Industrial mto reaction depth and catalyst activity intelligent control method and device

Abstract

The present application relates to the industrial MTO reaction depth and catalyst activity intelligent control method and device. Among them, the control method is to obtain the operation data of MTO production device, set the optimization target of MTO production, take the reaction residue content as the variable reflecting the reaction depth, calculate the reaction residue content optimization value and the reaction coke rate optimization value to realize the optimization target, adopt the automatic control algorithm or the automatic control model, calculate the regeneration air flow regulating valve valve position optimization value and the regeneration slide valve valve position optimization value to realize the reaction residue content optimization value and the reaction coke rate optimization value respectively, and control the regeneration air flow regulating valve valve position and the regeneration slide valve valve position. The device is provided with an intelligent optimization controller, the operation data is obtained through the DCS of MTO production device, the regeneration air flow regulating valve valve position optimization value and the regeneration slide valve valve position optimization value under the optimization target are calculated according to the intelligent control model, and the corresponding optimization control is realized through the DCS.

Description

Technical Field

[0001] This invention relates to a method for intelligent control of industrial MTO reaction depth and catalyst activity, and an apparatus for intelligent control of industrial MTO reaction depth and catalyst activity using this method. It belongs to the field of industrial control technology, and in particular to intelligent control technology for MTO production. Background Technology

[0002] Methanol-to-olefins (MTO) is an important coal chemical production process. In recent years, with rising oil prices, coal and natural gas chemical industries have developed rapidly, driving a significant increase in methanol production capacity. MTO uses methanol derived from coal or natural gas as feedstock, and reacts it under catalytic conditions to produce low-carbon olefins through dehydration. Currently, the basic process flow for industrial MTO production is largely the same, employing a fluidized bed reactor (reactor) – catalyst regenerator (regenerator) to achieve the MTO reaction and catalyst regeneration and recycling. Methanol reacts in the reactor (fluidized bed) under the action of a catalyst, producing olefins with a specific compositional distribution. The catalyst separated from the product gas (reaction gas) is recycled. Some catalyst is first introduced into the regenerator for coke burn-off regeneration before returning to the reactor to control and maintain the required catalyst activity. Production inputs, processes, and products (including compositional distribution and yield of each product) can be controlled by adjusting the reaction conditions (material composition, process parameters, etc.) and catalyst activity within the reactor, allowing for optimized control based on set optimization objectives. Controlling the reaction depth of MTO (Metal-to-Oxide) is a crucial aspect of MTO production control. Currently, this control is primarily performed manually by operators. Every four hours, the carbon content of the regenerator and the reactant is analyzed. Based on the reaction status, such as the residual amounts of ethylene, propylene, and dimethyl ether at the reactor outlet, operators adjust the regeneration air volume to maintain the carbon content at the desired level, thereby regulating catalyst activity and ultimately controlling the reaction depth. However, this method of reaction depth control has several unresolved issues in actual operation. These include long catalyst carbon content analysis cycles (over 4 hours), significant lag, large fluctuations in carbon content, and the inability to achieve ideal operating conditions. Furthermore, there is still room for optimization in methanol consumption. Therefore, developing an intelligent control and optimization system to optimize the reaction depth of the MTO unit is of great significance. This will further improve the automation and intelligence of the unit, reduce diene methanol consumption, increase diene yield, reduce energy consumption, and improve the economic efficiency of the unit while ensuring stable operation and safe production.

[0003] On the other hand, the continuous development of automatic detection and control technologies has provided strong support for the automation and intelligent control of MTO production. Currently, there is extensive research worldwide on methanol-to-olefins catalysts, reaction characteristics, and process technologies. Several internationally renowned oil and chemical companies, such as Mobil, BASF, Exxon, UOP, and Norsk Hydro, have invested significant resources in this area for many years. Domestically, institutions such as the Dalian Institute of Chemical Physics, the Beijing Research Institute of Petroleum Science, China University of Petroleum, Shanghai Research Institute of Petroleum and Chemical Industry, and East China University of Science and Technology have also conducted long-term research on MTO catalytic reactions. Several single-loop control methods have been developed for the basic MTO production process, including feed control, level control, pressure control, reaction temperature control, regeneration airflow control, and reactor catalyst level control, applying various automated control techniques. For example, Inner Mongolia University of Science and Technology applied fuzzy control technology to the PID control of MTO, attempting to perform correlation analysis on the reaction system variables from an engineering application perspective, and providing an advanced fuzzy PID control strategy for the system. [1] These control technologies have, to some extent, promoted the development of intelligent MTO production, provided a reference for the deep intelligent control of MTO reaction, and also put forward more urgent requirements. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art, this invention provides an intelligent control method and device for industrial MTO reaction depth and catalyst activity. Based on the relevant operating data of the MTO production unit (hereinafter referred to as MTO unit), the optimized values ​​of reaction depth and catalyst activity to achieve the optimization target, as well as the set values ​​(optimized values) of the corresponding operating variables, are calculated so that the reaction depth and catalyst activity of the MTO production unit can be adjusted to the corresponding optimized values ​​through the control system of the MTO production unit (usually a distributed control system).

[0005] The technical solution of this invention is: an intelligent control method for industrial MTO reaction depth and catalyst activity. This method acquires operating data of the MTO production unit, sets optimization targets for MTO production, uses the content of reaction residue as a variable reflecting reaction depth, and calculates optimized values ​​for the reaction residue content and coking rate to achieve the optimization targets based on the constraints of the MTO production process and the production unit. Using an automatic control algorithm or model, it calculates optimized values ​​for the regeneration airflow regulating valve position and the regeneration slide valve position (i.e., the regeneration catalyst circulation control valve, or the regeneration catalyst circulation flow regulating valve) to achieve the optimized values ​​for the reaction residue content and the regeneration coking rate, respectively. The regeneration airflow regulating valve position and the regeneration slide valve position are adjusted to the optimized values ​​for the regeneration airflow regulating valve position and the regeneration slide valve position, respectively, thereby achieving optimized control of reaction depth and catalyst activity.

