Reactor parameter processing method and device, computer equipment and readable storage medium

Abstract

This application relates to a reactor parameter processing method, apparatus, computer equipment, and readable storage medium. The method includes: predicting the changing trends of reactor performance indicators based on reactor-related operating status data; adjusting reactor parameter values ​​based on the obtained prediction results; the performance indicators include at least selectivity; after adjustment, if the actual values ​​of the reactor performance indicators do not meet stable operating conditions, performing the step of predicting the changing trends of reactor performance indicators based on reactor-related operating status data until the actual values ​​of the performance indicators meet stable operating conditions; and when the actual values ​​of the performance indicators meet stable operating conditions, adjusting reactor parameter values ​​based on product component feedback data from the separation unit until the reactor's selectivity and the product conversion rate indicated by the product component feedback data meet optimal steady-state conditions. Using the method of this application can improve product quality.

Description

Technical Field

[0001] This application relates to the field of selective control technology for chemical process reactions, and in particular to a reactor parameter processing method, apparatus, computer equipment, and readable storage medium. Background Technology

[0002] In the control of chemical reaction systems, selectivity and product conversion rate are key factors determining reaction efficiency and economy. Because the reaction process involves multiple devices and coupled parameters, from feed control in the pretreatment unit to parameter maintenance of equipment such as the thermal integration tower, and ensuring the reactor feed status, each link influences the others and exhibits lags due to physical transport and reaction kinetics.

[0003] In related technologies, automatic control is usually achieved through manual operation or simple single-parameter control. This method is difficult to respond precisely and synchronously, which makes the reaction system prone to oscillations from start-up to steady-state operation, resulting in low quality of the final product. Summary of the Invention

[0004] Therefore, it is necessary to provide a reactor parameter processing method, apparatus, computer equipment, computer-readable storage medium, and computer program product that can improve product quality in response to the above-mentioned technical problems.

[0005] In a first aspect, this application provides a reactor parameter processing method, including:

[0006] The performance indicators of the reactor are predicted based on the reactor's relevant operating status data, and the reactor's parameter values ​​are adjusted based on the obtained prediction results; the performance indicators include at least selectivity.

[0007] Once the adjustment is complete, if the actual value of the reactor's performance index does not meet the stable operating conditions, the step of predicting the trend of the reactor's performance index based on the reactor's relevant operating status data is executed until the actual value of the performance index meets the stable operating conditions.

[0008] When the actual values ​​of the performance indicators meet the stable operating conditions, the parameters of the reactor are adjusted based on the product component feedback data of the separation unit until the selectivity of the reactor and the product conversion rate indicated by the product component feedback data meet the optimal steady-state conditions.

[0009] Secondly, this application also provides a reactor parameter processing device, comprising:

[0010] A trend prediction module is used to predict the trend of performance indicators of the reactor based on reactor-related operating status data, and to adjust the parameter values ​​of the reactor based on the obtained prediction results; the performance indicators include at least selectivity;

[0011] The parameter value adjustment module is used to, after the adjustment is completed, if the actual value of the performance index of the reactor does not meet the stable operating conditions, execute the step of predicting the change trend of the performance index of the reactor based on the reactor-related operating status data, until the actual value of the performance index meets the stable operating conditions.

[0012] The feedback adjustment module is used to adjust the parameters of the reactor based on the product component feedback data of the separation unit, when the actual values ​​of the performance indicators meet the stable operating conditions, until the selectivity of the reactor and the product conversion rate indicated by the product component feedback data meet the optimal steady-state conditions.

[0013] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0014] The performance indicators of the reactor are predicted based on the reactor's relevant operating status data, and the reactor's parameter values ​​are adjusted based on the obtained prediction results; the performance indicators include at least selectivity.

[0015] Once the adjustment is complete, if the actual value of the reactor's performance index does not meet the stable operating conditions, the step of predicting the trend of the reactor's performance index based on the reactor's relevant operating status data is executed until the actual value of the performance index meets the stable operating conditions.

[0016] When the actual values ​​of the performance indicators meet the stable operating conditions, the parameters of the reactor are adjusted based on the product component feedback data of the separation unit until the selectivity of the reactor and the product conversion rate indicated by the product component feedback data meet the optimal steady-state conditions.

[0017] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0018] The performance indicators of the reactor are predicted based on the reactor's relevant operating status data, and the reactor's parameter values ​​are adjusted based on the obtained prediction results; the performance indicators include at least selectivity.

[0019] Once the adjustment is complete, if the actual value of the reactor's performance index does not meet the stable operating conditions, the step of predicting the trend of the reactor's performance index based on the reactor's relevant operating status data is executed until the actual value of the performance index meets the stable operating conditions.

[0020] When the actual values ​​of the performance indicators meet the stable operating conditions, the parameters of the reactor are adjusted based on the product component feedback data of the separation unit until the selectivity of the reactor and the product conversion rate indicated by the product component feedback data meet the optimal steady-state conditions.

[0021] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0022] The performance indicators of the reactor are predicted based on the reactor's relevant operating status data, and the reactor's parameter values ​​are adjusted based on the obtained prediction results; the performance indicators include at least selectivity.

