CN110252087B - Intelligent optimization control system for crude benzene recovery process - Google Patents
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Abstract
The invention discloses an intelligent optimization control system for a crude benzene recovery process, which comprises an elution benzene process analysis module, a recovery process intelligent control module and a data mining and coordination optimization module which are sequentially connected in series; the elution benzene process analysis module establishes a recovery process intelligent control module according to the basic data, calculates each control parameter value in a feedforward mode and sends the recovery process intelligent control module; the intelligent recovery process control module controls the system temperature and outputs control parameter values, and the controlled parameters are corrected in real time; data mining and coordination optimization is used to make aid decisions for system operation. The method optimizes the control flow and the control system, and can optimally control the crude benzene recovery process, thereby improving the system efficiency, improving the crude benzene yield, ensuring the crude benzene quality and having relatively low cost.
Description
Technical Field
The invention particularly relates to an intelligent optimization control system for a crude benzene recovery process.
Background
The recovery process of the coking crude benzene belongs to the mass transfer and separation process, the control process has the characteristics of nonlinearity, large hysteresis, strong coupling and the like, and the production process is easily influenced by factors such as production operation conditions, external environment, equipment state and the like. Thus, the coked crude benzene recovery process is relatively complicated.
Due to the complexity, key indexes in the production process, such as parameters of benzene content in gas entering and exiting sections, benzene content in lean oil, quality of wash oil and the like, cannot be detected in real time, so that process controlled variables cannot be adjusted in time according to changes of the crude benzene recovery rate, the benzene content behind the tower is high, the gas is impure, and the crude benzene recovery rate is reduced. On the other hand, the conventional system adopts a conventional control mode which is difficult to adapt to the requirement of multivariable coordination predictive control in the benzene elution process and lacks the coordination optimization operation among devices, so that the fluctuation of operation variables such as high benzene washing absorption temperature, small circulating wash oil amount, steam pressure and the like is large, and the control quality is poor; and the fluctuation range of the technological parameters is large, the production process is unstable, the labor intensity of the workshop section is increased, the production energy consumption is increased, and the yield and the quality of crude benzene are influenced.
Disclosure of Invention
The invention aims to provide an intelligent optimization control system for the crude benzene recovery process, which can perform optimization control on the crude benzene recovery process, thereby improving the system efficiency, improving the crude benzene yield, ensuring the crude benzene quality and having relatively low cost.
The system comprises an elution benzene process analysis module, a recovery process intelligent control module and a data mining and coordination optimization module; the elution benzene process analysis module, the recovery process intelligent control module and the data mining and coordination optimization module are sequentially connected in series; the elution benzene process analysis module is used for analyzing mass conservation, energy conservation law and gas-liquid phase mass transfer theory according to basic data, establishing a recovery process intelligent control module, and performing feedforward calculation on each control parameter value and sending the control parameter value to the established recovery process intelligent control module; the recovery process intelligent control module is used for controlling the system temperature according to the received control parameter values and the feedback values of the controlled parameters, outputting the control parameter values and correcting the controlled parameters in real time; and the data mining and coordination optimization module is used for optimizing the controlled parameters of the system and making an auxiliary decision for the system operation by adopting a data mining algorithm.
The basic data comprises analyzer data, pipeline equipment structure data, coking process data, gas collection process data, distributed control system data and assay data.
The recovery process intelligent control module comprises a final-cooling rear coal gas temperature control module, an upper-tower lean oil temperature control module, a rich oil preheating temperature control module, a debenzolization tower top temperature control module and a regenerator top temperature control module; the gas temperature control module after final cooling adopts a feedforward-feedback control mode, and the cooling water quantity feedforward calculation model can effectively inhibit the fluctuation influence of the gas quantity, the gas temperature and the cooling water quantity at the inlet of the final cooler and reduce the influence of disturbance on the system; the feedback correction of the cooling water quantity realizes the stable tracking control of the temperature of the finally cooled coal gas; the upper tower lean oil temperature control module adopts a feedforward-feedback control mode, a cooling water quantity feedforward calculation model can effectively inhibit the fluctuation influence of the lean oil quantity and the lean oil temperature at the inlet of the two-stage cooler, and the feedback correction of the cooling water quantity realizes the stable tracking control of the lean oil temperature at the outlet of the cooler; the rich oil preheating temperature control module adjusts the flow rate of rich oil entering the furnace, the flow rate of coal gas and the steam pressure in a coordinated control mode, and keeps the temperature of the rich oil at the outlet of the tubular furnace, the temperature of superheated steam and the pressure in a normal range; the debenzolization tower top temperature control module adopts a feedforward-cascade control mode, the rich oil flow is used for feedforward adjustment, the stability of the load of the tower is maintained, the outer ring in the cascade control adopts fuzzy control, and the debenzolization tower top temperature is used as a controlled variable; the sum of the feedforward output result and the outer ring output result is used as a set value for PID control of the inner ring crude benzene reflux, and the crude benzene reflux is used as a controlled variable; the regenerator top temperature control module adopts coordination control to adjust the flow of washing oil entering the regenerator and the flow of steam to keep the temperature of the oil gas at the outlet of the regenerator within a normal range.
