Method and system for enhancing degradation performance prediction precision of Fenton-like system

Abstract

The invention discloses a method and system for enhancing Fenton-like system degradation performance prediction precision, and relates to the technical field of environmental protection, and the method comprises the following steps: obtaining data of biochar characteristics, organic matter physical descriptors and Fenton-like system degradation performance lnkSA, and constructing a parameter data set; dividing the parameter data set into a training set and a test set according to a preset proportion; training different machine learning models by using the training set, and determining model parameters; and performing prediction performance evaluation on the two machine learning models by using the test set, and finally obtaining an optimal model for predicting the degradation performance of the Fenton-like system. According to the method, the accuracy of predicting the performance of the biochar / PMS system by the model can be improved, the trial and error probability of the traditional experiment is greatly reduced, and the consumption of manpower, material resources and time is saved.

Description

Technical Field

[0001] The present invention relates to the field of environmental protection technology, and more specifically to a method and system for enhancing the prediction accuracy of degradation performance of a Fenton-like system. Background Art

[0002] As an artificial intelligence technology, machine learning has attracted widespread attention in the environmental field because it is good at revealing complex and hidden relationships that are often overlooked by traditional analytical methods. Recently, researchers have applied machine learning technology to biochar / PMS systems to enhance the degradation of organic matter. However, after a series of model optimization (data optimization, model screening, feature engineering, cross-validation, hyperparameter adjustment), the R of the model test set was 2 When it is greater than 0.8, it is a better prediction model, which means that there is still a prediction error of about 20%. Therefore, it is very important to improve the accuracy of the prediction model of the degradation performance of the biochar / PMS system.

[0003] In addition to the above-mentioned methods for improving accuracy that have been used in the construction of the biochar / PMS system performance prediction model, the accuracy can be further improved by designing a new model. However, building a new model may require algorithm development, coding, debugging and optimization from scratch, requiring developers to have a deep theoretical foundation and be able to handle complex mathematical problems and algorithms in model design. This is a very challenging task.

[0004] The study showed that introducing unexplored features that may be relevant to the prediction of results is beneficial to improving the performance of the model, and the active sites on the surface of biochar directly affect its catalytic activity. However, in the study of machine learning to enhance the performance of biochar / PMS system, researchers used C, O, N element content or O / C ratio to represent the active sites on the surface of biochar, which may be the cause of model errors.

[0005] Therefore, it is necessary to re-examine the model for predicting the performance of the biochar / PMS system constructed with elements as input characteristics, and it is urgent to further explore the accuracy of the model in predicting the degradation performance of the biochar / PMS system when active sites are used as input characteristics. Summary of the invention

[0006] In view of this, the present invention provides a method and system for enhancing the prediction accuracy of degradation performance of a Fenton-like system, which solves the problems existing in the background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solutions:

[0008] In one aspect, the present invention provides a method for enhancing the prediction accuracy of degradation performance of a Fenton-like system, comprising the following steps:

[0009] Obtain biochar characteristics, physical descriptors of organic matter and degradation performance of Fenton-like systems lnk SA , construct parameter data set;

[0010] Divide the parameter data set into a training set and a test set according to a preset ratio;

[0011] Use the training set to train different machine learning models and determine the model parameters;

[0012] The prediction performance of the two machine learning models was evaluated using the test set, and finally the best model for predicting the degradation performance of the Fenton-like system was obtained.

[0013] Preferably, biochar characteristics, physical descriptors of organic matter and Fenton-like degradation performance lnk are obtained SA The data are as follows:

[0014] A variety of different biochars were prepared by pyrolysis modification, and the properties of the biochars were obtained by characterization methods;

[0015] The biochar was used to activate persulfate to degrade different organic matter under different catalyst concentrations to obtain the reaction rate constant k. The specific surface area was used to normalize k to obtain k SA , evaluate the degradation performance of the Fenton-like reaction system.

[0016] Preferably, the parameter data set includes a first data set and a second data set; wherein the first data set is C=C content, CN content, OC=O content, C=O content, CO content, pyridine nitrogen content, pyrrole nitrogen content, graphite nitrogen content, defect value I D / I G , the energy of the highest occupied molecular orbital E HOMO is the input feature, lnk SA as the target variable; the second data set is C content, O content, N content, C / N, C / O, defect value I D / I G , the energy of the highest occupied molecular orbital E HOMO is the input feature, lnk SA is the target variable.

[0017] Preferably, the ratio of the training set to the test set is 8:2.

[0018] Preferably, different machine learning models are trained using the training set, specifically:

[0019] Using the SVR algorithm, the first and second data sets were used to construct a model for predicting the performance of the biochar / PMS system, and the input variables were determined based on the evaluation indicators;

[0020] The training set was used to determine the hyperparameters and establish the relationship between biochar characteristics, organic matter structure and the degradation performance of the biochar / PMS system.

