CN107203193B - Intelligent control system for chemical product recovery process - Google Patents
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- CN107203193B CN107203193B CN201710532272.XA CN201710532272A CN107203193B CN 107203193 B CN107203193 B CN 107203193B CN 201710532272 A CN201710532272 A CN 201710532272A CN 107203193 B CN107203193 B CN 107203193B
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- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41865—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
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- G—PHYSICS
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract
The invention discloses an intelligent control system for a chemical product recovery process, which comprises a chemical product recovery process visualization module, a set value calculation module, a data acquisition and output module, a coordination optimization module and system hardware; the data acquisition and output module transmits the data result to the chemical product recovery process visualization module; the chemical product recovery process visualization module feeds back field equipment parameters and process parameters to the data acquisition and output module; the data acquisition and output module receives the operating parameter set value of the set value calculation module and feeds back the process parameter to the set value calculation module; the set value calculation module obtains a state parameter optimization set value from the coordination optimization module and transmits real-time and historical data to the coordination optimization module; and the coordination optimization module is used for realizing coordination optimization of the gas purification and recovery process. The system can realize the visualization display of the integrated product, data management, coordination optimization, data mining, energy conservation, consumption reduction, efficiency improvement and benefit maximization on the premise of ensuring the safe production.
Description
Technical Field
The invention belongs to the technical field of coking chemical product recovery, and particularly relates to an intelligent control system for a chemical product recovery process.
Background
Chemical recovery is a typical complex flow industrial process with many influencing factors, large process fluctuation, numerous variables, constraints and targets accompanied by a large number of physicochemical reactions and heat exchanges. The existing recovery process loop control is traditional control, feedforward analysis of a chemical product recovery process is lacked, analysis of production historical data is lacked, excavation of relation rules of production indexes, state parameters and operation parameters is lacked, consideration on aspects of coordination optimization of comprehensive production targets and the like is lacked, manual adjustment is frequent, feedback is delayed, the overall control effect is not ideal enough, and energy conservation, consumption reduction and benefit improvement of the chemical product recovery process are difficult to achieve.
Disclosure of Invention
The invention aims to solve the problems, provides an intelligent control system for a chemical product recovery process, overcomes the defects of lack of predictive control, data mining, coordinated optimization and high energy consumption of the existing recovery technology of sulfur, ammonia and benzene, and provides an integrated intelligent control system which integrates visual display, data management, predictive control, coordinated optimization, data mining, energy conservation, consumption reduction, efficiency improvement and maximum reduction of people on the premise of ensuring safe production.
In order to realize the purpose, the invention adopts the technical scheme that: an intelligent control system for a chemical product recovery process comprises a chemical product recovery process visualization module, a set value calculation module, a data acquisition and output module, a coordination optimization module and system hardware; the data acquisition and output module transmits the data result to a visualized module for the chemical product recovery process; the chemical product recovery process visualization module feeds back field equipment and process parameters to the data acquisition and output module; the data acquisition and output module receives the operation parameter set value sent by the set value calculation module and feeds back the process parameter to the set value calculation module; the set value calculation module obtains a state parameter optimization set value from the coordination optimization module and transmits real-time and historical data to the coordination optimization module; and the coordination optimization module is used for realizing coordination optimization of the gas purification and recovery process.
Further, the coordination optimization module obtains a relation model between the production index and the process parameter by using an expert rule module arranged in the data center; and the production index and process parameter relation model realizes coordination optimization through a multi-objective coordination optimization module.
Furthermore, the set value calculation module comprises an intelligent desulfurization control system, an intelligent ammonium sulfate control system and an intelligent benzene elution control system; the intelligent desulfurization control system, the intelligent ammonium sulfate control system and the intelligent benzene elution control system obtain operation parameter set values through analysis and calculation and send the operation parameter set values to the desulfurization sub-station, the ammonium sulfate sub-station and the benzene elution sub-station of the data acquisition and output module.
Furthermore, the desulfurization sub-station, the ammonium sulfate sub-station and the elution benzene sub-station convert the control signals sent by the set value calculation module into corresponding execution actions of the execution mechanism.