[0023] Once the adjustment is complete, if the actual value of the reactor's performance index does not meet the stable operating conditions, the step of predicting the trend of the reactor's performance index based on the reactor's relevant operating status data is executed until the actual value of the performance index meets the stable operating conditions.

[0024] When the actual values ​​of the performance indicators meet the stable operating conditions, the parameters of the reactor are adjusted based on the product component feedback data of the separation unit until the selectivity of the reactor and the product conversion rate indicated by the product component feedback data meet the optimal steady-state conditions.

[0025] The aforementioned reactor parameter processing method, apparatus, computer equipment, computer-readable storage medium, and computer program product predict the changing trends of reactor performance indicators based on reactor-related operating status data, and adjust reactor parameter values ​​based on the obtained prediction results. Performance indicators include at least selectivity. After adjustment, if the actual values ​​of reactor performance indicators do not meet stable operating conditions, a step of predicting the changing trends of reactor performance indicators based on reactor-related operating status data is executed until the actual values ​​of performance indicators meet stable operating conditions. Once the actual values ​​of performance indicators meet stable operating conditions, reactor parameter values ​​are adjusted based on product component feedback data from the separation unit until the reactor's selectivity and the product conversion rate indicated by the product component feedback data meet the optimal steady-state conditions. By using reactor operating status data to predict selectivity changing trends in advance, compensatory parameter adjustments are made before physical lag occurs, avoiding parameter oscillations caused by lag, reducing side reactions caused by parameter oscillations, stabilizing the main product content, and ensuring product quality stability. After performance indicators meet stable operating conditions, a closed-loop optimization of selectivity and conversion rate is constructed through product component feedback from the separation unit, breaking through the limitations of traditional single-parameter control, achieving synergy between high selectivity and high conversion rate, and further improving product quality. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a schematic diagram of the reactor system in one embodiment;

[0028] Figure 2 This is a flowchart illustrating a reactor parameter processing method in one embodiment;

[0029] Figure 3 This is a structural diagram of a multi-stage reactor in one embodiment;

[0030] Figure 4 This is a schematic diagram of the process for adjusting the parameter values ​​of the reactor in one embodiment;

[0031] Figure 5 This is a schematic diagram of the feed reference state matching control for a multi-stage reactor in one embodiment;

[0032] Figure 6 This is a schematic diagram of a reactor parameter processing method in one embodiment;

[0033] Figure 7 This is a schematic diagram of the reactor parameter processing method in another embodiment;

[0034] Figure 8 This is a structural block diagram of a reactor parameter processing device in one embodiment;

[0035] Figure 9 This is an internal structural diagram of a computer device in one embodiment;

[0036] Figure 10 This is a diagram of the internal structure of a computer device in another embodiment. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0038] In some exemplary embodiments, such as Figure 1 As shown, the reactor parameter processing method of this application can be applied to reactor systems. (Refer to...) Figure 1 After the raw materials enter the reactor system, the product is output through the following three steps:

[0039] Feeding stage: The raw material enters the pretreatment unit, is pressurized by the pump, and enters the CO1 tower (i.e., the thermal integration tower) for pretreatment. The temperature and composition are adjusted, and the gas and liquid are separated. The liquid phase at the bottom of the tower is used as the reactor feed.

[0040] Reaction stage: The pretreated material enters the multi-stage reactor R01, where the main reaction occurs under specific temperature, pressure and catalyst conditions to generate the target product and by-products.

[0041] Separation stage: The reaction products first enter the CO2 tower in the separation unit to separate light components such as CO2. The bottom material enters the CO3 tower for further separation and purification, and finally outputs the product. By-products are discharged from the bottom of the tower.

[0042] In some exemplary embodiments, a reactor parameter processing method is provided. This method is executed by a computer device, which can be a terminal or a server. The terminal can be, but is not limited to, various personal computers, laptops, smartphones, tablets, drones, low-altitude aircraft, IoT devices, and portable wearable devices. IoT devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, projection devices, etc. Portable wearable devices can be smartwatches, smart bracelets, head-mounted devices, etc. Head-mounted devices can be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. Figure 2 As shown, the method specifically includes steps 202 to 206. Wherein:

[0043] Step 202: Based on the reactor's relevant operating status data, predict the changing trends of the reactor's performance indicators, and adjust the reactor's parameter values ​​based on the obtained prediction results; the performance indicators include at least selectivity.

[0044] The reactor-related operational status data refers to various parameter information reflecting the real-time operating conditions of the reactor. This includes feed information and real-time operating parameters. Feed information includes feed components, feed flow rate, feed temperature, and the temperature / pressure of the thermal integration tower. Real-time operating parameters include temperature, pressure, and oxygen content. This operational status data is fundamental for understanding the reactor's current operating condition and can be acquired through equipment such as temperature sensors, pressure transmitters, flow meters, and component analyzers. Reactor performance indicators are used to reflect the reactor's reaction performance, and these indicators must include at least selectivity.

[0045] The prediction result is a conclusion drawn by using a specific prediction model to analyze and judge the future trend of performance indicators by inputting reactor operating status data. It can know in advance whether the performance indicators tend to stabilize, improve or deteriorate, and whether there are abnormal fluctuations. The prediction model can be, for example, a model built based on machine learning algorithms or a mechanism model.

