System and method for rapidly accounting material balance and product yield of DMTO device

Abstract

The invention relates to the technical field of chemical production, and discloses a system and method for rapidly accounting material balance and product yield of a DMTO device, and the system and method are connected with sensors and instruments at all points of the DMTO device, and collect material multi-dimensional data of the DMTO device in real time; the material multi-dimensional data acquired by the data acquisition module is transmitted to the data processing module; pre-processing the received data, and calculating the pre-processed data according to a preset material balance model and the product yield; storing the long-time series original acquisition data, the processed data, the material balance result of each accounting and the product yield value; displaying a real-time material balance condition and a product yield dynamic change curve through a visual interface; according to the method, automatic data extraction and calculation in the raw material production process are achieved, the data processing process is simple, efficient and accurate, and efficient, stable and excellent operation of the DMTO device is deeply monitored.

Description

Technical Field

[0001] The invention relates to the technical field of chemical production, and in particular to a system and method for quickly calculating material balance and product yield of a DMTO device. Background Art

[0002] In the current operation and management of DMTO units, the raw material conversion rate and product yield are usually simply calculated through the actual conversion of raw materials and product quantities. Various technical parameters in the production process require the process data to be exported from the production unit, and then go through a complicated data processing process before the so-called effective guidance data can be barely obtained. More importantly, the accounting technology involving the material balance of the DMTO unit in the entire system is almost blank. This traditional operating mode has many drawbacks, which seriously restricts the improvement of production efficiency and quality.

[0003] The calculation process of raw material conversion rate, product yield and technical parameters of the production process is extremely complicated; operators need to manually collect a large amount of field data from different points and different time periods. These data are not only scattered in source, but also have different formats and precisions, making integration and processing extremely difficult; due to the lack of automated data collection and integration methods, in order to ensure the reliability of the results as much as possible, the scope of data collection has to be expanded, resulting in an exponential increase in the amount of data, further increasing the calculation burden; the data is completely dependent on manual operation, from data reading, recording to calculation, each link may introduce errors due to human negligence, such as typos when transcribing data, incorrect formula application during the calculation process, etc.; the accumulation of many error factors has greatly reduced the credibility of the final raw material conversion rate, product yield and technical parameters, and cannot provide accurate support for production decisions; finally, the DMTO device involves material balance without accounting technology, and cannot monitor the flow and conversion of materials in the device in real time. Once a material imbalance occurs, it is difficult to detect and adjust in time, which can easily cause production failures and cause economic losses. Summary of the invention

[0004] The purpose of the present invention is to solve the above problems and to design a system and method for quickly calculating the material balance and product yield of a DMTO device.

[0005] The first aspect of the present invention provides a system for quickly calculating the material balance and product yield of a DMTO device, wherein the system comprises a data acquisition module, a data transmission module, a data processing module, a storage module and a display and interaction module, wherein:

[0006] A data acquisition module, which collects multi-dimensional material data of the DMTO device in real time by connecting to sensors and instruments at various points of the DMTO device, wherein the multi-dimensional material data at least includes material flow data, material temperature data and material pressure data;

[0007] A data transmission module, configured to transmit the multi-dimensional material data collected by the data acquisition module to the data processing module;

[0008] A data processing module, configured to preprocess the received data and perform operations on the preprocessed data according to a pre-set material balance model and product yield calculation;

[0009] A storage module, configured to store the original collected data in a long time series, the processed data, the material balance results of each calculation, and the product yield values;

[0010] A display and interaction module, configured to display the real-time material balance status and the dynamic change curve of the product yield through a visual interface.

[0011] Optionally, in the first implementation manner of the first aspect of the present invention, the data acquisition module includes a flow data acquisition sub-module, a temperature data acquisition sub-module, and a pressure data acquisition sub-module, wherein,

[0012] The flow data acquisition sub-module is configured to obtain the real-time monitoring value of the material flow velocity in the pipeline, automatically adjust the sampling frequency by using a dynamic adaptive sampling algorithm, and perform weighted fusion on the data of the turbine flowmeter and the mass flowmeter to obtain the material flow data;

[0013] The temperature data acquisition sub-module is configured to perform real-time filtering processing on the data collected by the thermocouple thermometer in the DMTO device by using Kalman filtering, remove noise interference, and perform temperature deviation compensation and correction to obtain the material temperature data;

[0014] The pressure data acquisition sub-module is configured to analyze the correlation of the pressure data fluctuations collected by the pressure sensor in adjacent time periods, determine whether there is an abnormal situation in the pipeline, and obtain the material pressure data, where the abnormal situation includes at least blockage and / or leakage.

