Low-temperature methanol washing system self-adaptive adjusting method and electronic equipment

Abstract

The application discloses a low-temperature methanol washing system self-adaptive adjusting method and electronic equipment, and the method comprises the following steps: acquiring process parameters in the low-temperature methanol washing system, wherein the process parameters at least include controlled variables and operation variables; determining the set value of the controlled variables; determining a target interval according to the set value of the controlled variables; determining the real-time control importance of the controlled variables, and taking the controlled variables with the real-time control importance exceeding a preset importance threshold as important controlled variables; when the important controlled variables exceed the target interval, determining the set value of the operation variables corresponding to the important controlled variables; and adjusting the important controlled variables to the target interval according to the set value of the operation variables. By adopting the scheme provided by the application, the self-adaptive adjustment of the process parameters of the low-temperature methanol washing system is realized, the work load of the operator is reduced, the process parameter fluctuation is reduced, and energy saving and consumption reduction are achieved.

Description

Technical Field

[0001] This invention relates to the field of Internet technology, and in particular to an adaptive adjustment method and electronic device for a low-temperature methanol washing system. Background Technology

[0002] Low-temperature methanol washing refers to the process of removing acidic gases such as CO2, H2S, and COS from raw gas under certain pressure and temperature using methanol. The solvent CH3OH has different Henry's coefficients for solutes such as H2, N2, CO, Ar, CO2, H2S, and COS, allowing for selective absorption of these gases and gas removal. The commonly used industrial method for removing and recovering CO2, H2S, and COS is solution absorption.

[0003] Because low-temperature methanol washing systems are relatively complex, automation is necessary to reduce operational workload and achieve good removal results. Existing technologies, including utility model patents for advanced control of the methanol washing tower, methanol regeneration tower, and methanol-water separation tower in low-temperature methanol washing systems, only address the control of local parameters. There is no model-based automatic control method for the entire low-temperature methanol washing system. Therefore, how to achieve adaptive adjustment of the low-temperature methanol washing system and reduce the workload of operators is a pressing technical solution. Summary of the Invention

[0004] Therefore, it is necessary to provide an adaptive adjustment method and electronic device for a low-temperature methanol washing system to address the technical problems of the existing technology.

[0005] This invention provides an adaptive adjustment method for a low-temperature methanol washing system, comprising:

[0006] Obtain the process parameters of the low-temperature methanol washing system, wherein the process parameters include at least: controlled variables and operating variables;

[0007] Determine the set value of the controlled variable;

[0008] The target range is determined based on the set value of the controlled variable;

[0009] Determine the real-time control importance of the controlled variable, and designate the controlled variable whose real-time control importance exceeds a preset importance threshold as an important controlled variable;

[0010] When the important controlled variable exceeds the target range, determine the set value of the operation variable corresponding to the important controlled variable;

[0011] Adjust the important controlled variable to the target range according to the set value of the operated variable.

[0012] Furthermore, the method also includes:

[0013] Obtain all process parameters of the low-temperature methanol washing system within the set historical operating cycle;

[0014] The historical data of the low-temperature methanol washing system were obtained by normalizing all the process parameters.

[0015] The optimal operating curves of each controlled variable are obtained by simulating the historical data for all time periods within the historical operating cycle.

[0016] Determining the set value of the controlled variable specifically includes:

[0017] The production process is divided into multiple production periods, and the value of the controlled variable corresponding to each production period in the optimal operating curve is used as the set value of the controlled variable corresponding to the production period.

[0018] Furthermore, determining the target interval based on the set value of the controlled variable specifically includes:

[0019] The target range of the controlled variable is obtained by adding the set value to the set tolerance deviation.

[0020] Further, determining the real-time control importance of the controlled variable specifically includes:

[0021] Determine the first importance of the controlled variable;

[0022] Determine the second importance of the controlled variable;

[0023] The real-time control importance of the controlled variable is determined based on its first importance and second importance as follows:

[0024] Z = w1Zm + w2Zn, where Z is the real-time control importance of the controlled variable, Zm is the first importance of the controlled variable, Zn is the second importance of the controlled variable, w1 is the first weight, and w2 is the second weight.

[0025] Further, determining the first importance of the controlled variable specifically includes:

[0026] Obtain the actual value of the controlled variable, the maximum and minimum values ​​of the target interval, and determine the tolerance value of the controlled variable:

[0027] If Cr > (Cmax + Cmin) / K1, then Cr = C - Cmax.

[0028] If Cr≤(Cmax+Cmin) / K1, then Cr=C-Cmin;

[0029] Determine the tolerance of the controlled variable: t = Cr / (Cmax - Cmin);

[0030] Determine the first importance of the controlled variable:

[0031] When Cmax > C > Cmin, and t ≥ t1 or t ≤ t2, Zm = A1.

[0032] When Cmax > C > Cmin, and t1 < t ≤ t3, Zm = 1 / t.

[0033] When Cmax > C > Cmin and t > t3, Zm = A2.

[0034] When C≥Cmax, Zm=A3+(C-Cmax) / (Cmax-Cmin)*A4,

[0035] When C ≤ Cmin, Zm = A3 + (Cmin - C) / (Cmax - Cmin) * A4.

[0036] Where C is the actual value of the controlled variable, Cr is the tolerance value of the controlled variable, Cmax is the maximum value of the target interval, Cmin is the minimum value of the target interval, t is the tolerance of the controlled variable, Zm is the first importance of the controlled variable, K1 is the first parameter, A1 is the second parameter, A2 is the third parameter, A3 is the fourth parameter, A4 is the fifth parameter, t1 is the sixth parameter, t2 is the seventh parameter, and t3 is the eighth parameter.

[0037] Further, determining the second importance of the controlled variable specifically includes:

[0038] Determine the deviation and rate of change of the controlled variable, wherein:

[0039] The deviation of the controlled variable is defined as:

[0040] eL = (CC * ) / (Cmax-Cmin),

[0041] The rate of change of the controlled variable is defined as:

[0042]

[0043] Where C is the actual value of the controlled variable, C * Here, eL is the setpoint of the controlled variable, eL is the deviation of the controlled variable, eCL is the rate of change of the controlled variable, Cmax is the maximum value of the target interval, and Cmin is the minimum value of the target interval.

[0044] The second importance of the controlled variable is determined based on the deviation and rate of change of the controlled variable.

[0045] Furthermore, determining the second importance of the controlled variable based on its deviation and rate of change specifically includes:

[0046] Obtain a first fuzzy set of the natural language domain corresponding to the deviation of the controlled variable and a second fuzzy set of the natural language domain corresponding to the rate of change of the controlled variable. The first fuzzy set includes multiple first subset elements, and the second fuzzy set includes multiple second subset elements.

[0047] Obtain the correspondence table between the elements of the first subset and the elements of the second subset for the second importance level;

[0048] The first subset element corresponding to the real value of the deviation of the controlled variable is determined as the first subset element to be checked, and the second subset element corresponding to the real value of the rate of change of the controlled variable is determined as the second subset element to be checked.

[0049] The control importance of the first subset element to be checked and the second subset element to be checked are obtained from the correspondence table and used as the second importance of the controlled variable.

