Methanol synthesis catalyst performance online prediction and health management method and system

Abstract

The invention discloses a methanol synthesis catalyst performance online prediction and health management method. The method comprises the following steps: S1, obtaining three groups of time sequence data of reactor inlet temperature, outlet gas CO2 concentration and reactor pressure; s2, the three groups of time sequence data are input into a pre-trained LSTM neural network model, an input layer of the LSTM neural network model comprises three nodes, a hidden layer of the LSTM neural network model comprises 128 nodes, an output layer of the LSTM neural network model comprises one node, and the LSTM neural network model is used for outputting a catalyst activity predicted value; and S3, when the predicted value of the catalyst activity is lower than 85.0%, triggering an early warning signal. According to the method, the catalyst performance prediction response delay and the false alarm rate are greatly reduced, the service life utilization rate of the catalyst is also improved, hardware modification is not needed, dependence on internal data of the catalyst is completely avoided, and high-benefit and zero-risk intelligent operation and maintenance are realized.

Description

Technical Field

[0001] This invention relates to a method for online prediction of the performance and health management of methanol synthesis catalysts, belonging to the field of intelligent automatic control technology. Background Technology

[0002] In projects utilizing wind and solar renewable energy to produce green methanol, the performance stability of the synthesis catalyst is crucial for ensuring continuous system operation and economic benefits. Catalysts inevitably experience activity decay during use; failure to predict these performance changes in a timely and accurate manner will directly lead to unplanned shutdowns, production capacity fluctuations, and significant economic losses.

[0003] Currently, the industry commonly employs two traditional methods for monitoring the status of methanol synthesis catalysts: The first is manual inspection and periodic testing, relying on operator experience to observe parameters such as reactor temperature and pressure, and periodically stopping the reactor to collect samples for analysis. This method is highly subjective, has a significant response lag, and cannot achieve real-time early warning. The second method is a fixed threshold alarm mechanism, which sets fixed upper and lower alarm limits for a single process parameter (such as reactor temperature or pressure) in a distributed control system (DCS). An alarm is triggered when the parameter exceeds the limit. However, the above-mentioned existing technologies have significant and long-standing drawbacks:

[0004] First, there is a significant prediction lag: because catalyst activity decay is a slow, non-linear process, it is difficult to capture its early gradual trend based solely on the instantaneous value of a single parameter or human experience. Existing solutions typically have a system response delay exceeding 24 hours, failing to provide sufficient lead time for operational and maintenance decisions.

[0005] Secondly, the false alarm rate remains high: the fixed threshold alarm mechanism cannot distinguish between normal fluctuations in the process and the actual degradation of catalyst performance. Because its threshold setting is usually based on a fuzzy range (such as activity below 80% or 90%), it frequently triggers invalid maintenance, with a false alarm rate of over 35%, which seriously disrupts production order and increases operation and maintenance costs.

[0006] Third, there are security and compliance risks: Some solutions attempting to improve accuracy rely on data regarding the physicochemical properties of the catalyst (such as the content of active components and microstructure). This data constitutes the core technical secrets of the catalyst supplier and can only be obtained through contact with the catalyst or reverse engineering in practice. This not only violates confidentiality agreements but also poses risks of intellectual property infringement and data security.

[0007] These fundamental flaws collectively lead to a series of problems in production systems, including frequent unplanned shutdowns, low catalyst lifetime utilization (the industry benchmark is only around 65%), and system availability of less than 95%. Furthermore, despite widespread discussion of advanced concepts such as "digital twins," many implementation schemes in the industry still rely on complex hardware modeling, resulting in high system implementation costs (exceeding 450,000 RMB), lengthy implementation cycles, and frequent compatibility conflicts with mainstream catalyst process packages (such as Johnson Matthey's process package), making large-scale application in existing production systems difficult.

[0008] Therefore, there is an urgent need in this field for a new method and system that can achieve accurate, real-time, and safe online prediction of catalyst performance and health management based solely on existing process operation data, without relying on internal catalyst data. Summary of the Invention

[0009] The purpose of this invention is to provide an online prediction and health management method for methanol synthesis catalyst performance, and also to provide an online prediction and health management system for methanol synthesis catalyst performance. This invention aims to address three major technical shortcomings of traditional monitoring schemes: excessively long prediction response delays, high false alarm rates, and the need for access to internal catalyst data. By integrating three sets of parameters—reactor inlet temperature, outlet CO2 concentration, and reactor pressure—and utilizing a lightweight LSTM neural network model, real-time and accurate prediction of catalyst activity is achieved. Ultimately, this significantly improves catalyst lifetime utilization and system availability while mitigating safety risks.

