Chromatographic column box unit and optimization method

Abstract

The invention discloses a chromatographic column box unit and an optimization method, and belongs to the technical field of chromatographic detection. The invention provides a chromatographic column box unit which comprises a data acquisition board, and the data acquisition board is integrated with a signal isolation unit used for blocking digital circuit interference; the power supply isolation unit is used for providing independent pure power supply; the precision amplification circuit consists of an instrument amplifier and a high-stability reference voltage source; and the high-precision ADC acquisition module adopts a sigma-delta technology. A microprocessor in the data acquisition board executes an optimized Kalman filtering algorithm, and real-time low-delay de-noising processing is carried out on data output by the ADC acquisition module. Through collaborative design of hardware physical isolation and software algorithm optimization, digital, power supply and random noise is systematically suppressed, and baseline noise is remarkably reduced, so that high-sensitivity and high-precision detection of low-concentration (< = 1 mu L / L) gas can be realized, and the anti-interference capability and stability of the system are improved.

Description

Technical Field

[0001] This application relates to the field of oil chromatography detection technology, and in particular to a chromatography column oven unit and its optimization method. Background Technology

[0002] Oil chromatography analysis technology detects the composition and content of dissolved gases in insulating oil to determine whether there are faults such as localized overheating or discharge inside power equipment, making it a key technology for power operation and maintenance. The column box unit, as the core of the oil chromatography analyzer, directly determines the accuracy and stability of the test results. However, existing column box units suffer from the following technical bottlenecks: Large baseline noise fluctuations: In circuit systems where the digital and analog sections are integrated on a single circuit board, interference from high-speed switching pulses and power supply ripples in the digital circuitry causes sawtooth-like fluctuations in the AD acquisition data. Baseline noise is typically ≥500 nVrms, which can easily lead to misjudgments in the detection of low-concentration gases (≤1 μL / L). Figure 1 As shown, the X-axis represents time (minutes), and the Y-axis represents the signal value (count). The Y-axis baseline fluctuates significantly in the range of 7674-7700, making it impossible to accurately identify low-concentration gas peak signals. The algorithm also suffers from low processing efficiency: traditional filtering algorithms exhibit a lag of ≥50ms, which easily smooths chromatographic peak signals, resulting in the masking of low-concentration gas peaks.

[0003] Existing technologies often employ improved filtering algorithms for optimization, but the optimization effect is limited and cannot meet the requirements of high-precision detection. Therefore, there is an urgent need to propose a chromatography column oven unit and optimization method to fundamentally solve the above-mentioned shortcomings. Summary of the Invention

[0004] This application provides a chromatographic column oven unit and optimization method, which is suitable for high-precision chromatographic analysis of dissolved gases (such as H2, CO, CO2, CH4, C2H5, C2H6, C2H2) in insulating oils of power equipment such as transformer oil and reactor oil. In particular, it solves the problems of large detection errors of low-concentration gas components and serious baseline noise interference, and can be widely used in power equipment condition monitoring and fault diagnosis scenarios.

[0005] This application is achieved through the following technical solution: A chromatography column oven unit includes a column oven body, a dual-channel chromatography column disposed within the column oven body, a heating unit, a temperature detection unit, and a gas sensor, and also includes a data acquisition board, on which the following are integrated: The signal isolation unit is connected between the digital control circuit and the analog signal acquisition circuit to block the coupling interference of the high-speed switching pulses of the digital control circuit to the analog signal. The power isolation unit is used to provide an independent power supply for the analog signal acquisition circuit, thereby isolating the motherboard power supply from the analog power supply. A precision amplifier circuit, the input of which is connected to the output of the gas sensor, includes an instrumentation amplifier and a reference voltage source; the gain resistor of the instrumentation amplifier is configured to achieve a predetermined gain, and a high- and low-frequency composite filter capacitor combination is connected in parallel to the output of the reference voltage source, which is connected to the instrumentation amplifier. An ADC acquisition module, connected to the output of the instrumentation amplifier, is used to convert analog signals into digital signals. The ADC acquisition module adopts Σ-Δ modulation technology and supports multi-level filtering modes. The microprocessor within the data acquisition board is configured to execute an optimized Kalman filter algorithm, including the following steps: The data output by the ADC acquisition module is used as the observation value to construct a one-dimensional state-space model. Based on the one-dimensional state space model, the state covariance matrix P is updated by introducing a forgetting factor, the noise covariance matrix Q is dynamically adjusted by predicting the residuals, and the observation noise covariance matrix R is dynamically adjusted by the residual sliding window variance. Based on the adjusted state covariance matrix P, the process noise covariance matrix Q, and the observation noise covariance matrix R, calculate the Kalman gain K; The Kalman gain K is limited, and the data output by the ADC acquisition module is filtered in real time using an optimized Kalman filtering algorithm.

[0006] By adopting the above technical solution, integrating a signal and power isolation unit, a precision amplification circuit, and a high-precision ADC acquisition module on the data acquisition board, and combining it with an optimized Kalman filter algorithm, the system systematically solves the problems of coupling interference between digital circuits and analog signals, power supply noise interference, and random noise during data acquisition through the synergistic effect of hardware physical isolation and software algorithm optimization. This solution achieves high signal-to-noise ratio, high-precision acquisition, and low-latency processing of weak signals from gas sensors, significantly reducing baseline noise from ≥500 nVrms in traditional technologies to within 61.3 nVrms. This provides a solid foundation for accurately identifying gas components with concentrations as low as 1 μL / L, fundamentally solving the core technical pain point of inaccurate or missed detection of low-concentration components due to high baseline noise in existing technologies.

[0007] Optionally, the step of constructing a one-dimensional state-space model includes: constructing a one-dimensional state equation x. k =x k-1 +w k and observation equation z k =x k +v k , where x kx represents the current actual signal state value. k-1 w represents the actual signal state value at the previous moment. k Process noise value ， z k v is the value acquired by the ADC. k To measure noise levels; The step of updating the state covariance matrix P by introducing a forgetting factor includes: introducing an adaptive forgetting factor λ, where 0.95 ≤ λ ≤ 1; and constructing the update formula for the state covariance matrix P: P k - =λP k-1 +Q; using P k - =λP k-1 +Q updates the state covariance matrix P k - P k-1 The state covariance matrix at the previous time step; The step of dynamically adjusting the noise covariance matrix Q through the predicted residual includes: calculating the residual ε between the observed value and the predicted value in real time. k , ε k =z k -x k - ,when At that time, the Q value increases exponentially, where the observed value is the ADC acquisition value z. k The predicted value is the prior estimate x at the current time. k - ; The step of dynamically adjusting the observation noise covariance matrix R through the residual sliding window variance includes: calculating the residual ε k The sliding window variance; based on the ratio of the sliding window variance to the reference noise standard deviation, the observation noise covariance matrix R is dynamically adjusted. k ; The step of calculating the Kalman gain K based on the adjusted state covariance matrix P, the process noise covariance matrix Q, and the observation noise covariance matrix R includes: The Kalman gain K is calculated using the formula: K = P k - / (P k - +R k ).

[0008] By adopting the above technical solution, the key parameters P, Q, and R of the Kalman filter are dynamically and adaptively adjusted by introducing a forgetting factor, prediction residual, and sliding window variance. This enables the filtering algorithm to intelligently adapt to dynamic changes in the signal. When the signal is stable, it provides strong smoothing to reduce baseline noise; when rapidly changing chromatographic peaks appear, it can respond quickly to accurately track the peak shape. Thus, it achieves an optimal balance between efficient noise reduction and maintaining signal authenticity, ensuring high-fidelity filtering performance with low latency (≤10ms).

[0009] Optionally, the data acquisition board adopts a multi-layer PCB design, and its layout is divided into an independent analog signal area, a digital signal area and a power supply area. An AGND isolation line with a width of ≥0.5mm is provided between the analog signal area and the digital signal area, and a PE grounding protection line with a width of ≥1mm is provided between the power supply area and other areas.

[0010] By adopting the above technical solution, and through a refined multi-layered partitioning layout of the data acquisition board, and by setting isolation lines and grounding protection lines of specific physical widths, high-frequency digital signals, power supply noise, and extremely sensitive analog signal paths are effectively isolated from each other in physical space. This design minimizes cross-coupling and electromagnetic interference between signals, forming another key line of defense against interference at the hardware level. It provides a solid hardware guarantee for achieving ultra-low baseline noise and enhances the stability and reliability of the overall system in complex electromagnetic environments.