[0050] Furthermore, determining the set value of the operational variable corresponding to the important controlled variable specifically includes:

[0051] From the process parameters, select one or more of the operating variables as the operating variables to be adjusted; predict the important controlled variable based on the transfer function of the response of the important controlled variable with respect to the operating variable to be adjusted; if the predicted value of the important controlled variable is within the target range, stop adjusting the operating variable to be adjusted and use the current value of the operating variable to be adjusted as the set value of the operating variable to be adjusted; if the predicted value of the important controlled variable is not within the target range, adjust the operating variable to be adjusted again.

[0052] Furthermore, the transfer function is:

[0053]

[0054] Wherein CV is the important controlled variable, MV is the variable to be adjusted, Ge is the gain, tau is the time constant, and Dt is the lag time.

[0055] This invention provides an electronic device, comprising:

[0056] At least one processor; and,

[0057] A memory communicatively connected to at least one of the processors; wherein,

[0058] The memory stores instructions that can be executed by at least one of the processors to enable at least one of the processors to perform the adaptive adjustment method for the low-temperature methanol washing system as described above.

[0059] This invention automatically adjusts the setpoints of controlled variables in a low-temperature methanol washing system based on an adaptive control model; it determines whether any controlled variables exceed the target range; and when such variables exist, it determines the operational variables to be adjusted based on a multivariable control self-correcting transfer function to bring the controlled variables back to the target range. This achieves adaptive regulation of each tower in the low-temperature methanol washing system, reducing the workload of operators and saving labor costs. Attached Figure Description

[0060] Figure 1 This is a flowchart illustrating the adaptive adjustment method for a low-temperature methanol washing system according to an embodiment of the present invention.

[0061] Figure 2 This is a flowchart illustrating the adaptive adjustment method for a low-temperature methanol washing system according to another embodiment of the present invention.

[0062] Figure 3 This is a flowchart illustrating the determination of the real-time control importance of each controlled variable in another embodiment of the present invention;

[0063] Figure 4 This is a flowchart illustrating the determination of the control importance of each controlled variable in another embodiment of the present invention;

[0064] Figure 5 This is a flowchart illustrating the adaptive adjustment method of a low-temperature methanol washing system in another embodiment of the present invention.

[0065] Figure 6 This is a flowchart illustrating the adaptive adjustment method of a low-temperature methanol washing system in another embodiment of the present invention.

[0066] Figure 7 This is a schematic diagram showing the connection relationship of a portion of the low-temperature methanol washing system in the preferred embodiment of the present invention;

[0067] Figure 8 This is a schematic diagram showing the connection relationship of a portion of the low-temperature methanol washing system in the preferred embodiment of the present invention;

[0068] Figure 9 This is a schematic diagram showing the connection relationship of a portion of the low-temperature methanol washing system in the preferred embodiment of the present invention;

[0069] Figure 10This is a schematic diagram showing the connection relationship of a portion of the low-temperature methanol washing system in the preferred embodiment of the present invention;

[0070] Figure 11 This is a schematic diagram of the hardware structure of an electronic device according to the present invention; Detailed Implementation

[0071] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings. Identical components are indicated by the same reference numerals. It should be noted that the terms "front," "rear," "left," "right," "up," and "down" used in the following description refer to directions in the accompanying drawings, while the terms "inner" and "outer" refer to directions toward or away from the geometric center of a specific component, respectively.

[0072] Example 1

[0073] like Figure 1 The diagram shown is a flowchart of an adaptive adjustment method for a low-temperature methanol washing system according to an embodiment of the present invention, including:

[0074] Step S101: Obtain the process parameters in the low-temperature methanol washing system, wherein the process parameters include at least: controlled variables and operating variables;

[0075] Step S102: Determine the set value of the controlled variable;

[0076] Step S103: Determine the target interval based on the set value of the controlled variable;

[0077] Step S104: Determine the real-time control importance of the controlled variable, and designate the controlled variable whose real-time control importance exceeds a preset importance threshold as an important controlled variable;

[0078] Step S105: When the important controlled variable exceeds the target range, determine the set value of the operation variable corresponding to the important controlled variable;

[0079] Step S106: Adjust the important controlled variable to the target range according to the set value of the operation variable.

[0080] Specifically, step S101 acquires the process parameters of the operating low-temperature methanol washing system, including at least controlled variables and operating variables. Step S102 determines the setpoints of the controlled variables by collecting and processing historical system operation data. Step S103 determines the target range of the controlled variables based on the setpoints and configured tolerance deviation values. Since the system is affected by multiple variables during operation, step S104 determines the real-time control importance of the controlled variables. Controlled variables whose real-time control importance exceeds a preset importance threshold are designated as important controlled variables. Based on the ranking of importance from high to low, the first, second, and third controlled variables of a certain stage of the system can be obtained. Three controlled variables, etc.; when a controlled variable whose real-time control importance exceeds a preset importance threshold exceeds the target range, in this embodiment, if the importance threshold is set to two, then the first controlled variable with the first high real-time control importance and the second controlled variable with the second high real-time control importance are taken, and it is determined whether the first controlled variable and the second controlled variable exceed the target range respectively. If they exceed the target range, step S105 is executed. When the important controlled variable exceeds the target range, the set value of the operation variable corresponding to the important controlled variable is determined, and finally step S106 is executed to adjust the controlled variable to the target range according to the set value of the operation variable.

[0081] This invention determines whether there are controlled variables that exceed the target range; when there are controlled variables that exceed the target range, it determines the operational variables that need to be adjusted according to the multivariate control self-correction transfer function, so as to adjust the controlled variables to the target range.

[0082] Example 2

[0083] like Figure 2 The diagram shown is a flowchart of an adaptive adjustment method for a low-temperature methanol washing system according to another embodiment of the present invention, comprising:

[0084] Step S201: During the operation of the low-temperature methanol washing system, the setpoint of the controlled variable is automatically adjusted according to the adaptive control model. Specifically, the process parameters in the low-temperature methanol washing system are obtained, including the controlled variable and the operating variable; the setpoint of the controlled variable is determined, and the target range of the controlled variable is determined.

[0085] In one embodiment, the method further includes: acquiring all process parameters of the low-temperature methanol washing system within a set operating cycle; normalizing all the process parameters to obtain historical data of the low-temperature methanol washing system; and simulating the historical data to obtain the optimal operating curves for all time periods of each of the controlled variables.

[0086] In one embodiment, determining the set value of the controlled variable specifically includes:

[0087] The production process is divided into multiple production periods, and the value of the controlled variable corresponding to each production period in the optimal operating curve is used as the set value of the controlled variable corresponding to the production period.

[0088] In one embodiment, determining the target interval based on the set value of the controlled variable specifically includes: adding a set tolerance deviation to the set value to obtain the target interval of the controlled variable.

[0089] In one embodiment, the real-time control importance of the controlled variable is determined, specifically, as follows: Figure 3 As shown, the steps for determining the real-time control importance of the controlled variable are as follows:

[0090] Step S301: Determine the first importance of the controlled variable;

[0091] Step S302: Determine the second importance of the controlled variable;

[0092] Step S303: Determine the real-time control importance of the controlled variable based on its first importance and second importance.

[0093] Z = w1Zm + w2Zn, where Z is the real-time control importance of the controlled variable, Zm is the first importance of the controlled variable, Zn is the second importance of the controlled variable, w1 is the first weight, and w2 is the second weight.

[0094] In one embodiment, determining the first importance of the controlled variable specifically includes:

[0095] Obtain the actual value of the controlled variable, the maximum and minimum values ​​of the target interval, and determine the tolerance value of the controlled variable:

[0096] If Cr > (Cmax + Cmin) / K1, then Cr = C - Cmax.

