Method for predicting pH value and iodide ion concentration of iodine-containing wastewater based on deep learning

Abstract

The invention discloses a method for predicting the pH value and iodide ion concentration of iodine-containing wastewater based on deep learning, and belongs to the field of analysis and detection. The method comprises the following steps: constructing a CNN-ResNet-RF prediction model by combining a light fluorescence spectrum with deep learning; the method comprises the following steps: extracting an internal relationship among a pH value, iodide ion concentration and fluorescence lifetime through a deep neural network, performing feature extraction on a one-dimensional spectrum by using a single convolutional layer and a pooling layer, constructing a decision tree through a random forest, performing a regression task according to a result of the decision tree, and outputting a final prediction result. Therefore, the accurate prediction of the pH value and the iodide ion concentration of the iodine-containing wastewater is realized; more efficient and more accurate prediction is realized, and the overall treatment efficiency of sewage treatment is improved.

Description

TECHNICAL FIELD

[0001] The application relates to a deep learning-based prediction method for pH value and iodine ion concentration of iodine-containing wastewater, and belongs to the field of analysis and detection. BACKGROUND

[0002] Traditional iodine-containing wastewater treatment processes include chemical oxidation, adsorption, ion exchange and electrochemistry; the chemical oxidation method utilizes the reducing property of iodine ions, and by adding an oxidizing agent, the iodine ions in the wastewater are oxidized into iodine elements, thereby realizing the removal or recovery of iodine; the adsorption method utilizes the adsorption of adsorbents on iodine ions, and the iodine ions in the wastewater are adsorbed onto the surface of the adsorbents, so that the iodine ions are removed from the wastewater; the ion exchange method utilizes the exchangeable ions on the ion exchange resin to exchange with the iodine ions in the wastewater, and the iodine ions are adsorbed onto the resin, thereby achieving the purpose of removing the iodine ions; the electrochemistry method utilizes electrode reactions to oxidize the iodine ions in the wastewater into iodine elements or reduce them into iodide ions, thereby realizing the removal or recovery of iodine. These methods usually involve multiple steps, one of which is the adjustment of the pH value of the wastewater; the use of chemical reagents to adjust the pH value is crucial to ensure the effectiveness of the subsequent treatment process, because the pH value plays an important role in improving the efficiency of removing different pollutants, the stability of chemical reactions and the performance of biological treatment systems; however, this process requires frequent monitoring of the pH value, which is not only tedious and time-consuming, but also requires frequent human intervention, and the adjustment effect is affected by the professional quality and experience of the operators, and the dosage of the reagent is difficult to accurately control; in addition, the reagent may react with other substances in the wastewater during the pH adjustment process to produce secondary pollutants, resulting in inconsistency and affecting the overall treatment efficiency.

[0003] Therefore, there is an urgent need for a method that can accurately predict the pH value and iodine ion concentration in the wastewater treatment process and thereby improve the efficiency of wastewater treatment. SUMMARY

[0004] In order to solve the problems existing in the prior art, the application provides a deep learning-based prediction method for pH value and iodine ion concentration of iodine-containing wastewater, which combines fluorescence spectrum and deep learning to construct a CNN-ResNet-RF prediction model, extracts the internal relationship between pH value, iodine ion concentration and fluorescence lifetime through a deep neural network, constructs a decision tree through a random forest, and performs a regression task according to the results of the decision tree to output the final prediction results, thereby realizing accurate prediction of the pH value and iodine ion concentration of the iodine-containing wastewater.

[0005] The technical scheme of the application is as follows:

[0006] Step 1: Prepare the fluorescein sodium standard solution with different concentrations of potassium iodide using two groups of PBS buffers with different pH values, respectively, as the experimental sample and the sewage sample, and perform time-resolved fluorescence spectrum detection after shaking for 1 min to obtain the time-resolved fluorescence spectrum of the experimental sample and the time-resolved fluorescence spectrum of the sewage sample, respectively;

[0007] Step 2: Construct a CNN-ResNet-RF prediction model;

[0008] Step 3: Normalize the time-resolved fluorescence spectrum of the experimental sample and the time-resolved fluorescence spectrum of the sewage sample obtained in step 1, and deslope the entire spectrum to obtain the experimental sample data set and the sewage sample data set, respectively;

[0009] Step 4: Train the CNN-ResNet-RF prediction model constructed in step 2 using the experimental sample data set obtained in step 3, and further constrain the training process of the model using a loss function;

[0010] Step 5: Input the sewage sample data set obtained in step 3 into the CNN-ResNet-RF prediction model trained in step 4 to verify the prediction performance of the model;

[0011] Step 6: Input the slope of the normalized sewage time-resolved fluorescence spectrum into the CNN-ResNet-RF prediction model to predict the pH value and the concentration of iodine ions in the sewage.