[0015] Optionally, in the second implementation manner of the first aspect of the present invention, the data transmission module includes a flow distribution sub-module, a data encryption sub-module, and a data verification sub-module, wherein,

[0016] The flow distribution sub-module is configured to form multiple paths to transmit data through an industrial Ethernet, and allocate the traffic of each path by using the MPTCP protocol;

[0017] The data encryption sub-module is configured to exchange the key for data transmission by using an asymmetric encryption algorithm, and then batch-encrypt the multi-dimensional material data by using a symmetric encryption algorithm;

[0018] The data verification sub-module is configured to generate a verification code for each data packet of the encrypted multi-dimensional material data by using the CRC algorithm to verify whether the data is complete through the verification code.

[0019] Optionally, in the third implementation manner of the first aspect of the present invention, the data processing module includes an outlier rejection sub-module, a missing data filling sub-module, a material balance calculation sub-module, and a yield calculation sub-module, where

[0020] The outlier rejection sub-module is used to calculate the statistical characteristics of each data sequence of the multi-dimensional material data, and determine the data points that deviate more than 3 times the standard deviation from the mean as outliers and reject them, where the statistical characteristics at least include the mean and the standard deviation;

[0021] The missing data filling sub-module is used to predict and fill the missing data by using the ARIMA model based on the time series;

[0022] The material balance calculation sub-module is used to construct a material flow analysis algorithm based on the DMTO reaction process through a material balance model, with the methanol-to-olefins reaction system as the core, analyze the reactant input, product output, and by-product generation in each reaction step, combine the measured data of the molar mass, reaction conversion rate, feed flow rate, and discharge flow rate of each substance, construct a linear equation system, solve the equation system by using the Gaussian elimination method, calculate the transfer amount and loss amount of each material, and obtain the material balance result of the entire device;

[0023] The yield calculation sub-module is used to determine the yield by the ratio of the actual output of each material to the raw material input, calculate the confidence interval of the yield according to the statistical distribution of multiple measurement data, and automatically trace back to the data acquisition module when the yield calculation result exceeds the confidence interval.

[0024] The second aspect of the present invention provides a method for quickly calculating the material balance and product yield of a DMTO device. The method for quickly calculating the material balance and product yield of a DMTO device includes the following steps:

[0025] By connecting to the sensors and instruments at each point of the DMTO device, the multi-dimensional material data of the DMTO device is collected in real time, where the multi-dimensional material data at least includes material flow data, material temperature data, and material pressure data;

[0026] Transmit the multi-dimensional material data collected by the data acquisition module to the data processing module;

[0027] Preprocess the received data, and perform operations on the preprocessed data according to the pre-set material balance model and product yield calculation;

[0028] Store the original collected data, processed data, material balance result of each calculation, and product yield value of the long time series;

[0029] The real-time material balance status and the dynamic change curve of product yield are displayed through a visual interface.

[0030] Optionally, in a first implementation of the second aspect of the present invention, the preprocessing of the received data and the operation of the preprocessed data according to a preset material balance model and product yield calculation include:

[0031] Calculate the statistical characteristics of each data sequence of the material multidimensional data, and determine the data points that deviate from the mean by more than 3 times the standard deviation as outliers and remove them. The statistical characteristics include at least the mean and standard deviation.

[0032] For missing data, the ARIMA model based on time series is used for forecasting and filling;

[0033] A material flow analysis algorithm based on the DMTO reaction process is constructed through a material balance model. Taking the methanol to olefins reaction system as the core, the reactant input, product output and by-product generation in each reaction step are analyzed. The linear equations are constructed by combining the molar mass of each substance, the reaction conversion rate, and the measured data of the feed flow rate and the discharge flow rate. The Gaussian elimination method is used to solve the equations, calculate the flow and loss of each material, and obtain the material balance result of the entire device.

[0034] The yield is determined by the ratio of the actual output of each material to the raw material input. The confidence interval of the yield is calculated based on the statistical distribution of multiple measurement data. When the yield calculation result exceeds the confidence interval, data collection is automatically traced back.

[0035] Optionally, in a second implementation of the second aspect of the present invention, the predictive filling of missing data using an ARIMA model based on a time series includes:

[0036] Perform first-order differences on the data after removing outliers, calculate the difference between two adjacent data points, form a new sequence, perform a stationarity test on the new sequence, and stabilize the new sequence through differential operations;

[0037] After confirming that the data is stationary, the least squares method is used to estimate the parameters of the ARIMA model, and the optimal parameter combination is found through multiple iterations to predict the missing data and fill the data gaps through the ARIMA model.