[0097] If Cr≤(Cmax+Cmin) / K1, then Cr=C-Cmin;

[0098] Determine the tolerance of the controlled variable: t = Cr / (Cmax - Cmin);

[0099] Determine the first importance of the controlled variable:

[0100] When Cmax > C > Cmin, and t ≥ t1 or t ≤ t2, Zm = A1.

[0101] When Cmax > C > Cmin, and t1 < t ≤ t3, Zm = 1 / t.

[0102] When Cmax > C > Cmin and t > t3, Zm = A2.

[0103] When C≥Cmax, Zm=A3+(C-Cmax) / (Cmax-Cmin)*A4,

[0104] When C ≤ Cmin, Zm = A3 + (Cmin - C) / (Cmax - Cmin) * A4.

[0105] Where C is the actual value of the controlled variable, Cr is the tolerance value of the controlled variable, Cmax is the maximum value of the target interval, Cmin is the minimum value of the target interval, t is the tolerance of the controlled variable, Zm is the first importance of the controlled variable, K1 is the first parameter, A1 is the second parameter, A2 is the third parameter, A3 is the fourth parameter, A4 is the fifth parameter, t1 is the sixth parameter, t2 is the seventh parameter, and t3 is the eighth parameter.

[0106] In one embodiment, a second importance level of the controlled variable is determined, specifically, as follows: Figure 4 As shown, the steps to determine the second importance of each controlled variable are as follows:

[0107] Step S401: Input the deviation and rate of change of each controlled variable as real values ​​into the fuzzy controller;

[0108] Step S402: Obtain the second importance of each controlled variable based on the real value determined by the fuzzy controller;

[0109] Determine the deviation and rate of change of the controlled variable:

[0110] The deviation of the controlled variable is defined as:

[0111] eL = (CC * ) / (Cmax-Cmin),

[0112] The rate of change of the controlled variable is defined as:

[0113]

[0114] Where C is the actual value of the controlled variable, C * Here, eL is the set value of the controlled variable, eL is the deviation of the controlled variable, eCL is the rate of change of the controlled variable, Cmax is the maximum value of the target interval, and Cmin is the minimum value of the target interval.

[0115] The deviations and rates of change of each of the controlled variables are used as the real values ​​of the input to the fuzzy controller;

[0116] The second importance of the controlled variable is determined based on the actual value;

[0117] In one embodiment, determining the second importance of the controlled variable based on its deviation and rate of change specifically includes:

[0118] Obtain a first fuzzy set of the natural language domain corresponding to the deviation of the controlled variable and a second fuzzy set of the natural language domain corresponding to the rate of change of the controlled variable. The first fuzzy set includes multiple first subset elements, and the second fuzzy set includes multiple second subset elements.

[0119] Obtain the correspondence table of the control importance levels with respect to the elements of the first subset and the elements of the second subset;

[0120] The first subset element corresponding to the real value of the deviation of the controlled variable is determined as the first subset element to be checked, and the second subset element corresponding to the real value of the rate of change of the controlled variable is determined as the second subset element to be checked.

[0121] The second importance level corresponding to the first subset element to be checked and the second subset element to be checked is obtained from the correspondence table and used as the second importance level of the controlled variable.

[0122] Step S202: Determine whether there is a first controlled variable that exceeds the target interval.

[0123] In one embodiment, the controlled variables are sorted from most important to least important according to their real-time control importance values ​​to obtain the first controlled variable, the second controlled variable, the third controlled variable, and so on. Based on a preset importance threshold, it is determined whether to select the first few controlled variables with the highest real-time control importance. For example, if the preset importance threshold is 3, then the first controlled variable, the second controlled variable, and the third controlled variable are selected, and it is determined whether they exceed the target range.

[0124] Step S203: When there is a first controlled variable that exceeds the target range, determine the operating variable that needs to be adjusted based on the self-tuning transfer function.

[0125] In one embodiment, one or more of the operation variables are selected from the process parameters as the operation variables to be adjusted;

[0126] The important controlled variable is predicted based on the transfer function of its response to the variable to be adjusted. If the predicted value of the important controlled variable is within the target range, the adjustment of the variable to be adjusted is stopped, and the current value of the variable to be adjusted is used as the set value of the variable to be adjusted. If the predicted value of the important controlled variable is not within the target range, the variable to be adjusted is adjusted again.

[0127] In one embodiment, the transfer function is:

[0128]

[0129] Where CV is the controlled variable; MV is the operated variable; Ge is the gain; tau is the time constant; and Dt is the lag time.

[0130] Step S204: Based on the valves and circuits corresponding to the corresponding operating variables, adjust the controlled variable back to the target range.

[0131] In one embodiment, adjusting the important controlled variable to the target range according to the set value of the operated variable specifically includes:

[0132] Regarding the aforementioned operating variable, when the low-temperature methanol washing system does not have an underlying electronic control loop, the operating variable is directly adjusted according to its set value.

[0133] Regarding the aforementioned operating variable, when the low-temperature methanol washing system has a bottom-level electronic control loop, the PID control action is determined based on the target range. The PID calculation formula is as follows:

[0134]

[0135] Where Pb is the output signal of the PID controller; Kp, Ti, and Td are all adjustment parameters obtained by the step response method; e(t) is the deviation between the actual value and the set value of the PID function block in the lower control loop; and t is time.

[0136] In this embodiment, during the operation of the low-temperature methanol washing system, the controlled variable, manipulated variable, and disturbance variable in the system are determined according to process requirements, and the setpoints of the controlled variable are automatically adjusted according to the adaptive control model. The controlled variable, manipulated variable, and disturbance variable are monitored. When the controlled variable changes, in order to reduce process fluctuations, the materials and heat in the system need to be controlled as stably as possible to achieve balance. However, due to various disturbances and local imbalances during system operation, the controlled variable is prone to change. Because the flow paths between systems are complex and highly coupled, adjustments to one part can easily have a ripple effect, leading to further deviations. Given the large workload and significant system fluctuations, this application considers using an adaptive control scheme to control the important parameters of the low-temperature methanol washing system. Compared to traditional PID single-loop control schemes, this scheme can ensure system stability under both load adjustment and stability conditions, reduce operator workload, minimize process parameter fluctuations, and save energy. In other words, this application not only adaptively adjusts the important parameters of the low-temperature methanol washing system to reduce process fluctuations, but also...

[0137] Specifically, in this invention, during the operation of the low-temperature methanol washing system, key controlled parameters, such as changes in liquid level, flow rate, and temperature, need to be monitored. Interference or other factors can cause changes in these key controlled parameters, triggering the execution entity (such as the adaptive control system connected to the low-temperature methanol washing system) to adjust the controlled variables. Simultaneously, changes in the operating variables that adjust these controlled variables can also cause changes in other controlled variables. If these changes exceed a specified range, the execution entity will adjust the operating variables corresponding to other controlled variables to bring the controlled variables back to the specified range as quickly as possible. In other words, a change in a single operating variable can cause changes in multiple controlled variables, and a change in a single controlled variable may also cause changes in the operating variables.

[0138] Therefore, in this invention, during the operation of the low-temperature methanol washing system, each key controlled variable and its corresponding manipulated variable and disturbance variable are determined. Due to system influences, each controlled variable will change; the reasons for these changes have been mentioned above, including but not limited to changes in a controlled variable caused by disturbances such as load changes. It is determined whether a first controlled variable exceeds the target range; when such a first controlled variable exists, the corresponding manipulated variable is adjusted based on the first controlled variable to bring it back into the target range.

