CN113468710A - Petrochemical production auxiliary operation method - Google Patents
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- 238000004519 manufacturing process Methods 0.000 title claims abstract description 146
- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000005094 computer simulation Methods 0.000 claims abstract description 35
- 238000012549 training Methods 0.000 claims abstract description 32
- 238000013486 operation strategy Methods 0.000 claims abstract description 21
- 230000009471 action Effects 0.000 claims abstract description 10
- 241000196324 Embryophyta Species 0.000 claims description 23
- 238000010977 unit operation Methods 0.000 claims description 10
- 238000013528 artificial neural network Methods 0.000 claims description 6
- 241000183024 Populus tremula Species 0.000 claims description 5
- 239000000463 material Substances 0.000 claims description 4
- 230000008569 process Effects 0.000 description 13
- VGGSQFUCUMXWEO-UHFFFAOYSA-N Ethene Chemical compound C=C VGGSQFUCUMXWEO-UHFFFAOYSA-N 0.000 description 8
- 239000005977 Ethylene Substances 0.000 description 8
- 238000010992 reflux Methods 0.000 description 8
- 238000011017 operating method Methods 0.000 description 5
- 238000000926 separation method Methods 0.000 description 5
- 238000004523 catalytic cracking Methods 0.000 description 3
- 239000002994 raw material Substances 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 239000002151 riboflavin Substances 0.000 description 2
- 230000009897 systematic effect Effects 0.000 description 2
- 239000004743 Polypropylene Substances 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 210000004556 brain Anatomy 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- -1 polypropylene Polymers 0.000 description 1
- 229920001155 polypropylene Polymers 0.000 description 1
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Abstract
The invention discloses an auxiliary operation method for petrochemical production, which comprises the following steps: step 1: establishing a dynamic simulation model of the production process; step 2: setting parameters of a dynamic simulation model to simulate scenes and operation actions in production and acquiring corresponding output data; and step 3: repeating the step 2 to obtain an operation strategy training data set; and 4, step 4: performing automatic auxiliary operation strategy training by using a data set to obtain an automatic auxiliary operation model; and 5: and taking the actual production parameters as input data of an automatic auxiliary operation model, and outputting the model for the auxiliary operation. According to the invention, the auxiliary operation model is obtained through the dynamic model and the training data set, when any fluctuation occurs in the production process, a large number of alarms and chain signals in the production can be quickly responded, and correct operation adjustment actions can be timely made, so that the production process is quickly recovered to a normal operation state, and production accidents caused by artificial response chaos and time delay are effectively avoided.
Description
Technical Field
The invention belongs to the field of petrochemical production, and particularly relates to an auxiliary operating method for petrochemical production.
Background
With the development of economy, the petrochemical industry, as a supporting industry of national economy, is rapidly developing. With the increasingly complex production process and the increasingly large-scale devices, petrochemical production operators rely on the automatic control system represented by the DCS (distributed control system) system to control the production process. When the production process is relatively stable, the relative pressure of an operator is low, the production process does not need to be interfered too much, and once the production process fluctuates, a large number of alarm and interlocking signals can be intensively gushed into the system to prompt the operator to adjust the operation parameters as soon as possible so as to recover the normal production. The operator needs to read a large amount of data, judge the production state and make operation adjustment actions in a very short time, which is a great challenge for the operator, and serious consequences can be caused once the operator does not properly respond.
In the industry, computer-assisted operation is always used to assist an operator to deal with the scenes, and a common scheme is an expert system, namely, different production scenes are preset, and an operation strategy for dealing with the scenes is customized in the system to realize auxiliary operation. However, the expert system is limited by the setting of the personnel for establishing the system on the production scene, and because the control of the current petrochemical production process depends on a control system consisting of thousands of sensors, control loops and instrument valves and has the characteristics of strong coupling and nonlinearity, the production scene which can be preset by the expert system is extremely limited, and the expert system cannot cope with the complex scene faced by the actual production process, which is also the main reason that the expert system is not widely popularized in the field of petrochemical production.
With the progress of computer technology, researchers have proposed using big data technology to generate an auxiliary operation strategy, and the main idea is to generate an auxiliary operation strategy by using a large amount of data generated in a daily petrochemical production process as a training data set. The problem with this approach is that although the petrochemical production process produces a huge amount of data, the data produced is essentially useless for performing operational strategy training since production is smoothly ongoing most of the time. Even if there is a small amount of data of working conditions such as production fluctuation, accidents and the like, the data are limited by the operation history of a specific production device, and coverage of different production scenes is difficult to realize, so that the applicability of the trained strategy is weakened.
