A quantitative detection and control method for flame stability of incinerator

Abstract

The invention discloses a quantitative detection and control method for the flame stability of an incinerator, comprising: at an incinerator site, using an industrial camera to continuously shoot a certain number of flame images, dividing the flame images into two categories of stable and unstable states according to the experience of operators, and marking them, so as to realize data collection; after completing the image data collection, filtering the shot flame images to obtain a binary image, and using an image processing algorithm to extract a flame characteristic parameter sequence; when measuring the flame, shooting the flame of the incinerator in actual combustion, performing grayscale processing on the image after the shooting is completed, and after the image is converted into a binary image, comparing the image with the data in a model and a database, so as to obtain a judgment result, and providing an intelligent adjustment strategy in combination with the data model, thereby realizing the ability to intelligently detect the flame of the incinerator and derive a control strategy based on the detection data.

Description

Technical Field

[0001] The invention relates to the technical field of incinerator flame detection, and in particular to a method for quantitatively detecting and regulating the flame stability of an incinerator. Background Art

[0002] The combustion state of the furnace flame is an important indicator for evaluating the combustion system. The safe operation of the incinerator is inseparable from the monitoring of the flame combustion state in the incinerator. With the rise and development of artificial intelligence, the current evaluation methods for image-based flame combustion states are concentrated on machine learning models, but the performance of machine learning models depends largely on the optimization of model hyperparameters. Adjusting hyperparameters requires experience and domain knowledge, which is a time-consuming and complicated process. In addition, most models only perform qualitative classification and divide the combustion state into two states: stable and unstable.

[0003] However, the existing flame control method has the following shortcomings:

[0004] Existing flame stability control methods mostly remain at the stage of prompting the model output flame stability. For operators, they still need to judge how to adjust the parameters and the adjustment range based on experience. The adjustment strategy may have the limitation of single parameter control, and it is easy to ignore the mutual influence between multiple parameters, resulting in low adjustment efficiency.

[0005] Therefore, we proposed a quantitative detection and control method for incinerator flame stability in order to solve the problems raised above. Summary of the invention

[0006] The purpose of the present invention is to provide a quantitative detection and control method for the flame stability of an incinerator. By establishing a detection model and a database, when measuring the flame, the flame of the incinerator in actual combustion is photographed, and after the shooting is completed, the image is gray-processed. After the image is converted into a binary image, the image is compared with the data in the model and the database to obtain a judgment result, and an intelligent adjustment strategy is given in combination with the data model, thereby realizing the ability to intelligently detect the flame of the incinerator and derive a control strategy based on the detection data, so as to solve the problems raised by the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solution: a method for quantitatively detecting and controlling the flame stability of an incinerator, comprising:

[0008] S1. At the incinerator site, use an industrial camera to continuously shoot a certain number of flame images, divide the flame images into two categories: stable and unstable states according to the operator's experience, and mark them to achieve data collection;

[0009] S2. After completing the image data acquisition, filter the captured flame image, convert the filtered flame image into a grayscale image, segment the grayscale image to obtain a binary image, and use an image processing algorithm to extract a flame characteristic parameter sequence;

[0010] S3. After the image data is processed, the data set is divided into a training set and a validation set and put into the random forest model for training. The whale optimization algorithm is used to optimize the hyperparameters of the random forest model to obtain the best training model.

[0011] S4. Complete the model establishment, input the test image, first process the test image to obtain the processed image and flame characteristic parameters, then predict the test image according to the best random forest model to obtain the judgment result, and combine the Shap interpretative method to make a comprehensive multi-parameter intelligent adjustment strategy to achieve the judgment and control suggestions on the flame stability of the incinerator.

[0012] Preferably, the industrial camera used for image acquisition is fixedly installed on the roof of the incinerator, and the flame image in the incinerator is acquired from the roof.

[0013] Preferably, the collected images need to include stable and unstable flame images of the incinerator, and the images are clearly visible. The operator can judge the flame combustion status according to the DCS data and CEMS data at the corresponding time of the flame image, including feed amount, dry-wet feed ratio, auxiliary oil gun flow, alkali solution spraying amount, oxygen content in the furnace, dilute phase temperature, dense phase temperature, NO x Emissions, SO2 emissions, and CO emissions are determined based on whether each parameter exceeds the parameter variation range under normal operating conditions.