[0012] The preparation process of the experimental sample in step 1 is as follows: 0.032g, 0.664g, 0.996g, 0.1328g, 0.166g, 0.1992g, 0.2324g, 0.265g, 0.332g, 0.398g, 0.498g, 0.664g, 0.83g, 0.996g, 1.162g and 1.328g of potassium iodide and 0.1mL of fluorescein sodium with a concentration of 2.04mM are mixed thoroughly, and then 100mM PBS buffer with different pH values is used to make up to 4mL, and the mixture is shaken for 1min at room temperature;

[0013] Among them, the pH of the PBS buffer is divided into 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0 and 8.5;

[0014] The preparation process of the sewage sample is as follows: different amounts of potassium iodide and 0.1mL of fluorescein sodium with a concentration of 2.04mM are mixed thoroughly, and then 100mM PBS buffer with different pH values is used to make up to 4mL, and the mixture is shaken for 1min at room temperature;

[0015] Wherein, the pH of PBS buffer is divided into 2.63, 3.7, 5.2, 6.4 and 7.3; the mass of potassium iodide under pH=2.63 is 0g, 0.664g, 0.1328g and 0.265g; the mass of potassium iodide under pH=3.7 is 0g, 0.664g, 0.996g, 0.1992g and 1.162g; the mass of potassium iodide under pH=5.2 is 0g, 0.664g, 0.996g, 0.1992g and 0.398g; the mass of potassium iodide under pH=6.4 is 0g, 0.664g, 0.996g, 0.1992g and 0.398g; the mass of potassium iodide under pH=7.3 is 0g, 0.032g, 0.166g, 0.1992g and 0.83g;

[0016] The time-resolved fluorescence spectrum of the obtained different potassium iodide concentration solutions is detected by time-correlated single photon counting (TCSPC), wherein the excitation wavelength is 470nm and the emission wavelength is 530nm;

[0017] The CNN-ResNet-RF prediction model in step 2 comprises an input layer, a convolution layer, a pooling layer, a full connection layer, a random forest and an output;

[0018] Wherein the size of the convolution layer is 2 and the step is 1; the size of the maximum pooling layer is 2 and the step is 2;

[0019] The normalization method in step 3 is maximum normalization, integral normalization, internal reference normalization or standard sample normalization;

[0020] The expression of the slope in step 3 is:

[0021]

[0022] In step 4, when the loss function converges, the training of the CNN-ResNet-RF prediction model is completed;

[0023] The loss function adopts RMSE (Root Mean Squared Error) to constrain the training of the CNN-ResNet-RF prediction model, and the average value of the square difference between the predicted value and the actual value is calculated, and then the square root is taken, which can give the scale of the error, which is consistent with the original unit of the data; the expression is:

[0024]

[0025] Wherein, y i is the i th actual value, is the i th predicted value, and n is the actual sample quantity.

[0026] The present application has the following beneficial effects:

[0027] The application provides a deep learning-based prediction method for pH value and iodine ion concentration of iodine-containing wastewater, a CNN-ResNet-RF prediction model is constructed by combining optical fluorescence spectrum and deep learning; the internal relationship between pH value, iodine ion concentration and fluorescence lifetime is extracted by a deep neural network, a single-layer convolution layer and a pooling layer are used for feature extraction on one-dimensional spectrum, a decision tree is constructed by a random forest, a regression task is performed according to the result of the decision tree, and finally a prediction result is output, so that the pH value and iodine ion concentration of the iodine-containing wastewater are accurately predicted; the slope of the time-resolved fluorescence spectrum is used to realize more efficient and more accurate prediction, and the overall treatment efficiency of the wastewater treatment is improved. BRIEF DESCRIPTION OF DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0029] Figure 1 The application provides a deep learning-based prediction method for pH value and iodine ion concentration of iodine-containing wastewater, a CNN-ResNet-RF prediction model is constructed by combining optical fluorescence spectrum and deep learning; the internal relationship between pH value, iodine ion concentration and fluorescence lifetime is extracted by a deep neural network, a single-layer convolution layer and a pooling layer are used for feature extraction on one-dimensional spectrum, a decision tree is constructed by a random forest, a regression task is performed according to the result of the decision tree, and finally a prediction result is output, so that the pH value and iodine ion concentration of the iodine-containing wastewater are accurately predicted; the slope of the time-resolved fluorescence spectrum is used to realize more efficient and more accurate prediction, and the overall treatment efficiency of the wastewater treatment is improved.

