A control method for isomerization dewaxing section in coal-to-liquids

Abstract

The present invention discloses a control method for an isomerization dewaxing section in coal-to-oil. The method adopts a multivariable predictive control method to modify and improve the control scheme of the isomerization dewaxing section, especially to modify the control logic of the extraction temperature of the lubricating oil product, so as to stabilize the operation of the entire distillation tower, the light vacuum tower and the heavy vacuum tower, reduce system fluctuations, improve the diversion effect, and enable the device to operate smoothly without human interference under normal production. On the basis of reducing the labor intensity of operators, the separation degree of base oil is further improved, the quality of lubricating oil base oil is improved, and the market competitiveness of the enterprise is enhanced.

Description

Technical Field

[0001] The invention belongs to the technical field of isomerization dewaxing control, and in particular relates to the control of an isomerization dewaxing section in a coal-to-liquid production process. Background Art

[0002] As an important part of coal-to-liquids in the new coal chemical industry, the isodewaxing production process has a complex mechanism. The feed components change with the coal quality in the actual production process. The separation of base oil products is difficult, and there is serious coupling between towers at each level. The control loops are highly correlated and the control process is complex. Whether the operation of each unit is coordinated and smooth directly affects the quality and yield of the base oil.

[0003] The isomerization dewaxing production process is that the base oil undergoes hydrogenation isomerization and post-hydrogenation refining reactions, and the resulting hydrogenated oil is then passed through the atmospheric distillation tower, light vacuum tower, product stripping tower, heavy vacuum tower, and stabilization tower to produce qualified base oil products and by-products. The main products are XLN, LN, LMN, MN, HN and blended lubricating oil base oil, and the by-products are light diesel, heavy / mixed diesel, stabilized naphtha, LPG and dry gas. Before the implementation of the project, the light vacuum tower, heavy vacuum tower and stabilization tower had large fluctuations. Among them, the light vacuum tower top temperature fluctuated by 70 degrees, the heavy vacuum tower top temperature fluctuated by more than 30 degrees, and the top gas phase temperature of several stripping towers fluctuated by as much as 100 degrees, which seriously affected normal production. The steam, tray liquid level, and production temperature of each tower mainly rely on the operator's experience, and the adjustment range is large, resulting in large fluctuations in the viscosity of the important parameters of the product lubricating oil product. The process of the entire isomerization dewaxing unit is relatively long and highly coupled. For example, the XLN reflux of the light vacuum tower provides heat for the tower kettle reboiler of the stabilization tower, and the LN reflux provides heat for the tower kettle reboiler of the light diesel gas tower of the fractionation tower. In this way, the operations of the light vacuum tower, stabilization tower and fractionation tower are closely linked together. Many liquid levels are cascade controlled in the DCS system. Although the liquid level is very stable, the flow used to control the liquid level fluctuates greatly, and even produces serious shocks that affect the operation of subsequent devices.

[0004] The heterogeneous dewaxing process is long and complex. In order to save energy and reduce consumption, a lot of material circulation is applied. There is also heat integration and utilization technology between the towers, which leads to serious coupling of the units. The conventional control based on PID single-loop control and manual experience cannot achieve the ideal control effect. When disturbances occur in the production process (such as heterogeneous dewaxing feed amount (production load), feed component changes, fluctuations in the hot and cold reflux volume of each tower, and changes in ambient temperature (season, day and night, rainfall)), it is easy to cause fluctuations in the device and reduce the stability of the system, which in turn affects the product quality.

[0005] For equipment such as fractionation towers and vacuum towers, temperature is the most important operating indicator. Because under the premise of stable temperature, on the one hand, the total yield of the device including base oil products such as XLN / LN / LMN / MN / HN and light diesel / heavy diesel / LPG / naphtha and other products can be improved, and on the other hand, the overlap of base oil products and light diesel / heavy diesel / LPG / naphtha and other products can be minimized, the yield of high value-added base oil products can be increased, and the separation accuracy of products can be improved, thereby maximizing the output of high value-added products. Therefore, it is necessary to develop and apply mature lubricant base oil production control technology to further improve the degree of base oil separation, improve the quality of lubricant base oil and thus enhance the market competitiveness of enterprises. Summary of the invention

[0006] The purpose of the invention of this project is to provide a control method for the isomerization dewaxing section in coal-to-liquids.

