A global cooperative and adaptive control system and method for DMTO

Abstract

The present application relates to the technical field of chemical production, and discloses a global cooperation and self-adaptive control system and method for DMTO, which comprises the following steps: the method is coupled and judged and controlled by combining the multivariate data such as catalyst activity value, catalyst inventory value, catalyst change value and bed pressure drop change value in the running process of the DMTO device with APC to establish a prediction model, so that the DMTO device can adaptively adjust parameters under different conditions, for example, when it is judged that the catalyst loss is abnormal, the parameters such as the outlet gas velocity of the regenerator and the operating pressure of the cyclone separator are adjusted, or when it is judged that the catalyst fluidization is abnormal, the parameters such as the bed gas velocity and the catalyst circulation amount are adjusted, or when it is judged that the catalyst activity is abnormal, the parameters such as the regeneration temperature, the regeneration air amount and the catalyst supplement amount are adjusted, so that manual operation control is avoided and the running efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of chemical production technology, and in particular to a global collaborative and adaptive control system and method for DMTO. Background Technology

[0002] Dimethyl ether and methanol to olefins (DMTO) technology uses dimethyl ether and methanol as feedstocks to produce low-carbon olefins such as ethylene and propylene through a catalytic reaction. DMTO technology operates through a coupled reactor and regenerator. During the reactor reaction, the catalyst gradually deactivates due to carbon deposition and structural changes, requiring regeneration via the regenerator to restore its activity and achieve continuous catalyst recycling.

[0003] However, catalyst loss is difficult to quantify accurately, and the amount of catalyst replenishment still needs to be manually controlled during the operation of the DMTO unit, resulting in low operating efficiency and high catalyst consumption. Summary of the Invention

[0004] This invention provides a global cooperative and adaptive control system and method for DMTO.

[0005] In a first aspect, the present invention provides a global collaborative and adaptive control method for DMTO (Digital-Dependent Tolerance) applied to electronic devices. The method includes: determining a first catalyst inventory value, a catalyst change value, a bed pressure drop change value, and a target product yield value for a DMTO device at a first time; updating the first catalyst inventory value to a second catalyst inventory value based on the catalyst change value and the bed pressure drop change value; determining the catalyst activity value of the DMTO device at the first time based on the target product yield value and the second catalyst inventory value; inputting the catalyst activity value, the bed pressure drop change value, and the catalyst change value into a preset first prediction model to obtain a first judgment result output by the first prediction model, wherein the first prediction model stores the target values ​​corresponding to the catalyst activity value, the catalyst inventory value, and the catalyst change value, respectively; and sending a first control command to the DMTO device based on the first judgment result, wherein the first control command is used to control the DMTO device to adjust parameters at a second time, the second time being a time after the first time.

[0006] In this embodiment of the invention, the method of the invention uses multivariate data such as catalyst activity value, catalyst inventory value, catalyst change value and bed pressure drop change value during the operation of the DMTO device to establish a prediction model for coupled judgment and control, so as to realize the adaptive parameter adjustment of the DMTO device under different conditions.

[0007] In one possible implementation of the first aspect above, the input of the catalyst activity value, bed pressure drop change value, and catalyst change value into a preset first prediction model to obtain a first judgment result output by the first prediction model includes: the first prediction model determining different sub-weight values ​​corresponding to the catalyst activity value, bed pressure drop change value, and catalyst change value according to different target values; and the first prediction model determining the first judgment result according to the different sub-weight values.

[0008] In one possible implementation of the first aspect described above, the first prediction model determines different sub-weight values ​​corresponding to the catalyst activity value, the bed pressure drop change value, and the catalyst change value based on different target values, including: the first prediction model determines the catalyst activity value, the bed pressure drop change value, and the difference between the catalyst change value and the corresponding target value; when the first prediction model determines that the difference between the catalyst activity value and the corresponding target value is the largest, the sub-weight value corresponding to the catalyst activity value is the largest, and the first control command is the first command; when the first prediction model determines that the difference between the bed pressure drop change value and the corresponding target value is the largest, the sub-weight value corresponding to the bed pressure drop change value is the largest, and the first control command is the second command; when the first prediction model determines that the difference between the catalyst change value and the corresponding target value is the largest, the sub-weight value corresponding to the catalyst change value is the largest, and the first control command is the third command; wherein the first command, the second command, and the third command are different.

[0009] In one possible implementation of the first aspect above, determining the first catalyst inventory value, catalyst change value, bed pressure drop change value, and target product yield value of the DMTO unit at the first moment includes: obtaining the first catalyst inventory value, bed pressure drop change value, target product yield value, catalyst replenishment value, flue gas flow rate value, and flue gas dust concentration value from the DMTO unit; determining the catalyst loss value based on the flue gas flow rate value and the flue gas dust concentration value; and determining the catalyst change value based on the catalyst replenishment value and the catalyst loss value.