[0038] Optionally, in a third implementation of the second aspect of the present invention, the yield is determined by the ratio of the actual output of each material to the raw material input, and the confidence interval of the yield is calculated according to the statistical distribution of multiple measurement data. When the yield calculation result exceeds the confidence interval, the data collection is automatically backtracked, including:

[0039] Based on the statistical distribution of multiple measurement data, the confidence interval of the yield is calculated using the normal distribution hypothesis: calculate the sample mean of the yield and the sample standard deviation s. According to the confidence level of 95%, the corresponding critical value z is obtained by looking up the normal distribution table. The lower limit of the confidence interval is The upper limit of the confidence interval is where n is the number of measurements.

[0040] In the technical solution provided by the present invention, by connecting with sensors and instruments at various points of the DMTO device, the multi-dimensional data of the materials of the DMTO device are collected in real time; the multi-dimensional data of the materials collected by the data collection module are transmitted to the data processing module; the received data is pre-processed, and based on the pre-set material balance model and product yield calculation, the pre-processed data is calculated; the original collected data, the processed data, the material balance results of each calculation and the product yield values of the long time series are stored; the real-time material balance status and the dynamic change curve of the product yield are displayed through the visualization interface; the present invention greatly shortens the calculation time, from several hours or even several days of manual calculation in the past to the minute level, meeting the requirements of real-time production decision-making; improves the calculation accuracy, reduces the errors caused by human factors, and provides a reliable basis for production optimization; through visualization display and convenient interaction, it enhances the operator's control over the operation status of the device, helps to promptly discover and solve production problems, and improves the overall operation efficiency and economic benefits of the DMTO device. Brief Description of the Drawings

[0041] By reading the detailed description of the preferred embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention.

[0042] Figure 1 It is a schematic diagram of the first embodiment of the method for quickly calculating the material balance and product yield of the DMTO device provided by the embodiment of the present invention;

[0043] Figure 2 It is a schematic diagram of the second embodiment of the method for quickly calculating the material balance and product yield of the DMTO device provided by the embodiment of the present invention;

[0044] Figure 3 It is a schematic diagram of the structure of the system for quickly calculating the material balance and product yield of the DMTO device provided by the embodiment of the present invention;

[0045] Figure 4 It is a schematic diagram of the structure of the data processing module provided by the embodiment of the present invention. Detailed Embodiments

[0046] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, device, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0047] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 A schematic diagram of a first embodiment of a method for rapidly calculating material balance and product yield of a DMTO device provided in an embodiment of the present invention, wherein the method specifically comprises the following steps:

[0048] Step 101, by connecting with sensors and instruments at various points of the DMTO device, real-time material multi-dimensional data of the DMTO device is collected, wherein the material multi-dimensional data at least includes material flow data, material temperature data and material pressure data;

[0049] Step 102: Transmit the material multi-dimensional data collected by the data collection module to the data processing module;

[0050] Step 103, preprocessing the received data, and performing calculations on the preprocessed data according to a preset material balance model and product yield calculation;

[0051] Step 104, storing the original collected data, processed data, material balance results of each calculation and product yield values ​​for a long time series;

[0052] Step 105: Display the real-time material balance status and the dynamic change curve of product yield through a visual interface.

[0053] See also Figure 2 , a schematic diagram of a second embodiment of a method for rapidly calculating material balance and product yield of a DMTO device provided in an embodiment of the present invention, the method comprising:

[0054] Step 201, calculate the statistical characteristics of each data sequence of the material multidimensional data, and determine the data points that deviate from the mean by more than 3 times the standard deviation as outliers and remove them, wherein the statistical characteristics at least include the mean and the standard deviation;

[0055] Step 202: For missing data, use the ARIMA model based on time series to perform prediction and filling;

[0056] Step 203: Construct a material flow analysis algorithm based on the DMTO reaction process through a material balance model. Taking the methanol-to-olefins reaction system as the core, analyze the input of reactants, output of products, and generation of by-products in each reaction step. Combine the molar masses of various substances, reaction conversion rates, and measured data of feed flow rates and discharge flow rates to construct a system of linear equations, and use the Gaussian elimination method to solve the equations to calculate the transfer amounts and loss amounts of each material, and obtain the material balance result of the entire device;

[0057] Step 204: Determine the yield by the ratio of the actual output of each material to the input of raw materials. According to the statistical distribution of multiple measurement data, calculate the confidence interval of the yield. When the calculated result of the yield exceeds the confidence interval, automatically trace back the data collection.

