Low-energy-consumption intelligent optimization method and system based on solid adsorption CO2 capture

Abstract

The invention relates to the technical field of carbon neutralization, in particular to a low-energy-consumption intelligent optimization method based on solid adsorption CO2 capture, which comprises the following steps: acquiring historical operation parameters, historical desulfurization outlet CO2 capture rates and historical equipment load parameters of all units in a CO2 capture system; a historical optimal energy consumption index is calculated according to the historical desulfurization outlet CO2 capture rate and the historical equipment load parameters, and historical operation parameters of the time period corresponding to the historical optimal energy consumption index are marked as historical optimal operation parameters; the regulation and control trend value of the historical operation parameters is calculated according to the historical operation parameters and the historical equipment load parameters of the corresponding time periods, the regulation and control trend amplitude value is calculated according to the regulation and control trend value, low-energy-consumption optimization can be continuously carried out, and finally the maximum CO2 capture efficiency and the minimum equipment load parameters are obtained.

Description

Technical Field

[0001] The present invention relates to the field of carbon neutrality technology, and in particular to a low-energy intelligent optimization method and system based on solid adsorption CO2 capture. Background Art

[0002] In the application of CCUS, post-combustion CO2 capture technology can be widely used in flue gas CO2 capture in thermal power, cement, steel, etc. because it does not affect the original industrial production operation and has flexible and adjustable scale. Solid adsorption material CO2 capture technology has the characteristics of strong adaptability, no corrosion, and no secondary pollution. It is suitable for large-scale CO2 capture and has great commercial application potential. Among them, CO2 capture technology is the basis and prerequisite for the development of CCUS technology, accounting for about 60%-70% of the total energy consumption of CCUS.

[0003] At present, the difficulty in promoting CCUS lies in its high energy consumption. The energy consumption of the capture system is an important indicator for evaluating the performance of the flue gas CO2 capture system. However, the energy consumption optimization technology of the solid adsorption CO2 capture system in the thermal power generation industry is currently lagging behind, and there is a lack of intelligent optimization methods for low energy consumption of solid adsorption CO2 capture. Summary of the invention

[0004] The main purpose of the present invention is to provide a low-energy intelligent optimization method for CO2 capture based on solid adsorption, aiming to solve the technical problems in the prior art.

[0005] The present invention proposes a low-energy intelligent optimization method for solid adsorption CO2 capture, comprising the following steps:

[0006] Obtain the historical operating parameters, historical desulfurization outlet CO2 capture rates and historical equipment load parameters of all units in the CO2 capture system;

[0007] The historical optimal energy consumption index is calculated based on the historical desulfurization outlet CO2 capture rate and the historical equipment load parameters, and the historical operating parameters of the time period corresponding to the historical optimal energy consumption index are marked as the historical optimal operating parameters;

[0008] Calculate the control trend value of the historical operating parameters according to the historical operating parameters and the historical equipment load parameters of the corresponding time period, and calculate the control trend amplitude value according to the control trend value;

[0009] Obtain the real-time operating parameters of all units in the CO2 capture system, the real-time desulfurization outlet CO2 capture rate and the real-time equipment load. The real-time operating parameters include at least the adsorption temperature, desorption temperature, adsorption pressure, desorption pressure and gas flow rate;

[0010] Calculate the real-time energy consumption index based on the real-time equipment load and the real-time desulfurization outlet CO2 capture rate;

[0011] Determine the size of the real-time energy consumption index of a single unit and the historical optimal energy consumption index;

[0012] If it is greater, the real-time operating parameters are adjusted toward the historical optimal operating parameters according to the control trend value and the control trend amplitude value until the historical optimal operating parameters are reached;

[0013] If it is less than, the real-time energy consumption index is updated to the historical optimal energy consumption index, and the real-time operating parameters corresponding to the real-time energy consumption index are marked as the historical optimal operating parameters;

[0014] Obtain optimal equipment load parameters based on historical optimal operating parameters.

