Logging double-chamber furnace main control and safety auxiliary system based on numerical control technology

Abstract

The invention relates to the technical field of logging equipment and control thereof, and provides a logging double-chamber furnace main control and safety auxiliary system based on a numerical control technology, which comprises a logging double-chamber furnace, an enhanced thermal field control module, a cooling module and an energy efficiency optimization module, and is characterized in that the enhanced thermal field control module and the cooling module are respectively arranged in the logging double-chamber furnace; the enhanced thermal field control module collects operation data in the logging double-chamber furnace, analyzes the operation state in the logging double-chamber furnace according to the operation data to form an analysis result, and controls thermal field data of the logging double-chamber furnace according to the analysis result; the cooling module is used for quickly cooling different areas based on a supercritical fluid cooling technology to form gradient cooling distribution; and the energy efficiency optimization module dynamically regulates energy efficiency optimization according to the analysis result.

Description

Technical Field

[0001] The invention relates to the technical field of well logging equipment and control thereof, and in particular to a well logging double-chamber furnace main control and safety auxiliary system based on numerical control technology. Background Art

[0002] A melting furnace is an industrial equipment used to heat and melt recycled metal or other fusible materials and then cast them. Its basic principle is to use electricity, heat energy generated by fuel combustion, etc. to heat the material in the furnace to above the melting point, turning it into a liquid state, and then injecting the liquid material into the mold through a specific casting system, and then cooling and solidifying to form the required casting.

[0003] The current logging double-chamber furnace mainly has the following problems:

[0004] 1. Traditional PID control is affected by thermal inertia, resulting in temperature fluctuations and large logging errors;

[0005] 2. Relying only on passive alarms, it is impossible to predict faults in advance and make proactive adjustments;

[0006] 3. The traditional thermocouple or infrared temperature measurement has a large error (±0.5°C), which affects the experimental stability;

[0007] 4. The heating power adjustment depends on experience, resulting in large energy loss and uneven distribution of the furnace cavity thermal field.

[0008] The present invention is made to solve the common problems in the field, such as poor control accuracy, inability to actively adjust, insufficient monitoring capability, uneven heat distribution and low intelligence. Summary of the invention

[0009] The purpose of the present invention is to propose a main control and safety auxiliary system for a well logging double-chamber furnace based on numerical control technology in view of the existing deficiencies.

[0010] In order to overcome the shortcomings of the prior art, the present invention adopts the following technical solutions:

[0011] A main control and safety auxiliary system for a well logging double-chamber furnace based on numerical control technology, the main control and safety auxiliary system for the well logging double-chamber furnace comprising a well logging double-chamber furnace, an enhanced thermal field control module, a cooling module, an energy efficiency optimization module, and an evaluation module, the enhanced thermal field control module and the cooling module are respectively arranged in the well logging double-chamber furnace, the enhanced thermal field control module collects operating data in the well logging double-chamber furnace, and analyzes the operating state of the well logging double-chamber furnace according to the operating data to form an analysis result, and controls the thermal field data of the well logging double-chamber furnace according to the analysis result; the cooling module quickly cools down different areas based on supercritical fluid cooling technology to form a gradient cooling distribution; the energy efficiency optimization module dynamically adjusts energy efficiency optimization according to the analysis result.

[0012] Optionally, the enhanced thermal field control module includes an intelligent sensing unit, a thermal field analysis unit and a temperature field optimization unit. The intelligent sensing unit is used to form a distributed temperature monitoring network inside the double-chamber furnace, and monitor the furnace cavity temperature distribution data inside the well logging double-chamber furnace in real time; the thermal field analysis unit analyzes the operating status of the well logging double-chamber furnace according to the furnace cavity temperature distribution data of the well logging double-chamber furnace to form an analysis result; the temperature field optimization unit dynamically adjusts the heating power distribution according to the analysis result to make the furnace cavity temperature distribution of the well logging double-chamber furnace more uniform.

[0013] Optionally, the cooling module includes a supercritical cooling unit, a microchannel fluid distribution unit, an intelligent cooling control unit, and a cooling efficiency monitoring unit. The supercritical cooling unit uses supercritical fluid as a cooling medium and cools the cooling area in the logging double-chamber furnace to achieve temperature gradient control of different areas. The microchannel fluid distribution unit arranges a high-density microchannel cooling pipeline network inside the logging double-chamber furnace; the cooling efficiency monitoring unit monitors the cooling data of the cooling fluid, and the intelligent cooling control unit adjusts the supercritical fluid flow rate in different areas according to the cooling data. Optionally, the energy efficiency optimization module includes a heating power control unit, a long-term cooling flow rate optimization unit, an intelligent energy efficiency calculation unit, and an energy consumption evaluation and prediction unit. The heating power control unit compares the analysis result with the set monitoring threshold. If it exceeds the set monitoring threshold, the power output of each heating unit is calculated and the calculation result is transmitted to the intelligent energy efficiency calculation unit; the long-term cooling flow rate optimization unit calculates the cooling flow rate of different cooling areas based on historical data and the long-term optimization result of the energy consumption evaluation and prediction unit, and transmits the optimized cooling flow rate to the intelligent energy efficiency calculation unit and the heating power control unit to coordinately optimize temperature control; the intelligent energy efficiency calculation unit receives the power output of the heating power control unit and the cooling flow rate of the long-term cooling flow rate optimization unit, calculates the real-time energy efficiency ratio of the furnace cavity of the logging double-chamber furnace, and transmits the real-time energy efficiency ratio to the energy consumption evaluation and prediction unit; the energy consumption evaluation and prediction unit dynamically adjusts the long-term control strategy of the heating power and cooling gradient based on historical data, real-time energy efficiency ratio and long-term cooling optimization strategy. Optionally, the analysis unit calculates the operating status index I of the logging double-chamber furnace according to the following formula R :

[0014]

[0015] In the formula, S T is the thermal stability coefficient of the furnace chamber, η E is the thermal energy utilization coefficient, S C is the cooling uniformity coefficient, R H is the thermal stress risk coefficient, and ε is the balance parameter, which is a very small number greater than 0.

