Gas chromatography system and method with diagnostic and predictive modules

Abstract

This invention provides an automated method for predicting when maintenance will be required in a gas chromatography system, and an automated troubleshooting support method that can instruct the user on appropriate actions to take. [Solution] The gas chromatography system is configured to perform chromatography performance monitoring, which includes comparing chromatography parameters with simulated chromatography separation and / or reference chromatography separation, to determine whether the chromatography parameters have fallen outside the performance control limits and to predict when the chromatography parameters will fall outside the performance control limits. Furthermore, it is configured to perform automated GC troubleshooting procedures to determine the cause of the performance problem.

Description

[Technical Field]

[0001] [Cross-reference of related applications] This application is a U.S. Provisional Patent Application No. 63 / 114,835, filed on November 17, 2020. Priority and interest are claimed, and the entirety of their respective contents is cited herein by reference. It shall form a section. [Background technology]

[0002] Gas chromatography (GC) is a method that analyzes many different substances in a sample. It is used to analyze and detect the presence of the analyte. The function of a gas chromatograph is to analyze the analyte The components of a chemical sample known as [a specific chemical] are separated, and the identity and / or concentration of those components are detected. This separation is often achieved using a capillary GC column. In that example, this column is essentially a fused silica tube (fu) with an inner coating. This is part of SED silica tubing. The column interacts with the sample to separate the components. It may include phases. The GC column remains isothermal throughout the analysis, or its temperature increases. It can be done.

[0003] Traditionally, when maintenance of GC equipment was required, the equipment underwent a hardware-related shutdown ( In other words, you may experience leakage at the septum due to excessive continuous injection, or clo Degradation of matrix performance (i.e., the stationary phase degrades due to widespread use, and the analyte Maintenance may be required due to (not being efficiently separated). In such situations, the user , analyze data from previous runs of the equipment, and identify hardware failures and / or chromatographic The cause of the performance degradation needs to be identified. Performance degradation can be caused by a shift in retention time and peak surface. This includes, but is not limited to, changes in product and / or changes in peak shape, as well as chromatographic changes. This can manifest as a change in characteristics. As a result, the user can determine which parts (e.g., liner, cylinder) to use. Determine whether to replace the vents, partitions, and / or columns, and continue until performance returns to an acceptable level. The replacement of parts must continue. The decision of which maintenance procedure to implement depends on the equipment, even if the equipment is Even if it is functioning normally or does not require hardware replacement, at certain time intervals Standard Operating Procedures (SPA) that propose hardware modifications. ) may be outlined or otherwise specified by the Standards of Operation. This procedure manual outlines what to do if the instrument malfunctions and / or the chromatographic performance deteriorates during sample analysis. In some cases, specific guidance on which maintenance procedures to perform may not be provided. Rather, the decision of which maintenance procedure to perform is likely to depend heavily on the user's experience. It is possible.

[0004] GC devices may face performance issues that are not easy to resolve, and the root cause of the problem Identifying the cause requires an extensive investigation. Therefore, the user should consult the equipment operation manual. Consult a website or specialist that deals with GC equipment repair to determine the cause of the performance problem. It may be necessary to determine. The current troubleshooting guide does not address specific symptoms (sympto An attempt is made to explain the relationship between m) and the recommended countermeasures. However, many In some cases, because there are many ways to deal with a single symptom, users can find the appropriate solution. Often, trial and error is necessary to get there.

[0005] Current chromat troubleshooting methods are available on external websites. Troubleshooting tools that can be accessed or provided by the GC equipment manufacturer They are using a writing guide. Several drawbacks of these methods are that they are from external sources. This may not be specific to the actual GC equipment configuration or equipment manufacturer / model. Data stored on devices that may be inaccessible to the user may be unusable. and / or troubleshoot specific chromatography problems observed on certain instruments. The only thing provided to users is general guidelines for logging. Thus, this means users spend time searching for troubleshooting assistance. Therefore, it becomes necessary to try certain maintenance procedures that are not suitable for specific equipment. , an automated method that can predict when maintenance will be needed, and specific GC machines Automated troubleshooting that can accurately instruct the user on what needs to be fixed in the device. Support is needed. [Overview of the Initiative]

[0006] One aspect of the present invention is a method for operating a gas chromatography (GC) system. This method is provided. This method uses a chromatography model based on the configuration of the GC system. Simulated chromatographic separation The step of generating the chromatography model is performed by a GC system. This includes calculating at least one chromatographic parameter of the sample to be analyzed. This method also performs sample chromatographic separation using a GC system, thereby generating a sample chromatogram of the sample analyzed by the GC system, and collecting performance data related to the sample chromatographic separation. The performance data includes at least one chromatographic parameter of the sample. This method also includes performing chromatographic performance monitoring configured to analyze the chromatographic separation of the sample. For example, the chromatographic performance monitoring includes comparing at least one chromatographic parameter from the sample chromatographic separation with a simulated chromatographic separation and / or a reference chromatographic separation, determining whether at least one chromatographic parameter is outside the performance control limits, and / or predicting whether and / or when at least one chromatographic parameter will be outside the performance control limits. This method also includes performing an automatic GC troubleshooting procedure that predicts the expected maintenance work using the results of the chromatographic performance monitoring and the chromatographic model, and sending a maintenance notification of the GC system including the expected maintenance work. of the sample analyzed by the GC system generating a sample chromatogram, and collecting performance data related to the sample chromatographic separation The performance data includes at least one chromatographic parameter of the sample. This method also includes performing chromatographic performance monitoring configured to analyze the chromatographic separation of the sample. For example, the chromatographic performance monitoring includes comparing at least one chromatographic parameter from the sample chromatographic separation with a simulated chromatographic separation and / or a reference chromatographic separation, determining whether at least one chromatographic parameter is outside the performance control limits, and / or predicting whether and / or when at least one chromatographic parameter will be outside the performance control limits. This method also includes performing an automatic GC troubleshooting procedure that predicts the expected maintenance work using the results of the chromatographic performance monitoring and the chromatographic model, and sending a maintenance notification of the GC system including the expected maintenance work using the results of the chromatographic performance monitoring and the chromatographic model to predict the expected maintenance work and sending a maintenance notification of the GC system including the expected maintenance work .

[0007] In another aspect, a gas chromatography (GC) system for analyzing a sample is provided. The GC system includes a GC column having an inlet and an outlet. The GC column has one or more It is configured for chromatographic separation of samples containing the analyte. The GC system is also , a GC detector fluidly connected to the outlet of the GC column, and at least a device capable of communicating with the GC detector. It is equipped with a controller connected to the GC system. The GC system controller is connected to the GC system Chromatography simulated using a chromatography model based on the configuration The chromatography model is configured to generate filtration separations, and the GC system This calculates at least one chromatographic parameter of the sample being analyzed. Trolla also performs chromatographic separation of samples loaded into the GC system and tries We collected performance data related to the chromatographic separation of materials, and the performance data was obtained with small sample sizes. It includes at least one chromatography parameter. The controller also includes the chromatograph of the sample. Chromatography performance monitoring configured to analyze tography separation. This is performed. For example, chromatography performance monitoring is performed using sample chromatography. At least one chromatography parameter from the separation and the simulated chromatogram Includes comparison with chromatographic separation and / or reference chromatographic separation, at least 1 Determine whether one of the chromatography parameters has fallen outside the performance control limits, and / Or whether at least one chromatography parameter falls outside the performance control limits. And / or predict when performance will be exceeded. The controller also uses chromatography. Using the results of fee performance monitoring and chromatography models, the GC system Perform automated GC troubleshooting steps to predict expected maintenance work. The controller then generates and sends a maintenance notification that includes the expected maintenance work for the GC system. For example, maintenance notifications can be sent to smartphones, computers, tablets, or other devices. It can be transmitted to external electronic devices such as electronic devices. Here, "A and / "Or B" refers to either A or B, or both A and B.

[0008] In yet another embodiment, a gas chromatography (GC) system for analyzing a sample is provided. The GC system includes a GC column with an inlet and an outlet, and the GC column is one The GC system is configured for chromatographic separation of samples containing the above analytes. Furthermore, a GC detector is fluid-connected to the outlet of the GC column, and instrument data from the GC system is collected. The GC system also includes at least one sensor configured to collect data. It includes a detector and a controller that is communicatively connected to at least one sensor. Trolla performs chromatographic separation of the sample loaded into the GC system, less Using instrument data collected by a single sensor, a simulated sample is created. It is configured to produce chromatography separation. The controller processes the chromatography of the sample. Generates real-time simulated chromatographic separation during Raffie separation. .

[0009] The methods and operations of the GC system described herein are as follows: By incorporating and / or communicatingly connected diagnostic and predictive modules into the roller It can be implemented.

[0010] This instruction is best understood when read in conjunction with the attached drawings, as detailed below. Those features are not necessarily depicted to scale. [Brief explanation of the drawing]

[0011] [Figure 1] This is a schematic block diagram of a GC system, including diagnostic and predictive modules, using a typical example. [Figure 2] This is a schematic flowchart illustrating the use of the diagnostic and predictive module shown in Figure 1 for chromatography performance monitoring, chromatography modeling, and automated GC troubleshooting procedures, using a typical example. [Figure 3] Figure 1 shows a control chart generated by the diagnostic and prediction module, illustrating the retention time shift of a sample using a typical example. [Figure 4] This is a schematic flowchart illustrating the execution of a chromatography modeling application using the diagnostic and predictive modules shown in Figure 1, with a typical example. [Figure 5A] This is a schematic flowchart illustrating the execution of a decision tree using the diagnostic and prediction modules shown in Figure 1, with a representative example. [Figure 5B] This is a schematic flowchart illustrating the execution of a decision tree using the diagnostic and prediction modules shown in Figure 1, with a representative example. [Figure 5C] This is a schematic flowchart illustrating the execution of a decision tree using the diagnostic and prediction modules shown in Figure 1, with a representative example. [Figure 6] Figure 1 is a schematic flowchart illustrating the execution of a decision tree using the diagnostic and predictive modules, with a representative example, demonstrating a reduction in potential solutions to provide a specific solution to the chromatography performance problem. [Figure 7A] Figure 1 is a graph chart generated by the diagnostic and prediction module, showing an overlay of a reference chromatogram and a simulated chromatogram using a typical example. [Figure 7B] Figure 1 is a graph chart generated by the diagnostic and prediction module, showing a comparison between a reference chromatogram and a sample chromatogram from a failed peak evaluation, using a typical example. [Figure 7C]Figure 1 shows a control chart generated by the diagnostic and prediction module, illustrating the retention time shift of a sample using a typical example. [Figure 8] This is a schematic flowchart outlining the user input questions required for troubleshooting, both when using information from the GC system and when not. [Figure 9] This is a schematic flowchart illustrating the process of enabling, configuring, and using the diagnostic and predictive modules. [Modes for carrying out the invention]

[0012] The GC system in this disclosure will be designed to address future equipment performance and / or maintenance issues before they arise. Chromatography performance monitoring as part of predictive diagnostic and maintenance tools. chromatographic modeling, and automated GC troubleshooting It is configured to utilize a maintenance procedure. In addition, diagnostic and predictive maintenance tools are used. Use to correct equipment performance and / or maintenance issues, and perform any specific maintenance work. It is possible to determine whether or not to perform the maintenance. The GC system of this disclosure is a diagnostic and predictive maintenance tool. By using this, we can make the device more intelligent (i.e., the interaction with the user that is required). (The number of users decreases, and the "knowing" of the equipment increases), Use becomes easier. In addition, the GC system of this disclosure does not actually experience failure or maintenance problems. Before proceeding, diagnostic and predictive maintenance tools will anticipate equipment failures, thus preventing unexpected downtime. This can be reduced. Furthermore, diagnostic and predictive maintenance tools can determine which maintenance tasks are needed for the GC system. In order to determine and suggest whether there is a higher probability of future failure or maintenance problems, This can reduce downtime. Here, "A and / or B" means It refers to A or B, or both A and B.

[0013] In several specific examples, the diagnostic and predictive maintenance tools of this disclosure are chromatographic. Performance evaluation (e.g., blank evaluation, detector evaluation, and peak evaluation), control chart creation, user Input, diagnostic test results (e.g., carrier gas pressure check, leakage limit test, bulkhead purge test) Tests, split vent limit tests, jet limit tests, FID leakage current tests, and pressure decay tests. Tests), and / or equipment sensor data (e.g., temperature, pressure, gas flow, valve status, motor). Chromatography combined with step count, sample injection count, motor current value, etc. Performance monitoring, chromatography modeling, and automated GC troubleshooting Using this procedure, we predict future GC system performance and / or maintenance issues. Currently, users of the GC system will not use it until performance and / or maintenance issues actually occur. Because the problem cannot be detected, the GC system of this disclosure is not the same as the current system. This provides an improvement to the current GC system. In other words, users of the current GC system are generally , proactive methods for performance monitoring and maintenance of GC systems (i.e.) Rather than identifying performance degradation and performing maintenance before a failure occurs, reactive measures are taken. It is necessary to take the approach of waiting until a failure occurs. In reactive methods, Samples were analyzed using a system that was not functioning properly, resulting in sample and analysis time issues. This may be wasted. In addition, the GC system of this disclosure has performance and / or maintenance issues. It was determined that this had occurred, and additional samples were being processed while the GC system was not functioning properly. Because the sample analysis sequence can be stopped immediately to prevent it from being interrupted, current GC systems To provide improvements to the system.

[0014] In several specific examples, the diagnostic and predictive maintenance tools of this disclosure perform performance and / or maintenance To fix the problem, we have incorporated automated diagnostic troubleshooting steps. The automated diagnostic troubleshooting steps relate to performance and / or maintenance issues. By guiding the user to investigate specific components, we can prevent users from making unnecessary repairs. Performing the necessary procedures or investigating irrelevant components of the GC system would require time and expense. To save money. Therefore, the diagnostic and predictive maintenance tools of this disclosure allow users to maintain performance and / or save money. Before a problem occurs, users can decide when they want to address it, and the GC system can troubleshoot. During shooting, it provides an intelligent starting point and performs necessary repairs on the GC system. Because it runs quickly, it reduces unexpected downtime for the GC system.

[0015] In several specific examples, the diagnostic and predictive maintenance tools of this disclosure enable the system to function optimally. By notifying the user when it is not working, the user experience is improved, and therefore, It provides excellent chromatography results. For example, chromatography modeling and By utilizing chromatography performance monitoring, the GC system (and system The system users optimize equipment performance and implement the "theoretical best-case scenario" (theoreti It becomes possible to compare with desired performance, such as the best-case scenario. If it is found that the user has a problem, the automatic GC troubleshooting procedure will be initiated to protect the user's memory. This diagnostic and predictive maintenance tool can guide users when resolving maintenance issues. The command generates instructions for equipment performance, ensuring the equipment performs as expected by the user and / or within the specifications of the equipment. Verify that it is working within the system.

