CN112749849A - Integrated learning online prediction method for key parameters of continuous catalytic reforming process - Google Patents

Integrated learning online prediction method for key parameters of continuous catalytic reforming process Download PDF

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CN112749849A
CN112749849A CN202110068102.7A CN202110068102A CN112749849A CN 112749849 A CN112749849 A CN 112749849A CN 202110068102 A CN202110068102 A CN 202110068102A CN 112749849 A CN112749849 A CN 112749849A
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钟伟民
杜文莉
钱锋
彭鑫
李智
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Abstract

本发明涉及连续催化重整工艺领域，更具体的说，涉及一种连续催化重整工艺过程关键参数的集成学习在线预测方法。本方法包括以下步骤：S1、选取连续催化重整工艺的辅助变量和主要变量；S2、对过程数据样本集进行预处理，生成数据集；S3、对数据集进行划分；S4、从训练数据集中选择spn个数据样本作为总体数据集，剩余数据样本作为校正数据集，分别将总体数据集作为子训练集构建三个自适应回归子模型；S5、从校正数据集中依次提取数据样本，更新对应的自适应回归子模型；S6、将评估数据集应用到每个自适应回归子模型中，建立集成模型；S7、基于输入的辅助变量，预测主要变量。本发明有效地提高了模型在连续催化重整工艺中的预测精度。

Figure 202110068102

The invention relates to the field of continuous catalytic reforming process, more particularly, to an integrated learning online prediction method of key parameters of continuous catalytic reforming process. The method includes the following steps: S1, selecting auxiliary variables and main variables of the continuous catalytic reforming process; S2, preprocessing the process data sample set to generate a data set; S3, dividing the data set; S4, extracting from the training data set Select the spn data samples as the overall data set, the remaining data samples as the correction data set, and use the overall data set as the sub-training set to construct three adaptive regression sub-models; S5, extract data samples from the correction data set in turn, and update the corresponding Adaptive regression sub-model; S6, applying the evaluation data set to each adaptive regression sub-model to establish an integrated model; S7, predicting the main variable based on the input auxiliary variables. The invention effectively improves the prediction accuracy of the model in the continuous catalytic reforming process.

Figure 202110068102

Description

Integrated learning online prediction method for key parameters of continuous catalytic reforming process
Technical Field
The invention relates to the field of continuous catalytic reforming processes, in particular to an integrated learning online prediction method for key parameters of a continuous catalytic reforming process.
Background
In recent years, with the increasing demand for automotive oils and the increasing demand for energy, catalytic reforming technology has also attracted considerable attention. The continuous regeneration reforming technology of the catalyst has epoch-making significance in the development history of the catalyst, and provides infinite possibility for the high-efficiency production of high-quality gasoline and other chemical raw materials.
The continuous catalytic reforming process is mainly used for increasing the octane number in gasoline refineries and aromatic-rich petrochemicals, is the most popular naphtha reforming process in the world at present, and realizes continuous extraction of catalyst in the steam operation process of a reactor.
Figure 1 discloses a flow diagram of a continuous catalytic reforming process, as shown in figure 1, in which a heavy naphtha liquid is pumped 300 to a heat exchanger 400 and mixed with a hydrogen rich recycle gas from a recycle compressor 600 during the catalytic cracking process.
The temperature of the liquid gas feed mixture is raised to the reaction temperature in the heater (heater No. 1, 101 to heater No. 4, 104) before entering each reactor (reactor No. 1, 201 to reactor No. 4, 204) to ensure complete vaporization of the mixture.
In the four reactors, several chemical reactions, mainly dehydrogenation, occur stably.
When the reactants flow rapidly through the catalyst bed, the temperature of the reactants drops dramatically. The process of heating and reacting continues until it exits reactor No. 4, 204, into the contacting and separating section.
Among them, the reforming catalyst is continuously consumed during operation, and is in contact with various substances to undergo chemical reactions, thereby gradually decreasing the catalyst activity, and thus it is necessary to continuously regenerate the catalyst to maintain a high and stable catalyst activity.
The regenerator 500 functions to achieve continuous circulation of the catalyst and to complete regeneration of the catalyst at the same time.
In the separator 700, the hydrogen-rich gas is separated and returned to the recycle compressor 600.
In addition, the net hydrogen rich gas is used for additional hydrogen consuming petrochemical processes and the bottom product of the stabilizer column is a high octane liquid reformate.
The catalytic reforming process takes naphtha as a raw material, and improves the octane number of the naphtha through reforming reaction.
As a key parameter of the process, the online detection of the octane number index of the reformed gasoline is important in order to improve the economic benefit and the pollution problem and the safety problem of energy sources.
However, the continuous catalytic reforming process belongs to a very complicated industrial production technology and has the characteristics of large time lag, high coupling and nonlinearity.
In industrial engineering in recent decades, when variables cannot be directly obtained, soft measurement is widely applied to estimation of key performance indexes instead of hard measurement, and the performance is excellent in parameter prediction and process monitoring.
Many innovative soft measurement modeling methods such as artificial neural networks, gaussian process regression, and deep learning have been applied and developed.
However, traditional multivariate statistical methods such as principal component regression, partial least squares, and slow feature analysis are still popular in practical applications.
The partial least squares method is a common modeling technique because of its inherent structure and feature extraction capability, and it can project process data into low-dimensional latent variables, including the comprehensive information in the input and output data, and can well deal with the co-linearity between process variables. However, the partial least squares method is inherently limited by its linear characteristics, and cannot properly extract data characteristics when the data relationship is non-linear.
Therefore, a series of nonlinear improvement methods are proposed to integrate the nonlinear features in the linear partial least squares framework. These non-linear techniques combine neural network methods, kernel-based algorithms, and local modeling strategies.
Among them, LWPLS (local weighted partial least squares) is based on the combination of a sample weighting technique and a partial least squares method, and is one of the most widely applied local modeling methods.
However, due to the non-linear and time-varying characteristics of the actual industrial process, the conventional soft measurement model may have severe performance gradients when not matched with the originally designed model.
