CN113717022B - Optimal control method and system for carbon three-liquid-phase hydrogenation reactor - Google Patents
Optimal control method and system for carbon three-liquid-phase hydrogenation reactor Download PDFInfo
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- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
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Abstract
The invention discloses an optimal control method and system for a carbon three-liquid-phase hydrogenation reactor. The optimal control method of the carbon three-liquid phase hydrogenation reactor comprises the following steps: establishing a state control model based on the characteristics of the carbon three-liquid phase hydrogenation catalyst; carrying out classified statistics on real-time operation data of the hydrogenation reactor to obtain classified statistical data; based on the reaction characteristics of the carbon three liquid phase hydrogenation catalyst, carrying out on-line correction on the state control model by adopting a multiple optimization mode to obtain a corrected state control model; obtaining control parameters based on the classified statistical data and the corrected state control model; performing gain scheduling on the control parameters to obtain gain scheduling parameters; and controlling the carbon three-liquid phase hydrogenation reactor based on the control parameter and the gain scheduling parameter. The method and the system can achieve the aim of obtaining the optimal propylene yield.
Description
Technical Field
The invention belongs to the field of petrochemical industry, and particularly relates to an optimal control method and system for a carbon three-liquid-phase hydrogenation reactor.
Background
Ethylene technology is a petrochemical tap technology, and the ethylene technology level is regarded as an important sign for measuring the state of petrochemical development. The triene (ethylene, propylene and butadiene) produced by the ethylene cracking device is a basic raw material of petrochemical industry, and the level of the triene yield is a main mark for measuring the national petrochemical development level.
After the liquid hydrocarbon raw materials such as naphtha and the like in the ethylene cracking device are subjected to steam cracking and separation, the carbon three-fraction contains propylene, propane and a small amount of propyne and propadiene (MAPD for short), and the content of MAPD is about 1-5% (volume). In propylene polymerization, MAPD reduces the activity of polypropylene catalysts, affecting the product quality of polymerization grade propylene. To remove MAPD from the carbon three fraction, catalytic selective hydrogenation and solvent absorption processes are currently used in industry to remove MAPD. The carbon three-liquid phase catalytic hydrogenation method has the advantages of simple process flow and no environmental pollution, so the application of the catalytic hydrogenation method is increasingly popular.
The carbon three liquid phase hydrogenation reactor device is an important device of a propylene device recovery system, and is used for converting MAPD in the carbon three fraction into propylene through selective hydrogenation under the action of a catalyst. MAPD, if excessively hydrogenated, will produce propane, oligomers and polymers, resulting in the loss of propylene; if the MAPD effect is poor, the concentration of MAPD at the outlet of the reactor is not controlled within the re-index requirement range, so that the propylene product is unqualified and the production of a downstream device is influenced, and the operation quality of the hydrogenation reactor directly influences the purity and yield of the propylene product.
The carbon three-liquid phase hydrogenation catalyst generally adopts transition metals such as palladium, nickel and the like as active components, the reaction thermodynamic parameters, the surface adsorption and desorption reaction rate and the process sensitivity of different catalysts are different, and the optimal performance can be ensured by targeted adjustment and optimization.
At present, the production control of the carbon three liquid phase hydrogenation reactor generally adopts manual regulation and control, and related parameters are manually regulated and controlled by technicians. Because of the lengthy cracking separation process, complex process and limited personnel energy, the real-time monitoring and expert adjustment optimization of the carbon three liquid phase hydrogenation reactor cannot be realized. When unstable conditions such as material composition, pressure, temperature, flow and hydrogen fluctuation occur in the carbon three hydrogenation system, the stability is very slow by the liquid phase hydrogenation system, and superposition phenomenon generated by repeated fluctuation ensures that the system is in a metastable state for a long time, so that the acetylene leakage at the outlet of the reactor and the excessive hydrogenation of propylene are easily caused to be serious, and the yield of propylene and the separation effect of a rectifying tower are influenced.
Disclosure of Invention
In view of the above, the invention provides an optimal control method and system for a carbon three-liquid phase hydrogenation reactor, which at least solve the problem of poor propylene yield in the prior art.