[0016] Figure 1 is a simplified schematic block diagram of a typical GC system 100. Many embodiments of TEM100 are known and widely used. Therefore, this specification will not describe them. The disclosed GC system 100 broadly represents available and / or modified GC systems. The intention is that the specific selection and details of the various components of the GC system 100 are as follows: The user or others in the field may choose. The GC system 100 is divided A sample inlet or injection port 102 for injecting the sample into the GC system 100 for analysis. Prepare. For example, the sample is injected into the injection port 102, where it is already not in a gaseous state. The mixture is then vaporized into a gaseous state for analysis by GC System 100. Furthermore, carriers The gas supply unit 103 is fluidly connected to the injection port 102 and can supply helium, hydrogen, etc., though not limited to these. A carrier gas such as nitrogen or another such inert gas is supplied, and the carrier gas is injected. The sample is transported from the injection port 102 through the GC system 100.

[0017] Using a sample introduction system or sampler (not shown), inject the sample into the injection port 102. It can be injected. The type of sampler used depends on the phase of the sample being injected (liquid or gas). It can depend on the body. Different types of samplers include automated liquid samplers (ALS: automated). c liquid sampler), headspace sampler, valves of various configurations, thermal desorption sampler, This includes, but is not limited to, other types of sample introduction systems.

[0018] In various specific examples, the injection port 102 is also fluidly connected to the column 104. The column 104 is used to achieve the separation of sample components by gas chromatography. A wide variety of columns can be selected for use. One column is shown, but GC... Please understand that a specific example of a stem can contain multiple columns. For example, For backflushing, detector splitting, or other pneumatic switching The configured GC system may include multiple columns. The carrier gas is used for separation. The material is transported to column 104, which separates the components of the gaseous sample, such as the vaporized sample. Then, one or more analytes are generated for analysis by the GC system 100. In a specific example, column 104 is a capillary column and This may include a fused silica tube having a coating on the inner portion of the tube. This can be done. In some specific examples, the stationary phase coating is injected into the injection port 102. It interacts with the sample and separates the components of the sample. In various specific examples, column 10 The dimensions of 4 are an inner diameter range of 100 micrometers to 530 micrometers and 5 meters. This includes a length range of 60 meters. However, other column dimensions may differ in this GC system. It will be understood that this can be used in that context.

[0019] In the illustrated example, column 104 is also fluidly connected to detector 106. The detector 106 detects the separated components (i.e., after the sample has been transported through the column 104) The detector then receives the analyte of the sample. Therefore, the detector 106 separates the separated sample components. Analyze the sample to detect the presence and / or amount of the analyte separated by column 104. In a specific example, the detector 106 is a flame ionization detector (FID). (zation detector), mass-selective detector (MSD), thermal conductivity detector Thermal conductivity detector (TCD), electron capture detector (ECD) (Capture detector), nitrogen phosphorus detector (NPD), sulfur Sulfur chemiluminescence detector (SCD), nitrogen chemiluminescence detector (NCD: nitrogen chemiluminescence detector), Flame photometer (FPD: flame photometer) Hotometric detectors, and helium ionization detectors (HID: helium ionization det) A GC detector selected from the group consisting of (ectors). However, one or more such detectors. The use of the detector is merely illustrative, and many other analyte detectors are used in GC systems. It should be understood that this may be used. Two or more detectors flow to the outlet of the GC system column. It will also be understood that physical connections can be made.

[0020] The GC system 100 includes an oven, convection heater, conduction heater, air bath, or certain Other heating devices for heating GC system components, such as a column heater 108 It further includes the following. More specifically, the column heater 108 is controlled via the controller 110. The column 104 and other flow path components can be heated or cooled to the desired temperature. For example, the column heater 108 raises the column 104 to 450°C depending on the analysis being performed. It is configured to heat in the following way. In various specific examples, the column heater 108 is configured to heat column 1 Heating 04 to ensure that column 104 remains isothermal during sample analysis is a possible configuration. Yes, it is possible. Alternatively, the column heater 108 can raise the temperature of column 104 during sample analysis. It can be configured to do so. In addition, the column heater 108 cools the column below ambient temperature. It can be configured with a cryogenic cooling system for injection port 102 and detection The ejector 106 is equipped with separate outlets for maintaining the temperature of the injection port 102 and the detector 106. It will be understood that this may include a number of heating devices. In some specific examples, GC Additional heaters not directly described herein may be present to heat other components of the system. It is possible.

[0021] In the illustrated example, the controller 110 includes a column heater 108 and a detector 10 6. Injection port 102, one or more sensors 111, and other components of the GC system 100 They are connected directly or indirectly to enable communication. In a particular example, the controller It can be an onboard computing component, such as a column, a detector, and a column. The heater and other components of the GC system are physically incorporated into the GC system enclosure. It is included. In some other specific example, the controller is the GC system housing One or more separate computing devices located internally and / or externally and / or other Such a control device may be one or more sensors 111 of the GC system 100 It is positioned at a location and configured to collect operational and / or diagnostic data. One or more sensors 111 used by the GC system 100 are (for example, inlet, inlet, inlet) Inlet pressure sensor (above or inside) dispenser, heater, sample introduction device, valve, etc., total inlet flow rate Sensors, bulkhead purge pressure sensors, auxiliary pressure sensors, heater duty cycle sensors, detection Includes sensors such as output signal sensors, temperature zone sensors, or other GC system sensors. This is possible (but not limited to these).

[0022] In several specific examples, the controller 110 controls the data and information of the GC system 100. A single-core application configured to execute, analyze, and process data, but not limited to, Processors, multicore processors, logic devices, or other such data processing circuits, etc. Includes a processor 112. The controller 110 is communicably connected to the processor 112. It may include a memory device 114. The memory device 114 is a volatile memory device ( For example, SRAM and DRAM), non-volatile memory devices (for example, flash memory) (ROM and hard disk drive), or any combination thereof This may occur. In various specific examples, the memory device 114 operates during the operation of the GC system 100. executable code and other such information generated and / or processed by processor 112 It can store information.

[0023] In the illustrated example, the GC system 100 is also able to communicate with the controller 110. Includes a connected input / output device 116. The input / output device 116 is connected to the operator and / or the user receives information from controller 110 and controls the information and parameters It is configured to allow input to the roller 110. In various specific examples, The information and parameters are stored in the memory device 114 and processed by the processor 112. It can be accessed and output to input / output device 116. For example, input / output device The Chair 116 includes a monitor, display device, touchscreen device, and keyboard. , microphone, joystick, dial, button, or input of information and parameters This may include other devices that enable force and output. Therefore, input / output devices Using S116, information is input to controller 110, and the GC system 100 process Output or otherwise display the information and data generated by 112. It is possible.

[0024] The GC system 100 further includes a diagnostic and predictive module 118. In some specific examples... In this configuration, the diagnostic and predictive module 118 is integrated into the controller 110 and processes It is communicated to the 112 and / or memory device 114. The diagnostic and predictive module 118 then determines and / or determines the performance degradation of the GC system 100. To make predictions, chromatography performance monitoring and chromatography modeling are used. , and perform the automated GC troubleshooting procedure, or do so by other means. Therefore, the diagnostic and predictive module 118 performs chromatography performance monitoring, Graphography modeling, automated GC troubleshooting procedures, and / or GC systems To perform any other such diagnostic monitoring on Stem 100, one or more hard Wearable devices, software, firmware, and / or any combination thereof It may include combinations.

[0025] In various specific examples, the diagnostic and predictive module 118 is a programmer of the controller 110. Processor 118a and memory device separate from setter 112 and memory device 114 This may include S118b. In such a specific example, processor 118a executes the instruction, The data stored in memory device 118b is analyzed. Furthermore, memory device 118 b is by processor 118a for the execution of instructions of the diagnostic and predictive module 118 It stores the software and / or firmware containing the executable code to be processed. Furthermore, the memory device 118b is used for the automatic GC troubleshooting of the GC system 100. During the procedure, the diagnostic and predictive module 118 utilizes multiple different aspects of the GC system 100. To store data and information related to one or more expected maintenance tasks from the maintenance work performed. This is possible. The diagnostic and predictive module 118 is integrated into the controller 110. Although it is shown that, in a certain specific example, the diagnostic and predictive module controls Please understand that this can be a separate component from Laura.

[0026] In various specific examples, the diagnostic and predictive module 118 of the GC system 100 is reliable. To improve performance and reduce unexpected downtime of the GC system, conventional methods are being used in contrast to... This offers significant advantages. One benefit provided by the diagnostic and predictive module 118 is The point predicts the timeframe for future performance degradation and / or maintenance issues of the GC system 100, and This is the ability to predict failure modes related to the causes of future performance degradation and / or maintenance problems. In other words, the diagnostic and predictive module 118 determines when, for example, after how many injections, and / or Does a failure occur after a specified amount of equipment execution time, and how long does it take to correct the failure? It is possible to determine whether maintenance work should be performed on the sample. Therefore, the user can determine whether the sample should be executed. Alternatively, instead of failures and / or maintenance issues occurring during the analysis, maintenance of the GC system This allows you to plan when you want to carry it out. This also allows you to anticipate any unexpected events that may occur during the sample analysis. This avoids the need to rerun the sample due to unintended failures, thus saving time and costs. Both are saved.

[0027] Another advantage of the diagnostic and predictive module 118 is that it can detect multiple sample injections over time (e.g., multiple sample injections or The instrument status and chromatography performance (over a certain amount of instrument execution time) are linked. It is the ability to continuously monitor. As mentioned above, the diagnostic and predictive module 118 uses chroma Toography performance monitoring, chromatography modeling, and automated GC troubleshooting. Using the shooting procedure, the GC system 100 dynamically monitors its own functionality. , predict future chromatography performance and / or maintenance issues and implement certain specific measures. It enables the automatic suggestion of maintenance tasks. After the maintenance work is performed, diagnostic and predictive modules are used. The 118 also features the GC System 100, which offers ideal chromatographic performance. Compared to Lamb, chromatography performance has returned to an acceptable baseline performance level. This makes it possible to automatically verify this.

[0028] For example, the ability to compare current chromatography performance with reference chromatography separation. By incorporating troubleshooting that includes this, the GC system 100 is easy to maintain. The results can be automatically checked after the process is completed. Therefore, the user can check the GC system. To quickly determine whether the performance of the Tem100 has returned to an acceptable initial baseline. Furthermore, the ratio of current chromatography performance to standard chromatography separation can be calculated. By incorporating chromatography modeling into the comparison, real-time data collection of the GC system can be performed. This allows for comparison between collected data and theoretical datasets, enabling verification of post-maintenance results. This will be further improved. The ratio of current chromatography performance to standard chromatography separation. By incorporating chromatography modeling into comparison, users can further utilize the previously known " The "good" (known good) criteria (for example, samples that have never been run on a GC system) Even if no problems occur during analysis or equipment installation, there may be issues with the equipment's performance and / or maintenance. This makes it possible to solve the problem.

[0029] (Calculation performance monitoring) As described above and as shown in Figure 2, the diagnostic and predictive module 118 is software Includes the software and / or firmware 200. 200 is used for chromatography performance monitoring, chromatography modeling, and By combining automated GC troubleshooting procedures, chromatography performance monitoring The chromatography modeling, chromatography modeling, and automated GC troubleshooting procedures are separate. Compared to using it in other ways, it provides additional functionality to the GC system 100. In the example, chromatography performance monitoring includes blank evaluation, detector evaluation, and / or perform a specific performance evaluation of the GC system 100, such as peak evaluation, and the GC system Whether M100 is functioning correctly (i.e., whether the analysis results meet the specified control limits or This includes determining whether the value is within a threshold. For example, diagnostic and predictive module 118 These were collected during one or more blank runs (i.e., during analyses in which no analytes were present). Using sample data, a blank evaluation was performed to determine the baseline cloning of the GC system 100. It can analyze matrix performance. Diagnostic and predictive modules are available during blank evaluation. 118 is the baseline signal, noise, and combined peak area over the selected time window. , predefined thresholds (e.g., user-defined control limits or device-defined control limits) )By determining whether or not any carryover material is outside, the presence or absence of any carryover material can be determined. Determine whether it is present.

[0030] In another non-limiting example, the diagnostic and predictive module 118 may also perform detector evaluation. This detector evaluation uses a specific sample to determine peak retention time, peak area, and peaks. The height is the standard value and / or limit that the manufacturer has determined to represent the nominal or nominal performance. The detector performance is confirmed by comparing it with the set.

[0031] In yet another non-limiting example, the diagnostic and predictive module 118 utilizes peak assessment. The sample data of the current sample being analyzed by GC system 100 is predefined Compare it to a reference chromatogram. More specifically, peak evaluation In terms of valuation, the reference chromatogram, or alternatively or additionally, the chromatogram of the GC system Using a simulated chromatogram generated from the Raffy model, the sample For multiple peaks (e.g., 5 peaks, 10 peaks, 20 peaks, etc.), retain Time, relative retention time, retention index, adjusted retention time, peak height, peak area, peak Width, peak symmetry, peak resolution, peak capacity, skew, kurtosis, number of separations, volume These include ratio, selectivity, efficiency, apparent efficiency, tailing coefficient, concentration, and molar amount, etc. Define certain expected chromatography parameters, which are not limited to those listed above. The diagnostic and predictive module 118 uses a reference chromatogram and / or simulates The chromatography of the current sample being analyzed is compared with the chromatogram obtained. Evaluate one or more parameters to indicate whether the GC system 100 is functioning correctly. For example, the reference chromatogram and the simulated chromatogram are nominal chromatograms. It can provide tography performance, GC system 100 or GC system 100 Users can define a set of control limits based on nominal chromatographic performance. Therefore, the diagnostic and predictive module 118 can perform one or more chromatographic measurements of the sample data. Evaluate the chromatographic parameters and check if one or more chromatographic parameters are within the control limits. Determine whether it exceeds the set.

[0032] In various specific examples, chromatography performance monitoring is performed using a reference chromatogram. Instead, chromatography modeling is used to specifically address chromatography performance problems. It can be determined. More specifically, in cases where no known good reference chromatogram exists. In addition, the nominal chromatography model described below is... Using a graphic model for baseline comparison, the expected chromatogram of the GC system The performance of the Laffey can be determined. For example, the user can analyze it using a GC system. If a reference chromatogram for the sample is not available, the user enters the analytes of the sample. The GC system can use the setpoint as input to the model to determine the nominal value of the sample. Simulated chromatographic separation (nominal simulated chromatographic separation) To generate a ration. In another specific example, chromatography modeling is used to The reference chromatogram can be verified. For example, if you generate a reference chromatogram... Using the instrument data acquired during the process, a nominal chromatography model or a model generated from the model is used. The resulting chromatogram was compared to the reference chromatogram, and the reference chromatogram was deemed acceptable. It is possible to determine whether or not it represents the performance of a capable GC system.