[0010] In one possible implementation of the first aspect described above, the method further includes: using the first catalyst inventory value, catalyst change value, bed pressure drop change value, target product output value, and first control command determined at different times within a first time interval as a first dataset; establishing a second prediction model based on the first dataset, wherein the second prediction model includes the correspondence between the first catalyst inventory value, catalyst change value, bed pressure drop change value, target product output value, and first control command at different times; obtaining a second judgment result from the second prediction model at a preset time interval; and sending a second control command to the DMTO device based on the second judgment result, wherein the second control command is used to control the DMTO device to perform parameter adjustments.

[0011] In one possible implementation of the first aspect above, the method further includes: establishing a second prediction model based on the first dataset, including: preprocessing the first dataset to obtain a preprocessed dataset; performing time synchronization processing on different types of data in the preprocessed dataset, and establishing a second prediction model based on the time-synchronized data.

[0012] In one possible implementation of the first aspect above, the first prediction model and / or the second prediction model are deployed inside an electronic device; and / or, the first prediction model and / or the second prediction model are deployed in the cloud.

[0013] Secondly, the present invention provides a global collaborative and adaptive control system for DMTO, characterized in that the system includes: a first determining module, used to determine a first catalyst inventory value, a catalyst change value, a bed pressure drop change value, and a target product yield value of the DMTO device at a first time; an updating module, used to update the first catalyst inventory value to a second catalyst inventory value based on the catalyst change value and the bed pressure drop change value; a second determining module, used to determine the catalyst activity value of the DMTO device at the first time based on the target product yield value and the second catalyst inventory value; a judging module, used to input the catalyst activity value, the bed pressure drop change value, and the catalyst change value into a preset first prediction model to obtain a first judging result output by the first prediction model, wherein the first prediction model stores the target values ​​corresponding to the catalyst activity value, the catalyst inventory value, and the catalyst change value respectively; and a control module, used to send a control command to the DMTO device based on the first judging result, wherein the control command is used to control the DMTO device to adjust parameters at a second time, the second time being a time after the first time.

[0014] Thirdly, embodiments of the present invention provide a computer-readable storage medium storing instructions that, when executed on an electronic device, cause the electronic device to implement any of the global cooperative and adaptive control methods of DMTO provided in the first aspect and various possible implementations of the first aspect.

[0015] Fourthly, embodiments of the present invention provide an electronic device comprising: a memory for storing instructions executed by one or more processors of the electronic device; and a processor, one of the processors of the electronic device, for executing the instructions stored in the memory to implement any of the global cooperative and adaptive control methods of DMTO provided by the first aspect and various possible implementations of the first aspect.

[0016] Fifthly, embodiments of the present invention provide a program product including instructions that, when executed by an electronic device, enable the electronic device to implement any of the global cooperative and adaptive control methods of DMTO provided in the first aspect and various possible implementations of the first aspect. Attached Figure Description

[0017] Figure 1 According to some embodiments of the present invention, a schematic diagram of a global cooperative and adaptive control method for DMTO is shown. Figure 2 According to some embodiments of the present invention, a schematic diagram of a module example of a global cooperative and adaptive control system for DMTO is shown; Figure 3 According to some embodiments of the present invention, an example schematic diagram of an electronic device is shown. Detailed Implementation

[0018] The illustrative embodiments of the present invention include, but are not limited to, a global cooperative and adaptive control system and method for DMTO.

[0019] The following description is intended to disclose the present invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art. The basic principles of the invention defined in the following description can be applied to other embodiments, modifications, improvements, equivalents, and other technical solutions that do not depart from the spirit and scope of the invention.

[0020] As mentioned earlier, catalyst loss is difficult to quantify accurately, and the amount of catalyst replenishment still needs to be manually controlled during the operation of the DMTO unit, resulting in low operating efficiency and high catalyst consumption.

[0021] Therefore, this invention provides a global coordinated and adaptive control method for DMTO. This method combines multivariate data such as catalyst activity, catalyst inventory, catalyst change, and bed pressure drop during DMTO unit operation with advanced process control (APC) to establish a predictive model for coupled judgment and control. This enables the DMTO unit to adaptively adjust parameters under different conditions. For example, when abnormal catalyst loss is detected, parameters such as regenerator outlet gas velocity and cyclone separator operating pressure are adjusted; when abnormal catalyst fluidization is detected, parameters such as bed gas velocity and catalyst circulation rate are adjusted; or when abnormal catalyst activity is detected, parameters such as regeneration temperature, regeneration air volume, and catalyst replenishment are adjusted. This avoids manual operation and control, improving operating efficiency.

[0022] The following is in conjunction with the appendix Figures 1 to 3The technical solution of the present invention will be described.