[0030] Figure 2 The application provides a deep learning-based prediction method for pH value and iodine ion concentration of iodine-containing wastewater, a CNN-ResNet-RF prediction model is constructed by combining optical fluorescence spectrum and deep learning; the internal relationship between pH value, iodine ion concentration and fluorescence lifetime is extracted by a deep neural network, a single-layer convolution layer and a pooling layer are used for feature extraction on one-dimensional spectrum, a decision tree is constructed by a random forest, a regression task is performed according to the result of the decision tree, and finally a prediction result is output, so that the pH value and iodine ion concentration of the iodine-containing wastewater are accurately predicted; the slope of the time-resolved fluorescence spectrum is used to realize more efficient and more accurate prediction, and the overall treatment efficiency of the wastewater treatment is improved.

[0031] Figure 3 The application provides a deep learning-based prediction method for pH value and iodine ion concentration of iodine-containing wastewater, a CNN-ResNet-RF prediction model is constructed by combining optical fluorescence spectrum and deep learning; the internal relationship between pH value, iodine ion concentration and fluorescence lifetime is extracted by a deep neural network, a single-layer convolution layer and a pooling layer are used for feature extraction on one-dimensional spectrum, a decision tree is constructed by a random forest, a regression task is performed according to the result of the decision tree, and finally a prediction result is output, so that the pH value and iodine ion concentration of the iodine-containing wastewater are accurately predicted; the slope of the time-resolved fluorescence spectrum is used to realize more efficient and more accurate prediction, and the overall treatment efficiency of the wastewater treatment is improved. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical scheme and advantages of the present application more clear, the embodiments of the present application will be further described in detail below with reference to the drawings.

[0033] Embodiment one

[0034] The embodiment provides a deep learning-based prediction method for pH value and iodine ion concentration of iodine-containing wastewater, which combines fluorescence spectrum and deep learning, constructs a CNN-ResNet-RF prediction model, extracts the internal relationship among the pH value, iodine ion concentration and fluorescence lifetime through a deep neural network, then constructs a decision tree through a random forest, performs a regression task according to the result of the decision tree, and outputs the final prediction result, so as to realize accurate prediction of the pH value and iodine ion concentration of the iodine-containing wastewater.

[0035] The technical scheme of the present application is as follows:

[0036] Step 1: two groups of PBS buffers with different pH values are used to prepare fluorescein sodium standard solutions with different potassium iodide concentrations, which are used as experimental samples and sewage samples respectively, and time-resolved fluorescence spectrum detection is performed after oscillation for 1 min, so as to obtain the time-resolved fluorescence spectrum of the experimental sample and the time-resolved fluorescence spectrum of the sewage sample respectively;

[0037] Step 2: a CNN-ResNet-RF prediction model is constructed;

[0038] Step 3: the time-resolved fluorescence spectrum of the experimental sample and the time-resolved fluorescence spectrum of the sewage sample obtained in step 1 are normalized, and then the slopes are taken as the experimental sample data set and the sewage sample data set respectively;

[0039] Step 4: the experimental sample data set obtained in step 3 is used to train the CNN-ResNet-RF prediction model constructed in step 2, and the training process of the model is further constrained by using a loss function;

[0040] Step 5: the sewage sample data set obtained in step 3 is input into the CNN-ResNet-RF prediction model trained in step 4 to verify the prediction performance of the model;

[0041] Step 6: the slope of the normalized sewage time-resolved fluorescence spectrum is input into the CNN-ResNet-RF prediction model to predict the pH value and iodine ion concentration of the sewage.

[0042] The preparation process of the experimental sample in step 1 is as follows: 0.032g, 0.664g, 0.996g, 0.1328g, 0.166g, 0.1992g, 0.2324g, 0.265g, 0.332g, 0.398g, 0.498g, 0.664g, 0.83g, 0.996g, 1.162g and 1.328g of potassium iodide and 0.1mL of fluorescein sodium with a concentration of 2.04mM are fully mixed, then 100mM PBS buffer with different pH values is used to make up to 4mL, and oscillation is performed at room temperature for 1min.