The intelligent optimization control system for the crude benzene recovery process is divided into a process parameter optimization part, a control parameter optimization part and a data processing part; the process parameter optimization part analyzes the balance relation between the recovery rate of the crude benzene product and the energy consumption, establishes an optimization model based on production evaluation, and obtains and stores the optimal real-time process parameters by solving the optimization model; the control parameter optimization part is used for controlling the process parameters and the temperature of the elution benzene process analysis module and the recovery process intelligent control module; the data processing part is used for analyzing and processing the data of the bottom instrument, transmitting the data to the server through the network, converting the control instruction transmitted by the server into a signal which can be executed by the execution mechanism, and thus completing the control of the field device.
The control parameter optimization part comprises a process unit mechanism model and a temperature control model; the process unit mechanism model comprises a lean oil benzene-containing model, a benzene-containing soft measurement model of the gas before the benzene washing tower, a crude benzene recovery rate model, a heat exchange process model and a rectification process model; the lean oil benzene-containing model is calculated according to the top temperature of the debenzolization tower, the top pressure of the debenzolization tower, the bottom temperature of the debenzolization tower, the bottom pressure of the debenzolization tower, the rich oil preheating temperature and the superheated steam temperature; the benzene-containing soft measurement model of the coal gas before the benzene washing tower is calculated according to the volatile matter of the blended coal, the yield of crude benzene, the dry coal amount per hour of a coke oven, the coal gas amount before entering the benzene washing tower, the coal gas temperature and the coal gas pressure; the crude benzene recovery rate model is calculated according to the absorption area of the benzene washing tower, the absorption temperature, the quality of the circulating wash oil, the benzene content of lean oil, the benzene content before the tower and the quantity of the circulating wash oil; the heat exchange process model is used for calculating model parameters of the heat exchange process; the rectification process model is used for calculating model parameters in the rectification process; a temperature control model of a final cooler rear gas temperature adaptive fuzzy control model, a two-stage cooler outlet lean oil temperature adaptive fuzzy control model, a tubular furnace coordination prediction control model, a regenerator coordination prediction control model and a debenzolization tower top temperature feedforward-cascade control model; the gas temperature control model behind the final cooler inputs the fuzzy controller according to the deviation and the deviation change rate of the set value and the measured value of the gas temperature behind the final cooler, and the cooling water flow compensation quantity is output by the controller; the corrected set value is sent to DCS for control; the lean oil temperature fuzzy control model at the outlet of the second-stage cooler inputs the deviation and the deviation change rate of the set value and the measured value of the lean oil temperature after the second-stage cooler into the fuzzy controller, and the cooling water flow compensation quantity is output by the controller; the corrected set value is sent to DCS for control; the tubular furnace coordination prediction control model realizes a prediction control decision on an upper computer according to a prediction control algorithm, calculates DMC controller output, and uses each output control variable to set given values of the inlet rich oil quantity, the steam flow and the gas flow of the tubular furnace in the DCS; the regeneration coordination prediction control model establishes a model with the output of the temperature of oil gas at the outlet of the regenerator and the temperature of slag discharged at the bottom of the regenerator through a test method, and the input of the model is the flow of rich oil at the inlet of the regenerator and the flow of steam; calculating set values of the oil-rich quantity and the steam flow at the inlet of the regenerator in the DCS according to a predictive control algorithm; the debenzolization tower top temperature feedforward-cascade control model takes the rich oil temperature as a feedforward variable, effectively inhibits the fluctuation influence of the rich oil quantity, the temperature and the superheated steam temperature, and realizes the stable tracking control of the debenzolization tower top temperature by the feedback correction of the crude benzene reflux quantity.