[0016] Optionally, the intelligent energy efficiency calculation unit calculates the real-time energy efficiency ratio COP according to the following formula:

[0017]

[0018] Where M is the number of independent electric heating elements used for heating in the double-chamber furnace for logging, Q useful,i is the effective heat actually transferred to the furnace cavity by the ith heating unit, P heat,i is the electric energy consumed by the heating element of the logging double-chamber furnace per unit time, and △t is the time of the evaluation cycle.

[0019] Optionally, the operating data collected by the data collection unit includes temperature data, heating data, cooling data, and energy consumption data.

[0020] Optionally, the data acquisition unit includes a carbon nanotube temperature sensor array, an infrared thermal imaging sensor, an intelligent power meter, a flow meter, and a pressure sensor. The carbon nanotube temperature sensor array is arranged inside the furnace cavity of the well logging double-chamber furnace, and measures the temperature inside the furnace cavity of the well logging double-chamber furnace; the infrared thermal imaging sensor is arranged to face the furnace cavity of the well logging double-chamber furnace, and remotely measures the furnace wall and thermal field distribution of the well logging double-chamber furnace; the intelligent power meter remotely measures the furnace wall and thermal field distribution of the well logging double-chamber furnace, the flow meter detects the flow of the cooling pipe network of the cooling module, and the pressure sensor detects the pressure of the cooling pipe network of the cooling module.

[0021] Optionally, the supercritical fluid is set to H 2 O.

[0022] Optionally, the intelligent cooling control unit obtains current cooling data and calculates the local flow rate v according to the following formula: cool,i :

[0023] v cool,i =v base +α·(T i -T target );

[0024] In the formula, v base is the reference flow rate, α is the temperature deviation correction coefficient, T i -T target is the temperature deviation.

[0025] The beneficial effects achieved by the present invention are:

[0026] 1. By enhancing the cooperation between the thermal field control module and the cooling module, the temperature distribution inside the furnace chamber is made more uniform, and the temperature gradient is controlled to ensure that the entire system has higher temperature stability, reduce the impact of thermal stress, and improve the heat treatment quality of logging tools;

[0027] 2. By enhancing the cooperation between the thermal field control module and the energy efficiency optimization module, the heating power can be dynamically adjusted according to the temperature distribution, and the energy efficiency optimization module can reduce unnecessary energy consumption, ensuring that the entire system reduces energy consumption while maintaining the target temperature and improves energy utilization;

[0028] 3. Through the cooperation between the cooling module and the energy efficiency optimization module, the cooling system can adjust the cooling flow rate according to the real-time temperature and thermal field distribution, and avoid overcooling or insufficient cooling, so as to ensure that the whole system can quickly stabilize the furnace chamber temperature while saving energy and improving the cooling efficiency;

[0029] 4. By enhancing the coordination of the thermal field control module, cooling module and energy efficiency optimization module, the heating and cooling systems are always in a dynamic equilibrium state, and the temperature gradient control of the heat treatment process is optimized, ensuring that the entire system has higher temperature stability, energy efficiency and equipment operation reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The present invention can be further understood from the following description in conjunction with the accompanying drawings. The components in the figures are not necessarily drawn to scale, but the emphasis is placed on illustrating the principles of the embodiments. In different views, the same reference numerals designate the same parts.

[0031] Figure 1 It is an overall block diagram of the present invention.

[0032] Figure 2 It is a block diagram of the enhanced thermal field control module of the present invention.

[0033] Figure 3 It is a block diagram of a cooling module of the present invention.

[0034] Figure 4 It is a block diagram of the heating power control unit of the present invention.

[0035] Figure 5 It is a block diagram of the energy efficiency optimization module of the present invention. DETAILED DESCRIPTION

[0036] The following is an explanation of the embodiments of the present invention through specific embodiments. Those skilled in the art can understand the advantages and effects of the present invention from the contents disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and the details in this specification can also be modified and changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. In addition, the drawings of the present invention are only simple schematic illustrations and are not depicted according to actual sizes. It is stated in advance. The following embodiments will further explain the relevant technical contents of the present invention in detail, but the disclosed contents are not intended to limit the scope of protection of the present invention.

[0037] Embodiment 1: According to Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5As shown, this embodiment provides a main control and safety auxiliary system for a well logging double-chamber furnace based on numerical control technology, the main control and safety auxiliary system for the well logging double-chamber furnace includes a double-chamber furnace, an enhanced thermal field control module, a cooling module, an energy efficiency optimization module, and an evaluation module. The enhanced thermal field control module and the cooling module are respectively arranged in the well logging double-chamber furnace. The enhanced thermal field control module collects operating data in the well logging double-chamber furnace, and analyzes the operating status of the well logging double-chamber furnace according to the operating data to form an analysis result, and controls the thermal field data of the well logging double-chamber furnace according to the analysis result; the cooling module quickly cools down different areas based on supercritical fluid cooling technology to form a gradient cooling distribution; the energy efficiency optimization module dynamically adjusts energy efficiency optimization according to the analysis result.

[0038] Optionally, the enhanced thermal field control module includes an intelligent sensing unit, a thermal field analysis unit and a temperature field optimization unit. The intelligent sensing unit is used to form a distributed temperature monitoring network inside the double-chamber furnace, and monitor the furnace cavity temperature distribution data inside the well logging double-chamber furnace in real time; the thermal field analysis unit analyzes the operating status of the well logging double-chamber furnace according to the furnace cavity temperature distribution data of the well logging double-chamber furnace to form an analysis result; the temperature field optimization unit dynamically adjusts the heating power distribution according to the analysis result to make the furnace cavity temperature distribution of the well logging double-chamber furnace more uniform.

