An asphalt production online control method based on asphalt density method

Abstract

The application discloses a kind of asphalt production online control method based on asphalt density method, comprising the following steps: step 1: linear negative correlation association model between the conversion density of asphalt at standard temperature and penetration is established in advance;Step 2: real-time acquisition of apparent density ρ of asphalt delivery pipeline and apparent temperature T The table shows;Step 3: according to formula ρ 标 =ρ 表 × 1000 + (T 表 ‑ T0) × K, the real-time apparent density is converted to the conversion density ρ 标 At standard temperature T0, wherein K is the density temperature variation coefficient of asphalt;Step 4: the conversion density ρ 标 Computed and compared with the association model of step 1, according to the comparison result, the operating parameters of asphalt production are adjusted in real time, and the above standard temperature T0 is 150 DEG C.

Description

Technical Field

[0001] This invention belongs to the field of petrochemical production technology, specifically relating to a real-time online quality control method for guiding the asphalt production process, which is particularly suitable for the production of road asphalt from raw materials with variable properties such as Marai crude oil. Background Technology

[0002] In the industrial production of heavy-duty asphalt, penetration is commonly used as a key quality indicator to measure the hardness and grade (e.g., 70#, 90#) of the asphalt. The level of penetration depends on the content of heavy components in the asphalt. However, traditional asphalt quality control methods suffer from significant lag: obtaining results such as penetration after on-site sampling and laboratory analysis usually takes a considerable amount of time (e.g., several hours). During this period, fluctuations in raw material properties (e.g., moisture content) or operating parameters are difficult to detect and adjust in a timely manner, leading to the production of a large number of substandard asphalt products.

[0003] Especially during the process of switching asphalt grades (e.g., from 90# to 70#), because it is impossible to know in real time whether the product grade has met the standard, operators can only transfer the product during the switch to the unqualified tank and wait for the test results to pass before switching to the qualified tank. This process is not only inefficient (usually taking more than 2 hours), but also easily causes cross-contamination between products of different grades, increasing production costs and inventory pressure.

[0004] Therefore, developing a method that can reflect changes in asphalt penetration in real time, online, and indirectly, thereby guiding adjustments to production operations, is of great significance for improving the quality of asphalt products, reducing defective products, and optimizing grade switching efficiency. Summary of the Invention

[0005] In view of the shortcomings of the prior art described above, this invention proposes an online control method for asphalt production based on the asphalt density method. This method utilizes the linear negative correlation between asphalt density and its penetration, combined with temperature compensation, to achieve online, real-time, and non-waiting-for-test-results quality judgment and production guidance.

[0006] The technical solution adopted by this invention to solve its technical problem is: It includes the following steps: Step 1: Pre-determine the actual density of asphalt products with different penetration grades under different production conditions of the same production unit, and establish a database of the correspondence between penetration and density; determine that the penetration of asphalt is linearly negatively correlated with the converted density, that is, the higher the converted density, the lower the penetration; the lower the converted density, the higher the penetration. Step 2: Using a mass flow meter installed on the asphalt delivery pipeline, read the apparent density ρ of the current asphalt in real time (unit: kg / m³). 3 or g / cm3 ) and the displayed temperature T gauge (unit: °C); Step 3: Perform temperature compensation conversion on the real-time density using the following formula: Based on formula (I), convert the real-time displayed density to the density ρ at the standard temperature. 标 ρ 标 = ρ 表 × 1000 + (T 表 - T0) × K (I) where, ρ 标 This is the converted density at standard temperature, in kg / m³. 3 ;ρ 表 The density displayed by the asphalt delivery flow meter is in g / cm³. 3 Multiply by 1000 to convert to kg / m 3 T0 is the preset standard temperature, preferably 150℃; K is the temperature coefficient of asphalt density, in kg / (m³). 3 (℃), obtained through calibration experiments or by consulting the petroleum density temperature coefficient table; Step 4: Real-time production control based on converted density. The calculated real-time converted density ρ... 标 Compare with the penetration and density correlation model established in step 1; If the converted density exceeds the density range corresponding to the current target product penetration, it is determined that the asphalt quality is about to or has already deviated, and at least one asphalt production operation parameter is adjusted in real time accordingly. The operation parameters include, but are not limited to: vacuum degree of pressure reducing tower, cleaning oil flow rate, and heating furnace outlet temperature. During the asphalt grade switching process, the converted density ρ was continuously monitored. 标 The change when ρ 标 Once the density range corresponding to the target grade of asphalt is entered and remains stable, the switching is immediately determined to be complete, and the product tank switching operation can be carried out.