In a first aspect, the invention provides a method for optimally controlling a carbon three liquid phase hydrogenation reactor, comprising the following steps:
establishing a state control model based on the characteristics of the carbon three-liquid phase hydrogenation catalyst;
carrying out classified statistics on real-time operation data of the hydrogenation reactor to obtain classified statistical data;
based on the reaction characteristics of the carbon three liquid phase hydrogenation catalyst, carrying out on-line correction on the state control model by adopting a multiple optimization mode to obtain a corrected state control model;
obtaining control parameters based on the classified statistical data and the corrected state control model;
performing gain scheduling on the control parameters to obtain gain scheduling parameters;
and controlling the carbon three-liquid phase hydrogenation reactor based on the control parameter and the gain scheduling parameter.
Optionally, the state control model is a catalyst main performance change model established based on a limited number of parameter optimization operations;
the main properties include propylene selectivity and outlet MAPD concentration.
Optionally, the state control model is expressed as:
wherein Yd (T) is the selectivity, MAPD (T) is the outlet MAPD concentration, T i (T), i=1, 2,3 is the bed temperature, T 0 (t) is the reactor inlet temperature, P A0 (t) is the reactor inlet hydrogen to MAPD split ratio, G (t) is the reactor inlet fresh feed plus recycle mass flow rate, K i I=1, 2,3 is zero, C, D, E is a state model parameter.
Optionally, the multiple optimization mode includes:
an index control model, a propylene yield model and a yield prediction model.
Optionally, the index control model is:
Tg＝Tg st ×Coeff Model ，
wherein Tg is a reactor control index, and the values comprise at least one of outlet MAPD concentration, propylene selectivity, outlet hydrogen concentration and the generation amount above C5; tg of (Tg) st Is a reactor control index under the standard process and material composition conditions; coeff (r) Model Controlling index transfer coefficients for the reactor from standard working conditions to actual working conditions;
and/or
The index control model considering the influence of the material property parameters on the optimized operation mode is as follows:
Tg＝Tg 0 +ΔTg，
wherein Tg is an index of reactor control in the optimization mode, tg 0 Initial value of reactor control index, tg 0 Is a fixed value; Δtg is the amount of improvement corresponding to the material property parameter in the optimization mode.
Optionally, under the condition that the propylene yield of the reactor takes a fixed value under the optimized operation and the material composition change is compensated, the propylene yield model is as follows:
Yd＝Yd 0 +f m (ΔTg)，
in the formula Yd 0 A base value for optimizing propylene yield under operation; Δtg is the amount of improvement in the control index corresponding to the material property parameter under the optimization operation; f (f) m (ΔTg) yield compensation value for optimizing process material variation, f m (ΔTg) as a function of ΔTg.
Optionally, the profit prediction model is:
Gn＝M×Yd+f p (1-Yd)，
wherein Gn is the prediction of total benefit brought by hydrogenation of the reactor; yd is propylene yield under optimized operation; m is propylene unit price; f (f) p (1-Yd) for the return of propane under optimized operation, f p (1-Yd) as a function of (1-Yd); (1-Yd) is the propane yield of the reactor hydrogenation.
Optionally, the multiple optimization mode includes:
based on the index control model, establishing a continuous model between a propylene yield model and the index control model according to initial test operation data;
calculating a propylene yield interval value by using the yield prediction model based on the continuous model;
and transmitting the propylene yield interval value into a state control model to assign the operation variable.
Optionally, the multiple optimization mode includes:
based on the index control model, compensating and correcting the propylene yield model and the index control model according to the optimized control operation data;
calculating a corrected propylene yield interval value by using the yield prediction model based on the corrected propylene yield model and the index control index model;
and transmitting the corrected propylene yield interval value into a state control model to correct the operation variable.