[0010] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: a method for online prediction of methanol synthesis catalyst performance and health management, comprising the following steps:

[0011] S1. Obtain three sets of time series data: reactor inlet temperature, outlet gas CO2 concentration, and reactor pressure;

[0012] S2. Input the three sets of time series data into a pre-trained LSTM neural network model. The LSTM neural network model has 3 nodes in the input layer, 128 nodes in the hidden layer, and 1 node in the output layer, which is used to output the predicted value of catalyst activity.

[0013] S3. When the predicted catalyst activity is below 85.0%, an early warning signal is triggered; otherwise, monitoring continues.

[0014] The aforementioned method includes training parameters for the LSTM neural network model, including a learning rate of 0.001, a batch size of 32, and 150 training epochs.

[0015] The aforementioned method involves deploying the LSTM neural network model in a DCS system simulation environment using a Python script.

[0016] The aforementioned method involves generating the three sets of time series data using Aspen Plus process simulation software, with a sampling interval of 10 minutes and a simulation runtime of 720 hours.

[0017] The aforementioned method generates simulation data based on the following typical catalyst operating parameters:

[0018] The reactor inlet temperature is 210℃;

[0019] The CO2 concentration in the outlet gas is 12.5%;

[0020] The reactor pressure is 6 MPa.

[0021] An online performance prediction and health management system for methanol synthesis catalysts, comprising:

[0022] The data input layer is used to obtain three sets of time series data—reactor inlet temperature, outlet gas CO2 concentration, and reactor pressure—from the historical database of the DCS system.

[0023] The core processing layer includes a data source processing module, an LSTM neural network model, and a threshold comparator. The data source processing module is used to preprocess the three sets of time series data. The LSTM neural network model has 3 nodes in the input layer, 128 nodes in the hidden layer, and 1 node in the output layer. It is used to output the catalyst activity prediction value based on the three sets of time series data. The threshold comparator is used to judge the catalyst activity prediction value. When the catalyst activity prediction value is lower than 85.0%, an early warning signal is triggered.

[0024] The output quality layer includes a prediction triggering module, which is used to respond to the warning signal and generate an alarm.

[0025] The aforementioned system, in its output quality layer, further includes a health management dashboard and a maintenance decision support module. The health management dashboard is used to display real-time activity curves and remaining life predictions, and the maintenance decision support module is used to generate unplanned shutdown predictions and maintenance suggestions based on the warning signals.

[0026] The training parameters of the aforementioned system's LSTM neural network model include: a learning rate of 0.001, a batch size of 32, and 150 training epochs.

[0027] The aforementioned system is deployed in a DCS system simulation environment using Python scripts, without requiring any additional hardware.

[0028] The aforementioned system uses three sets of time-series data generated by Aspen Plus process simulation software, with a sampling interval of 10 minutes and a simulation runtime of 720 hours. The simulation data is constructed based on the following typical operating parameters of foreign catalysts:

[0029] The reactor inlet temperature is 210℃;

[0030] The CO2 concentration of the outlet gas is 12.5%;

[0031] The reactor pressure is 6 MPa.

[0032] Compared with the prior art, the present invention has at least the following beneficial effects:

[0033] (1) This invention uses the fusion analysis of three parameters—reactor inlet temperature, outlet CO2 concentration, and pressure—and dynamic prediction by LSTM model to shorten the response delay of catalyst activity prediction from more than 24 hours in the traditional scheme to 4.1 hours, and improve the prediction accuracy to 94.5%. This achieves a fundamental shift from delayed alarm to early and accurate warning, and greatly improves the accuracy and timeliness of prediction.

[0034] (2) The present invention precisely limits the warning threshold to 85.0%, which fundamentally avoids the frequent false alarms caused by the traditional fuzzy threshold, reduces the false alarm rate from 35.2% to 8.9%, effectively reduces unnecessary shutdown maintenance, makes operation and maintenance decisions more scientific and reliable, and significantly enhances operation and maintenance reliability.

[0035] (3) This invention significantly improves catalyst lifetime utilization and system availability, and reduces the number of unplanned shutdowns per year from 5 to 1.1, directly reducing the huge economic losses caused by shutdowns, and comprehensively improving economic benefits and system performance.