[0021] Preferably, the hyperparameters include: random_state, kernel, C, epsilon, and tol.

[0022] Preferably, when using the test set to evaluate the predictive performance of the two machine learning models respectively, the test set is used to test the performance of the model, and the accuracy of the model is evaluated by the evaluation index.

[0023] Preferably, the evaluation indicators include mean absolute error, root mean square error and determination coefficient.

[0024] On the other hand, the present invention also provides a system for enhancing the prediction accuracy of degradation performance of a Fenton-like system, which is used to implement any of the above-mentioned methods for enhancing the prediction accuracy of degradation performance of a Fenton-like system, comprising a data set construction module, a partitioning module, a training module and a testing module connected in sequence;

[0025] Dataset building blocks for obtaining biochar characteristics, physical descriptors of organic matter, and Fenton-like degradation performance lnk SA , construct parameter data set;

[0026] A partitioning module is used to divide the parameter data set into a training set and a test set according to a preset ratio;

[0027] The training module is used to train different machine learning models using the training set and determine the model parameters;

[0028] The test module is used to evaluate the prediction performance of the two machine learning models using the test set, and finally obtain the best model for predicting the degradation performance of the Fenton-like system.

[0029] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a method and system for enhancing the prediction accuracy of degradation performance of a Fenton-like system, which has the following beneficial effects:

[0030] Using active sites instead of element ratios as input features can improve the accuracy of the model in predicting the performance of the biochar / PMS system, help to further clarify the structure-activity relationship of biochar, greatly reduce the probability of traditional experimental trial and error, and save manpower, material resources and time. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0032] Figure 1 A flow chart of a method for enhancing the prediction accuracy of degradation performance of a Fenton-like system provided by the present invention;

[0033] Figure 2 The present invention provides the C content, O content, N content, C / N, C / O, I D / I G 、E HOMO Schematic diagram of the prediction of the performance of the biochar / PMS system by the model constructed for the input parameters;

[0034] Figure 3 The present invention provides a carbon nanotube with C=C content, CN content, OC=O content, C=O content, CO content, pyridine nitrogen content, pyrrole nitrogen content, graphite nitrogen content, I D / I G 、E HOMO Schematic diagram of the prediction of the performance of the biochar / PMS system by the model constructed for the input parameters;

[0035] Figure 4 A schematic diagram of the importance of input parameters provided by the present invention using a data set containing active sites as input;

[0036] Figure 5 A structural block diagram of a system for enhancing the prediction accuracy of degradation performance of a Fenton-like system provided by the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] The embodiment of the present invention discloses a method for enhancing the prediction accuracy of degradation performance of a Fenton-like system, such as Figure 1 As shown, the following steps are included:

[0039] Obtain biochar characteristics, physical descriptors of organic matter and degradation performance of Fenton-like systems lnk SA , construct parameter data set;

[0040] Divide the parameter data set into a training set and a test set according to a preset ratio;

[0041] Use the training set to train different machine learning models and determine the model parameters;

[0042] The prediction performance of the two machine learning models was evaluated using the test set, and finally the best model for predicting the degradation performance of the Fenton-like system was obtained.

[0043] Furthermore, the biochar characteristics, physical descriptors of organic matter and degradation performance of Fenton-like system lnk were obtained SA The data are as follows:

[0044] A variety of different biochars were prepared by pyrolysis modification, and the properties of the biochars were obtained by characterization methods;

[0045] The biochar was used to activate persulfate to degrade different organic matter under different catalyst concentrations to obtain the reaction rate constant k. The specific surface area was used to normalize k to obtain k SA , evaluate the degradation performance of the Fenton-like reaction system.

[0046] Furthermore, the parameter data set includes a first data set and a second data set; wherein the first data set is based on C=C content, CN content, OC=O content, C=O content, CO content, pyridine nitrogen content, pyrrole nitrogen content, graphite nitrogen content, defect value I D / I G , the energy of the highest occupied molecular orbital E HOMO is the input feature, lnk SA as the target variable; the second data set is C content, O content, N content, C / N, C / O, defect value I D / I G , the energy of the highest occupied molecular orbital E HOMO is the input feature, lnk SA is the target variable.

[0047] Furthermore, the division ratio of the training set and the test set is 8:2.

[0048] Furthermore, the training set is used to train different machine learning models, specifically:

[0049] Using the SVR algorithm, the first and second data sets were used to construct a model for predicting the performance of the biochar / PMS system, and the input variables were determined based on the evaluation indicators;

[0050] The training set was used to determine the hyperparameters and establish the relationship between biochar characteristics, organic matter structure and the degradation performance of the biochar / PMS system.