[0011] Optionally, the output terminal of the power isolation unit is connected in series with a PI-type filter circuit, which includes a connected tantalum capacitor and a metal film resistor. The PI-type filter circuit has a ripple suppression ratio of ≥40dB for the 100Hz-1MHz frequency band.

[0012] By adopting the above technical solution, a PI-type filter circuit is further added to the power isolation, which can efficiently filter out the power supply ripple noise in a specific frequency band (100Hz-1MHz) remaining at the output of the power isolation module. This design provides a cleaner and more stable power supply (ripple noise ≤5mV) for analog signal acquisition circuits (especially precision amplifiers and ADCs), effectively avoiding the interference of power supply noise on the precision amplification and high-precision ADC conversion process, thereby further improving the accuracy and stability of signal acquisition, and is an important part of reducing the overall system noise floor.

[0013] Optionally, the data acquisition board also integrates a silicon controlled rectifier (SCR) temperature control circuit, including a bidirectional SCR and a zero-crossing detection module. The zero-crossing detection module is based on an optocoupler isolation design and is used to detect the zero-crossing point of the AC power supply. The microprocessor on the data acquisition board is configured to generate an adjustable-width trigger pulse based on the temperature deviation and control the conduction of the bidirectional SCR at the zero-crossing point.

[0014] By adopting the above technical solution, a thyristor temperature control circuit based on zero-crossing detection is integrated. By triggering the thyristor to conduct when the AC power supply voltage is zero, "soft switching" control of the heating unit power is achieved. This control method effectively avoids the large number of high-order harmonics and strong electromagnetic interference (EMI) generated by traditional phase control or random triggering. It eliminates the possibility of secondary pollution to the precision signal acquisition circuit by the temperature control system itself from the source, ensuring that the low noise characteristics of the entire system can be maintained while achieving precise temperature control.

[0015] Optionally, the microprocessor within the data acquisition board is configured to execute the following algorithm: Incremental PID temperature control algorithm: Combining the dual-point feedback of the sample inlet temperature and the sample outlet temperature collected by the temperature detection unit, when the temperature difference between the sample inlet temperature and the sample outlet temperature exceeds a preset threshold, the output of the heating unit is dynamically adjusted to eliminate the temperature difference. The preset threshold is 0.03℃. Temperature compensation algorithm: Based on a preset temperature-output characteristic curve model, the output data of the gas sensor is compensated; the preset temperature-output characteristic curve model is y=a0+a1T+a2T 2 +a3T 3 Where y is the compensated output, T is the temperature, and a 0、 a 1、 a 2、 a3 is the fitting coefficient.

[0016] By adopting the above technical solution, and by executing an incremental PID temperature control algorithm combined with dual-point temperature feedback, high-precision and uniform control of the temperature field inside the column box (temperature difference ≤ 0.03℃, fluctuation ≤ ±0.02℃) was achieved, effectively eliminating the temperature gradient inside the column box. Simultaneously, by executing a temperature compensation algorithm based on a polynomial model, the output drift of the gas sensor caused by changes in ambient temperature was actively corrected. This dual optimization at the algorithm level collaboratively solved the two key problems affecting the accuracy of detection results: imprecise temperature control and temperature drift, ensuring high accuracy and repeatability of detection results under different operating conditions (RSD ≤ 1.5%).

[0017] Optionally, the inner wall of the column box body is fitted with a high-temperature resistant glass wool insulation layer, and a high-temperature resistant silicone sealing ring is provided at the sealing gap between the column box body and its cover.

[0018] By adopting the above technical solution, and by setting an insulation layer inside the column box body and efficiently sealing its gaps, the insulation and heat insulation performance of the column box is enhanced from a physical structure perspective. This effectively reduces the loss of internal heat to the outside and the impact of external temperature fluctuations on the inside of the column box, creating extremely favorable passive thermodynamic conditions for the stable operation of the high-precision temperature control algorithm. This not only further improves the stability and uniformity of the column box temperature but also effectively reduces the energy consumption for maintaining a constant temperature in the system.

[0019] Optionally, it also includes a carrier gas treatment unit, which is connected in series in the carrier gas pipeline and includes: The dehumidification column is filled with molecular sieves to adsorb moisture from the carrier gas. The impurity removal column is filled with a composite packing material of activated carbon and alumina to adsorb organic and inorganic impurities in the carrier gas.

[0020] By adopting the above technical solution and adding dehumidification and impurity removal units in series in the carrier gas pipeline, trace amounts of moisture and organic / inorganic impurities that may be present in the carrier gas can be effectively removed, ensuring that the carrier gas entering the chromatographic column has high purity (≥99.999%) and low humidity (dew point ≤-40℃). This avoids a series of problems caused by poor carrier gas quality, such as loss of the chromatographic column stationary phase, decreased sensitivity of the gas sensor, peak broadening, tailing, and baseline drift, thus ensuring the high efficiency of the chromatographic separation process and the accuracy and reliability of the analytical results.

[0021] This application also provides a method for optimizing a chromatography column oven unit, comprising the following steps: By setting up a signal isolation unit between the digital control circuit and the analog signal acquisition circuit, the coupling interference of the high-speed switching pulse of the digital control circuit to the analog signal is blocked, and the analog signal acquisition circuit is powered separately through the power isolation unit. A precision amplification circuit is set at the output of the gas sensor. The precision amplification circuit includes a connected instrumentation amplifier and a reference voltage source. The resistance value of the gain resistor of the instrumentation amplifier is adjusted to achieve a predetermined multiple gain. A combination of high and low frequency composite filter capacitors is connected in parallel to the output of the reference voltage source. An ADC acquisition module is set at the output of the instrumentation amplifier. The ADC acquisition module adopts Σ-Δ modulation technology and supports multi-level filtering modes. The data output by the ADC acquisition module is subjected to an optimized Kalman filter algorithm for noise reduction. The algorithm includes: using the data output by the ADC acquisition module as observations to construct a one-dimensional state-space model. Based on the one-dimensional state space model, the state covariance matrix P is updated by introducing a forgetting factor, the noise covariance matrix Q is dynamically adjusted by predicting the residuals, and the observation noise covariance matrix R is dynamically adjusted by the residual sliding window variance. Based on the adjusted state covariance matrix P, the process noise covariance matrix Q, and the observation noise covariance matrix R, calculate the Kalman gain K; The Kalman gain K is limited, and the data output by the ADC acquisition module is filtered in real time using an optimized Kalman filtering algorithm.

[0022] Optionally, the following steps may also be included: Temperature control steps: acquire temperature data at the injection and discharge ends of the column oven; calculate the temperature difference between the injection and discharge ends; when the temperature difference between the injection and discharge ends exceeds a preset threshold, dynamically adjust the output of the heating unit using an incremental PID algorithm to eliminate the temperature difference, wherein the preset threshold is 0.03℃. Temperature compensation steps: First, establish a temperature-output characteristic curve model based on the temperature and gas sensor output: y = a0 + a1T + a2T 2 +a3T 3 Where y is the compensated output, T is the temperature, and a 0、 a 1、 a 2、 a3 is the fitting coefficient; based on the real-time collected temperature data, the output of the gas sensor is automatically corrected using the temperature-output characteristic curve model.

[0023] In summary, this application includes at least one of the following beneficial technical effects: 1. Achieved ultra-low baseline noise and high sensitivity detection: Through the synergistic optimization of hardware (signal isolation, power isolation, PCB partitioning) and software (optimized Kalman filtering), various noise sources were systematically suppressed, reducing baseline noise to an industry-leading level (61.3 nVrms). This enabled the accurate identification and quantitative analysis of low-concentration (≤1 μL / L) gas components that were submerged in noise, significantly improving the sensitivity and accuracy of detection.

[0024] 2. High-precision and high-stability temperature control is achieved: By combining a high-precision PID temperature control algorithm with dual-point feedback, active temperature compensation algorithm, and reinforced physical insulation and sealing structure, extremely high uniformity (temperature difference ≤ 0.03℃) and stability (fluctuation ≤ ±0.02℃) of the internal temperature field of the column oven are achieved, and the influence of gas sensor temperature drift is eliminated, ensuring high repeatability (RSD ≤ 1.5%) and accuracy of analysis results in a wide temperature range and complex environment.