While many new approaches have been used to alleviate this problem, each approach focuses only on certain aspects of the model features, and thus a comprehensive framework is needed to combine these features.
Disclosure of Invention
The invention aims to provide an integrated learning online prediction method for key parameters of a continuous catalytic reforming process, which solves the problems of poor prediction precision and poor adaptability of the key parameters of the conventional continuous catalytic reforming process.
In order to achieve the aim, the invention provides an integrated learning online prediction method for key parameters of a continuous catalytic reforming process, which comprises the following steps:
s1, selecting auxiliary variables and main variables of the continuous catalytic reforming process, and performing data acquisition to form a process data sample set, wherein the auxiliary variables reflect the operation condition of the continuous catalytic reforming process, and the main variables reflect the quality of finished products;
s2, preprocessing the collected process data sample set of the continuous catalytic reforming process to generate a data set;
s3, dividing the data set, including a training data set, an evaluation data set and a test data set;
s4, selecting spn data samples from the training data set as a total data set, using the residual data samples as a correction data set, and respectively using the total data set as a sub-training set to construct three adaptive regression submodels, wherein the three adaptive regression submodels comprise a moving window model based on a local weighted partial least square method, a time difference model based on the local weighted partial least square method and an instant learning model based on the local weighted partial least square method;
s5, sequentially extracting data samples from the correction data set, verifying each self-adaptive regression sub-model, adding the current data sample into a sub-training set of the self-adaptive regression sub-model with the minimum estimation error value, and updating the corresponding self-adaptive regression sub-model;
s6, applying the evaluation data set to each self-adaptive regression sub-model, calculating the root mean square error of the predicted value of each self-adaptive regression sub-model, determining the weight coefficient of each self-adaptive regression sub-model by Bayesian estimation, and establishing an integrated model;
and S7, predicting the final output value of the main variable based on the input auxiliary variable by adopting the constructed integrated model.
In one embodiment, in step S1:
auxiliary variables including re-contact temperature, separation tank temperature, hydrocracked naphtha, reactor temperature, separation tank pressure, recycle hydrogen flow, primary alkane content, compressor pressure, reflux flow, tray temperature, bottom temperature, and reactor outlet temperature;
the main variable is the octane number, and the corresponding expression is as follows,
Figure BDA0002904937290000041
wherein, YRON、
Figure BDA0002904937290000042
And
Figure BDA0002904937290000043
measured value parameter, X, for the output of a continuous catalytic reforming processFeedIs a feed variable for a continuous catalytic reforming process.
In an embodiment, the preprocessing method in step S2 further includes:
removing abnormal values and missing values, and removing measurement noise.
In an embodiment, in the step S4, based on the moving window model of the local weighted partial least squares, the modeling process is as follows:
s411, mixing { X1，Y1I.e. the window size H is set to the training data matrix in the initial window, where X1For the initial H input variables, Y, in the selected overall dataset1For the initial H output variables in the selected overall dataset;
s412, establishing a local weighted partial least square regression model for the output of the plurality of sampled data points after the window;
s413, moving the window forward according to the step length D to enable the { Xw，YwRetraining a locally weighted partial least squares regression model in a window, where XwFor H input variables, Y, after w D steps in the overall datasetwH output variables after w D steps in the overall data set.
In an embodiment, in the step S4, based on the time difference model of the local weighted partial least squares, the modeling process is as follows:
s421, respectively calculating a first-order difference delta x (t) between input variables and a first-order difference delta y (t) between output variables of adjacent sampling data points;
s422, constructing a relation model between the delta x (t) and the delta y (t) by using a local weighted partial least square regression model;
s423, calculating a query sample xq(t) first order difference Deltaxq(t)；
S424, converting delta xq(t) predicting the differential value Deltay of the response variable in the input relational modelp(t)；
S425, based on adjacent sampling data points yp(t-1) and the difference value Deltayp(t) calculating the predicted value y of the response variablep(t), the expression is as follows,
yp(t)＝Δyp(t)+yp(t-1)。
in an embodiment, in the step S4, the modeling process based on the instantaneous learning model of the local weighted partial least squares is as follows:
s431, when the traversal inputs a query sample xq∈RmOn arrival, calculate xqAnd xiThe distance between the two or more of the two or more,
Figure BDA0002904937290000051
wherein, n data sample points (x)i，yi)i＝1～nThe constituent data set, x ∈ RmAnd y ∈ RlRepresenting input and output training sets, x, respectivelyiRepresents the ith input training value;
s432, selecting N data samples with the minimum distance to form a related sample set;
s433, constructing a local weighted partial least square regression model based on the relevant sample set;
s434, obtaining a query sample x through an LWPLS regression modelqCorresponding response variable ypThe predicted value of (2).
In one embodiment, in step S5:
the estimation error is an error value between the predicted value and the actual value;
the data sample with the smallest estimated error value is added to the corresponding sub-training set.
In one embodiment, in step S6, the root mean square error of the predicted value of each adaptive regression submodel is calculated as follows:
root Mean Square Error (RMSE) of predicted value of ith adaptive regression sub-modeli，
Figure BDA0002904937290000052
Wherein, ydRepresents the actual value, ydp，iRepresents the predicted value of the ith adaptive regression submodel, and D represents the number of evaluation samples of the evaluation data set.
In one embodiment, the output result y of the model is integrated in the step S6pThe corresponding expression is:
Figure BDA0002904937290000053
wherein, yp，iThe predicted output value of the ith adaptive regression submodel is represented, C is the current process state, P (M)iI C) is the ith adaptive regression submodel M in the current process state CiProbability of (M)1，M2，...，MIThere are I different adaptive regression submodels.