[0007] Preferably, the method for determining the density-temperature variation coefficient K includes: acquiring at least two sets of apparent density data at different temperatures within a period of time during which the asphalt quality (penetration) remains basically stable, and substituting them into formula (II) for solution: ρ 表1 ×1000 + (T 表1 - T0) × K = ρ 表2 ×1000 + (T 表2 - T0) × K (II) Solve for the value of K; Alternatively, the recommended coefficient for asphalt within the corresponding temperature range can be directly adopted from the petroleum product density temperature coefficient table, with K=0.52 kg / (m³) preferred. 3 ·℃).

[0008] Preferably, the method further includes an online prediction step of the raw material moisture content: using the pre-desorption temperature T of the raw oil before it enters the unit. 前 and the density ρ displayed on the pre-desorption flow meter 前 According to formula (III), the water content percentage of the raw material X is estimated in real time: X = [(ρ 后 - ρ 前 ) × 100 + (T 后 - T 前 ) × K 油 ] / (ρ 水 - ρ 前 (III) Wherein, ρ 后 The apparent density of the raw material after it has been moistened; The temperature after T is the temperature of the raw material after it has been mixed with water; K oil is the density temperature coefficient of the feed oil; ρ_water is the density of water at the current temperature; If the estimated moisture content X exceeds the preset threshold, the raw material tank water cutting will be initiated or the pre-dewatering operation will be adjusted.

[0009] Preferably, the standard temperature T0 is 150°C, which corresponds to the normal operating temperature range of the asphalt delivery pipeline.

[0010] As a preferred method, when continuously producing asphalt of the same grade, the density ρ is calculated. 标 Real-time trend monitoring, if ρ 标 If the pressure continues to rise, it indicates a decrease in penetration, prompting measures to reduce vacuum or increase cleaning oil flow in advance; if ρ 标 If the penetration rate continues to decline, it is predicted that the penetration rate will increase. Measures such as increasing the vacuum level or reducing the cleaning oil flow rate should be taken in advance. Based on this operation, the penetration rate of asphalt can be controlled within the optimal range.

[0011] As a preferred option, when switching between 70# and 90# asphalt grades, it is pre-determined through statistical analysis that the equivalent density of 70# asphalt is 1.8~2.0 kg / m³ higher than that of 90# asphalt. 3 When the converted density ρ 标 Once the difference is raised or lowered and stabilized, the switch is considered to be in place, and the dispatcher is immediately contacted to perform the tank relocation operation.

[0012] Compared with the prior art, the present invention has the following beneficial effects: Significantly improved real-time performance: No need to wait for test results (which usually takes more than 2 hours), it can achieve online monitoring of asphalt quality in seconds, enabling operators to detect quality fluctuations and intervene immediately.

[0013] Significantly reduces the defect rate: By predicting the penetration direction in advance through density change trends, adjustments can be made before production parameters deviate and cause quality defects, effectively avoiding the generation of a large number of substandard asphalt.

[0014] Significantly shorten the label switching time: The waiting time for product repackaging during label switching is reduced from more than 2 hours in the traditional method to about half an hour, and the switching qualification rate reaches 100%, avoiding mixed contamination of products with different labels.

[0015] Highly adaptable: It is not only suitable for continuous production in a single unit, but also for density benchmark deviations caused by differences in raw materials and operating conditions in different units (such as heavy traffic asphalt units I and II). It can be self-corrected through periodic data statistics.

[0016] It also has a raw material early warning function: it can be extended to online estimation of raw material moisture content, providing early warning of the risk of water in crude oil and providing a basis for dehydration operations. Attached Figure Description

[0017] The present invention will be further described below with reference to the accompanying drawings and embodiments; Figure 1 This is a trend chart showing the relationship between asphalt penetration and equivalent density in June for heavy-duty asphalt device I in this embodiment of the invention (showing a negative linear correlation). Figure 2 This is a bar chart of the average converted density of heavy-duty asphalt device I in September according to different penetration intervals in this embodiment of the invention (the coefficients may have deviations). Figure 3 This is a statistical table of the asphalt penetration range and converted density of the heavy-duty asphalt unit I from May to September in this embodiment of the invention; Figure 4 The production data of heavy-duty asphalt unit I from May to September 2022 were statistically analyzed. Figure 5 This is a data table showing the practical application of the density method in guiding the switching from 90# to 70# asphalt in this embodiment of the invention. Figure 6 This is a data table showing the practical application of the density method in guiding the switching from 70# to 90# asphalt in this embodiment of the invention. Detailed Implementation

[0018] The present invention will be further described below through embodiments. Those skilled in the art should understand that the disclosed embodiments are merely specific implementation examples of the present technical solution. Those skilled in the art can understand the present technical solution from the content disclosed in the following examples. Based on the understanding of the present technical solution, further technical extensions can be made. If such technical extensions cover the present technical solution as a whole, they should fall within the scope of protection of this patent.