[0046] For example, a computer device can construct a mathematical model based on reaction kinetics and thermodynamics to correlate temperature, oxygen content, feed parameters, and selectivity as a predictive model. When changes are detected in the reactor's operating status data, the predictive model is used to predict the trend of selectivity change, resulting in a prediction result indicating the trend of selectivity change. The predictive model can be as follows:

[0047] Y = Gs * Us + Ds * Ws;

[0048] Where Y represents selectivity, Gs represents the process model describing the deterministic response, Us represents input parameters such as reactor temperature and feed flow rate, Ws represents white noise, such as raw material impurity fluctuations and sensor measurement noise, and Ds represents the instantaneous response of the controlled variable to the operated variable, which is generally 0.

[0049] For example, the Gs process model can be as follows:

[0050]

[0051] Where K is the gain, is the time constant, DT is the pure time delay, and S is the Laplace operator.

[0052] The above formula shows that during the prediction process, by obtaining K, τ1, and DT of the actual process, a precise Gs can be established. Combined with the current Us and Ws, the trend of Y can be predicted. If the predicted Y deviates from the target, the gradient of Us that needs to be adjusted can be calculated through the inverse model to compensate for lag and inertia, thereby suppressing performance degradation in advance.

[0053] For example, the computer device can also use the reactor's reaction temperature and oxygen content as performance indicators, and then predict the reaction temperature or oxygen content based on the reactor's feed information to obtain prediction results indicating the trend of reaction temperature change or oxygen content change.

[0054] Furthermore, when the prediction results indicate an abnormal trend in the performance indicators, the computer equipment can determine a first parameter value adjustment strategy for the reactor based on the prediction results, and adjust the parameters of the reactor based on the first parameter value adjustment strategy.

[0055] For example, a computer device can deduce the adjustment amount for adjusting the parameters of the reactor based on a constructed prediction model, and then issue an adjustment command to the reactor based on the calculated adjustment amount.

[0056] Step 204: After the adjustment is completed, if the actual value of the reactor's performance index does not meet the stable operating conditions, perform the step of predicting the change trend of the reactor's performance index based on the reactor's relevant operating status data until the actual value of the performance index meets the stable operating conditions.

[0057] Among them, the stable operating condition refers to the continuous fluctuation of the value greater than the preset threshold. The preset threshold can be adjusted and set as needed. For example, if the fluctuation of selectivity is continuously monitored to be greater than 3%, it can be determined that the actual value of selectivity does not meet the stable operating condition.

[0058] Specifically, after the reactor parameter values ​​are adjusted, the computer equipment can further obtain the actual values ​​of the reactor. If the actual values ​​of the reactor do not meet the stable operating conditions, the process continues to execute the steps of predicting the changing trend of the reactor's performance indicators based on the reactor's relevant operating status data, and adjusting the reactor parameter values ​​based on the obtained prediction results, until the actual values ​​of the performance indicators meet the stable operating conditions.

[0059] Step 206: When the actual values ​​of the performance indicators meet the stable operating conditions, adjust the parameters of the reactor based on the product component feedback data of the separation unit until the selectivity of the reactor and the product conversion rate indicated by the product component feedback data meet the optimal steady-state conditions.

[0060] The optimal steady-state condition refers to the optimal values ​​of both the reactor selectivity and the product conversion rate indicated by the product component feedback data, with the fluctuation range within a preset range. For example, the selectivity is greater than the first preset value and the conversion rate is greater than the second preset value, with the fluctuation range within a preset range.

[0061] For example, the computer device can acquire product component feedback data of the separation unit within a preset time period, analyze the time series of product component feedback data within the preset time period, determine the type of interference for the reactor, determine a second parameter value adjustment strategy for the reactor based on the type of interference, adjust the parameters of the reactor based on the second parameter value adjustment strategy, and execute the step of acquiring product component feedback data of the separation unit within the preset time period until the selectivity of the reactor and the product conversion rate indicated by the product component feedback data meet the optimal steady-state conditions.

[0062] The aforementioned reactor parameter processing method predicts the changing trends of reactor performance indicators based on relevant reactor operating status data, and adjusts reactor parameter values ​​based on the obtained prediction results. Performance indicators include at least selectivity. After adjustment, if the actual values ​​of reactor performance indicators do not meet stable operating conditions, the method executes a step of predicting the changing trends of reactor performance indicators based on relevant reactor operating status data until the actual values ​​meet stable operating conditions. Once the actual values ​​meet stable operating conditions, the method adjusts reactor parameter values ​​based on product component feedback data from the separation unit until the reactor's selectivity and the product conversion rate indicated by the product component feedback data meet the optimal steady-state conditions. By using reactor operating status data to predict selectivity trends in advance, compensatory parameter adjustments are made before physical lag occurs, avoiding parameter oscillations caused by lag, reducing side reactions caused by parameter oscillations, stabilizing the main product content, and ensuring product quality stability. After performance indicators meet stable operating conditions, a closed-loop optimization of selectivity and conversion rate is constructed through product component feedback from the separation unit, breaking through the limitations of traditional single-parameter control, achieving synergy between high selectivity and high conversion rate, and further improving product quality.