Optionally, the carbon three liquid phase hydrogenation catalyst comprises a carrier, a main active component and a cocatalyst component which are loaded on the carrier;
the main active component is at least one selected from Pd, ni, pt, rh and Ru; the content of the main active component is preferably 0.05 to 0.8wt% based on the weight of the carbon three liquid phase hydrogenation catalyst;
the promoter component is at least one selected from Ag, cu, au, la, ce, ga, pb, W, mo, halogen family, alkali metal family and alkaline earth metal family; the content of the cocatalyst is preferably 0.01 to 1.0wt% based on the weight of the carbon three liquid phase hydrogenation catalyst;
the carrier of the carbon three-liquid phase hydrogenation catalyst is at least one selected from alumina, molecular sieve, silicon oxide, gallium oxide, titanium oxide and active carbon.
In a second aspect, the present invention provides an optimal control system for a carbon three liquid phase hydrogenation reactor, comprising: the system comprises an expert optimization module, a gain scheduling module, a soft measurement module, an analysis and evaluation module and a state model controller module;
the soft measurement module and the analysis and evaluation module are used for carrying out classification statistics on the real-time operation data of the hydrogenation reactor to obtain classification statistics data;
the expert optimization module is used for carrying out on-line correction on the state control model by adopting a multiple optimization mode based on the reaction characteristics of the carbon three-liquid phase hydrogenation catalyst;
the state model controller module is used for establishing a state control model based on the characteristics of the carbon three liquid phase hydrogenation catalyst and obtaining control parameters based on the classification statistical data and the corrected state control model;
and the gain scheduling module is used for carrying out gain scheduling on the control parameters to obtain gain scheduling parameters.
Optionally, the state model controller module compares the classified statistical data with the assigned operation variable to obtain comparison data;
the gain scheduling module sets an adjustment amplitude of an operating variable based on the comparison data.
Optionally, the output end of the soft measurement module is connected with the input ends of the expert optimization module and the state model controller module respectively, the output end of the analysis evaluation module is connected with the input end of the expert optimization module, the output end of the expert optimization module is connected with the input end of the state model controller module, and the output end of the state model controller module is connected with the output end of the gain scheduling module.
According to the invention, after classifying, counting, analyzing and fitting operation data of the hydrogenation reactor, on-line correction is carried out on the state control model by adopting a multiple optimization mode based on the reaction characteristics of the carbon three liquid phase hydrogenation catalyst, and control parameters are obtained based on the classifying, counting data and the corrected state control model; and carrying out gain scheduling on the control parameters to obtain gain scheduling parameters. Therefore, different control is carried out aiming at different carbon three liquid phase hydrogenation catalysts, and the maximum efficiency of the carbon three liquid phase hydrogenation catalyst is exerted, so that the aim of obtaining the optimal propylene yield is fulfilled.
And establishing an expert optimization and state model controller module, and realizing automatic control of the carbon three-liquid-phase hydrogenation reactor through a multiple optimization mode of the expert optimization module. The optimal triene yield is obtained by maximizing the hydrogenation income of the reactor, and the long-period operation of the catalyst is ensured while the consumption of energy and materials is reduced. Additional features and advantages of the invention will be set forth in the detailed description which follows.
Drawings
Exemplary embodiments of the present invention will be described in more detail with reference to the accompanying drawings.
FIG. 1 illustrates a flow chart of a method for optimizing control of a carbon three liquid phase hydrogenation reactor in accordance with one embodiment of the present invention;
FIG. 2 illustrates a functional block diagram of a carbon three liquid phase hydrogenation reactor optimization control system in accordance with one embodiment of the invention;
FIG. 3a shows a flow rate variation graph for outlet MAPD control using a carbon three liquid phase hydrogenation reactor control method in accordance with one embodiment of the invention;
FIG. 3b shows a flow rate variation graph of the hydrogen-to-alkyne ratio using a carbon three liquid phase hydrogenation reactor control method in accordance with one embodiment of the present invention;
FIG. 3c illustrates a graph of hydrogen flow rate variation using a carbon three liquid phase hydrogenation reactor control method in accordance with one embodiment of the present invention;
FIG. 4 shows a schematic diagram of a prior art carbon three liquid phase hydrogenation unit process;
wherein, the hydrogenation reactor is 1-C3, and the condenser is 2-condenser.