Furthermore, the intelligent desulfurization control system, the intelligent ammonium sulfate control system and the intelligent benzene elution control system respectively comprise a mechanism analysis module, an intelligent process control module and a coordination optimization module; the mechanism analysis module transmits basic parameters of the pipeline and equipment structure to a coal raw gas desulfurization, deamination and debenzolization process analysis module for obtaining an operation parameter set value; obtaining the set value of the operation parameter and transmitting the set value to a set value correction module; the set value correction module finishes the set value correction and transmits the corrected value to the process intelligent control module; the process intelligent control module comprises a precooling tower rear coal gas temperature control module, a regeneration tower regeneration liquid level control module, an ammonia distillation tower top temperature control module, a preheater rear coal gas temperature control module, a saturator mother liquor acidity control module, a dryer inner temperature control module, a final cooler rear coal gas temperature control module, a benzene washing tower lean oil temperature control module, a regenerator top oil gas temperature control module, a tubular furnace outlet rich oil temperature control module and a benzene removal tower top temperature control module; the gas temperature control module behind the pre-cooling tower, the regeneration liquid level control module of the regeneration tower and the ammonia distillation tower top temperature control module complete desulfurization analysis control; the temperature control module of the coal gas behind the preheater, the mother liquor acidity control module of the saturator and the temperature control module in the dryer complete deamination analysis control; the final cooler rear gas temperature control module, the benzene washing tower lean oil temperature control module, the regenerator top oil gas temperature control module, the tubular furnace outlet rich oil temperature control module and the benzene removal tower top temperature control module complete benzene removal analysis control; the process intelligent control module transmits a control signal to a sulfur, ammonia and benzene recovery working section; the coordination optimization module collects field sensor detection data of a desulfurization, deamination and benzene elution section, manually records the data and uploads the data to a data center; and the coordination optimization module feeds the optimized state parameter set value back to each submodule of the process intelligent control module.
Furthermore, the system hardware comprises a field control level, a process monitoring level and a production management level; the field control stage comprises an actuating mechanism, a detection instrument, a chemical product PLC system and a DCS system; the process monitoring module monitors the chemical product recovery work flow in real time through the monitoring operation end; the production management module comprises a server, a redundant server, a data center server and a production decision management center; the production decision management center and the data center server are both connected with the server; the server and the redundant server are connected with the monitoring operation end; the monitoring operation end is connected with a chemical product PLC system and a DCS system; the DCS and the chemical product PLC are connected with an actuating mechanism and a detection instrument; and the DCS is connected with the PLC system.
Furthermore, the data acquisition and output module comprises a DCS system, a chemical product PLC system, an actuating mechanism and a detection instrument.
Further, the data center forms an expert rule module through a rule mining and state extraction module; the data center is connected with the interfaces of the internal and external systems.
Further, the detection instrument feeds back information to a chemical production PLC system or a DCS system in the desulfurization substation, the ammonium sulfate substation and the elution benzene substation.
The invention has the beneficial effects that:
1. the coordination optimization module of the invention integrates data by means of artificial intelligence and big data technology, excavates rules, optimizes process parameters according to expert rules and in combination with economic benefits, equipment states, operating conditions and the like, and guides the production of the recovery process.
2. The set value calculation module calculates the set value of the state parameter according to the energy consumption of the system, the yield and the quality of the product; and performing feedforward analysis on the operation set value according to the structure of the pipeline equipment and the like, and performing feedback correction by acquiring field data.
3. The data acquisition module of the invention has great influence on product quality and yield due to the process parameters of lean oil containing benzene, wash oil quality and the like, and can not carry out on-line measurement, and solves the problem of artificial assay lag by establishing a data soft measurement model.
4. The invention can realize automatic operation of the system when the working section meets the start condition, and can carry out operation under special conditions (power failure, steam stop and the like) according to the subsystems when the working section is stopped.
5. The invention realizes the increase of the yield of crude benzene, reduces the production energy consumption, reduces the equipment loss, reduces part of operators, improves the working environment and improves the production efficiency of enterprises.
6. The invention overcomes the defects of lack of predictive control, data mining, coordinated optimization and high energy consumption in the prior art for recovering sulfur, ammonia and benzene.
Drawings
FIG. 1 is a schematic block diagram of the present invention.
FIG. 2 is a control module relationship diagram of the present invention.
FIG. 3 is a block diagram of the hardware system of the present invention.
Detailed Description
The following detailed description of the present invention is given for the purpose of better understanding technical solutions of the present invention by those skilled in the art, and the present description is only exemplary and explanatory and should not be construed as limiting the scope of the present invention in any way.