[0051] Furthermore, the hyperparameters include: random_state, kernel, C, epsilon, and tol.

[0052] Furthermore, when the prediction performance of the two machine learning models is evaluated using the test set, the performance of the model is tested using the test set, and the accuracy of the model is evaluated using the evaluation index.

[0053] Furthermore, the evaluation indicators include mean absolute error, root mean square error and coefficient of determination.

[0054] Next, the technical solution of the present invention is further described by means of specific implementation modes in combination with the accompanying drawings. It should be noted that the following specific embodiments are only used as examples, and the protection scope of the present invention is not limited thereto.

[0055] Specific implementation method 1: A method for enhancing the prediction accuracy of degradation performance of a Fenton-like system in this implementation method is carried out by the following steps:

[0056] (1) Eight different biochars were prepared by pyrolysis modification, and the biochar properties were obtained by characterization. The prepared biochar catalysts were used to activate PMS to degrade pollutants with different structures, and the first-order rate constant k was obtained. The specific surface area was used to normalize k to obtain k SA , set it as the target variable;

[0057] (2) According to the characteristics of biochar, it can be divided into element-related, active site-related and I D / I G . C content, O content, N content, C / N, C / O, I D / I G 、E HOMO As an input parameter, use lnk SA Construct a data set (A data set) with input variables;

[0058] (3) Using the SVR algorithm, the prediction model is constructed using the A dataset and the input variables are determined based on the evaluation indicators;

[0059] (4) Dataset A is randomly divided into training set and test set in a ratio of 8:2, with the training set accounting for 80% and the test set accounting for 20%;

[0060] (5) Using the SVR model and the training set to determine the hyperparameters, the biochar / PMS system lnk SA For output, the relationship between biochar characteristics, organic matter structure and degradation performance of biochar / PMS system was established;

[0061] (6) Use the test set to test the performance of the model and use evaluation indicators to evaluate the accuracy of the model.

[0062] Figure 2 In this embodiment, C content, O content, N content, C / N, C / O, I D / I G 、E HOMO As input parameters, the predicted biochar / PMS system lnk was constructed SA Model, RMSE, MAE and R of the training set 2 The RMSE, MAE and R of the test set are 0.64, 0.30 and 0.74 respectively. 2 They are 0.79, 0.44 and 0.66 respectively, so the model has low accuracy.

[0063] Specific implementation method 2: A method for enhancing the prediction accuracy of degradation performance of a Fenton-like system in this implementation method is carried out by the following steps:

[0064] (1) Eight different biochars were prepared by pyrolysis modification, and the biochar properties were obtained by characterization. The prepared biochar catalysts were used to activate PMS to degrade pollutants with different structures, and the first-order rate constant k was obtained. The specific surface area was used to normalize k to obtain k SA , set it as the target variable;

[0065] (2) According to the characteristics of biochar, it can be divided into element-related, active site-related and I D / I G . C=C content, CN content, OC=O content, C=O content, CO content, pyridine nitrogen content, pyrrole nitrogen content, graphite nitrogen content, I D / I G 、E HOMO As an input parameter, use lnk SA Construct a data set (E data set) with input variables;

[0066] (3) Using the SVR algorithm, the prediction model is constructed using the E dataset and the input variables are determined based on the evaluation indicators;

[0067] (4) The E dataset is randomly divided into a training set and a test set in a ratio of 8:2, with the training set accounting for 80% and the test set accounting for 20%;

[0068] (5) Using the SVR model and the training set to determine the hyperparameters, the biochar / PMS system lnk SA For output, the relationship between biochar characteristics, organic matter structure and degradation performance of biochar / PMS system was established;

[0069] (6) Use the test set to test the performance of the model and use evaluation indicators to evaluate the accuracy of the model.

[0070] Figure 3In this embodiment, the C=C content, CN content, OC=O content, C=O content, CO content, pyridinic nitrogen content, pyrrolic nitrogen content, graphite nitrogen content, I D / I G 、E HOMO As input parameters, the predicted biochar / PMS system lnk was constructed SA Model, RMSE, MAE and R of the training set 2 The RMSE, MAE and R of the test set are 0.19, 0.13 and 0.98 respectively. 2 They are 0.35, 0.23 and 0.94 respectively, so the model has high accuracy.

[0071] Figure 4 The effects of different input parameters on the biochar / PMS system lnk in the E dataset SA The feature importance diagram shows that C = C has a significant effect on lnk SA The most important, followed by CO and CN.