[0063] In some exemplary embodiments, the changing trends of reactor performance indicators are predicted based on reactor-related operating status data, and the reactor parameter values ​​are adjusted based on the obtained prediction results, including:

[0064] Based on the reactor's operating status data, the changing trends of the reactor's performance indicators are predicted to obtain prediction results; when the prediction results indicate that the changing trends of the performance indicators are abnormal, a first parameter value adjustment strategy for the reactor is determined based on the prediction results; and the reactor's parameter values ​​are adjusted based on the first parameter value adjustment strategy.

[0065] The first parameter adjustment strategy refers to the specific plan for adjusting various operating parameters of the reactor when the prediction results show abnormal trends in performance indicators. This plan takes into account the reactor's operating principle, process requirements, and the correlation between parameters. It includes which parameters need to be adjusted, the direction of adjustment (increase or decrease), and the magnitude of adjustment, so as to bring the performance indicators back to normal and optimize the reaction process.

[0066] For example, by collecting reactor operating status data, appropriate predictive models can be used to predict the changing trends of performance indicators. For instance, a machine learning model trained on a large amount of historical reaction data can predict, based on current feed composition, temperature, and other data, whether the selectivity of the target product will decrease due to increased side reactions caused by rising temperature, or whether it will improve in a favorable direction due to pressure adjustments. When the prediction results show an abnormal trend in performance indicators, an adjustment strategy for the first parameter value is determined based on the parameter influence relationships analyzed during the prediction process. For example, if it is predicted that as the reaction proceeds, rising temperature will make side reactions more likely to occur, thus reducing selectivity, then the adjustment strategy might be to moderately reduce the reactor temperature while fine-tuning the feed flow rate to maintain reaction stability.

[0067] In this embodiment, on the one hand, predictive intervention compensates for the physical lag in the reaction process, avoiding significant parameter fluctuations due to lag when adjustments are made after the actual indicators have deteriorated. This stabilizes reaction conditions, ensures the stability and consistency of product quality, and reduces losses caused by quality fluctuations. On the other hand, the proactive adjustment method based on prediction accurately determines the reaction direction, keeping performance indicators at a better level, thereby increasing the yield and purity of the target product and improving product quality.

[0068] In some exemplary embodiments, the reactor is a multi-stage reactor. Based on the reactor's operating status data, the changing trends of the reactor's performance indicators are predicted to obtain prediction results, including:

[0069] Obtain the operating status data of each reactor level; based on the operating status data of each reactor level, predict the changing trends of the performance indicators of each reactor level, and obtain the prediction results for each reactor level.

[0070] Among them, a multi-stage reactor refers to a staged reaction system consisting of multiple independent reactors connected in series. Each stage can independently control parameters such as temperature, pressure, and feed to achieve stepwise reactions and adapt to complex reaction pathways. For example... Figure 3 The diagram shown is a structural diagram of a multi-stage reactor, consisting of... Figure 3 It can be seen that this multi-stage reactor consists of multiple reactors connected in series.

[0071] For example, considering that in a multi-stage reaction system, there are problems such as differences in reaction mechanisms, material transfer lag, and cross-stage coupling interference at each stage, if a unified prediction is adopted for the entire process, the prediction will be distorted and the control will be inaccurate due to insufficient model generalization and data interference. Therefore, computer equipment can acquire the operating status data of each stage reactor, and based on the operating status data of each stage reactor, predict the changing trend of the performance indicators of each stage reactor, and obtain the prediction results of each stage reactor.

[0072] In this embodiment, hierarchical data collection and hierarchical trend prediction can overcome hierarchical coupling interference and improve prediction accuracy.

[0073] In some exemplary embodiments, such as Figure 4 As shown, the reactor parameters are adjusted based on the product component feedback data from the separation unit until the reactor selectivity and the product conversion rate indicated by the product component feedback data meet the optimal steady-state conditions, including:

[0074] Step 402: Obtain product component feedback data from the separation unit within a preset time period.

[0075] The separation unit refers to the core equipment that achieves product separation and unreacted material recovery. Examples include distillation columns, absorption columns, and membrane separators. It separates the target product, byproducts, and unreacted feedstock in the reactor effluent through phase equilibrium / adsorption principles. Product component feedback data refers to the component concentration sequence obtained from online detection of the product by the separation unit, such as the content of target product A, byproduct B, and unreacted feedstock C. This data reflects the final conversion efficiency and selectivity of the reaction.

[0076] Step 404: Determine whether the selectivity of the reactor and the product conversion rate indicated by the product component feedback data meet the optimal steady-state conditions. If not, proceed to step 406; if yes, proceed to step 410.

[0077] For example, the computer device can determine whether the selectivity of the reactor and the product conversion rate indicated by the product component feedback data meet the optimal steady-state conditions. If yes, step 410 is executed; otherwise, step 406 is executed.

[0078] Step 406: Analyze the time series of product component feedback data within a preset time period to determine the type of interference to the reactor.

[0079] Interference type is a classification based on different causes of interference. For example, interference types may include trend interference, abrupt interference, and periodic interference. Trend interference may be caused by catalyst deactivation, abrupt interference may be caused by feed contamination, and periodic interference may be caused by fluctuations in circulating water temperature.

[0080] Specifically, the computer equipment uses a time series model to analyze the time series of product component feedback data within a preset time period to determine the type of interference.