As shown in fig. 1 to 3, the specific structure of the present invention is: an intelligent control system for a chemical product recovery process comprises a chemical product recovery process visualization module, a set value calculation module, a data acquisition and output module, a coordination optimization module and system hardware; the data acquisition and output module transmits the data result to a visualized module for the chemical product recovery process; the chemical product recovery process visualization module feeds back field equipment and process parameters to the data acquisition and output module; the data acquisition and output module receives the operation parameter set value sent by the set value calculation module and feeds back the process parameter to the set value calculation module; the set value calculation module obtains a state parameter optimization set value from the coordination optimization module and transmits real-time and historical data to the coordination optimization module; and the coordination optimization module is used for realizing coordination optimization of the gas purification and recovery process.
Preferably, the coordination optimization module obtains a relation model between the production index and the process parameter by using an expert rule module arranged in a data center; and the production index and process parameter relation model realizes coordination optimization through a multi-objective coordination optimization module.
Preferably, the set value calculation module comprises an intelligent desulfurization control system, an intelligent ammonium sulfate control system and an intelligent benzene elution control system; the intelligent desulfurization control system, the intelligent ammonium sulfate control system and the intelligent benzene elution control system obtain operation parameter set values through analysis and calculation and send the operation parameter set values to the desulfurization sub-station, the ammonium sulfate sub-station and the benzene elution sub-station of the data acquisition and output module.
Preferably, the desulfurization sub-station, the ammonium sulfate sub-station and the elution benzene sub-station convert the control signal sent by the set value calculation module into the corresponding execution action of the execution mechanism.
Preferably, the intelligent desulfurization control system, the intelligent ammonium sulfate control system and the intelligent benzene elution control system respectively comprise a mechanism analysis module, an intelligent process control module and a coordination optimization module; the mechanism analysis module transmits basic parameters of the pipeline and equipment structure to a coal raw gas desulfurization, deamination and debenzolization process analysis module for obtaining an operation parameter set value; obtaining the set value of the operation parameter and transmitting the set value to a set value correction module; the set value correction module finishes the set value correction and transmits the corrected value to the process intelligent control module; the process intelligent control module comprises a precooling tower rear coal gas temperature control module, a regeneration tower regeneration liquid level control module, an ammonia distillation tower top temperature control module, a preheater rear coal gas temperature control module, a saturator mother liquor acidity control module, a dryer inner temperature control module, a final cooler rear coal gas temperature control module, a benzene washing tower lean oil temperature control module, a regenerator top oil gas temperature control module, a tubular furnace outlet rich oil temperature control module and a benzene removal tower top temperature control module; the gas temperature control module behind the pre-cooling tower, the regeneration liquid level control module of the regeneration tower and the ammonia distillation tower top temperature control module complete desulfurization analysis control; the temperature control module of the coal gas behind the preheater, the mother liquor acidity control module of the saturator and the temperature control module in the dryer complete deamination analysis control; the final cooler rear gas temperature control module, the benzene washing tower lean oil temperature control module, the regenerator top oil gas temperature control module, the tubular furnace outlet rich oil temperature control module and the benzene removal tower top temperature control module complete benzene removal analysis control; the process intelligent control module transmits a control signal to a sulfur, ammonia and benzene recovery working section; the coordination optimization module collects field sensor detection data of a desulfurization, deamination and benzene elution section, manually records the data and uploads the data to a data center; and the coordination optimization module feeds the optimized state parameter set value back to each submodule of the process intelligent control module.
Preferably, the system hardware comprises a field control level, a process monitoring level and a production management level; the field control stage comprises an actuating mechanism, a detection instrument, a chemical product PLC system and a DCS system; the process monitoring module monitors the chemical product recovery work flow in real time through the monitoring operation end; the production management module comprises a server, a redundant server, a data center server and a production decision management center; the production decision management center and the data center server are both connected with the server; the server and the redundant server are connected with the monitoring operation end; the monitoring operation end is connected with a chemical product PLC system and a DCS system; the DCS and the chemical product PLC are connected with an actuating mechanism and a detection instrument; and the DCS is connected with the PLC system.
Preferably, the data acquisition and output module comprises a DCS system, a chemical product PLC system, an execution mechanism and a detection instrument.
