CN113723686A - Multitask ash box prediction method and system for energy consumption in organic silicon monomer fractionation process - Google Patents

Multitask ash box prediction method and system for energy consumption in organic silicon monomer fractionation process Download PDF

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CN113723686A
CN113723686A CN202111016250.0A CN202111016250A CN113723686A CN 113723686 A CN113723686 A CN 113723686A CN 202111016250 A CN202111016250 A CN 202111016250A CN 113723686 A CN113723686 A CN 113723686A
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CN113723686B (en
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栾小丽
平晓静
赵顺毅
倪雨青
刘飞
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Jiangnan University
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    • B01D—SEPARATION
    • B01D3/00—Distillation or related exchange processes in which liquids are contacted with gaseous media, e.g. stripping
    • B01D3/14—Fractional distillation or use of a fractionation or rectification column
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    • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
    • B01D—SEPARATION
    • B01D3/00—Distillation or related exchange processes in which liquids are contacted with gaseous media, e.g. stripping
    • B01D3/14—Fractional distillation or use of a fractionation or rectification column
    • B01D3/143—Fractional distillation or use of a fractionation or rectification column by two or more of a fractionation, separation or rectification step
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Abstract

The invention relates to a multitask ash box prediction method for energy consumption in an organic silicon monomer fractionation process, which comprises the steps of determining a first principle model of a rectifying tower; acquiring operation data, and dividing the operation data into a training set and a test set; training the first principle model by using a training set to obtain a model parameter vector to be estimated, and assuming the model parameter vector wtBy sharing a parameter w0And a specific parameter vtComposition, estimating model parameters at the same time; establishing an energy consumption model of the rectifying tower; and testing the energy consumption model by using the test set to obtain the energy consumption predicted value of the test sample in the test set, and comparing the energy consumption predicted value with the energy consumption actual value of the test sample to evaluate the performance of the energy consumption model. The inventionBy utilizing the dynamic characteristics of the rectifying tower and the similarity between different rectifying towers in the organic silicon monomer fractionation process, the model generalization performance is improved, and the defects that the energy consumption prediction method in the organic silicon monomer fractionation process in the prior art is high in modeling cost, ignores the correlation among the towers and is difficult to model under the condition of insufficient data are solved.

Description

Multitask ash box prediction method and system for energy consumption in organic silicon monomer fractionation process
Technical Field
The invention relates to the technical field of process industrial production and processing, in particular to a multitask ash box prediction method and a multitask ash box prediction system for energy consumption in an organic silicon monomer fractionation process.
Background
The organosilicon material has the excellent characteristics of corrosion resistance, radiation resistance, high and low temperature resistance, electric insulation, flame retardance, good biocompatibility and the like, and is widely applied to the fields of food, textile, building, automobile, chemical industry, medicine, instruments, aerospace and the like. The organosilicon monomer produced by the direct synthesis method has more components, close boiling points, small relative volatility and difficult separation. Therefore, the organic silicon monomer fractionation process needs more substances to be separated and has high separation requirements, and the monomer is generally purified by adopting a multi-tower continuous rectification process, so that the organic silicon monomer fractionation process is a unit with higher energy consumption in the organic silicon production process. Wherein the energy consumption of each column is proportional to its separation capacity. The method has the advantages that an explanatory high-precision model of the relation between the energy consumption of each tower and the process variable in the fractionation process is established at low cost, and the method has important significance for improving the economic benefit of the organic silicon industry.
In the prior art, a data model established based on fractionation process operation data is simple in structure but lacks interpretable physical significance; the accuracy of the first principle model established based on the dynamic characteristics of rectification requires an extremely complex model structure and a large number of parameters which are difficult to calculate, and the ash box model estimates the parameters in the first principle model by using a data-driven method, so that the model complexity is reduced while the interpretability is maintained. However, modeling each rectifying column separately is not only costly, but also ignores the potential relationships and dependencies between columns. Also in case it is difficult to collect enough modeling data for a certain tower, e.g. sensor damage for a certain key variable, etc., it is not possible to model the tower alone.