[0006] Furthermore, the calculation process for the optimized value of the regenerated air flow regulating valve position includes: using a reaction residue intelligent control model, with the reaction residue content (e.g., diethanol residue, or the diethanol residue content in the reaction gas) as the controlled variable and the regenerator carbon determination (regenerated catalyst carbon determination, or the carbon content of the regenerated catalyst) as the operating variable, to calculate the optimized regenerator carbon determination value that achieves the optimized value of the reaction residue content; using a regenerator carbon determination intelligent control model, with the regenerator carbon determination as the controlled variable and the regenerated air flow as the operating variable, to calculate the optimized regenerated air flow rate that achieves the optimized regenerator carbon determination value; using a regenerated air intelligent control model, with the regenerated air flow as the controlled variable and the regenerated air flow regulating valve position (opening degree) as the operating variable, to calculate the optimized regenerated air flow regulating valve position that achieves the optimized regenerated air flow rate value.

[0007] Furthermore, the intelligent control model for reaction residues, the intelligent control model for regenerator carbon determination, and the intelligent control model for regeneration air all adopt PID feedback control models, or in other words, they all adopt PID feedback control algorithms.

[0008] Preferably, an auxiliary regulating valve for regenerated air flow is provided. The auxiliary regulating valve for regenerated air flow includes a makeup air valve, a small vent valve, and a large vent valve. The air inlet side (air inlet) of the regenerator is connected to the main fan through the regenerated air main pipe. A main valve is provided on the outlet side of the main fan. The makeup air valve is set on the makeup air pipe. One end of the makeup air pipe is the air inlet end, which is connected to the makeup air source (usually the factory air source), and the other end is the air outlet end, which is connected to the regenerated air main pipe or the air inlet side of the regenerator. The small vent valve is set on the small valve vent pipe. One end of the small valve vent pipe is the air inlet end, which is connected to the regenerated air main pipe or the air inlet side of the regenerator, and the other end is the air outlet end, which is connected to the atmosphere or connected to the venting and exhaust facilities. The large vent valve is set on the large valve vent pipe. One end of the large valve vent pipe is the air inlet end, which is connected to the regenerated air main pipe or the air inlet side of the regenerator, and the other end is the air outlet end, which is connected to the atmosphere or connected to the venting and exhaust facilities.

[0009] Preferably, in the calculation involving the optimized value of the regenerated air flow regulating valve position, the main valve is used as the regenerated air flow regulating valve. The optimized value of the main valve position is calculated, and the main valve position is controlled to be at its optimized value. Then, according to the following control logic, the regenerated air flow auxiliary regulating valve is adjusted based on the real-time regenerated air flow:

[0010] 1) When the real-time regenerated airflow is lower than the optimized regenerated airflow value, gradually increase the opening of the make-up air valve until the real-time regenerated airflow matches the optimized regenerated airflow value or the difference between the real-time regenerated airflow and the optimized regenerated airflow value is less than the effective adjustment range of the make-up air valve.

[0011] If there is a certain amount of venting in the above situation, or if the opening of the venting valve or the venting valve exceeds a certain limit (for example, the opening of the valve or valve is >0.1%), the venting valve and / or the venting valve with a certain opening should be gradually reduced first, until the real-time regenerated air flow rate is consistent with the optimized regenerated air flow rate or the opening of both the venting valve and the venting valve is less than a certain limit (the limit can be zero at this time, or it can be non-zero for reasons such as operational convenience or system stability).

[0012] 2) If the real-time regenerative airflow exceeds the optimized regenerative airflow value, and the difference is large (within the effective adjustment range of the main vent valve), first control the main vent valve for coarse adjustment until the difference between the real-time regenerative airflow and the optimized regenerative airflow value falls within the effective adjustment range of the small vent valve. Then control the small vent valve for fine adjustment until the real-time regenerative airflow matches the optimized regenerative airflow value. If the difference is small (within the adjustment range of the small vent valve), control the small vent valve for fine adjustment until the real-time regenerative airflow matches the optimized regenerative airflow value.

[0013] If there is a certain amount of make-up air in the above situation, or if the opening of the make-up air valve exceeds a certain limit (for example, opening > 0.1%), the opening of the make-up air valve should be gradually reduced until the real-time regenerated air flow rate is consistent with the optimized value of the regenerated air flow rate or the opening of the make-up air valve is less than a certain limit (the limit can be zero at this time, or it can be non-zero for reasons such as operational convenience or system stability).

[0014] The adjustment of the above-mentioned make-up air valve, vent valve, and vent valve can also be achieved by first calculating the optimal adjustment method under the corresponding conditions using a model, and then controlling the valve opening accordingly based on the optimal adjustment method under the corresponding conditions. The model used and its application / calculation method can be the same as or similar to the model and its application / calculation method involved in the calculation of the optimized value of the regenerated air flow.

[0015] A small vent valve with appropriate performance can be used, making its adjustment accuracy significantly better than that of the large vent valve, and even significantly better than that of the make-up air valve. This setup significantly improves the control accuracy of the regenerated air flow, better achieving the optimization goal. The so-called effective adjustment range refers to the effective and feasible adjustment range in relevant industrial practice. When the effective adjustment range of the small vent valve partially overlaps with that of the large vent valve or make-up air valve, the adjustment in the overlapping area can be arbitrarily selected based on practical convenience.

[0016] The operating data of the MTO production unit can include raw operating data that can be directly obtained from the MTO production unit and obtained through online monitoring, as well as calculated operating data based on the raw operating data. Specific parameters are determined according to actual needs.

[0017] Preferably, the method of directly acquiring and online detecting raw operating data from the MTO production unit is that the distributed control system of the MTO production unit obtains raw operating data and / or raw detection data from the process parameter detection instruments and reactor inlet and outlet component analysis instruments of the MTO production unit, and generates corresponding raw operating data based on the raw detection data.

[0018] Preferably, the intelligent optimization controller reads the required raw operating data through the real-time data reading interface of the distributed control system.

[0019] An optimizer can be constructed based on the constraints of the MTO production process and equipment. The optimizer calculates the optimized values ​​of reaction residue content and reaction coking rate based on the externally output optimization objectives of MTO production.

[0020] An intelligent control device for industrial MTO reaction depth and catalyst activity, employing any of the intelligent control methods disclosed in this invention, performs intelligent optimization control of industrial MTO reaction depth and catalyst activity. It includes a distributed control system and an intelligent optimization controller for the MTO production unit. The distributed control system obtains raw operating data and / or raw detection data from process parameter monitoring instruments and reactor inlet / outlet component analysis instruments of the MTO production unit and generates corresponding raw operating data based on the raw detection data. The intelligent optimization controller calculates and obtains estimated operating data based on the raw operating data and, based on the externally output MTO... The optimization objectives for production are determined by calculating the optimal values ​​for the content of reaction residue and the rate of reaction coking, based on the constraints of the MTO production process and equipment. Using an automatic control algorithm or model, the optimal values ​​for the regeneration airflow regulating valve position and the regeneration slide valve position, respectively, are calculated to achieve these optimal values. These optimal values ​​are then written back to the distributed control system. The distributed control system controls the regeneration airflow regulating valve position and the regeneration slide valve position of the MTO production unit to these optimized values, thereby achieving intelligent optimization control of the MTO production unit.