[0025] A comprehensive anti-interference system has been constructed, resulting in high system reliability: This application not only solves the problem of electromagnetic interference within the circuit, but also avoids interference from the heating system through zero-crossing temperature control technology, and eliminates interference from the gas source through a carrier gas purification unit. This multi-dimensional and systematic anti-interference design enables the equipment to maintain high-performance and stable operation even in harsh industrial environments such as strong electric fields, demonstrating extremely strong environmental adaptability and reliability.

[0026] The method is highly versatile: the optimization method can be extended to other types of chromatographic column oven units (such as column oven optimization for gas chromatography and liquid chromatography), and has broad application prospects. Attached Figure Description

[0027] Figure 1 It is a baseline map in related technologies; Figure 2 This is an overall structural block diagram of the chromatography column oven unit in this application; Figure 3 This is a schematic diagram of the precision amplifier circuit of this application; Figure 4 A PCB layout diagram of a data acquisition board provided in this application; Figure 5 A hardware structure block diagram of a high-precision data acquisition board provided in this application; Figure 6 A flowchart illustrating the Kalman filter algorithm provided in this application; Figure 7 A comparison chart showing the output of the thyristor temperature control circuit with and without temperature compensation algorithm provided in this application as a function of temperature; Figure 8 The chromatogram shows the components containing CO2, C2H5, C2H6, and C2H2 obtained using the method described in this application. Figure 9 The image shows a chromatogram of H2, CO, and CH4 components obtained using the method described in this application.

[0028] Explanation of reference numerals in the attached figures: 100. Column oven body; 110. Dual-channel chromatographic column; 120. Heating unit; 130. Temperature detection unit; 140. Data acquisition board; 200. Precision amplifier circuit; 210. Input filter network; 220. Instrumentation amplifier; 230. Reference voltage generation circuit; 240. ADC acquisition module; 300. Analog signal area; 310. Digital signal area; 320. Power supply area. Detailed Implementation

[0029] To make the technical means, creative features, objectives, and effects of this application easier to understand, specific implementation methods will be described below, with reference to the appendix. Figure 1-9This application will be further described in detail below. It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of this application can be combined with each other.

[0030] The core idea of ​​this application is to systematically improve the detection sensitivity, accuracy, and stability of the chromatography column oven unit for low-concentration dissolved gases by implementing in-depth anti-interference design at the hardware level (including signal isolation, power isolation, and refined PCB layout) on the data acquisition board of the chromatography column oven unit, and combining it with high-precision signal processing algorithms at the software level (optimized Kalman filtering, incremental PID temperature control, and temperature compensation algorithm). This is achieved by synergistically suppressing system noise, improving temperature control accuracy, and eliminating temperature drift from multiple dimensions.

[0031] Example 1. This example provides a chromatography column oven unit. Please refer to... Figure 2 The column oven unit includes a column oven body 100, a dual-channel chromatographic column 110 disposed within the column oven body 100, a heating unit 120, a temperature detection unit 130, a gas sensor, and a data acquisition board 140.

[0032] The column oven body 100 is a box with good thermal insulation performance, used to house the core components of chromatographic analysis. Preferably, the column oven body 100 is a sealed box made of 304 stainless steel with a thickness of 1.5mm and is resistant to high temperatures (≥200℃).

[0033] A dual-channel chromatographic column 110 is installed within the column oven body 100. For example, one channel is configured as a packed column for separating H2, O2, N2, CH4, and CO, while the other channel is configured as a capillary column for separating CO2, C2H4, C2H6, and C2H2, thereby achieving full component separation of common fault gases in insulating oil. Preferably, the dual-channel chromatographic column 110 is an HP-5 column with a length of 30 m, an inner diameter of 0.32 mm, a stationary phase of 5% phenylmethyl polysiloxane, and a maximum operating temperature of 325 °C.

[0034] Preferably, the heating unit 120 is a 220V / 50W stainless steel heating tube, installed at the bottom of the column oven body 100, and its power is adjusted through a silicon controlled rectifier (SCR) temperature control circuit. The temperature detection unit 130, for example, can be a high-precision platinum resistance thermometer (such as Pt100). The temperature detection unit 130 includes two Class A PT100 probes, which are respectively installed at the injection end (50mm from the injection port) and the outlet end (50mm from the outlet) of the dual-channel chromatographic column 110. Its detection range is -200℃ to 650℃, and its accuracy is ≤0.01℃. Temperature data is transmitted via an RS485 bus.

[0035] Optionally, the gas sensor is a dual-channel catalytic combustion sensor (not shown in the figure). The dual-channel catalytic combustion sensor is located inside the column oven body 100 and has two detection units: one for measurement (active bead) and one for reference (a compensation bead without catalyst coating, also called an inert bead or white bead). By measuring the difference between the two, a more accurate signal output can be achieved, while compensating for the effects of changes in ambient temperature and humidity. Preferably, the dual-channel catalytic combustion sensor is model MQ-4, with a detection range of 0-100% LEL, a response time ≤10s, and a recovery time ≤30s. The bridge power supply is connected to an ADI ultra-high PSRRLDO, which can effectively suppress the influence of power fluctuations on the bridge balance. Theoretically, when there is no combustible gas on the sensor surface, the bridge output voltage difference is ≤0.1mV, significantly reducing the sensor's own noise.

[0036] The key improvement of this application lies in the integrated design of the data acquisition board 140. The data acquisition board 140 integrates a signal isolation unit, a power isolation unit, a precision amplifier circuit 200, an ADC acquisition module 240, and digital control circuits such as a microprocessor. When the digital circuit is working, it will generate high-speed switching pulse signals. In order to prevent these digital noises from being coupled to the extremely weak analog signal (usually at the microvolt level) path through conduction or radiation, this embodiment provides a signal isolation unit.

[0037] Preferably, the signal isolation unit can be a high-speed digital optocoupler (such as an ADuM series chip), which is physically connected between the digital control circuit and the analog signal acquisition circuit. Its isolation voltage is ≥2500Vrms, which blocks the coupling interference of high-speed switching pulses (frequency ≥100MHz) generated by the CPU, clock chip, etc. in the digital control circuit to the analog signal, thereby reducing the interference noise of the analog signal acquisition circuit by ≥80%.

[0038] Preferably, the power isolation unit uses a 1500V power isolation module, which provides a separate power supply for the analog signal acquisition circuit. It receives the motherboard's general-purpose power supply (e.g., +5V) and outputs one or more independent power supplies (e.g., ±15V) dedicated to the analog circuit and completely isolated from the motherboard power supply. This design achieves complete isolation between the motherboard power supply and the analog power supply, providing a clean energy foundation for subsequent precision amplification and acquisition. This hardware isolation design is a key safeguard for significantly reducing baseline noise from ≥500nVrms to below 61.3nVrms.

[0039] In a preferred embodiment, a PI-type filter circuit is connected in series at the output of the power isolation unit. The PI-type filter circuit includes a connected tantalum capacitor and a metal film resistor. Preferably, the tantalum capacitor is a 100μF / 16V tantalum capacitor, and the metal film resistor is a 10Ω / 1W metal film resistor. This PI-type filter circuit has a ripple rejection ratio ≥40dB in the 100Hz-1MHz frequency band, making the DC power supply to the analog signal acquisition circuit cleaner (e.g., ripple noise ≤5mV), thereby further improving the stability and accuracy of signal acquisition.

[0040] Reference Figure 3 In this embodiment, the input terminal of the precision amplifier circuit 200 is differentially connected to the output of the gas sensor. Specifically, the precision amplifier circuit 200 includes an input filter network 210, an instrumentation amplifier 220, and a reference voltage generation circuit 230.

[0041] Reference Figure 3 The input filter network 210 includes an input terminal IN+, an input terminal IN-, a first input resistor R1-1, a second input resistor R2-1, a first common-mode filter capacitor C1-1, a second common-mode filter capacitor C2-1, and a differential-mode filter capacitor C3-1. The input terminals IN+ and IN- are respectively used to connect to the differential signal output terminals of the dual-channel catalytic combustion sensor. The input terminal IN+ is connected to the positive input terminal (+) of the instrumentation amplifier 220 through the first input resistor R1-1, and the input terminal IN- is connected to the negative input terminal (-) of the instrumentation amplifier 220 through the second input resistor R2-1.