In one embodiment, the current process state C in the step S6 is the ith adaptive regression submodel MiProbability P (M)i| C), the corresponding expression is:
Figure BDA0002904937290000061
wherein, P (M)i) Is the prior probability of the ith adaptive regression submodel, P (C | M)i) For the ith adaptive regression submodel MiIs the probability of the current process state C, p is the tuning parameter, RMSEiThe root mean square error of the predicted value of the ith self-adaptive regression sub-model;
P(C|Mi) The corresponding expression of (a) is,
Figure BDA0002904937290000062
P(Mi) The corresponding expression of (a) is,
Figure BDA0002904937290000063
rithe corresponding expression of (a) is,
Figure BDA0002904937290000064
the integrated learning online prediction method for key parameters of the continuous catalytic reforming process, provided by the invention, is based on MW, TD and JITL adaptive regression submodels of an LWPLS regression algorithm, and combines a Bayesian method to fuse data to obtain an integrated model, so that different process characteristic problems of nonlinearity, time variation, mutation and the like in the soft measurement of the key parameters of the continuous catalytic reforming process are respectively solved, the performance and efficiency of the respective adaptive regression submodels are greatly improved, the method can adapt to complex environments, and the prediction precision is effectively improved.
Drawings
The above and other features, properties and advantages of the present invention will become more apparent from the following description of the embodiments with reference to the accompanying drawings in which like reference numerals denote like features throughout the several views, wherein:
FIG. 1 discloses a flow diagram of a continuous catalytic reforming process;
FIG. 2 discloses a flow chart of a continuous catalytic reforming process key parameter ensemble learning online prediction method according to an embodiment of the present invention;
FIG. 3 discloses a modeling flow diagram of an integration model according to an embodiment of the invention;
FIG. 4a discloses a flow chart of modeling a moving window model based on local weighted partial least squares according to an embodiment of the present invention;
FIG. 4b discloses a flow chart of modeling a local weighted partial least squares based time difference model according to an embodiment of the invention;
FIG. 5 discloses a flow chart of modeling an instantaneous learning model based on local weighted partial least squares according to an embodiment of the invention;
FIG. 6 discloses a flow chart of a method for partitioning a training data set according to an embodiment of the invention;
fig. 7 reveals octane number prediction maps for the five models.
The meanings of the reference symbols in the figures are as follows:
no. 1011 heater;
1022. the heater;
1033 # heater;
no. 1044 heater;
2011 reactor;
reactor No. 2022;
2033 reactor no;
number 2044 reactor;
300 pumps;
400 heat exchanger;
a 500 CCR regenerator;
600 circulating compressor;
700 divider.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Aiming at different process characteristics of nonlinearity, time variation, mutation and the like in a continuous catalytic reforming process, the invention provides an integrated learning online prediction method of key parameters of the continuous catalytic reforming process based on a self-adaptive local weighted partial least square method, which comprises the steps of analyzing a continuous catalytic reforming process mechanism, selecting proper auxiliary variables, dividing a training data set into a total data set and a correction data set, training a moving window model, a time difference model and an immediate learning model based on the local weighted partial least square method, combining the moving window model, the time difference model and the immediate learning model based on the local weighted partial least square method, comprehensively considering all characteristic information by adopting an integration mode of Bayesian theorem, carrying out fusion calculation on prediction output, and establishing a soft measurement integrated model with higher cost performance, the method is suitable for complex environments, the change situation of the main variable octane value is correctly predicted, and the efficiency and the accuracy of the model are improved.
Fig. 2 discloses a flowchart of an ensemble learning online prediction method for key parameters of a continuous catalytic reforming process according to an embodiment of the present invention, fig. 3 discloses a flowchart of an ensemble model according to an embodiment of the present invention, and fig. 2 and fig. 3 illustrate an ensemble learning online prediction method for key parameters of a continuous catalytic reforming process according to the present invention, which includes the following steps:
s1, selecting auxiliary variables and main variables of the continuous catalytic reforming process, and performing data acquisition to form a process data sample set, wherein the auxiliary variables reflect the operation condition of the continuous catalytic reforming process, and the main variables reflect the quality of finished products;
s2, preprocessing the collected process data sample set of the continuous catalytic reforming process to generate a data set;
s3, dividing the data set, including a training data set, an evaluation data set and a test data set;
s4, selecting spn data samples from the training data set as a total data set, using the residual data samples as a correction data set, and respectively using the total data set as a sub-training set to construct three adaptive regression submodels, wherein the three adaptive regression submodels comprise a moving window model based on a local weighted partial least square method, a time difference model based on the local weighted partial least square method and an instant learning model based on the local weighted partial least square method;
s5, sequentially extracting data samples from the correction data set, verifying each self-adaptive regression sub-model, adding the current data sample into a sub-training set of the self-adaptive regression sub-model with the minimum estimation error value, and updating the corresponding self-adaptive regression sub-model;
s6, applying the evaluation data set to each self-adaptive regression sub-model, calculating the root mean square error of the predicted value of each self-adaptive regression sub-model, determining the weight coefficient of each self-adaptive regression sub-model by Bayesian estimation, and establishing an integrated model;
and S7, predicting the final output value of the main variable based on the input auxiliary variable by adopting the constructed integrated model.
Each step is described in detail below.
And S1, selecting auxiliary variables and main variables of the continuous catalytic reforming process, and performing data acquisition to form a process data sample set, wherein the auxiliary variables reflect the operation condition of the continuous catalytic reforming process, and the main variables reflect the quality of finished products.
Suitable auxiliary variables and main variables are selected according to the mechanism of the continuous catalytic reforming process.
The selection of the secondary and primary variables can be directly summarized from the actual continuous catalytic reforming process.
The auxiliary variable is required to reflect the operating conditions of the continuous catalytic reforming process.
Selected auxiliary variables include, but are not limited to:
the temperature of the secondary contact, the temperature of the liquid separation tank, hydrocracked naphtha, the temperature of the reactor, the pressure of the separation tank, the flow rate of the circulating hydrogen, the content of primary alkane, the pressure of a compressor, the flow rate of reflux, the temperature of a tray, the flow rate of reflux, the temperature of the tray, the temperature of the bottom, the temperature of the outlet of the reactor I and the like.
The main variables, need to be able to reflect the quality of the finished product.
The octane number can reflect the quality of finished products and is an important performance index.
Thus, octane number was chosen as the primary variable.
