Efficient gas supply method for forming CNG (compressed natural gas) by gasifying and pressurizing LNG recycled gas

Abstract

The invention discloses an efficient gas supply method for forming CNG (compressed natural gas) by gasifying and pressurizing LNG recovered gas, and relates to the technical field of gas supply by gasifying and pressurizing LNG recovered gas, and the method specifically comprises the following steps: when a cold section area of a vaporizer is in a thermal inert state, acquiring cold section heat exchange state information through a sensor network arranged in the cold section area of the vaporizer, and analyzing after acquiring; determining the thermal inertia level of the cold section area of the vaporizer; based on the determined thermal inertia level, executing a corresponding heat source preloading control strategy and an initial air inlet regulation and control strategy; in the starting process of the vaporizer, the temperature change of the cold section area of the vaporizer and the stability index of outlet gas are monitored in real time, and heat source input and gas inlet intensity are dynamically adjusted according to the monitoring result till the gasification state is stable. According to the invention, the problem of insufficient thermal inertia state identification and response during cold start of the vaporizer is solved, and dynamic regulation and control and stable gas supply effects based on the thermal inertia level are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of gasification, pressurization and gas supply of LNG recovered gas, and in particular to a high-efficiency gas supply method for gasification, pressurization and gasification of LNG recovered gas to form CNG. Background Art

[0002] With the widespread application of natural gas energy in transportation, industrial fuel, and other fields, LNG (liquefied natural gas), as a high-energy-density storage and transportation method, has gradually become a vital link in the natural gas supply chain. In practical applications, to achieve efficient supply of LNG to end users, LNG is often converted into CNG (compressed natural gas) through a gasification process for storage and transportation. The gasification process is usually completed with the help of a vaporizer, whose main function is to rapidly convert low-temperature liquid natural gas into a gaseous state under controlled conditions and cooperate with the boosting system to increase the gas pressure to meet the pressure and flow requirements of the CNG filling or distribution system. As a key device connecting low-temperature storage and transportation with room-temperature gas supply, the performance of the vaporizer is directly related to the efficiency and stability of the entire gasification and boosting gas supply system. Therefore, in the process of converting LNG to CNG, the design and optimization of the vaporizer has become an important direction of research and engineering practice.

[0003] Existing efficient gas supply technology for converting recycled LNG into CNG through gasification and pressurization primarily achieves the conversion and transportation of low-temperature liquid natural gas into high-pressure gaseous natural gas through the following key steps: First, during the use of LNG, a certain amount of recycled gas (also known as BOG, or Boil-Off Gas) is often produced in the storage tank. This gas is collected and fed into the vaporization system. Second, the core vaporizer is responsible for rapidly converting the low-temperature LNG or partially ungasified recycled gas into a room-temperature gas, typically using a water bath, air, or electric heating method to achieve efficient heat exchange. Next, the vaporized natural gas is pressurized by a booster system (such as a compressor unit) to bring it to the pressure range required for CNG storage, transportation, or use. Finally, after pressure regulation and safety control, it is output to gas stations, gas cylinder groups, or downstream users. The entire gas supply process relies on the vaporizer as a key device for heat energy conversion and process initiation. Its gasification capacity and thermal efficiency directly affect the gas supply speed and energy efficiency of the entire system.

[0004] The existing technology has the following deficiencies:

[0005] During the efficient CNG gasification and pressurization process of recycled LNG, when the vaporizer is restarted after a long shutdown, the entire internal heat exchange structure of the vaporizer is prone to being in a cold state, especially the cold section near the air inlet, which fails to restore effective heat exchange capacity in a timely manner. Because it takes some time for the heat source to build up sufficient heat capacity after activation, the first batch of low-temperature recycled LNG gas directly contacts the cold section, which has not yet heated up. This causes a lag in heat transfer, which in turn prevents the initial vaporization process from proceeding stably, resulting in gas-liquid mixing or insufficient vaporization. This will cause the gas outlet state to fluctuate violently, and the temperature and pressure cannot rise smoothly. The existing LNG recovered gas gasification and pressurization to form CNG efficient gas supply technology cannot dynamically adjust the vaporizer's heat source preload and initial air intake intensity according to the thermal inertia state of the cold section area of the vaporizer during the initial startup after a long shutdown. As a result, the vaporizer is forced to bear normal load before it has sufficient heat exchange capacity, which may eventually cause incompletely vaporized natural gas to enter the compressor, causing liquid hammer risk, and at the same time causing gas supply pressure fluctuations, frequent start and stop of the compressor or protective shutdown, thereby affecting the continuity and safety of the entire gas filling system.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0007] The object of the present invention is to provide an efficient gas supply method for gasifying and pressurizing LNG recovered gas to form CNG, so as to solve the problems in the above-mentioned background technology.

[0008] To achieve the above objectives, the present invention provides the following technical solution: an efficient gas supply method for gasifying and pressurizing LNG recovered gas to form CNG, specifically comprising the following steps:

[0009] Before the carburetor is started, the temperature information of the cold section of the carburetor, the ambient temperature information and the shutdown duration information are collected, and based on the collected information, it is determined whether the cold section of the carburetor is in a thermal inertia state;

[0010] When the cold section of the evaporator is in a thermally inert state, the sensor network deployed in the cold section of the evaporator obtains the cold section heat exchange status information, and analyzes it after acquisition to determine the thermal inertia level of the cold section of the evaporator;

[0011] Based on the determined thermal inertia level, executing a corresponding heat source preload control strategy and an initial air intake regulation strategy;

[0012] During the vaporizer startup process, the temperature changes in the cold section of the vaporizer and the stability index of the outlet gas are monitored in real time. Based on the monitoring results, the heat source input and air intake intensity are dynamically adjusted until the vaporization state reaches a stable state.

[0013] After the vaporizer enters a stable operating state, the thermal inertia state of the vaporizer cold section, the corresponding control strategy execution results, and the stability monitoring information of the outlet gas are recorded and stored for subsequent strategy optimization and control updates.

[0014] Preferably, when the cold section area of the evaporator is in a thermal inertia state, the cold section heat exchange state information is obtained by a sensor network arranged in the cold section area of the evaporator, and analyzed after the acquisition to determine the thermal inertia level of the cold section area of the evaporator, specifically including the following steps:

[0015] When the cold section of the evaporator is in a thermally inert state, the cold section heat exchange state information is obtained through a sensor network deployed in the cold section of the evaporator and preprocessed after acquisition;

[0016] The cold segment thermal response trend information and the cold segment heat flux fluctuation information are extracted from the preprocessed cold segment heat exchange state information, and analyzed to generate the thermal response delay index and the unit heat flux fluctuation coefficient respectively;

[0017] A thermal inertia evaluation model is constructed based on the generated thermal response delay index and unit heat flux fluctuation coefficient, and a thermal inertia index is generated through weighted summation.

[0018] Determine the pre-set thermal inertia index threshold range, and compare it with the generated thermal inertia index after determination. According to the comparison results, evaluate the thermal inertia level of the vaporizer cold section area and divide it into low thermal inertia level, medium thermal inertia level and high thermal inertia level.

[0019] Preferably, the logic for obtaining the thermal response delay index is as follows:

[0020] The cold section thermal response trend information is extracted from the pre-processed cold section heat exchange state information, including the average temperature value of the cold section of the vaporizer at different times over a period of time, the average temperature value of the surrounding environment, and the temperature value of the heat source contact end, and calibrated as TC m TE m and TH m , TC m It represents the average temperature of the cold section of the vaporizer at time m within a period of time, TE m Indicates the average temperature of the surrounding environment in the cold section of the vaporizer at time m within a period of time, TH m represents the temperature of the heat source contact end of the cold section of the vaporizer at time m within a period of time, where m = 1, 2, 3, ..., k, and k is a positive integer;

[0021] The exponential decay model of temperature change is used for fitting to obtain the temperature response delay factor of the cold section of the vaporizer at different times within a period of time. The specific calculation formula is:

[0022]

[0023] Where, α m is the temperature response delay factor of the cold section of the vaporizer at time m within a period of time;

[0024] Calculate the thermal response delay index. The specific calculation formula is as follows:

[0025]

[0026] Where TDRI is the thermal response delay index and ∈ is a small constant to prevent division by zero.

[0027] Preferably, the logic for obtaining the unit heat flux fluctuation coefficient is as follows:

[0028] The cold section heat flux fluctuation information is extracted from the preprocessed cold section heat exchange state information, including the heat flux density value, cold section surface average temperature value and heat source input power value of the cold section of the vaporizer at different times within a period of time, and calibrated as Q m 、TBC m and PH m , Q m Indicates the heat flux density value of the cold section of the vaporizer at time m within a period of time, TBC m Indicates the average surface temperature of the cold section of the vaporizer at time m within a period of time, PH m represents the heat source input power value of the cold section of the evaporator at time m within a period of time, m = 1, 2, 3, ..., k, where k is a positive integer;

[0029] Calculate the unit heat flux fluctuation coefficient. The specific calculation formula is as follows:

[0030]

[0031] Where QFVC is the unit heat flux fluctuation coefficient, and δ is a small constant to prevent division by zero.