The hardware platform of the intelligent optimization control system for the crude benzene recovery process comprises an operation end, a server, a master control PLC, a sensor, a communication module, an online analyzer and an execution mechanism; the dual-redundancy network, the dual-redundancy server and the dual-redundancy main control unit ensure the reliable operation of the whole system, and the system has flexible structure, convenient expansion and low maintenance cost; the system network is communicated with a plant-level big data server, a coking system, a gas collection process system and a laboratory in a networking way, and the plant-level historical data, the current production operation condition and the equipment state are utilized to optimize and calculate the production process and realize the stable control of each control loop; meanwhile, the recovery process flow, combustible gas and toxic and harmful gas are monitored in real time, the operation condition of the production process and the load state of equipment are observed, and safe and stable operation is realized; the control network realizes the data acquisition of the newly added sensor equipment and receives a control instruction issued by an upper layer to automatically control the equipment.
The intelligent optimization control system for the crude benzene recovery process can predict the benzene content, crude benzene yield and coal gas generation amount of coal gas by acquiring laboratory data, DCS data and soft measurement model data and utilizing a process model to perform simulation calculation, and guide production in real time; the real-time and historical data can be comprehensively analyzed, and a key equipment fault diagnosis knowledge base can be established; when the production data of the related equipment is abnormal, a rule is triggered, a fault is prompted on the upper computer, a production operator is guided to find out the equipment problem in time, and the fluctuation of the production process is reduced; meanwhile, the time when the equipment possibly fails is analyzed according to the historical data trend, so that prevention is carried out in advance, and the equipment state is optimized; establishing a crude benzene production optimization model by analyzing the relationship between the benzene and crude benzene yield after the tower and the relationship between energy consumption and material consumption, and improving the efficiency of benzene washing and benzene removal to enable the system to reach a corresponding target value; optimizing the set value of the operation parameter by adopting a feed-forward analysis and feedback correction strategy, and reducing the fluctuation range of the controlled variable; finally, the method optimizes the control flow and the control system, and can optimally control the crude benzene recovery process, thereby improving the system efficiency, improving the crude benzene yield, ensuring the quality and having relatively low cost.
Drawings
FIG. 1 is a block diagram of a system according to the present invention.
FIG. 2 is a schematic diagram of the process parameter optimization section of the present invention.
FIG. 3 is a schematic diagram of the control parameter optimization portion of the present invention.
FIG. 4 is a schematic diagram of a data processing portion of the present invention.
FIG. 5 is a diagram of a hardware platform according to the present invention.
Detailed Description
FIG. 1 is a schematic diagram of the system module of the present invention: the system comprises an elution benzene process analysis module, a recovery process intelligent control module and a data mining and coordination optimization module; the elution benzene process analysis module, the recovery process intelligent control module and the data mining and coordination optimization module are sequentially connected in series; the elution benzene process analysis module is used for analyzing mass conservation, energy conservation law and gas-liquid phase mass transfer theory according to basic data, establishing an intelligent recovery process control module, and performing feedforward calculation on each control parameter value and sending the control parameter value to the established intelligent recovery process control module; the recovery process intelligent control module is used for controlling the system temperature according to the received control parameter values, outputting control parameter values and correcting the controlled parameters in real time; and the data mining and coordination optimization module is used for optimizing the process parameters and making an auxiliary decision for system operation by adopting a data mining algorithm.
The basic data includes analyzer data, pipeline equipment structure data, coking process data, gas collection process data, distributed control system data and assay data.