[0014] Preferably, after the flame image is collected, the captured flame image needs to be subjected to Gaussian filtering, with a filter kernel size of 5, and the grayscale image is segmented using a bimodal method to determine the optimal segmentation grayscale threshold, including the following steps:

[0015] S1, converting the flame grayscale image into a grayscale level statistical graph;

[0016] S2. Define the peak of the grayscale statistical graph as the position where the frequency corresponding to the current grayscale is the largest among the frequencies corresponding to the previous and next 25 grayscales, and the valley as the position where the frequency between two adjacent peaks is the smallest;

[0017] S3. Compare the frequencies corresponding to all valleys. The grayscale level corresponding to the valley with the minimum frequency is the optimal segmentation grayscale threshold. At the same time, the grayscale threshold must be no less than 50.

[0018] Preferably, the extracted flame characteristic parameter sequence includes three categories: geometric parameters, brightness parameters and thermodynamic parameters; the geometric parameters include flame centroid, flame effective area ratio and flame high temperature area ratio; the brightness parameters include average grayscale, grayscale standard deviation and non-uniformity; the thermodynamic parameters include maximum temperature, average temperature and flicker frequency.

[0019] Preferably, the data set includes the aforementioned DCS data, CEMS data, flame characteristic parameters and flame stability calibration state data, which are divided into a training set and a test set and put into a random forest model for training, and the ratio of the training set to the test set is 4:1.

[0020] Preferably, the image processing uses a whale optimization algorithm to optimize the hyperparameters of the random forest model to improve the prediction ability of the model. The optimized hyperparameters are n_estimators (number of trees) and max_depth (maximum depth) of the random forest model. The specific steps are as follows:

[0021] Parameter initialization, setting the parameters of the whale optimization algorithm: number of whale individuals, maximum number of iterations, variable dimension, upper and lower limits of variables;

[0022] Furthermore, the population is initialized, and the position values ​​of each dimension of all whale individuals are randomly initialized. The position values ​​of each dimension of each whale individual represent the input hyperparameters.

[0023] Furthermore, the fitness value is calculated, and the random forest classification error rate (CER) is used to calculate the fitness value (Fit) of each whale individual:

[0024]

[0025] Further, according to the fitness function (Fit), the fitness value of each whale, the optimal position of the individual and the global optimal position of all whales are calculated, and the position of the optimal solution is used to update the position of each individual whale;

[0026] Furthermore, the position of each individual whale is updated. When the probability of selecting the shrinking and encircling mechanism or the spiral update position is p < 0.5 and |A| < 1, the position of each individual whale is updated according to the following formula:

[0027]

[0028] In the formula, is the individual whale position vector with the best objective function value in the current whale group, The current individual whale position vector, represents the distance vector between the optimal solution of the current whale group and the current solution of the individual whale, t is the number of iterations, and the coefficient vector and Defined as:

[0029]

[0030]

[0031] The control parameter vector As the number of iterations increases, it decreases linearly from 2 to 0. is a random vector distributed between [0, 1];

[0032] If |A|≥1, a position vector is randomly selected and updated according to the following formula:

[0033]

[0034] In the formula, is the position vector of a whale individual randomly selected from the current whale group;

[0035] When the probability p ≥ 0.5, the position is updated by the following formula:

[0036]

[0037] In the formula, Represents the distance vector between the best position whale and the current whale individual; the constant coefficient b determines the spiral shape of the whale individual when it spirals forward, and l is a random number between [-1, 1];

[0038] Furthermore, the iteration is repeated until the maximum number of iterations is reached to obtain the optimal hyperparameters, which are then substituted into the random forest model for further training.

[0039] Preferably, the result output by the random forest model is the probability Probability (0-1) of belonging to the flame stability state, expressed as:

[0040]

[0041] When Probability>0.8, the flame is stable; when 0.8>Probability>0.6, the flame is relatively stable; when Probability<0.6, the flame is unstable;

[0042] Calculate the Shap distribution of the test set to obtain the contribution and importance of each feature in the random forest model to the model output target Probability. The calculation formula is:

[0043]

[0044] In the formula, Represents the contribution of feature i in function f, f is the random forest model, N s is a set of all features, S represents a feature subset, N s / {i} represents the s Remove feature i from the set S, S∪{i} means adding feature i to the subset S, and |S| means the number of elements in the set S.