[0139] Secondly, in this invention, during the operation of the low-temperature methanol washing system, each key controlled variable and its corresponding operating variable and disturbance variable are determined; when there is a second controlled variable that exceeds the target range, the corresponding operating variable is adjusted according to the second controlled variable to bring the second controlled variable that exceeds the target range back to the target range.

[0140] It is understandable that, because a change in a single operated variable can cause changes in multiple controlled variables, and a change in a single controlled variable may also cause a change in the operated variable, and the real-time control importance of the controlled variable changes constantly, a change in a certain controlled variable will also trigger the executing entity of this application to adjust the changes of all subsequent controlled variables. Furthermore, when the second controlled variable exceeds the specified range, in order to bring the second controlled variable back to the specified range as quickly as possible, the executing entity will adjust the operated variable corresponding to the second controlled variable. Therefore, the second controlled variable and the first controlled variable can be the same controlled variable.

[0141] In this invention, the first step is to determine the controlled variables, corresponding operating variables, and disturbance variables required within the system. The selection of controlled variables, operating variables, and disturbance variables includes, but is not limited to, obtaining them based on process and control experience and historical trends, and segmenting the model according to the actual situation.

[0142] When determining the real-time control importance of each controlled variable within a group based on system information, the first importance Zm of each controlled variable is determined; the second importance Zn of each controlled variable is determined; and the real-time control importance Z of each controlled variable is determined based on the first importance Zm and the second importance Zn of each controlled variable.

[0143] Among them, determining the first importance Zm of each controlled variable includes:

[0144] If Cmax > C > Cmin, then when t ≥ 1 / 3 or t ≤ 0, Zm = 0; when 1 / 3 < t ≤ 1 / 10, Zm = 1 / t; and when t > 1 / 10, Zm = 12.

[0145] If C≥Cmax, then Zm=12+(C-Cmax) / (Cmax-Cmin)*5;

[0146] If C≤Cmin, then Zm=12+(Cmin-C) / (Cmax-Cmin)*5;

[0147] Where t represents the current tolerance of the controlled variable, calculated using the following formula:

[0148] t = Cr / (Cmax - Cmin);

[0149] Where Cr is the tolerance value of the controlled variable; C is the actual value of the controlled variable; Cmax is the upper tolerance limit of the controlled variable; and Cmin is the lower tolerance limit of the controlled variable.

[0150] The calculation method for Cr is as follows: if C > (Cmax + Cmin) / 2, then Cr = Cmax - C; if Cr ≤ (Cmax + Cmin) / 2, then Cr = C - Cmin.

[0151] Determine the second importance Zn of the liquid level in each column, including:

[0152] The deviation eL between the actual value and the set value of each controlled variable is defined as:

[0153] eL = (CC * ) / (Cmax-Cmin)

[0154] In the formula: C represents the actual value of each controlled variable, C * These are the setpoints for each controlled variable;

[0155] The rate of change (ecL) of each controlled variable is defined as:

[0156]

[0157] The deviation eL and rate of change eCL of each controlled variable are input into the fuzzy controller as the actual values ​​of each tower.

[0158] The second importance Zn of each controlled variable is determined by the fuzzy controller based on real values.

[0159] The fuzzy controller determines the second importance Zn of each controlled variable based on real values ​​as follows:

[0160] The real values ​​of each controlled variable are converted into fuzzy quantities described in natural language. The real value domain U of the controlled variable deviation eL is in the range of [-3, 3], and the corresponding fuzzy set of the natural language domain U is {NB, NS, ZO, PS, PB}, with subset elements being negative large, negative small, zero, positive small, and positive large, respectively. The real value domain U of the controlled variable rate of change eCL is in the range of [-3, 3], and the corresponding fuzzy set of the natural language domain U is {SB, SS, ZO, FS, FB}, with subset elements being negative fast, negative slow, zero, positive slow, and positive fast, respectively.

[0161] The second importance Zn of each controlled variable is determined based on the subset elements corresponding to the real values ​​of each controlled variable. Specifically, the real-time control importance Z of the controlled variable is determined according to Table 1 below:

[0162] Table 1

[0163]

[0164]

[0165] For example, if the deviation of a controlled variable eL is 10% (corresponding to PS) and the rate of change of the controlled variable eCL is 0 (corresponding to Z0), then the importance of real-time control of the liquid level of a single tower is Z = 1.

[0166] The real-time control importance Z of each controlled variable is determined based on its first importance Zm and second importance Zn, including: determining the real-time control importance Z of each controlled variable according to the following formula:

[0167] Z = 0.3Zm + 0.2Zn;

[0168] Where Zm represents the first importance of each controlled variable; Zn represents the second importance of each controlled variable.

[0169] Specifically, after identifying the first controlled variable that exceeds the controlled range, the operational variable that needs to be adjusted needs to be determined based on the transfer function. The transfer function between each controlled variable, operational variable, and disturbance variable is obtained through a step test. When the system is stable, other process parameters are kept constant, and only a single operational variable or disturbance variable is adjusted to observe the response of the controlled variable, thereby obtaining the transfer function.

[0170]

[0171] Where CV is a controlled variable; MV is an operand corresponding to a controlled variable CV; Ge is the gain in the transfer function; tau is the time constant in the transfer function; and Dt is the lag time in the transfer function.

[0172] When the controlled variable changes, the corresponding change in the operated variable is determined based on the transfer function relationship.

[0173] Specifically, for an operating variable corresponding to multiple controlled variables, the change in the operating variable is the result of the combined effect of the multiple controlled variables;

[0174] For the manipulated variable, if a lower-level control loop exists, the PID control action is determined based on the deviation between its actual value and the setpoint. Specifically, the change in PID action is calculated using the following formula:

[0175]

[0176] Where Pb is the output signal of the PID controller; Kp, Ti, and Td are all adjustment parameters obtained by the step response method; e(t) is the deviation between the actual value and the set value of the PID function block in the lower control loop; and t is time.

[0177] The controlled variables (including but not limited to temperature and liquid level), operating variables (including but not limited to steam flow and outlet valve), and disturbance variables (including but not limited to feed flow) within the low-temperature methanol washing system can be determined using the above formula. Then, each controlled variable, operating variable, and disturbance variable can be sent to the corresponding control host of the low-temperature methanol washing system, thereby enabling each control host to control each operating variable.

[0178] This invention identifies key controlled variables and their corresponding manipulated and disturbing variables within a low-temperature methanol washing system during operation. These controlled variables change due to system influences, including but not limited to changes caused by load variations or other disturbances. The invention determines whether a first controlled variable exceeds a target range. If so, the corresponding manipulated variable is adjusted to bring the first controlled variable within the target range back to the target range. Furthermore, during system operation, the invention identifies all controlled variables and their corresponding manipulated and disturbing variables. If a second controlled variable exceeds a target range, the corresponding manipulated variable is adjusted to bring the second controlled variable within the target range back to the target range.

[0179] In one embodiment, the method may also be implemented as follows:

[0180] Step S501: During the operation of the low-temperature methanol washing system, the set value of the controlled variable is automatically adjusted according to the adaptive control model, and the system is divided into groups.

