Methanol cracking hydrogen production device capable of efficiently adjusting temperature gradient

Abstract

The invention relates to the field of hydrogen production, and discloses a methanol cracking hydrogen production device capable of efficiently adjusting temperature gradient, comprising: a pretreatment module for mixing methanol and deionized water according to a preset proportion and vaporizing to obtain a mixed gas; the reactor module is used for receiving the mixed gas and enabling the mixed gas to be subjected to cracking reaction through a catalyst; the sensing acquisition module is used for acquiring a three-dimensional temperature field, catalyst activity, an inlet flow field and outlet component concentration in the reactor module to form multi-dimensional data; the temperature analysis module is used for constructing and solving a reaction kinetics-heat transfer-catalyst activity coupling model based on the multi-dimensional data and outputting real-time three-dimensional field distribution; and the temperature solving module is used for constructing a target functional based on the three-dimensional field distribution. By collecting multi-dimensional data, a reaction kinetics-heat transfer-catalyst activity coupling model is constructed and solved to output real-time three-dimensional field distribution, and a target functional is constructed and solved to obtain an optimized real-time temperature field.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of hydrogen production, in particular to a methanol cracking hydrogen production device capable of efficiently adjusting temperature gradient. BACKGROUND

[0002] Hydrogen energy, as a clean and efficient secondary energy source, has a wide application prospect in fuel cell power generation, industrial hydrogenation and other fields. The scale and economy of its preparation technology are the core key to promoting the development of hydrogen energy industry. Among them, methanol cracking hydrogen production technology is particularly suitable for distributed hydrogen supply scenarios due to the convenient storage and transportation of raw material methanol, mild reaction conditions and high equipment integration, and has become one of the mainstream technologies in the current small and medium scale hydrogen production field.

[0003] The existing methanol cracking hydrogen production device adopts axial segmented heating combined with single-point thermocouple temperature measurement when adjusting temperature, which leads to temperature difference in the radial direction due to heat lag, temperature fluctuation in the circumferential direction due to airflow deflection, and mismatch of axial and radial, circumferential temperature gradient coupling, so that the local catalyst bed exceeds the activity window, which not only affects the methanol conversion efficiency and hydrogen selectivity, but also accelerates the catalyst deactivation, and restricts the long-term stable operation of the device. SUMMARY

[0004] In view of the shortcomings of the prior art, the present application provides a methanol cracking hydrogen production device capable of efficiently adjusting temperature gradient, which solves the problem of mismatch of axial and radial, circumferential temperature gradient coupling of the existing methanol cracking hydrogen production device, and restricts the long-term stable operation of the device.

[0005] To achieve the above purpose, the present application is realized by the following technical scheme: a methanol cracking hydrogen production device capable of efficiently adjusting temperature gradient, comprising:

[0006] A pretreatment module is used to mix methanol and deionized water at a predetermined ratio and vaporize to obtain a mixed gas;

[0007] A reactor module is used to receive the mixed gas and make the mixed gas undergo a cracking reaction through a catalyst;

[0008] A sensing and collecting module is used to collect the three-dimensional temperature field, catalyst activity, inlet flow field and outlet component concentration in the reactor module to form multi-dimensional data;

[0009] A temperature analysis module is used to construct and solve a reaction kinetics-heat transfer-catalyst activity coupling model based on the multi-dimensional data, and output real-time three-dimensional field distribution;

[0010] A temperature solving module is used to construct a target functional based on the three-dimensional field distribution, and solve the optimized real-time temperature field under the predetermined constraint condition;

[0011] The adjustment control module is configured to output a temperature control amount of the reactor module by an Actor-Critic neural network according to a deviation of the three-dimensional temperature field from the real-time temperature field and a catalyst activity attenuation amount.

[0012] The adjustment execution module is configured to adjust the axial, radial and circumferential temperatures of the reactor module according to the temperature control amount.