[0045] Preferably, the Shap distribution of the test set is calculated to obtain the contribution and importance of each feature in the random forest model to the model output target Probability; according to the feature importance ranking of the calculated test set Shap, the DCS parameters that rank first and are easy to directly control are selected as the control objects, such as feed amount, dry-wet feed ratio, auxiliary oil gun flow rate, and alkali solution spraying amount; when Probability>0.8, that is, the flame is stable, no control suggestion is made; when Probability<0.6, that is, the flame is unstable, under the condition that other features remain unchanged, the whale optimization algorithm is used to optimize the two selected DCS parameters, and the fitness value (Fit) is Probability, until Probability>0.6, at which time the control object parameter is the adjustment suggestion of the model.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] The present invention can effectively reduce the interference of flame images by using Gaussian filtering, and the bimodal method determines the optimal grayscale threshold for flame image segmentation. The flame image features can well reflect the combustion conditions inside the incinerator furnace. The whale optimization algorithm has a good optimization effect on the prediction performance of the random forest model. The probability of belonging to the flame stability state is defined as the quantitative index of flame stability, and the quantitative detection of combustion stability based on the furnace flame image is realized. By introducing the Shap interpretive method, we deeply understand how the model judges the flame state and the degree of influence of each feature on the flame stability. Based on the Shap interpretive technology, we propose a comprehensive multi-parameter intelligent adjustment method to overcome the limitations of the single parameter adjustment strategy and provide operators with more intuitive and flexible adjustment suggestions. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A diagram of the stability quantification steps in a method for quantitatively detecting and controlling flame stability of an incinerator according to the present invention;

[0049] Figure 2 This is a model architecture diagram of a method for quantitatively detecting and controlling flame stability of an incinerator according to the present invention;

[0050] Figure 3 This is a grayscale statistical diagram in a method for quantitatively detecting and controlling flame stability of an incinerator according to the present invention. DETAILED DESCRIPTION

[0051] 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 implementation regulations described 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.

[0052] See also Figure 1-3 As shown, the present invention provides a technical solution: a method for quantitatively detecting and controlling the flame stability of an incinerator, comprising:

[0053] S1. At the incinerator site, use an industrial camera to continuously shoot a certain number of flame images, divide the flame images into two categories: stable and unstable states according to the operator's experience, and mark them to achieve data collection;

[0054] S2. After completing the image data acquisition, filter the captured flame image, convert the filtered flame image into a grayscale image, segment the grayscale image to obtain a binary image, and use an image processing algorithm to extract a flame characteristic parameter sequence;

[0055] S3. After the image data is processed, the data set is divided into a training set and a validation set and put into the random forest model for training. The whale optimization algorithm is used to optimize the hyperparameters of the random forest model to obtain the best training model.

[0056] S4. Complete the model establishment, input the test image, first process the test image to obtain the processed image and flame characteristic parameters, then predict the test image according to the best random forest model to obtain the judgment result, and combine the Shap interpretative method to make a comprehensive multi-parameter intelligent adjustment strategy to achieve the judgment and control suggestions on the flame stability of the incinerator.

[0057] according to Figure 1-3 As shown, the industrial camera used for image acquisition is fixedly installed on the top of the incinerator, and the flame image in the incinerator is acquired from the top of the incinerator.

[0058] according to Figure 1-3 As shown, the collected images must include stable and unstable flame images of the incinerator. The images are clearly visible. The operator judges the flame combustion status according to the DCS data and CEMS data at the corresponding time of the flame image, including feed amount, dry-wet feed ratio, auxiliary oil gun flow, alkali solution spraying amount, oxygen content in the furnace, dilute phase temperature, dense phase temperature, NO xEmissions, SO2 emissions, and CO emissions are determined based on whether each parameter exceeds the parameter variation range under normal operating conditions.