[0033] Chromatography performance monitoring of the diagnostic and predictive module 118 is also performed using control charts. (For example, control chart 300 in Figure 3) is used to determine the expected chromatography parameters. Track and communicate any discrepancies between the sample data of the analyzed sample and the sample data of the analyzed sample. It is possible to predict whether the control limit will be exceeded. For example, diagnostic and predictive module 118 As explained below, the reference criterion generated from the chromatography system model Using chromatograms and / or simulated chromatograms, the expected chromatograms are used. The matrix values ​​(e.g., retention time) are determined, and the GC system 100 or the user... The control limits defined by either method are within the tolerance range of the nominal or expected chromatographic values. These limits apply as follows: These control limits are the absolute or hundreds of the expected chromatographic values. It can be defined as a fraction. During sample analysis, the diagnostic and predictive module 118 predicts the expected criterion. Extrapolating chromatography values ​​may cause chromatography parameters to fall outside the control limits. Predict whether and / or when it may go beyond the control limits.

[0034] In some specific examples, the diagnostic and predictive module 118 is provided by the GC system 100. This includes data related to monitoring the peak retention time of the specific analyte being analyzed. A control chart 300 is generated. As shown in the figure, the control chart 300 is generated by the GC system 100. The specific analyte being analyzed has an expected retention time of 200 minutes, 310 minutes, and an upward retention time of 210 minutes. It is shown that there is a limit of 320 and a downward control limit of 330 for 190 minutes. Illustrated exemplary In a specific example, the actual retention time of the analyte during sample analysis is recorded after each sample injection. The diagnostic and predictive module 118 analyzes the actual retention time data (for example, linearly). Alternatively, using nonlinear regression, the retention time is based on the peak retention time point 342 of the control chart 300. Determine the inter-trend line 340. In the illustrated example, the peak retention time point 342 is for each sample. It is plotted against the number of injections. Therefore, the holding time trend line 340 is each Regarding sample injection, it shows that the peak retention time decreases at a predictable rate. In detail, the retention time trend line 340 indicates that the expected retention time is lower at the 15th sample injection. This indicates that the control limit of 330 is exceeded. Therefore, diagnostic and predictive module 11 8 indicates that the peak retention time falls outside the lower control limit of 330 during the 15th sample injection. Generate a notification message and send it to the user.

[0035] Additionally or alternatively, control charts are used by the diagnostic and predictive module 118. Temperature value, pressure value, valve status, motor step, syringe injection count, motor current, Heater current, heater duty cycle, flow sensor value, detector signal level, detector current level Bell, on-time value, valve duty cycle, and other such instrument sensor values, etc. Monitoring specific device data collected by a sensor (for example, sensor 111 in Figure 1) This is possible. In such a specific example, the diagnostic and predictive module 118 predicts in a different way. To predict possible failures of the GC system 100 that cannot be performed, use the following equipment data. This is displayed in a control chart. In other words, the diagnostic and predictive module 118 uses certain device data. If sample data and / or chromatography performance values ​​are not monitored, the GC system will fail. It is extremely difficult to determine performance and / or maintenance issues before a failure occurs. The nominal values ​​and control limits of the data are set values, the mean values ​​of these values ​​determined at the factory, and the standard deviation. The difference, instrumental data collected while generating the reference chromatogram, or other means are used to determine the difference. It can be determined.

[0036] In this use case, the split at the outlet of the injection port while the user is injecting the sample... One example is when the split vent trap starts to become clogged. Each time, the user injects a contaminated sample that gradually clogs the split vent trap. This can occur in combination. This will ultimately result in additional limitations on the system, and as a result As a result, the same flow rate, and therefore the split ratio (i.e., the flow rate through the column and the split ratio) While maintaining the ratio of the flow rate through the lit vent trap, the split vent trap The duty cycle of the split vent valve, which controls the flow rate, compensates for the new limitations. It decreases in order to do so (to keep the valve more "open"). Initially, the actual division ratio changes It does not change (therefore, the user obtains the same chromatography result), but over time The duty cycle of the split vent valve continues to decrease. This problem is progressing significantly. Until the procedure is performed, it will not affect the chromatography results, but the system (through the control chart) If you are monitoring the duty cycle of a split vent valve, the valve duty cycle When the cycle decreases, it is detected by the device. Finally, the split vent valve When fully open, the inlet pressure increases due to the restriction, and the actual split ratio and the user's desired split ratio Because discrepancies can arise, detecting problems at an early stage is beneficial for users. This change in the splitting ratio can lead to inaccurate chromatography results and affect the user's data. This would impair the process and ultimately lead to failures in peak evaluation results due to an increase in peak area. By using control charts, users can identify any chromatography problem. Long before that, users were notified of the reduction in the split vent duty cycle. This makes it possible to take action before any sample (or result) is damaged. Therefore, the diagnostic and predictive module 118 is used to dynamically monitor certain device data. By monitoring, the GC system waits to notify the user after a fault is detected. Instead, it becomes possible to predict when a failure may occur. If you continue to perform further analysis instead of stopping, the analysis will continue in real time. Simulated chromatographic separation (this is chromatographic, as explained below) The actual values ​​of the equipment for temperature and pressure settings are used as input to the tography model. (and nominal simulated chromatography separation, which is explained below) Automatic comparison (using method settings as input to the chromatography model) Therefore, changes in the reconstitution ratio and the resulting changes in chromatography can also be affected by the instrument. It will be marked. As a result, the user will find that the chromatography results are insufficient and P Further opportunities to troubleshoot and perform maintenance before evaluation becomes impossible. You can obtain this.

[0037] (Chromatography Modeling) As described above, the diagnostic and predictive module 118 monitors chromatography performance. Combining chromatography modeling and automated GC troubleshooting procedures. In addition, the chromatography performance and functionality of GC system 100 are dynamically monitored. In various specific examples, the diagnostic and predictive module 118 uses chromatography modeling. Using this, performance data and the sample or analyte analyzed by GC system 100 Retention time, relative retention time, retention index, adjusted retention time, peak height, peak area, Peak width, peak symmetry, peak resolution, peak capacity, skew, kurtosis, number of separations, A specific volume ratio, selectivity, efficiency, apparent efficiency, tailing coefficient, concentration, and molar amount, etc. Determine the expected chromatography parameters. For example, chromatography The modeling involves the instrument configuration, the instrument settings for the chromatography method of sample separation, and several... In that specific example, using real-time device data, the GC system 100 To simulate chromatographic separation of the sample being analyzed. The graphi model combines the analyte-column-specific thermodynamic properties with the carrier gas species. GC system 10, including column dimensions, detector parameters, inlet pressure, outlet pressure, and temperature. By utilizing the physical properties of 0, the chromatographic separation of a sample or analyte is simulated. Using simulated chromatography separation, the GC system 100... The expected retention time, peak width, and / or other chromatographic information of the sample to be analyzed. The parameters can be determined.

[0038] Referring to Figure 4, the diagnostic and predictive module 118 is a chromatography model. Run ring application 400 to generate a chromatography model. Therefore, before generating simulated chromatographic separation, GC system 10 User 0 initializes certain parameters for the diagnostic and predictive module 118. Determine the GC system configuration. For example, the diagnostic and predictive module 118 determines the GC system The following parameters are derived from the column configuration, namely, column parameters (e.g., length, inner diameter, fixed) Phase thickness, stationary phase type, carrier gas type, column and / or detector outlet pressure, air control Mode (flow rate or pressure), predetermined time window (Δt), column heater temperature, heating rate and / or Isothermal maintenance (to determine the nominal temperature calculation for each predetermined time window), and the desired column Initialize and / or define the flow rate and / or pressure. Other parameters of GC system 100 Please understand that the meter values ​​may be used by the diagnostic and predictive module 118.

[0039] In various specific examples, the chromatography modeling application 400 is used at time Using an inter-based iterative model, Snijders, H. et. al. (Journal of Chromatography A The GC separation of the sample is mathematically simulated using a method similar to that described in (718, 1995, pp. 339-355). Chromatography modeling application 400 predefined complete GC separation. Using the defined time window (Δt), it is simulated as a collection of many short isothermal separations. Within each predefined time window (Δt), the retention coefficient (k') of each analyte is determined by other instrument data. Along with analyte-column specific data obtained from Van't Hoff data, It is calculated using thermodynamic values. The analyte rate is then calculated from the retention coefficient, and each Δ The distance the analyte travels within time t is calculated from the analyte velocity and a predetermined time window (Δt). It is possible to perform chromatography modeling between each segment of the simulation. Application 400 will continue until a certain numerical threshold is met (for example, total analytes). When the migration distance exceeds the column length, a series of calculations of the relevant chromatography equations are performed. Implement. The chromatography modeling application 400 is determined by the user. As intended, the expected retention time, peak width, peak height, and peak of the analyte in the sample are obtained. It is possible to generate the area and peak symmetry.

[0040] In various specific examples, the chromatography modeling application 400 is G The method setting values ​​from C system 100 are used as input to the chromatography model. This produces what is called nominal simulated chromatographic separation. GC Controller 110 is used by a user of the GC system 100 in a specific way. You may be instructed to define the setting values. In some specific examples, the column heater temperature and The inlet pressure and other settings are two values ​​set by the user. Chromatography model The ring application 400 performs the necessary calculations within each predefined time window (Δt). These settings are used when implementing the model. This model uses settings entered by the user. Based on the fixed values, it represents what the user expected from the equipment. Other GC System 100 The instrument parameter settings are determined by the chromatography modeling application 400. Please understand that it may be used. Or, nominal simulated chromatography. Fee separation generates a reference chromatogram as input to the chromatography model. It can be generated by using the device data collected in between.

[0041] In various specific examples, the chromatography modeling application 400 is used Another type of chromatography model that is generated is G during chromatography analysis. Real-time instrument data measured and / or determined by system C 100 (for example, Using column heater temperature values, inlet pressure sensor values, etc., the chromatography model It generates simulated chromatographic separations. Therefore, it is generated in real time. The chromatographic model used is similar to other models that utilize nominal or ideal settings. It offers several advantages over other methods. More specifically, it utilizes real-time device data. The chromatography model generated by the diagnostic and predictive module 118. This is not assumed to be what the system is doing, but rather what the GC system 100 is doing during sample separation. It accurately reflects what was actually being done. For example, the air around the heat zone of the GC system The flow and / or heatsink will change the actual zone temperature compared to the zone temperature setpoint. This is possible. In addition, pressure fluctuations can change the outlet pressure of the column, affecting the nominal or ideal pressure. Compared to the assumed gas velocity for separation, the actual gas velocity in the column changes during sample separation. This may be the case. Therefore, real-time data is used instead of set values ​​or ideal device data. Using instrument data improves the accuracy of chromatography models. Real-time device data collected during fee isolation can be stored for later use. Please note the capabilities. For example, real-time from previous chromatographic separations. Save the instrument data, and after the chromatographic separation is complete, Used as input to generate simulated chromatographic separations in the model. This replicates the collected chromatograms, but it is not possible to do this offline. can.

[0042] (Automatic GC troubleshooting) As described above, the diagnostic and predictive module 118 monitors chromatography performance. Combining chromatography modeling and automated GC troubleshooting procedures. In addition, the chromatography performance and functionality of GC system 100 are dynamically monitored. In various specific cases, the user is warned about chromatography performance and / or instrument problems. Afterward, the automated GC troubleshooting procedure will diagnose and repair the GC system 100. This guides the user. Therefore, "automated" troubleshooting eliminates human intervention. Rather, it includes troubleshooting that is made easier by automated steps. Normally, when the GC system 100 fails, the user analyzes the data to determine what the problem is. The user must decide for themselves what repairs are needed to fix the problem. However, the diagnostic and predictive module 118 of this disclosure is a GC system We guide users through 100 troubleshooting and maintenance tasks.

[0043] In some specific examples, the automated GC troubleshooting procedure is a decision tree. Decision trees can take the form of a tree. Decision trees are the most effective way to solve observed or predicted problems. This may include a series of questions or observations to guide the user to the most likely maintenance items. In one example, the automated GC troubleshooting procedure involves monitoring chromatography performance. The ring results (i.e., which chromatography parameters fell outside the control limits) Whether the lower or upper control limit has been exceeded, or whether any device data has gone outside the control limits, etc. Use this to determine the starting point for the automated GC troubleshooting procedure. For example, one The retention times of the above peaks were determined by chromatography performance monitoring. If it is observed that the system is outside the limits, the automated GC troubleshooting procedure will ask questions. To retrieve information stored in the system related to the cause of the retention time shift of the analyte. It can be started by gathering people together.

[0044] Some of the decisions in the decision tree can be presented to the user for input. These are then garbage collected (GC). This may include items that the system cannot answer, or items that the GC system requires the user to verify. This may include items that the user wants to include. For example, the user may want to include different modules installed on the device. The user can answer questions to verify the configuration of the method / parameters. (For example, verify the column type and dimensions, syringe size, sample location, etc.) and the system It can be confirmed that the system is correctly configured for the analysis being performed.

[0045] In addition to questions presented to the user for input and / or verification, the system also... Mathematics performance monitoring results, instrument data, simulated chromatographs. Based on separation and / or diagnostic testing, the user can be guided to a different branch of the decision tree. In other words, the GC system can access the information stored internally. Alternatively, additional information can be collected by initiating a diagnostic test, so the user can In some cases, the user does not need to answer all the questions in the decision tree. Because this information is inaccessible, the automatic GC troubleshooting procedure... This improves the ability to perform troubleshooting, rather than what individual users can do. ru.

[0046] For example, using chromatography performance monitoring, sample chromatography from the latest analysis can be performed. The peaks of the chromatogram are shown in the reference chromatogram and / or simulated chromatogram. By comparing with the peak, we can answer the question, "Is the retention time short or long?" It is possible to obtain additional information monitored by peak assessment. This may result in the retention time of two or more analytes being too short or too long, or a problem occurring. It is also possible to determine whether only one of the analytes in the sample was affected. If the retention time of the analyte is affected, the decision will be linked to the issue with the entry point. The user can be guided to a part of the tree, while the retention time of two or more analytes is affected. If this occurs, you can begin asking questions or gathering information to investigate the problem with the column heater. can.

[0047] Instrument data confirms that the setpoint matches the actual value achieved during chromatographic analysis. This allows us to verify that there was a match or not, and guide the decision tree to a different branch depending on whether they matched or not. This is possible. For example, if the set value of the column heater temperature rises at a rate that the equipment cannot achieve... In total, the system analyzes the deviation between the instrument data and the setpoint for analysis, and the temperature is expected to be... It can be determined that the low level may be the cause of the retention time being longer than expected. The method involves temperature value, pressure value, valve status, motor step, motor current, heater voltage, and heater. Duty cycle, flow sensor value, detector signal level, detector current level, on-time These include, but are not limited to, values ​​such as valve duty cycle and other such instrument sensor values. It may not be used for data from other devices.