[0015] As a preferred method, the steps for obtaining the historical desulfurization outlet CO2 capture rate are as follows:

[0016] Obtain the inlet CO2 concentration and outlet CO2 concentration per unit time of each unit;

[0017] Obtain the inlet and outlet flue gas flow rates per unit time for each unit;

[0018] Obtain the inlet and outlet smoke density per unit time of each unit;

[0019] The desulfurization outlet CO2 capture rate per unit time is calculated based on the inlet CO2 concentration, outlet CO2 concentration, inlet flue gas flow rate, outlet flue gas flow rate, inlet flue gas density and outlet flue gas density, where the calculation formula is:

[0020]

[0021] Among them, C b Indicates the desulfurization outlet CO2 capture rate per unit time, Q O Indicates the outlet flue gas flow rate, P O Indicates the outlet smoke density, C O Indicates the inlet CO2 concentration, Q i represents the inlet flue gas flow rate, P i Indicates the inlet smoke density, C I Indicates the inlet CO2 concentration;

[0022] The desulfurization outlet CO2 capture rate per unit time of all units is statistically analyzed to obtain the historical desulfurization outlet CO2 capture rate.

[0023] As a preferred method, the steps for calculating the historical optimal energy consumption index based on the historical desulfurization outlet CO2 capture rate and the historical equipment load parameters are as follows:

[0024] Obtain the unit time equipment load parameters of all units through historical equipment load parameters;

[0025] The energy consumption index per unit time of a single unit is calculated based on the equipment load parameter per unit time of a single unit and the CO2 capture rate per unit time of the desulfurization outlet of a single unit, where the calculation formula is:

[0026] F = ω1*E(z)+ω2*C(z);

[0027] F represents the energy consumption index per unit time of a single unit, E(z) represents the equipment load parameter per unit time of a single unit, ω1 represents the load weight, C(z) represents the CO2 capture rate per unit time of the desulfurization outlet of a single unit, and ω2 represents the CO2 capture rate weight;

[0028] The energy consumption per unit time of all units is counted, and the energy consumption per unit time with the lowest value is the historical optimal energy consumption index.

[0029] As a preferred embodiment, it is characterized in that the calculation formula for calculating the control trend value of the historical operating parameters according to the historical operating parameters and the historical equipment load parameters of the corresponding time period is:

[0030]

[0031] Among them, y k and h k They represent the historical operating parameters and the historical equipment load parameters of the corresponding time period at the time of the kth regulation, y k+i and h k+i They respectively represent the values ​​of the historical operating parameters and the historical equipment load parameters of the corresponding time period at the time of the k+ith regulation, and v represents the regulation trend value.

[0032] Preferably, the formula for calculating the control trend amplitude value according to the control trend value is:

[0033]

[0034] Among them, ω3 represents the amplitude of the regulation trend, h k+i and h k represents the values ​​of the historical equipment load parameters at the k+ith regulation and the kth regulation, respectively, and Δv represents the value of the historical equipment load parameters at the kth regulation. k+i The control trend value and h k The difference in the control trend value at .

[0035] Preferably, the step of regulating the real-time operating parameters toward the historical optimal operating parameters according to the regulation trend value and the regulation trend amplitude value until the historical optimal operating parameters are reached includes:

[0036] Obtain the environmental impact value of a single unit and the environmental impact value of all units;

[0037] Obtain the optimal operating parameter value of a single unit and the historical optimal operating parameter value;

[0038] Obtain the historical optimal operating parameters of a single unit;

[0039] According to the environmental impact value of a single unit, the environmental impact values ​​of all units, the importance value of the optimal operating parameters of a single unit, the importance value of the historical optimal operating parameters, the historical optimal operating parameters of a single unit and the historical optimal operating parameters, the control trend value of the real-time operating parameters of a single unit at the time of the k+1th control of the corresponding real-time equipment load parameters is calculated;

[0040]

[0041] in, represents the control trend value of the real-time operating parameters of a single unit when the corresponding real-time equipment load parameters are regulated for the k+1th time, c1 represents the importance value of the optimal operating parameters of a single unit, c2 represents the importance value of the historical optimal operating parameters, r1 represents the environmental impact value of a single unit, r2 represents the environmental impact value of all units, ω3 represents the control trend value, i represents the real-time operating parameters, d represents the real-time equipment load parameters, It represents the parameters of the real-time operating parameters at the time of the kth adjustment of the corresponding real-time equipment load. represents the control trend value of the real-time operating parameter when the corresponding real-time equipment load parameter is adjusted for the kth time, g d represents the historical optimal operating parameters, P i,d Indicates the historical optimal operating parameters of a single unit;

