A Methanol Distillation System Intelligent Control Method and Device

Abstract

The present invention provides a method and device for intelligent control of a methanol distillation system, wherein the method for controlling a methanol distillation system comprises the following steps: Step 1: constructing a pressure-temperature correction two-dimensional matrix, wherein the pressure-temperature correction two-dimensional matrix is ​​a two-dimensional matrix of a*b, and the elements in the pressure-temperature correction two-dimensional matrix are c de , d=1, 2...a, e=1, 2...b; Step 2: Obtain the pressure value f and the pressure value change g per unit time, and calculate the first activation coefficient f' and the second activation coefficient g'; Step 3: Calculate the temperature correction value h1; Step 4: Obtain the temperature detection value h2, calculate the temperature calculation value h, and the calculation formula of h is as follows: h=h1+h2. The intelligent control method of the present invention has the beneficial effects of high precision and easy programming implementation.

Description

Technical Field

[0001] The present invention relates to the technical field of methanol distillation, and in particular to an intelligent control method and device for a methanol distillation system. Background Art

[0002] Distillation is a process of separating mixtures by partial vaporization and partial condensation at the same temperature to obtain purer components based on the different volatilities of different components in the same liquid mixture. The methanol distillation process is a complex control process with multiple variables and strong coupling, especially for the pressurized / normal pressure two-tower operation. Currently, the device is mainly based on open-loop manual control.

[0003] The patent (CN112295255B) proposes an intelligent control system and control method for a methanol distillation device to achieve dynamic optimization of the device. As long as the process allows and the control loops of the measurement and control instruments involved are intact, they can be put into full automatic operation, which can reduce the labor intensity of operators and achieve quasi-unmanned operation of the device.

[0004] However, based on this patent solution, the methanol distillation system still has the following problems: the large changes in pressure in the pressure tower cause the equilibrium temperature of the material system to change significantly, and the temperature measurement value is affected by the pressure change more than the change in the composition of the material system, resulting in the measured temperature value being unable to directly reflect the composition change.

[0005] In view of this, the present invention is proposed. Summary of the invention

[0006] The purpose of the present invention is to provide an intelligent control method for a methanol distillation system, which is used for correcting the temperature in the methanol distillation system. The intelligent control method of the present invention has the beneficial effects of high precision and easy programming. Based on this, the present invention also provides an intelligent control device for a methanol distillation system.

[0007] In order to solve the above technical problems, the present invention provides an intelligent control method for a methanol distillation system, comprising the following steps:

[0008] Step 1: Construct a pressure-temperature correction two-dimensional matrix. The pressure-temperature correction two-dimensional matrix is ​​a two-dimensional matrix of a*b. The element in the pressure-temperature correction two-dimensional matrix is ​​c de , d=1, 2...a, e=1, 2...b;

[0009] Step 2: Obtain the pressure value f and the change g of the pressure value per unit time, and calculate the first activation coefficient f' and the second activation coefficient g';

[0010] Step 3: Calculate the temperature correction value h 1 , and its calculation formula is as follows:

[0011]

[0012] Step 4: Get the temperature detection value h 2 , calculate the temperature value h, the calculation formula of h is as follows:

[0013] .

[0014] Furthermore, constructing the pressure-temperature correction two-dimensional matrix in step 1 includes the following steps:

[0015] Step 1.1: Construct an initial pressure-temperature correction two-dimensional matrix, in which all elements are 0;

[0016] Step 1.2: Set the count value l=0;

[0017] Step 1.3: Get k sets of historical data, each set of historical data includes pressure value f, pressure value change per unit time g and standard value h for temperature correction 4 ;

[0018] Step 1.4: Randomly select 7 / 10 groups of data from the k groups of historical data as the first data set, and the remaining data as the second data set;

[0019] Step 1.5: Take the data in the first data set and calculate the temperature correction value h according to steps 2 and 3 3 ;

[0020] Step 1.6: Calculate h 3 With h 4 The difference k 1 ;

[0021] Step 1.7: Determine k 1 Is it greater than a preset first difference threshold? If so, execute step 1.8; if not, execute step 1.9;

[0022] Step 1.8: Let c ij =c ij +k 1 / 9;

[0023] Step 1.9: Determine whether all the data in the first data set have executed steps 1.5 to 1.7. If yes, execute step 1.10. If not, return to step 1.5.