[0007] The purpose of the present invention is achieved through the following technical solutions A method for controlling the isomerization dewaxing section in coal-to-liquids comprises the following steps: Step 1: Collect and monitor the variables of the entire heterogeneous dewaxing process, and collect all the heterogeneous dewaxing related instrument numbers in the DCS system, including temperature, pressure, liquid level and other parameters. The collection frequency is set to once per minute.

[0008] Step 2: Determine the variables that need to be controlled for isomerization dewaxing, mainly controlling the post-hydrogenation refining liquid level, the top temperature of the fractionation tower, the bottom liquid level, and the light diesel oil production level; the bottom liquid level, top temperature, heavy diesel oil production level, and the production temperature and level of LN, LMN, and XLN products of the light vacuum tower; the bottom liquid level, top temperature, and MN production temperature and level of the heavy vacuum tower. The operating variables are selected according to the following principles: The tower top temperature is the bubble point temperature of the liquid phase oil at the tower top. Optimizing the temperature distribution in the tower. The change of the tower top temperature has an important reference significance for the quality index of the base oil. The advanced control of the tower top temperature and other aspects realizes the balance of temperature distribution and material distribution in the tower, and ensures the quality of each product of the base oil. There are three towers for tower top temperature control: fractionation tower, light vacuum tower, and heavy vacuum tower. The factors affecting the tower top temperature mainly include cold reflux, hot reflux, tray liquid level, raw material flow and temperature entering the tower, steam flow and temperature entering the tower, etc.

[0009] Large fluctuations in liquid level directly destroy the overall heat balance of the tower, causing energy flow disorder in the tower, destroying the gas-liquid distribution of the product in the tower, and affecting the product separation effect. The main factors affecting the liquid level are the opening of the outflow valve and the opening of the inflow valve.

[0010] Step 3: Use multivariable predictive control to model the above-mentioned control points. Take these control points as controlled variables, select corresponding controlled variables according to the specific situation, and establish models of liquid level and temperature respectively. Use historical data to perform multivariable predictive control and training on the heterogeneous dewaxing process.

[0011] Among them, liquid level control is relatively simple: liquid level control involves the process of liquid flowing in and out of a container. Compared with other control problems, the dynamic characteristics of this system are relatively stable. The system dynamics are relatively linear, and in many cases, the dynamic characteristics of the liquid level control system can be approximated as linear. Liquid level control needs to consider fewer variables and does not need to deal with multiple variables that affect each other at the same time. Liquid level control prediction models are relatively easy to build, which can accurately predict future changes in liquid level and make optimized control decisions based on these predictions.

[0012] The produced temperature is directly related to the quality of lubricating oil products, involving many variables and a relatively complex control system. In this method, the produced temperature is divided into two different control modes. One is to use the produced valve as the operating variable. It is found that the model effect of only selecting the produced valve as the operating variable is very poor. After adding the interference variable, the model is better than the actual effect. The other is to use the upstream reflux flow to control the downstream produced temperature. Especially for the MN produced temperature controlled variable, the MN reflux flow is selected, and the interference variable selects the MN flow returning to the heavy vacuum tower and the flow entering the heavy vacuum tower. The model of the MN produced temperature is set to reach a steady state in 60 minutes, in which the operating variable MN reflux valve is a negative gain, the gain is between 0.35-0.45, preferably between 0.38-0.43, and more preferably 0.42, that is, the valve action is reduced by 1 valve position, and the produced temperature will rise by 0.42°C; the MN flow of the interference variable returning to the heavy vacuum tower is a negative gain, the gain is between 0.18-0.22, and preferably 0.195. The flow rate of the disturbance variable entering the heavy and vacuum tower is a positive gain, and the gain is between 0.6 and 0.7, preferably 0.67.