[0042] The parameters of the real-time operating parameters at the k+1th regulation of the corresponding real-time equipment load are calculated according to the regulation trend value of the real-time operating parameters of a single unit at the k+1th regulation of the corresponding real-time equipment load and the value of the real-time operating parameters of a single unit at the kth regulation, wherein the calculation formula is:

[0043]

[0044] in, It represents the real-time operating parameters of a single unit at the time of the k+1th adjustment of the corresponding real-time equipment load. The control trend value of the real-time operating parameters of a single unit when the corresponding real-time equipment load parameters are adjusted for the k+1th time, Indicates the value of the real-time operating parameters of a single unit at the kth regulation;

[0045] judge When the real-time energy consumption index under the optimal energy consumption index is less than or equal to the historical optimal energy consumption index, the regulation is stopped. Updated to historically optimal operating parameters.

[0046] The present invention also discloses a low-energy-consumption intelligent optimization system based on solid adsorption CO2 capture, which is used for a low-energy-consumption intelligent optimization method based on solid adsorption CO2 capture, including:

[0047] Real-time monitoring module, used to monitor the operating status of all units in the CO2 capture system in real time;

[0048] A historical data storage module, used to store historical operating parameters of all units in the CO2 capture system, historical desulfurization outlet CO2 capture rates and historical equipment load parameters;

[0049] The intelligent control module is used to calculate and update the historical optimal operating parameters based on the data in the historical data storage module and the real-time data.

[0050] Environmental impact assessment module: used to evaluate the impact of the operating environment of each unit in the CO2 capture system on the real-time operating parameters.

[0051] Preferably, the intelligent control module includes:

[0052] The fault detection and diagnosis unit is used to determine whether all units are in normal operation or abnormal operation through real-time monitoring and data analysis, and to alarm when abnormal operation is detected, thereby reducing the risk of unplanned downtime and improving system reliability and availability.

[0053] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned low-energy intelligent optimization method based on solid adsorption CO2 capture are implemented.

[0054] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned low-energy intelligent optimization method for solid adsorption-based CO2 capture are implemented.

[0055] The beneficial effects of the present invention are: it can continuously perform low energy consumption optimization, and by obtaining historical operating parameters, historical desulfurization outlet CO2 capture rate and historical equipment load parameters to calculate the historical optimal energy consumption index and the historical optimal operating parameters, it can form an initial optimization target for the real-time operating parameters. On this basis, each single unit of all units in the CO2 capture system is independently regulated, so that the real-time operating parameters are continuously approached to the historical optimal operating parameters, and the historical optimal operating parameters are updated at any time for optimization, and finally the CO2 capture efficiency is maximized and the equipment load parameters are minimized. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 The figure is a schematic diagram of a method flow according to an embodiment of the present invention.

[0057] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0058] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0059] like Figure 1 As shown, the present application provides a low-energy intelligent optimization method for solid adsorption CO2 capture, comprising the following steps:

[0060] S1. Obtain the historical operating parameters, historical desulfurization outlet CO2 capture rate and historical equipment load parameters of all units in the CO2 capture system;

[0061] S2. Calculate the historical optimal energy consumption index based on the historical desulfurization outlet CO2 capture rate and the historical equipment load parameters, and mark the historical operating parameters of the time period corresponding to the historical optimal energy consumption index as the historical optimal operating parameters;

[0062] S3. Calculate the control trend value of the historical operating parameters according to the historical operating parameters and the historical equipment load parameters of the corresponding time period, and calculate the control trend amplitude value according to the control trend value;

[0063] S4. Obtaining real-time operating parameters of all units in the CO2 capture system, real-time desulfurization outlet CO2 capture rate and real-time equipment load, wherein the real-time operating parameters at least include adsorption temperature, desorption temperature, adsorption pressure, desorption pressure and gas flow rate;

[0064] S5. Calculate the real-time energy consumption index based on the real-time equipment load and the real-time desulfurization outlet CO2 capture rate;

[0065] S6. Determine the magnitude of the real-time energy consumption index of a single unit and the historical optimal energy consumption index;

[0066] If it is greater, the real-time operating parameters are adjusted toward the historical optimal operating parameters according to the control trend value and the control trend amplitude value until the historical optimal operating parameters are reached;

[0067] If it is less than, the real-time energy consumption index is updated to the historical optimal energy consumption index, and the real-time operating parameters corresponding to the real-time energy consumption index are marked as the historical optimal operating parameters;

[0068] S7. Obtain optimal equipment load parameters based on historical optimal operating parameters.