The recovery process intelligent control module comprises a final-cooling coal gas temperature control module, an upper-tower lean oil temperature control module, a rich oil preheating temperature control module, a debenzolization tower top temperature control module and a regenerator top temperature control module; the gas temperature control module after final cooling adopts a feedforward-feedback control mode, and the cooling water quantity feedforward calculation model can effectively inhibit the fluctuation influence of the gas quantity, the gas temperature and the cooling water quantity at the inlet of the final cooler and reduce the influence of disturbance on the system; the feedback correction of the cooling water quantity realizes the stable tracking control of the temperature of the finally cooled coal gas; the upper tower lean oil temperature control module adopts a feedforward-feedback control mode, a cooling water quantity feedforward calculation model can effectively inhibit the fluctuation influence of the lean oil quantity and the lean oil temperature at the inlet of the two-stage cooler, and the feedback correction of the cooling water quantity realizes the stable tracking control of the lean oil temperature at the outlet of the cooler; the rich oil preheating temperature control module adjusts the flow rate of rich oil entering the furnace, the flow rate of coal gas and the steam pressure in a coordinated control mode, keeps the temperature of the rich oil at the outlet of the tubular furnace, the temperature of superheated steam and the pressure in a normal range, and adjusts the flow rate of the rich oil entering the furnace and the flow rate of the coal gas entering the furnace when the production working condition is greatly changed (when the crude gas quantity is greatly changed) and the gas pressure and the heat value are insufficient, and is combined with a turnover plate of the tubular furnace for adjustment; the debenzolization tower top temperature control module adopts a feedforward-cascade control mode, the rich oil flow is used for feedforward adjustment, the stability of the load of the tower is maintained, the outer ring in the cascade control adopts fuzzy control, and the debenzolization tower top temperature is used as a controlled variable; the sum of the feedforward output result and the outer ring output result is used as a set value for PID control of the inner ring crude benzene reflux, and the crude benzene reflux is used as a controlled variable; the regenerator top temperature control module adopts coordination control to adjust the flow of rich oil entering the regenerator and the flow of steam to keep the temperature of the oil and the steam at the outlet of the regenerator within a normal range.
The coordination optimization module is used for assisting in finding problems and making an auxiliary decision according to related information such as raw material cost, coal blending components, energy cost, environmental protection requirements, product quality requirements, product yield requirements, product market price, operation cost, equipment states and the like, adjusting the set values and control ranges of key parameters in time, and mainly achieving the functions of material distribution, regulation guidance, fault prediction and the like.
FIG. 2 is a schematic diagram of the process parameter optimization part of the present invention: the process parameter optimization part analyzes the balance relation between the recovery rate of the crude benzene product and the energy consumption, establishes an optimization model based on production evaluation, and obtains and stores the optimal real-time process parameters by solving the optimization model;
the process parameter optimization part analyzes the balance relation between the yield of the crude benzene product and the energy consumption according to the current production operation condition, the equipment state, the market condition and the like, and establishes an optimization model based on production evaluation; the optimization model comprises an objective function, constraint conditions and decision variables; the objective function is determined according to the maximum crude benzene yield and the minimum energy consumption and material consumption (steam, coal gas, cooling water and washing oil consumption); the constraint conditions comprise crude benzene quality, crude benzene recovery rate and model constraint; the decision variables comprise the gas temperature after final cooling, the lean oil temperature after a two-stage cooler, the rich oil and superheated steam temperature at the outlet of the tubular furnace, the oil gas temperature at the top of the regenerator, the slag discharging temperature at the bottom and the top temperature of the debenzolization tower, and optimized optimal process parameters are stored in a rule base. Meanwhile, historical data of the final cooling benzene washing and crude benzene distillation process are analyzed by a data mining technology, and a comprehensive index-controlled parameter-operating parameter rule, a production operating condition-energy consumption-profit rule, an equipment fault judgment rule and the like are mined, so that rule support is provided for optimization control;
FIG. 3 is a schematic diagram of the control parameter optimization portion of the present invention: the control parameter optimization part is used for carrying out process parameter control and temperature control on the elution benzene process analysis module and the recovery process intelligent control module; the control parameter optimization part comprises a process unit mechanism model and a temperature control model; the process unit mechanism model comprises a lean oil benzene-containing model, a benzene-containing soft measurement model of the gas before the benzene washing tower, a crude benzene recovery rate model, a heat exchange process model and a rectification process model.
And the lean oil benzene-containing model calculates the benzene content of the lean oil by adopting a multiple regression algorithm according to the top temperature of the debenzolization tower, the top pressure of the debenzolization tower, the bottom temperature of the debenzolization tower, the bottom pressure of the debenzolization tower, the rich oil preheating temperature and the superheated steam temperature.
And the benzene content of the gas before the benzene washing tower is calculated by adopting a multiple regression algorithm according to the volatile matter of the blended coal, the crude benzene yield, the dry coal loading of the coke oven per hour, the gas quantity before entering the benzene washing tower, the gas temperature and the gas pressure by using the benzene-containing soft measurement model of the gas before the benzene washing tower.