[0039] Optionally, the intelligent sensing unit includes a carbon nanotube temperature sensor array, an infrared thermal imaging sensor, an intelligent power meter, a flow meter, and a pressure sensor. The carbon nanotube temperature sensor array is arranged inside the furnace cavity of the well logging double-chamber furnace, and measures the temperature inside the furnace cavity of the well logging double-chamber furnace; the infrared thermal imaging sensor is arranged to face the furnace cavity of the well logging double-chamber furnace, and remotely measures the furnace wall and thermal field distribution of the well logging double-chamber furnace; the intelligent power meter remotely measures the furnace wall and thermal field distribution of the well logging double-chamber furnace, the flow meter detects the flow of the cooling pipe network of the cooling module, and the pressure sensor detects the pressure of the cooling pipe network of the cooling module.

[0040] Optionally, the thermal field analysis unit calculates the operating state index I of the well logging double-chamber furnace according to the following formula: R :

[0041]

[0042] In the formula, S T is the thermal stability coefficient of the furnace chamber, η E is the thermal energy utilization coefficient, S C is the cooling uniformity coefficient, R H is the thermal stress risk coefficient, ε is the balance parameter, and its value is a very small number greater than 0.

[0043] In this embodiment, the furnace chamber thermal stability coefficient S T Calculated according to the following formula:

[0044]

[0045] Where, T i is the temperature of the i-th measuring point in the furnace chamber, N is the total number of measuring points, Represents the temperature gradient (spatial rate of change) and is used to measure the uniformity of the temperature inside the furnace chamber. Specifically, is the temperature gradient in the x direction, is the temperature gradient in the y direction, is the temperature gradient in the z direction;

[0046] In this embodiment, a double-chamber logging furnace with a regular rectangular shape is set, and the length direction of the furnace cavity is set as the x-axis, the width direction is set as the y-axis, and the height direction is set as the z-axis.

[0047] The origin (0,0,0) is set at a corner of the furnace cavity, and the temperature sensors are arranged at different spatial positions. i ,y j ,z k ), where i = 1, 2, ..., N x ; j = 1, 2, ..., N y ; k = 1, 2, ..., N z Among them, N x 、N y 、N z are the number of measurement points in three directions respectively.

[0048] Temperature sensors are installed in key areas of different heights, widths and depths of the furnace cavity to form a grid-like array of temperature measurement points. Carbon nanotube temperature sensors, fiber grating sensors (FBGs) and infrared thermal imagers are used to collect temperature distribution data inside the furnace cavity.

[0049] With a fixed sampling interval Δt (e.g., 1 second), the temperatures of all measuring points are collected every second, and the temperature data are processed by a data acquisition controller (DAQ) and stored in a time series database (InfluxDB). At the same time, the data is transmitted to the temperature field analysis unit for real-time gradient calculation.

[0050] The temperature field analysis unit performs gradient calculation according to the following formula (using the finite difference method):

[0051]

[0052] Where, T(x,y,z) is the temperature at position (x,y,z), T(x+Δx,y,z) is the temperature at position (x,y+Δy,z), and T(x,y,z+Δz) is the temperature at position (x,y,z). is the temperature gradient in the x direction, is the temperature gradient in the y direction, is the temperature gradient in the z direction, Δx is the spacing in the x direction, that is, the distance between two temperature measurement points, Δy is the spacing in the y direction, and Δz is the spacing in the z direction.

[0053] Thermal energy utilization coefficient η E Calculated according to the following formula:

[0054]

[0055] In the formula, Q j seful is the energy effectively transferred to the furnace cavity by the jth heating unit, Q j input is the total input energy of the jth heating unit, and M is the total number of heating units.

[0056] Cooling uniformity coefficient S C Calculate according to the following formula:

[0057]

[0058] In the formula, v k is the flow rate of the kth cooling channel, K is the total number of cooling channels, and its value is set according to the actual situation. Represents the gradient change in flow rate and is used to evaluate cooling uniformity.

[0059] Among them, in the rectangular coordinate system inside the furnace chamber, the x-axis is the mainstream direction of the fluid (usually the flow direction of the cooling fluid), the y-axis is the lateral diffusion direction, and the z-axis is the vertical direction (there may be natural convection or auxiliary flow);

[0060] Flow velocity measuring points are set at different positions in the furnace chamber, and (u, v, w) are recorded. The flow velocity is measured using ultrasonic flowmeter, hot wire anemometer, fiber optic flow velocity sensor and other equipment.

[0061]

[0062] Where u, v, w are the velocity components of the fluid in the x, y, z directions respectively, and Δx, Δy, Δz are the distances between the measuring points.

[0063] If S C If it is larger, it means that the cooling is uneven, which may cause local overcooling or overheating.

[0064] If S C Close to zero, indicating stable flow and uniform cooling.

[0065] Heat stress risk factor R H Calculate according to the following formula:

[0066]

[0067] In the formula, σ m thermal is the thermal stress in the mth region, and its value is calculated by the following formula: m thermal =E m *α m *ΔT m ; E m The elastic modulus of the material, whose value is determined based on the known physical properties of the furnace cavity material (experimental data or material database), α m is the thermal expansion coefficient of the furnace chamber material, ΔT m is the regional temperature difference, and the temperature difference in the furnace cavity is measured by deploying temperature sensors (thermocouples, optical fiber sensors, infrared temperature measurement), P is the number of furnace cavity partitions, ∈ m is the regional weight factor, based on the regional heating area A m ,∈ m =A m / A total ;

[0068] The heating area of ​​the furnace cavity is set as the design area of ​​the furnace cavity.

[0069] The temperature field optimization unit determines the new heating power P according to the following formula: heat,i new :

[0070]

[0071] Where P heat,i lod is the original power of the i-th heating unit, k 1 is the temperature error correction coefficient, k 2 is the operating status index correction coefficient, T avg is the target temperature mean, T i is the real-time temperature near the i-th heating unit, I R It is the running status index.

[0072] Among them, the target temperature mean T avg Calculated according to the following formula:

[0073]

[0074] Where, T i is the real-time temperature of the i-th heating zone, and N is the number of heating zones in the furnace cavity.