Detailed Description
The following describes specific embodiments of the present invention in detail. It should be understood that the detailed description and specific examples, while indicating and illustrating the invention, are not intended to limit the invention.
The present invention will be further described with reference to examples, but the scope of the present invention is not limited to these examples.
Embodiment one:
as shown in fig. 1, the method for optimally controlling the carbon three-liquid phase hydrogenation reactor comprises the following steps:
step S101: establishing a state control model based on the characteristics of the carbon three-liquid phase hydrogenation catalyst;
step S102: carrying out classified statistics on real-time operation data of the hydrogenation reactor to obtain classified statistical data;
the real-time operation data of the hydrogenation reactor comprises the composition and content of materials before and after the carbon three liquid phase hydrogenation reactor, the composition and content of the top of the depropanizer tower or the composition and content of the bottom of the deethanizer tower and the composition and content of circulating carbon three materials, and the contents of catalyst on-line operation time, material flow, reaction temperature, pressure, hydrogen before and after the reaction, carbon three composition, carbon four composition, water and the like are changed.
The classification statistics are based on the data obtained after the composition and content of the materials before and after the carbon three liquid phase hydrogenation reactor, the composition and content of the top of the depropanizer or the composition and content of the bottom of the deethanizer, and the composition and content of the circulating carbon three materials are filtered, so that the numerical value and trend of each material composition and content are obtained.
Based on the content change data of the catalyst such as on-line operation time, material flow, reaction temperature, pressure, hydrogen, carbon three composition, carbon four composition, water and the like before and after the reaction, the operation life, activity, propylene selectivity, MAPD conversion rate, MAPD tolerance and the like of the catalyst are obtained.
Step S103: based on the reaction characteristics of the carbon three liquid phase hydrogenation catalyst, carrying out on-line correction on the state control model by adopting a multiple optimization mode to obtain a corrected state control model;
step S104: obtaining control parameters based on the classified statistical data and the corrected state control model;
step S105: performing gain scheduling on the control parameters to obtain gain scheduling parameters;
step S106: and controlling the carbon three-liquid phase hydrogenation reactor based on the control parameter and the gain scheduling parameter.
Optionally, the state control model is a catalyst main performance change model established based on a limited number of parameter optimization operations;
the main properties include propylene selectivity and outlet MAPD concentration.
Optionally, the state control model is expressed as:
wherein Yd (T) is the selectivity, MAPD (T) is the outlet MAPD concentration, T i (T), i=1, 2,3 is the bed temperature, T 0 (t) is the reactor inlet temperature, P A0 (t) is the reactor inlet hydrogen to MAPD split ratio, G (t) is the reactor inlet fresh feed plus recycle mass flow rate, K i I=1, 2,3 is zero, C, D, E is a state model parameter.
Optionally, the multiple optimization mode includes:
an index control model, a propylene yield model and a yield prediction model.
Optionally, the index control model is:
Tg＝Tg st ×Coeff Model ，
wherein Tg is a reactor control index, and the values include the outlet MAPD concentration and propylene selectivityAt least one of outlet hydrogen concentration and C5 or higher generation amount; tg of (Tg) st Is a reactor control index under the standard process and material composition conditions; coeff (r) Model Controlling index transfer coefficients for the reactor from standard working conditions to actual working conditions;
and/or
The index control model considering the influence of the material property parameters on the optimized operation mode is as follows:
Tg＝Tg 0 +ΔTg，
wherein Tg is an index of reactor control in the optimization mode, tg 0 Initial value of reactor control index, tg 0 Is a fixed value; the delta Tg corresponds to the improvement of the material property parameter in the optimization mode. The material property parameters are material property parameters such as inlet MAPD concentration, hydrogen concentration and the like.
Optionally, under the condition that the propylene yield of the reactor takes a fixed value under the optimized operation and the material composition change is compensated, the propylene yield model is as follows:
Yd＝Yd 0 +f m (ΔTg)，
in the formula Yd 0 A base value for optimizing propylene yield under operation; Δtg is the amount of improvement in the control index corresponding to the material property parameter under the optimization operation; f (f) m (ΔTg) yield compensation value for optimizing process material variation, f m (ΔTg) as a function of ΔTg.