[0013] The product separation module is configured to collect reaction products after the cracking reaction of the reactor module, and separate and purify the reaction products to complete hydrogen production.

[0014] By adopting the above technical solutions, the multi-dimensional data is collected, the reaction kinetics-heat transfer-catalyst activity coupling model is constructed and solved to output the real-time three-dimensional field distribution, the target functional is constructed and solved to obtain the optimized real-time temperature field, the temperature control amount is generated by the Actor-Critic neural network in combination with the temperature deviation and the catalyst activity attenuation amount, and the axial, radial and circumferential temperatures are adjusted according to the control amount, so as to capture the dynamic change of the temperature field in real time, dynamically match the optimal temperature gradient, realize the full-dimensional temperature regulation in the reactor, and realize the stable temperature of the catalyst bed within the activity window, guarantee the reaction efficiency and the catalyst life, and solve the problem of mismatching of the axial and radial, circumferential temperature gradient coupling in the existing methanol cracking hydrogen production device, which restricts the long-term stable operation of the device.

[0015] Preferably, the preset ratio is 1:1.2-1:1.5, the mixing rate is 60-100 rpm, the time is not less than 10 minutes, the vaporization is performed by a vaporizer, the temperature is 150-180℃, and the three-dimensional temperature field includes the axial, radial and circumferential temperatures of the reactor module.

[0016] Preferably, the construction and solving specifically include the following steps:

[0017] Based on the multi-dimensional data, the kinetics characteristics of the methanol cracking reaction and the water-gas shift reaction, the three-dimensional heat transfer law in the reactor module, and the time-space characteristics of the catalyst activity attenuation, the reaction kinetics-heat transfer-catalyst activity coupling model is established.

[0018] The multi-dimensional data is input as a boundary condition into the coupling model to obtain a to-be-solved equation group including the correlation of temperature, component concentration and catalyst activity;

[0019] The finite element method is adopted to solve the to-be-solved equation group, the reactor module is divided, and then iterative calculation is performed to output the real-time three-dimensional field distribution.

[0020] Preferably, the inlet flow field and the outlet component concentration in the multi-dimensional data are used to correct the reaction kinetics parameters, the three-dimensional temperature field and the catalyst activity are used to calibrate the heat transfer coefficient and the activity attenuation parameters, the kinetic characteristics include the variation of the methanol cracking reaction and the water gas shift reaction rate with temperature, component concentration and catalyst activity, the three-dimensional heat transfer law includes the three-dimensional heat transfer law of the heat conduction, convection and reaction heat coupling in the reactor module, the catalyst activity attenuation time-space characteristics include the sintering rate of the copper active site in the catalyst with temperature and service time, the three-dimensional field distribution includes the temperature field, the concentration field and the catalyst activity field, the concentration field includes the concentration distribution of methanol, carbon monoxide, carbon dioxide, hydrogen and water vapor, and the catalyst activity field includes the catalyst activity factor distribution at different positions in the reactor module, and the activity factor is calculated by detecting the characteristic peak intensity of the copper active site in the catalyst.

[0021] Preferably, the construction of the target functional specifically includes the following steps:

[0022] The parameters of the target functional are defined by maximizing the comprehensive benefits of hydrogen yield, heating energy consumption and catalyst life in the global domain of the reactor module;

[0023] The parameters are integrated into an integral function, and the global space of the reactor module is taken as the integral domain to construct the target functional containing the integral function;

[0024] The three-dimensional field distribution is taken as a dynamic constraint input into the target functional, the preset constraint condition is combined, the variational method-finite element hybrid algorithm is used to discretize the target functional, the global domain is divided into units and converted into a discrete extreme value problem;

[0025] The discrete extreme value problem is solved by a sequential quadratic programming algorithm to obtain an optimized real-time temperature field.

