WO2020147461A1 - Procédé de modélisation et d'optimisation piloté par des données destiné à un dispositif de désasphaltage par solvant - Google Patents

Procédé de modélisation et d'optimisation piloté par des données destiné à un dispositif de désasphaltage par solvant Download PDF

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WO2020147461A1
WO2020147461A1 PCT/CN2019/124325 CN2019124325W WO2020147461A1 WO 2020147461 A1 WO2020147461 A1 WO 2020147461A1 CN 2019124325 W CN2019124325 W CN 2019124325W WO 2020147461 A1 WO2020147461 A1 WO 2020147461A1
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data
model
content
dao
solvent
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钱锋
钟伟民
隆建
杨明磊
杜文莉
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华东理工大学
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent

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  • the invention relates to a data-driven method for optimizing simulation and operating conditions of a solvent deasphalting device.
  • This method can be used to simulate the solvent deasphalting process, predict product yield and properties, and quickly and intelligently optimize the main operating conditions of the device.
  • the solvent deasphalting device is divided into two parts: the solvent extraction section and the solvent recovery section.
  • the principle of the core extraction section is the liquid-liquid extraction of the solvent-based on the solvent's ability to dissolve different chemical components in the residue to separate light and heavy Liquid phase.
  • the composition of residual oil is extremely complex.
  • Most of the existing studies use thermodynamic prediction of phase equilibrium data, supplemented by a small amount of experimental data as an extension, or divide new family components for each temperature fraction according to different K values.
  • the mechanism model mostly adopts trial and error method in the division of residue virtual family components, which requires continuous debugging based on experience, which has high complexity.
  • the invention aims to provide a data-driven simulation and optimization method for a solvent deasphalting device.
  • a data-driven modeling method for solvent deasphalting equipment includes:
  • the on-site data includes production and operation data of the solvent deasphalting device, raw material and product property analysis data;
  • PCA Principal component analysis
  • BP back propagation neural network
  • the preprocessing of the data includes data reconciliation, cleaning, transformation and reduction.
  • the preprocessing of the data includes after removing the abnormal data points, processing the DCS data into a 12h mean value form, and performing an interpolation operation on the default value of the property data for the corresponding time period and operation Combine the data and normalize it to the interval [-1, 1].
  • the characteristic variables include input variables for the DAO yield model and variables of the DAO desulfurization and carbon residue removal model;
  • the input variables of the DAO yield model include residual oil density ⁇ VR , kinematic viscosity ⁇ VR , residual carbon mass fraction w VR , sulfur content w S , iron content w Fe , nickel content w Ni , vanadium content w V.
  • Saturated content w SH aromatic content w AH , colloidal content w R , asphaltene content w A , extraction tower inlet temperature T R1I , extraction tower top temperature T R1T , extraction tower bottom temperature T R1B , The settling tower inlet temperature T R2I , the settling tower top pressure P R2T , the settling tower sub-solvent temperature T R2FRJ , the cold slag feed flow rate F VR and the solvent ratio R total 19-dimensional input characteristic variables.
  • the variables of the DAO desulfurization and decarbonization model include residual oil density ⁇ VR , kinematic viscosity ⁇ VR , sulfur content w S , iron content W Fe , nickel content w Ni , saturation content w SH , aromatic content content w AH , colloid content w R , asphaltene content w A , extraction tower inlet temperature T R1I , extraction tower top temperature T R1T , settling tower top pressure P R2T , settling tower top temperature T R2T , settling tower secondary solvent temperature T R2FRJ , cold slag feed flow rate F VR and solvent ratio R total 16-dimensional characteristic variables.
  • the PCA method is used to reduce the dimensionality of feature variables, and components with feature contribution rates greater than 99% are selected as modeling variables.
  • a data-driven method for optimizing a solvent deasphalting device includes using the method provided by the present invention to construct a data-driven solvent deasphalting device model to optimize the solvent deasphalting device
  • the optimization includes: using a particle swarm algorithm based on differential evolution and using a trained model to optimize the operating conditions of the solvent deasphalting device according to the production target.