[0042] One end of the first common-mode filter capacitor C1-1 is connected between the first input resistor R1-1 and the positive input terminal of the instrumentation amplifier 220, and the other end is grounded. One end of the second common-mode filter capacitor C2-1 is connected between the second input resistor R2-1 and the negative input terminal of the instrumentation amplifier 220, and the other end is grounded. The differential-mode filter capacitor C3-1 is connected across the positive and negative input terminals of the instrumentation amplifier 220.

[0043] The input filtering network 210 together constitutes a differential low-pass filter, used to initially filter out common-mode and differential-mode high-frequency noise in the input signal and prevent external electromagnetic interference from entering the amplification link. In a preferred embodiment, the resistance values ​​of R1-1 and R2-1 are 53.6Ω, and the capacitance values ​​of C1-1, C2-1, and C3-1 are all 10μF.

[0044] Preferably, the instrumentation amplifier 220 uses a chip with ultra-low noise and high common-mode rejection ratio, with a common-mode rejection ratio ≥120dB@50 / 60Hz and an input offset voltage ≤10μV. Its positive and negative input terminals are respectively connected to the output terminals of the input filter network 210.

[0045] The gain of the instrumentation amplifier 220 is determined by the gain setting resistor R1-2 connected between its two RG pins. In this embodiment, the resistance value of R1-2 is preferably 200Ω, thereby setting the gain of the instrumentation amplifier 220 to 100 times, achieving high-magnification and high-precision amplification of weak differential signals.

[0046] The output (OUT) of instrumentation amplifier 220 is used to output an amplified analog signal. This output is connected to the analog signal input of ADC acquisition module 240 via a third resistor R1-3. The reference voltage pin (REF) of instrumentation amplifier 220 is connected to the output of reference voltage generation circuit 230 described below, and is used to set the DC bias level of the amplifier output signal.

[0047] The reference voltage generation circuit 230 provides a highly stable, low-noise reference voltage for the instrumentation amplifier 220 and the ADC acquisition module 240. This circuit includes a reference voltage source U1ref-2 and a voltage buffer U2ref-2.

[0048] In a preferred embodiment, the reference voltage source U1ref-2 is an ADR4525A chip from Analog Devices, which can output a precise 2.5V voltage with extremely low temperature drift (≤5ppm / ℃). Its output terminal is connected to the non-inverting input terminal of the voltage buffer U2ref-2 through a first reference resistor R1ref-2, and grounded through a second reference resistor R2ref-2. A first reference filter capacitor C1ref-2 (e.g., 1μF) is connected in parallel between the output terminal of the reference voltage source U1ref-2 and ground to filter out noise in the reference voltage.

[0049] In a preferred embodiment, to further improve the stability of the reference voltage, a large-capacity tantalum capacitor (e.g., 10μF, not shown in the figure) and a small-capacity ceramic capacitor (e.g., 4.7μF) can be connected in parallel at the output of the reference voltage source U1ref-2 to form a high-low frequency composite filter capacitor combination, which effectively suppresses power supply ripple and noise across the entire frequency band.

[0050] Optionally, the voltage buffer U2ref-2 uses an Analog Devices MAX40023 operational amplifier chip. Its non-inverting input is connected to the output of the reference voltage source U1ref-2, and its negative input is directly connected to its output, forming a voltage follower. Its output serves as the output of the entire reference voltage generation circuit 230 and is connected to the REF pin of the instrumentation amplifier 220. The introduction of the voltage buffer U2ref-2 enhances the driving capability of the reference voltage, avoids the load effect of subsequent circuits on the reference voltage source, and ensures the stability and consistency of the reference voltage.

[0051] In a preferred embodiment, the ADC acquisition module 240 uses the AD7175-2 chip from Analog Devices (ADI) with low-noise Σ-Δ modulation technology. This chip supports a 1ksps sampling rate and multiple filtering modes; preferably, the multi-level filtering mode is the Sinc5+1 filtering mode. The chip's acquisition noise is ≤14nVrms, ensuring high-precision conversion from analog to digital signals.

[0052] The analog signal input terminal of the ADC acquisition module 240 is connected to the output terminal of the instrumentation amplifier 220 through a third resistor R1-3. An ADC input filter capacitor C1-3 is connected between the analog signal input terminal of the ADC acquisition module 240 and ground. The third resistor R1-3 and the ADC input filter capacitor C1-3 together form an RC low-pass filter, used for a final filtering before the signal enters the ADC, further filtering out any residual or generated high-frequency noise in the signal chain, ensuring the purity of the input ADC signal.

[0053] The REF pin of the ADC acquisition module 240 is connected to the output terminal of a reference voltage generation circuit 230 similar to the one described above. The circuit is similar to that described above and will not be repeated here. This is to obtain a stable 2.5V reference voltage, thereby ensuring the quantization accuracy of the analog-to-digital signal conversion.

[0054] The digital output (OUT) of the ADC acquisition module 240 outputs the converted digital signal to the microcontroller (e.g., STM32F103) on the data acquisition board 140 for subsequent processing by the Kalman filtering algorithm and temperature compensation algorithm.

[0055] The working principle of the precision amplifier circuit 200 is as follows: When the weak differential voltage signal carrying gas concentration information from the dual-channel catalytic combustion sensor is input to the precision amplifier circuit 200, it first passes through the input filter network 210 to filter out high-frequency interference. Subsequently, the clean differential signal is amplified 100 times by the instrumentation amplifier 220 based on the ultra-stable reference voltage (provided by the reference voltage generation circuit 230) with low noise. The amplified signal is then sent to the high-precision ADC acquisition module 240 for digital conversion after passing through a first-stage RC low-pass filter. The entire signal chain, through the selection of precision components, multi-stage filtering design, and the introduction of a highly stable reference voltage, forms a low-noise processing scheme for the entire process from analog signal acquisition and amplification to digital signal conversion. This ensures the accurate capture and quantization of low-concentration gas signals at the μL / L level, providing high-quality raw data for subsequent algorithm processing.

[0056] Furthermore, the data acquisition board 140 adopts a multi-layer PCB design, such as a four-layer board design, with the four layers being the signal layer, ground, power, and signal layer respectively.

[0057] Reference Figure 4The top and bottom layers of the PCB consist of three independent areas: analog signal area 300, digital signal area 310, and power supply area 320. Analog signal area 300 contains only sensitive components such as precision amplifier circuit 200, ADC acquisition module 240, and reference voltage source; digital signal area 310 contains high-frequency noise sources such as microprocessor, memory, and communication interface; and power supply area 320 contains power isolation modules, low dropout linear regulators (LDOs), etc.

[0058] In this design, a dividing trench is formed between the analog signal area 300 and the digital signal area 310 by hollowing out the intermediate ground and power layers. Analog ground (AGND) isolation lines with a width of ≥0.5mm are laid on the top and bottom layers. The "ground" of the two areas is connected at only one point through an isolation device (such as a power isolation module), completely cutting off the path for digital ground noise to pollute the analog ground. Simultaneously, a PE grounding protection line with a width of ≥1mm is installed between the power area 320 and other areas to absorb and shield external electromagnetic interference (EMI), enhancing overall electromagnetic compatibility (EMC). Signal lines in each area adopt a short-path, low-crossing layout, with analog signal line spacing ≥0.2mm to avoid signal crosstalk and further reduce circuit noise.

[0059] Reference Figure 5 , Figure 5 The hardware structure block diagram of a high-precision data acquisition board provided in this application is as follows: the motherboard interface serves as the input terminal of the external power supply, receiving 12V DC power from the external main control board.

[0060] The 12V power supply from the motherboard interface is split into two paths: one path is electrically connected to the input of a 12V to 5V power isolation module. This module converts the 12V power supply to an isolated 5.0V power supply, which serves as the "dirty power" (i.e., primary isolation power) for the entire analog signal processing area, achieving electrical isolation between analog ground and digital ground. The other path is electrically connected to the input of a 12V to 3.3V power conversion module, whose 3.3V output is specifically used to power the isolated master chip described later.

[0061] The isolated 5.0V power supply is electrically connected to the input of a negative low-noise linear regulator LT3094 and the input of a positive ultra-low noise linear regulator LT3045. The LT3094 outputs a stable negative voltage of -4.8V, and the first LT3045 outputs a stable positive voltage of +4.8V. These -4.8V and +4.8V voltages together form a high-precision dual positive and negative power supply, specifically used to power the operational amplifiers in the op-amp channels described later.