Preferably, the data center forms an expert rule module through a rule mining and state extraction module; the data center is connected with the interfaces of the internal and external systems.
Preferably, the detection instrument feeds back information to a chemical production PLC system or a DCS system in the desulfurization substation, the ammonium sulfate substation and the elution benzene substation.
In specific implementation of the invention, as shown in fig. 1, an intelligent control system for a chemical product recovery process comprises a chemical product recovery process visualization module, a data acquisition and output module, a set value calculation module and a coordination optimization module.
The chemical product recovery process visualization module receives the data result of the data acquisition and output module, displays the information of the state of the field equipment, the process parameters, the development trend state and the like in real time, and is convenient for various related personnel to operate, query, print and the like on the process parameters and the control parameters of the production process. Meanwhile, the parameters of the field equipment and the technological parameters are fed back to the data acquisition and output module; the field device parameters comprise device running state, pipeline height, radius and the like; the process parameters comprise temperature, pressure, flow rate and the like; the process parameters include valve opening, process parameters, and the like.
The data acquisition and output module mainly comprises a DCS system, a PLC system, an actuating mechanism, a detection instrument and the like, mainly comprises a desulfurization sub-station, an ammonium sulfate sub-station and an elution benzene sub-station, and is an implementation part of field data acquisition and system control functions. When the control signal sent by the set value calculation module is obtained, the module converts the control signal into the corresponding execution action of the execution mechanism. The signal detected by the detecting instrument is fed back to the DCS and the PLC of each substation. Meanwhile, the process parameters of the module are also fed back to the inverse set value calculation module.
The set value calculation module receives real-time data of the data acquisition and output module and mainly completes data analysis, storage and intelligent optimization control of the set value of the operating parameter. And calculating by adopting an intelligent integrated modeling and control technology to obtain an operation parameter set value control instruction and transmitting the operation parameter set value control instruction to the data acquisition and output module. Mainly comprises a desulfurization intelligent control system, an ammonium sulfate intelligent control system and a benzene elution intelligent control system; the three intelligent control systems mainly comprise a mechanism analysis module, a process intelligent control module and a coordination optimization module. The set value calculation module feeds back the process parameters to the coordination optimization module so as to adjust all process parameters in real time.
The coordination optimization module receives real-time and historical data of other modules to realize optimization calculation of process state parameter set values of local systems, real-time evaluation of working conditions, data storage and the like; the real-time and historical data includes all field devices and process parameters during the operation of the device. The data center mainly carries out centralized storage and management on data of all links on site, analyzes and mines potential rules in the data, and shares the data to form a rule mining and state extraction module. On one hand, the data center carries out preprocessing and mining analysis on different types of data to obtain different professional rules and form an expert rule module. The expert rules are rules between the relevant variables. On the other hand, the system is connected with interfaces of internal and external systems to acquire scheduling information or sharing information to other systems. By tracking the process parameters in the recovery process and adopting a data mining technology, the association rule, the coordination control rule and the like of the energy consumption, the material consumption and the process parameters are mined, such as: the correlation rules of energy consumption and material consumption and process parameters, the correlation rules of product quality and control parameters, the correlation rules of operating conditions and process parameters and equipment states, and the like; and providing rule support for the coordination optimization module control. And analyzing a relation model between the production indexes of the gas purification and recovery process and the process state parameters based on an expert rule module provided by the data center, and correcting the state parameter set values of each local system by adopting multi-objective coordination optimization when the recovery working condition changes. The analysis and calculation results can be displayed on a chemical product recovery visualization module so as to deepen the control of related personnel on the operation rule of the chemical product recovery process.