Optionally, the profit prediction model is:
Gn＝M×Yd+f p (1-Yd)，
wherein Gn is the prediction of total benefit brought by hydrogenation of the reactor; yd is propylene yield under optimized operation; m is propylene unit price; f (f) p (1-Yd) for the return of propane under optimized operation, f p (1-Yd) as a function of (1-Yd); (1-Yd) is the propane yield of the reactor hydrogenation. The profit prediction model is a mathematical model for establishing the hydrogenation profit prediction of the reactor according to production operation statistical data.
Optionally, the multiple optimization mode includes:
based on the index control model, establishing a continuous model between a propylene yield model and the index control model according to initial test operation data;
calculating a propylene yield interval value by using the yield prediction model based on the continuous model;
and transmitting the propylene yield interval value into a state control model to assign the operation variable.
Optionally, the multiple optimization mode includes:
based on the index control model, compensating and correcting the propylene yield model and the index control model according to the optimized control operation data;
calculating a corrected propylene yield interval value by using the yield prediction model based on the corrected propylene yield model and the index control index model;
and transmitting the corrected propylene yield interval value into a state control model to correct the operation variable.
Optionally, the carbon three liquid phase hydrogenation catalyst comprises a carrier, a main active component and a cocatalyst component which are loaded on the carrier;
the main active component is at least one selected from Pd, ni, pt, rh and Ru; the content of the main active component is preferably 0.05 to 0.8wt% based on the weight of the carbon three liquid phase hydrogenation catalyst;
the promoter component is at least one selected from Ag, cu, au, la, ce, ga, pb, W, mo, halogen family, alkali metal family and alkaline earth metal family; the content of the cocatalyst is preferably 0.01 to 1.0wt% based on the weight of the carbon three liquid phase hydrogenation catalyst;
the carrier of the carbon three-liquid phase hydrogenation catalyst is at least one selected from alumina, molecular sieve, silicon oxide, gallium oxide, titanium oxide and active carbon.
Embodiment two:
as shown in fig. 2, an optimization control system for a carbon three liquid phase hydrogenation reactor comprises: the system comprises an expert optimization module, a gain scheduling module, a soft measurement module, an analysis and evaluation module and a state model controller module;
the soft measurement module and the analysis and evaluation module are used for carrying out classification statistics on the real-time operation data of the hydrogenation reactor to obtain classification statistics data;
the expert optimization module is used for carrying out on-line correction on the state control model by adopting a multiple optimization mode based on the reaction characteristics of the carbon three-liquid phase hydrogenation catalyst;
the state model controller module is used for establishing a state control model based on the characteristics of the carbon three liquid phase hydrogenation catalyst and obtaining control parameters based on the classification statistical data and the corrected state control model;
and the gain scheduling module is used for carrying out gain scheduling on the control parameters to obtain gain scheduling parameters.
Optionally, the state model controller module compares the classified statistical data with the assigned operation variable to obtain comparison data;
the gain scheduling module sets an adjustment amplitude of an operating variable based on the comparison data.
Optionally, the output end of the soft measurement module is connected with the input ends of the expert optimization module and the state model controller module respectively, the output end of the analysis evaluation module is connected with the input end of the expert optimization module, the output end of the expert optimization module is connected with the input end of the state model controller module, and the output end of the state model controller module is connected with the output end of the gain scheduling module.
The optimal control system of the carbon three liquid phase hydrogenation reactor is positioned in a server connected with a distributed control system, namely a DCS system, of the carbon three liquid phase hydrogenation reactor. The state model controller module is positioned at the lower layer of the automatic control system of the carbon three liquid phase hydrogenation reactor, is connected with the DCS system through the OPC Server, and directly issues optimization control signals and commands to the DCS; the gain scheduling module is also positioned at the lower layer of the system and provides the gain and frequency of each adjustment parameter for the state model controller module; the expert optimization module, the soft measurement module and the analysis and evaluation module are all positioned at the upper layer of the automatic control system of the carbon three-liquid-phase hydrogenation reactor, and the expert optimization module is used for calculating an optimal propylene yield interval by combining the soft measurement and analysis and evaluation module and adopting a multiple optimization mode to assign and correct the state model controller module on line.