[0013] Step 4: Set the corresponding operating range for the controlled variables and operating variables, with an emphasis on setting upper and lower limits for the operating variables and controlled variables, to ensure that the system is stable during use and that the system does not fluctuate or stop due to commissioning.

[0014] Step 5: According to the actual results, the prediction model is modified and adjusted, and the operating parameters (including the maximum step of a single change, damping coefficient, priority control level between variables, etc.) and optimized to improve the control accuracy and system stability.

[0015] The working process and principle of the present invention are: Multivariable Predictive Control (MPC) is a control method that can be used to optimize and improve complex industrial processes. Below is an overview of the working process and principles of MPC in a heterogeneous dewaxing project: Data collection and modeling: First, real-time data on various input and output variables related to the dewaxing process needs to be collected. These variables may include temperature, pressure, flow, chemical concentration, etc. The collected data is used to build a mathematical model, which can be a model based on physical principles or a statistical model based on data.

[0016] Multivariate Modeling: Using the collected data, a multivariate model describing the heterogeneous dewaxing process was constructed. This model was able to relate the input variables to the output variables to describe the dynamic behavior of the system.

[0017] Controller Design: Based on the established multivariable model, a predictive controller is designed. The MPC controller is able to generate the best control strategy by predicting the future system response. The controller will consider the current state of the system and the desired goal and use the model to make predictions to calculate the optimal control action.

[0018] Optimization and constraints: When designing a controller, you also need to consider the performance and operational limitations of the system. You can set some optimization goals and use them as constraints for the optimization problem.

[0019] Real-time execution: During real-time operation, the MPC controller will make predictions based on the current system state and calculate the optimal control strategy. The output of the controller will be sent to the actuator as control instructions to adjust the operating variables in the heterogeneous dewaxing process.

[0020] Feedback and Iteration: The MPC controller is a model predictive controller that performs feedback and iteration based on real-time system responses. By continuously collecting and updating data from the actual operation process, the model can be corrected and improved to improve the performance and adaptability of the controller.

[0021] In general, multivariable predictive control can achieve precise control and optimization of heterogeneous dewaxing projects and improve the efficiency and quality of the production process by establishing mathematical models, designing controllers, optimizing and constraining conditions, and executing and feedback iterative processes in real time. DETAILED DESCRIPTION

[0022] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0023] A method for controlling the isomerization dewaxing section in coal-to-liquids comprises the following steps: Step 1: Collect and monitor the variables of the entire heterogeneous dewaxing process, including temperature, pressure, liquid level and other parameters, and collect all the instrument numbers related to heterogeneous dewaxing in the DCS system. The collection frequency is set to once per minute.

[0024] Step 2: Determine the variables that need to be controlled for isomerization dewaxing, mainly controlling the post-hydrogenation refining liquid level, the top temperature of the fractionation tower, the bottom liquid level, and the light diesel oil production level; the bottom liquid level, top temperature, heavy diesel oil production level, and the production temperature and level of LN, LMN, and XLN products of the light vacuum tower; the bottom liquid level, top temperature, and MN production temperature and level of the heavy vacuum tower. The operating variables are selected according to the following principles: The factors affecting the tower top temperature mainly include the cold reflux flow, hot reflux flow, tower plate liquid level, raw material flow and temperature entering the tower, steam flow and temperature entering the tower, etc.

[0025] The main factors affecting the liquid level are the opening of the outflow valve and the opening of the inflow valve.