And calculating the crude benzene recovery rate by adopting a neural network algorithm according to the absorption area of the benzene washing tower, the absorption temperature, the quality of the circulating wash oil, the benzene content of the lean oil, the benzene content before the tower and the quantity of the circulating wash oil by using the crude benzene recovery rate model.
The heat exchange process model is used for calculating model parameters of the heat exchange process; the heat exchange process model comprises a gas final cooling process model, a lean oil cooling process model and a tubular furnace heating process model; the gas final cooling process model establishes a cooling water flow feedforward model according to the energy balance relation among gas, circulating water and cooling water in a final cooler, and solves the model to obtain the regulating quantity of the final cooling water flow; establishing a cooling water flow feedforward model according to the energy balance relation of lean oil, circulating water and cooling water in the first-stage and second-stage lean oil coolers by the lean oil cooling process model, and solving the model to obtain the regulating quantity of the cooling water flow of the second-stage cooler; the heating process of the tubular furnace belongs to a multivariable adjusting process, a system multivariate regression model is established according to the flow rate of gas entering the furnace, the steam pressure entering the furnace, the flow rate of rich oil entering the furnace, the temperature of steam leaving the furnace and the temperature of rich oil, and the model is solved to obtain the flow rate adjusting quantity of gas entering the furnace, the pressure adjusting quantity of steam entering the furnace and the flow rate adjusting quantity of rich oil entering the furnace.
The rectification process model is used for calculating model parameters in the rectification process; the rectification process model comprises an oil washing regeneration process model and a crude benzene rectification process model: in the crude benzene rectification process, an intelligent modeling method is adopted to establish a crude benzene reflux model, the top temperature of the debenzolization tower, the steam flow entering the tower and the rich oil temperature are taken as model inputs, and the crude benzene reflux is taken as a model output; and the washing oil regeneration process model establishes a steam flow feedforward model according to the washing oil regeneration amount and the energy balance relation of steam, and solves the model to obtain the steam flow regulating amount.
The temperature control model comprises a final cooler rear gas temperature self-adaptive fuzzy control model, a two-stage cooler outlet lean oil temperature self-adaptive fuzzy control model, a tubular furnace coordination prediction control model, a regenerator coordination prediction control model and a debenzolization tower top temperature feedforward-cascade control model.
The gas temperature control model behind the final cooler is input into a fuzzy controller according to the deviation and the deviation change rate of the set value and the measured value of the gas temperature behind the final cooler, a self-adaptive fuzzy control model of the cooling water flow is established, and the compensation flow of the cooling water flow is output by the controller; the corrected set value is issued to DCS for control
The lean oil temperature fuzzy control model at the outlet of the second-stage cooler is input into a fuzzy controller according to the deviation and the deviation change rate of the set value and the measured value of the lean oil temperature after the second-stage cooler, so as to establish an adaptive fuzzy control model of cooling water flow, and the compensation quantity of the cooling water flow is output by the controller; the corrected set value is issued to DCS for control
The tubular furnace coordination prediction control model realizes a prediction control decision on an upper computer according to a prediction control algorithm, calculates DMC controller output, and uses each output control variable to set given values of the inlet rich oil quantity, the steam flow and the gas flow of the tubular furnace in the DCS;
the regeneration coordination prediction control model establishes a model with the output of the temperature of oil gas at the outlet of the regenerator and the temperature of slag discharged at the bottom of the regenerator through a test method, and the input of the model is the flow of rich oil at the inlet of the regenerator and the flow of steam; calculating set values of the oil-rich quantity and the steam flow at the inlet of the regenerator in the DCS according to a predictive control algorithm;
the debenzolization tower top temperature feedforward-cascade control model takes the rich oil temperature as a feedforward variable, effectively inhibits the fluctuation influence of the rich oil quantity and the temperature and the superheated steam temperature, and realizes the stable tracking control of the debenzolization tower top temperature by the feedback correction of the crude benzene reflux quantity.