[0036] (4) The present invention adopts a pure software-driven solution, which does not require the addition of new hardware equipment. It can be achieved by upgrading the DCS system software, and its cost is less than RMB 300,000. It completely avoids the dependence on the internal characteristic data of the catalyst, realizes intelligent operation and maintenance with zero safety risk, and is 100% compatible with existing foreign process packages. Attached Figure Description

[0037] Figure 1 This is an overall flowchart of the method of the present invention;

[0038] Figure 2 This is a modular block diagram of the system of the present invention.

[0039] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. Detailed Implementation

[0040] Example 1 of the present invention: A method for online prediction of the performance and health management of methanol synthesis catalysts, comprising the following steps:

[0041] S1. Obtain three sets of time-series data: reactor inlet temperature, outlet gas CO2 concentration, and reactor pressure. Specifically, using Aspen Plus process simulation software, a carbon dioxide hydrogenation to methanol process model was constructed strictly based on typical operating parameters published by foreign catalyst manufacturers. The reactor inlet temperature (T_in) was set to 210℃, the outlet gas CO2 concentration (X_CO2) to 12.5%, and the reactor pressure (P) to 6MPa. With a sampling interval of 10 minutes, continuous operation for 720 hours was simulated, generating three sets of time-series data (T_in, X_CO2, P) containing 100,000 data points, thus simulating the complete process of catalyst activity from high activity to degradation.

[0042] S2. Input the three sets of time series data into a pre-trained LSTM neural network model. The LSTM neural network model has an input layer of 3 nodes, a hidden layer of 128 nodes, and an output layer of 1 node, used to output the predicted catalyst activity value. Specifically, the generated 100,000 sets of data are divided into a training set (80,000 sets) and a test set (20,000 sets) in an 8:2 ratio. The LSTM neural network model is used for training, and its structure configuration is: 3 nodes in the input layer (corresponding to T_in, X_CO2, P), 128 nodes in the hidden layer, and 1 node in the output layer (outputting the predicted catalyst activity value). The training parameters are set as follows: learning rate 0.001, batch size 32, and training epochs 150.

[0043] S3. When the predicted catalyst activity is below 85.0%, an early warning signal is triggered; otherwise, monitoring continues.

[0044] This embodiment achieves a prediction accuracy of 92.3% and a false positive rate of 18.7% on the test set, with prediction response latency optimized to 8.5 hours. System availability is improved from the industry benchmark of 95% to 99.1%, and catalyst lifetime utilization is improved from the industry benchmark of 64.9% to 81.7%.

[0045] Example 2 of the present invention: An online prediction and health management system for methanol synthesis catalyst performance, comprising a data input layer, a core processing layer, and an output quality layer, wherein:

[0046] The data input layer is used to obtain three sets of time-series data—reactor inlet temperature, outlet gas CO2 concentration, and reactor pressure—from the DCS system's historical database. This layer is primarily a data acquisition interface deployed on the DCS historical data server. This interface automatically reads the time-series data of these three key process parameters from the DCS real-time database every 10 minutes using standard industrial communication protocols such as OPC UA or Modbus TCP.

[0047] T_in: Reactor inlet temperature (unit: °C);

[0048] X_CO2: CO2 concentration in the reactor outlet gas (unit: %);

[0049] P: Reactor pressure (unit: MPa).

[0050] The core processing layer, the intelligent hub of the system, can be deployed on the application server of a DCS system or a dedicated industrial computer. It includes a data source processing module, an LSTM neural network model, and a threshold comparator. The data source processing module preprocesses the three sets of time-series data. The LSTM neural network model has an input layer of 3 nodes, a hidden layer of 128 nodes, and an output layer of 1 node, used to output predicted catalyst activity values ​​based on the three sets of time-series data. The threshold comparator judges the predicted catalyst activity values, triggering an early warning signal when the predicted catalyst activity value is below 85.0%. Specifically:

[0051] The data source processing module receives the raw data and performs preprocessing. This module, implemented using Python scripts, cleans the three sets of input time series data (e.g., using median filtering to eliminate instantaneous spike noise) and standardizes them to form a standard dataset that meets the input requirements of the LSTM model.

[0052] The LSTM neural network model is the core prediction engine. In this embodiment, a pre-trained lightweight LSTM model is loaded. This model has a structure of 3 input layers, 128 hidden layers, and 1 output layer. It receives pre-processed data and outputs a catalyst activity prediction value between 0 and 100%.

[0053] The threshold comparator is the decision-making unit. It uses a simple logical judgment procedure to compare the activity prediction value output by the model with a strictly fixed 85.0% threshold. It generates a digital "warning signal" if and only if the prediction value is lower than 85.0%.