The octane number can be derived by the following formula:
Figure BDA0002904937290000091
wherein, YRON、
Figure BDA0002904937290000092
And
Figure BDA0002904937290000093
for 3 measurements of output, Y, of a continuous catalytic reforming processRONTo investigate octane number, XFeedIs a feed variable for a continuous catalytic reforming process.
And S2, preprocessing the collected process data sample set of the continuous catalytic reforming process to generate a data set.
The measurement of continuous catalytic reforming industrial process variables often contains measurement noise and therefore requires pre-processing of the collected continuous catalytic reforming process data sample sets.
The pretreatment method comprises the following steps: removing abnormal values and missing values, and removing measurement noise.
In this embodiment, outliers are removed and the 3-sigma criterion is applied. The 3-sigma criterion is also called Laudea criterion, and is characterized by that firstly, a group of detection data only contains random error, and makes calculation treatment to obtain standard deviation, and according to a certain probability a zone is defined, and when the error exceeding said zone is not random error but coarse error, the data containing said error can be removed.
In this embodiment, measurement noise is removed, and white noise is removed using a gaussian smoothing filter.
The expression of the gaussian smoothing filter is as follows:
Figure BDA0002904937290000101
where μ represents the mean of the data sample x and σ represents the standard deviation of the data sample x.
And S3, dividing the data set, including a training data set, an evaluation data set and a test data set.
By dividing the preprocessed process data sample set, an accurate model is constructed and the accuracy of the model can be evaluated.
Typically, 45% of the data set is divided into the training data set, 10% into the evaluation data set, and the remaining 45% into the test data set.
The training data set is used for training a model, inputting and corresponding output data to the model, and training the model to learn the relation between the input data and the output data;
the evaluation dataset, which is used to verify the training level of the evaluation model, such as the classification accuracy of the classifier, the prediction error, etc., may be used to select the best model based on the performance of the evaluation dataset.
And the test data set is used for testing the final model after training is finished, and the new input data is simulated in the final model to obtain a prediction result and output the prediction result.
S4, selecting spn data samples from the training data set as a total data set, using the residual data samples as correction data sets, and respectively using the total data set as a sub-training set to construct three adaptive regression sub-models, wherein the three adaptive regression sub-models comprise a Moving Window model (MW-LWPLS, Moving Window-LWPLS) based on a local weighted partial least square Method, a Time Difference model (TD-LWPLS, Time Difference-LWPLS) based on a local weighted partial least square Method, and a Just-in-Time Learning Method-LWPLS based on a local weighted partial least square Method.
Spn data samples are selected from the training data set as a total data set, and the remaining data samples are used as a correction data set.
Based on the overall data set, three adaptive regression sub-models are constructed.
Three adaptive regression submodels, including:
a moving window model based on local weighted partial least squares (MW-LWPLS);
a time difference model based on local weighted partial least squares (TD-LWPLS);
a local weighted partial least squares based just-in-time learning model (JITL-LWPLS).
The size of the initially partitioned overall data set determines to a large extent the performance of the final regression integration model, and thus the optimal value of the spn parameter needs to be determined by trial and error.
Fig. 4a discloses a modeling flow chart of a moving window model based on a local weighted partial least square method according to an embodiment of the present invention, such as the moving window model based on the local weighted partial least square method shown in fig. 4a, the modeling flow chart is as follows:
s411, mixing { X1，Y1I.e. the window size H is set to the training data matrix in the initial window, where X1For the initial H input variables, Y, in the selected overall dataset1For the initial H output variables in the selected overall dataset;
s412, establishing an LWPLS regression model for the output of the windowed multiple sampling data points;
s413, moving the window forward according to the step length D to enable the { Xw，YwRetraining the LWPLS regression model in a window, where XwFor H input variables, Y, after w D steps in the overall datasetwRepresenting H output variables after w D steps in the overall data set.
The key step of the moving window method is to determine the appropriate window size H according to the actual process, and usually a trial and error method is used to determine the window size H.
Further, for convenience, the step size D of the moving window is set to 1.
Fig. 4b discloses a modeling flow chart of the local weighted partial least square method-based time difference model according to an embodiment of the invention, and the local weighted partial least square method-based time difference model shown in fig. 4b is composed of time differences between variables, and the modeling flow chart is as follows:
s421, respectively calculating a first order difference Δ x (t) between input variables and a first order difference Δ y (t) between output variables of adjacent sampling data points, where the corresponding expression is,
Δx(t)＝x(t)-x(t-1)；
Δy(t)＝y(t)-y(t-1)；
s422, constructing a relation model between the delta x (t) and the delta y (t) by using an LWPLS regression model, wherein the corresponding expression is as follows,
Δy(t)＝f(Δx(t))；
where f represents the called LWPLS regression model function.
S423, calculating a query sample xq(t) first order difference Deltaxq(t) corresponding to the expression of,
Δxq(t)＝xq(t)-xq(t-1)；
s424, converting delta xq(t) predicting the differential value Deltay of the response variable in the input relational modelp(t), the corresponding expression is as follows,
Δyp(t)＝f(Δxq(t))；
s425, based on adjacent sampling data points yp(t-1) and the difference value Deltayp(t) calculating the predicted value y of the response variablep(t), the corresponding expression is as follows,
yp(t)＝Δyp(t)+yp(t-1)。
the most critical step in the just-in-time learning framework is the selection of a neighborhood sample set for model construction.
Similarity is generally evaluated using a distance-based metric, i.e., euclidean distance. The greater the distance, the lower the similarity of the current historical sample point and the query sample, and vice versa.
Fig. 5 discloses a modeling flow chart of an instantaneous learning model based on a local weighted partial least square method according to an embodiment of the present invention, such as the instantaneous learning model based on the local weighted partial least square method shown in fig. 5, the modeling flow chart is as follows:
suppose a block consisting of n data sample points (x)i，yi)i＝1～nComposed data set, where x ∈ RmAnd y ∈ RlRepresenting input and output training sets, respectively.
And S431, measuring the similarity.
When traversing and inputting a query sample xq∈RmOn arrival, calculate xqAnd xiThe distance therebetween, as shown in the following formula,
Figure BDA0002904937290000121
wherein x isiRepresenting the ith input training value.