[0024] Step 1.10: Output the intermediate pressure-temperature correction two-dimensional matrix.

[0025] Furthermore, constructing the pressure-temperature correction two-dimensional matrix in step 1 also includes the following steps:

[0026] Step 1.11: Calculate k when executing steps 1.5 to 1.7 for all data in the first data set 1 The sum of k 2 ;

[0027] Step 1.12: Determine the k calculated in step 1.11 2 Is it greater than a preset second difference threshold? If so, return to step 1.5; if not, execute step 1.13;

[0028] Step 1.13: Take the data in the second data set and calculate the temperature correction value h according to steps 2 and 3 3 ;

[0029] Step 1.14: Calculate h 3 With h 4 The difference k 1 ;

[0030] Step 1.15: Determine whether all the data in the second data set have completed step 1.13 and step 1.14. If yes, execute step 1.16. If not, return to step 1.13.

[0031] Step 1.16: Calculate k when executing steps 1.13 and 1.14 for all data in the second data set 1 The sum of k 2 , let l=l+1.

[0032] Furthermore, constructing the pressure-temperature correction two-dimensional matrix in step 1 also includes the following steps:

[0033] Step 1.17: Determine the k calculated in step 1.16 2 Is it greater than a preset second difference threshold? If so, execute step 1.18; if not, execute step 1.19;

[0034] Step 1.18: Output the pressure-temperature correction two-dimensional matrix;

[0035] Step 1.19: Determine whether l is greater than a preset calculation threshold, if so, return to step 1.3, if not, execute step 1.20;

[0036] Step 1.20: Let a=a+a 1 , b=b+b 1 , and return to step 1.2.

[0037] Further, the calculation of the first activation coefficient f' in step 2 includes the following steps:

[0038] Step 2.1: Get the value range of f and divide it into a parts, and get a value intervals arranged in sequence;

[0039] Step 2.2: Get f' according to the interval to which f belongs.

[0040] Further, the calculation of the second activation coefficient g' in step 2 includes the following steps:

[0041] Step 2.3: Get the value range of g, and divide it into b equal parts, and get b value intervals arranged in sequence;

[0042] Step 2.4: Get g' according to the interval to which g belongs.

[0043] Further, through steps 1 to 4, the temperature calculation values ​​h of the pre-tower bottom temperature, the pre-tower middle temperature, the pre-tower top temperature, the pre-tower reflux tank temperature, the pressure tower bottom temperature, the pressure tower top temperature, the atmospheric tower bottom temperature, the atmospheric tower top temperature, the recovery tower bottom temperature, the recovery tower middle temperature and the recovery tower top temperature are respectively calculated.

[0044] The present invention also provides an intelligent control device for a methanol distillation system, comprising: a memory, a processor, and an intelligent control program for a methanol distillation system stored in the memory and executable on the processor. When the intelligent control program for a methanol distillation system is executed by the processor, the steps of any one of the intelligent control methods for a methanol distillation system provided by the present invention are implemented.

[0045] The present invention provides an intelligent control method for a methanol distillation system, which has the following beneficial effects: a temperature correction value h is calculated according to a pressure value f and a change g of the pressure value per unit time. 1 , used for temperature detection value h 2 correction, reducing the impact of pressure changes on temperature detection; in addition, according to different pressure values ​​f and different changes in pressure values ​​per unit time g, the sum of different elements in the pressure-temperature correction two-dimensional matrix is ​​calculated. Therefore, the method of this implementation also has the beneficial effects of high accuracy and easy programming implementation. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 The figure is a flow chart of an intelligent control method for a methanol distillation system. DETAILED DESCRIPTION

[0047] The core of the invention is to provide an intelligent control method for a methanol distillation system, which is used for correcting the temperature in the methanol distillation system.

[0048] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention is clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] Embodiment 1:

[0050] A method for intelligent control of a methanol distillation system, such as Figure 1 As shown, the following steps are included:

[0051] Step 1: Construct a pressure-temperature correction two-dimensional matrix. The pressure-temperature correction two-dimensional matrix is ​​a two-dimensional matrix of a*b. The element in the pressure-temperature correction two-dimensional matrix is ​​c de , d=1, 2...a, e=1, 2...b.

[0052] In this embodiment, the size of the pressure-temperature correction two-dimensional matrix, that is, the values ​​of a and b, are set based on experience or automatically adjusted according to a training process.