[0058] In this embodiment, for missing data, use the ARIMA model based on time series for prediction and filling, including:

[0059] Perform first-order differencing on the data after removing outliers, calculate the difference between adjacent two data points to form a new sequence, and perform a stationarity test on the new sequence to make the new sequence stationary through differencing operations;

[0060] After determining that the data is stationary, use the least squares method to estimate the parameters of the ARIMA model, and find the optimal parameter combination through multiple iterations to predict the missing data through the ARIMA model and fill the data gap.

[0061] In this embodiment, determine the yield by the ratio of the actual output of each material to the input of raw materials. According to the statistical distribution of multiple measurement data, calculate the confidence interval of the yield. When the calculated result of the yield exceeds the confidence interval, automatically trace back the data collection, including:

[0062] According to the statistical distribution of multiple measurement data, calculate the confidence interval of the yield by assuming a normal distribution: calculate the sample mean of the yield and the sample standard deviation s, look up the corresponding critical value z in the normal distribution table according to the confidence level of 95%, the lower limit of the confidence interval is The upper limit of the confidence interval is where n is the number of measurements.

[0063] Please refer to Figure 3 , the structural schematic diagram of the system for quickly calculating the material balance and product yield of the DMTO device provided by the embodiment of the present invention. The system includes a data collection module, a data transmission module, a data processing module, a storage module, and a display and interaction module. Among them,

[0064] The data acquisition module is connected to the sensors and instruments at various points of the DMTO device to collect the multi-dimensional data of the materials of the DMTO device in real time. The multi-dimensional data of the materials includes at least the material flow rate data, the material temperature data, and the material pressure data;

[0065] The data transmission module is used to transmit the multi-dimensional data of the materials collected by the data acquisition module to the data processing module;

[0066] The data processing module is used to preprocess the received data and perform operations on the preprocessed data according to the pre-set material balance model and product yield calculation;

[0067] The storage module is used to store the original collected data in a long time series, the processed data, the material balance results of each calculation, and the product yield values;

[0068] The display and interaction module is used to display the real-time material balance status and the dynamic change curve of the product yield through a visual interface.

[0069] In this embodiment, the data acquisition module includes a flow rate data acquisition sub-module, a temperature data acquisition sub-module, and a pressure data acquisition sub-module. Among them,

[0070] The flow rate data acquisition sub-module is used to obtain the real-time monitoring value of the material flow velocity in the pipeline, use the dynamic adaptive sampling algorithm to automatically adjust the sampling frequency, and perform weighted fusion on the data of the turbine flowmeter and the mass flowmeter to obtain the material flow rate data;

[0071] The temperature data acquisition sub-module is used to perform real-time filtering on the data collected by the thermocouple thermometer in the DMTO device by using the Kalman filter, remove the noise interference, and perform temperature deviation compensation and correction to obtain the material temperature data;

[0072] The pressure data acquisition sub-module is used to analyze the correlation of the pressure data fluctuations collected by the pressure sensor in adjacent time periods, judge whether there is an abnormal situation in the pipeline, and obtain the material pressure data. The abnormal situation includes at least blockage and / or leakage.

[0073] In this embodiment, the data transmission module includes a flow rate allocation sub-module, a data encryption sub-module, and a data verification sub-module. Among them,

[0074] The flow rate allocation sub-module is used to form multiple paths to transmit data through the industrial Ethernet and allocate the flow rate of each path by using the MPTCP protocol;

[0075] The data encryption sub-module is used to exchange the key for data transmission by using the asymmetric encryption algorithm, and then batch-encrypt the multi-dimensional data of the materials by using the symmetric encryption algorithm;

[0076] A data verification sub-module, which is used to generate a verification code for each data packet of the encrypted multi-dimensional material data by using the CRC algorithm, so as to verify whether the data is complete through the verification code.