The distance d to be calculatediSorted in ascending order.
And S432, correlating the sample set.
Selecting the N data samples with the smallest distance, i.e. the highest similarity, to form a correlated sample set for building the LWPLS regression model, because these data samples and the query sample xqMost similar.
Set of correlated samples { Xlocal，Ylocal}＝{(x1，y1)，...，(xi，yi)，...，(xN，yN) Where N represents the number of samples of the LWPLS regression model.
S433, constructing an LWPLS regression model based on the related sample set, wherein the expression is as follows,
yi＝f(xi)，
wherein (x)i，yi) Data sample points of a correlated sample set;
s434, obtaining a query sample x through an LWPLS regression modelqAnd outputting the predicted value of the corresponding response variable.
Query sample xqThe LWPLS regression model of (1), the expression is as follows,
yp＝f(xq)，
wherein x isqTo query the sample, ypIs the predicted value of the response variable.
The LWPLS regression model needs to be re-established every time of prediction, and after the prediction output is completed, the constructed model is abandoned, and a new LWPLS regression model is established when a new query sample comes.
Notably, the window size in the MW-LWPLS sub-model and the local area size in the JITL-LWPLS sub-model should not exceed spn in case the model fails.
The optimal values for the window size in the MW-LWPLS sub-model and the local area size in the JITL-LWPLS sub-model also need to be determined by trial and error.
When the total data set is used as a sub-training set of the three adaptive regression submodels, not only all characteristics of nonlinearity, time variation, mutation and the like need to be covered, but also the characteristics which are unique to the three adaptive regression submodels of MW-LWPLS, TD-LWPLS and JITL-LWPLS need to be adapted.
The time-varying characteristics of the sub-training set of the MW-LWPLS adaptive regression submodel are dominant;
the mutation characteristics of the sub-training set of the TD-LWPLS adaptive regression sub-model are dominant;
the nonlinear characteristics of the sub-training set for the JITL-LWPLS adaptive regression submodel dominate.
Therefore, in the embodiment of the present invention, first, the total data set is respectively used as the sub-training sets of the three adaptive regression sub-models of MW-LWPLS, TD-LWPLS and JITL-LWPLS, so that the sub-training sets of the three adaptive regression sub-models can cover all the features;
and then, by updating the sub-training sets, the sub-training sets of the three self-adaptive regression sub-models can respectively obtain data of corresponding characteristics.
And S5, sequentially extracting data samples from the correction data set, verifying each self-adaptive regression sub-model, calculating a corresponding estimation error, adding the current data sample into a sub-training set of the self-adaptive regression sub-model with the minimum estimation error value, and updating the corresponding self-adaptive regression sub-model.
Fig. 6 is a flowchart of a method for dividing a training data set according to an embodiment of the present invention, and as shown in fig. 6, data samples of a correction data set are added to three adaptive regression submodels, MW-LWPLS, TD-LWPLS and JITL-LWPLS, for training and prediction, and corresponding estimation errors are calculated and sorted in ascending order.
The estimation error is an error between the calculated predicted value and the actual value.
And adding the data sample with the minimum estimated error value in the three adaptive regression submodels to the end of the sub training set corresponding to the adaptive regression submodel.
The estimated error value is used as a performance index to describe the capability of the model processing process characteristic, and the smaller the error value is, the more interpretable the corresponding self-adaptive regression sub-model can be improved corresponding to the specific characteristic.
Thus, the current data sample point with a particular feature interprets the adaptive regression sub-model with the prediction dependency of the smallest estimation error value and adds these data sample points to the corresponding sub-training set of the adaptive regression sub-model.
And S6, applying the evaluation data set to each self-adaptive regression sub-model, calculating the root mean square error of the predicted value of each self-adaptive regression sub-model, determining the weight coefficient of each self-adaptive regression sub-model by Bayesian estimation, and establishing an integrated model.
Evaluation samples of the evaluation data set are applied to each of the adaptive regression sub-models.
And calculating the Root Mean Square Error (RMSE) of each predicted value, determining a weight coefficient by adopting a Bayesian estimation method, and establishing an integrated model for predicting output.
Assume that there are I different adaptive regression submodels, denoted M respectively1，M2，...，MI. In this embodiment, I is 3.
The current process state is denoted C, then the current process state C is the ith adaptive regression sub-model MiHas a probability of P (M)iC), which can be the weight value of the fused prediction output estimated for each adaptive regression submodel.
For better illustration of the integration model, M thereiniAnd C are both meaningful characters.
Then, the output result y of the integrated modelpThe corresponding expression is:
Figure BDA0002904937290000151
wherein, yp，iThe predicted output value, P (M), representing the ith adaptive regression submodeliI C) is the ith self-adaptive regression submodel M of the current process state CiThe probability of (c).
P(Mi| C) can be calculated by the following formula:
Figure BDA0002904937290000152
wherein, P (C | M)i) For the ith adaptive regression submodel MiIs the probability of the current process state C, P (M)i) Is the prior probability of the ith adaptive regression submodel.
Assuming that the prior probabilities of each adaptive regression submodel are the same, i.e.
Figure BDA0002904937290000153
For a regression task, the Root Mean Square Error (RMSE) must be small if a high prediction accuracy is to be achieved, i.e. the prediction performance is inversely proportional to the value of RMSE.
P(C|Mi) The value of (d) can be obtained by:
Figure BDA0002904937290000154
wherein r isiThe corresponding expression is that,
Figure BDA0002904937290000155
the parameter p is an adjustment coefficient according to the actual application.
Root Mean Square Error (RMSE) of predicted value of ith adaptive regression sub-modeliCalculated from the following formula:
Figure BDA0002904937290000156
wherein, ydRepresents the actual value, ydp，iRepresents the predicted value of the ith adaptive regression submodel, and D represents the number of evaluation samples of the evaluation data set.
S7, predicting the final output value of the main variable based on the input auxiliary variable by adopting the constructed integrated model
In this embodiment, the constructed integrated model is used to predict the octane number of the main variable based on the test data set, and the final output is obtained.