[0062] The +4.8V output of the first LT3045 is electrically connected to the input of a second positive-voltage ultra-low noise linear regulator, the LT3045, which outputs a stable 2.7V voltage. This 2.7V voltage is specifically used to provide bias or operating voltage for gas sensors (such as dual-channel catalytic combustion sensors) at the column interface.

[0063] The above structure forms a complete power supply link from external power supply to precision analog circuit. This link realizes the functions of power isolation, positive and negative dual power supply generation, multi-level LDO filtering and noise reduction, and dedicated sensor power supply, ensuring the stability and low noise characteristics of analog circuit operation from the source.

[0064] Meanwhile, the column interface receives 2.7V power from the power isolation module and is connected to the gas sensor. The weak analog signal (e.g., differential signal) output by the gas sensor is output from the column interface and electrically connected to the signal input terminals of operational amplifier channel 1 and operational amplifier channel 2, respectively.

[0065] The reference module (e.g., a built-in ADR4525A reference voltage source) is powered by a +4.8V supply from the power isolation module and outputs a highly stable reference voltage (e.g., 2.5V). The output of this reference voltage is electrically connected to the reference voltage input (REF pin) of op-amp channel 1 and op-amp channel 2, as well as the reference voltage input of the ADC channel.

[0066] Operational amplifier channels 1 and 2 (e.g., built-in AD8422 instrumentation amplifier 220) receive analog signals from the column interface and perform high-gain, high-common-mode rejection ratio differential amplification using a ±4.8V dual power supply provided by the power isolation module, with reference to the voltage provided by the reference module. The amplified analog signals are then output from their respective output terminals.

[0067] The temperature module (e.g., containing a PT100 detection unit) is used to acquire ambient or column oven temperature, and its output temperature signal is also an analog signal.

[0068] The ADC channel (e.g., the built-in AD7175-2 high-precision ADC) has multiple analog input channels. Its first analog input channel is electrically connected to the output of operational amplifier channel 1, the second analog input channel is electrically connected to the output of operational amplifier channel 2, and the third analog input channel is electrically connected to the output of the temperature module. The ADC channel uses the voltage provided by the reference module as a reference to convert the received analog signals from multiple channels into high-resolution digital signals.

[0069] Simultaneously, the digital signal output terminal of the ADC channel is electrically connected to the input terminal of the isolated slave terminal. The isolated slave terminal and the isolated master terminal (for example, together forming the ADUM1400 signal isolation unit) achieve electrical isolation transmission of signals through magnetic coupling or optical coupling. The isolated slave terminal operates in the analog power domain, while the isolated master terminal operates in the digital power domain.

[0070] The output of the isolated master terminal is electrically connected to the motherboard interface, and the acquired digital signals are transmitted to an external master control system (e.g., a microcontroller or processor) for subsequent data analysis and processing through the motherboard interface.

[0071] In summary, this embodiment achieves end-to-end isolation and low-noise design from power input to signal output through the aforementioned hardware structure. The power isolation module provides a clean energy foundation for signal processing; the instrumentation amplifier 220 effectively amplifies and accurately digitizes weak signals under the constraint of a high-precision reference; and the signal isolation module constructs a robust "firewall," preventing digital domain noise from contaminating the analog domain, thereby ensuring a high signal-to-noise ratio and high measurement accuracy for the entire data acquisition system.

[0072] In a preferred embodiment, the microprocessor on the data acquisition board receives digital signals from the ADC acquisition module. To further filter out random noise introduced during the acquisition process and avoid signal lag and peak flattening problems caused by traditional filtering algorithms (such as moving average filtering), the microprocessor is configured to execute an optimized Kalman filter algorithm. This algorithm has been significantly improved to address the characteristics of chromatographic signals (slow baseline drift and rapid peak changes), referring to... Figure 6 The specific implementation logic is as follows: S41. Use the data output by the ADC acquisition module as the observation value to construct a one-dimensional state space model.

[0073] To meet the stringent requirement of a filtering lag time ≤10ms, this embodiment simplifies traditional high-dimensional matrix operations into scalar operations. A one-dimensional state-space model is constructed, where the state vector contains only the ADC acquisition values.

[0074] Preferably, the steps for constructing a one-dimensional state-space model include: constructing a one-dimensional state equation x k =x k-1 +w k and observation equation z k =x k +v k , where x k x represents the current actual signal state value. k-1 w represents the actual signal state value at the previous moment. k Process noise value ， z k v is the value acquired by the ADC.k To measure noise levels.

[0075] Let the ADC sampling frequency be fs. The execution time of the microprocessor-controlled single-pass filtering algorithm must be ≤10ms⁻¹ / fs to reserve sufficient sampling and transmission time and ensure real-time data processing. For example, if fs = 1kHz, the execution time must be ≤9ms.

[0076] S42. Based on the one-dimensional state space model, the state covariance matrix P is updated by introducing a forgetting factor, the noise covariance matrix Q is dynamically adjusted by predicting the residuals, and the observation noise covariance matrix R is dynamically adjusted by the residual sliding window variance.

[0077] Preferably, the step of updating the state covariance matrix P by introducing a forgetting factor includes: During initialization, the microprocessor determines the range of the ADC acquisition module [0, V]. max Set an initial value P0, P0 = (V max / 2) 2 This is to cover the maximum error range of the initial estimate.

[0078] Introduce an adaptive forgetting factor λ, where 0.95 ≤ λ ≤ 1; construct the update formula for the state covariance matrix P: P k - =λP k-1 +Q, P k-1 Let P be the state covariance matrix at the previous time step; using P k - =λP k-1 +Q updates the state covariance matrix P k - When the signal is stable, λ is taken to a value close to 1, so that P decays slowly; when the signal changes rapidly, λ decreases, accelerating the decay of P, thereby improving the algorithm's sensitivity to changes in system state.

[0079] Preferably, the step of dynamically adjusting the process noise covariance matrix Q by predicting the residuals includes: Real-time calculation of the residual ε between observed and predicted values k , ε k =z k -x k - ,when At that time, the Q value increases exponentially, where the observed value is the ADC acquisition value z. k The predicted value is the prior estimate x at the current time. k - .

[0080] Specifically, the Q-value represents the noise of the system's "unmodeled dynamics." This embodiment employs a strategy of "offline calibration + online adjustment": Offline calibration: Acquire static ADC data (e.g., short-circuit the ADC input and acquire pure noise), and calculate the variance σ of the data. 2 static Acquire ADC dynamic calibration data (input a known sinusoidal / step signal) and calculate the variance σ of the prediction error. 2 dynamic Combined with σ 2 static and σ 2 dynamic Calculate the basic Q value: Q base =ασ 2 dynamic +(1-α)σ 2 static , wherein α is preferably 0.7.

[0081] Online adjustment: Microprocessor calculates residual ε in real time k =z k -x k - ,.when When a signal abrupt change is detected (e.g., chromatographic peak elution), the formula is used. (β is an adjustment coefficient, for example, 0.1) Temporarily increase the Q value to make the filter quickly track signal changes; when the residual returns to normal, the Q value decays exponentially back to Q. base .

[0082] Preferably, the step of dynamically adjusting the observation noise covariance matrix R through the residual sliding window variance includes: Calculate the residual ε k The sliding window variance; based on the ratio of the sliding window variance to the reference noise standard deviation, the observation noise covariance matrix R is dynamically adjusted. k .

[0083] Specifically, the baseline noise standard deviation σ of the hardware noise is calculated through multiple sets of static sampling by the ADC. 2 ADC Set the basic observation noise covariance R of the Kalman filter. base R is usually configured base Equal to or slightly greater than σ 2 ADC To ensure filtering stability, during online dynamic adjustment, the microprocessor maintains a residual sliding window of length N (e.g., N=5~10), and calculates the variance Var(ε) within the residual sliding window. k ) win The formula for calculating the real-time R value is: R k =R baseVar(ε k ) win / σ 2 ADC The physical meaning of this formula is: using the ratio of the current residual fluctuation to the hardware standard noise floor as the gain coefficient, the basic parameter R is adjusted. base Perform dynamic scaling. When noise within the window increases, R... k Automatically increase R, reducing the weight of the current observation in the update, thereby suppressing sudden disturbances; conversely, decrease R. k This improves tracking accuracy.