  • the optimization target is defined as:
  • the decision variable X is the operating conditions of the solvent deasphalting device.
  • y DAO represents the output of the DAO yield model, which is the yield of the product deasphalted oil.
  • the DAO yield model is linked with the desulfurization and decarbonization model, and the device is operated through the DAO yield model according to the production goal of maximizing the yield of deasphalted oil
  • the conditions are optimized, and the operating conditions generated in each iteration are sent to the desulfurization and decarbonization model to obtain the sulfur and carbon residue content of the deasphalted oil, and determine whether the value meets the constraints, and set the temperature at the top of the extraction tower to be greater than The temperature at the bottom of the extraction tower to ensure the executable of the optimized results.
  • the present invention solves the problems in the prior art by providing a simulation method for a solvent deasphalting device.
  • Figure 1 is a schematic diagram of the solvent deasphalting process.
  • Figure 2 is a distribution diagram of the DAO sample yield.
  • Figure 3 is the PCA-BP model structure.
  • Figure 4 shows the effect of DAO yield model on yield.
  • Figure 5 shows the simulation effect of DAO desulfurization and carbon residue removal model on carbon residue.
  • Figure 6 is the simulation effect of the DAO desulfurization and decarbonization model on the sulfur content.
  • Figure 7 shows the effect of simulated operating conditions on the DAO yield.
  • Figure 8 shows the effect of simulated operating conditions on the DAO sulfur and carbon residue content.
  • the inventor provided a method for predicting the product yield and sulfur and residual carbon content of a solvent deasphalting device based on industrial data, and an optimization method for device operating conditions.
  • this method uses the methods of data reconciliation, cleaning, transformation, and reduction to preprocess the field data to eliminate fault points and smooth the data; after preprocessing
  • the characteristic variables are selected according to the process principle and the characteristics of the production process, and the characteristic variables that have a greater impact on the DAO yield, DAO sulfur content and residual carbon content are screened out; the principal component analysis method ( PCA) dimensionality reduction to reduce the impact of noise;
  • the dimensionality reduction data is input into the neural network model for training, and the DAO yield and DAO desulfurization and carbon residue removal model are obtained;
  • the particle swarm algorithm based on differential evolution is used to build The model optimizes the operating conditions, and obtains the operating conditions that the DAO
  • the data-driven simulation and optimization method of a solvent deasphalting device includes the following steps:
  • Step 1 Collect production operation data, raw material and product property analysis data of solvent deasphalting device
  • Step 2 Perform data reconciliation, cleaning, transformation and reduction of the missing and redundant parts of the field data
  • Step 3 Preliminarily screen the characteristic variables in the data obtained in Step 2 according to the process principle and process characteristics;
  • Step 4 Use principal component analysis (PCA) to reduce the dimensionality of the data set filtered by the feature variable in step 3 to eliminate abnormal noise;
  • PCA principal component analysis
  • Step 5 The data set after dimensionality reduction in step 4 is modeled by back propagation (BP) neural network, and the DAO yield model and the desulfurization and decarbonization model are established respectively;
  • BP back propagation
  • Step 6 Use the particle swarm algorithm based on differential evolution and use the trained model to optimize the operating conditions of the solvent deasphalting device according to the production target.
  • the optimization target is defined as:
  • the decision variable X is the operating conditions of the solvent deasphalting device.
  • y DAO represents the output of the DAO yield model, which is the yield of the product deasphalted oil.
  • step one the production operation data, raw material and product analysis data of the solvent deasphalting device are collected on site.
  • the production operation data is generally measured in seconds. Considering that there is a certain time lag in the industrial process, in one embodiment of the present invention, after removing data points with abnormal noise, the production operation data is processed into a 12h average form.
  • the analysis data usually has a daily analysis cycle, and the default value of the material analysis data corresponding to the production operation data is interpolated, and finally combined with the production operation data. Finally, a data set corresponding to the properties of raw materials, operating conditions and product properties is obtained and normalized to the interval [-1, 1].
  • modeling variables are screened for DAO yield and sulfur and carbon residue indicators respectively.