[0181] Step S502: Determine the real-time control importance of each controlled variable within the group based on the controlled variable information;

[0182] Step S503: When the second most important controlled variable whose real-time control importance changes exceeds the target range, the set value of the corresponding operation variable is calculated based on the transfer function.

[0183] Step S504: Adjust the operating variable according to the set value of the operating variable to bring the corresponding controlled variable back to the target range.

[0184] In this embodiment, during the operation of the low-temperature methanol washing system, the real-time control importance of each controlled variable within the system group is determined. It is easy to understand that the steps for determining the information of each controlled variable within the system can be performed simultaneously with the aforementioned steps for determining the feed flow rate.

[0185] When the low-temperature methanol washing system reaches a certain point in time, if the second controlled variable changes due to disturbance and exceeds the target range, the real-time control importance of the second controlled variable will increase. At this time, the real-time control importance of the second controlled variable can be reduced by adjusting the value of the corresponding operation variable calculated according to the transfer function, that is, reducing the second controlled variable back to the target range.

[0186] Because it requires adjustments to the manipulated variables, and a single manipulated variable may adjust multiple controlled variables, while a single controlled variable can be adjusted by multiple manipulated variables, as long as one controlled variable, manipulated variable, or disturbance variable changes, it will affect some or all of the controlled variables, manipulated variables, and disturbance variables in the system, thereby triggering their respective adaptive systems, so that the entire system can maintain dynamic equilibrium during operation.

[0187] Based on the above description, it is easy to understand that when steps S201-S204 and S501-S504 are executed together, one possible execution scheme is as follows:

[0188] Step S601: During the operation of the low-temperature methanol washing system, determine the information of each controlled variable in the system, and determine the set value of each controlled variable according to the adaptive system.

[0189] Determine the information of each operational variable based on the information of the controlled variable;

[0190] When there is a second important controlled variable whose real-time control importance changes under the same operated variable, adjust other operated variables of the second important controlled variable to reduce the real-time control importance of the second important controlled variable.

[0191] Step S602: Determine whether there is a first important controlled variable that exceeds the target range based on the information of each controlled variable, and determine whether there is a second important controlled variable whose real-time control importance changes based on the information of the controlled variables.

[0192] Step S603: When there is a second important controlled variable whose real-time control importance changes, calculate the setpoint of the operating variable corresponding to the second important controlled variable according to the self-tuning transfer function;

[0193] Step S604: Adjust the operating variable according to the set value of the operating variable to reduce the real-time control importance of the second important controlled variable;

[0194] The beneficial effects of this embodiment are as follows: During the operation of the low-temperature methanol washing system, the information of each controlled variable in the system can be determined; the real-time control importance of each controlled variable in the system can be determined based on the information of each controlled variable; when there is a second controlled variable whose real-time control importance changes, the operation variable corresponding to the second controlled variable is adjusted through an adaptive transfer function to reduce the real-time control importance of the second controlled variable; thereby realizing adaptive adjustment of each controlled variable in the system, reducing the workload of operators and saving labor costs.

[0195] Best Practice

[0196] The connection method of the low-temperature methanol washing system is as follows Figures 7 to 10 The diagram shows the connection relationship between the four parts: methanol synthesis-absorption tower, nitrogen stripping, regeneration separation, and tail gas absorption. Figure 7 The meanings of the symbols in the text are as follows:

[0197] 701—CO composition entering the methanol synthesis tower; 702—CO2 composition entering the methanol synthesis tower; 703—T1 tower pressure; 704—T1 tower temperature; 705—T1 tower bottom liquid level; 706—T2 tower pressure; 707—T2 tower temperature; 708—T2 tower bottom liquid level; 709—T1 upper tower top outlet valve; 710—T1 upper tower middle outlet valve; 711—T1 upper tower methanol circulation rate; 712—T1 lower tower methanol circulation rate; 713—T1 tower bottom liquid level outlet valve; 714—T2 upper tower top outlet valve; 715—T2 upper tower methanol circulation rate; 716—T2 lower tower methanol circulation rate; 717—T1 tower bottom liquid level outlet valve; 718—T1 load; 719—T2 load.

[0198] Figure 8 The meanings of the symbols in the text are as follows:

[0199] 801—CO2 composition at the top of T3 tower; 802—H2S composition at the top of T3 tower; 803—Liquid level at the bottom of T3 tower; 804—Liquid level at the bottom of T6 tower; 805—Nitrogen flow rate at the bottom of T3 tower; 806—Liquid level outlet valve at the bottom of T3 tower; 807—Nitrogen flow rate in T6 tower; 808—Liquid level outlet valve at the bottom of T6 tower; 809—Load.

[0200] Figure 9 The meanings of the symbols in the text are as follows:

[0201] 901—Temperature in column T4; 902—Liquid level in the top tank of column T4; 903—Liquid level in the bottom tank of column T4; 904—Temperature of the sensitive plate in column T5; 905—Liquid level in the bottom tank of column T5; 906—Steam feed rate to the bottom tank of column T4; 907—Recirculation flow rate in the top tank of column T4; 908—Liquid level outlet valve in column T4; 909—Steam feed rate to the bottom tank of column T5; 910—Liquid level outlet valve in column T5; 911—Load of column T4; 912—Load of column T5.

[0202] Figure 10 The meanings of the symbols in the text are as follows:

[0203] 1001—Methanol content in the tail gas at the top of tower T7; 1002—Bottom liquid level of tower T7; 1003—Desalinated water flow rate of tower T7; 1004—Bottom liquid level outlet valve of tower T7; 1005—Tower load of tower T7.

[0204] The low-temperature methanol washing system of a certain gasification unit needs to remove acidic gases such as CO2, H2S, and COS contained in the raw gas of the gasification process to obtain relatively pure CO and H2. It is necessary to ensure the removal effect of acidic gases and to control the stability of process parameters such as liquid level and temperature of each tower, its auxiliary tanks, and heat exchangers in the system as much as possible to achieve efficient and stable control of the system.

[0205] The low-temperature methanol washing device includes a variable gas absorption tower (hereinafter referred to as T1 tower), an unvariable gas absorption tower (hereinafter referred to as T2 tower), an H2S concentration tower (hereinafter referred to as T3 tower), a thermal regeneration tower (hereinafter referred to as T4 tower), a methanol-water separation tower (hereinafter referred to as T5 tower), a CO2 stripping tower (hereinafter referred to as T6 tower), a tail gas washing tower (hereinafter referred to as T7 tower), auxiliary heat exchangers, and execution components. The execution components are located in towers T1-T7. The methanol synthesis section needs to ensure that the contents of CO, CO2, and H2 are within a certain range. Therefore, it is necessary to control towers T1 and T2 to supply CO, CO2, and H2 to the methanol synthesis section. Other towers and their auxiliary tanks and heat exchangers are to be concentrated, thermally regenerated, separated, and washed according to requirements.

[0206] The process flow of towers T1 and T2 in the low-temperature methanol washing system is as follows: The shift gas from the gasification process is sent to tower T1, where CO2 and sulfur components are removed. The purified gas exiting the tower is cooled and recovered before being supplied to the subsequent PSA hydrogen production section for H2 production. A side stream from the tower is used to adjust the hydrogen-to-carbon ratio of the methanol synthesis gas and is then sent to methanol synthesis.

[0207] The methanol-rich methanol from the bottom of column T1 is cooled by heat exchange in E7 and E22. After initial cooling, the methanol expands and flashes to an intermediate pressure in flash tank V4 through a pressure reducing valve to recover dissolved H2 and CO. The liquid-phase methanol is then depressurized and sent to the upper packing layer of column T3.