[0059] according to Figure 1-3 As shown, after the flame image is collected, the captured flame image needs to be Gaussian filtered with a filter kernel size of 5, and the grayscale image is segmented using a bimodal method to determine the optimal segmentation grayscale threshold, including the following steps:

[0060] S1, converting the flame grayscale image into a grayscale level statistical graph;

[0061] S2. Define the peak of the grayscale statistical graph as the position where the frequency corresponding to the current grayscale is the largest among the frequencies corresponding to the previous and next 25 grayscales, and the valley as the position where the frequency between two adjacent peaks is the smallest;

[0062] S3. Compare the frequencies corresponding to all valleys. The grayscale level corresponding to the valley with the minimum frequency is the optimal segmentation grayscale threshold. At the same time, the grayscale threshold must be no less than 50.

[0063] according to Figure 1-3 As shown, the extracted flame characteristic parameter sequence includes three categories: geometric parameters, brightness parameters and thermodynamic parameters; the geometric parameters include flame centroid, flame effective area ratio and flame high temperature area ratio; the brightness parameters include average grayscale, grayscale standard deviation and non-uniformity; the thermodynamic parameters include maximum temperature, average temperature and flicker frequency.

[0064] according to Figure 1-3 As shown, the data set includes the aforementioned DCS data, CEMS data, flame characteristic parameters and flame stability calibration state data, which are divided into a training set and a test set and put into a random forest model for training, and the ratio of the training set to the test set is 4:1.

[0065] according to Figure 1-3 As shown, the image processing uses the whale optimization algorithm to optimize the hyperparameters of the random forest model to improve the prediction ability of the model. The optimized hyperparameters are n_estimators (number of trees) and max_depth (maximum depth) of the random forest model. The specific steps are as follows:

[0066] Parameter initialization, setting the parameters of the whale optimization algorithm: number of whale individuals, maximum number of iterations, variable dimension, upper and lower limits of variables;

[0067] Furthermore, the population is initialized, and the position values ​​of each dimension of all whale individuals are randomly initialized. The position values ​​of each dimension of each whale individual represent the input hyperparameters.

[0068] Furthermore, the fitness value is calculated, and the random forest classification error rate (CER) is used to calculate the fitness value (Fit) of each whale individual:

[0069]

[0070] Further, according to the fitness function (Fit), the fitness value of each whale, the optimal position of the individual and the global optimal position of all whales are calculated, and the position of the optimal solution is used to update the position of each individual whale;

[0071] Furthermore, the position of each individual whale is updated. When the probability of selecting the shrinking and encircling mechanism or the spiral update position is p < 0.5 and |A| < 1, the position of each individual whale is updated according to the following formula:

[0072]

[0073] In the formula, is the individual whale position vector with the best objective function value in the current whale group, The current individual whale position vector, represents the distance vector between the optimal solution of the current whale group and the current solution of the individual whale, t is the number of iterations, and the coefficient vector and Defined as:

[0074]

[0075]

[0076] The control parameter vector As the number of iterations increases, it decreases linearly from 2 to 0. is a random vector distributed between [0, 1];

[0077] If |A|≥1, a position vector is randomly selected and updated according to the following formula:

[0078]

[0079] In the formula, is the position vector of a whale individual randomly selected from the current whale group;

[0080] When the probability p ≥ 0.5, the position is updated by the following formula:

[0081]

[0082] In the formula, Represents the distance vector between the best position whale and the current whale individual; the constant coefficient b determines the spiral shape of the whale individual when it spirals forward, and l is a random number between [-1, 1];

[0083] Furthermore, the iteration is repeated until the maximum number of iterations is reached to obtain the optimal hyperparameters, which are then substituted into the random forest model for further training.

[0084] according to Figure 1-3 As shown, the result output by the random forest model is the probability Probability (0-1) of belonging to the flame stability state, expressed as:

[0085]

[0086] When Probability>0.8, the flame is stable; when 0.8>Probability>0.6, the flame is relatively stable; when Probability<0.6, the flame is unstable;

[0087] Calculate the Shap distribution of the test set to obtain the contribution and importance of each feature in the random forest model to the model output target Probability. The calculation formula is:

[0088]

[0089] In the formula, Represents the contribution of feature i in function f, f is the random forest model, N S is a set of all features, S represents a feature subset, N S / {i} represents the S Remove feature i from the set S, S∪{i} means adding feature i to the subset S, and |S| means the number of elements in the set S.