[0048] Certain diagnostic tests, with or without user assistance, G This can be done by the C system. For example, a user can access a leak located inside the entrance bulkhead. If the chromatogram has a "no peak" problem for output, the GC system The system can access internally stored information as needed, and / or automatically diagnose By performing a test, the user can be guided through the decision tree. GC system By using the information stored in the system and automatically performing diagnostic tests, troubleshooting can be performed. Fewer questions are asked of the user during testing, resulting in a better user experience. In the example described below, the user asks the question, "No peaks?" regarding the last chromatogram collected. The problem is that the root cause is leakage at the location of the inlet bulkhead. Warning: Peak The user was notified that the evaluation had failed and that no peaks were found in the chromatogram. The problem is identified.

[0049] The automated GC troubleshooting procedure begins with the GC symptom "no peak". The user is responsible for determining the root cause of the chromatography problem they are observing. You will be asked a series of questions about the problems you have (or asked to perform a task). ) In some cases, the user may be asked to complete some of the tasks that may be required to complete, such as leaks in the GC flow path. The task is to find it, or to verify that the FID jet is not clogged. Skills can determine the quality of results and information provided to the GC system, Therefore, it is possible to determine how well the instrument can determine the root cause of the chromatography problem. It is determined whether it is possible. Using the automated GC troubleshooting procedure, user interaction is not required. The GC system will assist users by answering some of these questions.

[0050] Figure 8 shows how to solve the "no peak" problem, which leads to the resolution of leakage at the inlet bulkhead. This shows the process or step 800 that the user takes. The top of Figure 8 shows the troubleshooting. If the testing does not use information from the GC system or does not automatically perform diagnostic tests This shows the user interaction. The bottom of Figure 8 shows troubleshooting for the GC system. This shows the user interaction when performing automated diagnostic tests using the information above. If the "no peak" problem arises due to leakage at the entry point, the user will usually suggest or To arrive at the expected maintenance task, you must answer five questions from a user-guided decision tree. It is necessary. Information stored in the GC system and / or collected by the GC system In the automated GC troubleshooting procedure using the report, the user can determine the timing of the injections that were performed. Answer one question related to verifying the properties (e.g., split, splitless, etc.) All you need to do is check. After the user verifies the type of injection performed, the GC system will detect the leak. Perform a limit diagnostic test. The leakage limit test first involves holding the inlet at the pressure setpoint. Therefore, we will verify the inlet control. Next, we will examine the error between the actual flow rate and the target setpoint column flow rate. Commence monitoring. If leakage is present in the inlet stub, the system will be required by the column. The system detects a flow rate higher than the normal flow rate and determines that a leak exists within the system. Based on this, the automated GC troubleshooting procedure will inform the user about leakage into the GC system path. We will notify you of the situation and propose the following: 1) replace the partition, 2) reinstall the column. 3) Install, replace the liner and liner O-ring, and 4) split vent The trap can be opened to allow the user to check the seating of the O-ring. Replace the split vent trap as needed. The instrument will automatically perform a leak limit test. If this is not done, the user will be presented with a longer list of potential issues that need to be checked and fixed. (For example, eight potential solutions are provided.)

[0051] Another aspect of troubleshooting is that the GC system may have recently experienced an issue. This refers to the ability to store and use information related to maintenance work or hardware changes. GC system If the application was working correctly before, the problem may lie in an area that the user recently modified. The possibility increases. By using maintenance information stored on the GC system, -za provides a direct route to a solution that has a higher probability of fixing the problem that is occurring. This will be provided. One example is when a user has recently performed maintenance on the entrance (such as replacing the entrance bulkhead). In this case, the automatic GC troubleshooting procedure is based on the most recent information stored in the GC system. Using recent maintenance information, guide the user to start from the entry section of the decision tree.

[0052] In various specific examples, chromatography modeling is used for automated GC troubleshooting. A specific maintenance procedure used to correct chromatography performance problems. The work can be determined. For example, nominal simulated chromatograms and The real-time simulated chromatogram and the reference chromatogram match each other. However, the chromatogram of the experimental sample performed on the current sample is the simulated chromatogram. If it does not match the Ram and reference chromatogram, the automated GC troubleshooting procedure is The GC system was controlling things as expected, but the GC system, and therefore the GC model, was recognized It can be determined that something unnoticed may have changed. That is, heat and air. The pressure setting is controlled during sample execution, and is outside the scope of the GC system's control and knowledge. The column changes, leading to chromatography performance problems (for example, if the wrong sample is injected). (This is due to factors such as the data being truncated, parameters not being updated, or columns starting to fail.) This may be causing the problem. Therefore, the automatic GC troubleshooting procedure is as follows: Check if any changes have been made to the system, verify that the configuration is correct, or A decision tree that instructs the user to investigate performance problems related to column degradation, flow path contamination, etc. You can proceed to that section.

[0053] In another example, a real-time simulated chromatogram and experiment of the current run. The sample chromatogram matches, but the reference chromatogram and / or nominal simulation are not. The generated chromatogram is a real-time simulated chromatogram and experimental sample. If the chromatogram does not match, the automated GC troubleshooting procedure will be performed by the GC system. It can be determined that the system was not controlling things as expected. For example, a certain sensor The value does not match the set value (i.e., the column heater temperature does not match the set value, inlet pressure) (There are cases where the sensor does not match the set value, or the expected gas flow rate does not match the set value.) In these cases, the actual instrument data from the current analysis is real-time chromatography. - Used in the model, any impact of device data that does not match the set values ​​will be in real time. This becomes clear in the chromatography model results. Therefore, automatic GC trouble The shooting procedure guides the user to a part of the decision tree, including the heater, flow control module, Alternatively, other components of the GC system, such as other components, can be further investigated. Diagnostic tests can be implemented to narrow down the possibilities and / or confirm the problem. Alternatively, the automated GC troubleshooting procedure is the most likely way to resolve the issue. High-priority maintenance items include the replacement or repair of some hardware components of the GC system (for example) In such cases, cleaning, adjustment, or changing the settings may be recommended.

[0054] In yet another example, the chromatography model can be compared to itself. This is when the GC system is in a known good state and / or when using the instrument settings. Sometimes, nominal simulated chromatography separations are generated in real time. This is compared to simulated chromatographic separation. Therefore, nominal stains Simulated chromatographic separation and real-time simulated chromatography If the Raffie isolation does not match, the automated GC troubleshooting procedure will be performed by the GC system. It can be determined that there is a hardware problem. For example, real-time simulation The chromatographic separation performed is nominally simulated chromatographic separation. If the analyte retention time is longer than expected, this indicates that the flow rate or temperature is lower than anticipated. This may suggest that the automatic GC troubleshooting procedure involves checking the path cold spot. This suggests that flow path leakage or other such flow path problems may be the cause of longer retention times. It is possible that this may occur. In such cases, the GC system configuration (i.e., column type / dimensions, (Gas species, etc.) are compared in real-time chromatography models and nominal chromatography models. The same applies to the real-time chromatography model, but the actual thermal performance of the GC system is different. It uses thermal and pneumatic values. Therefore, real Time-simulated chromatographic separation is performed with different thermal and / or pneumatic values. In this case, it will differ from nominally simulated chromatographic separation.

[0055] For example, if a user requests a column heater ramp speed that the system cannot meet (column When the heater ramp rate is input to the GC system, the set value (i.e., expected ramp rate) is entered. A nominal simulated chromatogram can be generated based on the degree. However, the real-time simulated chromatogram shows the actual column heater It is generated using temperature values ​​and cannot meet the expected temperature ramp rate, therefore, The ram heater temperature will be lower than expected. Therefore, the nominal model will have a faster ram speed. Because it uses degrees, the real-time simulated chromatogram is nominal. The results do not match the chromatogram obtained through chromatography modeling. Therefore, the system uses equipment data (e.g., measured thermal values) to allow the user to... The input expected heat setting value can be compared with this value. In this example, the column heater temperature is set The value may not have been close to the normal value, and the system may not have achieved the desired column heater ramp speed. The user can be notified if the column heater ramp speed is not working. This is useful when the user is unaware that the goal has not been achieved. If a reference chromatogram is generated using an impossible ramp speed, then its "known good" The chromatogram was not collected with the expected settings, so the problem does not surface. In some cases, the user has entered an oven temperature ramp rate that GC can achieve. If, for any reason, this could not be achieved in the sample execution, this is a hardware issue. The diagnostic and predictive module 118 indicates a fair error, and the column heater is functioning as expected. It can be shown that it is not the case.

[0056] In various specific examples, automated GC troubleshooting procedures also involve chromatography. By using Chromatography, anticipated maintenance tasks can be chromatographed before performing maintenance work. It can be verified that the Laffey performance problem has been successfully corrected. More specifically, The and / or GC system knows what changes will be made during maintenance work, If the model can use device settings as input, before performing maintenance work, It is possible to generate simulated chromatographic separations. For example, the user can In some cases, columns are periodically trimmed to remove contamination. Each time the ram was shortened, it's possible that a new length was being added to the equipment configuration. The shortened lamb caused the retention time to shift outside the established limits of chromatography. If detected by Laffey performance monitoring, automatic GC troubleshooting steps Next, to fix the chromatography performance issue or resolve it in other ways, the column We can suggest replacing it. The chromatography model will show the column dimensions of the new column. By using the method, phase type, and other such parameters, chromatography can be performed by changing the column. It is necessary to verify that the fee performance issues will be fixed or resolved in some other way. can.

[0057] In several specific examples, after being guided through the automated GC troubleshooting procedure, A single maintenance task, or two or more weighted or ranked possible maintenance tasks A list of tasks will be provided to the user. Each of these maintenance tasks will resolve the current performance issue. Based on the possibilities provided by the user during the automated GC troubleshooting procedure The responses can be weighted or ranked according to the diagnostic tests performed by the device. And the user will be provided with guidance on how to carry out the proposed maintenance work. After the user performs maintenance work, the maintenance work resolves the original chromatography problem. To verify this, the ability to perform verification is proposed. The proposed maintenance work is used If the chromatography problem is resolved, the user can update the reference chromatogram. Therefore, the option to continue normal equipment operation is available. The proposed maintenance work is for the user's chromatograph. If the graphics issue is not resolved, the user will need to run the automatic GC troubleshooting again. You can choose to proceed with the process or be provided with additional support information (such as manufacturer contact information). It has options.

[0058] In various specific examples, the diagnostic and predictive module 118 performs chromatography performance measurements. Nutting, chromatography modeling, and automated GC troubleshooting procedures This is used in combination with machine learning and / or neural networks to improve equipment performance and / Alternatively, diagnostic tools can predict the timeframe and failure modes of maintenance issues before they occur. It constitutes a network. For example, the diagnostic and prediction module 118 utilizes a neural network. This addresses the potential chromatographic performance and / or maintenance issues of the GC system 100. Multiple different maintenance operations can be ranked and ordered. That is, the neural network can analyze chromatographic performance monitoring data, equipment data, data from diagnostic tests, and / or simulated chromatograms to correlate the data with multiple different maintenance operations. Therefore, the diagnostic and prediction module 118 can utilize the neural network to assign weights or ranks to each of the different maintenance operations based on the likelihood that the maintenance operation will solve equipment performance and / or maintenance problems.

[0059] In various specific examples, the diagnostic and prediction module 118 can also incorporate machine learning to teach the GC system 100 that certain sample data and / or equipment data is associated with a specific failure or maintenance problem or a limited number of possible problems of the GC system 100. That is, the diagnostic and prediction module 118 can analyze past chromatographic performance monitoring results, sample data, equipment data, data from diagnostic tests, and / or simulated chromatograms with different performed maintenance operations to correlate equipment failures with the performed maintenance. Therefore, the diagnostic and prediction module 118 can learn that certain sample data and / or equipment data indicates one or more failures or maintenance problems of the GC system 100. Therefore, over time, the GC system 100 can, based on past troubleshooting and maintenance of the GC system, determine that certain chromatographic performance monitoring results, sample data, equipment data, data from The system learns that it can identify a specific failure mode of the GC system 100. .

[0060] Another aspect of troubleshooting is to help guide the user through a decision tree. This involves using neural networks and / or machine learning processes. By utilizing a network and / or machine learning process, the GC system can determine how Have the eel problem occurred repeatedly, and what related issues have been used to resolve these problems? Helping users learn solutions to similar problems, such as leaks at the entrance bulkhead. This includes cases where the following occurs repeatedly. The neural network of the GC system and / or if the machine learning process notices that this leakage pattern continues to occur The GC system does not explain the entire decision tree process to the user, but rather first shows the entry point leak. The output is checked. This reduces the number of questions the device asks the user, and the user The decision tree provides a direct route to a previously working solution to fix the problem. It will be provided.

[0061] Another benefit of GC systems that utilize neural networks and / or machine learning processes The point is, if the same problem continues to occur repeatedly for the user, the GC system may have other potential This is the ability to propose a solution. In this example, leakage occurs at the entrance located in the entrance bulkhead. If this continues, the GC system will be the root cause of the problem. To solve this, we can begin proposing other solutions. Repeated positioning within the entrance bulkhead. In an example of leakage, the GC system would have the user check the syringe for burrs inside the needle. It can be suggested to verify that there is no burr. Burrs inside the syringe needle This repeatedly causes leakage problems within the partition, but the user is answering questions from the decision tree. If it's just that, it might go unnoticed by the user or during troubleshooting. The device utilizes neural networks and / or machine learning, thereby enabling the device to perform various tasks. This allows us to provide users with more insights and determine the root cause of the problem.

[0062] Once the maintenance work is complete, the diagnostic and predictive module 118 will indicate that the maintenance has been performed. Record and display (for example, maintenance instruction line 350 on control chart 300 in Figure 3). Then, automatic G C. Troubleshooting procedure to correct chromatography performance and / or maintenance issues To verify that this has been done, a verification run will be performed using the same sample and separation process. Instruct the user to do so. The results of the verification run will be the previous reference chromatogram and / or chromatogram. The results are compared with a matrix model to check if they match. The verification process is then completed. The result matched the previous reference chromatogram and / or simulated chromatogram. If the result of the verification run is the same as before, the reference chromatogram will be updated and the instrument will return to normal operation. If it does not match the reference chromatogram and / or simulated chromatogram, The user will then return to the automatic GC troubleshooting to identify the cause of the problem. The system can also accept or reject the results of the validation run and, if desired, perform an automatic garbage collection (GC) You can return to the bullshooting. The user can then view the results of the chromatography model and Even if they do not match, but do match the previous reference chromatogram, the results of the validation run will be... It can also be accepted. When it is determined that the problem has been solved, the management diagram can be updated, reinitialized, and / or cleared as necessary.

[0063] (Example 1) Chromatographic analysis methods developed to qualitatively and quantitatively understand the components of complex sample matrices are diverse. There are many regulatory agencies such as ASTM, NIST, and EPA that design and provide methods for analyzing various samples. These methods often include complex method settings developed to obtain the desired chromatographic results. Some methods aim to quantify analytes at very low concentrations (i.e., parts per billion), while the goal of other methods may be to quantify compounds at very high concentration levels (percent levels). Some methods employ a combination of isothermal and temperature-programmed settings to separate both volatile and semi-volatile compounds. In other methods, a complex inlet temperature program or inlet flow dynamics may be used to vaporize thermally unstable analytes.