FIG. 4 is a schematic diagram of a data processing portion of the present invention: the data processing mainly comprises a PLC system, a final cooler regulating valve, a lean oil two-stage cooler regulating valve, a tube furnace regulating valve, a debenzolization tower regulating valve, a regenerator regulating valve, a benzene on-line analyzer, temperature and flow sensors and the like, and is used for analyzing and processing the data of a bottom instrument and transmitting the data to a server through a network. Meanwhile, the control instruction transmitted by the server is converted into a signal which can be executed by an execution mechanism, and the control of the field device is finished.
FIG. 5 is a diagram of a hardware platform of the present invention: the hardware platform of the intelligent optimization control system for the crude benzene recovery process comprises an operation end, a server, a master control PLC, a sensor, a communication module, an online analyzer and an execution mechanism; the dual-redundancy network, the dual-redundancy server and the dual-redundancy main control unit ensure the reliable operation of the whole system, and the system has flexible structure, convenient expansion and low maintenance cost; the system network is communicated with a plant-level big data server, a coking system, a gas collection process system and a laboratory in a networking way, and the plant-level historical data, the current production operation condition and the equipment state are utilized to optimize and calculate the production process and realize the stable control of each control loop; meanwhile, the recovery process flow, combustible gas and toxic and harmful gas are monitored in real time, the operation condition of the production process and the load state of equipment are observed, and safe and stable operation is realized; the control network realizes the data acquisition of the newly added sensor equipment and receives a control instruction issued by an upper layer to automatically control the equipment.
Claims (1)
1. An intelligent optimization control system for a crude benzene recovery process is characterized by comprising an elution benzene process analysis module, a recovery process intelligent control module and a data mining and coordination optimization module; the elution benzene process analysis module, the recovery process intelligent control module and the data mining and coordination optimization module are sequentially connected in series; the elution benzene process analysis module is used for analyzing mass conservation, energy conservation law and gas-liquid phase mass transfer theory according to basic data, establishing an intelligent recovery process control module, calculating each control parameter value in a feedforward mode and issuing the established intelligent recovery process control module; the recovery process intelligent control module is used for controlling the system temperature according to the received control parameter values, outputting control parameter values and correcting the controlled parameters in real time; the data mining and coordination optimization module is used for optimizing process parameters and making an auxiliary decision for system operation by adopting a data mining algorithm;
the basic data comprises analyzer data, pipeline equipment structure data, coking process data, gas collection process data, distributed control system data and assay data;
the recovery process intelligent control module comprises a final-cooling rear coal gas temperature control module, an upper-tower lean oil temperature control module, a rich oil preheating temperature control module, a debenzolization tower top temperature control module and a regenerator top temperature control module; the gas temperature control module after final cooling adopts a feedforward-feedback control mode, and the cooling water quantity feedforward calculation model can effectively inhibit the fluctuation influence of the gas quantity, the gas temperature and the cooling water quantity at the inlet of the final cooler and reduce the influence of disturbance on the system; the feedback correction of the cooling water quantity realizes the stable tracking control of the temperature of the finally cooled coal gas; the upper tower lean oil temperature control module adopts a feedforward-feedback control mode, a cooling water quantity feedforward calculation model can effectively inhibit the fluctuation influence of the lean oil quantity and the lean oil temperature at the inlet of the two-stage cooler, and the feedback correction of the cooling water quantity realizes the stable tracking control of the lean oil temperature at the outlet of the cooler; the rich oil preheating temperature control module adjusts the flow rate of rich oil entering the furnace, the flow rate of coal gas and the steam pressure in a coordinated control mode, and keeps the temperature of the rich oil at the outlet of the tubular furnace, the temperature of superheated steam and the pressure in a normal range; the debenzolization tower top temperature control module adopts a feedforward-cascade control mode, the rich oil flow is used for feedforward adjustment, the stability of the load of the tower is maintained, the outer ring in the cascade control adopts fuzzy control, and the debenzolization tower top temperature is used as a controlled variable; the sum of the feedforward output result and the outer ring output result is used as a set value for PID control of the inner ring crude benzene reflux, and the crude benzene reflux is used as a controlled variable; the regenerator top temperature control module adopts coordination control to adjust the flow of rich oil entering the regenerator and the flow of steam to keep the temperature of the oil and the steam at the outlet of the regenerator within a normal range;