[0208] The sulfur-free, rich methanol collected from the bottom of column T1 is partially sent to the lower column for H2S absorption. The remaining portion is cooled via E23, E7, and E21, then passes through a pressure-reducing valve and enters flash tank V3 for expansion and flash evaporation. The liquid sulfur-free, rich methanol exiting from the bottom is further depressurized and sent to the top of column T3, while the gaseous phase enters the top of tank V4.

[0209] Unconverted gas from the gasification process is sent to the low-temperature methanol wash tower T2, where CO2 and sulfur components are removed. The purified gas from tower T2 is sent to the molecular sieve adsorption station to remove trace amounts of CH3OH and CO2 before returning to the low-temperature methanol wash unit to recover cooling energy. Finally, it is sent to the cold box system to produce CO. The methanol rich in H2S and CO2, cooled and depressurized at E27 from the bottom of tower T2, expands and flashes to an intermediate pressure of 1.250 MPa in the methanol flash tank V5, recovering dissolved CO and H2. The flash vapor from V5 and V4 is pressurized by flash compressor C1 and sent to the unconverted feed gas cooler E2 for effective gas recovery. The liquid methanol rich in V5 enters the methanol flash tank V6 for further expansion and mixes with the two streams of methanol rich from the H2S concentration section after heat exchange and heating.

[0210] After data mining and step testing of the system, the monitoring indicators for towers T1 and T2 were determined:

[0211] The external influences of T1 tower include the CO (CV1) and CO2 composition (CV2) entering the synthesis tower from the methanol synthesis section; the controlled parameters include tower pressure (CV3), tower temperature (CV4), and tower bottom liquid level (CV5); the adjustable operating variables include the upper tower outlet valve (MV1), the upper tower middle outlet valve (MV2), the upper tower methanol circulation rate (MV3), the lower tower methanol circulation rate (MV4), and the tower bottom liquid level outlet valve (MV5); the disturbance variable is the load (DV1).

[0212] The external influences of T2 tower include the composition of CO and CO2 entering the synthesis tower from the methanol synthesis section; the controlled parameters include tower pressure (CV6), tower temperature (CV7), and tower bottom liquid level (CV8); the adjustable operating variables include the upper tower outlet valve (MV6), upper tower methanol circulation rate (MV7), lower tower methanol circulation rate (MV8), and tower bottom liquid level outlet valve (MV9); the disturbance variable is the load (DV2).

[0213] The process flow for towers T3 and T6 is as follows: Tower T3, the H2S concentration tower, is divided into upper and lower sections. The sulfur-free, H2S-rich methanol from V3, after depressurization and expansion, is sent to the 33rd tray at the top of tower T3. The flashed gas directly enters the tail gas, while the flash liquid serves as the reflux liquid in the upper part of tower T3, washing away the sulfides produced in the lower tower. The CO2 and H2S-rich methanol from V4, after depressurization and expansion, enters the upper part of the intermediate packing layer in the upper section of tower T3.

[0214] To further desorb CO2 from the methanol solution entering the hydrogen sulfide concentration tower, low-pressure nitrogen gas is introduced at the bottom of the tower to disrupt the original gas-liquid balance, reduce the partial pressure of carbon dioxide and hydrogen sulfide in the gas phase, and further desorb the dissolved CO2. Simultaneously, the desorbed hydrogen sulfide is washed down by the reflux liquid. Above the bottom packing layer of the lower tower, a stream of CO2 exits from the top of the T6 stripping tower and eventually enters the tail gas.

[0215] The monitoring indicators for the T3 tower include the CO2 content at the top of the tower (CV1), the H2S content (CV2), and the bottom liquid level (CV3). The adjustable operating variables include the nitrogen flow rate at the bottom of the tower (MV1) and the bottom liquid level outlet valve (MV2). The disturbance is the load (DV1).

[0216] The monitoring indicators for the T6 tower include the CO2 content at the top of the T3 tower (CV1), the H2S content at the top of the T3 tower (CV2), and the bottom liquid level (CV4). The adjustable operating variables include the nitrogen quantity in the tower (MV3) and the bottom liquid level outlet valve (MV4). The disturbance variable is the load (DV1).

[0217] The process flow of the T4 and T5 towers is as follows: The methanol rich in H2S and COS entering the T4 tower is stripped by methanol vapor from the reboiler E12 at the bottom of the tower and the top of the methanol-water separator, so that CO2, H2S and COS are completely desorbed.

[0218] V53 performs gas-liquid separation, with the liquid phase sent to T5 for distillation. Lean methanol from T4 is cooled in E16 and then enters the top of T5 as reflux to capture moisture. The heating medium in reboiler E15 is low-pressure steam. Methanol vapor from the top of methanol-water separator T5 enters thermal regeneration tower T4. Wastewater from the bottom of the tower is cooled to pressurization in heat exchanger E20 before being discharged into the boundary area.

[0219] The monitoring indicators for the T4 column include the temperature in the T4 column (CV1), the liquid level in the top tank (CV2), and the liquid level in the bottom tank (CV3). The adjustable operating variables include the steam input to the bottom tank (MV1), the reflux flow rate in the top tank (MV2), and the liquid level outlet valve in the bottom tank (MV3). The disturbance variable is the load (DV1).

[0220] The monitoring indicators for the T5 tower include the temperature of the sensitive plate in the tower (CV4), the liquid level in the tower bottom (CV5), the adjustable operating variables include the steam injection rate in the tower bottom (MV4), the liquid level outlet valve in the tower bottom (MV5), and the disturbance variable is the load (DV2).

[0221] The process flow of the T7 tower is as follows: Part of the tail gas from E1, E51, and E2 enters the bottom of the T7 water washing tower, where it is washed with demineralized water from the top to remove entrained methanol. The tail gas is then safely vented from the top. The methanol-containing water at the bottom of the T7 tower is pressurized by a bottom pump, heated by heat exchanger E20, and then enters the methanol / water separator T5 for methanol and water distillation to recover methanol. Before entering E20, 10% NaOH is added to control the pH of the wastewater at the bottom of the T5 tower between 8 and 10. The pressurized liquid from the outlet of P9 / 59 is either sent to a gasification plant to replace PW water.

[0222] The monitoring indicators for the T7 tower include the methanol content in the tail gas at the top of the tower (CV1) and the bottom liquid level (CV2). The adjustable operating variables include the demineralized water (MV1), the bottom liquid level outlet valve (MV2), and the disturbance variable is the load (DV1).

[0223] The steps for determining the setpoints of each controlled variable according to the adaptive control system are as follows: First, acquire the first data of all process parameters of the low-temperature methanol washing system within two operating cycles, including data for different operating durations and system loads; preprocess the first data to obtain normalized historical data; divide the normalized historical data into training, validation, and test sets according to a preset ratio of 3:2:5; first, iteratively train a preset deep learning model, using the training set to train multiple single deep learning models, and use the test set to determine the prediction error; then validate the preset deep learning model based on the validation set to obtain the detection model to be tested; finally, test the model to be tested based on the test set to obtain a controlled variable control model that meets the requirements. Based on the combined deep learning model, automatically read the optimal controlled variable data set setpoints at the current and historical times, and output the optimal controlled variable dataset for the next time step.