[0064] Due to the very large number of combinations of different chromatographic method parameters, it is very difficult to understand and interpret all the different possible interactions when a problem occurs. Often, users of GC systems utilize methods developed elsewhere and may not know why the method settings were selected as such. One of the goals of the development of the diagnostic and prediction module 118 described herein is not only to determine when a problem has occurred but also to assist in identifying where the problem exists when it occurs. ​This allows users to troubleshoot complex situations related to chromatography. The goal is to help with navigation. The aim is to quickly identify problems and resolve them as quickly as possible. It helps users recover quickly. One of its key features is involvement. Without requiring the user to have prior knowledge or understanding of chromatography, How do we use rhomography modeling to determine what the expected behavior of the system is? It is about being able to show the user what they should do.

[0065] In the following example, Figures 1, 3, 4, 5A, 5B, 5C, 6, 7A, 7B, Referring to Figures 7C and 9, a hypothetical analysis method and workflow are used to diagnose and predict The features of the measurement module 118 will be highlighted and explained. Figure 9 shows the diagnostic and predictive module 118. This flowchart shows the process of enabling, configuring, and using the sample analysis. Before starting, the user activates the diagnostic and predictive module 118 to GC system 1 Dynamically monitors the chromatography performance and functionality of 00. Diagnostic and predictive module 1 At startup, the user can check the chromatography performance and functionality of GC system 100. At least one chromatographic evaluation used for dynamic monitoring (e.g., bra Specify (indicator evaluation, detector evaluation, or peak evaluation). In this example, peak evaluation is used. Peak evaluation allows the user to determine which diagnostic and predictive module 118 is performing during sample analysis. You can choose whether to monitor the peak. The user (or GC system 100) also , certain peak parameters of the sample or analyte monitored by the GC system 100 (For example, retention time, peak height, peak area, peak width, peak symmetry, and peak fraction) Defines the resolution, reference chromatogram, and performance control limits. The reference chromatogram is G The target sample can be stored by system C 100, or alternatively, analyzed. Previously, it can be generated by the GC system 100. The user can monitor the peak When specified, chromatography modeling application 400 will configure the GC configuration and method Using the specified parameters, a nominal simulated chromatogram is generated, allowing the garbage collector to perform the GC. Verify that the process is being carried out as intended. Then, the user will use the sample as part of the operating procedure. The system will begin execution. It will monitor chromatography performance and display the results in a control chart. If a problem is detected (for example, if peak evaluation fails or the control chart shows future problems), then... (Predicting the problem), the user will start troubleshooting to diagnose the issue. The user is prompted to proceed. Once the problem is resolved, the user can resume the analysis of the sample.

[0066] In the illustrated example, the selected analyte is eicosane (nC 20 H 42 ), Docosan (Docosane) (nC 22 H 46 ), Tetracosane (n- C 24 H 50 ), and hexacosane (nC 26 H 54 These are. The compound can be analyzed by detailed hydrocarbon analysis (DHA) or by staining. Similar to simulated distillation (SIMDIST), different substances in the sample The separation and speciation of hydrocarbons are selected to represent a desirable part of hydrocarbon analysis. Selected. However, a wide range of compounds suitable for analysis by GC exist, and this specification It should be noted that the procedures described are not limited to hydrocarbon-type samples. Therefore, with peak evaluation, users can monitor the system status and performance using chroma The chromatographic performance of up to 10 peaks in the togram can be tracked. However, it is important to understand that you can monitor a larger or smaller number of peaks. The relevant experimental parameters are as follows: The column is 86 μm × 250 μm. It is m × 1.5 μm, HP-1 ms, has a constant flow rate of 1.0 mL / min, and has an atmospheric pressure outlet. Use the available helium carrier gas. The column heater program starts at an initial temperature of 30°C. It was started at [temperature], held for 5 minutes, and then increased at 1.5°C / min to a final temperature of 350°C. The detector used was a flame ionization detector (FID). It was used to determine the expected retention time in the chromatography model. Thermodynamic parameters were collected from a series of isothermal experiments to obtain the Van't Hoff value. The value was determined.

[0067] The diagnostic and predictive module 118 utilizes the current GC system configuration and method settings. It generates nominal simulated chromatograms. And diagnostic and predictive modules Rule 118 compares a nominal simulated chromatogram with a reference chromatogram. In the illustrated example, the diagnostic and predictive module 118 maintains the peak of the reference chromatogram. The time limit, nominal chromatography model, and GC instrument settings are used as input to the model. We compare it with the nominal simulated chromatogram generated using [the specified method]. The results are as follows: This is shown in Table 1 and Figure 7A, overlake chromatogram 710. - The model includes additional chromatography parameters (peak width, peak area, peak height). It is possible to generate peak symmetry, but in this example only the retention time is shown. Please understand that other chromatography parameters can be used in a similar manner.

[0068] [Table 1]

[0069] In the illustrated example, the reference chromatogram and the nominally simulated chromatogram are shown. The retention time difference or percentage error determined between them is approximately 0.2%. Such a difference is typical. The diagnostic and predictive module 118 uses a reference chromatogram and a nominal simulated chromatogram. Determine if the retention time difference or percentage error between the romatag and the overlay is acceptable. Peak height of simulated chromatograms, as shown in chromatogram 710 This refers to the peak between the reference chromatogram and the nominally simulated chromatogram. Please understand that this value has been lowered to better demonstrate consistency in retention times. Thus, the modeling results show how the equipment behaves in the current configuration and method settings. This is useful for indicating what is expected to happen. In short, users have no way of knowing whether the retention time generated from the experimental results is good or not. In this example, the modeling results using the nominal chromatography model are based on experimental standards. The rhomogram matches, suggesting the system is functioning correctly.

[0070] Once the GC system 100 is determined to be functioning correctly, the user can proceed with sample analysis. You can select the previously set peak evaluation method. Alternatively, the peak evaluation method If the peak evaluation parameter is not set for the sample, the user will set it in the GC system. You can input data and set up a new peak evaluation method. The user can use this later. The method, including these input peak evaluation parameters, can be saved during sample analysis. The GC system uses a peak evaluation method to chromatographically analyze the peaks of the target sample. Track and / or monitor data (e.g., retention time) and analyte peaks in a predefined tube. Ensure that the values ​​remain within the theoretical limits. An example set of peak evaluation parameters is shown below. This is shown in Table 2. In the illustrated example, the peak evaluation parameter is the reference chromatogram peak. Retention time, retention time limit or % error, and lower and upper control limits for retention time. This includes limits. The diagnostic and predictive module 118 uses the reference chromatogram peak retention time. The lower and upper control limits are determined by multiplying them by the holding time limit percentage error. Therefore, the lower control limit defines the acceptable limit for the reduction in retention time, and the upper control limit defines the acceptable limit for the reduction in retention time. The limit specifies the permissible limit for increasing the retention time. In the illustrated example, it is + / - 5%. The retention time limits were used to determine the lower and upper control limits, but different Please understand that retention time limits may be used. The chromatogram 710 in Figure 7A is As the vertical dashed line at the retention time listed in Table 2, the upper limit and 26 the lower limit of hexadecane (C ) are shown.

[0071]

Table 2

[0072] As described above, when the user determines that the chromatography performance is satisfactory and selects the peak evaluation method, the GC system 100 starts to execute the sample analysis. During the sample analysis, the diagnosis and prediction module 118 performs peak evaluation and monitors the analyte peak retention time of the sample being analyzed by the GC system 100. Therefore, at the start of the sample analysis, the diagnosis and prediction module 118 starts collecting sample data and dynamically displays the user-defined chromatography parameters of the sample data in a control chart. Therefore, during the sample analysis, when the diagnosis and prediction module 118 determines that one or more user-defined chromatography parameters (e.g., retention time) are outside the predefined performance management limits (e.g., upper control limit 320 and lower control limit 330) over a certain time frame (e.g., a specified number of sample injections), the diagnosis and prediction module 118 notifies the user that the user-defined chromatography parameters (e.g., retention time) will be out of range in the near future (e.g., after multiple injections). As shown in the control chart 730 of FIG. 7C, the chromatography performance monitoring of the diagnosis and prediction module 118 generates the control chart 730, on which the peak evaluation results for each analyte peak after each sample injection are plotted. In the illustrated example, the control chart 73

[0073] 0 shows that... 0 is analyte C 26 Evaluate the retention time of this analyte. Therefore, control chart 730 is used to evaluate the retention time of this analyte. Table 2 shows the upper and lower control limits defined therein. Control limits exist for all analytes being monitored, but to clarify, C2 Please note that this is only shown for item 6. (Cross of Diagnostic and Predictive Module 118) Analysis of control chart 730 using matrix performance monitoring is performed on analyte C 26 Retention time After the 6th sample injection, it approached the lower control limit, and after the 7th sample injection, it reached the lower control limit. It is determined that it exceeds the limit. Therefore, the diagnostic and predictive module 118 determines that when the peak is held in the future... The system notifies the user of any failures and allows the user to use the automated GC troubleshooting procedure. This can make it possible to correct peak retention failures before a failure occurs. In this example, Warnings about future retention time failures were ignored, and the system continued to run. However, seven times After injection into the eye, the diagnostic and predictive module 118 reports a failed peak evaluation. Figure 7B This shows the original reference chromatogram 722 with the expected chromatographic results, and the abnormal results. The chromatogram of sample 724, which failed to perform peak evaluation as shown in the results, is also shown.

[0074] In various specific cases, the user decided to accept troubleshooting assistance. In this case, the diagnostic and predictive module 118, through a series of questions displayed to the user, / or through the use of simulated chromatograms, instrumental data, and / or diagnostic tests. Then, additional input or information is collected. More specifically, the diagnostic and predictive module 118 This is to guide the user through troubleshooting the GC system 100. User-guided decision trees that utilize provided information (and / or system-provided information) The process proceeds through the step tree.

[0075] As shown in Figure 5A, the weighted decision tree portion 500 is comprised of the diagnostic and predictive module 118. 2. To start the automated intelligent troubleshooting of the GC System 100 Two common methods are shown. The automated GC troubleshooting procedure can be initiated. One method is to detect GC performance problems through chromatography performance monitoring. For example, as shown in the examples described herein, based on peak evaluation failure. The GC performance problem occurs when one or more of the user-defined peak data parameters are close to the upper control limit. Or, if it is outside the lower control limit, or if it is expected to be outside the upper or lower control limit in the near future. It can be detected if it is determined that this is the case. Therefore, diagnosis and prediction are performed according to the performance results. Module 118 generates a message indicating that a performance and / or maintenance problem has been detected. This is displayed to the user, and the user is asked if they would like troubleshooting assistance. If the user requests troubleshooting assistance, the diagnostic and predictive module 11 8 uses information from chromatography performance monitoring to induce Determine where to initiate troubleshooting support. For example, diagnostic and predictive modules. Schematic performance monitoring within 118 showed that the peak retention time was outside the control limit. If it is determined that a future failure will occur due to a certain cause, the diagnostic and predictive module 118 The internal automated GC troubleshooting procedure, as shown in Figure 5C, relates to retention time shifts. Guide the user to the weighted decision tree section.

[0076] Referring back to Figure 5A, the second automatic GC troubleshooting procedure can be initiated. This method allows the user to notice any performance issues during sample chromatography separation, and then GC This involves manually initiating the automatic GC troubleshooting procedure for System 100. The system is located in the diagnostics tab of the diagnostics and prediction module 118 or in other such menu options. By accessing this information, you can begin troubleshooting performance issues. When the user begins troubleshooting the GC system 100, diagnostic and predictive modes are displayed. Joule 118 determines whether the user has recently made any hardware changes, and / or G Ask the user whether they have performed maintenance work on System C 100. If you respond that the software was not changed or maintenance work was not performed, then diagnosis and pre- The measurement module 118 directs the user to the weighted decision tree portion 510, as shown in Figure 5B. Then, ask the user about the chromatography problem they are currently facing. And then, diagnose and Prediction module 118 can handle peak-free, low-response, high-response, retention time shift, and peak spread. To select from among peak tailing, peak fronting, and resolution loss, etc. The diagnostic and predictive module 118 displays multiple different performance issues to the user. Please understand that this may display other performance issues for selection. When you select a matrix problem, the guided troubleshooting will guide you through the troubleshooting process related to that problem. Proceed to the troubleshooting section.

[0077] On the other hand, if the user says that the hardware has been recently changed or that maintenance work has been recently performed If a response is received, the diagnostic and predictive module 118 will detect performance issues in the GC system 100 (for example) To address the retention time shift, ask the user what recent changes have been implemented. The diagnostic and predictive module 118 then provides the user with weighted information, as shown in Figure 5B. Point to decision tree section 510 to ask the user about the chromatography problem they are currently facing. Sleep. And the diagnostic and predictive module 118 detects no peak, low response, high response, and hold time. Inter-shift, peak broadening, peak tailing, peak fronting, and resolution loss, etc. To help the user choose from the options, multiple different performance issues are displayed. Diagnostic and predictive modules... Please understand that Rune 118 may display other performance issues for the user to choose from. When the user selects a chromatography problem they have observed, the system will guide them through troubleshooting. Next, proceed to the troubleshooting section related to that problem. For example, if a user recently... They responded that they had repaired the hardware or performed maintenance work related to the hold time shift. In that case, guided troubleshooting will further investigate the problem, as shown in Figure 5C. Therefore, we proceed to the weighted decision tree part 520. However, in the illustrated example, the hardware has recently changed It hasn't been updated.

[0078] As described above, the diagnostic and predictive module 118 performs automated GC troubleshooting. The procedure is used to determine the cause of the peak evaluation failure and what corrective actions may be necessary. It can be determined. In this example, the peak evaluation is determined by the short retention time outside the retention time limit. Since the peak failed, the "retention time shift" path is selected in Figure 5B. In total, the GC determines the correct chromatography performance failure mode without asking the user. It is possible. Figure 5C is the decision tree that follows from Figure 5B. The first two questions, "All "Does the analyte shift its retention time?" and "Is the retention time short or long?" Chromatography performance monitoring and / or reference chromatogram, simulated Using the chromatogram and / or information from the current sample chromatogram, the diagnosis and This is determined by the prediction module 118. The next question following that path is the user interaction. While it may be necessary in some cases, in others, the diagnostic and predictive module 118 can determine the outcome. It can also be determined. A closer examination of the bottom chromatogram in Figure 7B reveals that the retention time shift Not only the peak, but also the baseline offset is shown. Peak due to retention time shift. Chromatograms that failed to evaluate also show high baseline offset values. The line offset was not selected as a parameter to monitor, so the system Because the system does not warn users about this phenomenon, user action may be required. Yes, it is. The answer to the next question in decision trees, "Is column bleed high?" is yes. Therefore, a possible cause of the degradation of chromatography performance is initially the stationary phase (st This is thought to be a deterioration of the ational phase.