[0032] Preferably, a thermal inertia evaluation model is constructed for the generated thermal response delay index TDRI and unit heat flux fluctuation coefficient QFVC, and a thermal inertia index is generated by weighted summation. The specific calculation formula is as follows:

[0033] TII=ω1*TDRI+ω2*QFVC

[0034] Where TII is the thermal inertia index, ω1 and ω2 are the non-zero weight coefficients of the thermal response delay index TDRI and the unit heat flux fluctuation coefficient QFVC, respectively, and ω1+ω2=1.

[0035] Preferably, a predetermined thermal inertia index threshold interval [TII min ,TII max ], and after determination, it is compared with the generated thermal inertia index TII. According to the comparison results, the thermal inertia level of the cold section of the vaporizer is evaluated and divided into low thermal inertia level, medium thermal inertia level and high thermal inertia level. The specific comparison analysis is as follows:

[0036] If TII <TII min , the thermal inertia level of the cold section of the vaporizer is low thermal inertia level;

[0037] If TII min ≤TII≤TII max , the thermal inertia level of the cold section of the vaporizer is medium thermal inertia level;

[0038] If TII>TII max , the thermal inertia level of the cold section area of the vaporizer is high thermal inertia level.

[0039] Preferably, based on the determined thermal inertia level, corresponding heat source preload control strategy and initial air intake control strategy are executed, specifically:

[0040] When the thermal inertia level is low, the heat source preload control strategy and initial air intake regulation strategy are as follows: the heat source is immediately turned on and quickly increased to the target heating power, so that the heat source output quickly responds to the intake load. Simultaneously, the vehicle air supply process is immediately started after the heat source is turned on, and the initial air intake intensity is set to a high level to achieve rapid gasification and efficient air supply.

[0041] When the thermal inertia level is medium, the heat source preloading control strategy and initial air intake control strategy are as follows: gradually load the heat source at medium power and maintain a stable heat input rate at the initial startup to prevent hot and cold shocks. Simultaneously, the air intake process is delayed, and the initial air intake intensity is set to a medium level. This is dynamically adjusted based on the actual temperature rise rate to ensure a stable heat exchange process.

[0042] When the thermal inertia level is high, the heat source preloading control strategy and initial air intake regulation strategy implemented are as follows: start the heat source in advance and load heat in stages in a slow-rising manner during the long preheating phase to ensure a uniform temperature rise in the cold section structure; at the same time, air intake is allowed only after the first stage of heat loading is completed, and the initial air intake intensity is set to a low level to avoid insufficient vaporization and gas-liquid coexistence due to excessively rapid air intake.

[0043] Preferably, during the vaporizer startup process, the temperature change of the vaporizer cold section area and the stability index of the outlet gas are monitored in real time, specifically:

[0044] Using temperature sensors placed at different locations in the cold section of the vaporizer, the temperature values of the cold section area at different times are continuously acquired according to the set sampling period. The temperature change rate within the continuous sampling period is calculated to determine the temperature rise trend of the cold section area.

[0045] A sensor device located at the outlet of the vaporizer monitors the stability indicators of the outlet gas in real time, including the temperature variation range, pressure fluctuation frequency, and the presence of liquid phase signals of the outlet gas;

[0046] If the temperature change rate is lower than the set temperature rise rate threshold in consecutive sampling periods, the temperature fluctuation range of the outlet gas is greater than the set fluctuation threshold, and the liquid phase signal is continuously detected, the current gasification state is determined to be unstable.

[0047] Preferably, when the gasification state is determined to be unstable, a dynamic adjustment strategy of heat source input and air intake intensity is executed according to the monitoring results, specifically:

[0048] When the temperature rise rate in the cold section continues to be low and the outlet gas temperature is lower than the lower limit of the temperature required for stable operation, increase the heat source input power to the next heating level and maintain this state until the temperature rise trend returns to normal;

[0049] When the outlet pressure fluctuation frequency exceeds the set fluctuation frequency threshold and the outlet gas temperature continues to rise, the intake valve opening is reduced to the next level of control to reduce the gas entry rate;

[0050] When the liquid phase signal is continuously detected and the pressure fluctuates frequently, the current heat source power is kept unchanged and the initial air intake intensity is reduced, and the subsequent air intake rhythm is delayed until the liquid phase signal disappears;

[0051] During the continuous sampling period, when the temperature rise rate in the cold section area is higher than the temperature rise rate threshold, the outlet gas temperature fluctuation range is within the allowable range, and no liquid phase signal is detected, it is determined that the gasification state has reached stability, dynamic adjustment is stopped, and the normal gas supply mode is entered.

[0052] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0053] 1. The present invention collects cold section temperature information, ambient temperature information, and shutdown duration information before the vaporizer is started, and combines the thermal response delay index and the unit heat flux fluctuation coefficient to quantitatively evaluate the thermal inertia state, so that the system can identify the heat conduction lag characteristics of the cold section area in advance at the beginning of the cold start, thereby dividing it into different thermal inertia levels. Based on this level, differentiated heat source preloading and air intake control strategies are implemented, which can effectively avoid blindly starting normal air supply when heat conduction has not yet been established, and significantly improve the thermal response efficiency and preheating adaptability of the cold section. This mechanism solves the risk of gas-liquid mixing caused by direct contact between heat source loading and low-temperature gas in the existing technology, and realizes rapid, safe, and controllable temperature rise under cold start conditions.

[0054] 2. During the carburetor startup process, the present invention jointly constructs an outlet gas stability index system through multi-point temperature sensing, pressure monitoring, and phase state detection, capturing key state parameters such as temperature change rate, pressure fluctuation frequency, and liquid phase signal in real time. After identifying unstable states such as temperature rise stagnation, outlet disturbance, and liquid phase output, the system automatically executes a refined adjustment strategy for heat source power and intake intensity based on a multi-condition trigger mechanism, effectively achieving dynamic closed-loop control. This type of response mechanism can intelligently intervene in the early stages of the system's deviation from thermal stability, preventing risks such as liquid hammer, gas fluctuations, and abnormal boost pressure, significantly improving the steady-state recovery speed and operational continuity of the gas supply system under complex startup conditions.

[0055] 3. After the vaporizer enters a stable operating state, this technical solution further records and structures the thermal inertia level, execution strategy parameters, and outlet gas stability data during the startup process, establishing an operating data archiving and control strategy backtracking mechanism. Through subsequent data analysis and model updates, continuous optimization and adaptive parameter adjustment of the control strategy can be achieved, thereby gradually improving gas supply efficiency and energy utilization over multiple operations. This closed-loop control and learning capability, which integrates monitoring, execution, feedback, and optimization, gives the system the potential for evolutionary and intelligent development, and can adapt to diverse gas supply scenarios under different downtime durations, environmental conditions, and load requirements in the long term. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0057] Figure 1 This is a schematic flow chart of an efficient gas supply method for gasifying and pressurizing recovered LNG to form CNG according to the present invention;

[0058] Figure 2This is a method mind map for the efficient gas supply method of gasifying and pressurizing LNG recovered gas to form CNG according to the present invention. DETAILED DESCRIPTION

[0059] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0060] The present invention provides Figure 1 and Figure 2 The high-efficiency gas supply method for gasifying and pressurizing recovered LNG to form CNG specifically includes the following steps:

[0061] Before the carburetor is started, the temperature information of the cold section of the carburetor, the ambient temperature information and the shutdown duration information are collected, and based on the collected information, it is determined whether the cold section of the carburetor is in a thermal inertia state;

[0062] Before the vaporizer is started, the wall temperature and the surface temperature of the internal heat exchange structure in the cold section of the vaporizer (i.e., near the LNG recovery gas inlet area) can be collected in real time through a temperature sensor network deployed in the cold section of the vaporizer to form the temperature information of the cold section. At the same time, the current atmospheric temperature, humidity and other environmental temperature information are obtained by using external environmental monitoring nodes. As for the downtime information, the time interval between the last shutdown and the current startup instruction can be automatically read through the operation status recording module integrated in the software system. The above information can be centrally scheduled and collected by the software system before the vaporizer is officially put into operation, and uploaded to the database in the main control module to form a data set for thermal inertia identification.

[0063] After acquiring temperature, environmental, and downtime information, the system uses a built-in thermal inertia judgment logic module to determine whether the cold segment is experiencing thermal inertia. This judgment logic can be based on a set of predefined rule models. For example, if a combination of these conditions is met, such as a cold segment temperature below a certain threshold, a significant temperature difference between the cold segment and the heat source, or a downtime exceeding a set period, the software will determine that the cold segment has significant thermal inertia. Furthermore, this judgment logic can be combined with historical operating condition characteristics to improve judgment accuracy through a simple rule-based inference engine or empirical curve mapping algorithm. The judgment result is transmitted to the subsequent control decision module in the form of a "thermal inertia status: yes / no" response.