The invention provides an integrated learning online prediction method for key parameters of a continuous catalytic reforming process, which comprises the steps of selecting proper auxiliary variables and main variables according to the mechanism of the continuous catalytic reforming process when performing soft measurement modeling of an integrated model, preprocessing an acquired process data sample set, dividing the acquired process data sample set into a training data set, an evaluation data set and a test data set, dividing the training data set into three adaptive regression submodels, updating corresponding sub-training sets so that the training data set not only covers all the characteristics of the three submodels but also respectively comprises all the characteristics, applying the evaluation data set to each adaptive submodel to calculate the RMSE of each predicted value, determining a weight coefficient by Bayesian estimation, establishing the integrated model, and performing prediction by using the integrated model based on the test data set to obtain final output.
The on-line prediction method based on the adaptive local weighted partial least square method for the integrated learning is described by the following key parameter prediction embodiment in the continuous catalytic reforming process, and the specific steps comprise:
step S1: data samples of the continuous catalytic reforming process are collected, and auxiliary variables and main variables are selected.
The data samples collected include 84 input measurements and 3 output measurements, RON, C6(%) and C7+(%), all measurements were sampled every 30 minutes.
Selecting 84 input measurement values as auxiliary variables, including but not limited to, re-contact temperature, temperature of the liquid separation tank, hydrocracked naphtha, reactor temperature, pressure of the separation tank, flow rate of recycle hydrogen, primary alkane content, compressor pressure, reflux flow rate, tray temperature, bottom temperature, reactor outlet temperature, etc.
Selecting the octane number as a performance index as a main variable, wherein the octane number can be derived by the following formula:
Figure BDA0002904937290000161
wherein, XFeedRepresenting the feed variables for the continuous catalytic reforming process.
Step S2: and (4) preprocessing data.
Noise elimination is required in consideration of measurement noise contained in the continuous catalytic reforming process.
White noise is removed by adopting a Gaussian smoothing filter, and the calculation is as follows:
Figure BDA0002904937290000162
where μ represents the mean of the data x and σ represents the standard deviation of the data x.
And removing outliers using the 3-sigma criterion while removing missing values.
737 sample data were finally obtained (approximately half a month).
Step S3: sample data is divided into three parts: 350 samples were used as training data set, 50 samples as evaluation data set, 337 samples as test data set.
And S4-S7, establishing sub models of MW-LWPLS, TD-LWPLS and JITL-LWPLS, and establishing an integrated model EA-LWPLS.
Meanwhile, an LWPLS model is established for comparison, the number of hidden variables in the LWPLS model is set to be 5, and a weight coefficient is set to be 1;
the window size in the MW-LWPLS submodel is set to 28;
for the integrated model EA-LWPLS, spn and the adjustment coefficients in the weight function are set to 280 and 0.5, respectively.
Of the three adaptive regression submodels, the JITL-LWPLS submodel improved most significantly after the JITL model was added, wherein RMSE and R20.417 and 0.819, respectively, where the degree of fit R is2Can be calculated from the following formula:
Figure BDA0002904937290000171
fig. 7 reveals octane number prediction comparison graphs of five models, and as shown in fig. 7, the JITL model can better reflect the variation trend of the octane number, but cannot accurately reflect the variation of the octane number.
Although MW-LWPLS and TD-LWPLS submodels cannot catch up with JITL-LWPLS submodels in reflecting the trend of changes, the submodels can reflect the change of octane value in the process. The results of the TD-LWPLS sub-model are closer to the actual process than the results of the MW-LWPLS sub-model, especially after the 300 th sample point, which, although not fully tracking the amplitude of the process, reflects the fluctuating changes of the process.
However, between the 50 th and 100 th sampling points, the predicted value of the TD-LWPLS sub-model does not change significantly when the actual octane number drops to a minimum, but remains relatively stable.
Compared with the TD-LWPLS submodel, the MW-LWPLS submodel can better reflect the slow change process of the system.
FIG. 7 (e) is a graph showing that the proposed integrated model of the present invention not only achieves optimal prediction performance, where RMSE and R20.385 and 0.846 respectively, and also accurately reflects the real trend of the octane number changing along with the time.
The integrated learning online prediction method for key parameters in the continuous catalytic reforming process provided by the invention effectively improves the prediction accuracy of the model in the continuous catalytic reforming process containing characteristics of nonlinearity, mutation, time variation and the like.
While, for purposes of simplicity of explanation, the methodologies are shown and described as a series of acts, it is to be understood and appreciated that the methodologies are not limited by the order of acts, as some acts may, in accordance with one or more embodiments, occur in different orders and/or concurrently with other acts from that shown and described herein or not shown and described herein, as would be understood by one skilled in the art.
As used in this application and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.
The embodiments described above are provided to enable persons skilled in the art to make or use the invention and that modifications or variations can be made to the embodiments described above by persons skilled in the art without departing from the inventive concept of the present invention, so that the scope of protection of the present invention is not limited by the embodiments described above but should be accorded the widest scope consistent with the innovative features set forth in the claims.