[0021] Furthermore, the intelligent optimization controller interacts with the operator station and the engineer station, and accepts information input from the engineer station and the operator station.

[0022] The information input at the engineer's workstation may include optimization objectives.

[0023] The information input at the operator station may include the upper and lower limits of each operational variable.

[0024] Preferably, the distributed control system is provided with a real-time data reading interface and a real-time data writing-back interface for communicating with the intelligent controller. The intelligent controller reads the original operating data through the real-time data reading interface and writes the calculated optimized values ​​of the regenerated airflow regulating valve position and the regenerated slide valve position into the distributed control system through the real-time data writing-back interface.

[0025] The beneficial effects of this invention are: it can realize intelligent control of MTO reaction depth and catalyst activity based on the raw operating data obtained by real-time detection. Through the distributed control system of the MTO device, the valve positions of the regeneration air flow regulating valve and the regeneration slide valve in the MTO device are automatically adjusted to their respective optimized values, thereby achieving optimized reaction depth and catalyst activity. This not only reduces the workload of adjustment but also improves the response speed of the control system, reduces adjustment time, and also helps to improve the stability of the production system.

[0026] Intelligent control and optimization systems based on mechanistic reaction models are highly reliable and do not require extensive historical data to correct or learn the model.

[0027] Fully automated optimization control of MTO reaction depth, carbon waiting time, and catalyst activity.

[0028] The use of virtual flow meters enhances the reliability of material balance calculations. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the structure of an MTO production apparatus according to the present invention;

[0030] Figure 2 This is the control flowchart of the present invention;

[0031] Figure 3 This invention relates to a schematic diagram of a catalyst recycling material balance model and related data processing flow.

[0032] Figure 4 This invention relates to a schematic diagram of the reaction residue control model and related data processing flow;

[0033] Figure 5 This is a schematic diagram of the data processing flow related to catalyst circulation volume (regenerator circulation volume and catalyst circulation volume) involved in the present invention;

[0034] Figure 6 This invention relates to a schematic diagram of the carbon differential / catalyst cycle quantity control model and related data processing flow;

[0035] Figure 7 This is a schematic diagram of the regenerant carbon determination control model and related data processing flow involved in the present invention;

[0036] Figure 8 This is a schematic diagram of the regenerated air duct and flow regulation / auxiliary regulation product structure involved in this invention;

[0037] Figure 9 This is a schematic diagram of the regenerated air flow control model and related data processing flow involved in the present invention;

[0038] Figure 10 The graph shows the optimization effect of combining the optimization control of this invention with other related optimization controls when applied to an industrial MTO unit, involving unit consumption, diene yield, coking rate and total catalyst reserves. These indicators are calculated based on a 30-day moving average, and the straight line represents the changing trend of the corresponding parameter.

[0039] Figure 11The graph shows the residual amount and fluctuation of dimethyl ether before and after the application of the optimized control of this invention in combination with other relevant optimized controls to an industrial MTO unit. The left curve is the change (fluctuation) curve of the dimethyl ether component concentration before application, and the right curve is the change (fluctuation) curve of the dimethyl ether component concentration under stable conditions after application. Detailed Implementation

[0040] See Figure 2 The overall intelligent optimization and control strategy involved in this invention is as follows:

[0041] 1) The MTO intelligent optimizer optimizes the optimal reaction and regeneration conditions based on the optimization objectives, including reaction temperature, reaction time, water-to-alcohol ratio, reaction depth, catalyst activity, etc.

[0042] 2) Based on existing technology, use intelligent control systems or intelligent controllers to describe in detail the intelligent control of reaction temperature, reaction time, and water-to-alcohol ratio. Through these intelligent controls, these reaction conditions are controlled at the optimized target.

[0043] 3) The intelligent controller describes how to achieve intelligent control of reaction depth and catalyst activity to meet the optimization objectives required by the optimizer;

[0044] 4) The content of reaction residues such as dimethyl ether and methanol in the reaction gas at the reactor outlet (which can be called residual amount) can quantify the reaction depth and be used as a reaction depth parameter. The reaction depth can be controlled by adjusting the concentration of reaction residues. In actual production units, when catalyst deactivation, catalyst runoff, or additive addition affects catalyst activity, changes in reaction residues can automatically adjust catalyst activity and provide the required setpoints for catalyst carbon / regeneration air, thus controlling the reaction depth.

[0045] 5) When the raw material feed changes, the optimized coking rate changes, adjust the catalyst circulation rate, and thus affect the carbon difference between the regenerated and recycled catalysts, so that the regenerated coking rate tracks the reaction coking rate and achieves a dynamic balance of catalyst carbon stability.

[0046] The control method of the present invention may include the following specific steps:

[0047] 1. Read process instrument data from the DCS interface.

[0048] Read relevant instrument data such as flow rate, temperature, pressure, and liquid level, including regeneration air flow rate / valve position, regeneration / waiting slide valve position, regeneration temperature, regeneration flue gas analysis, reactor catalyst content, regenerator catalyst content, and reactor outlet component analyzer data.

[0049] 2. Read the reaction residue control target from the MTO optimizer or input the target manually by the operator.

[0050] 3. Data processing and correction.

[0051] The following methods were used for data acquisition, fault diagnosis, and correction:

[0052] 1) Data validity and instrument fault diagnosis: Use instrument quality stamps, ranges, and rates of change from DCS and other systems to judge the validity of data and diagnose faults.

[0053] 2) If the data is invalid, the calculated value or other alternative value will be used for subsequent model calculations according to the settings, or the model calculation will be terminated; if the data is valid, the data correction process will be performed in the next step (step 3).

[0054] 3) Data processing and correction: Data can be corrected by algorithms such as filtering, material balance correction, and energy balance correction as needed.

[0055] 4) Carbon determination calculation of the catalyst: Carbon determination of the catalyst (catalyst) is calculated based on the catalyst circulation material balance model.

[0056] 5) Carbon determination calculation of regenerator: Carbon determination of regenerator (regenerated catalyst) is calculated based on the catalyst circulation material balance model.