[0064] When a vaporizer is restarted after a long shutdown, its cold section is prone to a state of heat conduction lag due to factors such as long periods of lack of heat, large structural volume, and high thermal inertia. If conventional load gas is directly introduced at this time, the cold section will have difficulty completing rapid vaporization because effective heat exchange has not yet been established. This may cause gas-liquid mixing or even liquid phase direct flow in the initial stage of the LNG recovery gas, leading to serious problems such as unstable outlet pressure and compressor liquid hammer. Therefore, proactively identifying whether the cold section of the vaporizer is in a state of thermal inertia before the system is started can enable subsequent software logic to initiate heat source preloading or current limiting strategies in advance, effectively reducing the risk of instability during the vaporizer's initial stage and improving the safety and response efficiency of the entire system. This predictive mechanism relies entirely on data-driven, judgment modeling, and software control, which not only meets real-time requirements but also has a high degree of automation. It is the prerequisite for ensuring the accuracy of subsequent control strategies.

[0065] When the cold section of the evaporator is in a thermally inert state, the sensor network deployed in the cold section of the evaporator obtains the cold section heat exchange status information, and analyzes it after acquisition to determine the thermal inertia level of the cold section of the evaporator;

[0066] In this embodiment, when the cold segment of the evaporator is in a thermally inert state, a sensor network deployed in the cold segment of the evaporator is used to obtain cold segment heat exchange status information. After the information is obtained, analysis is performed to determine the thermal inertia level of the cold segment of the evaporator. Specifically, the following steps are included:

[0067] When the cold section of the evaporator is in a thermally inert state, the cold section heat exchange state information is obtained through a sensor network deployed in the cold section of the evaporator and preprocessed after acquisition;

[0068] When the cold-end of the evaporator is identified as being in a thermally inert state, the system collects heat exchange-related physical quantities in real time through a multi-point sensor network deployed in the cold-end to construct cold-end heat exchange status information. This sensor network typically includes temperature sensors and heat flux sensors placed on the cold-end's outer wall and internal heat transfer contact surfaces, as well as power detection nodes connected to the heat source. These sensors continuously collect temperature data, heat flux data, and heat source input power data from the cold-end at different time points within a set time interval, and package and transmit this data in a time series structure. The software system integrates this multi-source heterogeneous data structure based on the sensor's network address, location information, and time tag, ultimately generating a set of cold-end heat exchange status information with a clear time series, spatial location, and data field identifiers, providing a data foundation for subsequent evaluation and calculation.

[0069] The purpose of preprocessing the cold section heat exchange state information is to improve the accuracy of data analysis, eliminate invalid or abnormal data interference, and normalize the original physical quantities into a standard format that can be used for modeling. First, the system performs integrity verification on the collected time series data such as temperature, heat flux density and heat source power, identifies and eliminates invalid data caused by missing fields or sensor failures; secondly, multi-source data is time synchronized to unify the sampling period and time series nodes; thirdly, for high-frequency anomalies caused by signal fluctuations or interference, sliding mean filtering or bilateral median smoothing algorithm is used for denoising; finally, data of different dimensions and units are normalized to a standard dimension range (such as 0 to 1) for subsequent exponential or logarithmic operations. All of the above preprocessing operations can be automatically performed by the data processing module deployed in the system in software without manual intervention, ensuring that the data structure received by the subsequent thermal inertia assessment model is stable, the values are reasonable, and the features are clear.

[0070] The cold segment thermal response trend information and the cold segment heat flux fluctuation information are extracted from the preprocessed cold segment heat exchange state information, and analyzed to generate the thermal response delay index and the unit heat flux fluctuation coefficient respectively;

[0071] Extracting cold-segment thermal response trend information and cold-segment heat flux fluctuation information from preprocessed cold-segment heat exchange status information can be achieved in software using a built-in data feature extraction module. This process first automatically categorizes the time-series data related to cold-segment surface temperature, ambient temperature, and heat source temperature in the preprocessed information based on data field labels to construct cold-segment thermal response trend information. Simultaneously, the time-series data of heat flux density, cold-segment temperature, and heat source input power are combined to extract cold-segment heat flux fluctuation information. Using a pre-set mathematical structure recognition template, the system applies a logarithmic curve fitting algorithm to the temperature-related data series to extract the time-dependent temperature decay trend. Normalized fluctuation analysis is applied to the heat flux-related data series to calculate the relative rate of change and fluctuation amplitude of the heat flux per unit heat input. These processes are automated through the software's pre-defined data path mapping logic, mathematical function library, and data labeling rules, eliminating the need for manual intervention. This ensures that the two types of extracted information are independent, time-series, and modelable, facilitating subsequent use in index generation and thermal inertia rating assessment.

[0072] A thermal inertia evaluation model is constructed based on the generated thermal response delay index and unit heat flux fluctuation coefficient, and a thermal inertia index is generated through weighted summation.

[0073] Determine the pre-set thermal inertia index threshold range, and compare it with the generated thermal inertia index after determination. According to the comparison results, evaluate the thermal inertia level of the vaporizer cold section area and divide it into low thermal inertia level, medium thermal inertia level and high thermal inertia level.

[0074] The pre-set thermal inertia index threshold range can be automatically determined by software by combining statistical analysis of historical operating data with expert experience rule modeling. The system first calls a large amount of historical cold section heat exchange status information and corresponding thermal inertia level assessment results stored in the database, extracts the thermal inertia index values generated under typical operating conditions known to be low, medium, and high thermal inertia levels, and performs cluster analysis or density estimation analysis on these thermal inertia indices to identify representative numerical distribution intervals. Subsequently, the system divides the thermal inertia index distribution into three level segments by setting a confidence interval or quantile strategy. For example, below the 33rd percentile is the low thermal inertia interval, 33 to 67 percentile is the medium thermal inertia interval, and above the 67th percentile is the high thermal inertia interval. In addition, based on a preset rule model, the system allows engineering technicians to fine-tune the threshold range or set upper and lower limits according to actual needs, forming a set of thermal inertia index threshold setting logic that is data-driven, rule-controllable, and structurally stable. The entire process is executed by the evaluation strategy configuration module in the software platform, which supports automatic loading and dynamic adjustment to adapt to the accuracy requirements under different working conditions.

[0075] In this embodiment, the logic for obtaining the thermal response delay index is as follows:

[0076] The cold section thermal response trend information is extracted from the pre-processed cold section heat exchange state information, including the average temperature value of the cold section of the vaporizer at different times over a period of time, the average temperature value of the surrounding environment, and the temperature value of the heat source contact end, and calibrated as TC m TE m and TH m , TC m It represents the average temperature of the cold section of the vaporizer at time m within a period of time, TE m Indicates the average temperature of the surrounding environment in the cold section of the vaporizer at time m within a period of time, TH m represents the temperature of the heat source contact end of the cold section of the vaporizer at time m within a period of time, where m = 1, 2, 3, ..., k, and k is a positive integer;

[0077] In order to obtain the average temperature value of the cold section area of the vaporizer at different times over a period of time, the average temperature value of the surrounding environment, and the temperature value of the heat source contact end in real time, this can be achieved by deploying a high-precision temperature sensor network at key positions of the vaporizer in conjunction with the data scheduling module of the software system. Specifically, multiple temperature sensors are evenly arranged on the outer wall and internal structure surface of the cold section area to collect the instantaneous temperature of different parts of the area at various time points. The system summarizes the temperature data of these points within the set sampling period and calculates the average value to obtain the average temperature of the cold section area at each moment; at the same time, an ambient temperature sensor is set at a suitable position outside the cold section to collect the temperature of the surrounding atmosphere or structural medium. The system automatically performs a sliding average of such data over a period of time to obtain the average ambient temperature at each moment; in addition, temperature sensors are also arranged in the heat conduction path or heat exchange pipe near the heat source contact interface to collect temperature changes at the heat source end. All sensor data is uploaded to the software platform via a real-time communication interface. The software system uses mechanisms such as data timestamp matching, spatial identifier binding, and sampling frequency coordination to time-align and logically categorize data from different sources. Ultimately, it generates a dataset containing the average temperature data in three dimensions at each time point. This dataset serves as the basic input for subsequent thermal response trend information extraction and latency index calculation. This entire process requires no human intervention and is automatically completed by the software scheduling and data processing modules, ensuring the continuity, accuracy, and structural consistency of data collection.