Therefore, there is a need for an auxiliary operating method for petrochemical production based on the characteristics of the petrochemical production process itself.
Disclosure of Invention
The invention aims to provide an auxiliary operating method for petrochemical production according to the characteristics of a petrochemical production process.
In order to achieve the above object, the present invention provides a petrochemical production auxiliary operating method, comprising: step 1: establishing a dynamic simulation model of the production process; step 2: setting parameters of the dynamic simulation model to simulate scenes and operation actions in production and obtain corresponding output data; and step 3: repeating the step 2, and operating a strategy training data set; and 4, step 4: performing automatic auxiliary operation strategy training by applying the data set to obtain an automatic auxiliary operation model; and 5: and taking actual production parameters as input data of the automatic auxiliary operation model, and outputting the model for auxiliary operation.
Preferably, the production process includes at least one of a full plant production process, a partial production process, and a unit operation production process of petrochemical production.
Preferably, the parameters include at least one of raw material, product, equipment and meter control parameters.
Preferably, the instrument control parameter comprises at least one of temperature, pressure, flow rate, and material composition.
Preferably, the production scenario includes a normal production state and an accident state.
Preferably, in the step 4, the set parameters of the dynamic simulation model are used as input data, the data set is used as output data, and the automatic auxiliary operation strategy training is performed through a neural network algorithm.
The dynamic simulation model is preferably established by dynamic simulation software.
Preferably, the apparatus comprises at least one of a column, a vessel, a pump, a heat exchanger, a compressor, a reactor, a dryer, and a filter.
Preferably, the dynamic simulation model comprises a plant-wide production process model or a unit operation model of a local plant.
Preferably, the dynamic simulation software comprises: aspen HYSYS software or dynmsi software.
The invention has the beneficial effects that: according to the auxiliary operation method for petrochemical production, a dynamic simulation model is established, training of an automatic auxiliary operation strategy is performed by using training data sets collected under different working conditions in different production scenes, an auxiliary operation model is obtained, and auxiliary operation of petrochemical production is achieved based on the auxiliary operation model. Through the dynamic simulation model, under the condition of less manpower and cost investment, the data of working conditions such as production fluctuation, production accidents and the like which are difficult to obtain in the actual petrochemical production process are obtained, and the data are not limited by the operation history of a specific production device, so that the collected training data set is comprehensive to the training of an automatic auxiliary strategy, systematic and effective.
The method of the present invention has other features and advantages which will be apparent from or are set forth in detail in the accompanying drawings and the following detailed description, which are incorporated herein, and which together serve to explain certain principles of the invention.
Drawings
The above and other objects, features and advantages of the present invention will become more apparent by describing in more detail exemplary embodiments thereof with reference to the attached drawings. Wherein like reference numerals generally refer to like parts throughout the exemplary embodiments of the invention.
FIG. 1 illustrates a flow diagram of a petrochemical production sub-operation method according to one embodiment of the present invention.
FIG. 2 illustrates a dynamic simulation model of an ethylene plant depropanizer unit of a petrochemical production assisted operation process according to one embodiment of the present invention.
FIC-010, flow controller; c-100, a depropanizing tower; e-101, a tower top condenser; v-102, a reflux tank; p-103 and a reflux pump.
Detailed Description
Preferred embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While the preferred embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
A petrochemical production auxiliary operation method according to the present invention comprises: step 1: establishing a dynamic simulation model of the production process; step 2: setting parameters of a dynamic simulation model to simulate scenes and operation actions in production and acquiring corresponding output data; and step 3: repeating the step 2 to obtain an operation strategy training data set; and 4, step 4: performing automatic auxiliary operation strategy training by using a data set to obtain an automatic auxiliary operation model; and 5: and taking the actual production parameters as input data of an automatic auxiliary operation model, and outputting the model for the auxiliary operation.
Specifically, (1) aiming at a specified petrochemical production process, a dynamic simulation model of the production process is established;
(2) in the established dynamic model, a production process scene is established by setting various parameters controlled by raw materials, products, equipment and instruments;
(3) in the established dynamic model, the operation action in the actual production is simulated by changing the operation parameters; if the opening of a valve of the equipment is adjusted or a certain valve is closed, various possible working conditions such as operation action, misoperation or failure of a certain control loop in actual production are simulated;
(4) collecting all output data of the dynamic simulation model after the operating parameters are changed;
(5) repeating the processes (2) to (4) to obtain simulation data of various production scenes under various working conditions to form a training data set;
(6) based on the training data set obtained in the step (5), carrying out automatic auxiliary operation strategy training by means of a neural network algorithm to obtain an automatic auxiliary operation model;
(7) and (4) applying the auxiliary operation model obtained by training in the step (6) to the production process to realize auxiliary operation on the production process under different production scenes. Specifically, parameters of working conditions and operation actions in actual production are used as input data of the automatic auxiliary operation model, the automatic auxiliary operation model judges a production scene according to the input data, and when the actual production scene is the same as the production scene existing in the model, an instruction corresponding to the production scene is output so as to carry out corresponding production operation, and auxiliary operation of a production process under different production scenes is realized.