[0019] Example 1: Calibration and Correction of Density Temperature Variation Coefficient K During the production process of the heavy-duty asphalt unit II in an oil refinery, it was found that after temperature conversion based on the initial empirical coefficient K=1.0, when the asphalt delivery temperature changed significantly (for example, the temperature dropped from 152.6℃ to 144.3℃ due to the use of deoxygenated water for cooling), the converted density fluctuated abnormally and drastically (from 959.0 to 954.1). However, the actual sampling penetration (approximately 70) did not change significantly, as shown in Tables 1 and 2. This indicates that the temperature compensation coefficient is inaccurate, and the overall correlation between samples is not obvious.

[0020] Take the steady-state operating points at two different temperatures with the same needle penetration (approximately 71): Operating Condition A: Temperature 142.2℃, apparent density 0.9615 g / cm³ 3 Condition B: Temperature 152.5℃, apparent density 0.9564 g / cm³ 3 According to formula (II): 0.9615×1000 + (142.2 - 150)×K = 0.9564×1000 + (152.5 - 150)×K 961.5 - 7.8K = 956.4 + 2.5K, solving for K, we get: K ≈ 0.495 Samples were extracted, and the sampling density was determined to be 0.9962 under 20-degree operating conditions. Consult the table of petroleum density temperature coefficients (e.g.) Figure 3 The density-temperature variation coefficient K of asphalt was found to be 0.52. Since the density-temperature variation coefficient K of asphalt is constant, the recommended coefficient for asphalt near 150℃ is approximately 0.52 kg / (m³). 3 ·℃). Therefore, the formula is revised to: ρ 150 = ρ 表 × 1000 + (T 表 -150) × 0.52 After correction, recalculate. Figure 2 The data from all sources showed a significantly enhanced correlation between the converted density and the penetration, validating the accuracy of the coefficient K=0.52.

[0021] Example 2: Establishment and Validation of the Penetration-Reduced Density Correlation Model Production data from the heavy-duty asphalt unit I for four consecutive months in June, July, August, and September 2022 were statistically analyzed. The asphalt penetration was divided into intervals of 60-65, 66-70, 71-75, 76-80, 81-85, 86-90, 91-95, and 96-100. The average density at 150℃ for all samples within each interval was calculated. The results are as follows: Figure 4As shown in the excerpt: The data clearly shows that as penetration increases, the equivalent density decreases systematically, with a significant linear relationship. Due to slight differences in raw material properties across different months, the absolute density values ​​fluctuate slightly, but the negative correlation remains consistent. This model can be directly used for production guidance.

[0022] Example 3: Using density method to guide the switch from 90# asphalt to 70# asphalt For asphalt grade adjustment, statistical data shows that the density of 70# asphalt is generally 1.8~2.0 kg / m3 higher than that of 90# asphalt. Using an external asphalt flow meter, the operator can read the current asphalt density and temperature, converting it to density at 150℃ using a formula. As the asphalt changes, the density changes as well. Based on the asphalt density, the real-time sample grade can be determined. When converting between 70# and 90# asphalt, the dispatcher can be contacted directly to change the tank. The entire conversion process is shortened from 2 hours to half an hour, and sample contamination between different asphalt grades is avoided. The first-time conversion pass rate is increased to 100%.

[0023] On September 13, 2022, the heavy traffic asphalt unit I needed to switch from producing 90# asphalt to 70# asphalt.

[0024] Before switching (90#): The density of 90# asphalt was 952 kg / m³, and the delivery temperature was 156.2℃. The density converted at 150℃ was 955.22 kg / m³. 3 The needle penetration was 93.

[0025] Operational adjustments: The internal operator increased the vacuum level of the pressure reducing tower from 95.1 kPa to 95.5 kPa and reduced the cleaning oil flow rate from 10.2 t / h to 8.8 t / h.

[0026] Real-time monitoring: After 45 minutes of adjustment, the asphalt delivery flow meter displayed a density of 953 kg / m³. 3 At a temperature of 156.7℃, substituting the values ​​into the formula, the calculated density at 150℃ is 956.48 kg / m³. 3 According to the model, this density value has entered the typical range for 70# asphalt (penetration approximately 75-80).

[0027] Decision and Result: The operators immediately contacted dispatch to switch tanks. Test results two hours later confirmed that the asphalt penetration was 76, fully meeting the 70# asphalt standard; no defective products were produced during the switchover process, and the tank switching time was shortened from more than two hours using the traditional method to 45 minutes.