[0053] In a specific example, when a=6, b=6, the pressure-temperature correction two-dimensional matrix is ​​as follows:

[0054]

[0055] c of each element in the matrix de The values ​​are obtained through training.

[0056] Step 2: Obtain the pressure value f and the change g of the pressure value per unit time, and calculate the first activation coefficient f' and the second activation coefficient g'.

[0057] In this embodiment, pressure refers to the pressure corresponding to the temperature to be corrected, which is detected by a pressure sensor, and the unit time can be set according to actual needs, such as 1 second.

[0058] Step 3: Calculate the temperature correction value h 1 , and its calculation formula is as follows:

[0059]

[0060] In this embodiment, f'=1, 2...a, g'=1, 2...b.

[0061] In a specific example, f'=2, g'=2, then c 11 、c 12 、c 13 、c 21 、c22 、c 23 、c 31 、c 32 、c 33 The values ​​of are added.

[0062] When f'=1, g'=1, f'=a, g'=b occurs, the excess elements are not calculated, such as f'=1, g'=1, then C 11 , C 12 , C 21 , C 22 The values ​​of are added.

[0063] Step 4: Get the temperature detection value h 2 , calculate the temperature value h, the calculation formula of h is as follows:

[0064] .

[0065] In this embodiment, the temperature detection value h 2 Detected by temperature sensor.

[0066] In summary, in this embodiment, the temperature correction value h is calculated based on the pressure value f and the pressure value change g per unit time. 1 , used for temperature detection value h 2 The correction reduces the impact of pressure changes on temperature detection.

[0067] In addition, in this embodiment, the sum of different elements in the pressure-temperature correction two-dimensional matrix is ​​calculated according to different pressure values ​​f and different changes in pressure values ​​per unit time g. Therefore, the method of this implementation also has the beneficial effects of high precision and easy programming implementation.

[0068] In an optional embodiment, constructing the pressure-temperature correction two-dimensional matrix in step 1 includes the following steps:

[0069] Step 1.1: Construct an initial pressure-temperature correction two-dimensional matrix, in which all elements are 0.

[0070] In this optional embodiment, the initial values ​​of a and b can be set as needed, such as a=5, b=5.

[0071] Step 1.2: At the same time, set the count value l=0.

[0072] In this optional embodiment, l is used to record the number of training times.

[0073] Step 1.3: Get k sets of historical data, each set of historical data includes pressure value f, pressure value change per unit time g and standard value h for temperature correction 4 .

[0074] In this optional embodiment, k groups of historical data can be obtained experimentally in a laboratory environment, or based on experience; the number k can be set flexibly, such as 100 groups; the k groups of historical data should cover the range of pressure values ​​f and the range of pressure value changes g per unit time as much as possible.

[0075] Step 1.4: Randomly select 7 / 10 groups of data from the k groups of historical data as the first data set, and the remaining data as the second data set.

[0076] In this optional embodiment, in a training, the data in the first data set is used to train the values ​​of the elements in the initial pressure-temperature correction two-dimensional matrix, and the data in the second data set is used to verify the results of the training.

[0077] Step 1.5: Take the data in the first data set and calculate the temperature correction value h according to steps 2 and 3 3 .

[0078] Step 1.6: Calculate h 3 With h 4 The difference k 1 .

[0079] Step 1.7: Determine k 1 Is it greater than a preset first difference threshold? If so, execute step 1.8; if not, execute step 1.9.

[0080] In this optional embodiment, the size of the first difference threshold can be set to a specific value as needed.

[0081] Step 1.8: Let c ij =c ij +k 1 / 9.

[0082] Step 1.9: Determine whether all data in the first data set have executed steps 1.5 to 1.7. If yes, execute step 1.10; if no, return to step 1.5.

[0083] Step 1.10: Output the intermediate pressure-temperature correction two-dimensional matrix.

[0084] In this optional embodiment, in this optional embodiment, steps 1.5 to 1.10 are a set training in a training, wherein steps 1.5 to 1.8 are a data training in a set training, and data training refers to training one data in the first data set once. In a data training process, if k 1 is greater than the preset first difference threshold, then the c used in the data training process is ijThe correction is made, and the set training refers to that all the data in the first data are trained once in sequence.

[0085] In an optional embodiment, constructing the pressure-temperature correction two-dimensional matrix in step 1 further includes the following steps:

[0086] Step 1.11: Calculate k when executing steps 1.5 to 1.7 for all data in the first data set 1 The sum of k 2 .