[0026] Preferably, the parameters include hydrogen yield, heating energy consumption, catalyst life and catalyst replacement cost, the hydrogen yield is determined based on the ratio of the outlet hydrogen concentration to the inlet methanol concentration, the heating energy consumption is determined based on the cumulative amount of power for temperature adjustment, the catalyst life is determined based on the attenuation trend of the activity factor, the numerator of the integral function is the product of the hydrogen yield and the catalyst life, and the denominator is the sum of the heating energy consumption and the catalyst replacement cost, and the preset constraint condition includes the temperature, the methanol conversion rate and the volume concentration of carbon monoxide in the outlet gas in the reactor module.

[0027] Preferably, the output temperature control amount of the reactor module specifically includes the following steps:

[0028] The three-dimensional temperature field and the measured point deviation value of the real-time temperature field, and the catalyst activity attenuation amount are integrated into state variables;

[0029] Axial, radial, and circumferential heating power and outlet water cooling flow rate are defined as control variables;

[0030] An Actor-Critic neural network is constructed based on the state variables and control variables. Through iterative training, the Actor-Critic neural network is made to satisfy the control criteria, thereby obtaining the temperature control quantity of the reactor module.

[0031] Preferably, the Actor-Critic neural network uses the ReLU function as the activation function, and the Critic network takes the state variable as input and the value function reflecting the control effect as output, while the Actor network takes the state variable as input and the control variable as output. During the iterative training process, the network parameters are optimized by minimizing the weighted sum of control deviation and energy consumption. The control deviation is the deviation between the three-dimensional temperature field and the real-time temperature field, and the control energy consumption is the consumption of axial, radial, and circumferential heating power and outlet water cooling flow rate. The control criterion is to evaluate the control effect through the Critic network and guide the Actor network to adjust the output until the three-dimensional temperature field reaches the optimized real-time temperature field.

[0032] Preferably, the axial, radial, and circumferential temperatures of the regulating reactor module are adjusted according to the temperature control parameters. The axial temperature gradient is adjusted by the power of the annular electric heating tube, the spiral heating rod, the water-cooled coil, and the water-cooled jacket. The radial temperature difference is balanced by the inner ring heat-conducting oil jacket and the outer ring semiconductor cooling chip. The temperature of the corresponding circumferential region is adjusted by the side-mounted infrared heater to correct circumferential temperature fluctuations.

[0033] A method for efficiently regulating the temperature gradient of methanol cracking to produce hydrogen, used in the aforementioned methanol cracking hydrogen production apparatus for efficiently regulating the temperature gradient, includes the following steps:

[0034] Pretreatment: Methanol and deionized water are mixed in a preset ratio and vaporized to obtain a mixed gas;

[0035] Reactor: Receives the mixed gas and causes it to undergo a cracking reaction through a catalyst;

[0036] Sensing and data acquisition: The three-dimensional temperature field, catalyst activity, inlet flow field, and outlet component concentration within the reactor module are collected to form multi-dimensional data;

[0037] Temperature analysis: Based on multidimensional data, a coupled reaction kinetics-heat transfer-catalyst activity model is constructed and solved to output the real-time three-dimensional field distribution;

[0038] Temperature solution: Based on the three-dimensional field distribution, an objective functional is constructed, and the optimized real-time temperature field is obtained by solving under preset constraints.

[0039] Adjustment control: according to the deviation of three-dimensional temperature field and real-time temperature field and the catalyst activity attenuation amount, the temperature control amount of the reactor module is output through the Actor-Critic neural network;

[0040] Adjustment execution: according to the temperature control amount, the axial, radial and circumferential temperatures of the reactor module are adjusted;

[0041] Product separation: collecting the reaction products after the cracking reaction of the reactor module, and separating and purifying the reaction products to complete hydrogen production.

[0042] The application provides a methanol cracking hydrogen production device with efficient temperature gradient adjustment.