[0028] Example 4: Using density method to guide the switch from 70# asphalt to 90# asphalt On September 20, 2022, the heavy traffic asphalt unit I needed to switch from producing 70# asphalt to 90# asphalt.

[0029] Before switching (70#): The density of 70# asphalt was 952 kg / m³, and the delivery temperature was 159.6℃. The density converted at 150℃ was 956.99 kg / m³. 3 The penetration depth is 68.

[0030] Operational adjustments: The internal operator reduced the vacuum level of the pressure reducing tower from 95.7 kPa to 95.4 kPa and increased the cleaning oil flow rate from 8.9 t / h to 10.6 t / h.

[0031] Real-time monitoring: After one hour of adjustment, the asphalt delivery flow meter displayed a density of 951 kg / m³. 3 At a temperature of 158.0℃, the calculated density at 150℃ is 955.16 kg / m³. 3 This value falls within the density range of 90# asphalt.

[0032] Decision and Result: The operator immediately switched to the new tank. Subsequent laboratory results showed a penetration of 88, indicating a successful switchover. There was no cross-contamination throughout the process, and the efficiency of the tank switchover was significantly improved.

[0033] Example 5: Using density method to prevent the formation of substandard asphalt On October 3, 2022, during the continuous production of 70# asphalt in the heavy-duty asphalt unit I: Sampling was conducted at 12:00, and the test results showed a penetration of 61, corresponding to a density of 958.65 kg / m³ at 150℃. 3 .

[0034] Before the second sampling at 16:00, the operators continuously monitored the online density calculation and found that it had risen to 959.08 kg / m³. 3 .

[0035] According to the density-penetration negative correlation model, an increase in converted density indicates that penetration will further decrease. The operators anticipated that the penetration of this sample might be below 60 (the lower limit for 70# asphalt), but it was too late.

[0036] The sampling and testing results at 16:00 confirmed a penetration of 59, which is unacceptable.

[0037] Further improvements: Based on this case, the density warning line was set at 958.8 kg / m³. 3 Once the calculated density exceeds the threshold, the cleaning oil flow rate will be increased automatically or manually, or the vacuum level will be reduced, thereby effectively preventing the production of similar defective products in the future.

[0038] In summary, the online control method for asphalt production based on real-time density monitoring provided by this invention is scientific, simple, and easy to implement. It can significantly improve the control accuracy and efficiency of the asphalt production process and has extremely high industrial application value.

Claims

1. A method for online control of asphalt production based on the asphalt density method, characterized in that: Includes the following steps: Step 1: Establish a linear negative correlation model between the converted density and penetration of asphalt at standard temperature in advance; Step 2: Real-time acquisition of the apparent density ρ and apparent temperature T on the asphalt delivery pipeline; Step 3: According to the formula ρ 标 = ρ 表 × 1000 + (T 表 - T0) × K, converting the real-time displayed density to the converted density ρ at standard temperature T0. 标 , where K is the density-temperature variation coefficient of asphalt; Step 4: Apply the calculated reduced density ρ 标 The operation parameters for asphalt production are adjusted in real time based on the comparison results, compared with the correlation model in step 1.

2. The method according to claim 1, characterized in that, The standard temperature T0 is 150℃.

3. The method according to claim 1, characterized in that, The density-temperature variation coefficient K is determined as follows: During a period when the asphalt penetration remains stable, at least two sets of apparent density data at different temperatures are obtained, and these data are substituted into the formula ρ. 表 ×1000 + (T 表1 -T0)×K = ρ 表2 ×1000 + (T 表2 The value of K is obtained by solving for -T0)×K.

4. The method according to claim 1, characterized in that, The asphalt production operation parameters adjusted in step 4 include at least one of the following: vacuum degree of pressure reducing tower, cleaning oil flow rate, and outlet temperature of heating furnace.

5. The method according to claim 1, characterized in that, During the asphalt grade switching process, when the calculated equivalent density ρ standard enters the density range corresponding to the target grade asphalt and remains stable, the switching is immediately determined to be complete and the product tank switching operation is performed.

6. The method according to claim 5, characterized in that, The difference in converted density between 70# asphalt and 90# asphalt is 1.8~2.0 kg / m³. 3 .

7. The method according to claim 1, characterized in that, When continuously producing the same grade of asphalt, if the equivalent density ρ index continues to rise, it is predicted that the penetration will decrease, and measures should be taken in advance to reduce the vacuum degree or increase the cleaning oil flow rate; if the equivalent density ρ index continues to fall, it is predicted that the penetration will increase, and measures should be taken in advance to increase the vacuum degree or reduce the cleaning oil flow rate.

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