Referring to fig. 2, the intelligent control system for desulfurization, the intelligent control system for ammonium sulfate, and the intelligent control system for benzene elution in fig. 1 will be further described. The intelligent desulfurization control system, the intelligent ammonium sulfate control system and the intelligent benzene elution control system transmit basic parameters such as a pipeline equipment structure to the analysis module of the desulfurization, deamination and debenzolization process of the crude coal gas; the analysis module for the desulphurization, deamination and debenzolization process of the coal raw gas mainly comprises the analysis of the desulphurization and ammonia evaporation process, the analysis of the deamination process, the analysis of the final cooling benzene washing and crude benzene distillation process, and the set values of process operation parameters are obtained through the analysis of the technological process. The analysis of the desulfurization and ammonia distillation process comprises the following steps: (1) a precooling tower cooling water flow model: according to the amount of gas entering the precooling tower, the temperature of the gas entering the precooling tower and the temperature of cooling water entering the precooling tower, taking the precooling tower as a research object, and combining the structure (heat transfer area) of the precooling tower to calculate an initial value of the flow rate of the required cooling water according to heat balance feedforward; (2) the regeneration tower compressed air volume model: the air volume of compressed air mainly influences the sulfur foam flotation effect and the desulfurization liquid oxidation regeneration effect, and the initial value of the required compressed air volume is calculated according to the conservation of sulfur simple substances in the desulfurization reaction process; (3) entering an ammonia still steam quantity model: and (3) carrying out heat balance calculation according to the flow rate of the residual ammonia water entering the ammonia still, the temperature of the ammonia water and the temperature of the top of the ammonia still, the steam amount, the steam temperature, the structure of the ammonia still and the like, and calculating the initial value of the required steam amount. The deamination process analysis comprises: (1) entering a preheater steam flow model: according to the gas quantity entering the preheater and the gas temperature entering and exiting the preheater, the preheater is taken as a research object, and the initial value of the required steam flow is calculated according to the heat balance by combining the structure (heat transfer area) of the preheater; (2) saturator sulfuric acid amount model: analyzing by the mass conservation of ammonia according to the ammonia content of the gas entering and exiting the saturator, and calculating the initial value of the required sulfuric acid amount; (3) entering a steam flow model of the air heater: calculating the initial value of the required steam flow according to the air quantity entering the air heater, the air temperature at the inlet of the air heater, the drying temperature of ammonium sulfate crystals in the dryer and the heat heater structure (heat transfer area) and the heat balance feedforward; the final cooling benzene washing and crude benzene distillation process analysis comprises the following steps: (1) final cooler cooling water model: according to the gas flow entering the final cooler, the gas temperature entering and exiting the final cooler and the temperature of cooling water entering and exiting, the final cooler is taken as a research object, and the initial value of the flow of the required cooling water is calculated in a feedforward mode according to the heat balance calculation by combining the structure (heat transfer area) of the final cooler; (2) cooling water model of the two-stage cooler: according to the lean oil mass entering the second-stage cooler, the lean oil temperature entering the second-stage cooler and the inlet and outlet temperature of cooling water, taking the second-stage cooler as a research object, combining a lean oil structure (heat transfer area) of the second-stage cooler, and calculating an initial value of the flow of the required cooling water according to heat balance calculation feedforward; (3) tubular furnace gas flow model: according to the flow rate of rich oil entering the tubular furnace, the inlet and outlet temperature of the rich oil, the flow rate of steam entering the furnace, the pressure and the outlet steam temperature, heat balance is carried out according to the coal gas amount, the coal gas heat value, the heat transfer area of the tubular furnace and the like, and the initial value of the required coal gas amount is calculated; (4) regenerator steam flow model: according to the flow and the temperature of rich oil entering the regenerator and the deslagging temperature of the regenerator, carrying out heat balance calculation according to the steam quantity, the steam temperature, the structure of the regenerator and the like, and calculating the initial value of the required steam quantity; (5) a crude benzene reflux quantity model of a debenzolization tower: and (4) carrying out heat balance calculation according to the flow rate of rich oil entering the debenzolization tower, the temperature of the rich oil, the steam quantity, the steam temperature, the tower top temperature, the structure of the debenzolization tower and the like, and calculating the initial value of the reflux quantity of the needed crude benzene.
The initial set value correction module of the operating parameters comprises the correction of the initial set values of the operating parameters of the desulfurization, deamination and benzene elution sections, analyzes the gas yield at the present stage according to the changes of the coking coal weight, the coking time and the raw coke gas components, collects the data of a field sensor, manually inputs the data, combines the process indexes of the state parameters of each section, and corrects the initial set values of each operating parameter on line by using the set value correction module.