the intelligent optimization control system for the crude benzene recovery process is divided into a process parameter optimization part, a control parameter optimization part and a data processing part; the process parameter optimization part analyzes the balance relation between the recovery rate of the crude benzene product and the energy consumption, establishes an optimization model based on production evaluation, and obtains and stores the optimal real-time process parameters by solving the optimization model; the control parameter optimization part is used for controlling the process parameters and the temperature of the elution benzene process analysis module and the recovery process intelligent control module; the data processing part is used for analyzing and processing the data of the bottom layer instrument, transmitting the data to the server through a network, converting a control instruction transmitted by the server into a signal which can be executed by an execution mechanism, and thus finishing the control of the field device;
the control parameter optimization part comprises a process unit mechanism model and a temperature control model; the process unit mechanism model comprises a lean oil benzene-containing model, a benzene-containing soft measurement model of the gas before the benzene washing tower, a crude benzene recovery rate model, a heat exchange process model and a rectification process model; the lean oil benzene-containing model is calculated according to the top temperature of the debenzolization tower, the top pressure of the debenzolization tower, the bottom temperature of the debenzolization tower, the bottom pressure of the debenzolization tower, the rich oil preheating temperature and the superheated steam temperature; the benzene-containing soft measurement model of the coal gas before the benzene washing tower is calculated according to the volatile matter of the blended coal, the yield of crude benzene, the dry coal amount per hour of a coke oven, the coal gas amount before entering the benzene washing tower, the coal gas temperature and the coal gas pressure; calculating a crude benzene recovery rate model according to the absorption area of a benzene washing tower, the absorption temperature, the quality of circulating wash oil, the benzene content of lean oil, the benzene content before the tower and the quantity of the circulating wash oil; the heat exchange process model is used for calculating model parameters of the heat exchange process; the rectification process model is used for calculating model parameters in the rectification process; a temperature control model end cooler rear gas temperature adaptive fuzzy control model, a secondary cooler outlet lean oil temperature adaptive fuzzy control model, a tubular furnace coordination prediction control model, a regenerator coordination prediction control model and a debenzolization tower top temperature feedforward-cascade control model; the gas temperature control model behind the final cooler inputs the fuzzy controller according to the deviation and the deviation change rate of the set value and the measured value of the gas temperature behind the final cooler, and the cooling water flow compensation quantity is output by the controller; the corrected set value is sent to DCS for control; the lean oil temperature fuzzy control model at the outlet of the second-stage cooler inputs the deviation and deviation change rate of a set value and an actual value of the lean oil temperature after the second-stage cooler into the fuzzy controller, and the cooling water flow compensation quantity is output by the controller; the corrected set value is sent to DCS for control; the tubular furnace coordination prediction control model realizes a prediction control decision on an upper computer according to a prediction control algorithm, calculates DMC controller output, and uses each output control variable to set given values of the inlet rich oil quantity, the steam flow and the gas flow of the tubular furnace in the DCS; the regeneration coordination prediction control model establishes a model with the output of the temperature of oil gas at the outlet of the regenerator and the temperature of slag discharged at the bottom of the regenerator through a test method, and the input of the model is the flow of rich oil at the inlet of the regenerator and the flow of steam; calculating set values of the oil-rich quantity and the steam flow at the inlet of the regenerator in the DCS according to a predictive control algorithm; the debenzolization tower top temperature feedforward-cascade control model takes the rich oil temperature as a feedforward variable, effectively inhibits the fluctuation influence of the rich oil quantity, the temperature and the superheated steam temperature, and realizes the stable tracking control of the debenzolization tower top temperature by the feedback correction of the crude benzene reflux quantity;
the hardware platform of the intelligent optimization control system for the crude benzene recovery process comprises an operation end, a server, a master control PLC, a sensor, a communication module, an online analyzer and an execution mechanism; the dual-redundancy network, the dual-redundancy server and the dual-redundancy main control unit ensure the reliable operation of the whole system, and the system has flexible structure, convenient expansion and low maintenance cost; the system network is communicated with a plant-level big data server, a coking system, a gas collection process system and a laboratory in a networking way, and the plant-level historical data, the current production operation condition and the equipment state are utilized to optimize and calculate the production process and realize the stable control of each control loop; meanwhile, the recovery process flow, combustible gas and toxic and harmful gas are monitored in real time, the operation condition in the production process and the equipment load state are observed, and safe and stable operation is realized; the control network realizes the data acquisition of the newly added sensor equipment and receives a control instruction issued by an upper layer to automatically control the equipment.
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