[0069] By obtaining historical operating parameters, historical desulfurization outlet CO2 capture rates and historical equipment load parameters to calculate historical optimal energy consumption indicators and historical optimal operating parameters, an initial optimization target can be formed for the real-time operating parameters. On this basis, each individual unit of all units in the CO2 capture system is independently regulated to make the real-time operating parameters approach the historical optimal operating parameters continuously, and the historical optimal operating parameters are updated at any time for optimization, ultimately obtaining the parameters that maximize CO2 capture efficiency and minimize equipment load.

[0070] Steps for obtaining historical desulfurization export CO2 capture rate:

[0071] Obtain the inlet CO2 concentration and outlet CO2 concentration per unit time of each unit;

[0072] Obtain the inlet and outlet flue gas flow rates per unit time for each unit;

[0073] Obtain the inlet and outlet smoke density per unit time of each unit;

[0074] The desulfurization outlet CO2 capture rate per unit time is calculated based on the inlet CO2 concentration, outlet CO2 concentration, inlet flue gas flow rate, outlet flue gas flow rate, inlet flue gas density and outlet flue gas density, where the calculation formula is:

[0075]

[0076] Among them, C b Indicates the desulfurization outlet CO2 capture rate per unit time, Q O Indicates the outlet flue gas flow rate, P O Indicates the outlet smoke density, C O Indicates the inlet CO2 concentration, Q i represents the inlet flue gas flow rate, P i Indicates the inlet smoke density, C I Indicates the inlet CO2 concentration;

[0077] The desulfurization outlet CO2 capture rate per unit time of all units is statistically analyzed to obtain the historical desulfurization outlet CO2 capture rate.

[0078] For example, we have two units A and B, and the following are their measurement data:

[0079] Unit A:

[0080] Inlet CO2 concentration = 1500ppm;

[0081] Outlet CO2 concentration = 450ppm;

[0082] Inlet flue gas flow = 10000m 3 / h;

[0083] Outlet flue gas flow = 9800m 3 / h

[0084] Inlet smoke density = 1.293kg / m 3

[0085] Outlet smoke density = 1.270kg / m 3

[0086] Unit B:

[0087] Inlet CO2 concentration = 1400ppm

[0088] Outlet CO2 concentration = 420ppm

[0089] Inlet flue gas flow = 11000m 3 / h

[0090] Outlet flue gas flow = 10500m 3 / h

[0091] Inlet smoke density = 1.293kg / m 3

[0092] Outlet smoke density = 1.280kg / m 3

[0093] CO2 capture rate at the desulfurization outlet of unit A:

[0094]

[0095] CO2 capture rate at the desulfurization outlet of unit B:

[0096]

[0097] For example, this is the data within a unit of time. These data are recorded and counted to form a historical data set, thereby obtaining the historical desulfurization export CO2 capture rate.

[0098] The steps to calculate the historical optimal energy consumption index based on the historical desulfurization outlet CO2 capture rate and historical equipment load parameters are as follows:

[0099] Obtain the unit time equipment load parameters of all units through historical equipment load parameters;

[0100] The energy consumption index per unit time of a single unit is calculated based on the equipment load parameter per unit time of a single unit and the CO2 capture rate per unit time of the desulfurization outlet of a single unit, where the calculation formula is:

[0101] F = ω1*E(z)+ω2*C(z);

[0102] F represents the energy consumption index per unit time of a single unit, E(z) represents the equipment load parameter per unit time of a single unit, ω1 represents the load weight, C(z) represents the CO2 capture rate per unit time of the desulfurization outlet of a single unit, and ω2 represents the CO2 capture rate weight; the size of ω2 and ω1 means the importance of CO2 capture rate and load, the higher ω2 represents the pursuit of higher CO2 capture rate, and the lower ω1 represents the pursuit of lower load. In order to achieve the maximum CO2 capture rate and the minimum load, for example, setting ω2 and ω1 to 10 and 0.1 respectively means that the operating parameters of the entire system will be adjusted in the direction of maximum CO2 capture rate and minimum load. These two data can be adjusted according to production indicators;

[0103] The energy consumption per unit time of all units is counted, and the energy consumption per unit time with the lowest value is the historical optimal energy consumption index.