The soft measurement module monitors the composition and content of materials before and after the carbon three hydrogenation reactor, the composition and content of the top of the depropanizer or the composition and content of the bottom of the deethanizer in real time, and the composition and content of the circulating carbon three materials are filtered and input into the soft measurement module to measure the numerical value and trend of each material.
The analysis and evaluation module monitors the online running time, material flow, reaction temperature, pressure, hydrogen, carbon three composition, carbon four composition, water and other content changes of the catalyst in real time, and inputs the real-time data into the analysis and evaluation module to measure the running life, activity, propylene selectivity, MAPD conversion rate, MAPD tolerance and other catalytic performances of the catalyst.
The expert optimization module is based on an index control model, establishes a continuous model between propylene yield and an index control index according to initial test operation data, calculates an optimal propylene yield interval value by using a yield prediction model, and then transmits the value to the state model controller module to assign values to operation variables. In the online operation process, optimizing control operation data, compensating and correcting the propylene yield and index control index model, calculating a corrected optimal propylene yield interval value by using a yield prediction model, and transmitting correction data into a state model controller module to correct operation variables.
The real-time operation data is processed by the soft measurement module and the analysis and evaluation module and then enters the expert optimization module and the state model controller module. After classifying and counting the real-time data, the soft measurement module enters a state model controller module, compares and calculates with assigned operation variables, and sets adjustment amplitude of each operation variable to a gain scheduling module
And (3) using C# language to realize the design of iterative learning control software of the optimal control system of the carbon three-liquid phase hydrogenation reactor. The software comprises a data acquisition part and a data storage and learning control algorithm part. The control system software uses OPC technology to communicate with the DCS system of the carbon three hydrogenation device, reads real-time process variable data, and realizes the optimal control of the carbon three hydrogenation reactor through writing operation. The data storage section is capable of storing history data.
An optimization control system of a carbon three liquid phase hydrogenation reactor comprises a controller for constructing a carbon three liquid phase hydrogenation space model and utilizing linear matrix optimization to solve; and setting up an expert optimization model of the multiple optimization modes. The control system writes expert control software in C# language and tests the reliability of the software.
The carbon three liquid phase hydrogenation reactor optimization control system is applied to a carbon three liquid phase hydrogenation reaction control unit of the olefin plant: the modularized automatic control system of the carbon three liquid phase hydrogenation reactor is connected with a device DCS through an OPC Server, optimizes and adjusts each process condition, and provides an adjusting target for the DCS in real time so as to realize automatic control of the carbon three hydrogenation reactor.
FIGS. 3a, 3b and 3c show MAPD control at the outlet of the carbon three hydrogenation reactor, the hydrogen-alkyne ratio and the hydrogen flow. Based on an index control model, the expert-optimized control system of the carbon three-liquid-phase hydrogenation reactor adjusts operating variables such as hydrogen, temperature, pressure and the like through a propylene yield and benefit prediction model, ensures that the outlet MAPD is less than 100ppm, and obtains the maximum propylene yield. Under the same conditions of reactor, catalyst and feed composition, the average propylene selectivity can be improved from 41.2% before use to 65.3% after use by introducing an automatic control system, and the by-product ethylene is 120 tons/year, so that the synergistic effect is very obvious.
Comparative example:
an olefin plant producing 100 ten thousand tons of ethylene in one year has 14 cracking furnaces, and can process various cracking raw materials from ethane to hydrogenated tail oil and the like, and can produce 50 ten thousand tons of propylene in one year. The separation process of the plant adopts a sequential separation flow, a carbon three hydrogenation reactor is positioned between a hot zone depropanizer and a propylene rectifying tower, carbon three fractions obtained from the top of the high-pressure depropanizer are subjected to heat exchange to a required temperature by a cooler (or a preheater), are boosted by a feed pump, enter the hydrogenation reactor through a raw material dearsenifier, are mixed with hydrogen in a pipeline, enter a catalytic bed of the reactor for selective hydrogenation reaction, and the process flow of the plant carbon three hydrogenation process is shown in figure 4. The normal operation of the carbon three-liquid phase hydrogenation reactor in the factory is manually adjusted.