[0079] List 600 in Figure 6 is an initial list of possible troubleshooting solutions. It indicates that, based on the chromatographic symptoms, the problem is initially with the column or oven. It was thought to be inside. The results of chromatographic modeling selected a list, This can be very useful in identifying problems. In this example, chromatography models and Nominal chromatogram and chromatograph generated using chromatography settings Realtime data based on equipment data from the execution (i.e., measured thermal and air pressure values) The simulated chromatogram of IMM matches that of the other. Furthermore, both simulations The rated chromatogram also matches the original reference chromatogram. Nominal chromatogram The agreement between the gram and the real-time simulated chromatogram is, The measured thermal and air pressure values ​​were within the expected setpoints and under control during operation. This means that the GC hardware can be considered to be functioning properly. We will analyze the equipment data regarding the oven temperature and compare it with the expected oven temperature setting. Therefore, verification is also possible. These were determined to be consistent. The same process with air pressure values This can also be done for the following: Visual inspection of the chromatogram in Figure 7B is performed when the same sample is injected. To indicate this, a similar-looking chromatogram, simply shifted to the left, is displayed. Therefore, several sample introduction system-related problems (e.g., ALS problem) have been ruled out. Furthermore, since both models match the reference chromatogram, GC-S It is inferred that something outside the control or knowledge of the stem altered the chromatographic performance. This is possible. In addition, GC maintains the same configuration throughout the entire sample analysis. Chromatography degradation can be caused by changes in configuration or maintenance issues (e.g., changes in the column). This is not due to ( ). A figure that meets all the criteria for chromatographic behavior. The only remaining solution to number 6 is that the column stationary phase may be changing or degrading over time. That is the case.

[0080] Different analyses affect the GC system in different ways, leading to a wide range of performance degradation. Allows for a wide range of durations. Many samples contain contaminants that could damage the system. It is "clean" in the sense that it contains few substances. This is because chromatographic degradation is not observed. This can result in a relatively long time until the desired outcome occurs. Other samples are contaminated and undesirable. It may leave behind residue, which can damage system components relatively quickly. This can cause performance degradation. In some cases, very high performance can damage the column stationary phase. A temperature program is required. Furthermore, due to contaminated carrier gas or leaks in fittings... This can allow oxygen to enter the system, potentially causing rapid damage to the column stationary phase. This is very useful for variability during the period before the system may show performance degradation. In one example, the failure occurred immediately (as shown in the control chart in Figure 7C), but in some cases... The system can withstand hundreds of injections before significant degradation in chromatography performance occurs. It is possible.

[0081] After the user has performed the suggested procedures and / or maintenance work, the GC system 100 will perform the inspection. Automatically perform verification execution (or instruct the user to perform it). Test from verification execution. The material chromatogram is compared to the reference chromatogram and / or the simulated chromatogram. The retention time returned to normal as determined by comparison (and the user was the same as the result) If (this is intended), the reference chromatogram can be replaced with the validation sample chromatogram. The reference chromatogram can be updated. Therefore, the GC system 100 is normally The equipment operation is resumed, and the diagnostic and predictive module 118 is on the maintenance indicator line 35 of the control chart 300. The value is updated to 0 to indicate the change in equipment performance based on the adjustments and / or maintenance work performed. If the retention time does not return to normal, the diagnostic and predictive module 118 will also... Continue investigating the components (e.g., inlet, sample introduction system, and / or detector). In a specific example, the diagnostic and predictive module 118 performs automatic GC troubleshooting. Maintenance including inputs provided by the user and / or GC system 100 during the maintenance procedure. The report is generated automatically (or by user command). Maintenance reports are generated automatically by GC troubleshooting. This further includes the work and / or maintenance work performed during the cutting procedure and the results thereof. The diagnostic and predictive modules then save maintenance reports for future reference.

[0082] All patents, publications, and documents specified herein are disclosed by reference. This shall form part of this specification.

[0083] The terminology used in this specification is intended solely to explain specific examples. It should be understood that this is not intended to be limiting. The terms that are defined are defined In addition to the technical and scientific meanings of the terms, this instruction also provides a general understanding within the technical field of this teaching. It has a meaning that is accepted and accepted.

[0084] As used in this specification and the appended claims, “one (a, an)” and “that The term "(the)" refers to a singular object and unless otherwise clearly indicated in the context. It includes both of multiple objects. Therefore, for example, "apparatus" can refer to one apparatus and multiple parts. Includes minutes. Unless otherwise indicated, the terms "1st", "2nd", "3rd", and other ordinal numbers In this specification, the terms are used to distinguish different elements of the apparatus and method, and numerically It is not intended to imply any limitations. References to the first and second elements imply that the apparatus has two It should not be interpreted as meaning that it only has elements. The first element and the second element The apparatus having the above may also include the third, fourth, fifth and subsequent devices unless otherwise indicated.

[0085] In this specification, the terms "nominal value" and "ideal value" are used. The terms "alue" or "setpoint" are used abstractly, theoretically, or from a standard. This refers to a determined value, not a value determined from actual measurements during operation. For example, the GC method. However, the column heater maintained the temperature at 40°C for 1 minute, and then raised the temperature from 40°C to 60°C for 20 minutes. When specifying an increase per second, the nominal value (at a particular point in time) is defined in the program. It is a temperature based on the column, and the exact column temperature at that specific point in time is measured by the sensor. This is not the temperature. However, the GC system may differ slightly from the specified nominal value. It has a temperature sensor that measures and records the actual temperature of the column heater.

[0086] When used in this specification and the appended claims, "chromatographic model" The term "Dell (chromatographic model)" has, in addition to its usual meaning, also encompasses GC methods and When subjected to chromatographic separation by and / or composition, one or more analyses in the sample To predict one or more chromatography parameters for a substance, a GC method and / or, in combination with data on composition, for a sample or one or more analytes in a sample A program, software, or algorithm that uses data on the chemical properties of To point.

[0087] When used in this specification and the appended claims, "chromatographic The term "chromatographic parameter" has, in addition to its usual meaning, G Any parameters that can be measured by the C system (retention time of analyte pairs, peak height, Peak area, peak width, peak symmetry, and peak resolution are examples, but are not limited to these. (This refers to something that is not done.)

[0088] When used in this specification and the appended claims, "performance data (performa The term "sample data" includes, in addition to its usual meaning, sample data and instrument data. However, these are not limited to those obtained from chromatographic separation, derived therefrom. It refers to data related to, or otherwise associated with, the sample data provided for the separation of the sample. This refers to data related to (for example, retention time and other chromatography parameters), and Equipment data refers to data about the equipment (for example, temperature, pressure, power demand, etc.).

[0089] As used herein and in the appended claims, "connected" The term d) is used in addition to their usual meanings when two components are fluidly connected. It means to be connected, physically connected, or both. The term refers to a state where two components are in fluid communication, and there is direct communication between the two components. Connection, and indirect connections between two components within a flow path where one or more other components are present. This means including connections. For example, the first component and the second component are The exit from the first component is physically connected to the inlet of the second component. In cases where, or when the conduit connects the first component and the second component, This occurs when the fluid flows from the first component to the second component or vice versa. One or more intervening components, such as valves, pumps, or other structures, are connected to two components. When located between two components, they are connected via a ferrule. By doing, brazing, and other appropriate methods, etc. They can be physically connected. Generally, liquid-tight and / or have minimal dead volume. A rational connection is desired for this device.

[0090] In the following detailed explanation, in order to ensure that you fully understand this instruction, we will use a more detailed and explanatory approach. For clarity, representative examples that disclose specific details are described. To avoid making things difficult to understand, known systems, devices, materials, operating methods and The explanation of the manufacturing method may be omitted. Nevertheless, it is within the understanding of a person skilled in the art. The systems, devices, materials, and methods described herein can be used according to representative examples. ru.

[0091] Generally, drawings and the various elements shown in them are not drawn to scale. It is understood. Furthermore, "upper", "lower", "top", "bottom", "upper side", "lower side", "left", Relative terms such as "right," "vertical," and "horizontal" may be used in various ways as shown in the attached drawings. These terms are used to describe the relationships between elements. In addition to the orientation shown, this includes different orientations of the microfluidic contaminant device and / or elements. It is understood that this is intended to be the case.

[0092] (Examples) Illustrative examples provided by the subject matter disclosed herein include, This is not limited to these.

[0093] (Specific example 1) A method for operating a gas chromatography (GC) system, At least one chromatographic parameter of a sample analyzed by a GC system The simulation uses a chromatography model based on the configuration of the GC system to calculate the data. Steps to generate a rated chromatographic separation, Sample chromatography separation is performed using a GC system, thereby enabling GC systems A step of generating a sample chromatogram of the sample to be analyzed by the stem, Sample chromatography including at least one chromatographic parameter of the sample. - A step of collecting performance data related to separation, A chromatography performance monitor configured to analyze sample chromatography separation. The step involves performing chromatography, and chromatography performance monitoring is performed on the sample. At least one chromatography parameter for chromatography separation and simulation Includes comparison of the chromatographic separation and / or reference chromatographic separation. , at least one chromatography parameter of sample chromatography separation is performance Determine whether the control limits have been exceeded, and / or perform sample chromatographic separation with less Whether one chromatography parameter can fall outside the performance control limits, and / Alternatively, it is a step that predicts when the performance management limits may be exceeded, Using chromatography performance monitoring and chromatography model results , an automated GC troubleshooting procedure that predicts expected maintenance work for the GC system. The steps to be implemented, The steps include sending a maintenance notification for the GC system, including the expected maintenance work, and A method that includes this.

[0094] (Specific example 2) At least one chromatography parameter is analyzed by the GC system. Retention time of the analyte, relative retention time, retention index, adjusted retention time, peak height, peak area, Peak width, peak symmetry, peak resolution, peak capacity, skew, kurtosis, number of resolutions , one of the following: volume ratio, selectivity, efficiency, apparent efficiency, tailing coefficient, concentration, and molar amount The method described in Specific Example 1, including one or more of the above.

[0095] (Specific example 3) Automated GC troubleshooting procedures also cover instrumentation from sample chromatography separation. The steps of using data to determine expected maintenance work and sending maintenance notifications are multiple. Determining the expected maintenance tasks from different maintenance tasks and providing information to GC system users The method described in Specific Example 1, which includes warning of maintenance work to be performed.

[0096] (Specific example 4) The equipment data includes the GC system's temperature value, pressure sensor value, valve status, motor step, Sample injection count, motor duty cycle, heater current value, heater duty cycle Motor current value, flow sensor value, detector signal value, detector current value, detector frequency value, calibration Table, auto-zero value, sensor-zero value, time-on value, and valve duty cycle The method described in Specific Example 3, which includes one or more of the values.

[0097] (Specific example 5) The automated GC troubleshooting procedure is one way to determine the expected maintenance work. The method described in Specific Example 1 for conducting the above diagnostic test.

[0098] (Specific example 6) The chromatography model is sample chromatography performed by a GC system. - A concrete example using actual instrument values ​​of the GC system collected in real time during separation. The method described in 1.

[0099] (Specific example 7) The automated GC troubleshooting procedure uses a decision tree to determine the expected maintenance work. The method described in Specific Example 1, which utilizes this method.

[0100] (Specific example 8) The method described in Specific Example 7, where the user inputs information into the decision tree.

[0101] (Specific example 9) The decision tree is a GC system that includes the sample introduction system, sample inlet, column, column heater, and Further determine the expected maintenance performance for one or more of the detectors, and limit the performance control. At least one chromatograph that is expected to be outside the bounds and / or outside the performance control limits The method used in Specific Example 7 for correcting the Raffie parameter.

[0102] (Specific example 10) The automated GC troubleshooting procedure further utilizes neural networks to predict... Expected maintenance work and work that is outside the performance control limits and / or is expected to be outside the performance control limits The method described in Specific Example 1 for determining the correlation between the chromatographic parameters being used.

[0103] (Specific example 11) The automated GC troubleshooting procedure further utilizes machine learning processes to anticipate and resolve unexpected issues. Maintenance work that is outside the performance control limits, and / or is expected to be outside the performance control limits Specific example 1: Instructing the GC system to associate with chromatographic parameters. Methods used.

[0104] (Specific example 12) The automated GC troubleshooting procedure uses a neural network to find one or more solutions. The above anticipated maintenance work is outside the performance control limits, and / or outside the performance control limits. Associated with expected modifications to chromatography parameters, and outside the performance control limits, And / or chromatography parameters that are expected to be outside the performance control limits are repeatedly If the GC system problem occurs, the neural network will repeatedly... The method described in Specific Example 1 for determining alternative maintenance work to fix a GC system problem.

[0105] (Specific example 13) The automated GC troubleshooting procedure covers the GC system's sample introduction system and sample inlet. Perform expected maintenance on one or more of the following: column, column heater, and detector. By applying this, the chromatographs that are outside the performance control limits and / or are expected to be outside the performance control limits The method according to Specific Example 1, further including a step of correcting the graphics parameters.

[0106] (Specific example 14) The step of performing validation chromatography separation after carrying out expected maintenance work. Furthermore, validation chromatographic separation is simulated chromatographic separation. Alternatively, it is compared to a previous reference chromatogram, and the expected maintenance work is at least one The rhombus parameters are outside the performance control limits, and / or outside the performance control limits The method described in Specific Example 1 verifies the need for correction based on the expected outcome.

[0107] (Specific example 15) Verification chromatography separation requires that at least one chromatography parameter is suitable. When verifying that the capacity is within the control limits, validation chromatography separation is performed using a reference chromatography. The method described in Specific Example 14, which replaces the image separation method.

[0108] (Specific example 16) Chromatography performance monitoring involves at least one chromatography of the sample. This includes plotting control charts that include parameters and sample injection counts, and the control charts are small At the very least, extrapolate the data of one chromatography parameter to obtain at least one chromatographic result. Whether the rhomography parameters fall outside the performance control limits, and / or when performance control is implemented. Used to predict whether the limit will be exceeded, the control chart shows at least one chromatogram of the sample. Before the graphics parameters exceed the performance management limits, and / or when they exceed the performance management limits Used to generate maintenance notifications for anticipated GC system failures before they occur. The method described in Specific Example 1.

[0109] (Specific example 17) Generating simulated chromatography separation is a nominal simulation. Generates a chromatogram and a real-time simulated chromatogram. Including this, using a chromatography model allows for real-time simulation. This includes comparing the generated chromatogram with a nominally simulated chromatogram. Hmm, the method described in Specific Example 1.

[0110] (Specific example 18) Using chromatography models during troubleshooting procedures is nominal. Simulated chromatograms, real-time simulated chromatograms Two or more of the following: , reference chromatography separation, and sample chromatography separation. The method described in Specific Example 1, including comparisons between them.

[0111] (Specific example 19) Real-time simulated chromatograms are nominally simulated chromatograms. It matches at least one of the matogram and the reference chromatographic separation, but The simulated chromatogram at 12:00 time does not match the sample chromatographic separation. If not, the automatic GC troubleshooting procedure will be performed if the GC system is controlled as expected. Therefore, something outside the control of the GC system is causing at least one chromatography parameter A method for determining that the performance is outside the limits of performance management, as described in Specific Example 18.