[0104] For example, there are two units A and B, and the following are their measurement data:

[0105] Unit A:

[0106] Equipment load parameter per unit time = 1000kW

[0107] Desulfurization outlet CO2 capture rate per unit time = 69.1%

[0108] Unit B:

[0109] Equipment load parameter per unit time = 1200kW

[0110] Desulfurization outlet CO2 capture rate per unit time = 68.8%

[0111] Assume that load weight = 0.1 and CO2 capture rate weight = 10.

[0112] Energy consumption per unit time of unit A:

[0113] 0.1*1000+10*0.691=106.91

[0114] Unit B's energy consumption per unit time:

[0115] 0.1*1200+10*0.688=126.88

[0116] Of the two units, the unit time energy consumption index of unit B is 126.88, while the unit time energy consumption index of unit A is 106.91. Therefore, the unit time energy consumption index with a smaller value is 106.91, which is the historical optimal energy consumption index.

[0117] It is characterized in that the calculation formula for calculating the control trend value of the historical operating parameters is calculated according to the historical operating parameters and the historical equipment load parameters of the corresponding time period:

[0118]

[0119] Among them, y k and h k They represent the historical operating parameters and the historical equipment load parameters of the corresponding time period at the time of the kth regulation, y k+i and h k+i They respectively represent the values ​​of the historical operating parameters and the historical equipment load parameters of the corresponding time period at the time of the k+ith regulation, and v represents the regulation trend value.

[0120] For example, there are the following historical data:

[0121] The first regulation (k=1):

[0122] Historical operating parameters = 350°C (adsorption temperature)

[0123] Historical equipment load parameter = 1000kW

[0124] The fifth regulation (k+i=5):

[0125] Historical operating parameters = 360°C

[0126] Historical equipment load parameter = 1020kW

[0127] Calculate the control trend value:

[0128]

[0129] This means that every time the equipment load parameter increases by 1kW, the adsorption temperature increases by approximately 0.5°C. This indicates that the adsorption temperature shows a positive correlation trend with the change of equipment load parameters.

[0130] The formula for calculating the control trend amplitude value based on the control trend value is:

[0131]

[0132] Among them, ω3 represents the amplitude of the regulation trend, h k+i and h k represents the values ​​of the historical equipment load parameters at the k+ith regulation and the kth regulation, respectively, and Δv represents the value of the historical equipment load parameters at the kth regulation. k+i The control trend value and h k The difference in the control trend value at .

[0133] Taking into account that as the equipment load parameters are adjusted, when the equipment load parameters reach a certain value, the operating parameters cannot show a linear proportional relationship with it. For example, when the pressure in the system is low, the increase in the equipment load parameters increases the pressure quickly. When the pressure is high, providing the equipment load parameters can only increase the pressure less. Therefore, setting the difference in the control trend value can achieve more accurate control.

[0134] The step of adjusting the real-time operating parameters toward the historical optimal operating parameters according to the control trend value and the control trend amplitude value until the historical optimal operating parameters are reached includes:

[0135] Obtain the environmental impact value of a single unit and the environmental impact value of all units; taking into account the difference between the operating environment of a single unit and the operating environment of all units, such as different flue gas components and concentrations, different environmental impact values ​​of a single unit and all units can be set. When the difference between the environment of a single unit and the average environment of all units is large, a larger environmental impact value of a single unit is set. For example, the environmental impact value of a single unit is set to 2.5, and the environmental impact value of all units is set to 1.5, so that the real-time operating parameters are more inclined to be adjusted to the historical optimal operating parameters of a single unit.

[0136] Obtain the optimal operating parameter emphasis value of a single unit and the historical optimal operating parameter emphasis value; (the historical optimal operating parameter emphasis value represents the optimal operating parameters of all units. Considering the differences between a single unit and all units other than environmental impacts, the optimal operating parameter emphasis value of a single unit can affect the extent to which the real-time operating parameters are adjusted toward the optimal operating parameters of a single unit, while the historical optimal operating parameter emphasis value can affect the extent to which the optimal operating parameters of a single unit are adjusted toward the historical optimal operating parameters. A larger optimal operating parameter emphasis value of a single unit will make the real-time operating parameters more inclined to be adjusted toward the optimal operating parameters of a single unit, while a larger historical optimal operating parameter emphasis value will make the real-time operating parameters more inclined to be adjusted toward the historical optimal operating parameters. For example, if the optimal operating parameter emphasis value of a single unit and the historical optimal operating parameter emphasis value are set to 0.6 and 0.4 respectively, the real-time operating parameters can be more inclined to be adjusted toward the optimal operating parameters of a single unit.)