[0090] according to Figure 1-3 As shown in the figure, the Shap distribution of the test set is calculated to obtain the contribution and importance of each feature in the random forest model to the model output target Probability; according to the feature importance ranking of the calculated test set Shap, the DCS parameters that rank first and are easy to directly control are selected as the control objects, such as feed amount, dry-wet feed ratio, auxiliary oil gun flow rate, and alkali solution spraying amount; when Probability>0.8, that is, the flame is stable, no control suggestion is made; when Probability<0.6, that is, the flame is unstable, under the condition that other features remain unchanged, the whale optimization algorithm is used to optimize the two selected DCS parameters, and the fitness value (Fit) is Probability, until Probability>0.6, at which time the control object parameter is the adjustment suggestion of the model.

[0091] The effects achieved by the whole method are as follows: the use of Gaussian filtering can effectively reduce the interference of flame images, the bimodal method determines the optimal grayscale threshold for flame image segmentation, the flame image features can well reflect the combustion conditions inside the incinerator furnace, the whale optimization algorithm has a good optimization effect on the prediction performance of the random forest model, and the probability of belonging to the flame stability state is defined as the quantitative index of flame stability, realizing the quantitative detection of combustion stability based on the furnace flame image. By introducing the Shap interpretive method, we can deeply understand how the model judges the flame state and the influence of each feature on the flame stability. Based on the Shap interpretive technology, we propose a comprehensive multi-parameter intelligent adjustment method to overcome the limitations of the single parameter adjustment strategy and provide operators with more intuitive and flexible adjustment suggestions.

[0092] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for quantitatively detecting and controlling the flame stability of an incinerator, comprising the following steps: S1. At the incinerator site, use an industrial camera to continuously shoot a certain number of flame images, divide the flame images into two categories: stable and unstable states according to the operator's experience, and mark them to achieve data collection; S2. After completing the image data acquisition, filter the captured flame image, convert the filtered flame image into a grayscale image, segment the grayscale image to obtain a binary image, and use an image processing algorithm to extract a flame characteristic parameter sequence; S3. After the image data is processed, the data set is divided into a training set and a validation set and put into the random forest model for training. The whale optimization algorithm WOA is used to optimize the hyperparameters of the random forest model to obtain the best training model. S4. Complete the model establishment, input the test image, first process the test image to obtain the processed image and flame characteristic parameters, then predict the test image according to the best random forest model to obtain the judgment result, and combine the Shap (SHapley Additive exPlanations) explanatory method to make a comprehensive multi-parameter intelligent adjustment strategy to achieve the judgment and control suggestions on the flame stability of the incinerator; The industrial camera used for image acquisition is fixedly installed on the top of the incinerator, and the flame image in the incinerator is acquired from the top of the incinerator; The image acquisition shall include stable and unstable flame images of the incinerator, and the images shall be clearly visible. The operator shall judge the flame combustion status according to the DCS data and CEMS data at the corresponding time of the flame image, including feed amount, dry-wet feed ratio, auxiliary oil gun flow, alkali solution spraying amount, oxygen content in the furnace, dilute phase temperature, dense phase temperature, NO x Emissions, SO2 emissions, and CO emissions are determined based on whether each parameter exceeds the parameter variation range under normal operating conditions; After the flame image is collected, the captured flame image needs to be subjected to Gaussian filtering, with a filter kernel size of 5, and the grayscale image is segmented using a bimodal method to determine the optimal segmentation grayscale threshold, including the following steps: S1, converting the flame grayscale image into a grayscale level statistical graph; S2. Define the peak of the grayscale statistical graph as the position where the frequency corresponding to the current grayscale is the largest among the frequencies corresponding to the previous and next 25 grayscales, and the valley as the position where the frequency between two adjacent peaks is the smallest; S3, compare the frequencies corresponding to all valleys, the gray level corresponding to the valley with the minimum frequency is the optimal segmentation gray level threshold, and the gray level threshold must be no less than 50; The extracted flame characteristic parameter sequence includes three categories: geometric parameters, brightness parameters and thermodynamic parameters; the geometric parameters include the flame centroid, flame effective area ratio and flame high temperature area ratio; the brightness parameters include average grayscale, grayscale standard deviation and unevenness; the thermodynamic parameters include maximum temperature, average temperature and flicker frequency; the result output by the random forest model is the probability of belonging to the flame stable state Probability is 0~1, the expression is: when Probability >0.8, the flame is stable. Probability >0.6, the flame is relatively stable. Probability <0.6, the flame is unstable; n_estimators is the number of trees in the random forest model and max_depth is the maximum depth of the random forest model; Calculate the Shap distribution of the test set to obtain the model output target for each feature in the random forest model Probability The contribution and importance of are calculated as follows: In the formula, Representative function Medium Features The contribution of is a random forest model, is the set of all features, represents a subset of features, Representatives in the collection Remove features , Representation Subset Add features , Representing a collection The number of elements in .