[0078] The exponential decay model of temperature change is used for fitting to obtain the temperature response delay factor of the cold section of the vaporizer at different times within a period of time. The specific calculation formula is:

[0079]

[0080] Where, α m is the temperature response delay factor of the cold section of the vaporizer at time m within a period of time;

[0081] The exponential decay model of temperature change describes the natural process by which temperature gradually approaches a stable value over time. In a thermal system, when a relatively low-temperature region (such as the cold leg of a carburetor) is heated by a heat source, its temperature does not immediately rise to the target temperature. Instead, it gradually increases, first rapidly and then slowly. This trend conforms to the characteristics of the "exponential decay" model. "Fitting the exponential decay model of temperature change" involves fitting temperature data collected at different times during actual operation of the cold leg region into this temperature trend model. Through mathematical fitting, the temperature variation patterns are analyzed and used to determine whether the temperature rise is slow or lagging. During the fitting process, the system generates a metric to measure the temperature response speed, called the "temperature response delay factor." This factor represents the cold leg region's sensitivity to changes in the heat source temperature. A larger delay factor indicates a slower temperature rise in that region, meaning it is less responsive to heat and exhibits greater thermal inertia. Conversely, a smaller delay factor indicates a faster temperature response and less thermal inertia. In this way, the thermal response characteristics of the cold section of the vaporizer can be quantified more accurately, providing a basis for subsequent intelligent control strategies.

[0082] Calculate the thermal response delay index. The specific calculation formula is as follows:

[0083]

[0084] Where TDRI is the thermal response delay index and ∈ is a small constant to prevent division by zero.

[0085] The calculation of the thermal response delay index involves operations such as the inverse and logarithm of the temperature response delay factor. If the delay factor at a given moment is close to or equal to zero, this can lead to mathematical anomalies such as zero divisor or undefined logarithm, compromising the stability of the calculation and the reliability of the evaluation results. To prevent this, a small constant is introduced into the calculation formula to prevent zero divisor. The value of this small constant is not manually set but dynamically determined by the software system based on historical operating data. Specifically, the system uses the data management module to access the distribution of delay factor values under known normal operating conditions from a historical cold-segment temperature fitting sample. These values are statistically analyzed to identify non-zero valid values and calculate their quantile intervals, typically taking the smallest non-zero value below the 95th percentile. This constant is then scaled with an empirical correction factor to obtain a small constant that prevents zero divisor and does not materially interfere with the overall evaluation logic. This process is automatically performed by the data processing and fault-tolerance mechanism module in the software, eliminating the need for manual intervention. This ensures that the calculation of the thermal response delay index maintains mathematical continuity and physical plausibility even for extremely small or marginal values.

[0086] The reason for calculating the thermal response delay index by extracting temperature data and applying an exponential decay model is that this process accurately reflects the dynamic response capability of the evaporator's cold-end region during heating. Specifically, an exponential decay model of temperature variation is first fitted to capture the true trend of how the cold-end temperature gradually approaches the heat source temperature over time. This trend conforms to the natural variation in physical heat conduction. During the fitting process, a value representing the temperature response speed at each time point is inferred based on the temperature performance at that point. This value, known as the temperature response delay factor, reflects the time required for the cold-end region's temperature to reach stability. These temperature response delay factors at different times are then combined and weighted averaged through a series of logarithmic operations to eliminate the interference of individual outliers and enhance the statistical characteristics of the overall temperature response lag. This results in a comprehensive assessment value, the thermal response delay index. A larger index indicates a slower overall temperature rise and greater thermal inertia in the cold-end region, while a smaller index indicates a faster response. Through this layer-by-layer calculation, not only can the dynamic process of thermal response be truly restored, but it also provides a highly quantitative criterion basis for the system to intelligently identify thermal inertia states, ensuring that subsequent control strategies are accurate and targeted.

[0087] The Thermal Response Delay Index (TDRI) is a comprehensive parameter used to assess the temperature response speed of the cold-end section of a carburetor. Its value directly reflects the degree of sluggishness of this section during heating, in other words, the strength of its thermal inertia. This index is calculated by normalizing the temperature response delay factors at multiple moments, performing a logarithmic operation, and taking the average. Therefore, it reflects the average lag in temperature changes in the cold-end section over the entire operating cycle. A large TDRI indicates a generally slow temperature response at all times. In other words, the cold-end section takes a long time to gradually heat up after being heated by the heat source. This indicates slow heat conduction through the structure and high thermal inertia. Conversely, a small TDRI indicates a rapid response to the heat source, rapid temperature rise, and high heat conduction efficiency, indicating low thermal inertia. Therefore, a larger TDRI value indicates a higher thermal inertia rating, while a smaller value indicates a lower thermal inertia rating. This quantitative relationship provides a reliable basis for intelligent identification of cold-end thermal conditions and the classification of control strategies.

[0088] In this embodiment, the logic for obtaining the unit heat flux fluctuation coefficient is as follows:

[0089] The cold section heat flux fluctuation information is extracted from the preprocessed cold section heat exchange state information, including the heat flux density value, cold section surface average temperature value and heat source input power value of the cold section of the vaporizer at different times within a period of time, and calibrated as Q m 、TBC m and PH m, Q m Indicates the heat flux density value of the cold section of the vaporizer at time m within a period of time, TBC m Indicates the average surface temperature of the cold section of the vaporizer at time m within a period of time, PH m represents the heat source input power value of the cold section of the evaporator at time m within a period of time, m = 1, 2, 3, ..., k, where k is a positive integer;

[0090] To obtain real-time heat flux, average surface temperature, and heat source input power values in the cold-segment region of the vaporizer at different times over a period of time, a high-precision sensor network can be deployed in the cold-segment region. Specifically, multiple heat flux sensors are deployed in the cold-segment region to collect real-time heat flux data (i.e., heat transfer rate). These sensors can be installed on the inner and outer surfaces of the cold segment to monitor heat flow in real time. Second, surface temperature data can be collected using an array of surface-mounted temperature sensors. Software processes and averages the data from each sensor to obtain the real-time average surface temperature. Finally, heat source input power can be measured using a power meter or current sensor installed at the heat source end, providing real-time heat source input power. All of this data is transmitted to a central data processing system. The software integrates, synchronizes, and calibrates the data based on timestamps and sensor identifiers, ensuring temporal and spatial alignment of the collected data, thereby generating accurate real-time heat flux, temperature, and power data. This data is automatically captured by the data acquisition module and fed into subsequent analysis and evaluation models for further calculations and control decisions.

[0091] Calculate the unit heat flux fluctuation coefficient. The specific calculation formula is as follows:

[0092]

[0093] Where QFVC is the unit heat flux fluctuation coefficient, and δ is a small constant to prevent division by zero.

[0094] In the calculation of the unit heat flux fluctuation coefficient, the small constant δ, which prevents division by zero, is used to prevent mathematical anomalies such as division by zero during the calculation process, ensuring the stability and computability of the formula. To determine the value of δ, statistical analysis can be performed using historical operating data. Specifically, the system first extracts relevant information about the cold-segment heat exchange state from historical operating data, specifically the minimum values of heat flux density, cold-segment surface temperature, and heat source input power. Within this data distribution, the smallest non-zero effective value is found and, based on this value, an appropriate proportional correction factor (e.g., 0.5 to 0.8) is set to determine the value of δ. In this way, the value of δ reflects the minimum effective value of physical quantities such as heat flux, temperature, or power under actual operating conditions, thereby avoiding the problem of division by zero in extreme cases. This process is automatically completed by the data processing module in the software, requiring no human intervention, ensuring numerical stability and consistency in each calculation and ensuring that the calculated results of the heat flux fluctuation coefficient are reasonable and unambiguous.

[0095] The calculation of unit heat flux fluctuation coefficient QFVC aims to quantify the fluctuation and instability of heat flux transfer in the cold section of the vaporizer. First, the heat flux density (Q m ), average surface temperature of cold section (TBC m ) and heat source input power (PH m ) and introduce a small constant δ to prevent division by zero, and get their ratio This ratio reflects the relative intensity of the change in heat flux relative to the cold segment temperature and heat source power. In this way, the heat flux fluctuations at different time points can be normalized so that the fluctuations can be compared with the changes in cold segment temperature and heat source input power. Next, the squares of these ratios are calculated, and the results of all time points are summed and averaged, and then the square root is taken to obtain the standard deviation of the fluctuations. To amplify the impact of heat flux fluctuations, the standard deviation is weighted using an exponential function, giving moments of greater fluctuation a higher weight and increasing their influence. Finally, by subtracting 1, the result is zero in the absence of fluctuations. This series of steps enables QFVC to accurately reflect the degree of heat flux fluctuation in the cold section of the vaporizer under varying heat sources, further revealing the stability or instability of the system during heat transfer and providing a reliable basis for subsequent thermal inertia assessments.