The process intelligent control comprises a pre-cooling tower rear coal gas temperature control module, a regeneration tower regeneration liquid level control module, an ammonia distillation tower top temperature control module, a pre-heater rear coal gas temperature control module, a saturator mother liquor acidity control module, a dryer inner temperature control module, a final cooler rear coal gas temperature control module, a benzene washing tower lean oil temperature control module, a tubular furnace outlet rich oil temperature control module, a regenerator top oil gas temperature control module and a benzene removal tower top temperature control module. The gas temperature control module behind the pre-cooling tower comprises a pre-cooling tower, a residual ammonia water cooler, an adjusting and executing mechanism and a bottom layer control loop system; the regeneration liquid level control module of the regeneration tower comprises a desulfurization tower, a regeneration tower, a barren liquid pump, a rich liquid pump, a sulfur foam tank, a sulfur foam pump, an adjusting and executing mechanism and a bottom control loop system; the ammonia distillation tower top temperature control module comprises an ammonia distillation tower, an alkali liquor tank, an alkali liquor pump, an ammonia distillation wastewater pump, a wastewater cooler, a residual ammonia water heat exchanger, an adjusting execution mechanism and a bottom layer control loop system; the rear gas temperature control module of the preheater comprises a preheater, an adjusting execution mechanism and a bottom layer control loop system; the saturator mother liquor acidity control module comprises a saturator, a full-flow groove, a high-position sulfuric acid groove, a crystallization groove, a large mother liquor pump, a small mother liquor pump, a crystallization pump, an adjusting execution mechanism and a bottom layer control loop system; the temperature control module in the dryer comprises a centrifuge, a screw conveyor, a cold and hot fan, the dryer, an adjusting and executing mechanism and a bottom layer control loop system; the gas temperature control module behind the final cooler comprises a final cooler, a condensate spraying pump, an adjusting and executing mechanism and a bottom layer control loop system; the benzene-entering tower lean oil temperature control module comprises an oil-oil heat exchanger, a first-stage lean oil cooler, a second-stage lean oil cooler, a benzene washing tower, a lean rich oil pump, an adjusting and executing mechanism and a bottom layer control loop system; the tube furnace outlet rich oil temperature control module comprises a crude benzene condensation cooler, an oil-oil heat exchanger, a tube furnace, an adjusting execution mechanism and a bottom layer control loop system; the regenerator top oil gas temperature control module comprises a regenerator, an adjusting execution mechanism and a bottom layer control loop system; the debenzolization tower top temperature control module comprises a debenzolization tower, a crude benzene condensation cooler, an oil-water separator, a crude benzene reflux tank, a reflux pump, an adjusting and executing mechanism and a bottom layer control loop system. The set values of the control operation parameters of the bottom control loop system are obtained through the analysis of the process mechanisms such as mass conservation, energy conservation and the like; because the nonlinear system can be approximated to a linear system in a small range, namely the system can be regarded as the linear system in a stable operation period, an operation parameter and a controlled object model are established according to a steady-state process; and finally, performing stable tracking control on the controlled object by adopting a feedforward-feedback control mode.
The process coordination optimization module carries out coordination optimization on the working sections of desulfurization, deamination and benzene elution, and can bring greater guiding significance to the optimal production operation as the production indexes of the production process can directly reflect the production conditions, the quality of products and the energy consumption and coordinate the relationship between the production indexes and the process parameters. According to an expert rule module mined by a data center, from the perspective of coordination and optimization of each local process, a multi-objective optimization model which takes the maximum yield and the minimum energy consumption as optimization targets, takes the product quality as a constraint condition and takes the state parameters of each local process as decision variables is established. When the production working conditions (normal working conditions, power failure working conditions, water cut working conditions, steam cut working conditions and equipment maintenance working conditions) change, the set values of the process state parameters are optimized on line by adopting a coordination optimization algorithm, the coordination optimization of the quality, yield and energy consumption in the chemical product recovery process is realized, and the set values of the optimized state parameters are fed back to the process intelligent control module.