[0075] In this embodiment, a temperature error correction coefficient k is provided. 1 , Operation status index correction coefficient k2 Example values ​​for :

[0076] 1) In the scenario of heat treatment (strict temperature control) of precision logging tools, k 1 =1.0; k 2 =10;

[0077] 2) In the scenario of metal heat treatment (conventional production), k 1 =0.8; k 2 =30;

[0078] 3) In the case of ceramic / glass sintering, k 1 =0.5; k 2 =20;

[0079] 4) In the scenario of rapid heating process (high production capacity demand), k 1 =2.5; k 2 =60;

[0080] 5) In the case of slow heating (to avoid thermal shock), k 1 =0.3; k 2 =15;

[0081] Those skilled in the art can select a specific temperature error correction coefficient k according to the actual usage scenario. 1 , Operation status index correction coefficient k 2 .

[0082] In this embodiment, if I R If the temperature is too low (uneven temperature field, unstable flow, high thermal stress), the heating power in the low temperature area is increased to compensate for the insufficient temperature, and the heating power in the high temperature area is reduced to prevent local overheating.

[0083] If I R If the temperature is moderate (the furnace chamber is stable), the current heating power distribution is maintained to ensure a stable temperature field.

[0084] If I R If it is too high (energy efficiency is too low), reduce the total power input and optimize the heating strategy:

[0085] P heat,i new =P heat,i old -k 3 ·P heat,i old ;

[0086] In the formula, k 3 is the global power optimization coefficient, and its value range is (0, 1];

[0087] In this embodiment, the corresponding global power optimization coefficient k is selected for different usage scenarios. 3 :

[0088] 1) In the control scenario of high-precision well logging double-chamber furnace, k 3 =0.05;

[0089] 2) In the control scenario of the double-chamber furnace for long-term stability logging, k 3 =0.2;

[0090] 3) In the control scenario where the pulse temperature switches rapidly, k 3 =0.5;

[0091] In this embodiment, the cooling module is arranged in the well logging double-chamber furnace and controls the temperature inside the well logging double-chamber furnace, especially controls the furnace chamber temperature and protects the well logging equipment;

[0092] Optionally, the cooling module includes a supercritical cooling unit, a microchannel fluid distribution unit, an intelligent cooling control unit, and a cooling efficiency monitoring unit. The supercritical cooling unit uses supercritical fluid as a cooling medium and cools the cooling area in the logging double-chamber furnace to achieve temperature gradient control of different areas. The microchannel fluid distribution unit arranges a high-density microchannel cooling pipeline network inside the logging double-chamber furnace, and allows supercritical fluids of different pressures to flow in the microchannel to achieve precise cooling of local areas; the cooling efficiency monitoring unit monitors the cooling data of the cooling fluid, and the intelligent cooling control unit adjusts the supercritical fluid flow rate in different areas according to the current cooling data.

[0093] In this embodiment, the cooling data includes flow rate, pressure, temperature, and heat exchange efficiency.

[0094] The supercritical cooling unit includes a supercritical fluid supply component, a cooling distribution component, and a cooling efficiency detection component. The supercritical fluid supply component supplies cooling medium to the cooling area in the well logging double-chamber furnace, and the cooling distribution component distributes the supercritical fluid to cool the cooling area in the well logging double-chamber furnace.

[0095] The supercritical fluid supply component includes a high-pressure storage tank, a booster pump, and a preheater. The high-pressure storage tank stores the supercritical fluid. The booster pump is used to maintain the supercritical fluid at a set pressure. The preheater heats the fluid to a supercritical state.

[0096] The cooling distribution component includes a microchannel cooling network and a high-pressure regulating valve. The microchannel cooling network is arranged inside the furnace cavity. The high-pressure regulating valve is used to control the flow rate and pressure of the fluid in different areas.

[0097] Among them, for high-heat areas, embedded cooling channels are used to improve heat exchange efficiency, and for transition areas, surface-attached cooling pipes are used to optimize temperature control.

[0098] The cooling efficiency detection unit includes the temperature sensor, the pressure sensor, and the flow meter. The temperature sensor detects the temperature of the cooling area in real time, the pressure sensor measures the pressure change of the cooling fluid, and the flow meter detects the flow rate of the supercritical fluid.

[0099] In this embodiment, the supercritical fluid is set to H 2 O(water).

[0100] This embodiment uses supercritical water cooling (H 2 O) technology to achieve efficient and accurate cooling of the logging double-chamber furnace. The supercritical cooling unit includes a supercritical fluid supply component, a cooling distribution component, and a cooling efficiency detection component. The three work together to ensure cooling uniformity, temperature control stability, and efficient energy consumption management.

[0101] First, the supercritical fluid supply component is responsible for providing a water-cooled medium in a supercritical state, wherein the high-pressure storage tank stores supercritical water (H 2 O), the booster pump maintains the supercritical water flowing steadily at the set pressure, and the preheater heats the fluid to the supercritical state to ensure that the cooling process is always within the optimal working parameter range. Subsequently, the cooling distribution component transports the supercritical water to different cooling areas of the logging double-chamber furnace, where the microchannel cooling pipe network is responsible for accurately distributing the supercritical water flow, and the high-pressure regulating valve controls the flow rate and pressure in different areas to meet the different heat load requirements in the furnace cavity. In the high-heat area, an embedded cooling channel is used to enhance the heat exchange effect between the supercritical water and the high-temperature surface and improve the cooling efficiency; in the temperature transition area, a surface-attached cooling pipe is used to optimize the cooling gradient and ensure a uniform temperature field.

[0102] During the entire cooling process, the cooling efficiency detection component monitors the supercritical water cooling status in real time, the temperature sensor collects the temperature of the cooling area, the pressure sensor monitors the pressure change of the supercritical water fluid, and the flow meter accurately detects the flow rate of supercritical water to ensure that the cooling is running under the optimal working conditions. The cooling data is analyzed by the intelligent cooling control unit, and the supercritical water flow rate and distribution strategy are dynamically adjusted according to the changes in the furnace chamber temperature to achieve precise cooling control.