  • the input variables of the DAO yield model include residual oil density ⁇ VR and kinematic viscosity ⁇ VR , residual carbon mass fraction w VR , sulfur content w S , iron content w Fe , nickel content w Ni , vanadium content w V , saturation content w SH , aromatic content w AH , colloidal content w R , asphaltene Content w A , extraction tower inlet temperature T R1I , extraction tower top temperature T R1T , extraction tower bottom temperature T R1B , settling tower inlet temperature T R2I , settling tower top pressure P R2T , settling tower secondary solvent temperature T R2FRJ ,
  • the cold slag feed flow rate F VR and the solvent ratio R are 19-dimensional input characteristic variables.
  • the DAO desulfurization and decarbonization model includes residual oil density ⁇ VR , kinematic viscosity ⁇ VR , sulfur content w S , iron content w Fe , and nickel content w Ni , saturation content w SH , aromatic content w AH , colloidal content w R , asphaltene content w A , extraction tower inlet temperature T R1I , extraction tower top temperature T R1T , settling tower top pressure P R2T , sedimentation The temperature at the top of the tower T R2T , the temperature of the secondary solvent in the settling tower T R2FRJ , the cold slag feed flow rate F VR and the solvent ratio R total 16-dimensional characteristic variables.
  • step four the principal component analysis method is used to reduce the dimension of the selected feature subset, compress it to a low-dimensional space, and remove the noise in the original feature.
  • PCA is used to reduce the dimensionality of the characteristic variables of the solvent deasphalting process, and components with a characteristic contribution rate greater than 99% are selected as modeling variables.
  • back-propagation neural network is used to establish the models of DAO yield, DAO sulfur content, and residual carbon content.
  • the input is 19-dimensional raw material properties and operating conditions, and the output is the DAO yield;
  • the input is 16-dimensional The nature of raw materials and operating conditions, the output is DAO carbon residue content and sulfur content.
  • the particle swarm optimization algorithm based on differential evolution is used to link the DAO yield model with the desulfurization and decarbonization model, and according to the production goal of maximizing the yield of deasphalted oil, through DAO
  • the yield model optimizes the operating conditions of the device, and sends the operating conditions generated in each iteration to the desulfurization and decarbonization model to obtain the sulfur and carbon residue content of the deasphalted oil, and determine whether the value meets the constraints and set
  • the temperature at the top of the extraction tower is greater than the temperature at the bottom of the extraction tower to ensure the executable of the optimized results.
  • step 6 a particle swarm algorithm based on differential evolution is used to optimize the model.
  • the optimization goal is defined as:
  • the decision variable X is the operating conditions of the solvent deasphalting unit, including extraction tower inlet temperature T R1I , extraction tower top temperature T R1T , extraction tower bottom temperature T R1B , settling tower inlet temperature T R2I , settling tower top pressure P R2T , the sub-solvent temperature T R2FRJ of the settling tower, the cold slag feed flow rate F VR and the solvent ratio R total 8 dimensional variables.
  • y DAO represents the output of the trained DAO yield model.
  • the method of the present invention is simple, fast and practical, and avoids the tediousness and uncertainty of artificially dividing the virtual components of the feed into groups and determining the content by trial and error.
  • the present invention uses a neural network to realize rapid prediction of product DAO yield and its sulfur and residual carbon content; adopts swarm intelligence optimization algorithm to optimize the operating conditions of the device, so that it can meet the demand for maximizing production benefits.
  • the process flow of the device is shown in Figure 1.
  • the whole device is mainly divided into two parts: subcritical extraction and supercritical solvent recovery.
  • the vacuum residue from the previous device is mixed with the solvent and then enters the extraction tower, and the heavy deasphalted solution from the top enters the colloid settling tower for further sedimentation and separation.
  • the DAO solution drawn from the top of the settling tower is heated and enters the stripping system for solvent recovery.
  • the colloidal solution drawn from the bottom of the settling tower and the DOA solution drawn from the bottom of the extraction tower are mixed and heated to enter the stripping tower to recover the solvent.
  • the recovered solvent is returned to the tower for recycling.