In conclusion, the energy consumption prediction method for the organic silicon monomer fractionation process in the prior art has the problems of high modeling cost, neglect of the correlation among towers and difficulty in modeling under the condition of insufficient data.
Disclosure of Invention
Therefore, the technical problem to be solved by the invention is to overcome the defects that the energy consumption prediction method in the organic silicon monomer fractionation process in the prior art has high modeling cost, ignores the correlation among towers and is difficult to model under the condition of insufficient data.
In order to solve the technical problem, the invention provides a multitask ash box prediction method for energy consumption in an organic silicon monomer fractionation process, which comprises the following steps:
determining an ash box energy consumption prediction model structure, including determining a first principle model of each rectifying tower;
acquiring operation data of a steady-state operation condition in an organic silicon fractionation process, randomly dividing the operation data into a training set and a test set, and carrying out normalization processing on the training set and the test set;
training the first principle model of the rectifying tower by using the training set after normalization processing to obtain a model parameter vector to be estimated, and assuming the model parameter vector w based on the correlation among a plurality of rectifying towerstBy sharing a parameter w0And a specific parameter vtComposition in which the parameter w is shared0Capturing similarity between rectification columns, specific parameter vtCapturing differences among the rectifying towers, and simultaneously estimating model parameters by using a multi-task learning algorithm;
establishing an energy consumption model of the rectifying tower according to the model parameters;
and testing the energy consumption model of the rectifying tower by using the test set after normalization processing to obtain an energy consumption predicted value of the test sample in the test set, and comparing the energy consumption predicted value of the test sample with the energy consumption actual value of the test sample to evaluate the performance of the energy consumption model.
In one embodiment of the invention, the operational data includes feed flow, reflux flow, draw flow, and condensate flow.
In one embodiment of the present invention, randomly partitioning the operational data into training sets comprises:
randomly partitioning the operational data into training sets { (x)it,yit)，i＝1,2,…,ntWherein (x)it,yit) I training sample, n, representing the t tower training settRepresenting the number of training samples for that tower.
In one embodiment of the present invention, randomly partitioning the operational data into test sets comprises:
randomly partitioning the operational data into test sets { (x)jt,yjt)，j＝1,2,…mtWherein (x)jt,yjt) J test sample, m, representing the t tower test settRepresenting the number of test samples for that tower.
In an embodiment of the present invention, training the first principle model of the rectifying tower by using the training set after the normalization processing, and obtaining the model parameter vector to be estimated includes:
the first principle model of the tth rectifying tower is established as follows:
Figure BDA0003239961130000031
wherein the model parameter vector to be estimated is wt＝[w1t w2t w3t w4t]TModel input matrix
Figure BDA0003239961130000032
Model output matrix
Figure BDA0003239961130000033
Representing model errors, wherein
Figure BDA0003239961130000034
Is ntA unit column vector of dimensions.
In one embodiment of the present invention, and using a multitask learning algorithm to simultaneously estimate model parameters comprises:
the objective function form of the multi-task learning algorithm is a least square support vector machine, and the objective function is as follows:
Figure BDA0003239961130000035
s.t.yit＝xit(w0+vt)+bt+ξit,
i＝1,2,…,nt,t＝1,2,…T
wherein ξitRepresenting a relaxation variable representing the model's tolerance to outliers, and gamma representing a constrained relaxation variable ξitλ represents the control-specific variable vtThe regularization parameter of (a);
introducing Lagrange multiplier alpha ═ alpha1 … αt … αT]TSolving the above minimization problem with equality constraints, each element in alpha is greater than 0, wherein,
Figure BDA0003239961130000041
calculating a deviation to obtain
Figure BDA0003239961130000042
Solving the system of linear equations and converting it into a matrix form:
Figure BDA0003239961130000043
wherein the content of the first and second substances,
Figure BDA0003239961130000044
representing the output vector, b ═ b1 b2 … bt…bT]TRepresenting an error vector, Zt×tAll 0 matrices, Z, representing dimensions t x ttRepresenting a full 0-column vector of dimension t,
Figure BDA0003239961130000045
denotes a block diagonal matrix, H ═ blockdiag (H)1,H2,…,Ht)∈n×nMeans positive definiteMatrix, diagonal elements being block diagonal matrix Ht＝blockdiag(ht,ht,…,ht)，
Figure BDA0003239961130000046
Obtaining alpha ═ alpha by matrix inversion1 … αt … αT]TAnd
Figure BDA0003239961130000047
obtaining an estimated parameter of the energy consumption prediction model as
Figure BDA0003239961130000048
In one embodiment of the invention, the regularization parameters γ and λ are selected to be solved by a hyper-parameter search algorithm.