[0023] Figure 1 According to some embodiments of the present invention, a flowchart of a global cooperative and adaptive control method for DMTO is shown. It can be understood that... Figure 1 The processes shown are all executed by electronic devices. For the sake of simplicity, the following description will be further elaborated upon. Figure 1 The execution entity will not be described again in the illustrated process. For example, as shown... Figure 1 As shown, the global cooperative and adaptive control method of DMTO includes, but is not limited to, the following process: S01: Determine the first catalyst inventory value, catalyst change value, bed pressure drop change value, and target product output value of the DMTO unit at the first moment.

[0024] In some embodiments, the electronic device can directly obtain the first catalyst inventory value, bed pressure drop change value, target product yield value (e.g., olefin yield value), catalyst replenishment value, flue gas flow rate value, and flue gas dust concentration value from the device control system of the DMTO unit.

[0025] For example, the first catalyst inventory value can be the sum of the initial catalyst addition value and each subsequent catalyst replenishment value, minus the catalyst loss value during each reaction (the specific process will be described below). As another example, the DMTO unit's bed is equipped with a pressure drop detection sensor, which can detect changes in bed pressure drop in real time and input the data into the unit's control system; exemplarily, the bed pressure drop change value can be the bed pressure drop change value of the reactor in the DMTO unit. As another example, the DMTO unit is equipped with a target product yield detection sensor, which detects the target product yield value and inputs it into the unit's control system. As another example, the catalyst replenishment value can be added manually or automatically by machine; each addition data is manually input into the unit's control system or the DMTO unit automatically weighs the data and inputs the weighing data into the unit's control system. As another example, the flue gas outlet of the DMTO unit is equipped with a flue gas flow detection sensor and a flue gas dust concentration sensor, which can detect the flue gas flow rate and flue gas dust concentration values ​​of the DMTO unit in real time and input them into the unit's control system.

[0026] In some embodiments, the electronic device determines the catalyst loss value based on the flue gas flow rate and flue gas dust concentration, and determines the catalyst change value based on the catalyst replenishment value and the catalyst loss value. For example, the electronic device can obtain the mass of catalyst carried away with the flue gas by multiplying the flue gas flow rate and flue gas dust concentration, thereby determining the catalyst loss value. Further, the electronic device determines the catalyst change value based on the difference between the catalyst replenishment value and the catalyst loss value. For example, see the following formula (1): W a =(F add –F loss )= F add –(QⅹC) Formula (1) In formula (1), W a F represents the change in catalyst value. add Indicates the catalyst replenishment value, F loss Let Q represent the catalyst loss value, Q represent the flue gas flow rate value, and C represent the flue gas dust concentration value. For example, the catalyst replenishment value is 4.0 t / h, and the flue gas flow rate is 120,000 Nm³. 3 / h, flue gas dust concentration value is 8 g / Nm 3 The catalyst loss value is 0.96 t / h, and the catalyst change value is 3.04 t / h.

[0027] It is understandable that when the catalyst change value is positive, it means that the catalyst in the DMTO unit is increasing; when the catalyst change value is negative, it means that the catalyst in the DMTO unit is decreasing.

[0028] In some embodiments, the first catalyst inventory value can be the sum of the first catalyst addition value and the catalyst change value each time. For example, if the first catalyst addition value is 200t and the catalyst change value is 3.04 t / h, then the first catalyst inventory value is 203.04 t.

[0029] S02: Update the first catalyst inventory value to the second catalyst inventory value based on the catalyst change value and the bed pressure drop change value.

[0030] It is understandable that when the change in bed pressure drop is positive, it indicates that the catalyst in the DMTO unit is increasing; when the change in bed pressure drop is negative, it indicates that the catalyst in the DMTO unit is decreasing.

[0031] In some embodiments, when the trend of the first catalyst change value differs from the trend of the bed pressure drop value, determining the increase or decrease of catalyst in the DMTO unit solely based on the catalyst change value may be inaccurate. For example, when the catalyst change value is positive, the trend is upward, indicating an increase in catalyst in the DMTO unit; when the bed pressure drop change value is negative, the trend is downward, indicating a decrease in catalyst in the DMTO unit. In this case, it can be understood that the flue gas flow rate and flue gas dust concentration (i.e., the catalyst change value) may not fully reflect the additional loss of catalyst. Or, for example, when the catalyst change value is negative, the trend is downward, indicating a decrease in catalyst in the DMTO unit; when the bed pressure drop change value is positive, the trend is upward, indicating an increase in catalyst in the DMTO unit. In this case, it can be understood that the catalyst in the DMTO unit is in an abnormal state, which may be caused by catalyst agglomeration, coking, or uneven fluidization. Thus, the electronic device combines the catalyst change value and the bed pressure drop change value to make a judgment and update the first catalyst inventory value to the second catalyst inventory value.