  • the sources of factory data are generally divided into two types, one is the device operating condition data collected by the distributed distributed control system (DCS), and the other is the raw material and product analysis data collected by the laboratory information management system (LIMS). Due to the limitation of the reliability of on-site testing instruments, the data directly obtained from DCS often has problems such as material imbalance and heat imbalance, so it cannot be directly used to establish device models. In order to ensure the accuracy of the model sample data, it is necessary to establish cleaning and reconciliation standards for the data collected in real time.
  • DCS distributed distributed control system
  • LIMS laboratory information management system
  • Normalization reduces the influence of the inconsistency of magnitude and dimension between features on the model, and speeds up the solution of the model.
  • the purpose of introducing principal component analysis is to use as few input variables as possible to complete the estimation of the variables to be measured, remove the interference information in the original variables, retain useful information, and simplify the input of the model.
  • ⁇ is the sample mean and ⁇ is the sample standard deviation.
  • the variance contribution rate of all variables is usually used to determine the principal elements that should be retained. Define the variance contribution rate of the first k inputs:
  • a principal element with a contribution rate greater than 99% is selected as the input of the subsequent neural network.
  • the data after dimensionality reduction by PCA is used as the input of BP neural network to establish DAO yield and DAO sulfur content and residual carbon content models respectively.
  • the present invention adopts back propagation (BP) neural network, which is a neural network structure most widely used in process control.
  • BP neural network is shown in Figure 3.
  • FS Feature Selection
  • the n features with the highest importance are selected as the new feature subset
  • X FS ⁇ x 1 , x 2 ,..., x n ⁇ .
  • X FS selects the first k principal elements P 1 , P 2 ,..., P k with the largest contribution after PCA dimensionality reduction as the input of the BP neural network.
  • the neural network is composed of an input layer, a hidden layer and an output layer: the input layer has a total of k neurons, the hidden layer has a total of q nodes, and the output layer has p outputs.
  • the connection weights between the input layer and the hidden layer, and between the hidden layer and the output layer are W k ⁇ q and V q ⁇ p respectively .
  • the hidden layer and output layer neurons of the neural network adopt the Sigmoid nonlinear activation function.
  • the purpose of learning is to find a series of weights so that after each set of input vectors of the sample acts on the network, the actual output vector of the network and the expected output of the sample The vectors are consistent.
  • the whole process is to adjust the connection weight of each neuron in the network to minimize the error energy function of the network:
  • I the predicted output of the input sample x k .
  • the error backpropagation (BP) algorithm is used for training, and the Levenberg-Marquart algorithm with faster search speed is used to replace the traditional gradient descent method to minimize the error square sum.
  • H is the number of neurons in the hidden layer
  • m is the number of neurons in the input layer
  • n is the number of neurons in the output layer
  • L is a constant between 1 and 10.
  • the PCA-BP models of DAO yield and sulfur and carbon residue content were established respectively.
  • the network structure of the DAO yield model is: 13-7-1
  • Figure 4 is the comparison between the simulated results of the test data on the yield model and the actual value
  • the network structure of the DAO desulfurization and carbon residue removal model is 13-6-2, Figure 4 5.
  • Figure 6 shows the comparison between the simulated results of the residual carbon content and the sulfur content and the actual values
  • Figures 7 and 8 show the effect of changes in operating conditions on the yield and properties of DAO.
  • Table 1 shows the prediction accuracy of the model. It can be seen that the model has a high simulation accuracy for DAO yield and carbon residue and sulfur content.
  • the production optimization direction of solvent deasphalting equipment is usually to find the best operating conditions that maximize the yield of deasphalted oil under the condition that the properties of deasphalted oil meet the constraint requirements.
  • the intelligent search algorithm based on the built DAO yield and property simulation model, the key operating conditions of the solvent deasphalting process can be optimized according to this production demand.
  • PSO Particle Swarm Optimization
  • PSO organically combines the individuality and sociality of the particles in the group to guide the search. It has the characteristics of simple and easy implementation and fast convergence speed, but it also has the problem of easily falling into a local optimal solution.
  • Differential evolution algorithm (Differential Evolution, DE), like PSO algorithm, is also an optimization algorithm based on the theory of swarm intelligence. The competition and cooperation between populations are the basis for optimized search among individuals within a group.
