A method for de-benzene rich oil temperature correction

Abstract

This invention discloses a method for correcting and adjusting the rich oil temperature in benzene removal, relating to the field of coking coal gas refining technology. The method includes: setting a target temperature range for the rich oil entering the benzene removal tower; collecting data on multiple process parameters related to the rich oil temperature; and correcting and adjusting the rich oil temperature based on these multiple process parameters using a prediction model or priority rules. This invention solves the problem of correcting and adjusting the rich oil temperature entering the benzene removal tower for the purpose of producing light benzene. It establishes a multi-parameter rapid optimization adjustment technique aimed at normal fluctuations in the rich oil temperature, avoiding the problems of lag, poor stability, and slow adjustment associated with single-parameter adjustments.

Description

Technical Field

[0001] This invention relates to the field of coking coal gas refining technology, specifically to a benzene removal distillation process for the purpose of producing light benzene, which is based on multivariate prediction and priority adjustment for benzene-rich oil temperature correction and callback. Background Technology

[0002] Light benzene is the first fraction obtained after preliminary fractionation of crude benzene from coking plants. It is a key intermediate product in the production of pure aromatics such as benzene, toluene, and xylene. In the crude benzene recovery process from coking plants, the rich oil (wash oil after absorbing benzene hydrocarbons) is heated and then enters a benzene removal tower for desorption distillation. When the main product is light benzene, the process control requirements are even more stringent.

[0003] The temperature at which the rich oil enters the benzene stripping tower is the most critical control parameter of this process: excessively high temperatures will lead to an increase in the content of heavy components in the product, exacerbating the deterioration of the wash oil; excessively low temperatures will result in incomplete removal of light benzene and a decrease in recovery rate. At the same time, fluctuations in the temperature of the rich oil will disrupt the vapor-liquid balance within the tower, leading to instability in the quality of the light benzene product (such as distillation range and dry point).

[0004] Existing technologies mostly employ a single PID controller for setpoint control, with the setpoint fixed based on experience. This method cannot effectively handle disturbances caused by changes in rich oil flow and composition, resulting in control lag, large fluctuations, and frequent exceedances of light benzene quality indicators. To maintain yield, companies often use excessively high temperature settings, leading to energy waste and washing oil losses. Summary of the Invention

[0005] This invention aims to solve the problems of lag, large fluctuations, and slow correction in the rich oil inlet temperature control in existing benzene removal processes for the purpose of producing light benzene, and provides a fast and stable rich oil temperature correction and correction method based on multi-parameter modeling and priority adjustment.

[0006] This invention provides a method for correcting and adjusting the temperature of benzene-rich oil during debenzene removal, comprising: Set the target temperature range for the rich oil entering the benzene removal tower; Collect data on multiple process parameters related to oil-rich temperature; Based on the aforementioned multiple process parameters, the oil-rich temperature is corrected and reverted using a prediction model or priority rules.

[0007] In some instances, the target temperature range is [179°C, 183°C].

[0008] In some instances, the multiple process parameters include: the flow rate into the rich oil heater, the medium-pressure steam temperature, the medium-pressure steam pressure, the direct steam flow rate into the regenerator, the bottom temperature of the benzene removal tower, and the rich oil temperature after the first-stage lean and rich oil heat exchanger.

[0009] In some instances, correction callbacks for oil-rich temperatures are performed using predictive models, including: A multiple regression prediction model was established with rich oil temperature as the dependent variable and the flow rate into the rich oil heater, medium-pressure steam temperature, medium-pressure steam pressure, direct steam flow rate into the regenerator, and bottom temperature of the benzene removal tower as independent variables. Based on the regression coefficients of the prediction model, the independent variable that has the greatest impact on oil-rich temperature is determined as the first adjustment factor. Adjust the first adjustment element to a preset normal fluctuation range; If the oil-rich temperature returns to the target temperature range, the adjustment ends; otherwise, based on the updated data, a new prediction model is established, and the process returns to the step of determining the independent variable with the greatest impact on the oil-rich temperature as the first adjustment element based on the regression coefficient of the prediction model, until the oil-rich temperature reaches the target.