[0032] In some embodiments, the electronic device pre-stores the correspondence between different bed pressure drop change values ​​and corresponding different catalyst renewal values; for example, when the bed pressure drop change value increases by 1 kPa, it means that the catalyst renewal value increases by 5t; when the bed pressure drop change value increases by 2 kPa, it means that the catalyst renewal value increases by 10t; when the bed pressure drop change value decreases by 1 kPa, it means that the catalyst renewal value decreases by 5t; when the bed pressure drop change value decreases by 2 kPa, it means that the catalyst renewal value decreases by 10t. It can be understood that the bed pressure drop change value can be determined based on the bed pressure drop change at the current time and the previous time, or the bed pressure drop change value can also be determined based on the pressure difference between the current bed pressure and the preset target bed pressure, without any specific limitation. For example, see the following formula (2): W real =W s + W a + P ⅹ K p Formula (2) In formula (2), W real W represents the inventory value of the second catalyst. s W represents the first catalyst inventory value. a The value represents the change in catalyst pressure, P represents the change in bed pressure drop, and K represents the change in catalyst pressure drop. p This represents the bed pressure drop correction factor. For example, if the first catalyst inventory is 200t, the catalyst change is 3.04 t / h, the bed pressure drop change is -3kPa, and the bed pressure drop correction factor is 5, then the second catalyst inventory is 188.04t.

[0033] It is understandable that, combined with formulas (1) and (2), the electronic device comprehensively estimates the catalyst inventory value by combining multiple variables such as flue gas flow rate, flue gas dust concentration, and bed pressure drop change value, thereby improving accuracy, stability and robustness.

[0034] In other embodiments, the first catalyst inventory value can be determined based on the first catalyst addition value, the catalyst change value each time, and the bed pressure drop change value each time, as shown in formula (2) above, which will not be elaborated here.

[0035] S03: Determine the catalyst activity value of the DMTO unit at the first moment based on the target product output value and the second catalyst inventory value.

[0036] In some embodiments, the electronic device determines the catalyst activity value of the DMTO unit at a first moment based on the quotient of the target product yield value and the second catalyst inventory value. For example, see the following formula (3): A(t) =Y / W real Formula (3) In formula (3), A(t) represents the catalyst activity value, Y represents the target product yield value, and W real This represents the inventory value of the second catalyst. For example, if the target product output is 20 t / h and the inventory value of the second catalyst is 188.04 t, then the catalyst activity value is approximately 0.106.

[0037] S04: Input the catalyst activity value, bed pressure drop change value, and catalyst change value into the preset first prediction model to obtain the first judgment result output by the first prediction model. The first prediction model stores the target values ​​corresponding to the catalyst activity value, catalyst inventory value, and catalyst change value, respectively.

[0038] In some embodiments, the first prediction model determines different sub-weight values ​​corresponding to the catalyst activity value, bed pressure drop change value, and catalyst change value based on different target values; the first prediction model determines a first judgment result based on the different sub-weight values. For example, the target values ​​corresponding to the catalyst activity value, catalyst inventory value, and catalyst change value may be preset values.

[0039] In some embodiments, the first prediction model determines different sub-weight values ​​corresponding to the catalyst activity value, the bed pressure drop change value, and the absolute value of the difference between the catalyst change value and the corresponding target value.

[0040] For example, when the absolute value of the difference between the catalyst activity value and the corresponding target value is the largest, the sub-weight value of the catalyst activity value is the highest, indicating that the catalyst activity value has the highest priority, and the first prediction model prioritizes determining the first judgment result based on the catalyst activity value. For example, when the absolute value of the difference between the catalyst activity value and the corresponding target value is the largest, and the catalyst activity value is less than the corresponding target value, it indicates poor catalyst activity, and the first judgment result of the first prediction model includes poor catalyst activity, and this result has the highest priority. Further, when the absolute value of the difference between the bed pressure drop change value and the catalyst change value and the corresponding target value is not the largest, the sub-weight values ​​of the bed pressure drop change value and the catalyst change value are not the highest. For example, when the bed pressure drop change value is greater than or less than the corresponding target value, it indicates that there may be fluidization anomalies, and the first judgment result of the first prediction model includes fluidization anomalies, and this result has a lower priority than the result corresponding to the catalyst activity value.