[0054] The output quality layer, primarily user-facing, is deployed on the DCS operator station and presented in a graphical interface. It includes a predictive triggering module, which responds to the warning signal and generates an alarm. Specifically:

[0055] The predictive trigger module is an alarm actuator. This module listens for warning signals from the core processing layer. Once triggered, it immediately activates an audible and visual alarm on the DCS operator station and generates a clear record in the alarm list, such as "Low catalyst activity alarm: 84.2% < 85.0%".

[0056] The health management dashboard serves as a visualization center, and can be presented as an interactive interface in the form of a web page or configuration software screen. It dynamically plots the real-time activity curve of the catalyst and estimates and displays the remaining life based on the activity decay trend using a built-in algorithm (e.g., "Estimated remaining operating time: 28 days"), providing an intuitive basis for planned maintenance.

[0057] The maintenance decision support module, acting as an advanced analysis and recommendation engine, performs logical reasoning based on early warning signals and historical activity data. It generates and pushes maintenance recommendations (such as "suggest scheduling planned parking within 2 weeks") and risk assessments (such as "risk of unplanned parking within the next week: high"). Simultaneously, the interface displays key performance indicators, such as the current system's "availability: 99.4%" and "false alarm rate this month: 8.9%", to quantify management effectiveness.

[0058] The system in this embodiment achieves 24 / 7 automatic monitoring of catalyst health through the closed-loop linkage of the above modules. During implementation, only the above software modules need to be packaged, tested and verified in the DCS simulation environment, and then deployed to the production environment. The entire implementation process does not require any new hardware sensors or equipment. Ultimately, it achieves the technical effects of reducing the prediction response delay to 4.1 hours, reducing the false alarm rate to 8.9%, and increasing the catalyst lifetime utilization rate to 82.6%, providing enterprises with a highly efficient and zero-risk intelligent operation and maintenance solution.

[0059] The technical solution of the present invention will be described in detail below with specific embodiments and comparative experiments to demonstrate its specific implementation process, technical effects, and significant progress compared with the prior art. All experiments are based on simulation data and industry standard parameters generated by Aspen Plus process simulation software and verified in the DCS software simulation environment without requiring any modifications to the existing hardware system.

[0060] Embodiment 3 of the present invention:

[0061] 100,000 sets of simulation data were generated based on the Aspen Plus process simulation software. The data were constructed strictly according to the typical operating parameters disclosed by foreign catalysts: the reactor inlet temperature was fixed at 210℃ (industry standard operating point, not range value), the outlet gas CO2 concentration was set at 12.5% ​​(typical value), and the reactor pressure was maintained at 6MPa (design standard value).

[0062] Using Aspen Plus, a continuous 720-hour run was simulated with a 10-minute sampling interval to generate three sets of time-series data (T_in, X_CO2, P), ensuring the data conforms to the actual fluctuation characteristics of the methanol synthesis process. The dataset was divided into a training set (80,000 sets) and a test set (20,000 sets) in an 8:2 ratio, and trained using an LSTM neural network model: 3 nodes in the input layer (corresponding to T_in, X_CO2, and P respectively), 128 nodes in the hidden layer, and 1 node in the output layer. The learning rate was precisely set to 0.001, the batch size to 32, and the training epochs to 150. The catalyst activity prediction threshold was strictly defined as 85% (non-fuzzy range), and an automatic warning was triggered when the model output activity fell below this value.

[0063] Model validation results show that the prediction accuracy reached 92.3% (test set), the false alarm rate decreased to 18.7% (compared to 35.2% for the conventional approach), and the activity prediction response delay was optimized to 8.5 hours (compared to over 24 hours for the conventional approach). System availability improved from a baseline of 95% to 99.1%, and catalyst lifetime utilization improved from an industry benchmark of 64.9% to 81.7%.

[0064] Embodiment 4 of the present invention:

[0065] Based on Example 3, the model performance was optimized by precisely fine-tuning the reactor inlet temperature threshold.

[0066] The experimental materials used were 100,000 sets of Aspen Plus simulation data that were completely consistent with those in Example 1 (reactor inlet temperature T_in = 210℃, outlet gas CO2 concentration X_CO2 = 12.5%, reactor pressure P = 6MPa) to ensure data homogeneity and comparability.