[0103] Ultimately, the supercritical water cooling enables the logging double-chamber furnace to have higher temperature uniformity, lower energy consumption and better cooling stability through precise liquid supply, intelligent distribution and real-time monitoring, ensuring the efficiency and reliability of the furnace chamber heat treatment process.

[0104] The cooling efficiency monitoring unit collects flow, pressure, temperature, and heat exchange efficiency data. The intelligent cooling control unit calculates the cooling demand of each area and dynamically adjusts the fluid pressure and flow rate.

[0105] The intelligent cooling control unit obtains the current cooling data and calculates the cooling power Q according to the following formula cooling :

[0106] Q cooling =m·C p ΔT;

[0107] In the formula, Q cooling is the cooling power (W), m is the supercritical fluid mass flow rate (kg / s), C p is the specific heat of the supercritical fluid (J / kg·K), and ΔT is the temperature drop of the cooling fluid (K).

[0108] In addition, the intelligent cooling control unit obtains the current cooling data and calculates the local flow rate v according to the following formula cool,i :

[0109] v cool,i =v base +α·(T i -T target );

[0110] In the formula, v base is the reference flow rate, α is the temperature deviation correction coefficient, T i -T target is the temperature deviation;

[0111] Among them, when the temperature of a certain area is higher than the target value T target , the intelligent cooling control unit increases the flow rate of the supercritical fluid in the area. When the temperature in a certain area is lower than the target value, the flow rate of the supercritical fluid is reduced to reduce the cooling effect.

[0112] In this embodiment, the temperature deviation correction coefficient selects a matching value according to the actual usage scenario and is brought into the above formula, specifically:

[0113] 1) In the use scenario of heat treatment of precision logging tools (maintaining uniform temperature, avoiding rapid local temperature changes, and reducing the impact of thermal stress), α = 0.02;

[0114] 2) In the scenario of metal heat treatment (high temperature alloy) (control the cooling rate to avoid cracks in the metal due to excessive temperature difference, but the cooling rate still needs to be moderately accelerated), then α = 0.1;

[0115] 3) In the scenario of rapid production (beat cooling) (need to cool to the target temperature in a short time to improve production efficiency), α = 0.4;

[0116] In short, it is necessary to select an appropriate temperature deviation correction coefficient α based on the specific usage scenario.

[0117] By enhancing the cooperation between the thermal field control module and the cooling module, the temperature distribution inside the furnace chamber is made more uniform, and the temperature gradient is controlled to ensure that the entire system has higher temperature stability, reduce the impact of thermal stress, and improve the heat treatment quality of logging tools.

[0118] Optionally, the energy efficiency optimization module includes a heating power control unit, a long-term cooling flow rate optimization unit, an intelligent energy efficiency calculation unit, and an energy consumption evaluation and prediction unit. The heating power control unit compares the analysis result with the set monitoring threshold. If it exceeds the set monitoring threshold, the power output of each heating unit is calculated and the calculation result is transmitted to the intelligent energy efficiency calculation unit; the long-term cooling flow rate optimization unit calculates the cooling flow rate of different cooling areas based on historical data and the long-term optimization result of the energy consumption evaluation and prediction unit, and transmits the optimized cooling flow rate to the intelligent energy efficiency calculation unit and the heating power control unit to coordinately optimize temperature control; the intelligent energy efficiency calculation unit receives the power output of the heating power control unit and the cooling flow rate of the long-term cooling flow rate optimization unit, calculates the real-time energy efficiency ratio of the furnace cavity of the logging double-chamber furnace, and transmits the real-time energy efficiency ratio to the energy consumption evaluation and prediction unit; the energy consumption evaluation and prediction unit dynamically adjusts the long-term control strategy of the heating power and the cooling gradient based on historical data, the real-time energy efficiency ratio and the long-term cooling optimization strategy.

[0119] The heating power control unit obtains the target temperature mean value T avg , and according to the target temperature mean T avg and target temperature T target The average temperature deviation of the furnace chamber ΔT:

[0120] ΔT=T target -T avg ;

[0121] At the same time, the heating power control unit calculates the average temperature deviation ΔT of the furnace cavity and the system set temperature error threshold θ T For comparison, if ΔT>θ T , then the heating power needs to be adjusted;

[0122] The heating power control unit calculates the heating power ΔP according to the following formula heat,i :

[0123] ΔP heat,i =k 5 ·(T target -T i )+k 6 I R ;

[0124] In the formula, k5 is the temperature error correction coefficient, k 6 is the operating status index correction coefficient, T target -T i is the temperature deviation of the heating unit, I R It is the operating status index of the double-chamber furnace for logging;

[0125] Wherein, the temperature error correction coefficient is calculated according to the following formula:

[0126]

[0127] Where P max , P min is the maximum / minimum heating power, T max ,T min The upper and lower limits of the furnace chamber temperature.

[0128] In addition, the operating status index correction factor k 6 Determined according to the following formula:

[0129]

[0130] Where Pmax is the maximum heating power of the heating unit, IR max The largest operating status index in the historical data of the well logging double-chamber furnace, IR min It is the smallest operating status index in the historical data of the logging double-chamber furnace.

[0131] The heating power adjustment unit calculates a new power output P according to the heating power. new heat，i :

[0132]

[0133] Where P new heat，i is the current power output, ΔP heat,i is the calculated heating power;

[0134] At the same time, the new power output needs to meet the limiting conditions to prevent power overload:

[0135] The restrictions are:

[0136]

[0137] Where P max is the maximum allowable power of the heating unit, P min is the minimum permissible power of the heating unit;

[0138] At the same time, in order to optimize the overall power of the furnace cavity, the heating power adjustment unit considers the global energy efficiency and determines the final power P final final,i :

[0139]

[0140] In the formula, k 7 is the energy efficiency optimization coefficient, and its value is determined according to the following formula:

[0141]

[0142] Where η E max is the maximum energy efficiency ratio of the system in historical data, η E min is the minimum energy efficiency ratio of the system in historical data.