2. The method for quantitatively detecting and controlling the flame stability of an incinerator according to claim 1, characterized in that: The data set includes the aforementioned DCS data, CEMS data, flame characteristic parameters and flame stability calibration state data, which are divided into a training set and a test set and put into a random forest model for training, and the ratio of the training set to the test set is 4:

1.

3. The method for quantitatively detecting and controlling the flame stability of an incinerator according to claim 2, characterized in that: The image processing uses the whale optimization algorithm to optimize the hyperparameters of the random forest model to improve the prediction ability of the model. The optimized hyperparameters are n_estimators and max_depth of the random forest model. The specific steps are as follows: Parameter initialization, setting the parameters of the whale optimization algorithm: number of whale individuals, maximum number of iterations, variable dimension, upper and lower limits of variables; Furthermore, the population is initialized, and the position values ​​of each dimension of all whale individuals are randomly initialized. The position values ​​of each dimension of each whale individual represent the input hyperparameters. Further, the fitness value is calculated using the random forest classification error rate ( CER ) to calculate the fitness value of each whale individual ( Fit ): Further, according to the fitness function ( Fit ), calculate the fitness value of each whale, the optimal position of the individual and the global optimal position of all whales, and the optimal position is used to update the position of each individual whale; Further, the position of each individual whale is updated, and the probability of choosing the shrinking and encircling mechanism or the spiral updating position is p <0.5 and When <1, the position of each individual whale is updated according to the following formula: In the formula, is the individual whale position vector with the best objective function value in the current whale group, The current individual whale position vector, Represents the distance vector between the optimal solution of the current whale group and the current solution of the individual whale, is the number of iterations, the coefficient vector and Defined as: The control parameter vector As the number of iterations increases, it decreases linearly from 2 to 0. is a random vector distributed between [0, 1]; like ≥1, a position vector is randomly selected and updated according to the following formula: In the formula, is the position vector of a whale individual randomly selected from the current whale group; When the probability p When ≥0.5, the position is updated by the following formula: In the formula, Represents the distance vector between the best whale position and the current whale individual; constant coefficient Determines the shape of the spiral line when the individual whale spirals forward. is a random number between [−1, 1]; Furthermore, the iteration is repeated until the maximum number of iterations is reached to obtain the optimal hyperparameters, which are then substituted into the random forest model for further training.

4. The method for quantitatively detecting and controlling the flame stability of an incinerator according to claim 1, characterized in that: Calculate the Shap distribution of the test set to obtain the model output target for each feature in the random forest model Probability The contribution and importance of the feature of the calculated test set Shap are calculated, and the top two DCS parameters that are easy to directly control are selected as the control objects, including feed rate, dry-wet feed ratio, auxiliary oil gun flow rate, and alkali solution spraying amount. Probability >0.8 means the flame is stable, no regulation suggestion is made. Probability <0.6 means that the flame is unstable. Under the condition that other characteristics remain unchanged, the whale optimization algorithm is used to optimize the two DCS parameters selected. The fitness value ( Fit )for Probability ,until Probability >0.6, the control object parameter at this time is the adjustment suggestion of the model.

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