[0087] In this optional embodiment, the size of the second difference threshold can be set to a specific value as needed.

[0088] Step 1.12: Determine the k calculated in step 1.11 2 Is it greater than a preset second difference threshold? If so, return to step 1.5; if not, execute step 1.13.

[0089] In this optional embodiment, steps 1.11 to 1.12 are used to determine whether a training session can be completed. If the completion conditions are met, verification is performed, i.e., a training session is completed. If the completion conditions are not met, collective training is performed again, i.e., a training session consists of at least one collective training session.

[0090] Step 1.13: Take the data in the second data set and calculate the temperature correction value h according to steps 2 and 3 3 .

[0091] Step 1.14: Calculate h 3 With h 4 The difference k 1 .

[0092] Step 1.15: Determine whether all the data in the second data set have completed steps 1.13 and 1.14. If yes, execute step 1.16; otherwise, return to step 1.13.

[0093] Step 1.16: Calculate k when executing steps 1.13 and 1.14 for all data in the second data set 1 The sum of k 2 , let l=l+1.

[0094] In this optional embodiment, steps 1.13 to 1.16 are used to verify the result of the training and record the total number of training times through l.

[0095] In an optional embodiment, constructing the pressure-temperature correction two-dimensional matrix in step 1 further includes the following steps:

[0096] Step 1.17: Determine the k calculated in step 1.162 Is it greater than a preset second difference threshold? If so, execute step 1.18; if not, execute step 1.19.

[0097] In this optional embodiment, k calculated by step 1.16 2 Whether it is greater than a preset second difference threshold value determines whether the training can be ended to output a pressure-temperature corrected two-dimensional matrix.

[0098] Step 1.18: Output the pressure-temperature corrected two-dimensional matrix.

[0099] Step 1.19: Determine whether l is greater than a preset calculation threshold. If so, return to step 1.3; otherwise, execute step 1.20.

[0100] Step 1.20: Let a=a+a 1 , b=b+b 1 , and return to step 1.2.

[0101] In this optional embodiment, if the conditions for ending training are not met according to the judgment in step 1.17, first determine whether the number of training times (i.e., I) is greater than the preset calculation threshold. If so, it means that the historical data may be incomplete. At this time, re-acquire k groups of historical data for training. If not (i.e., the total number of training times is large), it means that the current matrix size cannot meet the calculation. At this time, increase the size of the matrix and retrain (and recalculate the number of training times) until the conditions for ending training are met.

[0102] In this alternative embodiment, a 1 and b 1 They can be set as needed, such as both are equal to 5.

[0103] In an optional embodiment, calculating the first activation coefficient f' in step 2 includes the following steps:

[0104] Step 2.1: Get the value range of f and divide it into a parts, and get a value intervals arranged in sequence;

[0105] Step 2.2: Get f' according to the interval to which f belongs.

[0106] In a specific embodiment, assuming that the value range of f is 0≤f≤1, a=5, it is divided into 5 intervals, namely [0, 0.2), [0.2, 0.4), [0.4, 0.6), [0.6, 0.8), [0.8, 1]. When f=0.1, f'=1.

[0107] In an optional embodiment, calculating the second activation coefficient g' in step 2 includes the following steps:

[0108] Step 2.3: Get the value range of g, and divide it into b equal parts, and get b value intervals arranged in sequence;

[0109] Step 2.4: Get g' according to the interval to which g belongs.

[0110] In an optional embodiment, the temperature calculated values ​​h of the pre-tower bottom temperature, the pre-tower middle temperature, the pre-tower top temperature, the pre-tower reflux tank temperature, the pressure tower bottom temperature, the pressure tower top temperature, the atmospheric pressure tower bottom temperature, the atmospheric pressure tower top temperature, the recovery tower bottom temperature, the recovery tower middle temperature and the recovery tower top temperature are respectively calculated through steps 1 to 4.

[0111] Embodiment 2:

[0112] A methanol distillation system intelligent control device comprises: a memory, a processor and a methanol distillation system intelligent control program stored in the memory and executable on the processor, wherein the methanol distillation system intelligent control program, when executed by the processor, implements the steps of any one of the methanol distillation system intelligent control methods provided in Example 1.