[0096] The unit heat flux fluctuation coefficient (QFVC) directly correlates with the thermal inertia level of the evaporator cold-end region. QFVC measures the volatility and instability of heat flux transfer in the cold-end region. Larger QFVC values indicate more dramatic variations in heat flux relative to temperature and heat source power, resulting in more unstable heat flux transfer in the system. This indicates a slower response to heat source changes in the cold-end region and greater thermal inertia. Conversely, smaller QFVC values indicate less heat flux fluctuation, more stable system heat transfer, and a faster response to heat source changes in the cold-end region, resulting in less thermal inertia. Therefore, larger QFVC values indicate higher thermal inertia in the evaporator cold-end region, while smaller values indicate lower thermal inertia. This quantified degree of fluctuation enables more accurate thermal inertia assessment. By comparing the value with a preset threshold, the cold-end region can be classified as low, medium, or high thermal inertia, thereby guiding appropriate control strategies.

[0097] In this embodiment, a thermal inertia evaluation model is constructed based on the generated thermal response delay index TDRI and unit heat flux fluctuation coefficient QFVC, and the thermal inertia index is generated by weighted summation. The specific calculation formula is as follows:

[0098] TII=ω1*TDRI+ω2*QFVC

[0099] Where TII is the thermal inertia index, ω1 and ω2 are the non-zero weight coefficients of the thermal response delay index TDRI and the unit heat flux fluctuation coefficient QFVC, respectively, and ω1+ω2=1.

[0100] In the thermal inertia assessment model, the thermal response delay index (TDRI) and the unit heat flux fluctuation coefficient (QFVC) are weighted and summed to generate the thermal inertia index (TII). In implementation, real-time temperature, heat flux density, and heat source power data from the cold segment are acquired via a sensor network. After preprocessing, TDRI and QFVC are calculated. These two indicators reflect the thermal response speed and heat flux transfer stability of the cold segment. These two parameters are then combined using a weighted summation method to generate the thermal inertia index (TII). ω1 and ω2 are weight coefficients, indicating the importance of each parameter in the overall assessment. To ensure model stability and rationality, ω1 and ω2 must satisfy ω1 + ω2 = 1, meaning their sum is 1, and both must be nonzero. The determination of these two weight coefficients typically relies on historical data analysis or engineering experience. By adjusting these two coefficients, the thermal inertia assessment requirements of different cold segments can be tailored to the specific application scenarios. The selection of these weight coefficients can be adjusted through optimization algorithms or empirical experience to ensure that the calculated results accurately reflect the system's heat flux fluctuations and temperature response characteristics, thus providing a basis for subsequent control strategies.

[0101] In this embodiment, the predetermined thermal inertia index threshold interval [TII min ,TII max ], and after determination, it is compared with the generated thermal inertia index TII. According to the comparison results, the thermal inertia level of the cold section of the vaporizer is evaluated and divided into low thermal inertia level, medium thermal inertia level and high thermal inertia level. The specific comparison analysis is as follows:

[0102] If TII <TII min , the thermal inertia level of the cold section of the vaporizer is low thermal inertia level;

[0103] This situation indicates that the heat transfer process in the cold section of the evaporator is very rapid, the temperature response is very sensitive to changes in the heat source, and the system's heat flux transfer is relatively stable. In this case, the cold section can quickly absorb and release heat, and the system has low thermal inertia during operation. In this case, the impact is that the cold section can respond to temperature changes more quickly, thereby reducing heat accumulation and delays. This makes the system's thermal control more efficient, suitable for conditions requiring rapid thermal adjustments and large temperature changes in a short period of time. However, it may also lead to large temperature fluctuations, requiring a more sophisticated control system to avoid over-adjustments.

[0104] If TII min ≤TII≤TII max , the thermal inertia level of the cold section of the vaporizer is medium thermal inertia level;

[0105] This situation indicates a moderate level of thermal inertia in the cold section of the evaporator. Heat transfer in this cold section is neither extremely rapid nor extremely delayed, and the system's thermal response and heat conduction are in a moderate balance. In this case, the system is able to accumulate and release heat within a reasonable timeframe, making it suitable for operating conditions requiring a certain level of stability, such as ensuring good energy efficiency with minimal temperature fluctuations. The implication is that the system's heat transfer is relatively stable, but adjustment and optimization of the control strategy are still necessary to avoid excessive lag or overly rapid response to maintain stable system operation.

[0106] If TII>TII max , the thermal inertia level of the cold section area of the vaporizer is high thermal inertia level.

[0107] This situation indicates that the heat transfer process in the cold section of the evaporator is very slow, and the temperature response to changes in the heat source is very sluggish, resulting in high thermal inertia in the cold section. In this case, the system's heat transfer efficiency is low, and temperature changes are relatively delayed, which may lead to excessive heat accumulation or insufficient heat dissipation, thus affecting system efficiency. The impact of this situation is that the temperature in the cold section rises and falls relatively slowly, which may make the system unable to meet rapidly changing heat demands in a short period of time, increasing the system's response time to changes in the heat source. In this case, the system may need to implement more preheating or insulation control to ensure balanced heat transfer to avoid system efficiency loss or excessive temperature fluctuations.

[0108] Based on the determined thermal inertia level, executing a corresponding heat source preload control strategy and an initial air intake regulation strategy;

[0109] In this embodiment, based on the determined thermal inertia level, the corresponding heat source preload control strategy and initial air intake control strategy are executed, specifically:

[0110] When the thermal inertia level is low, the heat source preload control strategy and initial air intake regulation strategy are as follows: the heat source is immediately turned on and quickly increased to the target heating power, so that the heat source output quickly responds to the intake load. Simultaneously, the vehicle air supply process is immediately started after the heat source is turned on, and the initial air intake intensity is set to a high level to achieve rapid gasification and efficient air supply.

[0111] If the system determines, based on the evaluation results, that the cold-end region of the vaporizer has a low thermal inertia level, it demonstrates high heat transfer efficiency, rapid temperature rise, and quick response to heat source changes. To maximize the cold-end's rapid response, the software system invokes a pre-set high-response control strategy, initiating an "immediate activation + rapid ramp-up" mode in the heat source control module to quickly achieve the set target heating power. This process is controlled in real time by the heating system's power regulation unit, which uses a closed-loop power feedback signal to confirm stable output. Simultaneously, the intake control module immediately opens the intake valve train during the first control cycle after heat source activation and adjusts the initial intake flow rate to a pre-set high-load setting to rapidly maximize the vaporizer's heat exchange. This approach enables the vaporization and pressurization of recovered LNG gas to be completed in the shortest possible time, improving gas supply efficiency and making it suitable for applications requiring rapid response. The software system dynamically maintains a safe balance in rapid vaporization mode by collecting and integrating real-time information on heat source output, cold-end temperature feedback, and intake flow rate.

[0112] When the thermal inertia level is medium, the heat source preloading control strategy and initial air intake control strategy are as follows: gradually load the heat source at medium power and maintain a stable heat input rate at the initial startup to prevent hot and cold shocks. Simultaneously, the air intake process is delayed, and the initial air intake intensity is set to a medium level. This is dynamically adjusted based on the actual temperature rise rate to ensure a stable heat exchange process.

[0113] A system evaluation result of medium thermal inertia indicates that the cold-end of the carburetor responds moderately to the heat source, with some heat transfer lag, but not severe. To avoid fluctuations or thermal shock caused by a mismatch between heat input and intake air rate during initial startup, the software platform applies a medium-intensity heat source control command, ensuring a stable output at medium power and employing a slow-start strategy to prevent transient heat accumulation. The intake control module delays triggering, only initiating intake control after the temperature rise in the cold-end region reaches a set steady-state threshold, and sets the initial intake air intensity to a medium level. The system also continuously monitors the cold-end temperature rise rate and, based on the temperature gradient within a time window, dynamically fine-tunes the intake valve opening to achieve a thermodynamic balance between heat input and cold-end thermal response. This control strategy improves stability while maintaining a certain degree of response flexibility, making it suitable for medium-load operation scenarios where efficiency and smoothness are crucial. All logic is implemented collaboratively by the software's control logic engine and data closed-loop analysis module, ensuring real-time and effective thermal adaptation.

[0114] When the thermal inertia level is high, the heat source preloading control strategy and initial air intake regulation strategy implemented are as follows: start the heat source in advance and load heat in stages in a slow-rising manner during the long preheating phase to ensure a uniform temperature rise in the cold section structure; at the same time, air intake is allowed only after the first stage of heat loading is completed, and the initial air intake intensity is set to a low level to avoid insufficient vaporization and gas-liquid coexistence due to excessively rapid air intake.