[0084] S43. Preferably, the step of calculating the Kalman gain K based on the adjusted state covariance matrix P, the process noise covariance matrix Q, and the observation noise covariance matrix R includes: The Kalman gain K is calculated using the formula: K = P k - / (P k - +R k ).

[0085] S44. Preferably, the step of limiting the Kalman gain K includes: Set the upper limit of gain K max For slowly varying signals (such as a baseline), K max Set to 0.8; for rapidly changing signals (such as the peak-forming phase), K max Relaxed to 0.95.

[0086] The final gain value is: K = min(K) max ,P k - / (P k - +R k )).

[0087] Through the Kalman filter algorithm optimized by multiple parameters, this application can minimize the mean square error (MSE) within a very short time window (lag ≤ 10ms), which avoids the chromatographic peak broadening and lag caused by traditional low-pass filtering, and effectively filters out high-frequency digital noise, ensuring accurate restoration of weak signals of low-concentration (≤1μL / L) gas.

[0088] Through the synergistic effect of the aforementioned hardware and software, this embodiment can achieve low-noise processing of gas sensor signals across the entire chain, from source isolation to precision amplification, high-precision conversion, and intelligent filtering, thereby providing a high-quality data foundation for subsequent quantitative gas analysis.

[0089] Furthermore, this embodiment integrates a high-precision temperature control system to eliminate the impact of temperature fluctuations and drift on the test results.

[0090] Preferably, the data acquisition board 140 also integrates a thyristor temperature control circuit connected to the microprocessor. This circuit includes a bidirectional thyristor and a zero-crossing detection module. The bidirectional thyristor can be a BT136 type bidirectional thyristor. The zero-crossing detection module is based on an optocoupler isolation design (e.g., PC817) and is used to safely detect the voltage zero-crossing point of the AC power supply and send an interrupt signal to the microprocessor.

[0091] The microprocessor is configured to generate an adjustable-width trigger pulse (0.5-2ms) based on the deviation between the current temperature and the set temperature, and trigger the bidirectional thyristor to conduct at a precise moment after receiving a zero-crossing interrupt signal, thereby controlling the power flowing through the heating unit 120. This "soft-switching" method avoids the strong electromagnetic harmonic interference generated by traditional phase-shift power regulation, eliminating secondary pollution of the signal acquisition circuit by the temperature control system at the source, and ensuring that the heating power adjustment accuracy is ≤1%. The effect is as follows... Figure 7 As shown, at a 20% LEL concentration, the fluctuation range of the sensor output in the range of -30℃ to 60℃ is ≤2%.

[0092] Furthermore, the microprocessor is configured to execute the following two algorithms: Incremental PID temperature control algorithm: This algorithm is used to precisely control the output power of the heating unit. It employs incremental PID instead of positional PID, and through experimental tuning, a set of optimal PID parameters can be determined, such as proportional gain Kp = 2.5, integral time Ti = 10s, and derivative time Td = 2s.

[0093] In this embodiment, the temperature detection unit specifically consists of two Class A PT100 probes, installed at the injection and effluent ends of the dual-channel chromatographic column, respectively. The microprocessor not only monitors whether the temperature at a single point reaches the set value (e.g., 65°C), but also calculates the temperature difference between the injection and effluent ends in real time. When this temperature difference exceeds a very small preset threshold (e.g., 0.03°C), the algorithm dynamically adjusts the output of the heating unit to actively eliminate the temperature gradient within the column oven, ensuring that the internal temperature of the column oven remains stable within the set value ±0.02°C. This reduces temperature fluctuation by ≥96% compared to traditional PID control.

[0094] Temperature compensation algorithm: This algorithm is based on a pre-calibrated temperature-output characteristic curve model. For example, by experimentally measuring multiple sets of baseline output y from a gas sensor at temperature T, a polynomial curve is fitted: y = a0 + a1T + a2T 2 +a3T 3 Where y is the compensated output, T is the temperature, and a 0、 a 1、a3 is the fitting coefficient. In practical operation, the microprocessor uses this model to calculate the baseline drift at the current temperature based on the real-time acquired column oven temperature T, and subtracts this drift from the original output data of the gas sensor, thereby achieving automatic correction of temperature drift. Based on this model, automatic temperature compensation of the sensor output is achieved, ensuring that the fluctuation range of the sensor output within the range of -30℃ to 60℃ at 20% LEL and 60% LEL concentrations is ≤2%, eliminating the influence of temperature changes on the detection results.

[0095] Furthermore, this application also improves and optimizes the physical structure and air path of the column box unit, constructing a comprehensive optimization system.

[0096] Specifically, the inner wall of the column box body is tightly fitted with a layer of high-temperature resistant glass wool insulation material with a thickness of, for example, 6mm. Its thermal conductivity is ≤0.03W / (mK), and its heat loss rate at room temperature is ≤5%, significantly reducing heat loss inside the column box. Furthermore, high-temperature resistant silicone sealing rings are embedded in the sealing gaps between the column box body and its lid, achieving an IP54 sealing rating for the column box body. This effectively isolates the column box from the influence of external environmental temperature changes (such as fluctuations of ±5℃), ensuring uniform temperature distribution inside the column box, with a temperature difference of ≤0.05℃ between any two points.

[0097] In addition, the column oven unit also includes a carrier gas treatment unit connected in series in the carrier gas pipeline. The carrier gas treatment unit includes a dehumidification column and a purification column. The dehumidification column is preferably a 13X molecular sieve dehumidification column (50 mL capacity), with a molecular sieve pore size of 0.3 nm and an adsorption capacity for water molecules ≥20% (mass fraction). This can control the dew point of the carrier gas (nitrogen) to ≤-40℃, preventing moisture from causing stationary phase loss and sensor sensitivity degradation. The purification column is preferably an activated carbon-alumina composite purification column (100 mL capacity). Activated carbon adsorbs organic impurities (such as hydrocarbons and alcohols), while alumina adsorbs inorganic impurities such as oxygen and carbon dioxide. The impurity removal rate is ≥99.9%, ensuring the carrier gas purity reaches ≥99.999%, preventing impurities from interfering with chromatographic peak separation and ensuring accurate detection results.

[0098] In summary, this application, through a multi-dimensional collaborative technical solution encompassing hardware circuit optimization, software algorithm upgrades, physical structure enhancement, and gas path purification as described in the above embodiments, constructs a comprehensive, interference-resistant, high-precision, and highly stable chromatography column oven unit system. It not only solves the problems of digital / analog crosstalk and power supply noise at the circuit level, and the issues of signal processing delay and imprecise temperature control at the algorithm level, but also eliminates interference introduced by the environment and gas source at the physical and gas path levels. The final technical effects achieved are: extremely low baseline noise (≤61.3 nVrms), uniform and stable temperature field (temperature difference ≤0.03℃, fluctuation ≤±0.02℃), accurate and reliable detection and quantification of gaseous components with concentrations as low as 1 μL / L, and maintaining high performance even in complex industrial environments, demonstrating significant application value.

[0099] The test results are as follows: Baseline noise comparison: Before optimization, the baseline noise was ≥500 nVrms, and after optimization, the baseline noise was reduced to 61.3 nVrms, with an attenuation of ≥87.7%.

[0100] Temperature control accuracy comparison: The optimized front column box temperature fluctuation is ±0.5℃, while the optimized fluctuation is ±0.02℃, resulting in an accuracy improvement of ≥96%.

[0101] Comparison of low concentration detection capabilities: Before optimization, it could not accurately identify 1 μL / L gas peaks; after optimization, it can clearly identify each gas peak (e.g., Figure 8 , 9 The optimized graph shows that the X-axis represents time (minutes) and the Y-axis represents the signal value (count), with a detection error of ≤5%.

[0102] Comparison of anti-interference capabilities: Under strong electric field interference of 10kV, the detection error before optimization is ≥20%, and the detection error after optimization is ≤3%, indicating a significant improvement in anti-interference capability.

[0103] Repeatability comparison: For the same sample, 10 consecutive tests were performed. Before optimization, the relative standard deviation (RSD) was ≥8%, and after optimization, the RSD was ≤1.5%, indicating a significant improvement in repeatability.