[0043] wherein the pH of the PBS buffer is divided into 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0 and 8.5;

[0044] The preparation process of the sewage sample is as follows: different masses of potassium iodide and 0.1 mL of fluorescein sodium with a concentration of 2.04 mM are fully mixed, and then different pH values of PBS buffer with a concentration of 100 mM are used to make up to 4 mL, and the mixture is shaken at room temperature for 1 min;

[0045] wherein the pH of the PBS buffer is divided into 2.63, 3.7, 5.2, 6.4 and 7.3; the mass of potassium iodide at pH = 2.63 is 0 g, 0.664 g, 0.1328 g and 0.265 g; the mass of potassium iodide at pH = 3.7 is 0 g, 0.664 g, 0.996 g, 0.1992 g and 1.162 g; the mass of potassium iodide at pH = 5.2 is 0 g, 0.664 g, 0.996 g, 0.1992 g and 0.398 g; the mass of potassium iodide at pH = 6.4 is 0 g, 0.664 g, 0.996 g, 0.1992 g and 0.398 g; the mass of potassium iodide at pH = 7.3 is 0 g, 0.032 g, 0.166 g, 0.1992 g and 0.83 g;

[0046] The relationship between the lifetime of fluorescein sodium and the concentration of potassium iodide under the condition of PBS buffer pH = 2.5 is shown in Figure 2 The higher the concentration of potassium iodide, the shorter the lifetime of fluorescein sodium, which verifies the quenching phenomenon of iodine ions on fluorescein sodium.

[0047] The time-resolved fluorescence spectrum of the obtained different concentrations of potassium iodide solution is detected by time-correlated single photon counting (TCSPC), wherein the excitation wavelength is 470 nm and the emission wavelength is 530 nm;

[0048] The structure of the CNN-ResNet-RF prediction model in step 2 is shown in Figure 1 , which includes an input layer, a convolutional layer, a pooling layer, a fully connected layer, a random forest and an output;

[0049] The size of the convolutional layer is 2 and the step is 1; the size of the maximum pooling layer is 2 and the step is 2;

[0050] The normalization method in step 3 is maximum normalization, integral normalization, internal reference normalization or standard sample normalization;

[0051] The expression of the slope in step 3 is:

[0052]

[0053] The training of the CNN-ResNet-RF prediction model ends in step 4 when the loss function converges.

[0054] The loss function is used to constrain the training of the CNN-ResNet-RF prediction model, and the RMSE (Root Mean Squared Error) is used to calculate the average value of the square difference between the predicted value and the actual value, and then take the square root, which can give the error scale, and the original unit of the data is consistent; its expression is:

[0055]

[0056] Where y i is the i-th actual value, is the i-th predicted value, and n is the actual sample number.

[0057] Step 6: The slope of the normalized time-resolved fluorescence spectrum of the wastewater to be predicted is input into the CNN-ResNet-RF prediction model to predict the pH value and iodine ion concentration of the wastewater.

[0058] Example Two

[0059] The embodiment provides a deep learning-based prediction method for the pH value and iodine ion concentration of iodine-containing wastewater, which is based on the method described in example one, and the method comprises the following steps:

[0060] Step 1: Time-resolved fluorescence spectrum is obtained by time-resolved fluorescence spectrum detection of iodine-containing wastewater with unknown concentration, and normalization processing is performed;

[0061] Step 2: The slope of the spectrum obtained in step 1 is taken and input into the CNN-ResNet-RF prediction model;

[0062] Step 3: The CNN-ResNet-RF prediction model outputs the pH value and iodine ion concentration of the iodine-containing wastewater.

[0063] The obtained prediction result is shown in Figure 3 As Figure 3 can be seen, the coincidence rate of the predicted value and the true value obtained by the CNN-ResNet-RF prediction model is high, indicating that the CNN-ResNet-RF prediction model has high accuracy.

[0064] In order to verify the accuracy of the method, the prediction performance of the CNN-ResNet-RF prediction model is verified by using the MSE (Mean Square Error), MAE (Mean Absolute Error) and R 2 (determination coefficient);

[0065] The MSE measures the error of the model by calculating the average of the square difference between the predicted value and the actual value, and its expression is:

[0066]

[0067] Wherein, y i represents the actual value, represents the predicted value, and n represents the sample data amount;

[0068] The MAE calculates the average of the absolute error between the predicted value and the actual value, and its expression is:

[0069]

[0070] Wherein, y i represents the actual value, represents the predicted value, and n represents the sample data amount;

[0071] R 2 represents the fitting degree of the model, and the closer the value is to 1, the better the model fitting is, and its expression is:

[0072]

[0073] Wherein, y i represents the actual value, represents the predicted value, n represents the sample data amount, represents the average of the actual value.