[0010] In some instances, the prediction model is established based on historical data excluding maintenance periods and abnormal operating conditions, and the data collection range of each variable covers the extreme value range of each variable. Specifically, the extreme value range of the bottom temperature of the benzene removal tower is 110℃~190℃, the extreme value range of the flow rate into the rich oil heater is 220m³ / h~340m³ / h, the extreme value range of the steam flow rate into the rich oil heater is 10t / h~25t / h, the extreme value range of the medium-pressure steam pressure is 0.8MPa~2.5MPa, and the extreme value range of the medium-pressure steam temperature is 250℃~450℃.

[0011] In some instances, the normal fluctuation range of the first regulating element is as follows: the extreme range of the bottom temperature of the benzene removal tower is 170℃~178℃, the extreme range of the flow rate into the rich oil heater is 280m³ / h~320m³ / h, the extreme range of the steam flow rate into the rich oil heater is 13t / h~17t / h, the extreme range of the medium-pressure steam pressure is 1.2MPa~1.6MPa, and the extreme range of the medium-pressure steam temperature is 340℃~380℃.

[0012] In some instances, priority rules are used to correct for rich oil temperatures via callbacks, including: Set the normal fluctuation range for multiple process parameters; Real-time monitoring of various process parameters; when the measured values ​​of at least three parameters deviate from their normal range by more than a preset threshold, a pre-adjustment mechanism is triggered. According to the preset priority order, parameters that deviate from the normal range will be returned to the correct range. If the oil-rich temperature stabilizes within the target range after adjustment, the adjustment is complete; if the oil-rich temperature still does not meet the target, the adjustment is switched to the correction callback method based on the prediction model.

[0013] In some instances, the preset threshold is a deviation from the normal range of more than 5%, or a measured value that is more than 3% lower than the lower limit of the normal range.

[0014] In some instances, the preset priority order is as follows: First priority: Adjust the steam flow rate into the rich oil heater; Second priority: Adjust the flow rate into the rich oil heater; Third priority: Adjust the temperature and pressure of the medium-pressure steam entering the unit; Fourth priority: Adjust the rich oil temperature after the first stage of the rich and lean oil heat exchanger.

[0015] In some instances, before correcting the rich oil temperature using a prediction model or priority rules based on the multiple process parameters, the method further includes: Determine whether the rich oil temperature after the first stage of the rich-lean oil heat exchanger is within the reference range of 125℃-145℃. If not, prioritize adjusting it to this reference range.

[0016] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects: Precise control and rapid response: By establishing a multi-parameter prediction model, the key factors affecting the oil-rich temperature can be accurately located, enabling rapid and targeted correction and overcoming the lag of single PID control.

[0017] Orderly adjustment and stable operation: By setting the priority order of parameters, orderly intervention is carried out when multiple parameters are abnormal at the same time, avoiding system fluctuations caused by blind adjustment and ensuring the stable operation of the benzene removal tower.

[0018] Improve product quality and reduce energy consumption: Precisely control the oil-rich temperature within the optimal range to ensure the recovery rate of light benzene, avoid energy waste and washing oil loss caused by excessive temperature, and improve the stability of light benzene product quality.

[0019] Strong adaptability: The predictive model can be continuously updated based on the latest production data, enabling the control method to adapt to changes in raw materials, equipment and other operating conditions. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of the method provided in an embodiment of the present invention; Figure 2 This is the ranking of the influence of independent variable parameters in the rich oil temperature model of the benzene removal tower provided in the embodiments of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] In the following description, specific embodiments of the invention will be illustrated with reference to steps and symbols performed by one or more computers, unless otherwise stated. Therefore, these steps and operations will be referred to several times as being performed by a computer, and computer execution as referred to herein includes operations by a computer processing unit representing electronic signals of data in a structured format. This operation transforms the data or maintains it at a location in the computer's memory system, which can be reconfigured or otherwise alter the operation of the computer in a manner well known to those skilled in the art. The data structure maintained by the data is the physical location of the memory, which has specific characteristics defined by the data format. However, the principles of the invention described above are not intended to be limiting, and those skilled in the art will understand that many of the following steps and operations can also be implemented in hardware.

[0024] The terms "module" or "unit" as used herein can be considered as software objects executing on the computing system. Different components, modules, engines, and services described herein can be considered as implementations on the computing system. The apparatus and methods described herein are preferably implemented in software, but can also be implemented in hardware, both of which are within the scope of this invention.