In one embodiment of the present invention, the modeling the energy consumption of the rectifying tower according to the model parameters comprises:
the energy consumption model of the t-th rectifying tower is established as follows:
Figure BDA0003239961130000051
in the formula (I), the compound is shown in the specification,
Figure BDA0003239961130000052
representing the model prediction output.
In an embodiment of the present invention, comparing the predicted energy consumption value of the test sample with the actual energy consumption value of the test sample to evaluate the performance of the energy consumption model includes:
and (3) evaluating the performance of the model by using the mean absolute error MAE:
Figure BDA0003239961130000053
wherein the content of the first and second substances,
Figure BDA0003239961130000054
representing the predicted value of energy consumption, y, of the test samplejtRepresenting the actual value of energy consumption for the test sample.
In addition, the invention also provides a multitask ash box prediction system for the energy consumption in the organic silicon monomer fractionation process, which comprises the following steps:
the first principle model determining module is used for determining an ash box energy consumption prediction model structure and comprises a first principle model determining module for determining each rectifying tower;
the data acquisition module is used for acquiring the operation data of the steady-state operation condition in the organic silicon fractionation process, randomly dividing the operation data into a training set and a test set, and carrying out normalization processing on the training set and the test set;
a model parameter estimation module for training the first principle model of the rectifying tower by using the training set after normalization processing to obtain a model parameter vector to be estimated, and assuming a model parameter vector w based on the correlation among a plurality of rectifying towerstBy sharing a parameter w0And a specific parameter vtComposition in which the parameter w is shared0Capturing similarity between rectification columns, specific parameter vtCapturing differences among the rectifying towers, and simultaneously estimating model parameters by using a multi-task learning algorithm;
the model establishing module is used for establishing an energy consumption model of the rectifying tower according to the model parameters;
and the model test evaluation module is used for testing the energy consumption model of the rectifying tower by using the test set after normalization processing to obtain an energy consumption predicted value of the test sample in the test set, and comparing the energy consumption predicted value of the test sample with the energy consumption actual value of the test sample to evaluate the performance of the energy consumption model.
Compared with the prior art, the technical scheme of the invention has the following advantages:
the invention provides an energy consumption multi-task ash box prediction model by utilizing the dynamic characteristics of a rectifying tower and the similarity between different rectifying towers in the organic silicon monomer fractionation process, the ash box model has higher precision than a simplified mechanism model and is more explanatory than a data driving model, meanwhile, the multi-task algorithm fully utilizes the correlation between different rectifying towers, realizes the sharing of knowledge between towers, improves the generalization performance of the model, and solves the defects that the energy consumption prediction method in the organic silicon monomer fractionation process in the prior art has high modeling cost, neglects the correlation between towers and is difficult to model under the condition of insufficient data.
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In order that the present disclosure may be more readily and clearly understood, reference will now be made in detail to the present disclosure, examples of which are illustrated in the accompanying drawings.
FIG. 1 is a flow chart of a production process of a fractionation process of an organosilicon monomer.
Fig. 2 is a schematic structural diagram of a continuous binary rectification column according to the first embodiment.
FIG. 3 is a schematic diagram of the first embodiment and a diagram for analyzing the correlation between towers.
Fig. 4 is an error convergence curve of the optimization algorithm of the first embodiment for selecting the regularization parameter.