[0115] When the carburetor cold section is assessed as having a high thermal inertia rating, it indicates a slow thermal response and significant heat capacity load and heat transfer lag within the structure. To prevent the risk of gas-liquid mixing and liquid hammering caused by insufficient heat source heating or excessively rapid air intake during the initial startup phase, the software system activates an "early ramp-up preheating" strategy. Before the startup process enters regular operation, the software control platform applies a staged, zoned load to the heat source output. The loading intensity and duration of each stage are dynamically calculated based on historical modeling data to ensure that heat energy is transferred to the deep cold section structure before entering the next stage. Simultaneously, the air intake control process is delayed until the first stage of thermal loading has fully completed, and the initial air intake intensity is set to a low level to prevent rapid gas inrush, which could lead to instantaneous saturation of the heat exchanger or inadequate heat exchange. By monitoring the slope of the cold section temperature rise curve and the gas outlet conditions, the system implements closed-loop regulation of the heat source power release rate and air intake opening to ensure a steady-state transition throughout the entire heating and air intake process. This strategy is particularly suitable for the first startup condition after frequent shutdowns or long-term heat outages. The software control module automatically completes judgment and execution through real-time model matching and strategy library calls, ensuring that the system enters normal operation safely and stably.

[0116] During the vaporizer startup process, the temperature changes in the cold section of the vaporizer and the stability index of the outlet gas are monitored in real time. Based on the monitoring results, the heat source input and air intake intensity are dynamically adjusted until the vaporization state reaches a stable state.

[0117] In this embodiment, during the vaporizer startup process, the temperature change in the vaporizer cold section and the stability index of the outlet gas are monitored in real time, specifically:

[0118] Using temperature sensors placed at different locations in the cold section of the vaporizer, the temperature values of the cold section area at different times are continuously acquired according to the set sampling period. The temperature change rate within the continuous sampling period is calculated to determine the temperature rise trend of the cold section area.

[0119] To accurately assess the temperature rise trend in the cold section of the vaporizer, multiple high-precision temperature sensors can be deployed at different locations in the cold section to form a sensing network covering the critical heat conduction paths in the cold section. Temperature data is collected at each measuring point at a fixed sampling interval. This is achieved by setting a sampling interval (e.g., once per second or every 5 seconds) and recording temperature data from all sensing points during each sampling period. The temperature values from the same measuring point over multiple consecutive sampling periods are then differentiated to obtain the time-temperature rate of change, or temperature rate of change. To ensure data stability and robustness, the system applies a sliding average filter or median processing to the data within each sampling window to eliminate transient outliers and simultaneously correct for temperature drift errors across each sensor. By comparing temperature trends at different measuring points, localized areas within the cold section with poor heating uniformity or slow response can be identified. This approach is necessary because the cold section is the starting point of the heat exchange response during startup, and its temperature rise rate directly reflects the mismatch between the heat source input and the structural conduction efficiency. If the temperature rises too slowly, it indicates heat transfer sluggishness or insufficient power. If the temperature rise is dramatic and uneven, it may cause localized overheating. Therefore, by collecting and analyzing the temperature change rate, we can not only dynamically determine heat transfer efficiency but also provide key information for subsequent heat source adjustments and air intake strategies. The entire process can be automated by integrating the temperature data acquisition module and data processing algorithms into the software system, enabling high-frequency, low-latency thermal response monitoring and analysis.

[0120] A sensor device located at the outlet of the vaporizer monitors the stability indicators of the outlet gas in real time, including the temperature variation range, pressure fluctuation frequency, and the presence of liquid phase signals of the outlet gas;

[0121] To achieve real-time monitoring of gas stability indicators at the vaporizer outlet, various types of sensor devices can be installed at key locations in the outlet pipeline to form an outlet status acquisition chain. Specifically, these include: deploying temperature sensors to continuously collect the instantaneous temperature of the outlet gas and calculating the maximum and minimum temperature values within a set sampling period to determine the temperature variation range; deploying high-sensitivity pressure sensors to collect real-time pressure changes over time, and combining them with time series analysis methods such as short-period Fourier transform or moving window fluctuation analysis to calculate the frequency and amplitude of pressure fluctuations per unit time to assess whether the gas flow is subject to unstable shocks; At the same time, optical or capacitive phase sensors are installed to monitor the presence of liquid entrainment in the gas flow. When droplets, atomized particles, or condensation signals are continuously identified, it is determined that a liquid phase signal is present. All sensor data is transmitted to the data processing module at a set frequency. The system uses a rule engine to automatically analyze the temperature curve fluctuation range, pressure change pattern, and phase identification signal to determine whether they meet the abnormality judgment criteria. This is necessary because the state of the outlet gas directly reflects the results of the vaporization process. Excessive temperature fluctuations, frequent pressure oscillations, or liquid entrainment at the outlet indicate not only an unstable internal heat exchange process but also potential liquid hammer damage to the downstream supercharger. By monitoring these stability indicators in real time and feeding them back into the control logic, timely corrections to the heat source power and intake rhythm can be made to ensure smooth system operation and prevent potential accidents. This monitoring mechanism, comprising data acquisition, feature extraction, and judgment algorithms, operates continuously within the software platform, requiring no human intervention and supporting high-frequency dynamic response.

[0122] If the temperature change rate is lower than the set temperature rise rate threshold in consecutive sampling periods, the temperature fluctuation range of the outlet gas is greater than the set fluctuation threshold, and the liquid phase signal is continuously detected, the current gasification state is determined to be unstable.

[0123] In order to determine whether the gasification state is unstable, the state assessment logic in the software platform can be used to perform combined judgment and linkage analysis on the three core monitoring indicators collected in real time. The specific method is as follows: First, in each sampling cycle, the system calculates the temperature change rate from multiple temperature sensors in the cold section, and compares the change rate for several consecutive cycles (such as 3 or 5) with the preset temperature rise rate threshold. If the change rate in all cycles is lower than the threshold, it is considered that the temperature rise in the cold section shows obvious lag; Second, the system calculates the difference between the maximum and minimum temperatures collected by the outlet gas temperature sensor to form a temperature fluctuation range, and accumulates and statistics whether the fluctuation amplitude exceeds the set threshold in each time window to identify the unstable characteristics of the gasification output; Third, the system continuously reads the feedback data of the phase state detection device at the outlet end to determine whether the liquid phase signal is detected in multiple consecutive sampling points. If the "continuity" condition is met, it is considered that liquid phase entrainment is continuing. Once all three judgment conditions are met, the software determines that the current state is unstable according to the preset logic and uses this result as the logic signal to trigger the control process. The goal is to establish a rigorous anomaly identification mechanism by simultaneously meeting the three hard indicators of "sluggish thermal response," "increased output fluctuations," and "abnormal liquid phase output," thereby preventing misjudgments and premature adjustments. This comprehensive assessment approach, based on cross-validation of multi-source data, not only improves the accuracy and robustness of vaporization state identification but also provides a clear and reliable basis for subsequent dynamic control. This process is automated within the software platform through a rule-based logic engine and data-triggered mechanisms, supporting full closed-loop control and high-frequency response.

[0124] In this embodiment, when the gasification state is determined to be unstable, a dynamic adjustment strategy for heat source input and air intake intensity is executed according to the monitoring results, specifically:

[0125] When the temperature rise rate in the cold section continues to be low and the outlet gas temperature is lower than the lower limit of the temperature required for stable operation, increase the heat source input power to the next heating level and maintain this state until the temperature rise trend returns to normal;

[0126] In order to achieve the goal of "raising the heat source input power to the previous heating level when the temperature rise rate in the cold section remains at a low rate and the outlet gas temperature is lower than the lower limit of the temperature required for stable operation," the corresponding control trigger mechanism can be built into the software control logic by real-time acquisition and analysis of the combined trend of the temperature change rate in the cold section and the outlet gas temperature value. Specifically, the system first continuously monitors the temperature change rate of each measuring point in the cold section within a set sampling period and compares this change rate with the set lower limit of the temperature rise rate. If it is lower than this rate lower limit for multiple consecutive periods, the cold section thermal response is determined to be in a lagging state. At the same time, the system collects the outlet gas temperature value in real time and compares it with the set minimum gasification output temperature. If the current outlet temperature is continuously lower than this lower limit, it indicates that the gasification efficiency is insufficient and the heat exchange capacity does not meet the requirements. When the above two conditions are met, the system will call the preset heat source power adjustment instruction, switch the current output power of the heat source to the previous level, and maintain this output state. During this process, the control logic will continue to track the trend changes of the cold section temperature rise. When the temperature change rate returns to normal levels within multiple subsequent cycles, the system will release the current power holding state. The significance of this control method is that through "double-criteria confirmation" (cold section temperature rise does not meet the standard + outlet temperature is low), it can effectively avoid unnecessary power adjustments caused by fluctuations in a single indicator, ensure accurate heating under the premise of insufficient thermal response, improve the overall thermal response capability of the vaporizer, speed up the system heating process, and lay a thermal foundation for subsequent air intake stability. The entire identification and adjustment process is coordinated by data collection, indicator judgment, and power adjustment control logic in the software platform, with full automation and real-time dynamic response capabilities.