[0026] Step 3: Use multivariable predictive control to model the above-determined control points. Take these control points as controlled variables, select corresponding controlled variables according to specific circumstances, and establish models of liquid level and temperature respectively. Use historical data to perform multivariable predictive control and training on the heterogeneous dewaxing process. The produced temperature is directly related to the quality of lubricating oil products. There are many variables involved and the control system is relatively complex. In this method, the produced temperature is divided into two different control methods. One is to use the produced valve as the operating variable. It is found that the model that only selects the produced valve as the operating variable has a very poor effect. After adding the interference variable, the model is better than the actual effect. The other is to use the upstream reflux flow to control the downstream produced temperature. Especially for the MN produced temperature controlled variable, the MN reflux flow is selected, and the interference variable selects the MN flow returning to the heavy vacuum tower and the flow entering the heavy vacuum tower. The model of MN output temperature is set to reach steady state in 60 minutes, where the operating variable MN return flow valve is a negative gain, the gain is between 0.35-0.45, preferably between 0.38-0.43, and more preferably 0.42, that is, the valve action decreases by 1 valve position, and the output temperature rises by 0.42°C; the interference variable MN flow returning to the heavy vacuum tower is a negative gain, the gain is between 0.18-0.22, and preferably 0.195. The flow of the interference variable entering the heavy vacuum tower is a positive gain, the gain is between 0.6-0.7, and preferably 0.67.

[0027] Step 4: Set the corresponding operating range for the controlled variables and operating variables, with an emphasis on setting upper and lower limits for the operating variables and controlled variables, to ensure that the system is stable during use and that the system does not fluctuate or stop due to commissioning.

[0028] Step 5: According to the actual results, modify the prediction model, adjust the operating parameters and optimize it to improve the control accuracy and system stability.

[0029] The beneficial effects of the present invention are: There are many advantages to automating the identification of gasifier ash. Here are some of the main ones: Improved efficiency: Ash identification can achieve fast and accurate identification and classification to obtain residual carbon rate, which can greatly improve production efficiency. Compared with laboratory processing, the time to obtain residual carbon rate is greatly shortened. Accuracy and consistency: Accurately detect and identify the composition and characteristics of ash. This eliminates the errors that may be introduced by different manual experience and ensures consistent results. Quality control: Through ash identification, better quality control can be achieved. The system can monitor and record the composition, quality and other relevant parameters of ash, thereby helping to implement timely adjustments and improvements in the production process. Data analysis and decision support: Collect and generate statistical information and trend analysis about ash. These data can help companies make more informed decisions, optimize production processes, and implement more effective resource management strategies.

[0030] Multivariable predictive control has changed the traditional operating concept and solidified the excellent operating experience of operators. The production operation has been changed from the previous unit operation to systematic operation, from the monitoring and control of normal measurable variables to the direct monitoring and control of process reaction quality, and from the local optimization of simple parameters of a single loop to the optimization of all quality indicators. The operation and application results of the multivariable predictive control system show that this system is effective, mainly reflected in: (1) Multivariable predictive control can effectively overcome the fluctuations caused by changes in raw oil products and load adjustments, making the operation stable and reliable, effectively reducing the workload of operators, and realizing the role transformation of operators from "operating the device" to "optimizing the operation of the device", while greatly improving the automation and intelligence level of the device.

[0031] (2) Smooth operation and reduced standard deviation of key controlled variables. The fluctuation range of the main process parameter points of the heating furnace, distillation tower, light vacuum tower, stripping tower, and stabilization tower units has been significantly reduced, and the standard deviation of the controlled variables has been reduced. Optimize the temperature balance in the distillation tower, light vacuum tower, stabilization tower, heavy vacuum tower, etc.

[0032] (3) Improve base oil yield and increase base oil production. Through the implementation of multivariable predictive control, the base oil yield is increased, the quality of each base oil product is stable (distillation range), the material and energy consumption are relatively reduced, and the economic benefits of the device are significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 is the trend of the top temperature of the distillation tower before commissioning Figure 2 is the trend of the top temperature of the distillation tower after it was put into use Figure 3 is the trend diagram of LN produced liquid level before commissioning Figure 4 is the trend chart of LN produced liquid level after commissioning Figure 5 is the trend chart of MN extraction temperature before commissioning Figure 6 This is the trend chart of MN production temperature after it was put into use.