The comparison result shows that: by introducing the method and the system of the invention, compared with the manual control of the original factory, the propylene yield is obviously improved, and the hydrogen consumption is reduced.
The foregoing description of embodiments of the invention has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described.
The endpoints and any values of the ranges disclosed herein are not limited to the precise range or value, and are understood to encompass values approaching those ranges or values. For numerical ranges, one or more new numerical ranges may be found between the endpoints of each range, between the endpoint of each range and the individual point value, and between the individual point value, in combination with each other, and are to be considered as specifically disclosed herein.
Claims (13)
1. An optimal control method for a carbon three liquid phase hydrogenation reactor is characterized by comprising the following steps:
establishing a state control model based on the characteristics of the carbon three-liquid phase hydrogenation catalyst;
carrying out classified statistics on real-time operation data of the hydrogenation reactor to obtain classified statistical data;
based on the reaction characteristics of the carbon three liquid phase hydrogenation catalyst, carrying out on-line correction on the state control model by adopting a multiple optimization mode to obtain a corrected state control model;
obtaining control parameters based on the classified statistical data and the corrected state control model;
performing gain scheduling on the control parameters to obtain gain scheduling parameters;
controlling the carbon three liquid phase hydrogenation reactor based on the control parameters and the gain scheduling parameters;
the state control model is expressed as:
，
in the method, in the process of the invention,for selectivity (I)>For the outlet MAPD concentration, & lt + & gt>I=1, 2,3 is the bed temperature, +.>For the reactor inlet temperature, +.>For the partial pressure ratio of hydrogen to MAPD at the reactor inlet, +.>For the sum of fresh feed to the reactor inlet and the mass flow rate of the recycle stream, +.>I=1, 2,3 is zero, C, D, E is a state model parameter.
2. The optimal control method for a carbon three liquid phase hydrogenation reactor according to claim 1, wherein said multiple optimization modes comprise:
an index control model, a propylene yield model and a yield prediction model.
3. The optimal control method for the carbon three liquid phase hydrogenation reactor according to claim 2, wherein the index control model is:
Tg=Tg st ×Coeff Model ，
wherein Tg is a reactor control index, and the values comprise at least one of outlet MAPD concentration, propylene selectivity, outlet hydrogen concentration and the generation amount above C5; tg of (Tg) st Is a reactor control index under the standard process and material composition conditions; the CoeffModel is a transmission coefficient of a control index of the reactor from a standard working condition to an actual working condition;
and/or
The index control model considering the influence of the material property parameters on the optimized operation mode is as follows:
Tg= Tg 0 +ΔTg，
wherein Tg is an index of reactor control in the optimization mode, tg 0 Initial value of reactor control index, tg 0 Is a fixed value; Δtg is the amount of improvement corresponding to the material property parameter in the optimization mode.
4. The optimal control method for a carbon three liquid phase hydrogenation reactor according to claim 2 or 3, wherein under the condition that the propylene yield of the reactor takes a fixed value and compensates the material composition change under the optimal operation, the propylene yield model is as follows:
Yd=Yd 0 + f m (ΔTg)，
in the formula Yd 0 A base value for optimizing propylene yield under operation; Δtg is the amount of improvement in the control index corresponding to the material property parameter under the optimization operation; f (f) m (ΔTg) yield compensation value for optimizing process material variation, f m (ΔTg) as a function of ΔTg.
5. The optimal control method for a carbon three liquid phase hydrogenation reactor according to claim 4, wherein the profit prediction model is:
Gn=M×Yd+f p (1-Yd)，
wherein Gn is the prediction of total benefit brought by hydrogenation of the reactor; yd is propylene yield under optimized operation; m is propylene unit price; f (f) p (1-Yd) is an optimization operationMake return of propane, f p (1-Yd) as a function of (1-Yd); (1-Yd) is the propane yield of the reactor hydrogenation.