[0127] When the outlet pressure fluctuation frequency exceeds the set fluctuation frequency threshold and the outlet gas temperature continues to rise, the intake valve opening is reduced to the next level of control to reduce the gas entry rate;

[0128] To achieve the goal of reducing the intake valve opening to the next control level when the outlet pressure fluctuation frequency exceeds a set frequency threshold and the outlet gas temperature continues to rise, a multi-cycle dynamic analysis of the outlet pressure and temperature signals is performed, combined with a trend recognition algorithm, to determine whether to trigger the intake air reduction control strategy. Specifically, the system first collects real-time pressure data from the outlet pressure sensor and performs fluctuation analysis on consecutive sampling points using a set sliding time window. For example, this analysis calculates the number of peak pressure changes per unit time to determine the actual fluctuation frequency. If this frequency exceeds the set frequency threshold for multiple consecutive time periods, persistent flow instability is identified. Simultaneously, the system analyzes the outlet gas temperature trend, using a continuous slope detection method to determine whether the temperature is monotonically increasing and has not experienced a decrease or plateau over multiple consecutive cycles. If both conditions are met—that is, the outlet pressure exhibits high-frequency fluctuations and the outlet temperature continues to rise—the system automatically invokes the intake control command, adjusting the intake valve opening to the next preset level, thereby reducing the gas intake rate. This is necessary because the simultaneous occurrence of high-frequency pressure fluctuations and rapid temperature increases often indicates a mismatch between the air intake rate and the heat supply from the heat source: gas flowing in too quickly without sufficient heat exchange, leading to gas-liquid intermixing and increased disturbances within the vaporizer. By moderately reducing the air intake, not only can pressure fluctuations at the outlet be mitigated, but the cold section can also be given more time for heat exchange, thereby restoring system stability. The entire identification and control logic is automatically implemented in the software through data trend detection, threshold judgment, and adjustment instructions, eliminating the need for human intervention and ensuring that the system can respond promptly and accurately to typical unstable states.

[0129] When the liquid phase signal is continuously detected and the pressure fluctuates frequently, the current heat source power is kept unchanged and the initial air intake intensity is reduced, and the subsequent air intake rhythm is delayed until the liquid phase signal disappears;

[0130] In order to achieve "when the liquid phase signal is continuously detected and the pressure fluctuates frequently, the current heat source power is kept unchanged and the initial intake intensity is reduced, and the subsequent intake rhythm is delayed", the phase detection and pressure fluctuation data at the outlet can be used to make continuous judgments and linkage responses to achieve refined intake control logic. The specific method is: the system first collects the phase change signal in real time through the phase recognition sensor (such as capacitive, optical or thermal conductivity probe) installed in the outlet gas channel. If the presence of liquid phase is identified within the set continuous sampling period, it is determined to be a "continuous liquid phase signal"; at the same time, the system analyzes the data sequence collected by the outlet pressure sensor, and calculates its frequency and fluctuation amplitude based on the pressure peak and valley values within the fixed window. If the set fluctuation frequency and amplitude dual thresholds are reached, it is determined to be in a "high fluctuation state". When both the "persistent presence of a liquid phase signal" and "frequent pressure fluctuations" conditions are met simultaneously, the system triggers control logic: The current heat source input power remains unchanged to avoid introducing new thermal instabilities due to heat source adjustments. The initial air intake intensity is immediately reduced to the next control level to reduce the instantaneous amount of gas entering the system. Furthermore, the air intake control cycle is adjusted to delay the next air intake command by a certain window, prolonging the heat exchange and vaporization time in the vaporizer, giving the liquid more opportunity to transform into a gas phase. This strategy aims to prevent premature liquid vaporization from entering the outlet, leading to liquid hammer, gas-liquid coexistence, or drastic gas phase fluctuations. By maintaining a stable heat source and controlling the delay and slowing of the air intake rhythm, the system effectively mitigates the stagnation caused by thermal response lag within the vaporizer, improving system stability. The entire judgment-trigger-execution process, comprised of a data acquisition module, a state recognition model, and a control execution module embedded in the software platform, enables fully automated, real-time response without the need for human intervention.

[0131] During the continuous sampling period, when the temperature rise rate in the cold section area is higher than the temperature rise rate threshold, the outlet gas temperature fluctuation range is within the allowable range, and no liquid phase signal is detected, it is determined that the gasification state has reached stability, dynamic adjustment is stopped, and the normal gas supply mode is entered.

[0132] To achieve the goal of "determining that the gasification state has stabilized, discontinuing dynamic adjustments, and entering normal gas supply mode when the temperature rise rate in the cold-segment area exceeds the temperature rise rate threshold within a continuous sampling cycle, the outlet gas temperature fluctuation range is within the allowable range, and no liquid phase signals are detected," a software platform performs periodic comprehensive analysis and state classification of key monitoring data, enabling automatic identification of gasification stability and control strategy switching. Specifically, the system performs time series analysis on data continuously collected by temperature sensors in the cold-segment area, calculates the temperature rise rate for each sampling cycle, and compares this with a set temperature rise rate threshold. If the temperature rise rate at all sampling points exceeds the threshold for multiple consecutive cycles, the heat conduction process is deemed stable and effective. Simultaneously, the system calculates the fluctuation range of the outlet temperature data, assessing the difference between the maximum and minimum values within each cycle and determining whether it remains within the allowable fluctuation range, reflecting the thermal stability of the gas output. Simultaneously, the system continuously reads the signal feedback from the phase state detection device to confirm that no liquid phase signals are detected within the same time window, eliminating the risk of gas-liquid coexistence. Only when the three independent judgment conditions—"stable temperature rise in the cold section," "controllable outlet fluctuations," and "no liquid phase signal"—are met simultaneously over multiple consecutive cycles will the system classify the current gasification state as "stable" and automatically issue a command to end the dynamic control process. Subsequently, the heat source power and air intake rhythm will be transferred to the default normal operating parameters to achieve a smooth transition of the gas supply system. The reason for adopting a multi-cycle, multi-dimensional judgment mechanism is to avoid misjudgment caused by short-term data fluctuations. The premise for entering the normal mode is to ensure that the cold section thermal response, outlet output, and gas purity all meet the system's preset stability standards, thereby ensuring the continuity, safety, and efficiency of the entire gasification, pressurization, and gas supply process. This stability identification and mode switching logic is driven by monitoring data and is completed automatically through the condition judgment module and control process management module embedded in the software platform. Without manual intervention, it ensures a fast and accurate response.

[0133] After the vaporizer enters a stable operating state, the thermal inertia state of the vaporizer cold section, the corresponding control strategy execution results, and the stability monitoring information of the outlet gas are recorded and stored for subsequent strategy optimization and control updates.

[0134] To achieve the goal of "recording and storing the thermal inertia state of the vaporizer cold-end region, the corresponding control strategy execution results, and outlet gas stability monitoring information after the vaporizer enters a stable operating state for subsequent strategy optimization and control updates," a structured historical operation data archiving mechanism can be established in the software platform to archive key operating data and control behaviors in a time-correlated manner. Specifically, when the system confirms that the vaporization state has reached stability based on the comprehensive judgment results of consecutive sampling cycles, the platform will automatically invoke data archiving instructions to categorize and record the core information from the current startup process. The first category is the thermal inertia state of the vaporizer cold-end region, including the generated thermal inertia index, thermal response delay index, unit heat flux fluctuation coefficient, and its corresponding thermal inertia level label. The second category is the control strategy invoked during execution, such as the heat source preload gear adjustment, the intake air control intensity curve, the start and end times and frequency of the control response, etc. The third category is the outlet gas stability indicator information, including multi-dimensional data such as temperature fluctuation range, pressure fluctuation frequency, and whether a liquid phase signal is detected. After collection, all data are uniformly packaged with timestamps and written into the specified operation log structure in the database. At the same time, the operation number and operating condition tags (such as ambient temperature, downtime and other background parameters) are attached to ensure that the data has traceability and semantic integrity. The reason for doing this is to provide real, high-quality data support for subsequent strategy optimization and control logic updates. The system can compare the control behavior and operating results under different thermal inertia levels through offline analysis or model training, so as to continuously adjust the strategy parameters, optimize the heat source and intake scheduling curve, and improve the system's adaptive control capabilities and operating efficiency. The entire archiving process is completed by the acquisition logic, structured data packager and storage interface, and runs in the data management module of the software platform to ensure efficient and stable support for the closed-loop optimization mechanism.