[0057] The input and output variables of the catalyst cycle material balance model can be found in [reference needed]. Figure 3 .

[0058] Calculation objectives and methods for catalyst circulation material balance models:

[0059] a) Establish a catalyst circulation reserve model, and use the reserve detection data and the pressure difference and valve position data of the waiting valve and the regeneration valve to correct the catalyst circulation rate (existing relevant calculation models can be used) to meet the accuracy requirements of the catalyst material balance model.

[0060] b) Using the reactor model to calculate the reactor coke generation rate and the regeneration coke burning rate to calculate the carbon material balance, the carbon determination of the waiting agent and the carbon determination of the regenerator are calculated in real time.

[0061] The application areas of the online catalyst circulation material balance model are as follows:

[0062] a) The carbon determination of the pre-generation agent serves as an important basis for the control and optimization of catalyst activity;

[0063] b) The corrected catalyst circulation rate is used as an important optimization variable for reaction optimization.

[0064] 6) Calculation based on reaction residue control

[0065] To control the reaction depth to the optimal target, reaction residues (such as dimethyl ether) are read from the reaction gas analyzer as a controlled variable for intelligent control. The regenerator carbon determination can be assessed in real-time using the dimethyl ether content in the reaction gas. An increase in dimethyl ether content indicates a decrease in reaction depth and an increase in regenerated carbon determination. To maintain the regenerated carbon determination, the main regeneration air volume needs to be increased to burn more carbon. Conversely, the opposite is also true.

[0066] The intelligent control model for reaction residues and its related data processing flow can be found in [reference needed]. Figure 4 .

[0067] Specifically, it includes:

[0068] a) Control objectives

[0069] Based on the reaction residue optimization target calculated by the MTO optimizer, the required regeneration carbon setpoint is calculated, which in turn affects the catalyst activity and achieves the purpose of controlling the reaction depth.

[0070] b) Controlled variables:

[0071] i) Current value of reaction residue (PV, signal source: DCS);

[0072] ii) Optimization target for reaction residues (SP, signal source: MTO optimizer).

[0073] c) Operational variable: Regenerant carbon setpoint (signal destination: Regenerant carbon setpoint intelligent controller 11).

[0074] d) Control algorithm:

[0075] Since the relationship between reaction residue and regenerated carbon determination is non-linear, one of the following two algorithms is used:

[0076] i) Nonlinear PID control algorithm:

[0077] m = m0 + Kc (e + 1 / Ti ∫e dt + Td de / dt) (6-1)

[0078] In the formula

[0079] m: Operation variable;

[0080] m0: The variable from the previous operation;

[0081] e: Control deviation e = SP – PV

[0082] Kc: Control gain; Ti: Integral time; Td: Derivative time;

[0083] Considering the nonlinear relationship between the manipulated variable and the controlled variable, a nonlinear PID algorithm is adopted.

[0084] Kc = f1(e) (6-2)

[0085] Ti = f2(e) (6-3)

[0086] Td = f3(e) (6-4)

[0087] Different PID parameters Kc, Ti, and Td are used for different PV and SP deviation ranges.

[0088] ii) Mechanism-based control algorithms based on reaction models:

[0089] Model relationship between the carbon determination activity factor Ac of the pre-treatment agent and carbon determination:

[0090] Ac = f4(C_DS) (6-5)

[0091] Assuming unpredictable factors such as catalyst deactivation, catalyst runoff, and catalyst addition affect the inherent catalyst activity factor Ad, the reaction yield Xc of the reaction residue is expressed as follows:

[0092] Xc = f5(kc,Tr,W,Kw) (6-6)

[0093] kc = kc0 * Ac * Ad * e(-Ei / RT) (6-7)

[0094] In the formula

[0095] T: Reaction temperature;

[0096] Tr: Reaction time;

[0097] W, Kw: Water adsorption resistance factors;

[0098] Ei: Activation energy of the reaction;

[0099] The reaction yield model can be developed using existing techniques.

[0100] The algorithm steps are as follows:

[0101] Based on the analyzer detection data Xcd of the reaction residue from DCS and the calculated value of the current Ac, the inherent activity factor Ad of the catalyst is solved by using equations (6-6) and (6-7) to Xc=Xcd; based on the optimized target data Xco of the reaction residue and the calculated inherent activity factor Ad of the catalyst, the set value C_DS_SP of the carbon setpoint of the catalyst is solved by using equations (6-5), (6-6), and (6-7) to Xc=Xco.

[0102] 7) Calculation of catalyst circulation rate

[0103] See Figure 5 The real-time calculated value of the regenerated catalyst circulation volume is given by the measured pressure drop and opening degree of the regeneration slide valve;

[0104] CCR_D = f(aD,FVD,DPD) (7-1)

[0105] CCR_Z = f(aZ,FVZ,DPZ) (7-2)

[0106] CCR_D: Regenerator circulation rate;

[0107] CCR_Z: Regenerant circulation rate;

[0108] aD: Flow characteristic parameters of the valve to be generated;

[0109] aZ: Flow characteristic parameters of the regeneration valve;

[0110] FVD: Opening degree of the valve to be generated;

[0111] FVZ: Opening degree of the regeneration valve;

[0112] DPD: Pressure differential of the standby valve;

[0113] DPZ: Pressure differential of the regeneration valve.

[0114] The flow rates of the catalyst to be generated and the catalyst to be regenerated are determined based on the dynamic thermal balance and dynamic material balance of the regenerator and the reaction settling tank, respectively, with time lag. The real-time calculated value of the catalyst to be regenerated is corrected in real time using the regenerated catalyst flow rate with time lag. In one embodiment of the present invention, the observer provides the current catalyst flow rate value and information on possible faults and abnormal catalyst flow every 3-10 seconds, providing a basis for online real-time analysis and judgment of reaction depth, operation control and real-time optimization.

[0115] 8) Intelligent control calculation of carbon difference:

[0116] The product of the carbon difference between the pre-regenerated and regenerated catalysts (the difference between the pre-regenerated catalyst's fixed carbon and the regenerated catalyst's fixed carbon, referred to as carbon difference) and the catalyst circulation rate is the amount of carbon burned in the regenerator. From the perspective of material conservation, this value should be equal to the amount of coke produced in the reactor. Therefore, the theoretical control value of the circulation rate should be methanol processing rate * coke production rate / control carbon difference.