[0081] For example, computer equipment can use a time series model to obtain the initial disturbance type, and then combine it with chemical mechanism models, such as kinetic decay models and material balance equations, to infer the disturbance type. The time series model could be an LSTM (Long Short-Term Memory Network). For instance, if the LSTM identifies the disturbance type as monotonically decreasing, it calls the kinetic model to calculate the catalyst activity: if the activity decay rate is >4%, it is determined to be "catalyst deactivation" (trend-type disturbance); if the activity is normal, it is determined to be "slowly decreasing feed flow rate" (equipment-related disturbance).

[0082] Step 408: Based on the type of disturbance, determine the second parameter value adjustment strategy for the reactor, and adjust the parameters of the reactor based on the second parameter value adjustment strategy.

[0083] Step 410, End.

[0084] Specifically, different reactor parameter adjustment strategies are determined for different types of interference. For example, in the case of catalyst deactivation, the reactor temperature is slightly increased to compensate for the decrease in activity, while the oxygen content is finely adjusted to suppress side reactions and avoid a sharp increase in energy consumption in the separation unit due to changes in product composition. In the case of feed contamination, the feed flow rate is immediately reduced and a backup feed tank is switched. At the same time, the reactor pressure is increased to promote the formation of the target product and ensure the purity of the product in the separation unit.

[0085] After each parameter value adjustment, the new feedback data is used to verify whether the "selectivity-conversion rate" is closer to the optimal equilibrium. If there is still a deviation, the time series data is analyzed again and the strategy is adjusted until the reactor selectivity and the product conversion rate indicated by the product component feedback data meet the optimal steady-state conditions.

[0086] In this embodiment, by monitoring the product component feedback data of the separation unit in real time, analyzing the fluctuation pattern of the time series, matching the corresponding interference type, the interference identification accuracy is improved, thereby triggering targeted parameter adjustments, avoiding incorrect parameter tuning direction, and ultimately achieving stable operation and performance optimization of the system.

[0087] In some exemplary embodiments, determining a second parameter value adjustment strategy for the reactor based on the type of disturbance, and adjusting the parameters of the reactor based on the second parameter value adjustment strategy, includes: determining a parameter adjustment gradient for the reactor based on the type of disturbance; and adjusting the parameters of the reactor according to the parameter adjustment gradient.

[0088] Among them, the parameter adjustment gradient refers to the magnitude / step size of the parameter change each time it is adjusted. For example, the temperature is adjusted by 0.5℃ each time, and the flow rate is adjusted by 1% each time. Through multiple gradient adjustments, the target range or target value is approached.

[0089] Once the specific type of interference is identified through the product component feedback data from the separation unit, the reactor parameters need to be adjusted accordingly to eliminate the interference and restore the optimal steady state of the reaction. Considering that directly and significantly adjusting the parameters may cause more severe parameter oscillations due to the hysteresis and multi-parameter coupling of the reaction system, thereby compromising reaction stability, in this embodiment, the parameter adjustment gradient is first determined based on the identified type of interference, combined with the reactor's reaction kinetics characteristics, process requirements, and historical experience or model calculations.

[0090] For example, if the interference is a trend-type interference, such as slow catalyst deactivation, considering that the catalyst activity decay is a gradual process, in order to compensate for the decrease in activity without causing drastic reaction fluctuations, the parameter adjustment gradient may be set to small-amplitude, multi-batch adjustments. For example, at regular intervals, the reactor temperature may be gradually increased in a gradient of 0.5°C, while the oxygen content is fine-tuned. If the interference is a sudden type, such as feed contamination, in order to quickly suppress adverse effects, the parameter adjustment gradient may be adjusted urgently with a larger magnitude first, and then fine-tuned later according to the reaction recovery.

[0091] Furthermore, by adjusting the gradient according to the determined parameters, the relevant parameters of the reactor can be adjusted, thereby making the parameter adjustment process more stable and controllable, gradually offsetting the negative impact of disturbances on the reaction, and guiding the selectivity and conversion rate of the reactor to return to and stabilize at the optimal steady-state conditions.

[0092] For example, when the reactor is a multi-stage reactor, Model Predictive Control (MPC) or Deep Reinforcement Learning (DRL) can be used to take the parameter gradients of each stage as decision variables, take selectivity and conversion rate as dual optimization objectives, and perform optimization under set constraints to output the globally optimal cross-stage parameter gradient combination.

[0093] In the above embodiments, by determining the parameter adjustment gradient based on the type of interference, blind and large-scale parameter adjustments are avoided. For the characteristics of different interferences, appropriate adjustment ranges and rhythms are formulated, which effectively reduces new oscillations caused by improper parameter adjustments, enabling the reactor to cope with interference more smoothly, ensuring the stability of the reaction process, and thus maintaining the consistency of product quality.

[0094] In some exemplary embodiments, the reactor parameter processing of this application further includes the following steps:

[0095] The operating status of the pretreatment unit is monitored; when the pretreatment unit is detected to be in a stable operating condition, the operating mode of the control loop of the key process parameters of the pretreatment unit is switched to automatic control mode, and the operating mode of the feed side parameters of the reactor is switched to passive control mode; the key process parameters of the pretreatment unit are adjusted to the reference state value through automatic control mode, so that the feed side parameters of the reactor are adjusted to the reference state value under the control of passive control mode.