Claims (10)

1.一种连续催化重整工艺过程关键参数的集成学习在线预测方法，其特征在于，包括以下步骤：1. an integrated learning online prediction method of a continuous catalytic reforming process key parameter, is characterized in that, comprises the following steps: S1、选取连续催化重整工艺的辅助变量和主要变量，进行数据采集形成过程数据样本集，所述辅助变量反应连续催化重整工艺过程的运行状况，所述主要变量反映成品的品质；S1, select auxiliary variables and main variables of the continuous catalytic reforming process, carry out data collection to form a process data sample set, the auxiliary variables reflect the operating conditions of the continuous catalytic reforming process, and the main variables reflect the quality of the finished product; S2、对采集的连续催化重整工艺的过程数据样本集进行预处理，生成数据集；S2, preprocessing the collected process data sample set of the continuous catalytic reforming process to generate a data set; S3、对数据集进行划分，包括训练数据集、评估数据集和测试数据集；S3. Divide the data set, including training data set, evaluation data set and test data set; S4、从训练数据集中选择spn个数据样本作为总体数据集，剩余数据样本作为校正数据集，分别将总体数据集作为子训练集构建三个自适应回归子模型，三个自适应回归子模型包括基于局部加权偏最小二乘法的移动窗口模型、基于局部加权偏最小二乘法的时间差分模型和基于局部加权偏最小二乘法的即时学习模型；S4. Select spn data samples from the training data set as the overall data set, and the remaining data samples as the correction data set, respectively use the overall data set as the sub-training set to construct three adaptive regression sub-models. The three adaptive regression sub-models include A moving window model based on local weighted partial least squares, a time difference model based on local weighted partial least squares, and an instant learning model based on local weighted partial least squares; S5、从校正数据集中依次提取数据样本，对每个自适应回归子模型进行验证，将当前数据样本添加到估计误差值最小的自适应回归子模型的子训练集中，更新对应的自适应回归子模型；S5. Extract data samples in turn from the correction data set, verify each adaptive regression sub-model, add the current data sample to the sub-training set of the adaptive regression sub-model with the smallest estimated error value, and update the corresponding adaptive regression sub-model Model; S6、将评估数据集应用到每个自适应回归子模型中，计算每个自适应回归子模型的预测值的均方根误差，采用贝叶斯估计确定每个自适应回归子模型的权重系数，建立集成模型；S6. Apply the evaluation data set to each adaptive regression sub-model, calculate the root mean square error of the predicted value of each adaptive regression sub-model, and use Bayesian estimation to determine the weight coefficient of each adaptive regression sub-model , build an integrated model; S7、采用构建的集成模型，基于输入的辅助变量，预测主要变量的最终输出值。S7. Using the constructed ensemble model, based on the input auxiliary variables, predict the final output value of the main variable. 2.根据权利要求1所述的连续催化重整工艺过程关键参数的集成学习在线预测方法，其特征在于，所述步骤S1中：2. the integrated learning online prediction method of continuous catalytic reforming process key parameter according to claim 1, is characterized in that, in described step S1: 辅助变量，包括再接触温度、分液罐温度、加氢裂化石脑油、石脑油、反应器温度、分离罐压力、循环氢流量、初级烷烃含量、压缩机压力、回流流量、托盘温度、回流流量、托盘温度、底部温度和一号反应器出口温度；Auxiliary variables including recontact temperature, separator tank temperature, hydrocracked naphtha, naphtha, reactor temperature, knockout tank pressure, circulating hydrogen flow, primary paraffin content, compressor pressure, reflux flow, tray temperature, Reflux flow, tray temperature, bottom temperature and reactor No. 1 outlet temperature; 主要变量，为辛烷值，对应的表达式如下，The main variable is octane number, and the corresponding expression is as follows,
Figure FDA0002904937280000021
Figure FDA0002904937280000021
 其中，YRON、
Figure FDA0002904937280000022
和
Figure FDA0002904937280000023
为连续催化重整工艺过程的输出测量值参数，XFeed为连续催化重整工艺过程的进料变量。Among them, Y RON ,
Figure FDA0002904937280000022
and
Figure FDA0002904937280000023
is the output measurement parameter of the continuous catalytic reforming process and X Feed is the feed variable of the continuous catalytic reforming process. 3.根据权利要求1所述的连续催化重整工艺过程关键参数的集成学习在线预测方法，其特征在于，所述步骤S2中的预处理方法，进一步包括：3. the integrated learning online prediction method of the key parameter of continuous catalytic reforming process according to claim 1, is characterized in that, the pretreatment method in described step S2, further comprises: 去除异常值和缺失值、去除测量噪声。Remove outliers and missing values, remove measurement noise. 4.根据权利要求1所述的连续催化重整工艺过程关键参数的集成学习在线预测方法，其特征在于，所述步骤S4中，基于局部加权偏最小二乘法的移动窗口模型，建模流程如下：4. the integrated learning online prediction method of the key parameter of continuous catalytic reforming process according to claim 1, is characterized in that, in described step S4, based on the moving window model of local weighted partial least squares method, the modeling process is as follows : S411、将{X1，Y1}即窗口大小H设为初始窗口中的训练数据矩阵，其中，X1为选择的总体数据集中的初始H个输入变量，Y1为选择的总体数据集中的初始H个输出变量；S411. Set {X 1 , Y 1 }, that is, the window size H, as the training data matrix in the initial window, wherein X 1 is the initial H input variables in the selected overall data set, and Y 1 is the selected overall data set. Initial H output variables; S412、对窗后多个采样数据点的输出建立局部加权偏最小二乘法回归模型；S412, establishing a local weighted partial least squares regression model for the outputs of the multiple sampled data points behind the window; S413、让窗口按步长D向前移动，使{Xw，Yw}在窗口中重新训练局部加权偏最小二乘法回归模型，其中，Xw为在总体数据集中进行w个D步长后的H个输入变量，Yw为在总体数据集中进行w个D步长后的H个输出变量。S413. Let the window move forward by step D, so that {X w , Y w } retrain the local weighted partial least squares regression model in the window, where X w is after performing w D steps in the overall data set The H input variables of , and Y w are the H output variables after performing w D steps in the overall data set. 5.