[0143] The cooling flow rate and gradient optimization unit obtains the heat transfer Q of each area cool,i :

[0144] Q cool,i =m i ·C p ·(T inlet,i -T outlet,i );

[0145] In the formula, Q cool,i is the heat transfer of cooling area i, m i is the cooling fluid mass flow rate (kg / s), C p is the specific heat capacity of the cooling fluid, T inlet,i is the inlet of the fluid, T outlet,i is the outlet temperature of the fluid.

[0146] Cooling flow rate and gradient optimization unit according to the heat transfer Q of each area cool,i Calculation of the energy efficiency η of the cooling area cool,i :

[0147]

[0148] Where P coolant,i is the power consumption of the cooling pump.

[0149] Cooling flow rate and gradient optimization unit cooling area energy efficiency η cool,i Calculate the new cooling flow rate v cool,i new :

[0150]

[0151] In the formula, k vis the flow rate optimization coefficient (set according to experimental experience), η target is the target cooling energy efficiency, and its value is set by the system.

[0152] In this embodiment, the flow rate optimization coefficient k is provided v Example values ​​for :

[0153] In the scene of precision logging double chamber furnace for parts processing and heat treatment, k v =0.001;

[0154] In the scenario of metal heat treatment (high temperature alloy) in precision logging double chamber furnace, k v =0.005;

[0155] In the scenario of precision logging double chamber furnace for ceramic / glass sintering, k v =0.002;

[0156] In the scenario of fast cooling (beat-type production) of the precision logging double-chamber furnace, k v =0.01;

[0157] If η cool,i <η target , increase the cooling flow rate.

[0158] If η cool,i >η target , reduce the cooling flow rate.

[0159] In this embodiment, the fluid flow rate of each area is adjusted by an intelligent cooling control unit.

[0160] Optionally, the intelligent energy efficiency calculation unit calculates the real-time energy efficiency ratio COP according to the following formula:

[0161]

[0162] Where M is the number of independent electric heating elements used for heating in the double-chamber furnace for logging, Q useful,i is the effective heat actually transferred to the furnace cavity by the ith heating unit, P heat,i is the electric energy consumed by the heating element of the logging double-chamber furnace per unit time, and △t is the time of the evaluation cycle.

[0163] Among them, if the COP is lower than the set threshold, it means that the system energy efficiency is low and the energy consumption strategy needs to be optimized;

[0164] If the COP is higher than the set threshold, it means that the system is operating well and the control intensity can be reduced.

[0165] After the COP is determined, the result is transmitted to the energy consumption evaluation and prediction unit.

[0166] The energy consumption evaluation and prediction unit calculates energy consumption trends based on historical data

[0167]

[0168] Where η E (i) is the energy efficiency ratio of the ith time, and M is the number of evaluations.

[0169] If η E If the power consumption is lower than the system set evaluation threshold value set for a long time, it means that the energy utilization rate is low and the power allocation needs to be optimized.

[0170] if If the temperature is higher than the system-set evaluation threshold for a long time, the heating / cooling power will be reduced to save energy.

[0171] The energy consumption evaluation and prediction unit determines to adjust the heating power P new heat :

[0172]

[0173] In the formula, k H is the heating power adjustment coefficient, and its value satisfies: k H =ΔP max / Δη E , ΔP max The maximum allowable heating power adjustment is set by the system and is equivalent to the default value, Δη E is the acceptable adjustment range of the energy efficiency ratio (the default is 0.1 to 0.2). In this embodiment, the value corresponding to the midpoint of the adjustment range can be selected. old heat is the current heating power, η target is the target energy efficiency ratio;

[0174] The energy consumption evaluation and prediction unit determines the cooling flow rate v new cool :

[0175]

[0176] In the formula, k C is the cooling flow rate adjustment coefficient, k C =Δv max / Δη E , Δv max The maximum cooling flow rate adjustment allowed is set by the system and is equivalent to the default value, Δη E is the acceptable adjustment range of the energy efficiency ratio (the default is 0.1 to 0.2). In this embodiment, the value corresponding to the midpoint of the adjustment range can be selected. oldcool is the current cooling flow rate, η target is the target energy efficiency ratio, whose value is determined by the current parameters of the system and is equivalent to a known value.

[0177] By enhancing the cooperation between the thermal field control module and the energy efficiency optimization module, the heating power can be dynamically adjusted according to the temperature distribution, and the energy efficiency optimization module can reduce unnecessary energy consumption, ensuring that the entire system reduces energy consumption while maintaining the target temperature and improving energy utilization.

[0178] Through the cooperation of the cooling module and the energy efficiency optimization module, the cooling system can adjust the cooling flow rate according to the real-time temperature and thermal field distribution, and avoid overcooling or insufficient cooling, ensuring that the entire system quickly stabilizes the furnace chamber temperature while saving energy and improving cooling efficiency.

[0179] By enhancing the coordination among thermal field control module, cooling module and energy efficiency optimization module, the heating and cooling system is always in a dynamic equilibrium state, and the temperature gradient control of the heat treatment process is optimized, ensuring that the entire system has higher temperature stability, energy efficiency and equipment operation reliability.

[0180] Embodiment 2: This embodiment should be understood to include all the features of any of the above embodiments, and further improve on them. Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 As shown, the main control and safety auxiliary system of the logging double-chamber furnace includes an evaluation module, which collects energy efficiency data in an operation cycle, evaluates the energy efficiency in an operation cycle to form an evaluation result, and provides intelligent control suggestions for the logging double-chamber furnace and automatically adjusts the system operation status according to the evaluation result.

[0181] The evaluation module includes a data acquisition unit, an energy efficiency analysis and evaluation unit, a decision optimization unit and an adaptive adjustment unit. The data acquisition unit collects the operating data of the well logging double-chamber furnace and stores historical data; the energy efficiency analysis and evaluation unit calculates the well logging double-chamber furnace cavity energy efficiency index run and evaluates whether the heating / cooling strategy is reasonable; the decision optimization unit generates intelligent control suggestions based on the historical data and the well logging double-chamber furnace cavity energy efficiency index run; the adaptive adjustment unit automatically adjusts the heating, cooling and energy consumption management strategies according to the control suggestions.