[0096] Among them, stable operating condition refers to the deviation between the actual value and the reference value being ≤ the allowable range of the process. Key process parameters of the pretreatment unit include feed composition, feed flow rate, feed temperature, thermal integration tower temperature, pressure, and component state.

[0097] Specifically, when the pretreatment unit is detected to be in a stable operating state, the operating mode of the control loop of the key process parameters of the pretreatment unit is switched to automatic control mode, and the operating mode of the feed side parameters of the reactor is switched to passive control mode. This can automatically adjust the key process parameters of the pretreatment unit to the reference state value, thereby driving the feed side parameters of the reactor to automatically adjust to the reference state value.

[0098] For example, refer to Figure 5 After the six feed streams FEED1-FEED6 enter the thermal integration tower (C01), the series control of "stable multi-stream feed flow rate → stable tower temperature / level / pressure → stable discharge temperature / composition" is achieved through the automatic coordinated adjustment of FIC, TIC, and LIC. The discharge from the bottom of the thermal integration tower is directly used as the feed for the multi-stage reactor. The controlled mode ensures that the reactor feed parameters are consistent with the baseline values ​​of the pretreatment unit. Specifically:

[0099] First, use signals from instruments such as FIC (Flow Indication Controller), TIC (Temperature Indication Controller), and LIC (Level Indication Controller) to perform signal acquisition and logic checks to ensure that the following parameters are in a stable initial state:

[0100] Feed side: Feed flow rate, such as the flow rate of FEED1-FEED6, is monitored by the corresponding FIC or associated instrument, such as the flow rate of FEED1 being controlled by the FIC; Feed temperature, such as the temperature of FEED1-FEED6, such as the temperature of FEED4 being monitored by TIC03;

[0101] Thermal integration tower (C01) side: tower temperature, tower pressure, tower liquid level, material composition;

[0102] If the above parameters meet the baseline requirements, initialize the following control loop's operational status:

[0103] 1) Flow control loop: The FIC regulating valves of FEED1 and other pipelines, and the VIC01 (Valve Position Indication Controller) of FEED3 pipeline are in the "automatic commissioning preparation" state; 2) Temperature control loop: The PID controllers of TIC01 (thermal integration tower temperature), TIC02 (tower section temperature), and TIC03 (feed temperature) are initialized and the reference temperature value is set, for example, the reference value of TIC01 is set to 120℃ to ensure the thermal integration effect; 3) Liquid level control loop: The liquid level regulating valve of LIC is initialized to maintain the liquid level of the thermal integration tower at the reference value.

[0104] Furthermore, the actual control modes of the thermal integration tower temperature, tower pressure, and feed flow rate can be switched to automatic control mode by activating the "Activate" button. For example, TIC01 enters cascade control mode, using the thermal integration tower temperature as the feedback signal and FEED1 flow rate as the feedforward, to automatically adjust the reboiler steam quantity / condenser cooling quantity, accurately maintaining the stability of the tower temperature; the FEED3 pipeline is adjusted through the VIC01 valve position, in conjunction with TIC01 control, to ensure the stable coordination of feed and tower temperature.

[0105] Switch the actual control mode of the feed temperature (such as the TIC03 control loop of FEED4) and feed flow rate (such as the VIC01 associated with FEED3 and other feed flow rate loops not explicitly labeled) to the controlled mode. In this mode, the parameters are no longer adjusted independently, but are determined by the automatic control output of the thermal integration tower, and are coordinated with the operating conditions of the thermal integration tower. For example, the temperature of FEED4 (monitored by TIC03) is stably linked to the temperature of the thermal integration tower (controlled by TIC01), eliminating the need for separate adjustment of the FEED4 heating / cooling equipment; the flow rate of FEED3 is automatically adjusted according to the overall material balance requirements of the thermal integration tower via the VIC01 valve position.

[0106] Based on parameters such as feed flow rate, feed temperature, thermal integration tower temperature, tower pressure, material composition, and liquid level inside the tower, the optimal operating parameters of the thermal integration tower are automatically calculated, so that the temperature and composition of the liquid phase discharged from the bottom of the thermal integration tower are stabilized at the reference state value, and can be directly used as qualified feed for multi-stage reactors.

[0107] Optionally, when the temperature of the thermal integration tower is abnormal (e.g., the TIC01 detection value > process safety threshold), the "temperature output limit" is automatically triggered, cutting off the automatic control command of TIC01 and forcibly switching to manual intervention mode (corresponding to the "remove" button function) to avoid excessive vaporization / coking of materials due to temperature runaway; Optionally, when the valve position is abnormal (e.g., VIC01 opening > 90% or < 10%), the "valve position output limit" is automatically triggered, locking the opening of the regulating valve to prevent sudden changes in material flow from causing gas-liquid imbalance in the tower; Optionally, the "switching control" module can manually / automatically switch the control logic when the automatic control is abnormal to ensure the safe operation of the unit.

[0108] In the above embodiments, by switching the working mode of the control loop of the key process parameters of the pretreatment unit to automatic control mode and switching the working mode of the feed side parameters of the reactor to passive control mode, the pretreatment unit is automated, reducing the number of operators and lowering the probability of human error.