  • the mutation mechanism of the algorithm that is, the method of generating offspring is:
  • r′ r 1 +F(r 2 -r 3 ) (7)
  • r 1 , r 2 and r 3 are 3 different individuals randomly selected from the evolutionary group. r'is a new individual generated from three randomly selected individuals.
  • DE algorithm does not depend on specific problems and has the ability to memorize individual optimal solutions. At the same time, DE algorithm also has problems such as slow convergence speed in the later stage and insufficient robustness.
  • the present invention introduces a differential evolution correction strategy on the basis of PSO, that is, performs a differential after each iteration.
  • the evolution operator is based on the crossover and mutation of the individual's current optimal solution. Replace the current optimal solution with the optimal solution with better fitness after evolution, guide the PSO to search for the optimal solution, and increase its ability to jump out of the local optimal solution.
  • the industry generally hopes to maximize the yield of DAO while ensuring that the production index meets a certain requirement.
  • carbon residue content and sulfur content are more important indicators to measure the yield of DAO.
  • the DE-PSO algorithm is now used to optimize the main operating conditions of the solvent deasphalting unit.
  • the operating conditions generated by each iteration of the DAO yield model are sent to the desulfurization and decarbonization model, and then the deasphalted oil under the corresponding operating conditions is obtained.
  • the sulfur content and residual carbon value are compared with the set deasphalted oil sulfur and residual carbon constraint indicators to determine the final executable optimal operating conditions.
  • the given quality index is CCR ⁇ 7%, S ⁇ 1.8%.
  • the model prediction result is used as the individual fitness value:
  • F i is the fitness value of individual i
  • y DAOi is the model prediction value of the yield of individual i.
  • the optimization goal is defined as:
  • the decision variable X is the operating conditions of the solvent deasphalting unit, including extraction tower inlet temperature T R1I , extraction tower top temperature T R1T , extraction tower bottom temperature T R1B , settling tower inlet temperature T R2I , settling tower top pressure P R2T , the sub-solvent temperature T R2FRJ of the settling tower, the cold slag feed flow rate F VR and the solvent ratio R total 8 dimensional variables.
  • y DAO represents the output of the DAO yield model.
  • Three sets of LIMS raw material data are selected as shown in Table 2. Under the condition that the properties of the raw materials remain unchanged, the operating conditions that maximize F i are found. In order to obtain the optimal operating conditions within the set carbon residue index range and conform to the principle of the extraction process, set the constraint condition as T R1T >T R1B , that is to ensure that the temperature at the top of the extraction tower is greater than the temperature at the bottom of the tower during the optimization process . In each iteration process, the value obtained by the yield model is sent to the desulfurization and decarbonization model to obtain the residual carbon content, and the particles that do not meet the requirements are punished, as shown in formula (10).

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Abstract

L'invention concerne un procédé de modélisation et d'optimisation piloté par des données destiné à un dispositif de désasphaltage par un solvant. Le procédé de modélisation piloté par des données destiné à un dispositif de désasphaltage par un solvant consiste à : (1) pré-traiter des données sur site, les données sur site comprenant des données d'opération de production, des données d'analyse de matière première et de propriété de produit dudit dispositif ; (2) sélectionner des variables de caractéristique, la sélection étant réalisée selon des principes et des processus technologiques, (3) réduire la dimensionnalité des variables de caractéristique par une analyse en composantes principales (ACP) ; et modéliser selon un ensemble de données de dimensionnalité réduite à l'aide d'un réseau de neurones à propagation arrière (BP) pour créer un modèle de rendement d'huile désasphaltée (DAO) ainsi qu'un modèle de désulfuration et d'élimination du carbone résiduel, respectivement. Le procédé d'optimisation comprend une étape d'optimisation des conditions de fonctionnement dudit dispositif à l'aide du modèle piloté par des données construit dudit dispositif.
PCT/CN2019/124325 2019-01-18 2019-12-10 Procédé de modélisation et d'optimisation piloté par des données destiné à un dispositif de désasphaltage par solvant WO2020147461A1 (fr)

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