6. The optimal control method for a carbon three liquid phase hydrogenation reactor according to claim 2, wherein said multiple optimization modes comprise:
based on the index control model, establishing a continuous model between a propylene yield model and the index control model according to initial test operation data;
calculating a propylene yield interval value by using the yield prediction model based on the continuous model;
and transmitting the propylene yield interval value into a state control model to assign the operation variable.
7. The optimal control method for a carbon three liquid phase hydrogenation reactor according to claim 6, wherein said multiple optimization modes comprise:
based on the index control model, compensating and correcting the propylene yield model and the index control model according to the optimized control operation data;
calculating a corrected propylene yield interval value by using the yield prediction model based on the corrected propylene yield model and the index control index model;
and transmitting the corrected propylene yield interval value into a state control model to correct the operation variable.
8. The method for optimizing control of a carbon three liquid phase hydrogenation reactor according to claim 1, wherein said carbon three liquid phase hydrogenation catalyst comprises a carrier and a main active component and a cocatalyst component supported thereon;
the main active component is at least one selected from Pd, ni, pt, rh and Ru;
the promoter component is at least one selected from Ag, cu, au, la, ce, ga, pb, W, mo, halogen family, alkali metal family and alkaline earth metal family;
the carrier of the carbon three-liquid phase hydrogenation catalyst is at least one selected from alumina, molecular sieve, silicon oxide, gallium oxide, titanium oxide and active carbon.
9. The optimal control method for a carbon three liquid phase hydrogenation reactor according to claim 8, wherein the content of said main active component is 0.05 to 0.8wt% based on the weight of the carbon three liquid phase hydrogenation catalyst.
10. The method for optimizing control of a carbon three liquid phase hydrogenation reactor according to claim 8, wherein the content of said cocatalyst is 0.01 to 1.0wt% based on the weight of the carbon three liquid phase hydrogenation catalyst.
11. An optimal control system for a carbon three liquid phase hydrogenation reactor, comprising: the system comprises an expert optimization module, a gain scheduling module, a soft measurement module, an analysis and evaluation module and a state model controller module;
the soft measurement module and the analysis and evaluation module are used for carrying out classification statistics on the real-time operation data of the hydrogenation reactor to obtain classification statistics data;
the expert optimization module is used for carrying out on-line correction on the state control model by adopting a multiple optimization mode based on the reaction characteristics of the carbon three-liquid phase hydrogenation catalyst;
the state model controller module is used for establishing a state control model based on the characteristics of the carbon three liquid phase hydrogenation catalyst and obtaining control parameters based on the classification statistical data and the corrected state control model;
the gain scheduling module is used for performing gain scheduling on the control parameters to obtain gain scheduling parameters;
the state control model is expressed as:
，
in the method, in the process of the invention,for selectivity (I)>For the outlet MAPD concentration, & lt + & gt>I=1, 2,3 is the bed temperature, +.>For the reactor inlet temperature, +.>For the partial pressure ratio of hydrogen to MAPD at the reactor inlet, +.>For the sum of fresh feed to the reactor inlet and the mass flow rate of the recycle stream, +.>I=1, 2,3 is zero, C, D, E is a state model parameter.
12. The optimal control system for the carbon three liquid phase hydrogenation reactor according to claim 11, wherein said state model controller module obtains comparison data by comparing said classification statistical data with assigned operating variables;
the gain scheduling module sets an adjustment amplitude of an operating variable based on the comparison data.
13. The optimal control system for a carbon three liquid phase hydrogenation reactor according to claim 11, wherein,
the output end of the soft measurement module is respectively connected with the input ends of the expert optimization module and the state model controller module, the output end of the analysis and evaluation module is connected with the input end of the expert optimization module, the output end of the expert optimization module is connected with the input end of the state model controller module, and the output end of the state model controller module is connected with the output end of the gain scheduling module.
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