[0109] In a specific embodiment, such as Figure 6 As shown, a reactor parameter processing method is provided. The computer equipment can perform selective optimization based on the feed information of the multi-stage reactor, that is, predict the selectivity of the multi-stage reactor through the feed information, and drive the multi-stage reactor to adjust the parameter values ​​according to the prediction results. Furthermore, the product components separated by the separation unit can be monitored to obtain product component monitoring data. This product component monitoring data can be used as conversion rate feedback data. The computer equipment can drive the multi-stage reactor to adjust the parameters according to the conversion rate feedback data. The computer equipment can also adjust the separation unit parameters such as the distillation column temperature and reflux ratio in a coordinated manner, thereby matching the changes in reactor output and ensuring the authenticity of the product component monitoring data.

[0110] In a specific embodiment, such as Figure 7 As shown, a reactor parameter processing method is provided. The pretreatment unit initializes parameters through dynamic monitoring of operating conditions, adjusting the feed to the reactor's optimal initial operating conditions, laying the foundation for reaction stability. For the reactor, reaction selectivity prediction modeling enables the coordinated adjustment of key parameters, such as simultaneously adjusting temperature and oxygen content to balance selectivity and conversion. The separation unit detects product conversion and uses a feedback closed-loop for reverse adjustment; for example, if the conversion is too low, the reactor temperature / air flow rate is adjusted; if raw material impurities affect separation, feedback is sent to the pretreatment unit to enhance impurity removal, such as increasing the reflux ratio of the distillation column.

[0111] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0112] Based on the same inventive concept, this application also provides a reactor parameter processing apparatus for implementing the reactor parameter processing method described above. The solution provided by this apparatus is similar to the solution described in the above method; therefore, the specific limitations in one or more reactor parameter processing apparatus embodiments provided below can be found in the limitations of the reactor parameter processing method described above, and will not be repeated here.

[0113] In some exemplary embodiments, such as Figure 8 As shown, a reactor parameter processing device 800 is provided, comprising:

[0114] The trend prediction module 802 is used to predict the trend of changes in the performance indicators of the reactor based on the reactor-related operating status data, and to adjust the parameter values ​​of the reactor based on the obtained prediction results; the performance indicators include at least selectivity.

[0115] The parameter value adjustment module 804 is used to perform a step of predicting the change trend of the reactor's performance index based on the reactor's relevant operating status data when the actual value of the reactor's performance index does not meet the stable operating conditions after the adjustment is completed, until the actual value of the performance index meets the stable operating conditions.

[0116] The feedback adjustment module 806 is used to adjust the parameters of the reactor based on the product component feedback data of the separation unit, when the actual values ​​of the performance indicators meet the stable operating conditions, until the selectivity of the reactor and the product conversion rate indicated by the product component feedback data meet the optimal steady-state conditions.

[0117] The aforementioned reactor parameter processing device predicts the changing trends of reactor performance indicators based on reactor-related operating status data, and adjusts reactor parameter values ​​based on the obtained prediction results. Performance indicators include at least selectivity. After adjustment, if the actual values ​​of reactor performance indicators do not meet stable operating conditions, the device executes a step of predicting the changing trends of reactor performance indicators based on reactor-related operating status data until the actual values ​​of performance indicators meet stable operating conditions. Once the actual values ​​of performance indicators meet stable operating conditions, the device adjusts reactor parameter values ​​based on product component feedback data from the separation unit until the reactor's selectivity and the product conversion rate indicated by the product component feedback data meet the optimal steady-state conditions. By using reactor operating status data to predict selectivity changing trends in advance, the device compensates for parameter adjustments before physical lag occurs, avoiding parameter oscillations caused by lag, reducing side reactions caused by parameter oscillations, stabilizing the main product content, and ensuring product quality stability. After performance indicators meet stable operating conditions, the device constructs a closed-loop optimization of selectivity and conversion rate through product component feedback from the separation unit, breaking through the limitations of traditional single-parameter control, achieving synergy between high selectivity and high conversion rate, and further improving product quality.

[0118] In some embodiments, the parameter value adjustment module is further configured to predict the changing trend of the reactor's performance indicators based on the reactor's operating status data, and obtain a prediction result; when the prediction result indicates that the changing trend of the performance indicators is abnormal, determine a first parameter value adjustment strategy for the reactor based on the prediction result; and adjust the parameters of the reactor based on the first parameter value adjustment strategy.

[0119] In some embodiments, the trend prediction module is further configured to: acquire the operating status data of each level reactor; and based on the operating status data of each level reactor, predict the trend of performance indicators of each level reactor to obtain the prediction result of each level reactor.

[0120] In some embodiments, the feedback adjustment module is further configured to: acquire product component feedback data of the separation unit within a preset time period; analyze the time series of product component feedback data within the preset time period to determine the type of interference for the reactor; determine a second parameter value adjustment strategy for the reactor based on the type of interference; adjust the parameters of the reactor based on the second parameter value adjustment strategy; and execute the step of acquiring product component feedback data of the separation unit within a preset time period until the selectivity of the reactor and the product conversion rate indicated by the product component feedback data meet the optimal steady-state conditions.