FIG. 5 is a comparison chart of the evaluation indexes of the single task and multi-task models in the first embodiment.
Detailed Description
The present invention is further described below in conjunction with the following figures and specific examples so that those skilled in the art may better understand the present invention and practice it, but the examples are not intended to limit the present invention.
Example one
The production process of the silicone monomer fractionation process is shown in fig. 1, and the embodiment provides a multitask ash box prediction method for energy consumption of the silicone monomer fractionation process, wherein the method comprises the following steps:
the method comprises the following steps: and determining an ash box energy consumption prediction model structure, wherein the step of establishing a first principle model of each rectifying tower is included.
For example, in the fractionation process, each distillation column only focuses on one quality index, and is considered as a binary distillation column in the modeling process, and the structure of the binary distillation column can be shown in fig. 2. Because the mechanism of the rectification process is unchanged, the structure of the established first principle model of each rectification tower is consistent. For a continuous binary rectifying tower, constant molar flow assumption, full mixing grade assumption, vapor-liquid phase equilibrium assumption and uniform temperature and pressure distribution in the tower are carried out, and heat loss is ignored.
Illustratively, the energy consumption (change of reboiler heat load in unit time) y of the T ∈ {1,2, …, T } th rectifying tower is established by using an energy dynamic balance equation, a full tower energy balance equation, a material balance equation and a heat load definitiontAnd process variable
Figure BDA0003239961130000071
(feed flow rate F, reflux amount L, extraction amount D, condensate flow rate FC) The relationship between, i.e., the first principles model:
Figure BDA0003239961130000072
in the formula, btRepresenting the model error. w is at＝[w1t w2t w3t w4t]TIs the t-th rectifying tower model parameter vector to be estimated, wherein:
w1t＝HF+(q-1)HV'-qHL',
w2t＝HV'-HL',
w3t＝HV'-HL,
w4t＝cC(Tco-Tci).
in the formula, HFIs the enthalpy of the feed, q is the feed thermal condition parameter, HV'To raise the enthalpy of the vapour stream in the stripping section, HL'For decreasing the enthalpy of the liquid in the stripping section, HLLowering the enthalpy of the liquid in the rectification section, cCIs the specific heat capacity of the condensed water, TcoAnd TciRespectively condensate outlet temperature and inlet temperature.
Under a single stable operation condition, the model parameter wtThe first principle model is a linear model.
Step two: the method comprises the steps of obtaining operation data of an organosilicon fractionation process under a steady-state operation condition, randomly dividing the operation data into a training set and a testing set, and carrying out normalization processing on the training set and the testing set.
Illustratively, the input variables (feed flow F, reflux L, draw D, condensate flow F) of the energy consumption prediction models of the M3 and MH columns were collected via the Plant Information System (PI) real-time databaseC) The operation data is acquired by taking a mean value every 5min, calculating the model output (the change of the heat load of the reboiler in unit time) through the physical property parameters of the heat conducting oil at corresponding moments, selecting the operation data acquired by the M3 tower and the MH tower in a steady-state operation time period by combining an operation condition record table and PCA clustering, eliminating damaged sample points in the selected operation data, and performing normalization processing to eliminate the influence of dimensions.
Illustratively, the running data is randomly partitioned into training sets { (x)it,yit)，i＝1,2,…,ntWherein (x)it,yit) I training sample, n, representing the t tower training settRepresenting the number of training samples for that tower. Likewise, the operational data is randomly partitioned into test sets { (x)jt,yjt)，j＝1,2,…mtWherein (x)jt,yjt) J test sample, m, representing the t tower test settRepresenting the number of test samples for that tower.
Step three: training the first principle model of the rectifying tower by using the training set after normalization processing to obtain a model parameter vector to be estimated, and assuming the model parameter vector w based on the correlation among a plurality of rectifying towerstBy sharing a parameter w0And a specific parameter vtComposition in which the parameter w is shared0Capturing similarity between rectification columns, specific parameter vtCapturing differences between rectification columns and simultaneously estimating model parameters (w) using a multi-task learning algorithm0,v1,v2,…,vt,…,vT)。
Step four: and establishing an energy consumption model of the rectifying tower according to the model parameters.