[0224] The formula for calculating the setpoint of the controlled variable is as follows:

[0225] CV1 = {t1, F1, X1}

[0226] CV2 = {t2, F2, X2}

[0227] CV3 = {t3, F3, X3}

[0228] CV4 = {t4, F4, X4} ......

[0230] CVn = {tn, Fn, Xn}

[0231] CV = {CV1, CV2, ..., CVn}

[0232] Where: CV1,...CVn are the optimal values ​​of the controlled variable under different runtimes, loads, and maximum returns; CV is the optimal operating curve of a single controlled variable; t is the runtime; F is the system load; and X is the maximum return.

[0233] Based on historical operating data and the transfer function between the disturbance variable, manipulated variable, and controlled variable from step tests, the trend of the controlled variable is predicted according to the transfer function. If the predicted actual value of the controlled variable is within the target range, no adjustment is made. If the predicted actual value of the controlled variable is not within the target range, the setpoint of the manipulated variable is predicted and adjusted according to the transfer function to bring the controlled variable into the target range. The transfer function includes, but is not limited to, zero-order, first-order, and second-order transfer functions.

[0234] The transfer function undergoes self-calibration, with the following steps: When the system is running stably, if a single disturbance variable or manipulated variable changes, the corresponding controlled variable responds, and the system automatically stores the response. If the deviation of the transfer function results obtained from three consecutive responses is less than 5% and the deviation from the previous result is greater than 10%, then the average of the three transfer functions is taken as the new transfer function.

[0235] The calculation method for self-calibration is as follows:

[0236] Let Gy = (G1 + G2 + G3) / 3

[0237] Tauy = (tau1 + tau2 + tau3) / 3

[0238] Dty=(Dt1+Dt2+Dt3) / 3

[0239] If abs(G1-G2)<5%, abs(G1-G3)<5%, abs(G2-G3)<5%, abs(tau1-tau2)<5%, abs(tau1-tau3)<5%, abs(tau2-tau3)<5%, abs(Dt1-Dt2)<5%, abs(Dt1-Dt3)<5%, abs(Dt2-Dt3)<5%, and Gy-G0>10%, tauy-tau0>10%, and Dty-Dt0>10% are all satisfied, then

[0240] Let G=Gy; Tau=Tauy; Dt=Dty

[0241] Where: G0, G1, G2, and G3 are the gains at sampling times 0, 1, 2, and 3, respectively;

[0242] Tau0, tau1, tau2, and tau3 are the time constants for the data collection times at the 0th, 1st, 2nd, and 3rd data collection times, respectively.

[0243] Dt0, Dt1, Dt2, and Dt3 are the lag times for data collection at times 0, 1, 2, and 3, respectively.

[0244] Based on the actual situation, the low-temperature methanol washing adaptive model system is divided into four parts: methanol synthesis-absorption tower adaptive control, stripping nitrogen adaptive control, regeneration separation adaptive control, and tail gas absorption adaptive control. The transfer functions of the four adaptive models at a certain moment are as follows:

[0245] Table 2 shows the transfer function matrix for the adaptive control of the methanol synthesis-absorption tower.

[0246] Table 3. Transfer function matrix for adaptive control of nitrogen stripping;

[0247] Table 4. Transfer function matrix for regeneration separation adaptive control;

[0248] Table 5. Transfer function matrix for exhaust gas absorption adaptive control.

[0249] Table 2

[0250]

[0251]

[0252] Table 3

[0253]

[0254]

[0255] Table 4

[0256]

[0257] Table 5

[0258]

[0259]

[0260] Based on the setpoints of the manipulated variables obtained in the previous steps, if a lower-level control loop exists, the PID control action is determined based on the deviation between the actual value and the setpoint. Specifically, the change in PID action is calculated using the following formula:

[0261]

[0262] Where Pb is the output signal of the PID controller; Kp, Ti, and Td are all adjustment parameters obtained by the step response method; e(t) is the deviation between the actual value and the set value of the PID function block in the lower control loop; and t is time.

[0263] If there is no underlying control loop, the operation variable is directly adjusted to the set value.

[0264] For example, if the setpoint of the liquid phase withdrawal valve at the bottom of the T3 tower calculated by the model changes from 54.123% to 53.334% at a certain moment, then the valve setpoint should be directly adjusted to 73.334%.

[0265] like Figure 11 The diagram shown is a hardware structure schematic of an electronic device according to the present invention, comprising:

[0266] At least one processor 1102; and,

[0267] Memory 1104 communicatively connected to the at least one processor; wherein,

[0268] The memory stores instructions that can be executed by the processor, and the instructions are executed by the at least one processor to implement the adaptive adjustment method for the low-temperature methanol washing system described in any of the above embodiments.

[0269] Reference Figure 11 The low-temperature methanol washing system adaptive adjustment system 1100 may include one or more of the following components: processing component 1102, memory 1104, power supply component 1106, multimedia component 1108, audio component 1110, input / output (Z / O) interface 1112, sensor component 1114, and communication component 1116.

[0270] Processing component 1102 typically controls the overall operation of the cryogenic methanol washing system adaptive adjustment system 1100. Processing component 1102 may include one or more processors 1118 to execute instructions to complete all or part of the steps of the method described above. Furthermore, processing component 1102 may include one or more modules to facilitate interaction between processing component 1102 and other components. For example, processing component 1102 may include a multimedia module to facilitate interaction between multimedia component 1108 and processing component 1102.

[0271] Memory 1104 is configured to store various types of data to support the operation of the cryogenic methanol washing system adaptive regulation system 1100. Examples of this data include instructions for any application or method operating on the cryogenic methanol washing system adaptive regulation system 1100, such as text, images, videos, etc. Memory 1104 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0272] Power supply component 1106 provides power to various components of the cryogenic methanol wash system adaptive regulation system 1100. Power supply component 1106 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the cryogenic methanol wash system adaptive regulation system 1100.

[0273] The multimedia component 1108 includes a screen that provides an output interface between the cryogenic methanol washing system adaptive adjustment system 1100 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of touch or swipe actions but also the duration and pressure associated with the touch or swipe operation. In some embodiments, the multimedia component 1108 may also include a front-facing camera and / or a rear-facing camera. When the cryogenic methanol washing system adaptive adjustment system 1100 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0274] Audio component 1110 is configured to output and / or input audio signals. For example, audio component 1110 includes a microphone (MZC) configured to receive external audio signals when the cryogenic methanol wash system adaptive adjustment system 1100 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 1104 or transmitted via communication component 1116. In some embodiments, audio component 1110 also includes a speaker for outputting audio signals.

[0275] Z / O interface 1112 provides an interface between processing component 1102 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0276] Sensor assembly 1114 includes one or more sensors for providing status assessments of various aspects of the cryogenic methanol wash system adaptive adjustment system 1100. For example, sensor assembly 1114 may include a sound sensor. Additionally, sensor assembly 1114 can detect the on / off state of the cryogenic methanol wash system adaptive adjustment system 1100, the relative positioning of components (e.g., the display and keypad of the cryogenic methanol wash system adaptive adjustment system 1100), changes in the position of the cryogenic methanol wash system adaptive adjustment system 1100 or one of its components, the presence or absence of user contact with the cryogenic methanol wash system adaptive adjustment system 1100, the orientation or acceleration / deceleration of the cryogenic methanol wash system adaptive adjustment system 1100, and temperature changes of the cryogenic methanol wash system adaptive adjustment system 1100. Sensor assembly 1114 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 1114 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 1114 may further include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.