[0113] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for intelligent control of a methanol distillation system, characterized in that: The steps include: Step 1: Construct a pressure-temperature correction two-dimensional matrix. The pressure-temperature correction two-dimensional matrix is ​​a two-dimensional matrix of a*b. The element in the pressure-temperature correction two-dimensional matrix is ​​c de , d=1, 2...a, e=1, 2...b; Step 2: Obtain the pressure value f and the change g of the pressure value per unit time, and calculate the first activation coefficient f' and the second activation coefficient g'; Step 3: Calculate the temperature correction value h1, the calculation formula is as follows: Step 4: Get the temperature detection value h2 and calculate the temperature calculation value h. The calculation formula of h is as follows: The construction of the pressure-temperature correction two-dimensional matrix in step 1 includes the following steps: Step 1.1: Construct an initial pressure-temperature correction two-dimensional matrix, in which all elements are 0; Step 1.2: Let the count value l = 0, l is used to record the number of training times; Step 1.3: Obtain k groups of historical data, each group of historical data includes a pressure value f, a change in the pressure value per unit time g, and a standard value h4 for temperature correction; Step 1.4: Randomly select 7 / 10 groups of data from the k groups of historical data as the first data set, and the remaining data as the second data set; Step 1.5: Take the data in the first data set and calculate the temperature correction value h3 according to steps 2 and 3; Step 1.6: Calculate the difference k1 between h3 and h4; Step 1.7: Determine whether k1 is greater than a preset first difference threshold, if yes, execute step 1.8, if no, execute step 1.9; Step 1.8: Let c ij =c ij +k1 / 9; Step 1.9: Determine whether all the data in the first data set have executed steps 1.5 to 1.

7. If yes, execute step 1.

10. If not, return to step 1.

5. Step 1.10: Output the intermediate pressure-temperature correction two-dimensional matrix; Step 1.11: Calculate the sum k2 of k1 when executing steps 1.5 to 1.7 for all data in the first data set; Step 1.12: Determine whether k2 calculated in step 1.11 is greater than a preset second difference threshold, if so, return to step 1.5, if not, execute step 1.13; Step 1.13: Take the data in the second data set, and calculate the calculated value h3 for temperature correction according to steps 2 and 3; Step 1.14: Calculate the difference k1 between h3 and h4; Step 1.15: Determine whether all the data in the second data set have completed step 1.13 and step 1.

14. If yes, execute step 1.

16. If not, return to step 1.

13. Step 1.16: Calculate the sum k2 of k1 when executing steps 1.13 and 1.14 for all data in the second data set, and let l=l+1; Step 1.17: Determine whether k2 calculated in step 1.16 is greater than a preset second difference threshold, if yes, execute step 1.18, if no, execute step 1.19; Step 1.18: Output the pressure-temperature correction two-dimensional matrix; Step 1.19: Determine whether l is greater than a preset calculation threshold, if so, return to step 1.3, if not, execute step 1.20; Step 1.20: Let a=a+a1, b=b+b1, and return to Step 1.

2.

2. According to the intelligent control method of a methanol distillation system according to claim 1, it is characterized in that: Calculating the first activation coefficient f' in step 2 includes the following steps: Step 2.1: Get the value range of f and divide it into a parts, and get a value intervals arranged in sequence; Step 2.2: Get f' according to the interval to which f belongs.

3. According to the intelligent control method of a methanol distillation system according to claim 1, it is characterized in that: Calculating the second activation coefficient g' in step 2 includes the following steps: Step 2.3: Get the value range of g, and divide it into b equal parts, and get b value intervals arranged in sequence; Step 2.4: Get g' according to the interval to which g belongs.

4. The intelligent control method for a methanol distillation system according to claim 1, characterized in that: Through steps 1 to 4, the temperature calculated values ​​h of the pre-tower bottom temperature, the pre-tower middle temperature, the pre-tower top temperature, the pre-tower reflux tank temperature, the pressure tower bottom temperature, the pressure tower top temperature, the atmospheric tower bottom temperature, the atmospheric tower top temperature, the recovery tower bottom temperature, the recovery tower middle temperature and the recovery tower top temperature are respectively calculated.

5. An intelligent control device for a methanol distillation system, characterized in that: include: A memory, a processor, and a methanol distillation system intelligent control program stored in the memory and executable on the processor, wherein the methanol distillation system intelligent control program, when executed by the processor, implements the steps of the methanol distillation system intelligent control method according to any one of claims 1 to 4.

Images