[0041] For example, when the absolute value of the difference between the bed pressure drop change value and the corresponding target value is the largest, the sub-weight value of the bed pressure drop change value is the highest, indicating that the bed pressure drop change value has the highest priority, and the first prediction model prioritizes determining the first judgment result based on the bed pressure drop change value. For example, when the absolute value of the difference between the bed pressure drop change value and the corresponding target value is the largest, and the bed pressure drop change value is less than the corresponding target value, it indicates fluidization anomaly, and the first judgment result of the first prediction model includes fluidization anomaly, and this result has the highest priority. Further, when the absolute value of the catalyst activity value and the difference between the catalyst change value and the corresponding target value is not the largest, the sub-weight values ​​of the catalyst activity value and the catalyst change value are not the highest. For example, when the catalyst activity value is less than the corresponding target value, it indicates that there may be catalyst deactivation, and the first judgment result of the first prediction model includes catalyst deactivation, and this result has a lower priority than the result corresponding to the bed pressure drop change value.

[0042] For example, when the absolute value of the difference between the catalyst change value and the corresponding target value is the largest, the sub-weight value of the catalyst change value is the highest, indicating that the catalyst change value has the highest priority, and the first prediction model prioritizes determining the first judgment result based on the catalyst change value. For example, when the absolute value of the difference between the catalyst change value and the corresponding target value is the largest, and the catalyst change value is greater than the corresponding target value, it indicates that there is a large catalyst loss, and the first judgment result of the first prediction model includes a large catalyst loss, and this result has the highest priority. Further, when the absolute values ​​of the differences between the bed pressure drop change value and the catalyst activity value and the corresponding target value are not the largest, the sub-weight values ​​of the bed pressure drop change value and the catalyst activity value are not the highest. For example, when the bed pressure drop change value is greater than or less than the corresponding target value, it indicates that there may be a fluidization anomaly, and the first judgment result of the first prediction model includes a fluidization anomaly, and this result has a lower priority than the result corresponding to the catalyst activity value.

[0043] S05: Send a first control command to the DMTO device based on the first judgment result. The first control command is used to control the DMTO device to adjust parameters at a second time point, which is a time point after the first time point.

[0044] In some embodiments, the first prediction model determines the catalyst activity value, the bed pressure drop change value, and the degree of difference between the catalyst change value and the corresponding target value (the degree of difference can be found in the explanation of the absolute value of the difference described in S04 above).

[0045] For example, when the first prediction model determines that the difference between the catalyst activity value and the corresponding target value is the largest, the sub-weight value corresponding to the catalyst activity value is the largest, and the first control instruction is the first instruction. The first instruction is used to prioritize controlling the DMTO device to adjust parameters at the second time step based on the catalyst activity value, and then further control the DMTO device to adjust parameters at the second time step based on the bed pressure drop change value and the catalyst change value. For example, when the absolute value of the difference between the catalyst activity value and the corresponding target value is the largest, and the catalyst activity value is less than the corresponding target value, it indicates poor catalyst activity. The first instruction is used to control the DMTO device to adjust parameters such as regeneration temperature, air volume, and / or catalyst replenishment amount at the second time step, for example, by increasing the regeneration temperature, increasing the air volume, and / or replenishing the catalyst; subsequently... When the absolute value of the difference between the bed pressure drop change value and the catalyst change value and the corresponding target value is less than the preset threshold, the parameters of the DMTO unit do not need to be adjusted. Alternatively, when the absolute value of the difference between the bed pressure drop change value and the corresponding target value is greater than or equal to the preset threshold, parameters such as circulation rate / gas velocity can be adjusted. Or, when the absolute value of the difference between the catalyst change value and the corresponding target value is greater than or equal to the preset threshold, parameters such as gas velocity / cyclone separation degree can be adjusted. No specific restrictions are imposed.

[0046] For example, when the first prediction model determines that the difference between the bed pressure drop change value and the corresponding target value is the largest, the sub-weight value corresponding to the bed pressure drop change value is the largest, and the first control command is the second command. The second command is used to prioritize controlling the DMTO device to adjust parameters at the second time step based on the bed pressure drop change value, and then further control the DMTO device to adjust parameters at the second time step based on the catalyst activity value and the catalyst change value. For example, when the absolute value of the difference between the bed pressure drop change value and the corresponding target value is the largest, and the bed pressure drop change value is less than the corresponding target value, it indicates a fluidization anomaly. The first command is used to control the DMTO device to adjust parameters such as circulation volume and / or gas velocity at the second time step, for example, by increasing the circulation volume and / or gas velocity; subsequently... When the absolute value of the difference between the catalyst activity value and the catalyst change value and the corresponding target value is less than the preset threshold, the parameters of the DMTO unit do not need to be adjusted. Or when the absolute value of the difference between the catalyst activity value and the corresponding target value is greater than or equal to the preset threshold, parameters such as regeneration temperature, air volume and / or catalyst replenishment can be adjusted. Or when the absolute value of the difference between the catalyst change value and the corresponding target value is greater than or equal to the preset threshold, parameters such as gas velocity and / or cyclone separation degree can be adjusted. There are no specific restrictions.