[0025] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any units and all combinations of one or more associated listed items.

[0026] In this embodiment of the invention, a method for correcting and adjusting the temperature of benzene-rich oil removal is provided, such as... Figure 1 As shown, it includes the following steps: S101: Set the target temperature range for rich oil entering the benzene removal tower; S102: Collect data on multiple process parameters related to oil-rich temperature; S103: Based on multiple process parameters, the rich oil temperature is corrected and reverted through prediction models or priority rules.

[0027] Furthermore, the optimized control range for oil-rich temperature is 181±2℃.

[0028] Furthermore, when the rich oil temperature exceeds the range, it is mainly optimized and adjusted through the following 6 parameters: the recent database information of the 6 parameters, the flow rate of the rich oil heater (m³ / h), the medium-pressure steam temperature (°C), the medium-pressure steam pressure, the steam flow rate of the rich oil heater (t / h), the temperature at the bottom of the benzene removal tower (°C), and the rich oil temperature after the first-stage lean and rich oil heat exchanger (°C).

[0029] Furthermore, first determine if the rich oil temperature after the heat exchanger is within the range of 125-145℃. If not, adjust the temperature to bring it into the control range. If the rich oil temperature meets the target, maintain production; if not, continue with the following optimization adjustments: Establish a parameter prediction model for the recent rich oil temperature, specifically including: ① Collect five recent database information from the above parameters: flow rate into the rich oil heater (m³ / h), medium-pressure steam temperature (°C), medium-pressure steam pressure, steam flow rate into the rich oil heater (t / h), and bottom temperature of the benzene removal tower (°C). ② Exclude data from abnormal production periods; ③ Establish a prediction model with rich oil temperature as the dependent variable and the above 5 parameters as independent variables. The data range of the model parameters and rich oil temperature should be as wide as possible, excluding data ranges such as maintenance and abnormal periods.

[0030] Table 1. Control range of independent variable parameters for the rich oil temperature model of the benzene stripping tower.

[0031] While establishing the prediction model, the first factor affecting the dependent variable - oil-rich temperature - was identified, and the value of the first factor was adjusted according to Table 2.

[0032] The first factor can be determined as follows: based on several recent database information (data), rank the five factors in Table 1 in terms of their importance in influencing oil-rich temperatures.

[0033] As the data is continuously updated and new influencing factors emerge, adjustments are made according to Table 2. When the oil-rich temperature reaches 181±2℃, parameter adjustments are stopped.

[0034] Furthermore, it also includes parameter pre-adjustment methods: unlike the prediction model establishment, the pre-optimization parameter adjustment is mainly based on the normal production range, as shown in Table 2.

[0035] Establish normal control ranges for the following five parameters based on production conditions or procedures, and optimize and adjust each parameter within the control range. When ≥3 of the following five parameters exceed the standard by 5%, especially when the decrease value is ≥3%, adjust the following parameters in advance to reduce the exceeding parameters and values.

[0036] Table 2. Normal fluctuation range of independent variable parameters in the rich oil temperature model of the benzene stripping tower.

[0037] Dependent variable oil-rich temperature adjustment method 2: 1) Adjust each variable in advance according to Table 2 in the following order.

[0038] Among the above adjustment parameters, prioritize adjusting the "steam flow rate into the rich oil heater" and track the changes in each parameter after adjustment. Next, adjust the "flow rate into the rich oil heater," then adjust the "medium-pressure steam temperature into the unit" and "medium-pressure steam pressure," and finally adjust the "rich oil temperature after the first-stage lean and rich oil heat exchanger." Because the adjustment process involves the interaction of operating conditions, adjust parameters slowly and gradually to avoid large fluctuations or exceeding the range of other parameters, which could worsen the operating conditions.

[0039] 2) If the oil-rich temperature still does not meet the requirements after adjustment, a prediction model is established, and the first adjustment element is determined for fine-tuning.

[0040] In another embodiment of the present invention, a benzene removal section of a coking plant is taken as an example.

[0041] 1) The temperature fluctuation range of rich oil is 170-175℃, which is lower than the optimized control range of 181±2℃.

[0042] 2) If the oil-rich temperature is below the control range, the following 6 parameters will be optimized and adjusted.