[0067] The key innovation lies in precisely adjusting the T_in threshold from the benchmark value of 210℃ to 218.5℃ (based on the industry-disclosed parameter of the catalyst activity decay inflection point), while strictly maintaining X_CO2 = 12.5% ​​and P = 6MPa unchanged.

[0068] The model training parameters were completely reused from the configuration in Example 1: LSTM neural network with 3 input layer nodes (T_in / X_CO2 / P), 128 hidden layer nodes, and 1 output layer node, with a learning rate of 0.001, batch size of 32, and 150 training epochs. Validation results showed that the prediction accuracy was improved to 93.8% (test set), the false positive rate was significantly reduced to 12.4% (a 6.3% reduction compared to Example 3), and the live prediction response latency was optimized to 6.2 hours (a 2.3-hour reduction compared to Example 1).

[0069] Catalyst lifetime utilization improved from 81.7% to 82.3%, and system availability improved from 99.1% to 99.3%. This optimization validated the decisive impact of precise threshold fine-tuning on system performance: for every 0.5℃ increase in the T_in threshold (e.g., from 218.5℃ to 219℃), the false alarm rate was further reduced by 0.8%, and the catalyst utilization improved by 0.3%.

[0070] Embodiment 5 of the present invention:

[0071] Based on Example 3, this embodiment significantly improves the predictive performance by using the pressure parameter P of the fusion reactor.

[0072] The experimental materials used were 100,000 sets of Aspen Plus simulation data that were completely consistent with those in Example 1 (reactor inlet temperature T_in = 210℃, outlet gas CO2 concentration X_CO2 = 12.5%, reactor pressure P = 6MPa) to ensure data homogeneity and comparability.

[0073] The key innovation lies in adding the P parameter (fixed value 6MPa) as an independent input feature to the LSTM input layer, while optimizing the learning rate to 0.0008 and the batch size to 64. Other model parameters strictly reuse the configuration of Example 1 (3 nodes in the input layer, 128 nodes in the hidden layer, 1 node in the output layer, and 150 training epochs).

[0074] Validation results showed that the prediction accuracy reached 94.5% (test set), the false alarm rate decreased to 8.9% (a reduction of 9.8% compared to Example 3), and the activity prediction response delay was optimized to 4.1 hours (a reduction of 20.4 hours compared to Example 1). Catalyst lifetime utilization increased from 81.7% to 82.6%, and system availability increased from 99.1% to 99.4%. This fusion strategy confirmed the synergistic effect of pressure parameters: when the P threshold was finely adjusted within the range of 5.8-6.2 MPa, the false alarm rate decreased by 0.5% for every 0.1 MPa reduction, and the catalyst utilization increased by 0.2%.

[0075] Comparative examples of the present invention:

[0076] Traditional solutions employ fixed threshold alarm mechanisms (such as monitoring only a single parameter like reactor temperature or pressure), resulting in prediction response delays of up to 24 hours (failing to capture catalyst activity decay trends in a timely manner), false alarm rates as high as 35.2% (frequently triggering ineffective maintenance), catalyst lifetime utilization of only 64.9% (industry benchmark), system availability maintained at 95%, and up to 5 unplanned shutdowns per year (annual losses exceeding 10 million yuan).

[0077] This patent embodiment 5 uses three precise parameters—reactor inlet temperature (210°C), outlet gas CO2 concentration (12.5%), and reactor pressure (6 MPa)—and an LSTM neural network model to achieve real-time dynamic evaluation of catalyst activity (with a strictly limited threshold of 85%). This reduces the prediction response delay to 4.1 hours (an improvement of 82.9% compared to traditional methods), lowers the false alarm rate to 8.9% (a reduction of 74.6%), increases catalyst lifetime utilization to 82.6% (an increase of 27.3%), improves system availability to 99.4% (an increase of 4.4%), and reduces the number of unplanned shutdowns per year to 1.1 (a reduction of 78.0%).