[0117] See Figure 6 The corresponding control model / data processing method is as follows:

[0118] a) Control objectives

[0119] Based on the coking rate calculated by the intelligent controller for reaction depth, the required carbon difference and catalyst circulation amount are calculated, and then the opening degree of the slide valve is determined to achieve the purpose of controlling the carbon difference required for a certain reaction depth.

[0120] b) Controlled variable (PV):

[0121] i) Carbon determination of regenerator C_ZS (signal source: carbon determination calculation of regenerator);

[0122] ii) Carbon determination of the pre-regenerating agent C_DS (signal source: carbon determination calculation of the pre-regenerating agent);

[0123] iii) Carbon difference DC (calculated from the above two: carbon determination of the pre-regenerating agent - carbon determination of the regenerating agent).

[0124] c) Operating variable: Catalyst regeneration slide valve (regenerant circulation flow regulating valve).

[0125] d) Control Algorithm

[0126] i) Read the carbon determination of the regenerator (C_ZS), the carbon determination of the reactant (C_DS), and the coking rate (RX_CR), and perform data processing;

[0127] ii) Calculate the carbon difference (DC):

[0128] DC = C_DS – C_ZS (8-1)

[0129] iii) Calculate the target catalyst circulation rate CCR_Z_TG:

[0130] To control the carbon stability in the catalyst and the catalyst circulation rate, the regenerated coke rate RG_CR should be equal to the reaction coke rate.

[0131] RX_CR = RG_CR = CCR_Z_TG * DC (8-2)

[0132] Solve for CCR_Z_TG from the above equation;

[0133] CCR_Z_TG = RX_CR / DC (8-3)

[0134] iv) Control the regeneration catalyst circulation rate according to the conventional PID algorithm, so that CCR_Z is controlled to CCR_Z_TG, and the output control output is FVZ_OUT.

[0135] 9) Spool valve output diagnosis and write-back

[0136] The slide valve output FVZ_OUT calculated by the intelligent control of catalyst circulation is used for diagnosis and limiting, including upper and lower limits, other constraints, etc. The output after limiting is written back to the regeneration slide valve setting value of DCS.

[0137] 10) Catalyst slide valve control (DCS)

[0138] The DCS connects to the field slide valve, and the regeneration slide valve is automatically controlled according to the settings.

[0139] 11) Intelligent control calculation for carbon determination of regenerator

[0140] a) Intelligent control strategy:

[0141] Because the carbon determination testing cycle is long, using only the test values ​​for carbon determination cannot achieve real-time and accurate control. Therefore, the carbon determination results of the regenerator based on catalyst material balance are used, and the carbon determination of the regenerator is controlled by adjusting the regeneration air volume.

[0142] For the control model and overall control strategy, please refer to Figure 7 .

[0143] b) Control objectives

[0144] Based on the regeneration carbon setpoint given by the intelligent controller for reaction residues, the required regeneration air setpoint is calculated to achieve the purpose of controlling the carbon setpoint and activity of the catalyst.

[0145] c) Controlled variable (PV):

[0146] i) Current value of regenerated carbon determination (signal source: module 5 regenerator carbon determination calculation);

[0147] ii) Regeneration carbon setpoint (signal source: Module 6 Reaction Residue Intelligent Controller).

[0148] d) Operated variable: Regeneration air setpoint (Signal destination: Regeneration air intelligent controller 13)

[0149] e) Control Algorithm

[0150] i) Perform data processing on the input signals read by the DCS;

[0151] ii) Control is performed using a nonlinear PID algorithm, which is the same as formulas (6-1), (6-2), (6-3), and (6-4), but different tuning parameters are used depending on the actual situation;

[0152] or,

[0153] iii) Employ existing multivariate predictive control algorithms.

[0154] 12) Calculation of regenerated air flow rate

[0155] Figure 8An improved embodiment of regenerated air supply and flow regulation is presented. In this embodiment, the main valve on the regenerated air main pipe connected to the main fan can be regarded as a regenerated air flow regulating valve for corresponding optimization value calculation and control. After the main valve is regulated, further regulation is carried out through the make-up air valve, the large vent valve, and the small vent valve. Alternatively, the combination of the main valve, the make-up air valve, the large vent valve, and the small vent valve can be regarded as a regenerated air regulating valve. The relationship between the main valve, the make-up air valve, the large vent valve, and the small vent valve is established according to the regulation strategy. Based on the calculated optimized regenerated air flow value and the regulation strategy, the main valve, the make-up air valve, the large vent valve, and the small vent valve are regulated.

[0156] exist Figure 8 In the illustrated implementation, the regeneration air source for the regenerator has two paths: one through the main air flow controller (main valve) FC1, and the other through the supplementary air valve FV2. To regulate the flow rate, small valves FV3S and large valves FV3B are installed to discharge some air.

[0157] Because the venting air volumes F3S and F3B, and the make-up air volume F2 are relatively small under normal operating conditions, the venting valve and make-up air valve are adjusted with a low opening. Since the accuracy of ordinary industrial gas flow meters is limited, no flow meters or flow controllers are installed. To facilitate adjustment of the venting volume, the venting valve is equipped with a large valve and a small valve; the small valve is used for fine adjustment, and the large valve is used for coarse adjustment. Based on existing technology, F2, F3S, and F3B can be calculated using a flow valve model, specifically as follows:

[0158] F2 = f(a2,FV2,P21,P22) (12-1)

[0159] F3S = f(a3S,FV3S,P31,P32) (12-2)

[0160] F3 = f(a3B,FV3B,P31,P32) (12-3)

[0161] FRG = FC1 + F2 – F3S – F3B (12-4)

[0162] In the formula:

[0163] F2, F3S, and F3B: These represent the supplementary airflow and venting airflow, respectively, in Nm3 / h.

[0164] FV2, FV3S, and FV3B: These represent the opening degrees of the make-up air and vent air valves, respectively, in %;

[0165] P21 and P22: Pressures before and after the supplementary air valve, respectively, in kPa;

[0166] P31 and P32: These represent the pressures before and after the vent valve, in kPa, respectively.

[0167] a2, a3S, a3B: These are percentage parameters for the make-up air and vent air valves, respectively.

[0168] Since the valve opening and valve position are often not ideally equal percentage characteristics, the equal percentage characteristic parameters of the regenerated air and the vent valve were corrected according to the change of the regenerated flue gas slide valve opening, and a strategy of using different equal percentage parameters for different openings was adopted.