[0112] (Specific example 20) Real-time simulated chromatograms are used for sample chromatography separation and However, real-time simulated chromatograms and sample chromatography - Nominal chromatogram simulation and reference chromatographic separation If it does not match at least one of ours, the automatic GC troubleshooting procedure will GC The system is not being controlled as expected, and the control of the GC system is at least one chromosome The tography parameters are determined to be outside the performance control limits, as described in Specific Example 18. method.

[0113] (Specific example 21) A gas chromatography (GC) system for analyzing a sample, Inlet configured for chromatographic separation of a sample containing one or more analytes. A GC column equipped with an outlet, A GC detector is fluidly connected to the outlet of the GC column, A controller that is at least connected to the GC detector in a way that allows it to communicate with it. The controller is equipped with, At least one chromatographic parameter of a sample analyzed by a GC system The simulation uses a chromatography model based on the configuration of the GC system to calculate the data. To produce a chromatographic separation, Performing sample chromatography separation of the sample loaded into the GC system, Includes at least one chromatographic parameter for sample chromatographic separation. To collect performance data related to sample chromatography separation, A chromatography performance monitor configured to analyze sample chromatography separation. This involves performing chromatography, and chromatography performance monitoring is performed on the sample chromatograph. At least one chromatography parameter for tography separation and simulated This includes a comparison with the chromatographic separation and / or reference chromatographic separation. At least one chromatography parameter in the material chromatography separation is used for performance control. Determine whether the limit has been exceeded, and / or at least the sample chromatographic separation Whether and / or when one chromatography parameter falls outside the performance control limits. Predicting whether performance will exceed the limits of management, Using chromatography performance monitoring and chromatography model results , an automated GC troubleshooting procedure that predicts expected maintenance work for the GC system. To execute, Send maintenance notifications, including expected maintenance work, to GC system users. A GC system configured to perform this function.

[0114] (Specific example 22) At least one chromatography parameter is analyzed by the GC system. Retention time of the analyte, relative retention time, retention index, adjusted retention time, peak height, peak area, Peak width, peak symmetry, peak resolution, peak capacity, skew, kurtosis, number of resolutions , one of the following: volume ratio, selectivity, efficiency, apparent efficiency, tailing coefficient, concentration, and molar amount A GC system as described in Specific Example 21, including one or more of the above.

[0115] (Specific example 23) At least one device that is connected to the controller in a communicative manner and configured to collect device data. It also has another instrument sensor, and the instrument data includes the temperature value of the GC system, the pressure sensor value, Valve status, motor step, sample injection count, motor duty cycle, heater power Flow rate, heater duty cycle, motor current value, flow sensor value, detector signal value, detector Current value, detector frequency value, calibration table, auto-zero value, sensor zero value, time-on value, The GC system described in Specific Example 21 includes one or more of the valve duty cycle values. Tem.

[0116] (Specific example 24) The controller uses at least one instrument sensor to analyze the chromatography model. Specific example 23 provides real-time instrument values ​​from the GC system. The GC system.

[0117] (Specific example 25) The controller determines the expected maintenance work during the automated GC troubleshooting procedure. A GC system, as described in Specific Example 23, performs one or more diagnostic tests in order to do so.

[0118] (Specific example 26) The controller generates a decision tree for the automated GC troubleshooting procedure. The GC system described in Example 21.

[0119] (Specific example 27) The GC system described in Specific Example 26 is one in which the user inputs information into a decision tree.

[0120] (Specific example 28) The controller uses a decision tree to control the sample introduction system of the GC system, the sample inlet, and the Expected maintenance work to be performed on one or more of the following: ram, column heater, and detector. Determine which chromosomes are outside the performance control limits and / or are expected to be outside the performance control limits. A GC system for correcting matrix parameters, as described in Specific Example 26.

[0121] (Specific example 29) The controller performs the neural network during the automated GC troubleshooting procedure. Use this to manage expected maintenance work and / or performance management limits. The correlation between the expected chromatographic parameters and the actual parameters is determined, as described in Specific Example 21. The GC system installed.

[0122] (Specific example 30) The controller utilizes machine learning processes during automated GC troubleshooting procedures. And, if the anticipated maintenance work is outside the performance control limits and / or is outside the performance control limits Instruct the GC system to associate the expected chromatography parameters. , the GC system described in Specific Example 21.

[0123] (Specific example 31) The controller is outside of its performance management limits and / or is expected to be outside of its performance management limits. Associated with one or more expected maintenance tasks involving modifications to chromatography parameters Using a neural network, which is outside the performance management limits and / or outside the performance management limits GC system problems where chromatography parameters expected to be in a certain state repeatedly occur. If so, the neural network will fix the recurring GC system problem. The GC system described in Specific Example 21 determines alternative maintenance work for this purpose.

[0124] (Specific example 32) The controller performs validation chromatography separation after the expected maintenance work has been completed. , validate chromatographic separation, simulated chromatographic separation and / or Compared to the reference chromatographic separation, it is outside the performance control limits and / or within the performance control limits. Since it is expected to be outside, at least one chromatography parameter is expected. The GC system described in Specific Example 21 verifies that maintenance work performed will correct the issue.

[0125] (Specific example 33) Validation chromatography separation is performed when at least one chromatography parameter is found to be suitable. When verifying that it is within the capacity control limits, the controller uses reference chromatography separation. A GC system, as described in Specific Example 32, that replaces verification chromatography separation.

[0126] (Specific example 34) During chromatography performance monitoring, the controller detects at least one of the samples Generate control charts including chromatography parameters and sample injection counts, and control La extrapolates the data of at least one chromatography parameter, at least Whether and / or when one chromatography parameter falls outside the performance control limits. The GC system described in Specific Example 21 predicts whether the performance will exceed the limits of performance management.

[0127] (Specific example 35) Using chromatography models during troubleshooting procedures is useful for controlling Laura, nominal simulated chromatogram, real-time simulation Chromatogram, reference chromatographic separation, and chromatographic separation of the sample. A GC system as described in Specific Example 21, which includes comparing two or more of the following.

[0128] (Specific example 36) Real-time simulated chromatograms are nominally simulated It matches at least one of the chromatogram and the reference chromatographic separation, The simulated chromatogram at real time matches the chromatographic separation of the sample. If not, the automatic GC troubleshooting procedure will not ensure that the GC system is controlled as expected. Something outside the control of the GC system is causing at least one chromatography parameter to malfunction. The GC system described in Specific Example 35 determines that the data is outside the performance management limits.

[0129] (Specific example 37) Real-time simulated chromatograms enable chromatographic separation of the sample. They match, but the real-time simulated chromatogram and the sample chromatogram Fee separation nominally simulated chromatogram and reference chromatogram If it does not match at least one of the separations, the automatic GC troubleshooting procedure will be: The GC system is not being controlled as expected, and the control of the GC system is at least one It is determined that the rhomography parameters are outside the performance control limits, as described in specific example 35. The GC system installed.

[0130] (Specific example 38) A gas chromatography (GC) system for analyzing a sample, Inlet configured for chromatographic separation of a sample containing one or more analytes. A GC column equipped with an outlet, A GC detector is fluidly connected to the outlet of the GC column, At least one sensor configured to collect device data from a GC system, A controller that is communicatively connected to a GC detector and at least one sensor. The controller is equipped with, Performing chromatographic separation of the sample loaded into the GC system, Using instrumental data collected by at least one sensor, a simulation of the sample is performed. The process involves generating chromatographic separation of the sample, and the controller controls the chromatographic separation of the sample. Real-time simulated chromatographic separation during chromatographic separation It is configured to achieve A GC system configured to perform this function.

[0131] (Specific example 39) Instrument data collected by at least one sensor includes the temperature and pressure values ​​of the GC system. Force sensor value, valve status, motor step, sample injection count, motor duty cycle Heater current value, heater duty cycle, motor current value, flow sensor value, detector signal Number value, detector current value, detector frequency value, calibration table, auto zero value, sensor zero value, Specific example 38 includes one or more of the following: IMUON value and valve duty cycle value. The GC system installed.

[0132] (Specific example 40) Simulated chromatographic separation is based on the configuration of the GC system. A GC system, as described in Specific Example 38, generated from a tography model.

[0133] (Specific example 41) Chromatography models involve the retention time of the sample analyzed by the GC system, and P. At least one of the following: peak height, peak area, peak width, peak symmetry, and peak resolution Calculate at least one chromatography parameter including one of the parameters, as described in Specific Example 40. The GC system.

[0134] (Specific example 42) The controller is configured to analyze the chromatographic separation of the sample. Graphics performance monitoring is performed, and chromatography performance monitoring is performed less frequently. Both a single chromatography parameter and simulated chromatography separation and / or comparison with a reference chromatographic separation, including at least one chromatographic separation. Determine whether the fee parameter has fallen outside the performance management limit, and / or at least 1 Whether one chromatography parameter falls outside the performance control limits, and / or when A GC system, as described in specific example 38, that predicts whether the system will exceed its capacity management limits.

[0135] (Specific example 43) The controller uses a chromatograph to predict expected maintenance work on the GC system. Automated G using performance monitoring and simulated chromatography separation Perform the C troubleshooting procedure, and the automated GC troubleshooting procedure is multiple Determine the expected maintenance work from the different maintenance tasks, which are outside the performance management limits, and / or This indicates that at least one chromatography parameter is expected to be outside the performance control limits. The GC system described in Specific Example 42 provides the correction.

[0136] (Specific example 44) The controller anticipates that the GC system user has selected from several different maintenance tasks. After performing the necessary maintenance work, perform validation chromatography separation and validation chromatography. The separation is simulated chromatographic separation and / or reference chromatography. Compared to separation, the expected maintenance work involves at least one chromatography parameter. The meter is outside the performance control limits, and / or is expected to be outside the performance control limits. The GC system described in Specific Example 43 demonstrates the effectiveness of the correction.

[0137] (Specific example 45) Verification chromatography separation is performed if at least one chromatography parameter is in performance. To verify that it is within the control limits, the controller performs a reference chromatographic separation. A GC system, as described in Specific Example 44, to replace validation chromatography separation.

[0138] (Specific example 46) A method for operating a gas chromatography (GC) system, At least one chromatographic parameter of a sample analyzed by a GC system The simulation uses a chromatography model based on the configuration of the GC system to calculate the data. Steps to generate a rated chromatographic separation, Sample chromatography separation is performed using a GC system, thereby enabling GC systems A step of generating a sample chromatogram of the sample to be analyzed by the stem, Sample chromatography including at least one chromatographic parameter of the sample. - A step of collecting performance data related to separation, Using the chromatography model and the results of sample chromatography separation, GC Implement automated GC troubleshooting procedures to predict expected maintenance work for the system. Steps and The steps include sending a maintenance notification for the GC system, including the expected maintenance work, and A method that includes this.

[0139] (Specific example 47) A method for operating a gas chromatography (GC) system, Sample chromatography separation is performed using a GC system, thereby enabling GC systems A step of generating a sample chromatogram of the sample to be analyzed by the stem, Instrument data including at least one sensor value related to the chromatographic separation of the sample. Steps to collect data, A chromatography performance monitor configured to analyze sample chromatography separation. In the step of performing taring, chromatography performance monitoring is performed less It determines whether one sensor value has fallen outside the performance management limit, and / or at least Whether a single sensor value could fall outside the performance control limits, and / or when it could fall outside the performance control limits. Steps include predicting what is likely to happen, GC system chromatography performance monitoring and chromatography model Perform the automated GC troubleshooting procedure to determine the expected maintenance of the GC system. Steps to predict the maintenance work, The steps include sending a maintenance notification for the GC system, including the expected maintenance work, and A method that includes this.

[0140] With regard to this disclosure, it is possible to implement the methods and apparatus in accordance with this teaching. It is intended. Furthermore, various components, materials, structures, and parameters are merely illustrative and illustrative examples. This includes, but does not include in a restrictive sense. Considering this disclosure, this teaching is included in the attached claims. While remaining within the scope of, other uses and components necessary to carry out these uses. It can be implemented using materials, structures, and equipment.

Claims

1. A method for operating a gas chromatography (GC) system, Simulated using a chromatography model based on the configuration of the GC system. A step of generating chromatographic separation by the GC system At least one chromatographic parameter of the sample to be analyzed is determined by the chromatography. - The steps the model calculates, In order to generate a sample chromatogram of the sample analyzed by the GC system, The steps include performing sample chromatography separation using the GC system, The sample includes at least one chromatography parameter of the sample. A step of collecting performance data related to matrix separation, chromatographic performance configured to analyze the chromatographic separation of the sample. A step in which monitoring is performed, wherein the chromatography performance monitoring is , the at least one chromatographic parameter of the sample chromatographic separation The simulated chromatographic separation and / or reference chromatography - Including comparison with the separation, the at least one chromatograph of the sample chromatographic separation Determine whether the tography parameters have fallen outside the performance control limits, and / or the trial The at least one chromatographic parameter of the material chromatography separation is the Whether and / or when the performance limits may be exceeded. It is a prediction, a step, The results of the chromatography performance monitoring and the chromatography model This system is used to predict expected maintenance work on the GC system and to automate GC troubleshooting. Steps to carry out the routing procedure, The steps include sending a maintenance notification for the GC system, including the anticipated maintenance work, and A method that includes this.

2. The at least one chromatography parameter is determined by the GC system. Retention time, relative retention time, retention index, adjusted retention time, peak height, and peak of the analyte being analyzed. Peak area, peak width, peak symmetry, peak resolution, peak capacity, skew, kurtosis Number of separations, volume ratio, selectivity, efficiency, apparent efficiency, tailing coefficient, concentration, and molar amount The method according to claim 1, comprising one or more of the above.

3. The automated GC troubleshooting procedure also involves the sample chromatography separation or Using this equipment data, the expected maintenance work is determined and the maintenance notification is sent. The step is to determine the expected maintenance work from several different maintenance tasks, and Claim 1 includes warning the user of the GC system of the anticipated maintenance work. Method of loading.

4. The aforementioned equipment data includes the temperature value, pressure sensor value, valve status, and motor status of the GC system. Steps, sample injection count, motor duty cycle, heater current value, heater duty cycle Cycle, motor current value, flow sensor value, detector signal value, detector current value, detector frequency Value, calibration table, auto zero value, sensor zero value, time on value, and valve duty cycle The method according to claim 3, comprising one or more cycle values.

5. The aforementioned automated GC troubleshooting procedure is for determining the expected maintenance work. The method according to claim 1, wherein one or more diagnostic tests are performed.

6. The chromatography model is performed by the GC system on the sample Actual instrument values ​​of the GC system collected in real time during matrix separation. The method according to claim 1, which is used.

7. The aforementioned automated GC troubleshooting procedure is for determining the expected maintenance work. The method according to claim 1, wherein a decision tree is used.