[0137] Obtain the historical optimal operating parameters of a single unit; the operating parameters corresponding to the optimal energy consumption index per unit time of a single unit are the historical optimal operating parameters of the single unit;

[0138] According to the environmental impact value of a single unit, the environmental impact values ​​of all units, the importance value of the optimal operating parameters of a single unit, the importance value of the historical optimal operating parameters, the historical optimal operating parameters of a single unit and the historical optimal operating parameters, the control trend value of the real-time operating parameters of a single unit at the time of the k+1th control of the corresponding real-time equipment load parameters is calculated;

[0139]

[0140] in, represents the control trend value of the real-time operating parameters of a single unit when the corresponding real-time equipment load parameters are regulated for the k+1th time, c1 represents the importance value of the optimal operating parameters of a single unit, c2 represents the importance value of the historical optimal operating parameters, r1 represents the environmental impact value of a single unit, r2 represents the environmental impact value of all units, ω3 represents the control trend value, i represents the real-time operating parameters, d represents the real-time equipment load parameters, It represents the parameters of the real-time operating parameters at the time of the kth adjustment of the corresponding real-time equipment load. represents the control trend value of the real-time operating parameter when the corresponding real-time equipment load parameter is adjusted for the kth time, g d represents the historical optimal operating parameters, P i,d Indicates the historical optimal operating parameters of a single unit;

[0141] The parameters of the real-time operating parameters at the k+1th regulation of the corresponding real-time equipment load are calculated according to the regulation trend value of the real-time operating parameters of a single unit at the k+1th regulation of the corresponding real-time equipment load and the value of the real-time operating parameters of a single unit at the kth regulation, wherein the calculation formula is:

[0142]

[0143] in, It represents the real-time operating parameters of a single unit at the time of the k+1th adjustment of the corresponding real-time equipment load. The control trend value of the real-time operating parameters of a single unit when the corresponding real-time equipment load parameters are adjusted for the k+1th time, Indicates the value of the real-time operating parameters of a single unit at the kth regulation;

[0144] judge Is the real-time energy consumption index under the current state less than or equal to the historical optimal energy consumption index? If it is less than or equal to, stop the regulation and Updated to historically optimal operating parameters.

[0145] By calculating the real-time operating parameters of each unit in combination with the control trend value, the control trend amplitude value, the environmental impact value of a single unit, the environmental impact values ​​of all units, the importance value of the optimal operating parameters of a single unit and the importance value of the historical optimal operating parameters, it is possible to continuously update and optimize the control trend value under the corresponding real-time equipment load parameters, and then further update and control the real-time operating parameters based on the updated control trend value, thereby achieving continuous optimization and ultimately reaching the historical optimal operating parameters of a single unit, evaluating the historical optimal operating parameters of all single units, and the minimum real-time energy consumption index under the historical optimal operating parameters of all single units, which are the new historical optimal operating parameters, to achieve low energy consumption optimization.

[0146] The present invention also discloses a low-energy-consumption intelligent optimization system based on solid adsorption CO2 capture, which is used for a low-energy-consumption intelligent optimization method based on solid adsorption CO2 capture, including:

[0147] A real-time monitoring module is used to monitor the operating status of all units in the CO2 capture system in real time, including but not limited to adsorption temperature, desorption temperature, adsorption pressure, desorption pressure and gas flow rate, and transmit these data to the central processing unit for processing in real time;

[0148] A historical data storage module is used to store the historical operating parameters of all units in the CO2 capture system, the historical desulfurization outlet CO2 capture rate and the historical equipment load parameters so that the central processing unit can access these data for analysis and optimization calculation;

[0149] The intelligent control module is used to calculate and update the historical optimal operating parameters based on the data in the historical data storage module and the real-time data. It can automatically adjust the operating parameters of each unit in the CO2 capture system to minimize energy consumption and maximize CO2 capture efficiency.

[0150] Environmental impact assessment module: used to evaluate the impact of the operating environment of each unit in the CO2 capture system on the real-time operating parameters, and adjust the control trend value according to the environmental impact value of a single unit and the environmental impact value of all units to ensure that the real-time operating parameters are more in line with the actual operating environment.