[0072] Reference Figure 5 As shown, the present invention also provides a system for enhancing the prediction accuracy of degradation performance of a Fenton-like system, the system is used to implement any of the above-mentioned methods for enhancing the prediction accuracy of degradation performance of a Fenton-like system, comprising a data set construction module, a partitioning module, a training module and a testing module connected in sequence;

[0073] Dataset building blocks for obtaining biochar characteristics, physical descriptors of organic matter, and Fenton-like degradation performance lnk SA , construct parameter data set;

[0074] A partitioning module is used to divide the parameter data set into a training set and a test set according to a preset ratio;

[0075] The training module is used to train different machine learning models using the training set and determine the model parameters;

[0076] The test module is used to evaluate the prediction performance of the two machine learning models using the test set, and finally obtain the best model for predicting the degradation performance of the Fenton-like system.

[0077] At present, in the process of using machine learning to predict the pollutant degradation performance of the biochar / PMS system, element content and element ratio are used as input features, which reduces the accuracy of the model. Different from this, this embodiment uses active sites instead of elements as input features to improve the accuracy of the model in predicting the performance of the biochar / PMS system, which helps to further clarify the structure-activity relationship of biochar, greatly reduces the probability of traditional experimental trial and error, and saves manpower, material resources and time.

[0078] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0079] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one 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 invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for enhancing the prediction accuracy of degradation performance of a Fenton-like system, characterized in that: The following steps are involved: Obtain biochar characteristics, physical descriptors of organic matter and degradation performance of Fenton-like systems lnk SA , construct parameter data set; Divide the parameter data set into a training set and a test set according to a preset ratio; Use the training set to train different machine learning models and determine the model parameters; The prediction performance of the two machine learning models was evaluated using the test set, and finally the best model for predicting the degradation performance of the Fenton-like system was obtained.

2. A method for enhancing the prediction accuracy of degradation performance of a Fenton-like system according to claim 1, characterized in that: Obtain biochar characteristics, physical descriptors of organic matter and degradation performance of Fenton-like systems lnk SA The data are as follows: A variety of different biochars were prepared by pyrolysis modification, and the properties of the biochars were obtained by characterization methods; The biochar was used to activate persulfate to degrade different organic matter under different catalyst concentrations to obtain the reaction rate constant k. The specific surface area was used to normalize k to obtain k SA , evaluate the degradation performance of the Fenton-like reaction system.

3. The method for enhancing the prediction accuracy of degradation performance of a Fenton-like system according to claim 1, characterized in that: The parameter data set includes a first data set and a second data set; wherein the first data set is C=C content, CN content, OC=O content, C=O content, CO content, pyridine nitrogen content, pyrrole nitrogen content, graphite nitrogen content, defect value I D / I G , the energy of the highest occupied molecular orbital E HOMO is the input feature, lnk SA as the target variable; the second data set is C content, O content, N content, C / N, C / O, defect value I D / I G , the energy of the highest occupied molecular orbital E HOMO is the input feature, lnk SA is the target variable.

4. The method for enhancing the prediction accuracy of degradation performance of a Fenton-like system according to claim 1, characterized in that: The ratio of training set to test set is 8:

2.

5. The method for enhancing the prediction accuracy of degradation performance of a Fenton-like system according to claim 3, characterized in that: Use the training set to train different machine learning models, specifically: Using the SVR algorithm, the first and second data sets were used to construct a model for predicting the performance of the biochar / PMS system, and the input variables were determined based on the evaluation indicators; The training set was used to determine the hyperparameters and establish the relationship between biochar characteristics, organic matter structure and the degradation performance of the biochar / PMS system.

6. A method for enhancing the prediction accuracy of degradation performance of a Fenton-like system according to claim 5, characterized in that: Hyperparameters include: random_state, kernel, C, epsilon, and tol.

7. The method for enhancing the prediction accuracy of degradation performance of a Fenton-like system according to claim 1, characterized in that: When using the test set to evaluate the prediction performance of the two machine learning models respectively, the test set is used to test the performance of the model, and the evaluation index is used to evaluate the accuracy of the model.

8. A method for enhancing the prediction accuracy of degradation performance of a Fenton-like system according to claim 7, characterized in that: The evaluation indicators include mean absolute error, root mean square error and determination coefficient.

9. A system for enhancing the prediction accuracy of degradation performance of Fenton-like systems, characterized in that: A method for enhancing the prediction accuracy of degradation performance of a Fenton-like system as claimed in any one of claims 1 to 8, comprising a data set construction module, a partitioning module, a training module and a testing module connected in sequence; Dataset building blocks for obtaining biochar characteristics, physical descriptors of organic matter, and Fenton-like degradation performance lnk SA , construct parameter data set; A partitioning module is used to divide the parameter data set into a training set and a test set according to a preset ratio; The training module is used to train different machine learning models using the training set and determine the model parameters; The test module is used to evaluate the prediction performance of the two machine learning models using the test set, and finally obtain the best model for predicting the degradation performance of the Fenton-like system.

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