[0077] In this embodiment, please refer to Figure 4 , the data processing module includes an outlier removal sub-module, a missing data filling sub-module, a material balance calculation sub-module and a yield calculation sub-module, where

[0078] The outlier removal sub-module is used to calculate the statistical characteristics of each data sequence of the multi-dimensional material data, and determine the data points that deviate more than 3 times the standard deviation from the mean as outliers and remove them, where the statistical characteristics at least include the mean and the standard deviation;

[0079] The missing data filling sub-module is used to predict and fill the missing data by using the ARIMA model based on the time series;

[0080] The material balance calculation sub-module is used to construct a material flow analysis algorithm based on the DMTO reaction process through the material balance model, with the methanol-to-olefins reaction system as the core, analyze the reactant input, product output and by-product generation in each reaction step, combine the measured data of the molar mass, reaction conversion rate, feed flow rate and discharge flow rate of each substance, construct a linear equation system, solve the equation system by using the Gaussian elimination method, calculate the transfer amount and loss amount of each material, and obtain the material balance result of the entire device;

[0081] The yield calculation sub-module is used to determine the yield by the ratio of the actual output of each material to the raw material input, calculate the confidence interval of the yield according to the statistical distribution of multiple measurement data, and automatically trace back to the data acquisition module when the yield calculation result exceeds the confidence interval.

[0082] According to the production process of the DMTO device, extract the technical parameters in the DCS screen, process them, establish a "basic table of DMTO device production technical parameters", and automatically extract data; according to the component content accounting amount of the online product chromatographic analysis of the DMTO device, compare the actual output of each product, establish a "formula table of DMTO device material balance calculation", and automatically extract data; according to the cumulative daily consumption of raw materials and the cumulative daily output of products, establish a "table of DMTO device material conversion rate and product yield", and automatically extract data; integrate and process the automatically extracted data, establish a "technical model for quickly calculating the material balance and product yield of the DMTO device", and automatically extract data.

[0083] Through the implementation of the above solutions, the automation extraction and calculation of the technical parameters, conversion rate, product yield, and DMTO device material balance data in the raw material production process are realized. The data processing process is simple, efficient and accurate, and the high-efficiency, stable and excellent operation of the DMTO device is deeply monitored.

[0084] The foregoing has shown and described the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and the descriptions in the specification are only preferred examples of the present invention, which are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of the present invention claimed is defined by the appended claims and their equivalents.

Claims

1. A system for quickly calculating material balance and product yield of a DMTO device, characterized in that: The system for quickly calculating the material balance and product yield of the DMTO device includes a data acquisition module, a data transmission module, a data processing module, a storage module and a display and interaction module, wherein: A data acquisition module, which collects multi-dimensional material data of the DMTO device in real time by connecting to sensors and instruments at various points of the DMTO device, wherein the multi-dimensional material data at least includes material flow data, material temperature data and material pressure data; A data transmission module, used for transmitting the multi-dimensional data of the material collected by the data collection module to a data processing module; The data processing module is used to pre-process the received data and perform calculations on the pre-processed data according to a preset material balance model and product yield calculation; The storage module is used to store the original collected data of a long time series, the processed data, the material balance results of each calculation, and the product yield value; The display and interaction module is used to display the real-time material balance status and the dynamic change curve of product yield through a visual interface.

2. A system for rapidly calculating material balance and product yield of a DMTO device as claimed in claim 1, characterized in that: The data acquisition module includes a flow data acquisition submodule, a temperature data acquisition submodule and a pressure data acquisition submodule, wherein: The flow data acquisition submodule is used to obtain the real-time monitoring value of the material flow rate in the pipeline, use the dynamic adaptive sampling algorithm to automatically adjust the sampling frequency, and perform weighted fusion of the data of the turbine flowmeter and the mass flowmeter to obtain the material flow data; The temperature data acquisition submodule is used to use Kalman filtering to perform real-time filtering processing on the data collected by the thermocouple thermometer in the DMTO device, remove noise interference, and perform temperature deviation compensation correction to obtain material temperature data; The pressure data acquisition submodule is used to analyze the correlation of pressure data fluctuations collected by the pressure sensor in adjacent time periods, determine whether there are abnormal conditions in the pipeline, and obtain material pressure data, wherein the abnormal conditions at least include blockage and / or leakage.

3. A system for rapidly calculating material balance and product yield of a DMTO device as claimed in claim 1, characterized in that: The data transmission module includes a flow distribution submodule, a data encryption submodule and a data verification submodule, wherein: The traffic distribution submodule is used to form multiple paths for data transmission through industrial Ethernet and use the MPTCP protocol to distribute the traffic of each path; The data encryption submodule is used to exchange the key for data transmission using an asymmetric encryption algorithm, and then use a symmetric encryption algorithm to batch encrypt the multi-dimensional data of materials; The data verification submodule is used to generate a verification code for each data packet of the encrypted material multidimensional data by using the CRC algorithm, so as to verify whether the data is complete through the verification code.