8. The method according to claim 7, wherein a user inputs information into the decision tree.

9. The decision tree comprises the sample introduction system, sample inlet, column, and column heater of the GC system. The performance of the anticipated maintenance work is further determined for one or more of the ta and detectors. , the few that are outside the performance control limits and / or are expected to be outside the performance control limits The method according to claim 7, wherein at least one chromatography parameter is corrected.

10. The aforementioned automated GC troubleshooting procedure further utilizes a neural network. , the anticipated maintenance work and the performance control limits and / or the performance control limits Determine the correlation between the chromatography parameters expected to be outside the scope of the request. The method described in item 1.

11. The aforementioned automated GC troubleshooting procedure further utilizes a machine learning process, If the anticipated maintenance work is outside the performance control limits and / or outside the performance control limits The GC system is associated with the chromatography parameters that are expected to be associated with the GC system. The method according to claim 1, as taught to Tem.

12. The aforementioned automated GC troubleshooting procedure uses a neural network to perform 1 The above anticipated maintenance work is outside the performance control limits and / or within the performance control limits. In relation to the modification of the chromatography parameters that are expected to be outside the field, The chromatograph is outside the performance control limit and / or is expected to be outside the performance control limit. If the Raffie parameter is a recurring GC system problem, then the neural The network performs alternative maintenance work to correct the recurring GC system problems. The method according to claim 1, which determines the method.

13. The automated GC troubleshooting procedure involves the sample introduction system of the GC system. The expected Maintenance work is performed and the performance is outside the performance control limits and / or outside the performance control limits The claim further includes the step of correcting the expected chromatography parameters. The method described in 1.

14. Step 1: Perform verification chromatography separation after carrying out the aforementioned expected maintenance work. The verification chromatographic separation further includes the simulated chromatographic separation. Raffie separation or comparison with a previous reference chromatogram, and the expected maintenance work is compared to the previous Note that at least one chromatography parameter is outside the performance control limit, and / Alternatively, the claim 1 verifies that corrections should be made because it is expected to be outside the performance control limits. Methods used.

15. The verification chromatography separation is performed using the at least one chromatography parameter When verifying that the data is within the performance control limits, the verification chromatography separation The method according to claim 14, wherein the standard chromatographic separation is replaced.

16. The chromatography performance monitoring of the sample is performed by the at least one chromatograph of the sample. This includes plotting control charts that include tography parameters and sample injection counts. The control chart is obtained by extrapolating the data of at least one chromatography parameter. , whether at least one of the chromatography parameters falls outside the performance control limit It is used to predict when and / or when the performance management limits will be exceeded, and the management The figure shows that at least one chromatographic parameter of the sample exceeds the performance control limit. Before it goes outside the bounds and / or before it is expected to go outside the performance control limits, the expected G The method according to claim 1, which is used to generate the maintenance notification for a C system failure.

17. The step of generating the aforementioned simulated chromatographic separation is a nominal chromatographic separation. Mutated chromatograms and real-time simulated chromatograms This includes generating and utilizing the chromatography model, the realta The simulated chromatogram of IM is the nominal simulated chromatogram The method according to claim 1, comprising comparing with ram.

18. The use of the chromatography model during the troubleshooting procedure is Nominal simulated chromatogram, real-time simulated chromatogram Matogram, the reference chromatographic separation, and the sample chromatographic separation The method according to claim 1, comprising a comparison between two or more of them.

19. The aforementioned real-time simulated chromatogram is the aforementioned nominal simulation The obtained chromatogram and at least one of the above standard chromatographic separations. They match, but the real-time simulated chromatogram is the sample chromatogram If it does not match the graph isolation, the automatic GC troubleshooting procedure will The C system is being controlled as expected, and something outside the control of the GC system is causing the aforementioned minor issues. If at least one chromatography parameter is determined to be outside the performance control limit, The method according to claim 18.

20. The aforementioned real-time simulated chromatogram is the sample chromatography - Consistent with separation, but the real-time simulated chromatogram and the Sample chromatographic separation is performed using the aforementioned nominal simulated chromatogram and pre If it does not match at least one of the standard chromatographic separations, the automated GC The troubleshooting procedure is for when the GC system is not controlled as expected, The control of the GC system controls the performance tube of the at least one chromatography parameter. The method according to claim 18, which determines that the limit has been exceeded.

21. A gas chromatography (GC) system for analyzing a sample, An inlet configured for chromatographic separation of a sample containing one or more analytes. A GC column equipped with an outlet, A GC detector is fluidly connected to the outlet of the GC column, A controller that is at least communicably connected to the GC detector and The controller comprises, At least one chromatography of the sample analyzed by the GC system Using a chromatography model based on the configuration of the GC system, the parameters are calculated. To generate simulated chromatographic separation using, Performing sample chromatography separation of the sample loaded into the GC system. and, The at least one chromatographic parameter of the sample chromatographic separation To collect performance data related to the chromatographic separation of the sample, including the data, chromatographic performance configured to analyze the chromatographic separation of the sample. This involves performing monitoring, and the chromatography performance monitoring is performed before The at least one chromatographic parameter for the chromatographic separation of the sample and , the simulated chromatographic separation and / or reference chromatography Including comparison with the separation, the at least one chromatograph of the sample chromatographic separation Determine whether the graph parameters have fallen outside the performance control limits, and / or the sample The at least one chromatographic parameter of chromatographic separation is the property of To predict whether and / or when the performance will exceed the performance control limits. Toto, The results of the chromatography performance monitoring and the chromatography model This system uses an automated GC troubleshooting tool to predict expected maintenance work on the GC system. Perform the following steps: Sending a maintenance notice, including the anticipated maintenance work, to the user of the GC system. A GC system configured to perform the following actions.

22. The at least one chromatography parameter is determined by the GC system. Retention time, relative retention time, retention index, adjusted retention time, peak height, and peak of the analyte being analyzed. Peak area, peak width, peak symmetry, peak resolution, peak capacity, skew, kurtosis Number of separations, volume ratio, selectivity, efficiency, apparent efficiency, tailing coefficient, concentration, and molar amount The GC system according to claim 21, comprising one or more of the above.

23. A small number of devices are connected to the aforementioned controller in a communicative manner and configured to collect device data. The system further includes at least one instrument sensor, and the instrument data includes the temperature value and pressure of the GC system. Force sensor value, valve status, motor step, sample injection count, motor duty cycle Heater current value, heater duty cycle, motor current value, flow sensor value, detector signal Number value, detector current value, detector frequency value, calibration table, auto zero value, sensor zero value, Claim 21 includes one or more of the imon value and the valve duty cycle value. The included GC system.

24. The controller provides the chromatography model with the at least one instrument cluster The system provides actual device values ​​of the GC system collected in real time by the sensor. The GC system according to claim 23.

25. The controller, during the automatic GC troubleshooting procedure, the expected storage The GC system according to claim 23, which performs one or more diagnostic tests to determine the maintenance work Hmm.

26. The controller generates a decision tree for the automated GC troubleshooting procedure. The GC system according to claim 21.

27. The user of the GC system inputs information into the decision tree, as described in claim 26. GC system.

28. The controller uses the decision tree to control the sample introduction system of the GC system. The aforementioned preparatory steps are performed on one or more of the sample inlet, column, column heater, and detector. Determine the maintenance work to be performed, which is outside the performance control limits and / or the performance control limits. The chromatography parameters that are expected to be outside the normal range are corrected, as described in claim 26. The GC system.

29. The controller, during the automatic GC troubleshooting procedure, Using the workpiece, the expected maintenance work and the performance control limits and / or prior to the workpiece are performed. The correlation between the chromatography parameters that are expected to be outside the performance control limits is The GC system according to claim 21, which determines the system.

30. The controller processes the machine learning process during the automated GC troubleshooting procedure. Using this, the expected maintenance work is outside the performance control limits and / or performance Associated with the chromatography parameters that are expected to be outside the control limits. The GC system according to claim 21, which teaches the GC system the above.

31. The controller is outside the performance management limits and / or outside the performance management limits One or more expected maintenance, which involves modifications to the aforementioned chromatography parameters. Using the neural network associated with the task, and which is outside the performance management limits and / or the chromatography parameters that are expected to be outside the performance control limits are repeated In the case of a recurring GC system problem, the neural network will... The method described in claim 21, which determines alternative maintenance work to correct the GC system problem that occurs. The included GC system.

32. The controller performs verification chromatography separation after the expected maintenance work has been completed. The verification chromatographic separation is performed using the simulated chromatographic method described above. Compared to the FIEA separation and / or the standard chromatographic separation, outside the performance control limits Since it is expected that there is and / or that it is outside the performance control limits, at least one of the above-mentioned k The request verifies that the expected maintenance work corrects the rhomography parameters. The GC system described in item 21.

33. The verification chromatography separation is performed using the at least one chromatography parameter When verifying that the data is within the performance control limits, the controller uses the reference threshold. The method described in claim 32 replaces the chromatography separation with the verification chromatography separation. The included GC system.

34. During the chromatography performance monitoring, the controller controls the sample A control chart including at least one chromatography parameter and sample injection count is generated. The controller then processes the data of the at least one chromatography parameter. Extrapolating the parameters, the at least one chromatography parameter is at the performance control limit. Claim 21, predicting whether and / or when the performance will be outside the performance limit. The GC system described above.

35. The use of the chromatography model during the troubleshooting procedure is The controller nominally simulates a chromatogram, and in real time... The simulated chromatogram, the reference chromatographic separation, and the sample before The method according to claim 21, comprising comparing two or more of the chromatographic separations. GC system.

36. The aforementioned real-time simulated chromatogram is the aforementioned nominal simulation The obtained chromatogram and at least one of the above standard chromatographic separations. They match, but the real-time simulated chromatogram of the sample If the results do not match the chromatographic separation, the automated GC troubleshooting procedure is The GC system is being controlled as expected, and something outside the control of the GC system is not being controlled. The at least one of the aforementioned chromatography parameters is set outside the performance control limit. The GC system according to claim 35, which determines that...

37. The real-time simulated chromatogram of the sample This is consistent with Raffie separation, but the aforementioned real-time simulated chromatogram and The chromatographic separation of the sample is a nominal simulated chromatographic separation. If it does not match Gram and at least one of the above standard chromatographic separations, The aforementioned automated GC troubleshooting procedure is performed when the GC system is controlled as expected. The control of the GC system is not performed by the at least one chromatography parameter The GC system according to claim 35, which determines that the performance is outside the performance control limit.

38. A gas chromatography (GC) system for analyzing a sample, An inlet configured for chromatographic separation of a sample containing one or more analytes. A GC column equipped with an outlet, A GC detector is fluidly connected to the outlet of the GC column, At least one sensor configured to collect device data from the GC system and 、 A controller that is communicatively connected to the GC detector and the at least one sensor. and The controller comprises, Performing chromatographic separation of the sample loaded into the GC system, Using the instrument data collected by the at least one sensor, the sample To generate simulated chromatographic separation of the control The simulation is performed in real time during the chromatographic separation of the sample. It is configured to produce chromatographic separations. A GC system configured to perform the following actions.

39. The device data collected by the at least one sensor is used in the GC system Temperature value, pressure sensor value, valve status, motor step, sample injection count, motor duty Duty cycle, heater current value, heater duty cycle, motor current value, flow sensor Value, detector signal value, detector current value, detector frequency value, calibration table, auto-zero value, sensor Please include one or more of the following: sazor value, time-on value, and valve duty cycle value. The GC system described in item 38.

40. The simulated chromatographic separation described above is based on the configuration of the GC system. The GC system according to claim 38, which is generated from a chromatography model.

41. The chromatography model preserves the sample analyzed by the GC system. Of the following: duration, peak height, peak area, peak width, peak symmetry, and peak resolution Calculate at least one chromatography parameter, including at least one other parameter, invoice The GC system described in item 40.

42. The controller is configured to analyze the chromatographic separation of the sample. The chromatography performance monitoring is performed, and the chromatography performance monitor The ring has at least one chromatography parameter and the simulated ring This includes comparison with chromatography separation and / or reference chromatography separation, and the less Determine whether at least one chromatography parameter has fallen outside the performance control limit. , and / or the at least one chromatography parameter is outside the performance control limit Claim 38 predicts whether and / or when the performance management limit will be exceeded. The GC system described.

43. The controller predicts the expected maintenance work on the GC system. Chromatography performance monitoring and the simulated chromatography The automated GC troubleshooting procedure using the separation is executed, and the automated GC troubleshooting... The maintenance procedure determines the expected maintenance task from among several different maintenance tasks, At least the above is outside the performance control limits and / or is expected to be outside the performance control limits. The GC system according to claim 42, which corrects another chromatography parameter.

44. The controller allows the user of the GC system to select from the multiple different maintenance tasks. After performing the aforementioned expected maintenance work, perform verification chromatography separation. The chromatographic separation described above is the simulated chromatographic separation and and / or compared to the standard chromatographic separation, the expected maintenance work is the At least one chromatography parameter is outside the performance control limit, and / or Claim 43: Since it is expected to be outside the performance control limit, we will verify that it is corrected. The GC system described above.

45. The verification chromatography separation is performed using the at least one chromatography parameter When verifying that the performance control limit is within the aforementioned limits, the controller uses the aforementioned reference cross The method described in claim 44, wherein the chromatographic separation is replaced with the verification chromatographic separation. The GC system.

46. A method for operating a gas chromatography (GC) system, Simulated using a chromatography model based on the configuration of the GC system. A step of generating chromatographic separation by the GC system At least one chromatographic parameter of the sample to be analyzed is determined by the chromatography. —The model calculates the steps, Using the GC system, sample chromatography separation is performed, thereby, The steps include generating a sample chromatogram of the sample analyzed by the GC system and 、 A step of collecting performance data related to the chromatographic separation of the sample, The performance data includes the at least one chromatography parameter of the sample. It is a step, Using the above chromatography model and the results of the sample chromatography separation , an automated GC troubleshooting tool that predicts expected maintenance work for the GC system. The steps to carry out the sequence, The steps include sending a maintenance notification for the GC system, including the anticipated maintenance work, and A method that includes this.

47. A method for operating a gas chromatography (GC) system, Using the GC system, sample chromatography separation is performed, thereby, The steps include generating a sample chromatogram of the sample to be analyzed by the GC system, An instrument including at least one sensor value related to the chromatographic separation of the aforementioned sample. Steps to collect data, chromatographic performance configured to analyze the chromatographic separation of the sample. A step in which monitoring is performed, wherein the chromatography performance monitoring is , determine whether the value of at least one sensor has fallen outside the performance management limit, and / or This is whether the at least one sensor value can fall outside the performance control limit, and / or The steps include predicting when the performance management limits may be exceeded, The chromatography performance monitoring and chromatography of the GC system Perform an automated GC troubleshooting procedure using the model to troubleshoot the GC system Steps to predict expected maintenance work, The steps include sending a maintenance notification for the GC system, including the anticipated maintenance work, and A method that includes this.

Images