Referring to fig. 3, the hardware architecture of the present invention is composed of a field control stage, a process monitoring stage, and a production management stage, and includes a monitoring operation terminal, a server, a DCS system, a chemical production PLC, remote ET200M of each process section, an I/O substation, a field device, a detection instrument, an execution mechanism, an external system, and the like. Wherein the DCS system is a distributed control system. The field control level realizes automatic control, equipment load monitoring, alarming and accident handling of equipment, records system operation data, and provides basis for analysis, statistics and the like. The process monitoring stage realizes real-time monitoring on the chemical product recovery work flow and carries out production scheduling and management by applying a computer communication technology. The production management level can be networked with an enterprise upper management information system, and enterprise development strategies and production plans are formulated by using management information system data and production operation data to carry out benefit accounting. The operation end comprises a monitoring operation end and a remote operation end, wherein the monitoring operation end adopts a C/S mode, and the remote operation end adopts a B/S mode. The server adopts a DELL professional server, meets the system performance and data safety, and mainly loads an application program and a database. The PLC300 is adopted in the commercial PLC system, a PID module is embedded in the PLC system, data point acquisition and numerical calculation are carried out, and a part of control programs are operated, so that the program storage and operation efficiency of the system and the data acquisition requirements are met. An ET200M substation is arranged at each work section on site, and signals of the on-site sensors are collected by the on-site ET200M substation and are transmitted to a central control room chemical production PLC system. The PLC system transmits signals to the newly-added actuator through the ET200M substation, and the DCS system also transmits signals to the actuator. The detection instruments under the PLC system and the DCS system feed back the signal feedback to the PLC system and the DCS system respectively, and the DCS system also transmits the feedback signal to the PLC system. The PLC system receives signals to realize the functions of system prediction control, data mining and coordination optimization. The server and the redundant server form a dual redundant server, and the PLC system and the DCS system form a dual redundant system to ensure the reliable operation of the whole system.
The invention analyzes the coal gas generating and conveying process by considering the coking process and the gas collecting process as a unified whole, performs mass conservation, energy conservation law and gas-liquid phase mass transfer theory analysis on the coal gas purifying process on the basis of researching and organizing the influence factors of the recovery process and the prior art problems, establishes a mechanism model of the sulfur, ammonia and benzene recovery process, and obtains the set value of the operation parameter. And then, designing a feedback control model on the basis of a feedforward mechanism model, correcting a set value of an operation parameter of the feedforward model, and sending the set value to a desulfurization sub-station, an ammonium sulfate sub-station and an elution benzene sub-station for stable tracking control. And finally, coordinating and optimizing the operation between the processes and in the processes by adopting coordinated optimization control according to the field environment and the process parameter change through the association rule of data mining. The method mainly comprises the following steps: (1) according to the process flow of sulfur, ammonia and benzene recovery, the processes of desulfurization, deamination, ammonia evaporation and benzene elution are analyzed, the total material amount or certain component amount in the recovery process is subjected to material balance calculation by collecting the process parameters such as equipment, pipelines, coal gas flow, coal gas components and the like according to the law of conservation of mass and energy, a mechanism model of a sulfur, ammonia and benzene recovery system is established, and initial set values of all operation parameters are obtained. (2) The mechanism control model is used for serially connecting the data of the sulfur, ammonia and benzene working sections, acquiring partial data of bottom signals, equipment states, a coking process and a gas collection process of each working section in real time, and fusing the data to form a real-time database and a historical database of the sulfur, ammonia and benzene working sections. (3) By collecting data of each section, correcting initial set values of each operation parameter and sending the corrected initial set values to the desulfurization sub-station, the ammonium sulfate sub-station and the elution benzene sub-station to control field equipment. (4) The data of the real-time database and the historical database are subjected to statistical analysis, and expert rules such as 'energy consumption and material consumption-process index' association rule, 'product quality-operation parameter' association rule, 'production target-state parameter-operation parameter' association rule, abnormal working condition control rule and the like are obtained by adopting a data mining method. (5) According to the mined expert rules, from the perspective of coordination and optimization of each local process, a multi-objective optimization model is established, wherein the multi-objective optimization model takes the maximum yield and the minimum energy consumption as optimization targets, the product quality as constraint conditions, and the state parameters of each local process as decision variables. When the production working condition changes, the set value of the process state parameter is optimized on line by adopting a coordination optimization algorithm, so that the coordination optimization of the quality, the yield and the energy consumption in the production process is realized.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to assist in understanding the method and its core concepts of the present invention. The foregoing is only a preferred embodiment of the present invention, and it should be noted that there are objectively infinite specific structures due to the limited character expressions, and it will be apparent to those skilled in the art that a plurality of modifications, decorations or changes may be made without departing from the principle of the present invention, and the technical features described above may be combined in a suitable manner; such modifications, variations, combinations, or adaptations of the invention using its spirit and scope, as defined by the claims, may be directed to other uses and embodiments.