[0104] Example 2. This example discloses an optimization method for a chromatography column oven unit. This method utilizes the synergistic effects of four core aspects: hardware optimization, algorithm optimization, structural optimization, and carrier gas treatment optimization, to systematically improve the performance of the chromatography column oven unit, particularly by reducing baseline noise, improving temperature control accuracy, and enhancing overall anti-interference capabilities. The method of this example can be applied to the chromatography column oven unit described in Example 1.

[0105] The optimization method specifically includes the following steps: S1. By setting a signal isolation unit between the digital control circuit and the analog signal acquisition circuit, the coupling interference of the high-speed switching pulse of the digital control circuit to the analog signal is blocked, and the analog signal acquisition circuit is powered separately through the power isolation unit.

[0106] Specifically, by setting up a signal isolation unit (such as the ADUM1400 chip) between the digital control circuit and the analog signal acquisition circuit, the coupling interference of high-speed switching pulses generated by the digital control circuit (such as the microprocessor, clock, etc.) to the analog signal is physically blocked. At the same time, by setting up a power isolation unit (such as the R-78E5.0-0.5 module) to provide a separate power supply for the analog signal acquisition circuit, electrical isolation between the motherboard power supply and the analog power supply is achieved, cutting off the noise conduction path from the power supply.

[0107] S2. A precision amplifier circuit 200 is set at the output end of the gas sensor. The precision amplifier circuit 200 includes a connected instrumentation amplifier 220 and a reference voltage source. The resistance value of the gain resistor of the instrumentation amplifier 220 is adjusted to achieve a predetermined multiple gain. The high and low frequency composite filter capacitors are connected in parallel to the output end of the reference voltage source.

[0108] Specifically, a precision amplifier circuit 200 is installed at the output of the sensor (such as the MQ-4 catalytic combustion sensor). The core of this circuit is a connected instrumentation amplifier 220 (such as AD8422) and a reference voltage source (such as ADR4525A). Preferably, a preset signal gain of 100 times is achieved by adjusting the resistance value of the gain resistor (RG) of the instrumentation amplifier 220 to 200Ω. Simultaneously, a high- and low-frequency composite filter capacitor consisting of a 10μF tantalum capacitor and a 4.7μF ceramic capacitor is connected in parallel to the output of the reference voltage source to provide an extremely stable reference voltage.

[0109] S3. Set the ADC acquisition module 240 at the output terminal of the instrumentation amplifier 220. The ADC acquisition module 240 adopts Σ-Δ modulation technology and supports multi-level filtering modes.

[0110] Specifically, an ADC acquisition module 240 (such as AD7175-2) is provided at the output of the instrumentation amplifier 220. This module uses Σ-Δ modulation technology and is configured to operate in a mode that supports multi-level filtering (such as Sinc5+1 filtering mode) to convert the amplified analog signal into a digital signal with high precision.

[0111] S4. Perform noise reduction processing on the data output by the ADC acquisition module 240 using an optimized Kalman filter algorithm. The algorithm includes: using the data output by the ADC acquisition module 240 as observations to construct a one-dimensional state space model. Based on the one-dimensional state space model, the state covariance matrix P is updated by introducing a forgetting factor, the noise covariance matrix Q is dynamically adjusted by predicting the residuals, and the observation noise covariance matrix R is dynamically adjusted by the residual sliding window variance. Based on the adjusted state covariance matrix P, the process noise covariance matrix Q, and the observation noise covariance matrix R, calculate the Kalman gain K; The Kalman gain K is limited, and the data output by the ADC acquisition module 240 is filtered in real time using the optimized Kalman filtering algorithm.

[0112] The specific implementation logic is as described above and will not be repeated here.

[0113] S5. Temperature control steps: Obtain temperature data at the injection and output ends of the column oven; calculate the temperature difference between the injection and output ends; when the temperature difference between the injection and output ends exceeds a preset threshold, dynamically adjust the output of the heating unit 120 using an incremental PID algorithm to eliminate the temperature difference, where the preset threshold is 0.03℃.

[0114] Specifically, the temperature data of at least two key points inside the column oven, namely the temperature at the injection end and the temperature at the outlet end, are acquired in real time by the temperature detection unit 130 (such as two PT100 probes installed at the injection end and the outlet end of the chromatographic column).

[0115] Next, the temperature difference between the injection end temperature and the outlet end temperature is calculated within the microprocessor.

[0116] When the temperature difference exceeds a preset threshold (e.g., 0.03℃), or when the temperature at any point deviates from the set value, the temperature control algorithm is immediately triggered for adjustment. This embodiment utilizes an incremental PID algorithm (with optimized parameters Kp=2.5, Ti=10s, Td=2s) to dynamically adjust the output power of the heating unit 120 to eliminate temperature differences and deviations. This combination of dual-point feedback and a high-precision algorithm ensures a high degree of uniformity (temperature difference ≤ 0.03℃) and stability (fluctuation ≤ ±0.02℃) of the temperature field inside the column box.

[0117] More preferably, the power adjustment of the heating unit 120 adopts a zero-crossing trigger control method, that is, the thyristor is triggered to conduct at the zero-crossing point of the AC power supply to avoid electromagnetic interference.

[0118] S6. Temperature Compensation Steps: Pre-establish a temperature-output characteristic curve model based on the temperature and gas sensor output: y = a0 + a1T + a2T 2 +a3T 3 Where y is the compensated output, T is the temperature, and a 0、 a 1、 a2、 a3 is the fitting coefficient. Based on the real-time acquired temperature data, the output of the gas sensor is automatically corrected using the temperature-output characteristic curve model.

[0119] Specifically, a mathematical relationship between temperature and sensor output, i.e., a temperature-output characteristic curve model, is established beforehand through experimental calibration. In this embodiment, this model is constructed as a cubic polynomial function: y = a0 + a1T + a2T 2 +a3T 3 Where y is the compensated output, T is the temperature, and a 0、 a 1、 a 2、 a3 is the fitting coefficient.

[0120] In actual operation, based on the real-time collected column oven temperature data T, the microprocessor uses the preset temperature-output characteristic curve model to perform real-time automatic correction calculations on the raw sensor output data obtained by the ADC acquisition module 240, obtaining the compensated final result. This step effectively eliminates the impact of temperature drift on detection accuracy.

[0121] By further executing steps S5 and S6, this method synergistically solves two key problems affecting the accuracy of detection results: imprecise temperature control and temperature drift. It provides stable and reliable operating conditions for the entire chromatographic analysis process, ensuring high accuracy and repeatability of the analytical results (RSD ≤ 1.5%).

[0122] Compared with existing technologies, this optimized method and structure have the following significant advantages: Baseline noise is significantly reduced: Through hardware isolation, optimization of precision amplification circuits and improvement of PCB layout, the total circuit noise is controlled within 61.3 nVrms, which can accurately identify low concentration gas peaks ≤1 μL / L and avoid misjudgment.

[0123] Extremely high temperature control accuracy: The optimized incremental PID algorithm combined with the structural insulation design ensures that the temperature fluctuation of the column box is ≤ ±0.02℃. With the temperature compensation algorithm, the influence of temperature on the sensor output is eliminated, and the detection repeatability RSD is ≤1.5%.

[0124] Strong anti-interference capability: Signal and power isolation, PCB partition layout, and high-purity carrier gas treatment enable the system to work stably in environments with strong electric fields and fluctuating temperature and humidity, with a detection error of ≤3%.

[0125] Stable and reliable structure: The 304 stainless steel column box body and IP54 sealing design are resistant to high temperature and corrosion. The heat loss rate of the insulation cotton layer is ≤5%, and the service life of the equipment is extended by ≥50%.

[0126] The method is highly versatile: the optimization method can be extended to other types of chromatographic column oven units (such as column oven optimization for gas chromatography and liquid chromatography), and has broad application prospects.

[0127] The above description is merely a preferred embodiment of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application. Those skilled in the art can make various combinations, modifications, or equivalent substitutions to the above embodiments, and such combinations, modifications, or equivalent substitutions are all within the scope of protection claimed in this application.