[0182] Optionally, the operating data collected by the data collection unit includes temperature data, heating data, cooling data, and energy consumption data.

[0183] Optionally, the decision optimization suggestions include heating regulation, cooling regulation, energy consumption optimization and safety adjustment.

[0184] The data acquisition unit comprises a data acquisition device and a data storage device. The data acquisition device acquires the operation data of the well logging double-chamber furnace, and the data storage device stores the operation data acquired by the data acquisition device.

[0185] The energy efficiency analysis and evaluation unit calculates the energy efficiency index run of the well logging double-chamber furnace cavity according to the following formula:

[0186]

[0187] In the formula, Q useful is the effective heat transfer, P total The heating and cooling efficiency consumed by the entire system meets: P total =P heat +P cool , S T is the thermal stability coefficient of the furnace chamber, η cool is the cooling energy efficiency, satisfying: η cool =Q cool / P cool .

[0188] The effective heat transferred is Q useful Calculated according to the following formula:

[0189]

[0190] In the formula, m i is the mass of the heated object, C p,i is the specific heat capacity, ΔT i For temperature changes.

[0191] The decision optimization unit calculates the current operating state deviation of the furnace chamber:

[0192]

[0193] In the formula, I run is the furnace energy efficiency index, I target is the target energy efficiency index, ΔI run is the energy efficiency deviation, S T is the thermal stability coefficient of the furnace chamber, ΔS T To measure the temperature uniformity deviation, Δη cool is the current cooling energy efficiency deviation, S T target is the thermal stability coefficient of the furnace chamber, η target cool Target cooling efficiency.

[0194] The decision optimization unit calculates the minimum distance d of historical data:

[0195]

[0196] In the formula, ΔI run history is the historical furnace energy efficiency index deviation, ΔS T history is the historical temperature uniformity deviation, Δη cool is the current cooling energy efficiency deviation, Δη cool history is the historical cooling energy efficiency deviation.

[0197] Extract the best heating / cooling strategies from similar cases in the past:

[0198]

[0199] Where P heat best is the optimal heating power, K is the number of nearest neighbor samples, which is determined by the number provided in the system database and is equivalent to a known value, P heat history,i is the heating power of the i-th sample in history, v cool best is the optimal cooling flow rate, v cool history,i is the cooling flow rate of the i-th sample in history.

[0200] The decision optimization unit is based on historical data and determines the optimal heating power:

[0201]

[0202] In the formula, k H To optimize the heating power coefficient, control the adjustment strength to meet: k C =ΔP max / ΔI cool max , ω1 is the power weight factor, which determines the balance between the historical optimal heating power and the current heating adjustment strategy, P heat best is the historical optimal heating power, ΔI run is the energy efficiency deviation.

[0203] The decision optimization unit is based on historical data and determines the optimal cooling flow rate:

[0204]

[0205] In the formula, k C k is the cooling flow rate optimization coefficient, which controls the cooling flow rate adjustment strength. Its value satisfies: C =Δv max / Δη coolmax , ω2 is the cooling weight factor, which determines the balance between the historical optimal cooling flow rate and the current cooling adjustment strategy, V cool best is the historical optimal cooling flow rate, Δη cool is the current cooling energy efficiency deviation.

[0206] In this embodiment, examples of values ​​of the power weight factor ω1 and the cooling weight factor ω2 are provided:

[0207] 1) In the production scenario of precision tools, ω1 = 0.65, ω2 = 0.67;

[0208] 2) In the production scenario of metal heat treatment (high temperature alloy), ω1 = 0.57, ω2 = 0.56;

[0209] 3) In the production scenario of rapid production (beat heating), ω1 = 0.38, ω2 = 0.40;

[0210] The decision optimization suggestions include:

[0211] 1) Heating power adjustment:

[0212] Increase / decrease heating power P heat new ;

[0213] Optimize heating power distribution (adjust heating unit output in different areas);

[0214] If the temperature in a certain area is abnormal, the heating power will be automatically reduced and an alarm will be issued;

[0215] 2) Cooling strategy optimization;

[0216] Adjust the cooling flow rate v cool new ;

[0217] Optimize cooling fluid distribution (cooling gradients in different areas);

[0218] If the cooling system is abnormal, adjust the flow rate or enter emergency mode;

[0219] 3) Energy consumption optimization:

[0220] Automatically adjust energy management strategies to reduce unnecessary power consumption;

[0221] 4) Safety adjustment measures:

[0222] Detect temperature, equipment, power supply, and cooling status;

[0223] Automatically respond to temperature anomalies;

[0224] Switch to safe mode in case of failure;

[0225] Trigger safety shutdown in emergency situations;

[0226] The adaptive adjustment unit obtains the decision optimization suggestion and automatically adjusts the heating, cooling and energy consumption management strategies.

[0227] Through the cooperation between the evaluation module and the energy efficiency optimization module, the evaluation module can analyze the system energy efficiency data in real time and feed back to the energy efficiency optimization module for adaptive adjustment, ensuring that the entire system maintains optimal energy consumption management during long-term operation and improving the long-term stability and economy of the logging double-chamber furnace.

[0228] Through the cooperation between the evaluation module and the enhanced thermal field control module, the evaluation module can optimize the thermal field control strategy based on the temperature distribution data and make the temperature control more precise, ensuring that the entire system can maintain efficient heating and cooling control under various operating conditions and improving the processing consistency of the logging tools.

[0229] The contents disclosed above are only preferred feasible embodiments of the present invention, and do not limit the protection scope of the present invention. Therefore, all equivalent technical changes made using the contents of the present invention specification and drawings are included in the protection scope of the present invention. In addition, the elements therein can be updated as technology develops.

