively analyzing the quality of each distillate product;
adjusting the subsequent refinement processing technology of each distillate product based on the quality of each distillate product;
the calculation method of the process completion degree data comprises the following steps:
comprehensively calculating the completion weight index of the process procedure based on the acquired real-time operation images of operators in each procedure;
substituting the acquired real-time process parameters of the process and the standard parameters of the process into a parameter fitting degree calculation formula to calculate parameter fitting indexes of the process;
comprehensively calculating the process completion degree of the process procedure according to a completion degree calculation formula based on the completion weight index of the process procedure and the parameter fitting index of the process procedure;
the parameter fitting degree calculation formula specifically comprises the following steps:
wherein C is the process completion of the process, n is the total number of parameters of the process, alpha i Is the ith parameter of the processImportant weight value of number, x i Real-time process parameter, x, being the ith parameter of the process sequence i0 Standard parameters for the ith parameter of the process step;
the method specifically comprises the following steps of performing real-time operation on the completion weight index of the image comprehensive calculation process procedure by an operator based on each acquired procedure:
determining a standard operation image of an operator in each procedure, and extracting standard action skeleton points of the operator from the standard operation image;
extracting real-time action skeleton points of an operator from a real-time operation image of the operator;
based on a matching weight formula, calculating the matching weight between the real-time action skeleton points of the operators and the standard action skeleton points of the operators, namely, the completion weight index of the process procedure;
the matching weight formula specifically comprises the following steps:
wherein K is the completion weight index of the process, m is the total number of action skeleton points, and delta (P j ,P j0 ) The value range of the fitting function between the j-th real-time action skeleton point of the operator and the j-th standard action skeleton point of the operator is [0,1 ]]The j-th real-time action skeleton point P of the operator is described as the larger the function value of the fitting function j J standard action skeleton point P with operator j0 The greater the distance between them;
the completion degree calculation formula specifically comprises:
J＝K×C
wherein J is the process completion of the process.
2. The process adjustment method based on the coal tar production process according to claim 1, wherein the prediction model for determining the distillate product of the coal tar processing process based on the historical processing parameter training of the coal tar processing process specifically comprises:
randomly retrieving a plurality of model training sample data from historical processing parameters of a coal tar processing technology;
respectively obtaining the components of the distilled products of a plurality of each process based on model training sample data, and the process completion degree of each process corresponding to the components;
training a neural network based on model training sample data, taking the process completion degree of each process procedure as a neural network input layer, taking a distillate product corresponding to each procedure as a neural network output layer, and training node mapping logic of an implicit layer of the neural network to obtain a preliminary training model;
randomly retrieving a plurality of model verification sample data from historical processing parameters of a coal tar processing technology;
and verifying the preliminary training model by using model verification sample data, judging whether the preliminary training model can meet the component quality prediction of the distilled product, if so, taking the preliminary training model as a prediction model of the distilled product of the coal tar processing technology, and if not, returning to randomly retrieving a plurality of model training sample data from the historical processing parameters of the coal tar processing technology, and retraining the preliminary training model.
3. The process adjustment method based on the coal tar production process according to claim 2, wherein the number of model verification sample data and the number of model training sample data should satisfy:
wherein N is Verification Verifying the number of sample data for the model, N Training The number of sample data is trained for the model.
4. The process adjustment method based on the coal tar production process according to claim 3, wherein verifying the preliminary training model with model verification sample data, and judging whether the preliminary training model can meet the component quality prediction of the distillate product specifically comprises:
calculating regression determination coefficients of the preliminary training model based on the model verification sample data;
judging whether the regression determination coefficient of the preliminary training model is larger than a preset determination coefficient threshold value, if so, judging that the preliminary training model can meet the component quality prediction of the distilled product, and if not, judging that the preliminary training model cannot meet the component quality prediction of the distilled product;
the calculation formula of the regression determination coefficient is as follows:
wherein R is 2 Determining coefficients for the regression; RSS is the sum of squares of residuals of the preliminary training model; TSS is the sum of the total squares of the preliminary training models.
5. A process adjustment system based on a coal tar production process for implementing the process adjustment method based on a coal tar production process according to any one of claims 1 to 4, comprising:
the raw material detection module is used for detecting the performance of the coal tar raw material and obtaining the performance parameters of the coal tar raw material;
the process determining module is electrically connected with the raw material detecting module and is used for determining the grade of the coal tar raw material based on the performance parameter of the coal tar raw material and determining the coal tar processing process based on the grade of the coal tar raw material;
the storage module is used for storing historical processing parameters of the coal tar processing technology;
the model training module is electrically connected with the storage module and is used for training and determining a prediction model of a distillate product of the coal tar processing technology based on historical processing parameters of the coal tar processing technology;
the real-time monitoring module is used for monitoring the coal tar production flow in real time to obtain real-time monitoring data;
the data analysis module is electrically connected with the real-time monitoring module and is used for comprehensively analyzing and calculating process completion data based on the real-time monitoring data to obtain real-time process completion data;
the prediction module is electrically connected with the model training module and the data analysis module, and is used for inputting real-time process completion data into a prediction model of a distillate product of the coal tar processing process and comprehensively analyzing the quality of each distillate product.
6. The process adjustment system based on a coal tar production process according to claim 5, wherein the data analysis module is internally integrated with:
the first calculation unit is used for comprehensively calculating the completion weight index of the process procedure based on the acquired real-time operation images of the operators in each procedure;
the second calculation unit is used for substituting a parameter fitting degree calculation formula to calculate a parameter fitting index of the process based on the acquired real-time process parameters of the process and the standard parameters of the process;
and the third calculation unit is used for comprehensively calculating the process completion degree of the process procedure according to a completion degree calculation formula based on the completion weight index of the process procedure and the parameter fitting index of the process procedure.
7. A computer-readable storage medium having a computer-readable program stored thereon, wherein the computer-readable program when invoked performs the coal tar production process-based process adjustment method according to any one of claims 1 to 4.
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