[0074] According to the above calculation, the value of MSE is 0.022, the value of MAE is 0.129, and the value of R 2 is 0.991; it is proved that the CNN-ResNet-RF prediction model proposed in the application has good prediction performance.

[0075] Part of the steps in the embodiments of the application can be realized by software, and the corresponding software program can be stored in a readable storage medium, such as an optical disc or a hard disk.

[0076] The above only describes the preferred embodiments of the application, and does not limit the application, and any modification, equivalent replacement, improvement, etc. within the spirit and principle of the application shall be included in the protection scope of the application.

Claims

1. A method for predicting pH value and iodine ion concentration of iodine-containing wastewater based on deep learning, characterized in that: The method comprises: Step 1: Use two sets of PBS buffers with different pH values ​​to prepare sodium fluorescein standard solutions with different potassium iodide concentrations, respectively, as experimental samples and sewage samples, respectively. After shaking, perform time-resolved fluorescence spectroscopy detection to obtain the time-resolved fluorescence spectra of the experimental samples and the time-resolved fluorescence spectra of the sewage samples, respectively. Step 2: Build a CNN-ResNet-RF prediction model; Step 3: Normalize the time-resolved fluorescence spectra of the experimental sample and the time-resolved fluorescence spectra of the sewage sample obtained in step 1, and then take the slopes as the experimental sample dataset and the sewage sample dataset, respectively; Step 4: Use the experimental sample dataset obtained in step 3 to train the CNN-ResNet-RF prediction model constructed in step 2, and use the loss function to constrain the training process of the prediction model; Step 5: Input the sewage sample dataset obtained in step 3 into the CNN-ResNet-RF prediction model trained in step 4 to verify the prediction performance of the model; Step 6: Input the normalized slope of the time-resolved fluorescence spectrum of the sewage to be predicted into the CNN-ResNet-RF prediction model to predict the pH value and iodide ion concentration of the sewage.

2. The method according to claim 1, characterized in that The experimental samples in step 1 are obtained by respectively mixing 0.032 g, 0.664 g, 0.996 g, 0.1328 g, 0.166 g, 0.1992 g, 0.2324 g, 0.265 g, 0.332 g, 0.398 g, 0.498 g, 0.664 g, 0.83 g, 0.996 g, 1.162 g and 1.328 g of potassium iodide with 0.1 mL of 2.04 mM sodium fluorescein solution, then diluting the volume to 4 mL with 100 mM PBS buffer of different pH values, and shaking at room temperature for 1 min; Among them, the pH of PBS buffer is divided into 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0 and 8.5; The sewage samples were prepared by mixing different amounts of potassium iodide and 0.1 mL of 2.04 mM sodium fluorescein, then diluting the volume to 4 mL with 100 mM PBS buffer of different pH values, and shaking at room temperature for 1 minute. Among them, the pH of PBS buffer is divided into 2.63, 3.7, 5.2, 6.4 and 7.3; the mass of potassium iodide at pH = 2.63 is 0g, 0.664g, 0.1328g and 0.265g; the mass of potassium iodide at pH = 3.7 is 0g, 0.664g, 0.996g, 0.1992g and 1.162g; the mass of potassium iodide at pH = 5.2 is 0g, 0.664g, 0.996g, 0.1992g and 0.398g; the mass of potassium iodide at pH = 6.4 is 0g, 0.664g, 0.996g, 0.1992g and 0.398g; the mass of potassium iodide at pH = 7.3 is 0g, 0.032g, 0.166g, 0.1992g, and 0.83g.

3. The method according to claim 2, characterized in that The time-resolved fluorescence spectrum in step 1 is obtained by time-correlated single photon counting, wherein the excitation wavelength is 470 nm and the emission wavelength is 530 nm.

4. The method according to claim 3, characterized in that The CNN-ResNet-RF prediction model in step 2 includes an input layer, a convolutional layer, a pooling layer, a fully connected layer, a random forest, and an output layer; The convolutional layer size is 2 and the stride is 1; the maximum pooling layer size is 2 and the stride is 2.

5. The method according to claim 4, characterized in that The normalization method in step 3 includes one of maximum normalization, integral normalization, internal reference normalization or standard sample normalization.

6. The method according to claim 5, characterized in that In step 4, the loss function is expressed as: Among them, y i is the ith actual value, is the i-th predicted value, n is the actual sample size; When the loss function converges, the training of the CNN-ResNet-RF prediction model is completed.

7. A device for predicting the pH value and iodine ion concentration of iodine-containing wastewater, characterized in that: The device is implemented based on the method according to any one of claims 1 to 6.

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