[0121] In some embodiments, the feedback adjustment module is further configured to: determine the parameter adjustment gradient of the reactor based on the type of disturbance; and adjust the parameter values ​​of the reactor according to the parameter adjustment gradient.

[0122] In some embodiments, the reactor parameter processing device is further configured to: monitor the operating status of the pretreatment unit; when the pretreatment unit is detected to be in a stable operating state, switch the operating mode of the control loop of the key process parameters of the pretreatment unit to an automatic control mode, and switch the operating mode of the feed-side parameters of the reactor to a passive control mode; adjust the key process parameters of the pretreatment unit to a reference state value through the automatic control mode, so that the feed-side parameters of the reactor are adjusted to a reference state value under the control of the passive control mode.

[0123] Each module in the aforementioned reactor parameter processing device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0124] In some exemplary embodiments, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9 As shown. The computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a reactor parameter processing method.

[0125] In some exemplary embodiments, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 10As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a reactor parameter processing method. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0126] Those skilled in the art will understand that Figure 9 , Figure 10 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0127] In some exemplary embodiments, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the reactor parameter processing method in any of the above embodiments.

[0128] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the reactor parameter processing method in any of the above embodiments.

[0129] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the reactor parameter processing method in any of the above embodiments.

[0130] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0131] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0132] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method of processing reactor parameters, characterized by, The method includes: The performance indicators of the reactor are predicted based on the reactor's relevant operating status data, and the reactor's parameter values ​​are adjusted based on the obtained prediction results; the performance indicators include at least selectivity. Once the adjustment is complete, if the actual value of the reactor's performance index does not meet the stable operating conditions, the step of predicting the trend of the reactor's performance index based on the reactor's relevant operating status data is executed until the actual value of the performance index meets the stable operating conditions. When the actual values ​​of the performance indicators meet the stable operating conditions, the product component feedback data of the separation unit within a preset time period is obtained. The time series of product component feedback data within the preset time period is analyzed to determine the type of interference to the reactor. Based on the type of interference, a second parameter value adjustment strategy for the reactor is determined, and the reactor parameter values ​​are adjusted based on the second parameter value adjustment strategy. The step of obtaining product component feedback data of the separation unit within a preset time period is performed until the selectivity of the reactor and the product conversion rate indicated by the product component feedback data meet the optimal steady-state conditions.

2. The method of claim 1, wherein, The process of predicting the changing trends of the reactor's performance indicators based on reactor-related operational status data, and adjusting the reactor's parameter values ​​based on the obtained prediction results, includes: The performance indicators of the reactor are predicted based on the reactor's operating status data, and the prediction results are obtained. When the prediction results indicate an abnormal trend in the performance indicators, a first parameter value adjustment strategy for the reactor is determined based on the prediction results. The reactor parameters are adjusted based on the first parameter value adjustment strategy.

3. The method according to claim 2, characterized in that, The reactor is a multi-stage reactor. The prediction of the reactor's performance indicators based on its operating status data, to obtain prediction results, includes: Obtain operational status data for each reactor level; Based on the operating status data of each reactor level, the changing trends of the performance indicators of each reactor level are predicted, and the prediction results for each reactor level are obtained.

4. The method according to claim 1, characterized in that, The step of determining a second parameter adjustment strategy for the reactor based on the type of interference, and adjusting the parameters of the reactor based on the second parameter adjustment strategy, includes: Based on the type of disturbance, determine the parameter adjustment gradient of the reactor; Adjust the gradient according to the parameters, and adjust the parameter values ​​of the reactor accordingly.

5. The method according to any one of claims 1 to 4, characterized in that, The method further includes: Monitor the operating status of the preprocessing unit; When the pretreatment unit is detected to be in a stable operating condition, the operating mode of the control loop of the key process parameters of the pretreatment unit is switched to automatic control mode, and the operating mode of the feed side parameters of the reactor is switched to passive control mode. The key process parameters of the pretreatment unit are adjusted to baseline values ​​through the automatic control mode, so that the feed-side parameters of the reactor are adjusted to baseline values ​​under the control of the passive control mode.

6. A reactor parameter processing device, characterized in that, The device includes: A trend prediction module is used to predict the trend of performance indicators of the reactor based on reactor-related operating status data, and to adjust the parameter values ​​of the reactor based on the obtained prediction results; the performance indicators include at least selectivity; The parameter value adjustment module is used to, after the adjustment is completed, if the actual value of the performance index of the reactor does not meet the stable operating conditions, execute the step of predicting the change trend of the performance index of the reactor based on the reactor-related operating status data, until the actual value of the performance index meets the stable operating conditions. The feedback adjustment module is used to adjust the performance indicators when the actual values ​​meet the stable operating conditions. Acquire product component feedback data of the separation unit within a preset time period; analyze the time series of product component feedback data within the preset time period to determine the type of interference for the reactor; based on the type of interference, determine a second parameter value adjustment strategy for the reactor, and adjust the parameters of the reactor based on the second parameter value adjustment strategy; execute the step of acquiring product component feedback data of the separation unit within the preset time period until the selectivity of the reactor and the product conversion rate indicated by the product component feedback data meet the optimal steady-state conditions.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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