[0169] Based on actual production data, the following model is established to show how percentage parameters change with the opening degree:

[0170] a2 = g(FV2) (12-5)

[0171] a3S = g(FV3S) (12-6)

[0172] a3S = g(FV3B) (12-7)

[0173] Based on (12-1) to (12-7) above, the regenerated airflow can be calculated in real time and used for the following intelligent optimization control of regenerated airflow.

[0174] 13) Intelligent optimization control of regenerated air

[0175] The intelligent optimization control model and data flow for regenerated airflow can be found in [reference needed]. Figure 9 .

[0176] Since the air volume controller simultaneously controls the vent valve, the auxiliary valve, and the make-up valve, the control opening degree of the three auxiliary regulating valves is calculated according to the following control logic:

[0177] a) When the air volume change that needs to be adjusted is large, use the vent valve for coarse adjustment. After the coarse adjustment is completed, adjust the small valve to increase the adjustment accuracy.

[0178] b) When the required regenerated air setpoint is higher than the current FC (regenerated air flow rate through the main valve) setpoint, increase the supplementary air valve FV2 at a certain rate.

[0179] c) To save factory air, when the required regenerated air setting is lower than the current FC setting and there is venting (large valve or small valve opening > 0.1%), the supplementary air will be automatically reduced or shut off.

[0180] d) Based on the required change in air volume, use formulas (12-1) to (12-7) to back-calculate the set values ​​FV2, FV3S and FV3B for each valve position.

[0181] 14) Diagnosis and write-back of regenerated air valves

[0182] The intelligent control of regenerated air calculates the output of the control valve for diagnosis and limit, including upper and lower limits, other constraints, etc. The output after the limit is written back to the regenerated air valve setting value of the DCS.

[0183] 15) Regenerated air regulating valve control (DCS)

[0184] The distributed control system controls the valve position of the regenerated air flow regulating valve based on the valve position setting value written back by the optimized controller.

[0185] 16) HMI display

[0186] The principles and methods of efficient human-machine interfaces can be applied to select or set up intelligent control HMIs.

[0187] 17) Control cycle waiting.

[0188] Based on existing automatic control technology [2] Establish relevant control models and perform corresponding control calculations.

[0189] Figure 1 An example of an MTO production unit is shown, which is adapted to the control method and control device of the present invention. Its main process is as follows: Methanol from the methanol tank area enters the methanol buffer tank, is pressurized by the methanol feed pump, and then sequentially passes through a heat exchanger and a methanol heat exchanger in the reactor to exchange heat to 110°C. It is then divided into three heat exchange streams for mixing, and then enters the methanol-reaction gas heat exchanger to fully exchange heat with the high-temperature reaction gas from reactor 1 to recover the high-temperature heat. The methanol is heated to approximately 250°C and enters the feed distributor of reactor 1. Inside reactor 1, methanol directly contacts the high-temperature regeneration catalyst from regenerator 2, and a rapid exothermic reaction occurs under the action of the catalyst. The resulting reaction gas passes through a two-stage cyclone separator to remove most of the carried catalyst, and then through a three-stage cyclone separator in the reactor to remove the entrained catalyst before being led out. It is then heated to approximately 263°C by the methanol-reaction gas heat exchanger, and the corresponding heat is recovered and utilized. After heat recovery, the reaction gas rich in ethylene and propylene enters the quench tower 3 from the bottom. The quench tower is equipped with 14 layers of herringbone baffles. The reaction gas comes into countercurrent contact with the cooling water at the top of the quench tower from bottom to top, washing away any small amount of catalyst carried in the reaction gas and lowering its temperature. After quenching, the reaction gas enters the water washing tower 4 from the bottom of the quench tower. The water washing tower is equipped with 18 layers of floating valve trays and an oil separator at the bottom. The reaction gas comes into countercurrent contact with the washing water from bottom to top, washing away any remaining gas and lowering its temperature. Under normal operating conditions, the reaction gas at the top of the water washing tower is sent to the compressor inlet of the olefin separation and C4 comprehensive utilization unit for further processing.

[0190] The catalyst after reaction in the reactor needs to be regenerated and can be called the spent catalyst. The carbonized spent catalyst enters the spent stripper for stripping. The spent stripper is equipped with three stripping steam ring pipes for stripping the reaction gas carried by the spent catalyst. After stripping, the spent catalyst enters the spent tube after passing through the spent slide valve and enters the regenerator 2 under the delivery of N2. In the regenerator 2, it comes into countercurrent contact with the main air to burn off the coke and achieve regeneration. The regenerated catalyst enters the regeneration stripper for stripping. The regeneration stripper is equipped with three stripping steam ring pipes for stripping the flue gas carried by the regenerated catalyst. After stripping, the regenerated catalyst enters the regeneration tube after passing through the regeneration slide valve and enters the reactor 1 under the delivery of 1.0 MPaG steam. The regenerated flue gas passes through a two-stage cyclone separator to remove most of the carried catalyst, and then passes through a three-stage and a four-stage regenerated flue gas cyclone separator to further remove the entrained catalyst. Finally, it is sent to the CO incinerator or waste heat boiler for heat recovery through a double-acting slide valve and a pressure reducing orifice plate. The resulting flue gas is discharged into the atmosphere through the chimney.

[0191] The quench water generated in the quench tower is drawn out from the bottom of the tower in two streams (not shown). The first stream of quench water is pressurized by the bottom pump of the quench tower and then split into two paths: one path is sent to the olefin separation and C4 comprehensive utilization unit as a low-temperature heat source to reduce the steam consumption of the olefin separation and C4 comprehensive utilization unit. After heat exchange in the olefin separation and C4 comprehensive utilization unit, the quench water returned is cooled to 60°C by the quench water dry air cooler. Part (or all) of it is returned to the quench tower as a quenching agent, and the other part is sent outside the unit (normally not opened); the other path does not undergo heat exchange and directly enters the settling tank 5. The second stream of quench water is pressurized by the quench water hydrocyclone pump and enters the quench water hydrocyclone separator to remove the catalyst carried in the quench water. The clear quench water is discharged from the top of the hydrocyclone separator, filtered by the quench water filter and returned to the quench tower. The quench water carrying most of the catalyst is discharged from the bottom of the hydrocyclone separator and sent to the catalyst drying facility for drying or to the sewage pond or sewage tank.