Claims

1. A well logging double-chamber furnace main control and safety auxiliary system based on numerical control technology, characterized in that: The main control and safety auxiliary system of the logging double-chamber furnace includes a logging double-chamber furnace, an enhanced thermal field control module, a cooling module, and an energy efficiency optimization module. The enhanced thermal field control module and the cooling module are respectively arranged in the logging double-chamber furnace; the enhanced thermal field control module collects the operating data in the logging double-chamber furnace, and analyzes the operating status of the logging double-chamber furnace according to the operating data to form an analysis result, and controls the thermal field data of the logging double-chamber furnace according to the analysis result; the cooling module quickly cools down different areas based on supercritical fluid cooling technology to form a gradient cooling distribution; the energy efficiency optimization module dynamically adjusts the energy efficiency optimization according to the analysis result.

2. The well logging double-chamber furnace main control and safety auxiliary system based on numerical control technology according to claim 1 is characterized in that: The enhanced thermal field control module includes an intelligent sensing unit, a thermal field analysis unit and a temperature field optimization unit. The intelligent sensing unit is used to form a distributed temperature monitoring network inside the well logging double-chamber furnace, and monitor the furnace cavity temperature distribution data inside the well logging double-chamber furnace in real time; the thermal field analysis unit analyzes the operating status of the well logging double-chamber furnace according to the furnace cavity temperature distribution data of the well logging double-chamber furnace to form an analysis result; the temperature field optimization unit dynamically adjusts the heating power distribution of the well logging double-chamber furnace according to the analysis result, so that the furnace cavity temperature distribution of the well logging double-chamber furnace is more uniform.

3. The well logging double-chamber furnace main control and safety auxiliary system based on numerical control technology according to claim 2 is characterized in that: The cooling module includes a supercritical cooling unit, a microchannel fluid distribution unit, an intelligent cooling control unit, and a cooling efficiency monitoring unit. The supercritical cooling unit uses supercritical fluid as a cooling medium and cools the cooling area in the logging double-chamber furnace to achieve temperature gradient control of different areas. The microchannel fluid distribution unit arranges a high-density microchannel cooling pipeline network inside the logging double-chamber furnace; the cooling efficiency monitoring unit monitors the cooling data of the cooling fluid, and the intelligent cooling control unit adjusts the supercritical fluid flow rate in different areas according to the cooling data.

4. The well logging double-chamber furnace main control and safety auxiliary system based on numerical control technology according to claim 3 is characterized in that: The energy efficiency optimization module includes a heating power control unit, a long-term cooling flow rate optimization unit, an intelligent energy efficiency calculation unit, and an energy consumption evaluation and prediction unit. The heating power control unit compares the analysis result with the set monitoring threshold. If the set monitoring threshold is exceeded, the power output of each heating unit is calculated and the calculation result is transmitted to the intelligent energy efficiency calculation unit; the long-term cooling flow rate optimization unit calculates the cooling flow rate of different cooling areas based on historical data and the long-term optimization result of the energy consumption evaluation and prediction unit, and transmits the optimized cooling flow rate to the intelligent energy efficiency calculation unit and the heating power control unit to coordinately optimize temperature control; the intelligent energy efficiency calculation unit receives the power output of the heating power control unit and the cooling flow rate of the long-term cooling flow rate optimization unit, calculates the real-time energy efficiency ratio of the furnace cavity of the logging double-chamber furnace, and transmits the real-time energy efficiency ratio to the energy consumption evaluation and prediction unit; the energy consumption evaluation and prediction unit dynamically adjusts the long-term control strategy of the heating power and the cooling gradient based on historical data, the real-time energy efficiency ratio and the long-term cooling optimization strategy.

5. The well logging double-chamber furnace main control and safety auxiliary system based on numerical control technology according to claim 4 is characterized in that: The analysis unit calculates the operating status index I of the well logging double-chamber furnace according to the following formula: R : In the formula, S T is the thermal stability coefficient of the furnace chamber, η E is the thermal energy utilization coefficient, S C is the cooling uniformity coefficient, R H is the thermal stress risk coefficient, and ε is the balance parameter, which is a very small number greater than 0.

6. The well logging double-chamber furnace main control and safety auxiliary system based on numerical control technology according to claim 5 is characterized in that: The intelligent energy efficiency calculation unit calculates the real-time energy efficiency ratio COP according to the following formula: Where M is the number of independent electric heating elements used for heating in the double-chamber furnace for logging, Q useful,i is the effective heat actually transferred to the furnace cavity by the ith heating unit, P heat,i is the electric energy consumed by the heating element of the logging double-chamber furnace per unit time, and △t is the time of the evaluation cycle.

7. The well logging double-chamber furnace main control and safety auxiliary system based on numerical control technology according to claim 6 is characterized in that: The operating data collected by the data collection unit includes temperature data, heating data, cooling data, and energy consumption data.

8. The well logging double-chamber furnace main control and safety auxiliary system based on numerical control technology according to claim 7 is characterized in that: The data acquisition unit includes a carbon nanotube temperature sensor array, an infrared thermal imaging sensor, an intelligent power meter, a flow meter, and a pressure sensor. The carbon nanotube temperature sensor array is arranged inside the furnace cavity of the well logging double-chamber furnace, and measures the temperature inside the furnace cavity of the well logging double-chamber furnace; the infrared thermal imaging sensor is arranged to face the furnace cavity of the well logging double-chamber furnace, and remotely measures the furnace wall and thermal field distribution of the well logging double-chamber furnace; the intelligent power meter remotely measures the furnace wall and thermal field distribution of the well logging double-chamber furnace, the flow meter detects the flow of the cooling pipe network of the cooling module, and the pressure sensor detects the pressure of the cooling pipe network of the cooling module.

9. The well logging double-chamber furnace main control and safety auxiliary system based on numerical control technology according to any one of claims 3 or 8, characterized in that: The supercritical fluid is set to be H2O.

10. The well logging double-chamber furnace main control and safety auxiliary system based on numerical control technology according to claim 9 is characterized in that: The intelligent cooling control unit obtains the current cooling data and calculates the local flow rate v according to the following formula cool,i : v cool,i =v base +α·(T i -T target ); In the formula, v base is the reference flow rate, α is the temperature deviation correction coefficient, T i -T target is the temperature deviation.

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