Step five: and testing the energy consumption model of the rectifying tower by using the test set after normalization processing to obtain an energy consumption predicted value of the test sample in the test set, and comparing the energy consumption predicted value of the test sample with the energy consumption actual value of the test sample to evaluate the performance of the energy consumption model.
In step three, according to the first principle model of the tth rectifying tower in the known fractionation process of step one, the method comprises the following steps:
Figure BDA0003239961130000091
wherein the model parameter vector to be estimated is wt＝[w1t w2t w3t w4t]TModel input matrix
Figure BDA0003239961130000092
Model output matrix
Figure BDA0003239961130000093
Figure BDA0003239961130000094
Representing model errors, wherein
Figure BDA0003239961130000095
Is ntA unit column vector of dimensions. Based on the correlation between the M3 column and the MH column during fractionation, the model parameter w is assumedtBy sharing a parameter w0And a specific parameter vtComposition in which the parameter w is shared0Capturing similarity between rectification columns, specific parameter vtDifferences between the rectification columns were captured as shown in fig. 3.
In step three, the model parameters (w) are estimated simultaneously using a multitask learning algorithm0,v1,v2,…,vt,…,vT) The method comprises the following steps:
1) the objective function form of the multi-task learning algorithm is a least square support vector machine, and the objective function is as follows:
Figure BDA0003239961130000096
s.t.yit＝xit(w0+vt)+bt+ξit,
i＝1,2,…,nt,t＝1,2,…T
where γ is the constraint relaxation variable ξitThe relaxation variable represents the tolerance of the model to outliers, λ is the control-specific variable vtThe larger the lambda value, vtThe smaller the representative models are, the more similar the representative models are, the values of γ and λ are selected by the particle swarm optimization algorithm, the error convergence curve is shown in fig. 4, and the comparison model includes a Single-task gray-box (S-GB) model without the multi-task strategy and a gray-box (M-GB) model directly using mixed data for comparison. Multitask learning algorithm by solving simultaneously (w)0,v1,v2,…,vt,…,vT) And the sharing of knowledge among a plurality of towers is realized.
2) The minimization problem with equality constraints described above is solved using the lagrange multiplier method:
introducing Lagrange multiplier alpha ═ alpha1 … αt … αT]TSolving the above minimization problem with equality constraints, each element in alpha is greater than 0, wherein,
Figure BDA0003239961130000101
calculating a deviation to obtain
Figure BDA0003239961130000102
Solving the system of linear equations and converting it into a matrix form:
Figure BDA0003239961130000103
wherein the content of the first and second substances,
Figure BDA0003239961130000104
representing the output vector, b ═ b1 b2 … bt… bT]TRepresenting an error vector, Zt×tAll 0 matrices, Z, representing dimensions t x ttRepresenting a full 0-column vector of dimension t,
Figure BDA0003239961130000105
denotes a block diagonal matrix, H ═ blockdiag (H)1,H2,…,Ht)∈n×nRepresenting a positive definite matrix with diagonal elements as a block diagonal matrix Ht＝blockdiag(ht,ht,…,ht)，
Figure BDA0003239961130000106
3) Obtaining alpha ═ alpha by matrix inversion1 … αt … αT]TAnd
Figure BDA0003239961130000107
obtaining an estimated parameter of the energy consumption prediction model as
Figure BDA0003239961130000108
The regularization parameters γ and λ are selected and solved by a hyper-parameter search algorithm, which may be, but not limited to, an intelligent optimization algorithm such as grid search, random search, bayesian optimization, or particle swarm optimization, longicorn search, for example, as shown in fig. 4.
The objective function form of the multi-task learning algorithm may be, but is not limited to, a support vector machine and its extended form such as a least squares support vector machine, an approximate support vector machine, a sparse support vector machine, and the like.