[0192] The wash water drawn from the bottom of the washing tower 4 is pressurized by the bottom pump and divided into two streams. The first stream enters the wash water filter to remove the catalyst carried in the wash water. After being filtered, it is mixed with the condensate from the first stage of the olefin separation and C4 comprehensive utilization unit, the condensate from the second and third stages of the olefin separation unit, and the wash water from the olefin separation unit, and then enters the settling tank 5. The second stream is sent to the olefin separation and C4 comprehensive utilization unit (the bottom reboiler of the propylene distillation tower in this unit) as a heat source. After heat exchange, it is cooled to 55°C by the wash water dry air cooler and the wash water cooler and then divided into two paths. One path enters the 10th tray in the middle of the washing tower, and the other path is cooled to 37°C by the wash water cooler and enters the 18th tray in the upper part of the washing tower.

[0193] A small amount of gasoline separated by the oil separation facility at the bottom of the water washing tower is pumped out by the gasoline pump at the bottom of the water washing tower and sent to the olefin separation and C4 comprehensive utilization unit.

[0194] Since the wash water drawn from the bottom of the water washing tower 4 contains trace amounts of methanol, dimethyl ether, olefin components, and catalyst, it should be stripped for recovery. The wastewater settled in the settling tank 5 can be pressurized by the stripping tower feed pump, and then heat-exchanged by the stripping tower feed heat exchanger before entering the 41st tray of the wastewater stripping tower 10. The wastewater stripping tower has 52 high-efficiency floating valve trays from top to bottom. There are two wastewater stripping tower bottom reboilers at the bottom of the wastewater stripping tower 10. The wastewater stripping tower bottom reboilers use 240℃, 1.0MPaG low-pressure superheated steam as a heat source. The steam condensate is sent to the condensate tank after passing through the condensate tank. After mixing with the condensate from the methanol-steam heat exchanger, the mixture is pressurized by the condensate pump and sent to the methanol-condensate heat exchanger to exchange heat with methanol and control the methanol heat exchange temperature. Finally, the condensate is cooled to 100℃ by the condensate air cooler before being sent out of the unit.

[0195] The purified water from the bottom of the wastewater stripping tower is pumped into the stripping tower feed heat exchanger. After passing through the methanol-purified water heat exchanger, it is cooled to 40°C by the purified water dry air cooler and the purified water cooler, and then split into two streams. One stream is sent to the gasification unit for coal preparation as makeup water, and the other stream is sent to the olefin separation and C4 comprehensive utilization unit as washing water.

[0196] The stripping gas from the top of the wastewater stripping tower is heated by a methanol-stripping gas heat exchanger and cooled by a wastewater stripping tower top gas cooler before entering the wastewater stripping tower top reflux tank. The concentrated water (containing methanol or dimethyl ether) is pressurized by the stripping tower top reflux pump and can be partially or entirely returned to the upper part of the wastewater stripping tower 10 as top cold reflux. The remaining part can enter the concentrated water storage tank, be pressurized by a pump, mixed with the methanol feed, and then sent to reactor 1 for reprocessing.

[0197] The non-condensable gas from the top of the wastewater stripping tower reflux tank is sent to reactor 1 for recycling.

[0198] Compared with existing technologies in industrial practice, the present invention has the following characteristics:

[0199] 1) Based on a high-precision reaction mechanism model, a real-time optimization of MTO was constructed. It has high reliability and strong adaptability. It has been practically applied in industrial plants and has achieved good optimization results.

[0200] 2) It is well-suited for a centralized control interface.

[0201] 3) It can achieve good results in use. For example, see Figure 10Based on the applicant's experimental data at the industrial site, after combining the optimized control of this invention with other relevant optimized controls and applying it to an industrial MTO unit, the unit consumption decreased by 0.026 from 2.970 to 2.944; the diene yield increased by 0.12% from 33.8% to 33.92%; the coke yield decreased by 0.05% from 1.25% to 1.20%; and the total catalyst stock decreased by 8 tons from 110.5t to 102.5t.

[0202] 4) The application of an intelligent control system enables fully automated production operations:

[0203] a) Based on changes in the production environment (such as daytime temperature), the opening of the regenerated main air vent valve and the supplementary air valve are automatically adjusted to ensure stable air volume;

[0204] b) Based on changes in throughput and production environment, the system automatically optimizes and adjusts the main regeneration air volume, catalyst circulation volume, and quench methanol volume to ensure that the MTO unit always operates in an optimized production state and guarantees the safety of production operations.

[0205] 5) After the system is implemented, the workload of operators can be greatly reduced, and labor costs can be reduced.

[0206] 6) Adjust the vent valve to reduce the factory's air consumption by using the minimum opening degree of the supplementary air valve as the objective function.

[0207] 7) It also demonstrates good technical effectiveness in controlling reaction residues and carbon determination of waste products: see [link to relevant documentation]. Figure 11 In the aforementioned experiments, the variance fluctuation range of the reaction residue dimethyl ether detected by the real-time analyzer and the carbon determination by periodic analysis both decreased by more than 50%, showing a good trend.

[0208] Unless otherwise specified or further limited to one preferred or optional technical means being another, the preferred and optional technical means disclosed in this invention can be arbitrarily combined to form several different technical solutions.

[0209] References

[0210] [1] Jia Guohua, Cui Guimei. Analysis and research on the control of the reverse-regeneration system of MTO device based on DCS [J]. Chemical Engineering Communication Design, 2016, 42(8).

[0211] [2] Huang Dexian, Ye Xinyu, Zhu Jianmin, et al. Advanced Control of Chemical Processes [M]. Chemical Industry Press, 2006.

Claims

1. An intelligent control method for industrial MTO reaction depth and catalyst activity: This method acquires operating data from the MTO production unit, sets optimization targets for MTO production, uses the content of reaction residue as a variable reflecting reaction depth, and calculates optimized values ​​for reaction residue content and coking rate based on the constraints of the MTO production process and unit. Using an automatic control algorithm or model, it calculates optimized values ​​for the regeneration airflow regulating valve position and the regeneration slide valve position to achieve these optimized values, respectively. The method adjusts the regeneration airflow regulating valve position and the regeneration slide valve position to these optimized values, thereby achieving optimized control of reaction depth and catalyst activity. The calculation process for the optimized regeneration airflow regulating valve position includes: An intelligent control model for reaction residues was adopted, with the content of reaction residues as the controlled variable and the carbon determination of regenerator as the operated variable. The optimized carbon determination value of regenerator that achieves the optimized value of reaction residue content was calculated. Similarly, an intelligent control model for regenerator carbon determination was adopted, with the carbon determination of regenerator as the controlled variable and the regeneration air flow rate as the operated variable. Finally, an intelligent control model for regeneration air was adopted, with the regeneration air flow rate as the controlled variable and the valve position of the regeneration air flow regulating valve as the operated variable. The optimized valve position of the regeneration air flow regulating valve that achieves the optimized value of regeneration air flow rate was calculated.