[0047] For example, when the first prediction model determines that the difference between the catalyst change value and the corresponding target value is the largest, the sub-weight value corresponding to the catalyst change value is the largest, and the first control instruction is the third instruction. The third instruction is used to prioritize controlling the DMTO device to adjust parameters at the second time step based on the catalyst change value, and then further control the DMTO device to adjust parameters at the second time step based on the catalyst activity value and the bed pressure drop change value. For example, if the absolute value of the difference between the catalyst change value and the corresponding target value is the largest, and the catalyst change value is greater than the corresponding target value, it indicates a large catalyst loss. The first instruction is used to control the DMTO device to adjust parameters such as gas velocity and / or cyclone separation degree at the second time step, for example, by reducing the gas velocity and / or cyclone separation degree; subsequently... When the absolute value of the difference between the catalyst activity value and the bed pressure drop change value and the corresponding target value is less than the preset threshold, no further parameter adjustment of the DMTO unit is required. Alternatively, when the absolute value of the difference between the catalyst activity value and the corresponding target value is greater than or equal to the preset threshold, parameters such as regeneration temperature, air volume, and / or catalyst replenishment can be adjusted. Or, when the absolute value of the difference between the bed pressure drop change value and the corresponding target value is greater than or equal to the preset threshold, parameters such as circulation volume and / or gas velocity can be adjusted. No specific restrictions apply.

[0048] It is understandable that the first, second, and third instructions are different.

[0049] In some embodiments, the establishment and judgment of the aforementioned first prediction model can be achieved through APC.

[0050] Understandable, such as Figure 1 As shown, the method of the present invention uses multivariate data such as catalyst activity value, catalyst inventory value, catalyst change value and bed pressure drop change value during the operation of DMTO unit, combined with APC to establish a prediction model for coupled judgment and control, so as to realize the adaptive parameter adjustment of DMTO unit under different conditions.

[0051] In other embodiments, the electronic device may also use the first catalyst inventory value, catalyst change value, bed pressure drop change value, target product output value, and first control command determined at different times within a first time interval as the first dataset. For example, the electronic device may use data determined in historical first time intervals of ten seconds, thirty seconds, five minutes, or ten minutes, or longer or shorter, as the first dataset. Exemplarily, after acquiring the first dataset, the electronic device performs preprocessing such as filtering, noise reduction, and anomaly removal on the first dataset to obtain a preprocessed dataset, and performs time synchronization processing on different types of data in the preprocessed dataset.

[0052] For example, the electronic device establishes a second prediction model based on time-synchronized data. This second prediction model includes the correspondence between the first catalyst inventory value, catalyst change value, bed pressure drop change value, target product output value, and the first control command at different times. Further, the electronic device obtains a second judgment result from the second prediction model at preset time intervals. For example, the second prediction model outputs the second judgment result to the electronic device at preset time intervals of ten seconds, thirty seconds, five minutes, or ten minutes, or longer or shorter intervals.

[0053] In some embodiments, the second prediction model can determine the second judgment result based on the average of the catalyst activity value, bed pressure drop change value, and catalyst change value determined at each moment within the first time interval; or the second prediction model can also fit multiple catalyst activity values ​​determined at each moment within the first time interval into a single data point using the least squares method, and similarly fit multiple bed pressure drop change values ​​and catalyst change values ​​into a single data point, and then determine the second judgment result. Further, the electronic device sends a second control command to the DMTO device based on the second judgment result, wherein the second control command is used to control the DMTO device to perform parameter adjustments.

[0054] It is understood that the second judgment result and the second control command can be referred to in the description of the first judgment result and the first control command mentioned above, and will not be repeated here.

[0055] It is understandable that the difference between the second judgment result and the second control instruction and the first judgment result and the first control instruction is that the first judgment result and the first control instruction are real-time judgment and control, while the second judgment result and the second control instruction are not real-time judgment and control.

[0056] In some embodiments, the first prediction model and / or the second prediction model are deployed inside an electronic device; and / or, the first prediction model and / or the second prediction model are deployed in the cloud.

[0057] In some embodiments, this application also proposes a global cooperative and adaptive control system for DMTO. Exemplarily, Figure 2 According to some embodiments of the present invention, a block diagram of a global cooperative and adaptive control system for DMTO is shown. For example... Figure 2 As shown, the DMTO global cooperative and adaptive control system 200 includes, but is not limited to: a first determination module 21, an update module 22, a second determination module 23, a judgment module 24, and a control module 25. Among them, For example, the first determining module 21 is used to determine the first catalyst inventory value, catalyst change value, bed pressure drop change value, and target product yield value of the DMTO unit at a first moment.

[0058] For example, update module 22 is used to update the first catalyst inventory value to the second catalyst inventory value based on the catalyst change value and the bed pressure drop change value.

[0059] For example, the second determining module 23 is used to determine the catalyst activity value of the DMTO unit at a first moment based on the target product output value and the second catalyst inventory value.