[0043] 3) Collect recent database information for 7 parameters: rich oil temperature (°C), flow rate into rich oil heater (m³ / h), medium-pressure steam temperature (°C), medium-pressure steam pressure, steam flow rate into rich oil heater (t / h), bottom temperature of benzene removal tower (°C), and rich oil temperature after the first-stage lean and rich oil heat exchanger (°C).

[0044] 4) Determine whether the rich oil temperature after the first heat exchanger is within the range of 125-145℃. If the rich oil temperature after the first heat exchanger is 135±2℃, which is within the range of 125-145℃, no adjustment is required.

[0045] 5) The oil-rich temperature is optimized and adjusted, mainly through the following 6 parameters: The flow rate of the rich oil heater is m³ / h, the medium-pressure steam temperature is °C, the medium-pressure steam pressure is ℃, the steam flow rate of the rich oil heater is t / h, the bottom temperature of the benzene removal tower is °C, and the rich oil temperature after the first-stage lean and rich oil heat exchanger is °C.

[0046] 3) Establish a parameter prediction model for near-term oil-rich temperatures.

[0047] ① Collect recent database information for the above 6 parameters (excluding "rich oil temperature after a section of rich and poor oil heat exchanger in °C").

[0048] ② Exclude data from abnormal production periods.

[0049] ③ Establish a prediction model with oil-rich temperature as the dependent variable and the above 5 parameters as independent variables.

[0050] The data range for model parameters and rich oil temperature should be as wide as possible, excluding data from maintenance periods and abnormal periods.

[0051] 5) Establish a parameter prediction model for near-term oil-rich temperatures.

[0052] ① Collect five recent database information from the above parameters: flow rate into the rich oil heater (m³ / h), medium-pressure steam temperature (°C), medium-pressure steam pressure, steam flow rate into the rich oil heater (t / h), and bottom temperature of the benzene removal tower (°C).

[0053] ② Exclude data from abnormal production periods.

[0054] ③ Establish a prediction model with oil-rich temperature as the dependent variable and the above 5 parameters as independent variables.

[0055] The data range for model parameters and rich oil temperature should be as wide as possible, excluding data from maintenance periods and abnormal periods.

[0056] Establish a predictive model:

[0057] While establishing the predictive model, the primary factor affecting the dependent variable—oil-rich temperature—was identified, and the value of this primary factor was adjusted according to Table 2. For example... Figure 2 As shown in Table 2, the primary factor influencing historical data in this database is the bottom temperature of the benzene removal tower. The normal fluctuation range for the bottom temperature of the benzene removal tower is 170~180℃. Adjust the bottom temperature of the benzene removal tower to enter this normal temperature range. If the rich oil temperature still does not reach the required control range after adjustment, continue to adjust as follows.

[0058] 6) As the data is continuously updated and new influencing factors emerge, continue to adjust according to Table 2. When the oil-rich temperature reaches 181±2℃, stop adjusting the parameters.

[0059] 7) Parameter pre-adjustment method.

[0060] Unlike predictive model building, pre-optimization parameter adjustments are mainly based on the normal production range, as shown in Table 2.

[0061] Establish normal control ranges for the following five parameters based on production conditions or procedures, and optimize and adjust each parameter within the control range. When ≥3 of the following five parameters exceed the standard by 5%, especially when the decrease value is ≥3%, adjust the following parameters in advance to reduce the exceeding parameters and values.

[0062] Dependent variable oil-rich temperature adjustment method 2: When the rich oil temperature does not meet the requirements, adjust the variables in advance according to Table 2 and the following order. Among the above adjustment parameters, prioritize adjusting the "steam flow rate (tank) into the rich oil heater," and track the changes in each parameter after adjustment. Next, adjust the "flow rate into the rich oil heater," then adjust the "medium-pressure steam temperature entering the unit" and "medium-pressure steam pressure," and finally adjust the "rich oil temperature after the first-stage lean-rich oil heat exchanger." Because the adjustment process involves the interaction of operating conditions, adjust parameters slowly and gradually to avoid large fluctuations or exceeding the range of other parameters, which could worsen the operating conditions.

[0063] If the oil-rich temperature still does not meet the requirements after adjustment, a prediction model is established according to Method 1, and the first adjustment element is determined for fine-tuning.