2. The intelligent control method for industrial MTO reaction depth and catalyst activity as described in claim 1, characterized in that... The operating data of the MTO production unit includes raw operating data that can be directly obtained from the MTO production unit and obtained through online monitoring, as well as estimated operating data calculated based on the raw operating data.

3. The intelligent control method for industrial MTO reaction depth and catalyst activity as described in claim 2, characterized in that... The method of directly acquiring and online detecting raw operating data from the MTO production unit is as follows: the distributed control system of the MTO production unit obtains raw operating data and / or raw detection data from the process parameter detection instruments and reactor inlet and outlet component analysis instruments of the MTO production unit, and generates corresponding raw operating data based on the raw detection data. The intelligent optimization controller reads the required raw operating data through the real-time data reading interface of the distributed control system.

4. The intelligent control method for industrial MTO reaction depth and catalyst activity as described in claim 1, characterized in that... An optimizer is constructed based on the constraints of the MTO production process and equipment. The optimizer calculates the optimized values ​​of reaction residue content and reaction coking rate based on the externally output optimization target of MTO production.

5. The intelligent control method for industrial MTO reaction depth and catalyst activity as described in claim 1, characterized in that... The intelligent control models for reaction residues, regenerator carbon determination, and regeneration air all employ PID feedback control models.

6. The intelligent control method for industrial MTO reaction depth and catalyst activity as described in any one of claims 1-5, characterized in that... An auxiliary regulating valve for regenerated air flow is provided. The auxiliary regulating valve for regenerated air flow includes a makeup air valve, a small vent valve, and a large vent valve. The air inlet side of the regenerator is connected to the main fan through the regenerated air main pipe. A main valve is provided on the outlet side of the main fan. The makeup air valve is set on the makeup air pipe. One end of the makeup air pipe is the air inlet end, which is connected to the makeup air source. The other end is the air outlet end, which is connected to the regenerated air main pipe or the air inlet side of the regenerator. The small vent valve is set on the small valve vent pipe. One end of the small valve vent pipe is the air inlet end, which is connected to the regenerated air main pipe or the air inlet side of the regenerator. The other end is the air outlet end, which is connected to the atmosphere or connected to the venting and exhaust system. The large vent valve is set on the large valve vent pipe. One end of the large valve vent pipe is the air inlet end, which is connected to the regenerated air main pipe or the air inlet side of the regenerator. The other end is the air outlet end, which is connected to the atmosphere or connected to the venting and exhaust system.

7. The intelligent control method for industrial MTO reaction depth and catalyst activity as described in claim 6, characterized in that... In the calculation involving the optimized value of the regenerated air flow regulating valve position, the main valve is used as the regenerated air flow regulating valve. The optimized value of the main valve position is calculated, and the main valve position is controlled to be at its optimized value. Then, according to the following control logic, the make-up air valve, the small vent valve, and the large vent valve are adjusted based on the real-time regenerated air flow: 1) When the real-time regenerated airflow is lower than the optimized regenerated airflow value, gradually increase the opening of the make-up air valve until the real-time regenerated airflow matches the optimized regenerated airflow value or the difference between the real-time regenerated airflow and the optimized regenerated airflow value is less than the effective adjustment range of the make-up air valve. When the above situation has a certain amount of venting, or the opening of the venting valve or the venting valve exceeds a certain limit, the venting valve and / or the venting valve with a certain opening should be gradually reduced until the real-time regenerated air flow rate is consistent with the optimized value of the regenerated air flow rate or the opening of the venting valve and the venting valve is less than a certain limit. 2) If the real-time regenerative airflow exceeds the optimized regenerative airflow value, and the difference is within the effective adjustment range of the vent valve, first control the vent valve for coarse adjustment until the difference between the real-time regenerative airflow and the optimized regenerative airflow value falls within the adjustment range of the vent valve. Then control the vent valve for fine adjustment until the real-time regenerative airflow matches the optimized regenerative airflow value. If the difference is within the adjustment range of the vent valve, control the vent valve for fine adjustment until the real-time regenerative airflow matches the optimized regenerative airflow value. When the above situation involves a certain amount of make-up air, or the opening of the make-up air valve exceeds a certain limit, the opening of the make-up air valve should be gradually reduced until the real-time regenerated air flow rate matches the optimized value of the regenerated air flow rate or the opening of the make-up air valve is less than a certain limit.

8. An intelligent control device for industrial MTO reaction depth and catalyst activity, characterized in that... The intelligent control method for industrial MTO reaction depth and catalyst activity according to any one of claims 1-7 is used for intelligent optimization control of industrial MTO reaction depth and catalyst activity. It includes a distributed control system and an intelligent optimization controller for the MTO production unit. The distributed control system obtains raw operating data and / or raw detection data from process parameter detection instruments and reactor inlet / outlet component analysis instruments of the MTO production unit and generates corresponding raw operating data based on the raw detection data. The intelligent optimization controller calculates and obtains estimated operating data based on the raw operating data. Based on the externally output optimization target for MTO production and the constraints of the MTO production process and production unit, it calculates and obtains optimized values ​​for the reaction residue content and reaction coking rate to achieve the optimization target. Using an automatic control algorithm or automatic control model, it calculates and obtains optimized values ​​for the regeneration airflow regulating valve position and the regeneration slide valve position to achieve the optimized values ​​for the reaction residue content and reaction coking rate, respectively. These optimized values ​​are then written back to the distributed control system. The distributed control system controls the regeneration airflow regulating valve position and the regeneration slide valve position of the MTO production unit to their optimized values, thereby achieving intelligent optimization control of the MTO production unit.

9. The intelligent control device for industrial MTO reaction depth and catalyst activity as described in claim 8, characterized in that... The intelligent optimization controller interacts with the operator station and the engineer station, accepting information input from them. The information input from the engineer station includes the optimization target, and the information input from the operator station includes the upper and lower limits of each operational variable. The distributed control system is equipped with a real-time data reading interface and a real-time data writing-back interface for communication with the intelligent controller. The intelligent controller reads the original operating data through the real-time data reading interface and writes the calculated optimized values ​​of the regenerated airflow regulating valve position and the regenerated slide valve position into the distributed control system through the real-time data writing-back interface.

Images