According to an exemplary implementation mode, the auxiliary operation method for petrochemical production establishes a dynamic simulation model, utilizes operation strategy training data sets collected under different working conditions in different production scenes to perform automatic auxiliary operation strategy training to obtain an auxiliary operation model, and realizes auxiliary operation of petrochemical production based on the auxiliary operation model. Through the dynamic simulation model, under the condition of less manpower and cost investment, the data of working conditions such as production fluctuation, production accidents and the like which are difficult to obtain in the actual petrochemical production process are obtained, and the data are not limited by the operation history of a specific production device, so that the collected training data set is comprehensive to the training of an automatic auxiliary strategy, systematic and effective.
Preferably, the production process includes at least one of a full plant production process, a partial production process, and a unit operation production process of petrochemical production.
Specifically, the whole device production process is a whole integrated production process established according to actual needs, such as an integrated petrochemical process.
The local production process is one or more, but not all, of the whole plant production process, for example, an ethylene plant, a polypropylene plant production process, or a local plant production process such as a cold zone, a hot zone, etc. in an ethylene plant. The unit operation process may be one of the whole plant processes or may be a single process, such as a rectifying column separation process.
Preferably, the parameter comprises at least one of a feedstock, product, equipment and meter control parameter.
Preferably, the instrument control parameter comprises at least one of temperature, pressure, flow rate and material composition.
Preferably, the production scenario includes a normal production state and an accident state. Specifically, the production scene conditions include a normal production state, an accident state or several superimposed accident states. The accident state comprises a misoperation state, an instrument control loop failure state, equipment failure or instrument failure and the like.
Preferably, in step 4, the parameters of the set dynamic simulation model are used as input data, the data set is used as output data, and the automatic auxiliary operation strategy training is performed through a neural network algorithm.
Specifically, parameters of a set dynamic simulation model are used as input data, a data set is used as output data, automatic auxiliary operation strategy training is carried out by means of a neural network algorithm, the human brain is simulated, hierarchical training is carried out, an automatic auxiliary operation model, namely an automatic auxiliary operation method, is obtained, and meanwhile self evolution and optimization of the automatic auxiliary operation model are achieved in the practical application production process.
Preferably, the apparatus comprises at least one of a column, a vessel, a pump, a heat exchanger, a compressor, a reactor, a dryer, and a filter. Preferably, the production process comprises at least one of a depropanization process of a depropanizer of an ethylene plant, a separation process of a rectifying tower and a catalytic cracking process.
For example, the production process includes at least one of a depropanization process of a depropanizer of an ethylene plant, a rectification column separation process, a catalytic cracking process.
As a preferred scheme, a dynamic simulation model is established through dynamic simulation software.
Preferably, the dynamic simulation model comprises a plant-wide production process model or a unit operation model of a local plant.
Specifically, the dynamic simulation model for the production process may be a production process model of a full plant, or a unit operation model of a local plant, according to actual needs.
Preferably, the dynamic simulation software comprises: aspen HYSYS software or dynmsi software.
Specifically, the dynamic model may be created using Aspen HYSYS or DYNSIM software, or other software that can implement creation of a petrochemical plant process dynamic model.
Example one
FIG. 1 illustrates a flow diagram of a petrochemical production sub-operation method according to one embodiment of the present invention. FIG. 2 illustrates a dynamic simulation model of an ethylene plant depropanizer unit of a petrochemical production assisted operation process according to one embodiment of the present invention.
Referring to fig. 1 and 2, the petrochemical production auxiliary operating method includes:
step 1: establishing a dynamic simulation model of the production process;
wherein, a dynamic simulation model is established through dynamic simulation software.
Wherein, dynamic simulation software includes: aspen HYSYS software or dynmsi software.
Wherein the production process comprises at least one of a full-plant production process, a partial production process and a unit operation production process of petrochemical production.
Wherein the dynamic simulation model comprises a production process model of the whole plant or a unit operation model of the local plant.