Claims (5)
1. The utility model provides a process intelligence control system is retrieved in change product which characterized in that: the system comprises a chemical product recovery process visualization module, a set value calculation module, a data acquisition and output module, a coordination optimization module and system hardware; the data acquisition and output module transmits the data result to a visualized module for the chemical product recovery process; the chemical product recovery process visualization module feeds back field equipment parameters and process parameters to the data acquisition and output module; the data acquisition and output module receives the operation parameter set value sent by the set value calculation module and feeds back the process parameter to the set value calculation module; the set value calculation module obtains a state parameter optimization set value from the coordination optimization module and transmits real-time and historical data to the coordination optimization module; the coordination optimization module is used for realizing coordination optimization of the gas purification and recovery process, and obtains a relation model between production indexes and process parameters by utilizing an expert rule module arranged in a data center; the production index and process parameter relation model realizes coordination optimization through a multi-objective coordination optimization module, and the set value calculation module comprises a desulfurization intelligent control system, an ammonium sulfate intelligent control system and a benzene elution intelligent control system; the system comprises a desulfurization intelligent control system, an ammonium sulfate intelligent control system and an benzene elution intelligent control system, wherein the desulfurization intelligent control system, the ammonium sulfate intelligent control system and the benzene elution intelligent control system obtain operation parameter set values through analysis and calculation and send the operation parameter set values to a desulfurization sub-station, an ammonium sulfate sub-station and a benzene elution sub-station of a data acquisition and output module; the mechanism analysis module transmits basic parameters of the pipeline and equipment structure to a coal raw gas desulfurization, deamination and debenzolization process analysis module for obtaining an operation parameter set value; obtaining the set value of the operation parameter and transmitting the set value to a set value correction module; the set value correction module finishes the set value correction and transmits the corrected value to the process intelligent control module; the process intelligent control module comprises a precooling tower rear coal gas temperature control module, a regeneration tower regeneration liquid level control module, an ammonia distillation tower top temperature control module, a preheater rear coal gas temperature control module, a saturator mother liquor acidity control module, a dryer inner temperature control module, a final cooler rear coal gas temperature control module, a benzene washing tower lean oil temperature control module, a regenerator top oil gas temperature control module, a tubular furnace outlet rich oil temperature control module and a benzene removal tower top temperature control module; the gas temperature control module behind the pre-cooling tower, the regeneration liquid level control module of the regeneration tower and the ammonia distillation tower top temperature control module complete desulfurization analysis control; the temperature control module of the coal gas behind the preheater, the mother liquor acidity control module of the saturator and the temperature control module in the dryer complete deamination analysis control; the final cooler rear gas temperature control module, the benzene washing tower lean oil temperature control module, the regenerator top oil gas temperature control module, the tubular furnace outlet rich oil temperature control module and the benzene removal tower top temperature control module complete benzene removal analysis control; the process intelligent control module transmits a control signal to a sulfur, ammonia and benzene recovery working section; the coordination optimization module collects field sensor detection data of a desulfurization, deamination and benzene elution section, manually records the data and uploads the data to a data center; and the coordination optimization module feeds the optimized state parameter set value back to each submodule of the process intelligent control module.
2. The intelligent control system for the chemical product recovery process of claim 1, wherein the system hardware comprises a field control stage, a process monitoring stage, a production management stage; the field control stage comprises an actuating mechanism, a detection instrument, a chemical product PLC system and a DCS system; the process monitoring stage monitors the chemical product recovery work flow in real time through a monitoring operation terminal; the production management level comprises a server, a redundant server, a data center server and a production decision management center; the production decision management center and the data center server are both connected with the server; the server and the redundant server are connected with the monitoring operation end; the monitoring operation end is connected with a chemical product PLC system and a DCS system; the DCS and the chemical product PLC are connected with an actuating mechanism and a detection instrument; and the DCS is connected with the PLC system.
3. The intelligent control system for the chemical product recovery process of claim 1, wherein the data acquisition and output module comprises a DCS system, a chemical product PLC system, an actuator and a detection instrument.
4. The intelligent control system for the chemical product recovery process according to claim 1, wherein the data center forms an expert rule module through a rule mining and state extraction module; the data center is connected with the interfaces of the internal and external systems.
5. The system of claim 3, wherein the instrumentation feeds back information to the chemical production PLC system or DCS system in the desulfurization substation, the ammonium sulfate substation, and the elution benzene substation.
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