4. A system for rapidly calculating material balance and product yield of a DMTO device as claimed in claim 1, characterized in that: The data processing module includes an outlier elimination submodule, a missing data filling submodule, a material balance calculation submodule and a yield calculation submodule, wherein The outlier elimination submodule is used to calculate the statistical characteristics of each data sequence of the material multidimensional data, and to determine the data points that deviate from the mean by more than 3 times the standard deviation as outliers and eliminate them, where the statistical characteristics include at least the mean and standard deviation; The missing data filling submodule is used to fill in missing data using the ARIMA model based on time series; The material balance calculation submodule is used to construct a material flow analysis algorithm based on the DMTO reaction process through the material balance model. With the methanol to olefins reaction system as the core, it analyzes the reactant input, product output and by-product generation in each reaction step, and combines the molar mass of each substance, reaction conversion rate, and measured data of feed flow rate and discharge flow rate to construct a linear equation group, and solve the equation group using Gaussian elimination method to calculate the flow and loss of each material, and obtain the material balance result of the entire device; The yield calculation submodule is used to determine the yield by the ratio of the actual output of each material to the raw material input, and calculate the confidence interval of the yield based on the statistical distribution of multiple measurement data. When the yield calculation result exceeds the confidence interval, the data collection module is automatically traced back.

5. A method for quickly calculating the material balance and product yield of a DMTO device, characterized in that: The method for quickly calculating the material balance and product yield of a DMTO device comprises the following steps: By connecting with sensors and instruments at various points of the DMTO device, multi-dimensional material data of the DMTO device is collected in real time, wherein the multi-dimensional material data at least includes material flow data, material temperature data and material pressure data; Transmitting the material multidimensional data collected by the data collection module to the data processing module; Preprocess the received data and perform calculations on the preprocessed data according to a pre-set material balance model and product yield calculation; Stores long-term series of original collected data, processed data, material balance results of each calculation, and product yield values; The real-time material balance status and the dynamic change curve of product yield are displayed through a visual interface.

6. A method for rapidly calculating material balance and product yield of a DMTO device as claimed in claim 5, characterized in that: The received data is preprocessed, and the preprocessed data is operated according to a preset material balance model and product yield calculation, including: Calculate the statistical characteristics of each data sequence of the material multidimensional data, and determine the data points that deviate from the mean by more than 3 times the standard deviation as outliers and remove them. The statistical characteristics include at least the mean and standard deviation. For missing data, the ARIMA model based on time series is used for forecasting and filling; A material flow analysis algorithm based on the DMTO reaction process is constructed through the material balance model. Taking the methanol to olefins reaction system as the core, the reactant input, product output and by-product generation in each reaction step are analyzed. The linear equations are constructed by combining the molar mass of each substance, the reaction conversion rate, and the measured data of the feed flow rate and the discharge flow rate. The Gaussian elimination method is used to solve the equations, calculate the flow and loss of each material, and obtain the material balance result of the entire device. The yield is determined by the ratio of the actual output of each material to the raw material input. The confidence interval of the yield is calculated based on the statistical distribution of multiple measurement data. When the yield calculation result exceeds the confidence interval, data collection is automatically traced back.

7. A method for rapidly calculating material balance and product yield of a DMTO device as claimed in claim 6, characterized in that: For missing data, the ARIMA model based on time series is used for prediction and filling, including: Perform first-order differences on the data after removing outliers, calculate the difference between two adjacent data points, form a new sequence, perform a stationarity test on the new sequence, and stabilize the new sequence through differential operations; After confirming that the data is stationary, the least squares method is used to estimate the parameters of the ARIMA model, and the optimal parameter combination is found through multiple iterations to predict the missing data and fill the data gaps through the ARIMA model.

8. A method for rapidly calculating material balance and product yield of a DMTO device as claimed in claim 6, characterized in that: The yield is determined by the ratio of the actual output of each material to the input amount of raw materials, and the confidence interval of the yield is calculated according to the statistical distribution of multiple measurement data. When the yield calculation result exceeds the confidence interval, data collection is automatically traced back, including: According to the statistical distribution of multiple measurement data, the confidence interval of the yield is calculated using the normal distribution assumption: Calculate the sample mean of the yield And the sample standard deviation s, according to the confidence level 95% to find the normal distribution table to get the corresponding critical value z, the lower limit of the confidence interval is The upper limit of the confidence interval is Where n is the number of measurements.

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