For example, the dynamic simulation software Aspen HYSYS is adopted to create a dynamic simulation model of the depropanizer unit of the ethylene plant, and the created dynamic simulation model is shown in fig. 2;
step 2: setting parameters of a dynamic simulation model to simulate scenes and operation actions in production and acquiring corresponding output data;
wherein the parameters include at least one of raw material, product, equipment and instrument control parameters.
Wherein, the instrument control parameter comprises at least one of temperature, pressure, flow and material composition.
For example, the dynamic simulation model in fig. 2 is set with parameters such as the feed composition, the equipment size, the operation parameters, and the instrument control of the depropanizer, so as to establish a production process scenario of the separation operation unit of the rectification tower, and make the production scenario in a stable state during the operation.
Then, changing operation parameters, for example, adjusting the set flow of a flow controller FIC-010, namely adjusting the opening of an adjusting valve FCV-002, increasing the reflux amount from original 7125kg/h to 9000kg/h, showing that the temperature and pressure of the tower top tower kettle are all reduced, the light component content of the tower top composition is increased, the heavy component content is reduced and other various changes, and collecting and outputting various parameter changes caused by the increase of the reflux amount in a dynamic model; or adjusting the set flow rate of the depropanizer C-100 or the overhead condenser E-101 or the reflux drum V-102 or the reflux pump P-103, or adjusting the set flow rate of one or more of the depropanizer C-100 or the overhead condenser E-101 or the reflux drum V-102 or the reflux pump P-103.
And step 3: repeating the step 2 to obtain an operation strategy training data set;
the operation parameters are changed for many times, various production scenes and various operation working conditions which cannot be realized in actual production can be comprehensively covered, and a data set for training is obtained.
And 4, step 4: performing automatic auxiliary operation strategy training by using a data set to obtain an automatic auxiliary operation model;
in step 4, the parameters of the set dynamic simulation model are used as input data, the data set is used as output data, and automatic auxiliary operation strategy training is performed through a neural network algorithm.
And 5: and taking the actual production parameters as input data of an automatic auxiliary operation model, and outputting the model for the auxiliary operation.
Wherein the production process comprises at least one of a depropanization process, a rectifying tower separation process and a catalytic cracking process of a depropanization tower of the ethylene device.
The production scene comprises a normal production state and an accident state.
The accident state can be one or a plurality of superposition states, wherein the accident state comprises a misoperation state, an instrument control loop failure state, an equipment failure or an instrument failure.
Wherein the apparatus comprises at least one of a column, a vessel, a pump, a heat exchanger, a condenser, a compressor, a reactor, a dryer, and a filter.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the illustrated embodiments.
Claims (10)
1. A petrochemical production sub-operation method, comprising:
step 1: establishing a dynamic simulation model of the production process;
step 2: setting parameters of the dynamic simulation model to simulate scenes and operation actions in production and obtain corresponding output data;
and step 3: repeating the step 2 to obtain an operation strategy training data set;
and 4, step 4: performing automatic auxiliary operation strategy training by applying the data set to obtain an automatic auxiliary operation model;
and 5: and taking actual production parameters as input data of the automatic auxiliary operation model, and outputting the model for auxiliary operation.
2. The petrochemical production sub-operation method according to claim 1, wherein the production process comprises at least one of a full plant production process, a partial production process, and a unit operation production process of petrochemical production.
3. The petrochemical production sub-operation method according to claim 1, wherein the parameter comprises at least one of a feedstock, a product, a plant, and an instrument control parameter.
4. The petrochemical production sub-operation method according to claim 3, wherein the instrument control parameter comprises at least one of temperature, pressure, flow rate, material composition.
5. The petrochemical production sub-operation method according to claim 1, wherein the production scenario comprises a normal production state and an accident state.
6. The petrochemical production sub-operation method according to claim 1, wherein in the step 4, the automatic sub-operation strategy training is performed by a neural network algorithm using the set parameters of the dynamic simulation model as input data and the data set as output data.
7. The petrochemical production sub-operation method according to claim 1, wherein the dynamic simulation model is established by dynamic simulation software.
8. The petrochemical production sub-operation method according to claim 3, wherein the equipment comprises at least one of a tower, a vessel, a pump, a heat exchanger, a compressor, a reactor, a dryer, and a filter.
9. The petrochemical production sub-operation method according to claim 1, wherein the dynamic simulation model comprises a plant-wide production process model or a unit operation model of a local plant.
10. The petrochemical production sub-operation method according to claim 7, wherein the dynamic simulation software comprises: aspen HYSYS software or dynmsi software.
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