[0135] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0136] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0137] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0138] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0139] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0140] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0141] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0142] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. An efficient gas supply method for gasifying and pressurizing LNG recovered gas to form CNG, characterized in that: The specific steps include: Before the carburetor is started, the temperature information of the cold section of the carburetor, the ambient temperature information and the shutdown duration information are collected, and based on the collected information, it is determined whether the cold section of the carburetor is in a thermal inertia state; When the cold section of the evaporator is in a thermally inert state, the sensor network deployed in the cold section of the evaporator obtains the cold section heat exchange status information, and analyzes it after acquisition to determine the thermal inertia level of the cold section of the evaporator; Based on the determined thermal inertia level, executing a corresponding heat source preload control strategy and an initial air intake regulation strategy; During the vaporizer startup process, the temperature changes in the cold section of the vaporizer and the stability index of the outlet gas are monitored in real time. Based on the monitoring results, the heat source input and air intake intensity are dynamically adjusted until the vaporization state reaches a stable state. After the vaporizer enters a stable operating state, the thermal inertia state of the vaporizer cold section, the corresponding control strategy execution results, and the stability monitoring information of the outlet gas are recorded and stored for subsequent strategy optimization and control updates.

2. The efficient gas supply method for gasifying and pressurizing recovered LNG to form CNG according to claim 1 is characterized in that: When the cold section of the evaporator is in a thermally inert state, a sensor network deployed in the cold section of the evaporator is used to obtain the cold section heat exchange status information. After the information is obtained, it is analyzed to determine the thermal inertia level of the cold section of the evaporator. Specifically, the following steps are included: When the cold section of the evaporator is in a thermally inert state, the cold section heat exchange state information is obtained through a sensor network deployed in the cold section of the evaporator and preprocessed after acquisition; The cold segment thermal response trend information and the cold segment heat flux fluctuation information are extracted from the preprocessed cold segment heat exchange state information, and analyzed to generate the thermal response delay index and the unit heat flux fluctuation coefficient respectively; A thermal inertia evaluation model is constructed based on the generated thermal response delay index and unit heat flux fluctuation coefficient, and a thermal inertia index is generated through weighted summation. Determine the pre-set thermal inertia index threshold range, and compare it with the generated thermal inertia index after determination. According to the comparison results, evaluate the thermal inertia level of the vaporizer cold section area and divide it into low thermal inertia level, medium thermal inertia level and high thermal inertia level.

3. The efficient gas supply method for gasifying and pressurizing recovered LNG to form CNG according to claim 2, characterized in that: The logic for obtaining the thermal response delay index is as follows: The cold section thermal response trend information is extracted from the pre-processed cold section heat exchange state information, including the average temperature value of the cold section of the vaporizer at different times over a period of time, the average temperature value of the surrounding environment, and the temperature value of the heat source contact end, and calibrated as TC m TE m and TH m , TC m It represents the average temperature of the cold section of the vaporizer at time m within a period of time, TE m Indicates the average temperature of the surrounding environment in the cold section of the vaporizer at time m within a period of time, TH m represents the temperature of the heat source contact end of the cold section of the vaporizer at time m within a period of time, where m = 1, 2, 3, ..., k, and k is a positive integer; The exponential decay model of temperature change is used for fitting to obtain the temperature response delay factor of the cold section of the vaporizer at different times within a period of time. The specific calculation formula is: Where, α m is the temperature response delay factor of the cold section of the vaporizer at time m within a period of time; Calculate the thermal response delay index. The specific calculation formula is as follows: Where TDRI is the thermal response delay index and ∈ is a small constant to prevent division by zero.

4. The efficient gas supply method for gasifying and pressurizing recovered LNG to form CNG according to claim 3 is characterized in that: The logic for obtaining the unit heat flux fluctuation coefficient is as follows: The cold section heat flux fluctuation information is extracted from the preprocessed cold section heat exchange state information, including the heat flux density value, cold section surface average temperature value and heat source input power value of the cold section of the vaporizer at different times within a period of time, and calibrated as Q m 、TBC m and PH m , Q m Indicates the heat flux density value of the cold section of the vaporizer at time m within a period of time, TBC m Indicates the average surface temperature of the cold section of the vaporizer at time m within a period of time, PH m represents the heat source input power value of the cold section of the evaporator at time m within a period of time, m = 1, 2, 3, ..., k, where k is a positive integer; Calculate the unit heat flux fluctuation coefficient. The specific calculation formula is as follows: Where QFVC is the unit heat flux fluctuation coefficient, and δ is a small constant to prevent division by zero.

5. The efficient gas supply method for gasifying and pressurizing recovered LNG to form CNG according to claim 4 is characterized in that: A thermal inertia evaluation model is constructed based on the generated thermal response delay index TDRI and unit heat flux fluctuation coefficient QFVC. The thermal inertia index is generated by weighted summation. The specific calculation formula is as follows: TII=ω1*TDRI+ω2*QFVC Where TII is the thermal inertia index, ω1 and ω2 are the non-zero weight coefficients of the thermal response delay index TDRI and the unit heat flux fluctuation coefficient QFVC, respectively, and ω1+ω2=1.

6. The efficient gas supply method for gasifying and pressurizing recovered LNG to form CNG according to claim 5, characterized in that: Determine the preset thermal inertia index threshold range [TII min ,TII max ], and after determination, it is compared with the generated thermal inertia index TII. According to the comparison results, the thermal inertia level of the cold section of the vaporizer is evaluated and divided into low thermal inertia level, medium thermal inertia level and high thermal inertia level. The specific comparison analysis is as follows: If TII <TII min , the thermal inertia level of the cold section of the vaporizer is low thermal inertia level; If TII min ≤TII≤TII max , the thermal inertia level of the cold section of the vaporizer is medium thermal inertia level; If TII>TII max , the thermal inertia level of the cold section area of the vaporizer is high thermal inertia level.

7. The efficient gas supply method for gasifying and pressurizing recovered LNG to form CNG according to claim 6, characterized in that: Based on the determined thermal inertia level, the corresponding heat source preload control strategy and initial intake air control strategy are executed, specifically: When the thermal inertia level is low, the heat source preload control strategy and initial air intake regulation strategy are as follows: the heat source is immediately turned on and quickly increased to the target heating power, so that the heat source output quickly responds to the intake load. Simultaneously, the vehicle air supply process is immediately started after the heat source is turned on, and the initial air intake intensity is set to a high level to achieve rapid gasification and efficient air supply. When the thermal inertia level is medium, the heat source preloading control strategy and initial air intake control strategy are as follows: gradually load the heat source at medium power and maintain a stable heat input rate at the initial startup to prevent hot and cold shocks. Simultaneously, the air intake process is delayed, and the initial air intake intensity is set to a medium level. This is dynamically adjusted based on the actual temperature rise rate to ensure a stable heat exchange process. When the thermal inertia level is high, the heat source preloading control strategy and initial air intake regulation strategy implemented are as follows: start the heat source in advance and load heat in stages in a slow-rising manner during the long preheating phase to ensure a uniform temperature rise in the cold section structure; at the same time, air intake is allowed only after the first stage of heat loading is completed, and the initial air intake intensity is set to a low level to avoid insufficient vaporization and gas-liquid coexistence due to excessively rapid air intake.

8. The efficient gas supply method for gasifying and pressurizing recovered LNG to form CNG according to claim 7, characterized in that: During the carburetor startup process, the temperature changes in the cold section of the carburetor and the stability indicators of the outlet gas are monitored in real time, specifically: Using temperature sensors placed at different locations in the cold section of the vaporizer, the temperature values of the cold section area at different times are continuously acquired according to the set sampling period. The temperature change rate within the continuous sampling period is calculated to determine the temperature rise trend of the cold section area. A sensor device located at the outlet of the vaporizer monitors the stability indicators of the outlet gas in real time, including the temperature variation range, pressure fluctuation frequency, and the presence of liquid phase signals of the outlet gas; If the temperature change rate is lower than the set temperature rise rate threshold in consecutive sampling periods, the temperature fluctuation range of the outlet gas is greater than the set fluctuation threshold, and the liquid phase signal is continuously detected, the current gasification state is determined to be unstable.

9. The efficient gas supply method for gasifying and pressurizing recovered LNG to form CNG according to claim 8, characterized in that: When the gasification state is determined to be unstable, a dynamic adjustment strategy for heat source input and air intake intensity is implemented based on the monitoring results. Specifically: When the temperature rise rate in the cold section continues to be low and the outlet gas temperature is lower than the lower limit of the temperature required for stable operation, increase the heat source input power to the next heating level and maintain this state until the temperature rise trend returns to normal; When the outlet pressure fluctuation frequency exceeds the set fluctuation frequency threshold and the outlet gas temperature continues to rise, the intake valve opening is reduced to the next level of control to reduce the gas entry rate; When the liquid phase signal is continuously detected and the pressure fluctuates frequently, the current heat source power is kept unchanged and the initial air intake intensity is reduced, and the subsequent air intake rhythm is delayed until the liquid phase signal disappears; During the continuous sampling period, when the temperature rise rate in the cold section area is higher than the temperature rise rate threshold, the outlet gas temperature fluctuation range is within the allowable range, and no liquid phase signal is detected, it is determined that the gasification state has reached stability, dynamic adjustment is stopped, and the normal gas supply mode is entered.

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