Claims

1. A chromatographic column oven unit, comprising a column oven body (100), a dual-channel chromatographic column (110) disposed within the column oven body (100), a heating unit (120), a temperature detection unit (130), and a gas sensor, characterized in that, It also includes a data acquisition board (140), on which the following are integrated: The signal isolation unit is connected between the digital control circuit and the analog signal acquisition circuit to block the coupling interference of the high-speed switching pulses of the digital control circuit to the analog signal. The power isolation unit is used to provide an independent power supply for the analog signal acquisition circuit, thereby isolating the motherboard power supply from the analog power supply. A precision amplifier circuit (200) is connected to the output of the gas sensor at its input terminal. The precision amplifier circuit (200) includes an instrumentation amplifier (220) and a reference voltage source. The gain resistor of the instrumentation amplifier (220) is configured to achieve a predetermined gain. A high- and low-frequency composite filter capacitor combination is connected in parallel at the output terminal of the reference voltage source. The reference voltage source is connected to the instrumentation amplifier (220). An ADC acquisition module (240) is connected to the output of the instrumentation amplifier (220) and is used to convert analog signals into digital signals. The ADC acquisition module (240) adopts Σ-Δ modulation technology and supports multi-level filtering modes. The microprocessor within the data acquisition board (140) is configured to execute an optimized Kalman filter algorithm, including the following steps: The data output by the ADC acquisition module (240) is used as the observation value to construct a one-dimensional state space model; Based on the one-dimensional state space model, the state covariance matrix P is updated by introducing a forgetting factor, the noise covariance matrix Q is dynamically adjusted by predicting the residuals, and the observation noise covariance matrix R is dynamically adjusted by the residual sliding window variance. Based on the adjusted state covariance matrix P, the process noise covariance matrix Q, and the observation noise covariance matrix R, calculate the Kalman gain K; The Kalman gain K is limited, and the data output by the ADC acquisition module (240) is filtered in real time using the optimized Kalman filtering algorithm.

2. The chromatographic column oven unit according to claim 1, characterized in that, The steps for constructing a one-dimensional state-space model include: constructing a one-dimensional state equation x. k =x k-1 +w k and observation equation z k =x k +v k , where x k x represents the current actual signal state value. k-1 w represents the actual signal state value at the previous moment. k Process noise value ， z k v is the value acquired by the ADC. k To measure noise levels; The step of updating the state covariance matrix P by introducing a forgetting factor includes: introducing an adaptive forgetting factor λ, where 0.95 ≤ λ ≤ 1; and constructing the update formula for the state covariance matrix P: P k - =λP k-1 +Q; using P k - =λP k-1 +Q updates the state covariance matrix P k - P k-1 The state covariance matrix at the previous time step; The step of dynamically adjusting the noise covariance matrix Q through the predicted residual includes: calculating the residual ε between the observed value and the predicted value in real time. k , ε k =z k -x k - ,when At that time, the Q value increases exponentially, where the observed value is the ADC acquisition value z. k The predicted value is the prior estimate x at the current time. k - ; The step of dynamically adjusting the observation noise covariance matrix R through the residual sliding window variance includes: calculating the residual ε k The sliding window variance; based on the ratio of the sliding window variance to the reference noise standard deviation, the observation noise covariance matrix R is dynamically adjusted. k ; The step of calculating the Kalman gain K based on the adjusted state covariance matrix P, the process noise covariance matrix Q, and the observation noise covariance matrix R includes: The Kalman gain K is calculated using the formula: K = P k - / (P k - +R k ).

3. A chromatographic column oven unit according to claim 1, characterized in that: The data acquisition board (140) adopts a multi-layer PCB design, and its layout is divided into an independent analog signal area (300), a digital signal area (310), and a power supply area (320). An AGND isolation line with a width of ≥0.5mm is provided between the analog signal area (300) and the digital signal area (310), and a PE grounding protection line with a width of ≥1mm is provided between the power supply area (320) and other areas.

4. A column oven unit according to claim 1, characterized in that: The output terminal of the power isolation unit is connected in series with a PI-type filter circuit, which includes a connected tantalum capacitor and a metal film resistor. The PI-type filter circuit has a ripple rejection ratio of ≥40dB for the 100Hz-1MHz frequency band.

5. A chromatographic column oven unit according to claim 1, characterized in that, The data acquisition board (140) also integrates: A thyristor temperature control circuit includes a bidirectional thyristor and a zero-crossing detection module. The zero-crossing detection module is based on an optocoupler isolation design and is used to detect the zero-crossing point of the AC power supply. The microprocessor on the data acquisition board (140) is configured to generate an adjustable-width trigger pulse based on the temperature deviation, and to control the conduction of the bidirectional thyristor at the zero crossing point.

6. A chromatographic column oven unit according to claim 5, characterized in that, The microprocessor within the data acquisition board (140) is configured to execute the following algorithm: Incremental PID temperature control algorithm: Combining the dual-point feedback of the injection end temperature and the outlet end temperature collected by the temperature detection unit (130), when the temperature difference between the injection end temperature and the outlet end temperature exceeds a preset threshold, the output of the heating unit (120) is dynamically adjusted to eliminate the temperature difference. The preset threshold is 0.03℃. Temperature compensation algorithm: Based on a preset temperature-output characteristic curve model, the output data of the gas sensor is compensated; the preset temperature-output characteristic curve model is y=a0+a1T+a2T 2 +a3T 3 Where y is the compensated output, T is the temperature, and a 0、 a 1、 a 2、 a3 is the fitting coefficient.

7. A column oven unit according to claim 1 or 6, characterized in that: The inner wall of the column box body (100) is covered with a high-temperature resistant glass wool insulation layer, and a high-temperature resistant silicone sealing ring is provided at the sealing gap between the column box body (100) and its cover.

8. A chromatographic column oven unit according to claim 1, characterized in that, It also includes a carrier gas treatment unit, which is connected in series in the carrier gas pipeline and includes: The dehumidification column is filled with molecular sieves to adsorb moisture from the carrier gas. The impurity removal column is filled with a composite packing material of activated carbon and alumina to adsorb organic and inorganic impurities in the carrier gas.

9. A method for optimizing a chromatography column oven unit, characterized in that, Includes the following steps: By setting up a signal isolation unit between the digital control circuit and the analog signal acquisition circuit, the coupling interference of the high-speed switching pulse of the digital control circuit to the analog signal is blocked, and the analog signal acquisition circuit is powered separately through the power isolation unit. A precision amplifier circuit (200) is set at the output end of the gas sensor. The precision amplifier circuit (200) includes a connected instrumentation amplifier (220) and a reference voltage source. The resistance value of the gain resistor of the instrumentation amplifier (220) is adjusted to achieve a predetermined multiple gain. The high and low frequency composite filter capacitors are combined and connected in parallel to the output end of the reference voltage source. An ADC acquisition module (240) is set at the output of the instrumentation amplifier (220). The ADC acquisition module (240) adopts Σ-Δ modulation technology and supports multi-level filtering modes. The data output by the ADC acquisition module (240) is subjected to an optimized Kalman filter algorithm for noise reduction. The algorithm includes: using the data output by the ADC acquisition module (240) as observations to construct a one-dimensional state space model. Based on the one-dimensional state space model, the state covariance matrix P is updated by introducing a forgetting factor, the noise covariance matrix Q is dynamically adjusted by predicting the residuals, and the observation noise covariance matrix R is dynamically adjusted by the residual sliding window variance. Based on the adjusted state covariance matrix P, the process noise covariance matrix Q, and the observation noise covariance matrix R, calculate the Kalman gain K; The Kalman gain K is limited, and the data output by the ADC acquisition module (240) is filtered in real time using the optimized Kalman filtering algorithm.

10. The method for optimizing a column oven unit according to claim 9, characterized in that, It also includes the following steps: Temperature control steps: acquire temperature data of the injection end and the outlet end of the column oven; calculate the temperature difference between the injection end temperature and the outlet end temperature; when the temperature difference between the injection end temperature and the outlet end temperature exceeds a preset threshold, use an incremental PID algorithm to dynamically adjust the output of the heating unit (120) to eliminate the temperature difference, the preset threshold is 0.03℃. Temperature compensation steps: First, establish a temperature-output characteristic curve model based on the temperature and gas sensor output: y = a0 + a1T + a2T 2 +a3T 3 Where y is the compensated output, T is the temperature, and a 0、 a 1、 a 2、 a3 is the fitting coefficient; based on the real-time collected temperature data, the output of the gas sensor is automatically corrected using the temperature-output characteristic curve model.

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