Claims

1. A method for controlling the isomerization dewaxing section in coal-to-liquids, comprising the following steps: Step 1: Collect and monitor the variables of the whole process of isomerization dewaxing, and collect all the instrument numbers related to isomerization dewaxing in the DCS system, including temperature, pressure, liquid level and other parameters, and the collection frequency is set to collect once every minute. Step 2: Determine the variables that need to be controlled for isomerization dewaxing, mainly the post-hydrogenation refining liquid level, the top temperature of the fractionation tower, the bottom liquid level, and the light diesel oil production level; the bottom liquid level, top temperature, heavy diesel production level, and the production temperature and production level of LN, LMN, and XLN products of the light vacuum tower; the bottom liquid level, top temperature, and production temperature and production level of MN of the heavy vacuum tower. For the operating variables, select according to the following principles: There are three towers for the control of the top temperature: fractionation tower, light vacuum tower, and heavy vacuum tower. The factors affecting the top temperature of the tower mainly include cold reflux, hot reflux, tray liquid level, raw material flow and temperature entering the tower, steam flow and temperature entering the tower, etc. The main factors affecting the liquid level are the opening of the outflow valve and the opening of the inflow valve. Step 3: Use multivariable predictive control to model the above-determined control points. Take these control points as controlled variables, select corresponding controlled variables according to specific conditions, and establish models of liquid level and temperature respectively. Use historical data to perform multivariable predictive control and training on heterogeneous dewaxing process. Among them, liquid level control is relatively simple: liquid level control involves the inflow and outflow process of liquid in the container. Compared with other control problems, the dynamic characteristics of this system are relatively stable. The system dynamics are relatively linear. In many cases, the dynamic characteristics of the liquid level control system can be approximated as linear. Liquid level control needs to consider fewer variables and does not need to deal with multiple variables that affect each other at the same time. The liquid level control prediction model is relatively easy to build, which can accurately predict the future changes of liquid level and make optimal control decisions based on these predictions. The production temperature is directly related to the quality of lubricating oil products. The control system is more complex with more variables involved. In this method, the production temperature is divided into two different control methods. One is to use the production valve as the operating variable. It is found that the model with only the production valve as the operating variable is very poor. After adding the interference variable, the model is better than the actual effect. The other is to use the upstream reflux flow to control the downstream production temperature. Especially for the MN production temperature, the controlled variable selects the MN reflux flow, and the interference variable selects the MN flow returning to the heavy vacuum tower and the flow entering the heavy vacuum tower. The model of the MN production temperature is set to reach a steady state in 60 minutes, wherein the operating variable MN reflux valve has a negative gain, the gain is between 0.35-0.45, preferably between 0.38-0.43, and more preferably 0.42, that is, the valve action is reduced by 1 valve position, and the production temperature will rise by 0.42°C; the interference variable MN flow returning to the heavy vacuum tower has a negative gain, the gain is between 0.18-0.22, and preferably 0.

195. The interference variable flow entering the heavy vacuum tower has a positive gain, the gain is between 0.6-0.7, and preferably 0.67.Step 4: Set the corresponding operating range for the controlled variables and the operating variables, and focus on setting the upper and lower limits for the operating variables and the controlled variables to ensure that the system is stable during the use of the system, and that the system will not fluctuate or stop due to the use of the system. Step 5: According to the actual effect, the prediction model is modified and adjusted, and the operating parameters (including the maximum step of a single change, the damping coefficient, the priority control level between variables, etc.) and optimized to improve the control accuracy and system stability.

2. The method according to claim 1, wherein in the described step 3, the controlled variable for the MN output temperature is the MN reflux flow, and the interference variable is the MN flow returning to the heavy vacuum tower and the flow entering the heavy vacuum tower. The model of the MN output temperature is set to reach a steady state in 60 minutes, wherein the operating variable MN reflux valve has a negative gain, the gain is between 0.35-0.45, preferably between 0.38-0.43, and more preferably 0.42, that is, the valve action is reduced by 1 valve position, and the output temperature will rise by 0.42°C; the interference variable MN flow returning to the heavy vacuum tower has a negative gain, the gain is between 0.18-0.22, and preferably 0.

195. The interference variable flow entering the heavy vacuum tower has a positive gain, the gain is between 0.6-0.7, and preferably 0.

67. .

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