[0064] The above provides a detailed description of a benzene-removing rich oil temperature correction and callback method provided by the embodiments of the present invention. Specific examples have been used to illustrate the principle and implementation of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation and application scope based on the idea of ​​the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for correcting and adjusting the temperature of benzene-rich oil during debenzene removal, characterized in that, include: Set the target temperature range for the rich oil entering the benzene removal tower; Collect data on multiple process parameters related to oil-rich temperature; Based on the aforementioned multiple process parameters, the oil-rich temperature is corrected and reverted using a prediction model or priority rules.

2. The method according to claim 1, characterized in that, The target temperature range is [179℃, 183℃].

3. The method according to claim 2, characterized in that, The multiple process parameters include: flow rate into the rich oil heater, medium-pressure steam temperature, medium-pressure steam pressure, direct steam flow rate into the regenerator, bottom temperature of the benzene removal tower, and rich oil temperature after the first-stage rich and lean oil heat exchanger.

4. The method according to claim 3, characterized in that, Correction and callback of oil-rich temperature through predictive models, including: A multiple regression prediction model was established with rich oil temperature as the dependent variable and the flow rate into the rich oil heater, medium-pressure steam temperature, medium-pressure steam pressure, direct steam flow rate into the regenerator, and bottom temperature of the benzene removal tower as independent variables. Based on the regression coefficients of the prediction model, the independent variable that has the greatest impact on oil-rich temperature is determined as the first adjustment factor. Adjust the first adjustment element to a preset normal fluctuation range; If the oil-rich temperature returns to the target temperature range, the adjustment ends; otherwise, based on the updated data, a new prediction model is established, and the process returns to the step of determining the independent variable with the greatest impact on the oil-rich temperature as the first adjustment element based on the regression coefficient of the prediction model, until the oil-rich temperature reaches the target.

5. The method according to claim 4, characterized in that, The prediction model is established based on historical data after excluding maintenance periods and abnormal operating conditions, and the data collection range of each variable covers the extreme value range of each variable. Specifically, the extreme value range of the bottom temperature of the benzene removal tower is 110℃~190℃, the extreme value range of the flow rate into the rich oil heater is 220m³ / h~340m³ / h, the extreme value range of the steam flow rate into the rich oil heater is 10t / h~25t / h, the extreme value range of the medium-pressure steam pressure is 0.8MPa~2.5MPa, and the extreme value range of the medium-pressure steam temperature is 250℃~450℃.

6. The method according to claim 5, characterized in that, The normal fluctuation range of the first regulating element is as follows: the extreme range of the bottom temperature of the benzene removal tower is 170℃~178℃, the extreme range of the flow rate into the rich oil heater is 280m³ / h~320m³ / h, the extreme range of the steam flow rate into the rich oil heater is 13t / h~17t / h, the extreme range of the medium-pressure steam pressure is 1.2MPa~1.6MPa, and the extreme range of the medium-pressure steam temperature is 340℃~380℃.

7. The method according to claim 3, characterized in that, Correction callbacks for rich oil temperatures are performed using priority rules, including: Set the normal fluctuation range for multiple process parameters; Real-time monitoring of various process parameters; when the measured values ​​of at least three parameters deviate from their normal range by more than a preset threshold, a pre-adjustment mechanism is triggered. According to the preset priority order, parameters that deviate from the normal range will be returned to the correct range. If the oil-rich temperature stabilizes within the target range after adjustment, the adjustment is complete; if the oil-rich temperature still does not meet the target, the adjustment is switched to the correction callback method based on the prediction model.

8. The method according to claim 7, characterized in that, The preset threshold is defined as a deviation from the normal range exceeding 5%, or a measured value falling below the lower limit of the normal range exceeding 3%.

9. The method according to claim 8, characterized in that, The preset priority order is as follows: First priority: Adjust the steam flow rate into the rich oil heater; Second priority: Adjust the flow rate into the rich oil heater; Third priority: Adjust the temperature and pressure of the medium-pressure steam entering the unit; Fourth priority: Adjust the rich oil temperature after the first stage of the rich and lean oil heat exchanger.

10. The method according to claim 1, characterized in that, Before correcting the rich oil temperature based on the multiple process parameters using a prediction model or priority rules, the method further includes: Determine whether the rich oil temperature after the first stage of the rich-lean oil heat exchanger is within the reference range of 125℃-145℃. If not, prioritize adjusting it to this reference range.

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