The solution of the minimization problem with the constraint can be, but is not limited to, an intelligent optimization algorithm such as a sequence minimum optimization algorithm, a generalized sequence minimum optimization algorithm, a lagrange multiplier method, a penalty function method, a grid method, a feasible direction method, a linear approximation method, a generalized simple gradient method, a quadratic programming method or a particle swarm algorithm.
The solution of the large-scale linear equation system can be, but is not limited to, a matrix form solution, or an iterative algorithm such as an HS conjugate gradient method, Jacobi iteration, Gauss-Seidel, gradient descent and the like.
In the fourth step, the energy consumption model of the tth rectifying tower is as follows:
Figure BDA0003239961130000111
wherein the content of the first and second substances,
Figure BDA0003239961130000112
and (5) predicting output for the model.
In step five, for the prediction model of the t-th rectifying tower, the performance of the prediction model is evaluated by using the average Absolute error mae (mean Absolute error):
Figure BDA0003239961130000113
wherein the content of the first and second substances,
Figure BDA0003239961130000114
to test the predicted value of energy consumption of a sample, yjtThe actual value of the energy consumption of the test sample is obtained. FIG. 5 compares MAE of the single-tasking gray box model S-GB and the multi-tasking gray box model MT-GB of the trimethyl tower and the hydrogenous tower, and it can be clearly seen from the figure that the multi-tasking gray box model simultaneously improves the accuracy of the energy consumption prediction models of the trimethyl tower and the hydrogenous tower.
The index of the model performance evaluation may be, but is not limited to, mean square error, root mean square error, mean absolute percentage error, logarithm of mean square error, and median absolute error.
According to the method, the ash box model with the serial structure is used for energy consumption prediction, namely, unknown parameters of the first principle model are estimated through a multi-task learning algorithm, the first principle model solidifies mechanism knowledge of the rectifying tower, and model parameters related to process variables are estimated through operation data and the multi-task learning algorithm.
The invention provides an energy consumption multi-task ash box prediction model by utilizing the dynamic characteristics of a rectifying tower and the similarity between different rectifying towers in the organic silicon monomer fractionation process, the ash box model has higher precision than a simplified mechanism model and is more explanatory than a data driving model, meanwhile, the multi-task algorithm fully utilizes the correlation between different rectifying towers, realizes the sharing of knowledge between towers, improves the generalization performance of the model, and solves the defects that the energy consumption prediction method in the organic silicon monomer fractionation process in the prior art has high modeling cost, neglects the correlation between towers and is difficult to model under the condition of insufficient data.
Example two
The following introduces a multitask ash box prediction system for energy consumption in the organic silicon monomer fractionation process disclosed in the second embodiment of the present invention, and a multitask ash box prediction system for energy consumption in the organic silicon monomer fractionation process described below and a multitask ash box prediction method for energy consumption in the organic silicon monomer fractionation process described above may be referred to correspondingly.
The embodiment of the invention discloses a multitask ash box prediction system for energy consumption in an organic silicon monomer fractionation process.
The first principle model determining module is used for determining an ash box energy consumption prediction model structure and comprises a first principle model for each rectifying tower;
the data acquisition module is used for acquiring operation data of a steady-state operation condition in the organic silicon fractionation process, randomly dividing the operation data into a training set and a test set, and carrying out normalization processing on the training set and the test set;
the model parameter estimation module is used for utilizing the training set after normalization processingTraining the first principle model of the rectifying tower to obtain a model parameter vector to be estimated, and assuming the model parameter vector w based on the correlation among a plurality of rectifying towerstBy sharing a parameter w0And a specific parameter vtComposition in which the parameter w is shared0Capturing similarity between rectification columns, specific parameter vtCapturing differences between rectification columns and simultaneously estimating model parameters (w) using a multi-task learning algorithm0,v1,v2,…,vt,…,vT)；
The model establishing module is used for establishing an energy consumption model of the rectifying tower according to the model parameters;
the model test evaluation module is used for testing the energy consumption model of the rectifying tower by using the test set after normalization processing to obtain an energy consumption predicted value of the test sample in the test set, and comparing the energy consumption predicted value of the test sample with the energy consumption actual value of the test sample to evaluate the performance of the energy consumption model.