[0277] Communication component 1116 is configured to enable the cryogenic methanol washing system adaptive regulation system 1100 to provide wired or wireless communication capabilities with other devices and cloud platforms. The cryogenic methanol washing system adaptive regulation system 1100 can access wireless networks based on communication standards, such as WZFZ, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 1116 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 1116 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFZD) technology, infrared data association (ZrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0278] In an exemplary embodiment, the cryogenic methanol washing system adaptive adjustment system 1100 may be implemented by one or more application-specific integrated circuits (ASZC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field-programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described cryogenic methanol washing system adaptive adjustment method.

[0279] This application also provides a computer-readable storage medium, which, when the instructions in the storage medium are executed by the processor corresponding to the low-temperature methanol washing system adaptive adjustment system, enables the low-temperature methanol washing system adaptive adjustment system to implement the low-temperature methanol washing system adaptive adjustment method described in any of the above embodiments.

[0280] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0281] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0282] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0283] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0284] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

[0285] This invention identifies key controlled variables and their corresponding manipulated and disturbing variables within a low-temperature methanol washing system during operation. These controlled variables change due to system influences, including but not limited to changes caused by load variations or other disturbances. The system determines whether a first controlled variable exceeds a target range. If so, the corresponding manipulated variable is adjusted to bring it within the target range. Furthermore, during system operation, key controlled variables and their corresponding manipulated and disturbing variables are identified. If a second controlled variable exceeds a target range, the corresponding manipulated variable is adjusted to bring it within the target range. This achieves adaptive adjustment of the low-temperature methanol washing system, reducing operator workload, minimizing process parameter fluctuations, and saving energy.

[0286] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. An adaptive adjustment method for a low-temperature methanol washing system, characterized in that, include: Obtain the process parameters of the low-temperature methanol washing system, wherein the process parameters include at least: controlled variables and operating variables; Determine the set value of the controlled variable; The target range is determined based on the set value of the controlled variable; Determine the real-time control importance of the controlled variable, and designate the controlled variable whose real-time control importance exceeds a preset importance threshold as an important controlled variable; When the important controlled variable exceeds the target range, determine the set value of the operation variable corresponding to the important controlled variable; Adjust the important controlled variable to the target range according to the set value of the operation variable; Determining the real-time control importance of the controlled variable specifically includes: Determine the first importance of the controlled variable; Determine the second importance of the controlled variable; The real-time control importance of the controlled variable is determined based on its first importance and second importance as follows: Where Z is the real-time control importance of the controlled variable, Zm is the first importance of the controlled variable, Zn is the second importance of the controlled variable, w1 is the first weight, and w2 is the second weight; Determining the first importance of the controlled variable specifically includes: Obtain the actual value of the controlled variable, the maximum and minimum values ​​of the target interval, and determine the tolerance value of the controlled variable: If Cr > (Cmax + Cmin) / K1, then Cr = C - Cmax. If Cr≤(Cmax+Cmin) / K1, then Cr=C-Cmin; Determine the tolerance of the controlled variable: ; Determine the first importance of the controlled variable: When Cmax > C > Cmin, and t ≥ t1 or t ≤ t2, Zm = A1. When Cmax > C > Cmin, and t1 < t ≤ t3, Zm = 1 / t. When Cmax > C > Cmin and t > t3, Zm = A2. When C≥Cmax, Zm=A3+(C-Cmax) / (Cmax-Cmin)*A4. When C≤Cmin, Zm=A3+(Cmin-C) / (Cmax-Cmin)*A4. Where C is the actual value of the controlled variable, Cr is the tolerance value of the controlled variable, Cmax is the maximum value of the target interval, Cmin is the minimum value of the target interval, t is the tolerance of the controlled variable, Zm is the importance of the controlled variable, K1 is the first parameter, A1 is the second parameter, A2 is the third parameter, A3 is the fourth parameter, A4 is the fifth parameter, t1 is the sixth parameter, t2 is the seventh parameter, and t3 is the eighth parameter.

2. The adaptive adjustment method for the low-temperature methanol washing system according to claim 1, characterized in that, The method further includes: Obtain all process parameters of the low-temperature methanol washing system within the set historical operating cycle; The historical data of the low-temperature methanol washing system were obtained by normalizing all the process parameters. The optimal operating curves of each controlled variable are obtained by simulating the historical data for all time periods within the historical operating cycle. Determining the set value of the controlled variable specifically includes: The production process is divided into multiple production periods, and the value of the controlled variable corresponding to each production period in the optimal operating curve is used as the set value of the controlled variable corresponding to the production period.

3. The adaptive adjustment method for the low-temperature methanol washing system according to claim 1, characterized in that, Determining the target interval based on the set value of the controlled variable specifically includes: The target range of the controlled variable is obtained by adding the set value to the set tolerance deviation.

4. The adaptive adjustment method for the low-temperature methanol washing system according to claim 1, characterized in that, Determining the second importance of the controlled variable specifically includes: Determine the deviation and rate of change of the controlled variable, wherein: The deviation of the controlled variable is defined as: ， The rate of change of the controlled variable is defined as: ； Where C is the actual value of the controlled variable. Here, eL is the setpoint of the controlled variable, eL is the deviation of the controlled variable, eCL is the rate of change of the controlled variable, Cmax is the maximum value of the target interval, and Cmin is the minimum value of the target interval. The second importance of the controlled variable is determined based on the deviation and rate of change of the controlled variable.

5. The adaptive adjustment method for the low-temperature methanol washing system according to claim 4, characterized in that, The determination of the second importance of the controlled variable based on its deviation and rate of change specifically includes: Obtain a first fuzzy set of the natural language domain corresponding to the deviation of the controlled variable and a second fuzzy set of the natural language domain corresponding to the rate of change of the controlled variable. The first fuzzy set includes multiple first subset elements, and the second fuzzy set includes multiple second subset elements. Obtain the correspondence table between the elements of the first subset and the elements of the second subset for the second importance level; The first subset element corresponding to the real value of the deviation of the controlled variable is determined as the first subset element to be checked, and the second subset element corresponding to the real value of the rate of change of the controlled variable is determined as the second subset element to be checked. The control importance of the first subset element to be checked and the second subset element to be checked are obtained from the correspondence table and used as the second importance of the controlled variable.

6. The adaptive adjustment method for the low-temperature methanol washing system according to claim 1, characterized in that, The determination of the set value of the operational variable corresponding to the important controlled variable specifically includes: Select one or more of the operation variables from the process parameters as the operation variables to be adjusted; The important controlled variable is predicted based on the transfer function of its response to the variable to be adjusted. If the predicted value of the important controlled variable is within the target range, the adjustment of the variable to be adjusted is stopped, and the current value of the variable to be adjusted is used as the set value of the variable to be adjusted. If the predicted value of the important controlled variable is not within the target range, the variable to be adjusted is adjusted again.

7. The adaptive adjustment method for the low-temperature methanol washing system according to claim 6, characterized in that, The transfer function is: Wherein CV is the important controlled variable, MV is the variable to be adjusted, Ge is the gain, tau is the time constant, and Dt is the lag time.

8. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to at least one of the processors; wherein, The memory stores instructions executable by at least one of the processors, which enable the at least one processor to perform the adaptive adjustment method for the low-temperature methanol washing system as described in any one of claims 1 to 7.

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