[0078] All parameters are based on precise data generated by Aspen Plus simulation (e.g., T_in = 210℃, threshold 85.0%); verified in a DCS software simulation environment, with an implementation cost of less than 300,000 RMB, requiring no hardware modifications, and completely avoiding dependence on catalyst internal data. This comparison verifies the substantial innovation of this patent: threshold accuracy to 85% (non-fuzzy range) and multi-parameter synergistic effect (e.g., when the pressure parameter is finely adjusted within the 5.8-6.2MPa range, the false alarm rate can be reduced by 0.5% for every 0.1MPa reduction). Detailed data comparisons of the above specific embodiments and comparative experiments are shown in the following table: Table 1: Parameter Configuration and Verification Results of Example 1

[0079]

[0080] Table 2: Optimization parameters and results of Example 2

[0081]

[0082]

[0083] Table 3: Fusion parameters and results of Example 3

[0084]

[0085] Table 4: Comparison between the traditional solution and Embodiment 3 of this patent

[0086]

[0087]

[0088] The working principle of one embodiment of the present invention is as follows: The present invention uses a lightweight LSTM neural network model in a purely software-based manner to fuse and analyze three sets of time-series data—reactor inlet temperature, outlet CO2 concentration, and reactor pressure—to dynamically evaluate catalyst activity in real time. This method innovatively limits the warning threshold precisely to 85.0% and, by deploying the model within an existing DCS system, completely avoids dependence on internal catalyst characteristic data.

[0089] This technical solution has achieved significant beneficial effects: reducing the predictive response delay from over 24 hours in traditional solutions to 4.1 hours, and drastically decreasing the false alarm rate from 35.2% to 8.9%. Simultaneously, catalyst lifetime utilization has increased from the industry benchmark of 65% to 82.6%, system availability has increased from 95% to 99.4%, and the average number of unplanned shutdowns per year has decreased from 5 to 1.1. The entire solution has an implementation cost of less than 300,000 yuan, requires no additional hardware, and achieves highly efficient and risk-free intelligent operation and maintenance, providing core technical support for the stable and efficient operation of green methanol projects.

Claims

1. A method for online prediction of performance and health management of a methanol synthesis catalyst, characterized in that, The method comprises the following steps: S1, obtaining three groups of time series data of reactor inlet temperature, outlet gas CO2 concentration and reactor pressure; S2, inputting the three groups of time series data into a pre-trained LSTM neural network model, the input layer of the LSTM neural network model has 3 nodes, the hidden layer has 128 nodes, and the output layer has 1 node, which is used for outputting catalyst activity prediction value; S3, triggering a warning signal when the catalyst activity prediction value is lower than 85.0%, otherwise, continuously monitoring.

2. The method of claim 1, wherein, The training parameters of the LSTM neural network model include: learning rate is 0.001, batch size is 32, and training round is 150.

3. The method of claim 1, wherein, The LSTM neural network model is deployed in the DCS system simulation environment through a Python script.

4. The method of claim 1, wherein, The three groups of time series data are generated by Aspen Plus process simulation software, the sampling interval is 10 minutes, and the simulation running time is 720 hours.

5. The method of claim 4, wherein, The generated simulation data is constructed according to the following typical catalyst working condition parameters: The reactor inlet temperature is 210℃; The outlet gas CO2 concentration is 12.5%; The reactor pressure is 6MPa.

6. An online methanol synthesis catalyst performance prediction and health management system, characterized in that, It comprises: A data input layer for obtaining three groups of time series data of reactor inlet temperature, outlet gas CO2 concentration and reactor pressure from the DCS system historical database; A core processing layer comprising a data source processing module, an LSTM neural network model and a threshold comparator, the data source processing module is used for pre-processing the three groups of time series data, the input layer of the LSTM neural network model has 3 nodes, the hidden layer has 128 nodes, and the output layer has 1 node, which is used for outputting catalyst activity prediction value according to the three groups of time series data, and the threshold comparator is used for judging the catalyst activity prediction value, and triggering a warning signal when the catalyst activity prediction value is lower than 85.0%; An output quality layer comprising a prediction triggering module, the prediction triggering module is used for responding to the warning signal and generating an alarm.

7. The system of claim 6, wherein, The output quality layer also comprises a health management dashboard and a maintenance decision support module, the health management dashboard is used for displaying real-time activity curve and remaining life prediction, and the maintenance decision support module is used for generating non-scheduled shutdown prediction and maintenance suggestion based on the warning signal.

8. The system of claim 7, wherein, The training parameters of the LSTM neural network model include: learning rate is 0.001, batch size is 32, and training round is 150.

9. The system of claim 7, wherein, The system is deployed in the DCS system simulation environment through a Python script, without adding new hardware devices.

10. The system of claim 7, wherein, The three groups of time series data are generated by Aspen Plus process simulation software, the sampling interval is 10 minutes, and the simulation running time is 720 hours, and the simulation data is constructed according to the following typical working condition parameters of foreign catalysts disclosed: The reactor inlet temperature is 210℃; The outlet gas CO2 concentration is 12.5%; The reactor pressure is 6MPa.

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