[0151] The intelligent control module includes:

[0152] The fault detection and diagnosis unit is used to determine whether all units are in normal operation or abnormal operation through real-time monitoring and data analysis, and to alarm when abnormal operation is detected, thereby reducing the risk of unplanned downtime and improving system reliability and availability.

[0153] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned low-energy intelligent optimization method based on solid adsorption CO2 capture are implemented.

[0154] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned low-energy intelligent optimization method for solid adsorption-based CO2 capture are implemented.

[0155] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing related hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided in this application and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0156] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0157] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A low-energy intelligent optimization method for solid adsorption CO2 capture, characterized in that: The following steps are involved: Obtain the historical operating parameters, historical desulfurization outlet CO2 capture rates and historical equipment load parameters of all units in the CO2 capture system; The historical optimal energy consumption index is calculated based on the historical desulfurization outlet CO2 capture rate and the historical equipment load parameters, and the historical operating parameters of the time period corresponding to the historical optimal energy consumption index are marked as the historical optimal operating parameters; Calculate the control trend value of the historical operating parameters according to the historical operating parameters and the historical equipment load parameters of the corresponding time period, and calculate the control trend amplitude value according to the control trend value; Obtaining real-time operating parameters of all units in the CO2 capture system, real-time desulfurization outlet CO2 capture rate and real-time equipment load, wherein the real-time operating parameters at least include adsorption temperature, desorption temperature, adsorption pressure, desorption pressure and gas flow rate; Calculate the real-time energy consumption index based on the real-time equipment load and the real-time desulfurization outlet CO2 capture rate; Determine the magnitude of the real-time energy consumption index of a single unit and the historical optimal energy consumption index; If it is greater than, the real-time operating parameter is regulated toward the historical optimal operating parameter according to the regulation trend value and the regulation trend amplitude value until the historical optimal operating parameter is reached; if it is less than, the real-time energy consumption index is updated to the historical optimal energy consumption index, and the real-time operating parameter corresponding to the real-time energy consumption index is marked as the historical optimal operating parameter; Obtain optimal equipment load parameters based on historical optimal operating parameters.

2. According to claim 1, a low-energy intelligent optimization method based on solid adsorption CO2 capture is characterized in that: The steps for obtaining the historical desulfurization outlet CO2 capture rate are as follows: Obtain the inlet CO2 concentration and outlet CO2 concentration per unit time of each unit; Obtain the inlet and outlet flue gas flow rates per unit time for each unit; Obtain the inlet and outlet smoke density per unit time of each unit; The desulfurization outlet CO2 capture rate per unit time is calculated based on the inlet CO2 concentration, outlet CO2 concentration, inlet flue gas flow rate, outlet flue gas flow rate, inlet flue gas density and outlet flue gas density, where the calculation formula is: Among them, C b Indicates the desulfurization outlet CO2 capture rate per unit time, Q O Indicates the outlet flue gas flow rate, P O Indicates the outlet smoke density, C O Indicates the inlet CO2 concentration, Q i represents the inlet flue gas flow rate, P i Indicates the inlet smoke density, C I Indicates the inlet CO2 concentration; The desulfurization outlet CO2 capture rate per unit time of all units is statistically analyzed to obtain the historical desulfurization outlet CO2 capture rate.

3. According to claim 2, a low-energy intelligent optimization method for solid adsorption CO2 capture is characterized in that: The steps of calculating the historical optimal energy consumption index based on the historical desulfurization outlet CO2 capture rate and the historical equipment load parameters are as follows: Obtain the unit time equipment load parameters of all units through historical equipment load parameters; The energy consumption index per unit time of a single unit is calculated based on the equipment load parameter per unit time of a single unit and the CO2 capture rate per unit time of the desulfurization outlet of a single unit, where the calculation formula is: F = ω1*E(z)+ω2*C(z); F represents the energy consumption index per unit time of a single unit, E(z) represents the equipment load parameter per unit time of a single unit, ω1 represents the load weight, C(z) represents the CO2 capture rate per unit time of the desulfurization outlet of a single unit, and ω2 represents the CO2 capture rate weight; The energy consumption per unit time of all units is counted, and the energy consumption per unit time with the lowest value is the historical optimal energy consumption index.