[0060] For example, the judgment module 24 is used to input the catalyst activity value, the bed pressure drop change value and the catalyst change value into a preset first prediction model to obtain the first judgment result output by the first prediction model, wherein the first prediction model stores the target values ​​corresponding to the catalyst activity value, the catalyst inventory value and the catalyst change value respectively.

[0061] For example, the control module 25 is used to send a control command to the DMTO device based on the first judgment result, wherein the control command is used to control the DMTO device to adjust parameters at a second time, which is a time after the first time.

[0062] Understandable. Figure 2 For a detailed description of the functions and roles of the system modules shown, please refer to the aforementioned Figure 1 The process descriptions of S01 to S05 shown are not repeated here.

[0063] It needs to be explained that, Figure 2 This is merely one possible implementation of the present invention. In practical applications, the DMTO global cooperative and adaptive control system 200 may include more or fewer modules, which will not be elaborated here.

[0064] Figure 3 According to some embodiments of the present invention, a schematic diagram of the structure of an electronic device 100 is shown. For example... Figure 3 As shown, the electronic device 100 includes a processor 101, a communication interface 103, and a memory 102. The processor 101, communication interface 103, and memory 102 can be interconnected via an internal bus 104, or they can communicate via wireless transmission or other means. This embodiment of the invention uses the connection via bus 104 as an example. Bus 104 can be a peripheral component interconnect express (PCIe) bus, an extended industry standard architecture (EISA) bus, a unified bus (Ubus or UB), a compute express link (CXL), a cache coherent interconnect for accelerators (CCIX), etc. Bus 104 can be divided into address bus, data bus, control bus, etc. In addition to the data bus, bus 104 can also include a power bus, a control bus, and a status signal bus. For clarity, all buses are labeled as bus 104 in the figure.

[0065] Processor 101 may consist of at least one general-purpose processor, such as a central processing unit (CPU), or a combination of a CPU and a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof. Processor 101 executes various types of digital storage instructions, such as software or firmware programs stored in memory 102, enabling electronic device 100 to provide a variety of services.

[0066] Memory 102 may include volatile memory, such as random access memory (RAM); memory 102 may also include non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid-state drive (SSD); memory 102 may also include combinations of the above types.

[0067] The communication interface 103 can be a wired interface (e.g., an Ethernet interface), an internal interface (e.g., a PCIe bus interface), a wired interface (e.g., an Ethernet interface), or a wireless interface (e.g., a cellular network interface or a wireless LAN interface), for communicating with other devices or modules.

[0068] It needs to be explained that, Figure 3 This is merely one possible implementation of an embodiment of the present invention. In actual applications, the electronic device 100 may include more or fewer components, which will not be elaborated here.

[0069] In some embodiments, the present invention also provides a computer-readable medium storing program code that, when run on a computer, causes the computer to perform the methods described in the above aspects.

[0070] In some embodiments, the present invention also provides a computer program product comprising: computer program code that, when run on a computer, causes the computer to perform the methods described above.

[0071] In the accompanying drawings, some structural or methodological features may be shown in a specific arrangement and / or order. However, it should be understood that such a specific arrangement and / or order may not be necessary. Rather, in some embodiments, these features may be arranged in a manner and / or order different from that shown in the illustrative drawings. Furthermore, the inclusion of structural or methodological features in a particular figure does not imply that such features are required in all embodiments, and in some embodiments, these features may be omitted or may be combined with other features.

[0072] It should be noted that the units / modules mentioned in the various device embodiments of the present invention are all logical units / modules. Physically, a logical unit / module can be a physical unit / module, a part of a physical unit / module, or a combination of multiple physical units / modules. The physical implementation of these logical units / modules themselves is not the most important factor; the combination of functions implemented by these logical units / modules is the key to solving the technical problem proposed by the present invention. Furthermore, to highlight the innovative aspects of the present invention, the above-described device embodiments of the present invention have not introduced units / modules that are not closely related to solving the technical problem proposed by the present invention. This does not mean that the above-described device embodiments do not contain other units / modules.

[0073] It should be noted that in the examples and description of this patent, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0074] Although the invention has been illustrated and described with reference to certain preferred embodiments thereof, those skilled in the art will understand that various changes in form and detail may be made therein without departing from the scope of the invention.

Claims

1. A global cooperative and adaptive control method for DMTO, applied to electronic devices, characterized in that, The method includes: Determine the initial catalyst inventory value, catalyst change value, bed pressure drop change value, and target product yield value of the DMTO unit at the first moment; The first catalyst inventory value is updated to the second catalyst inventory value based on the catalyst change value and the bed pressure drop change value. The catalyst activity value of the DMTO unit at the first moment is determined based on the target product output value and the second catalyst inventory value. The catalyst activity value, the bed pressure drop change value, and the catalyst change value are input into a preset first prediction model to obtain a first judgment result output by the first prediction model. The first prediction model stores the target values ​​corresponding to the catalyst activity value, the catalyst inventory value, and the catalyst change value, respectively. Based on the first judgment result, a first control command is sent to the DMTO device, wherein the first control command is used to control the DMTO device to adjust parameters at a second time point, the second time point being a time point after the first time point.