根据权利要求1所述的连续催化重整工艺过程关键参数的集成学习在线预测方法，其特征在于，所述步骤S4中，基于局部加权偏最小二乘法的时间差分模型，建模流程如下：5. the integrated learning online prediction method of continuous catalytic reforming process key parameter according to claim 1, is characterized in that, in described step S4, based on the time difference model of local weighted partial least squares method, the modeling process is as follows : S421、分别计算相邻采样数据点的输入变量之间的一阶差分Δx(t)和输出变量之间的一阶差分Δy(t)；S421, respectively calculating the first-order difference Δx(t) between the input variables of adjacent sampling data points and the first-order difference Δy(t) between the output variables; S422、利用局部加权偏最小二乘法回归模型，构建Δx(t)和Δy(t)之间的关系模型；S422, using a locally weighted partial least squares regression model to construct a relationship model between Δx(t) and Δy(t); S423、计算查询样本xq(t)的一阶差分Δxq(t)；S423. Calculate the first-order difference Δx q (t) of the query sample x q (t); S424、将Δxq(t)输入关系模型中，预测响应变量的差分值Δyp(t)；S424, input Δx q (t) into the relational model, and predict the difference value Δy p (t) of the response variable; S425、基于相邻采样数据点yp(t-1)和差分值Δyp(t)，计算响应变量的预测值yp(t)，表达式如下，S425, based on the adjacent sampling data points y p (t-1) and the difference value Δy p (t), calculate the predicted value y p (t) of the response variable, and the expression is as follows, yp(t)＝Δyp(t)+yp(t-1)。y p (t)=Δy p (t)+y p (t-1). 6.根据权利要求1所述的连续催化重整工艺过程关键参数的集成学习在线预测方法，其特征在于，所述步骤S4中，基于局部加权偏最小二乘法的即时学习模型，建模流程如下：6. the integrated learning online prediction method of the key parameter of continuous catalytic reforming process according to claim 1, is characterized in that, in described step S4, based on the real-time learning model of local weighted partial least squares method, the modeling process is as follows : S431、当遍历输入一个查询样本xq∈Rm到达时，计算xq和xi之间的距离，S431. When a query sample x q ∈ R m arrives in the traversal input, calculate the distance between x q and x i ,
Figure FDA0002904937280000031
Figure FDA0002904937280000031
 其中，由n个数据样本点(xi，yi)i＝1～n组成的数据集，x∈Rm和y∈Rl分别代表输入和输出训练集，xi代表第i个输入训练值；Among them, a data set consisting of n data sample points (x i , y i ) i=1～n , x∈Rm and y∈Rl represent the input and output training sets, respectively, and xi represents the ith input training set value; S432、选择距离最小的N个数据样本，构成相关样本集；S432. Select N data samples with the smallest distance to form a relevant sample set; S433、基于相关样本集，构建局部加权偏最小二乘法回归模型；S433, constructing a locally weighted partial least squares regression model based on the relevant sample set; S434、通过LWPLS回归模型，得到查询样本xq对应的响应变量的预测值。S434. Obtain the predicted value of the response variable corresponding to the query sample x q through the LWPLS regression model. 7.根据权利要求1所述的连续催化重整工艺过程关键参数的集成学习在线预测方法，其特征在于，所述步骤S5中：7. the integrated learning online prediction method of continuous catalytic reforming process key parameter according to claim 1, is characterized in that, in described step S5: 估计误差为预测值和实际值之间的误差值；The estimated error is the error value between the predicted value and the actual value; 将估计误差值最小的数据样本加到对应子训练集。The data sample with the smallest estimated error value is added to the corresponding sub-training set. 8.根据权利要求1所述的连续催化重整工艺过程关键参数的集成学习在线预测方法，其特征在于，所述步骤S6，计算每个自适应回归子模型的预测值的均方根误差，表达式如下：8. the integrated learning online prediction method of the key parameter of continuous catalytic reforming process according to claim 1, is characterized in that, described step S6, calculates the root mean square error of the predicted value of each adaptive regression sub-model, The expression is as follows: 第i个自适应回归子模型的预测值的均方根误差RMSEi，The root mean square error RMSE i of the predicted values of the ith adaptive regression submodel,
Figure FDA0002904937280000041
Figure FDA0002904937280000041
 其中，yd代表实际值，ydp，i代表第i个自适应回归子模型的预测值，D表示评估数据集的评估样本个数。Among them, y d represents the actual value, y dp, i represents the predicted value of the ith adaptive regression sub-model, and D represents the number of evaluation samples in the evaluation data set. 9.根据权利要求8所述的连续催化重整工艺过程关键参数的集成学习在线预测方法，其特征在于，所述步骤S6中集成模型的输出结果yp，对应表达式为：9. the integrated learning online prediction method of the key parameter of continuous catalytic reforming process according to claim 8, is characterized in that, in described step S6, the output result y p of integrated model, corresponding expression is:
Figure FDA0002904937280000042
Figure FDA0002904937280000042
 其中，yp，i代表第i个自适应回归子模型的预测输出值，C为当前过程状态，P(Mi|C)为在当前过程状态C是第i个自适应回归子模型Mi的概率，M1，M2，...，MI为存在I个不同的自适应回归子模型。Among them, y p, i represents the predicted output value of the ith adaptive regression sub-model, C is the current process state, P(M i | C) is the current process state C is the ith adaptive regression sub-model M i The probability of , M 1 , M 2 , ..., M I is that there are I different adaptive regression sub-models. 10.根据权利要求9所述的连续催化重整工艺过程关键参数的集成学习在线预测方法，其特征在于，所述步骤S6中在当前过程状态C是第i个自适应回归子模型Mi的概率P(Mi|C)，对应表达式为：10. the integrated learning on-line prediction method of continuous catalytic reforming process key parameter according to claim 9, is characterized in that, in described step S6, in current process state C is the i -th self-adaptive regression sub-model Mi. The probability P(M i |C), the corresponding expression is:
Figure FDA0002904937280000043
Figure FDA0002904937280000043
 其中，P(Mi)为第i个自适应回归子模型的先验概率，P(C|Mi)为第i个自适应回归子模型Mi是当前过程状态C的概率，p为调节参数，RMSEi为第i个自适应回归子模型的预测值的均方根误差；Among them, P(M i ) is the prior probability of the ith adaptive regression sub-model, P(C|M i ) is the probability that the ith adaptive regression sub-model Mi is the current process state C, and p is the adjustment parameter, RMSE i is the root mean square error of the predicted value of the ith adaptive regression sub-model; P(C|Mi)的对应表达式为，The corresponding expression of P(C|M i ) is,
Figure FDA0002904937280000044
Figure FDA0002904937280000044
 P(Mi)的对应表达式为，The corresponding expression of P(M i ) is,
Figure FDA0002904937280000045
Figure FDA0002904937280000045
 ri的对应表达式为，The corresponding expression of ri is,
Figure FDA0002904937280000046
Figure FDA0002904937280000046
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