4. According to claim 3, a low-energy intelligent optimization method based on solid adsorption CO2 capture is characterized in that: The calculation formula for calculating the control trend value of the historical operating parameters based on the historical operating parameters and the historical equipment load parameters of the corresponding time period is: Among them, y k and h k They represent the historical operating parameters and the historical equipment load parameters of the corresponding time period at the kth regulation, y k+i and h k+i They respectively represent the values ​​of the historical operating parameters and the historical equipment load parameters of the corresponding time period at the time of the k+ith regulation, and v represents the regulation trend value.

5. According to claim 4, a low-energy intelligent optimization method based on solid adsorption CO2 capture is characterized in that: The formula for calculating the control trend amplitude value according to the control trend value is: Among them, ω3 represents the amplitude of the regulation trend, h k:i and h k represents the values ​​of the historical equipment load parameters at the k+ith regulation and the kth regulation, respectively, and Δv represents the value of the historical equipment load parameters at the kth regulation. k+i The control trend value and h k The difference in the control trend value at .

6. The low-energy intelligent optimization method for solid adsorption CO2 capture according to claim 5 is characterized in that: The step of regulating the real-time operating parameter toward the historical optimal operating parameter according to the regulation trend value and the regulation trend amplitude value until the historical optimal operating parameter is reached includes: Obtain the environmental impact value of a single unit and the environmental impact value of all units; Obtain the optimal operating parameter value of a single unit and the historical optimal operating parameter value; Obtain the historical optimal operating parameters of a single unit; According to the environmental impact value of a single unit, the environmental impact values ​​of all units, the importance value of the optimal operating parameters of a single unit, the importance value of the historical optimal operating parameters, the historical optimal operating parameters of a single unit and the historical optimal operating parameters, the control trend value of the real-time operating parameters of a single unit at the time of the k+1th control of the corresponding real-time equipment load parameters is calculated; in, represents the control trend value of the real-time operating parameters of a single unit when the corresponding real-time equipment load parameters are regulated for the k+1th time, c1 represents the importance value of the optimal operating parameters of a single unit, c2 represents the importance value of the historical optimal operating parameters, r1 represents the environmental impact value of a single unit, r2 represents the environmental impact value of all units, ω3 represents the control trend value, i represents the real-time operating parameters, d represents the real-time equipment load parameters, It represents the parameters of the real-time operating parameters at the time of the kth adjustment of the corresponding real-time equipment load. represents the control trend value of the real-time operating parameter when the corresponding real-time equipment load parameter is adjusted for the kth time, g d represents the historical optimal operating parameters, P i,d Indicates the historical optimal operating parameters of a single unit; The parameters of the real-time operating parameters at the k+1th regulation of the corresponding real-time equipment load are calculated according to the regulation trend value of the real-time operating parameters of a single unit at the k+1th regulation of the corresponding real-time equipment load and the value of the real-time operating parameters of a single unit at the kth regulation, wherein the calculation formula is: in, It represents the real-time operating parameters of a single unit at the time of the k+1th adjustment of the corresponding real-time equipment load. The control trend value of the real-time operating parameters of a single unit when the corresponding real-time equipment load parameters are adjusted for the k+1th time, Indicates the value of the real-time operating parameters of a single unit at the kth regulation; judge When the real-time energy consumption index under the optimal energy consumption index is less than or equal to the historical optimal energy consumption index, the regulation is stopped. Updated to historically optimal operating parameters.

7. A low-energy consumption intelligent optimization system based on solid adsorption CO2 capture, used for the low-energy consumption intelligent optimization method based on solid adsorption CO2 capture according to claim 6, characterized in that: include: Real-time monitoring module, used to monitor the operating status of all units in the CO2 capture system in real time; A historical data storage module, used to store historical operating parameters of all units in the CO2 capture system, historical desulfurization outlet CO2 capture rates and historical equipment load parameters; The intelligent control module is used to calculate and update the historical optimal operating parameters based on the data in the historical data storage module and the real-time data. Environmental impact assessment module: used to evaluate the impact of the operating environment of each unit in the CO2 capture system on the real-time operating parameters.

8. The low energy consumption intelligent optimization system based on solid adsorption CO2 capture according to claim 7 is characterized in that: The intelligent control module includes: The fault detection and diagnosis unit is used to determine whether all units are in normal operation or abnormal operation through real-time monitoring and data analysis, and to alarm when an abnormal operation state is detected.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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