2. The method according to claim 1, characterized in that, The step of inputting the catalyst activity value, the bed pressure drop change value, and the catalyst change value into a preset first prediction model to obtain a first judgment result output by the first prediction model includes: The first prediction model determines different sub-weight values ​​corresponding to the catalyst activity value, the bed pressure drop change value, and the catalyst change value, respectively, based on different target values. The first prediction model determines the first judgment result based on the different sub-weight values.

3. The method according to claim 2, characterized in that, The first prediction model determines different sub-weight values ​​corresponding to the catalyst activity value, the bed pressure drop change value, and the catalyst change value, respectively, based on different target values, including: The first prediction model determines the catalyst activity value, the bed pressure drop change value, and the difference between the catalyst change value and the corresponding target value; When the first prediction model determines that the difference between the catalyst activity value and the corresponding target value is the largest, the sub-weight value corresponding to the catalyst activity value is the largest, and the first control instruction is the first instruction. When the first prediction model determines that the difference between the bed pressure drop change value and the corresponding target value is the largest, the sub-weight value corresponding to the bed pressure drop change value is the largest, and the first control command is the second command. When the first prediction model determines that the difference between the catalyst change value and the corresponding target value is the largest, the sub-weight value corresponding to the catalyst change value is the largest, and the first control instruction is the third instruction. The first instruction, the second instruction, and the third instruction are different.

4. The method according to claim 1, characterized in that, The determination of the first catalyst inventory value, catalyst change value, bed pressure drop change value, and target product yield value of the DMTO unit at the first moment includes: The following values ​​are obtained from the DMTO unit: the first catalyst inventory value, the bed pressure drop change value, the target product output value, the catalyst replenishment value, the flue gas flow rate value, and the flue gas dust concentration value. The catalyst loss value is determined based on the flue gas flow rate and the flue gas dust concentration value, and the catalyst change value is determined based on the catalyst replenishment value and the catalyst loss value.

5. The method according to any one of claims 1 to 4, characterized in that, The method further includes: The first data set consists of the first catalyst inventory value, the catalyst change value, the bed pressure drop change value, the target product output value, and the first control command determined at different times within the first time interval. A second prediction model is established based on the first dataset. The second prediction model includes the correspondence between the first catalyst inventory value, the catalyst change value, the bed pressure drop change value, the target product output value, and the first control command at different times. A second judgment result is obtained from the second prediction model at a preset time interval; Based on the second judgment result, a second control command is sent to the DMTO device, wherein the second control command is used to control the DMTO device to perform parameter adjustment.

6. The method according to claim 5, characterized in that, The establishment of the second prediction model based on the first dataset includes: The first dataset is preprocessed to obtain a preprocessed dataset; The different types of data in the preprocessed dataset are subjected to time synchronization processing, and the second prediction model is established based on the time-synchronized data.

7. The method according to claim 5, characterized in that, The first prediction model and / or the second prediction model are deployed inside the electronic device; And / or, the first prediction model and / or the second prediction model are deployed in the cloud.

8. A global cooperative and adaptive control system for DMTO, characterized in that, The system includes: The first determining module is used to determine the first catalyst inventory value, catalyst change value, bed pressure drop change value, and target product output value of the DMTO unit at the first moment. The update module is used to update the first catalyst inventory value to the second catalyst inventory value based on the catalyst change value and the bed pressure drop change value; The second determining module is used to determine the catalyst activity value of the DMTO device at the first moment based on the target product output value and the second catalyst inventory value; The judgment module is used to input the catalyst activity value, the bed pressure drop change value and the catalyst change value into a preset first prediction model to obtain a first judgment result output by the first prediction model. The first prediction model stores the target values ​​corresponding to the catalyst activity value, the catalyst inventory value and the catalyst change value, respectively. The control module is used to send a control command to the DMTO device based on the first judgment result, wherein the control command is used to control the DMTO device to adjust parameters at a second time point, the second time point being a time point after the first time point.

9. A computer-readable storage medium, characterized in that, The readable storage medium stores instructions that, when executed on an electronic device, cause the electronic device to perform the method of any one of claims 1 to 7.

10. An electronic device, characterized in that, include: Memory is used to store instructions executed by one or more processors of an electronic device; And a processor, one of the processors of the electronic device, for executing instructions stored in the memory to implement the method of any one of claims 1 to 7.

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