The multitask grey box prediction system for the energy consumption of the silicone monomer fractionation process is used for implementing the multitask grey box prediction method for the energy consumption of the silicone monomer fractionation process, so that the specific implementation of the system can be seen in the above example section of the multitask grey box prediction method for the energy consumption of the silicone monomer fractionation process, and therefore, the specific implementation of the system can refer to the description of the corresponding section example, and will not be further described herein.
In addition, since the multitask ash box prediction system for the energy consumption in the silicone monomer fractionation process is used for implementing the multitask ash box prediction method for the energy consumption in the silicone monomer fractionation process, the function of the multitask ash box prediction system corresponds to the function of the method, and details are not repeated here.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications of the invention may be made without departing from the spirit or scope of the invention.

Claims (10)

1. A multitask ash box prediction method for energy consumption in an organic silicon monomer fractionation process is characterized by comprising the following steps:
determining an ash box energy consumption prediction model structure, including determining a first principle model of each rectifying tower;
acquiring operation data of a steady-state operation condition in an organic silicon fractionation process, randomly dividing the operation data into a training set and a test set, and carrying out normalization processing on the training set and the test set;
training the first principle model of the rectifying tower by using the training set after normalization processing to obtain a model parameter vector to be estimated, and assuming the model parameter vector w based on the correlation among a plurality of rectifying towerstBy sharing a parameter w0And a specific parameter vtComposition in which the parameter w is shared0Capturing similarity between rectification columns, specific parameter vtCapturing differences among the rectifying towers, and simultaneously estimating model parameters by using a multi-task learning algorithm;
establishing an energy consumption model of the rectifying tower according to the model parameters;
and testing the energy consumption model of the rectifying tower by using the test set after normalization processing to obtain an energy consumption predicted value of the test sample in the test set, and comparing the energy consumption predicted value of the test sample with the energy consumption actual value of the test sample to evaluate the performance of the energy consumption model.
2. The multitask ash box prediction method for the energy consumption of the organic silicon monomer fractionation process according to claim 1, characterized in that: the operational data includes feed flow, reflux, draw and condensate flow.
3. The multitask ash box prediction method for the energy consumption of the organic silicon monomer fractionation process according to claim 1, characterized in that: randomly partitioning the operational data into training sets comprises:
randomly partitioning the operational data into training sets { (x)it,yit)，i＝1,2,…,ntWherein (x)it,yit) I training sample, n, representing the t tower training settRepresenting the number of training samples for that tower.
4. The multitask ash box prediction method for the energy consumption of the organic silicon monomer fractionation process according to claim 1, characterized in that: randomly partitioning the operational data into test sets comprises:
randomly partitioning the operational data into test sets { (x)jt,yjt)，j＝1,2,…mtWherein (x)jt,yjt) J test sample, m, representing the t tower test settRepresenting the number of test samples for that tower.
5. The multitask ash box prediction method for the energy consumption of the organic silicon monomer fractionation process according to claim 1, characterized in that: training the first principle model of the rectifying tower by using the training set after normalization processing to obtain a model parameter vector to be estimated, wherein the model parameter vector to be estimated comprises:
the first principle model of the determined tth rectifying tower is as follows:
Figure FDA0003239961120000021
wherein the model parameter vector to be estimated is wt＝[w1t w2t w3t w4t]TModel input matrix
Figure FDA0003239961120000022
Model output matrix
Figure FDA0003239961120000023
Representing model errors, wherein
Figure FDA0003239961120000024
Is ntA unit column vector of dimensions.
6. The method for multi-task grey box prediction of energy consumption in silicone monomer fractionation process according to claim 5, characterized in that: and simultaneously estimating the model parameters using a multi-task learning algorithm comprises:
the objective function form of the multi-task learning algorithm is a least square support vector machine, and the objective function is as follows:
Figure FDA0003239961120000025
s.t.yit＝xit(w0+vt)+bt+ξit,
i＝1,2,…,nt,t＝1,2,…T
wherein ξitRepresenting a relaxation variable representing the model's tolerance to outliers, and gamma representing a constrained relaxation variable ξitλ represents the control-specific variable vtThe regularization parameter of (a);
introducing Lagrange multiplier alpha ═ alpha1…αt…αT]TSolving the above minimization problem with equality constraints, each element in alpha is greater than 0, wherein,
Figure FDA0003239961120000031
calculating a deviation to obtain
Figure FDA0003239961120000032
Solving the system of linear equations and converting it into a matrix form:
Figure FDA0003239961120000033
wherein the content of the first and second substances,
Figure FDA0003239961120000034
representing the output vector, b ═ b1 b2…bt…bT]TRepresenting an error vector, Zt×tAll 0 matrices, Z, representing dimensions t x ttRepresenting a full 0-column vector of dimension t,
Figure FDA0003239961120000035
denotes a block diagonal matrix, H ═ blockdiag (H)1,H2,…,Ht)∈n×nRepresenting a positive definite matrix with diagonal elements as a block diagonal matrix Ht＝blockdiag(ht,ht,…,ht)，
Figure FDA0003239961120000036
Obtaining alpha ═ alpha by matrix inversion1…αt…αT]TAnd
Figure FDA0003239961120000037
obtaining an estimated parameter of the energy consumption prediction model as
Figure FDA0003239961120000038
7. The method for multi-task grey box prediction of energy consumption in silicone monomer fractionation process according to claim 6, characterized in that: and the regularization parameters gamma and lambda are selected and solved through a hyper-parameter search algorithm.
8. The method for multi-task grey box prediction of energy consumption in silicone monomer fractionation process according to claim 6, characterized in that: establishing the energy consumption model of the rectifying tower according to the model parameters comprises the following steps:
the energy consumption model of the t-th rectifying tower is established as follows:
Figure FDA0003239961120000041
in the formula (I), the compound is shown in the specification,
Figure FDA0003239961120000042
representing the model prediction output.
9. The multitask ash box prediction method for the energy consumption of the organic silicon monomer fractionation process according to claim 1, characterized in that: comparing the energy consumption predicted value of the test sample with the energy consumption actual value of the test sample to evaluate the performance of the energy consumption model comprises:
and (3) evaluating the performance of the model by using the mean absolute error MAE:
Figure FDA0003239961120000043
wherein the content of the first and second substances,
Figure FDA0003239961120000044
representing the predicted value of energy consumption, y, of the test samplejtRepresenting the actual value of energy consumption for the test sample.
10. A multitask ash box prediction system for energy consumption in an organic silicon monomer fractionation process is characterized by comprising the following steps:
the first principle model determining module is used for determining an ash box energy consumption prediction model structure and comprises a first principle model determining module for determining each rectifying tower;
the data acquisition module is used for acquiring the operation data of the steady-state operation condition in the organic silicon fractionation process, randomly dividing the operation data into a training set and a test set, and carrying out normalization processing on the training set and the test set;
a model parameter estimation module for training the first principle model of the rectifying tower by using the training set after normalization processing to obtain a model parameter vector to be estimated, and the model parameter vector is based on the phases among a plurality of rectifying towersRelevance, assuming a model parameter vector wtBy sharing a parameter w0And a specific parameter vtComposition in which the parameter w is shared0Capturing similarity between rectification columns, specific parameter vtCapturing differences among the rectifying towers, and simultaneously estimating model parameters by using a multi-task learning algorithm;
the model establishing module is used for establishing an energy consumption model of the rectifying tower according to the model parameters;
and the model test evaluation module is used for testing the energy consumption model of the rectifying tower by using the test set after normalization processing to obtain an energy consumption predicted value of the test sample in the test set, and comparing the energy consumption predicted value of the test sample with the energy consumption actual value of the test sample to evaluate the performance of the energy consumption model.
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