[0043] 1. The application acquires multi-dimensional data, constructs and solves a reaction kinetics-heat transfer-catalyst activity coupling model to output real-time three-dimensional field distribution, constructs a target functional and solves to obtain an optimized real-time temperature field, generates a temperature control amount through the Actor-Critic neural network in combination with the temperature deviation and the catalyst activity attenuation amount, adjusts the axial, radial and circumferential temperatures according to the control amount, thereby capturing the dynamic change of the temperature field in real time, dynamically matching the optimal temperature gradient, realizing full-dimensional temperature regulation in the reactor, stabilizing the catalyst bed temperature in the activity window, ensuring the reaction efficiency and the catalyst life, and solving the problem of mismatching of the axial and radial, circumferential temperature gradient coupling in the existing methanol cracking hydrogen production device, which restricts the long-term stable operation of the device.

[0044] 2. The application introduces the catalyst activity attenuation characteristics into the target functional optimization through the temperature solving module, constructs a comprehensive benefit target taking the catalyst life and replacement cost as important parameters, ensures that the optimal temperature field meets the reaction kinetics demand and avoids high temperature from aggravating catalyst sintering; at the same time, the sensing and collecting module monitors the catalyst activity change in real time, the adjustment control module dynamically adjusts the temperature control strategy according to the activity attenuation amount, reduces the activity loss caused by temperature fluctuation, makes the catalyst maintain stable activity in long-term operation, prolongs the replacement cycle, and reduces the equipment downtime and material cost caused by catalyst replacement.

[0045] 3. The application comprehensively weighs the hydrogen yield, heating energy consumption and catalyst life through the target functional, takes the maximization of the ratio of the hydrogen yield-catalyst life product to the sum of the heating energy consumption-catalyst replacement cost as the target, and optimizes the parameters of the Actor-Critic neural network of the adjustment control module in the iterative training by minimizing the weighted sum of the control deviation and the energy consumption, so that the temperature adjustment action of the adjustment execution module corrects the temperature deviation and avoids unnecessary energy consumption, reduces the unit hydrogen energy consumption, reduces the energy waste in the operation of the device, and improves the overall economy. BRIEF DESCRIPTION OF DRAWINGS

[0046] Figure 1 This is a schematic diagram of an efficient methanol cracking hydrogen production device for adjusting the temperature gradient proposed in this invention.

[0047] Figure 2 This is a flowchart of a method for producing hydrogen from methanol by efficiently adjusting the temperature gradient, as proposed in this invention. Detailed Implementation

[0048] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] Example 1:

[0050] In a first embodiment of the present invention, the present invention provides a methanol cracking hydrogen production apparatus with high efficiency for adjusting the temperature gradient, such as... Figure 1 As shown, it includes: a pretreatment module: used to mix methanol and deionized water in a preset ratio and vaporize them to obtain a mixed gas;

[0051] Furthermore, the preset ratio is 1:1.2-1:1.5, the mixing rate is 60-100 rpm, the time is not less than 10 minutes, and vaporization is carried out through a vaporizer at a temperature of 150-180℃.

[0052] Specifically, the pretreatment module pretreats methanol and deionized water to provide a mixed gas that meets the requirements of the cracking reaction for the subsequent reactor module. Typically, the preset mixing ratio of methanol to deionized water is 1:1.2-1:1.5, adapted to... The active window of the catalyst is determined to balance the amount of methanol required for the cracking reaction and the amount of water required for the shift reaction. The mixing process is achieved through a mechanical stirrer, with the stirring rate controlled at 60-100 rpm and the stirring time at least 10 minutes. The vaporization process is completed through a vaporizer, with the vaporization temperature set at 150-180℃. The vaporizer can be a tubular structure, utilizing the waste heat from subsequent reactions as a heating source, which reduces system energy consumption while ensuring complete vaporization of the mixture, providing a stable and uniform feedstock for the cracking reaction in the subsequent reactor modules.

[0053] Reactor module: Used to receive the gas mixture and cause it to undergo a cracking reaction via a catalyst.

[0054] Specifically, the reactor module receives the mixed gas output from the pretreatment module and, through the catalytic action of a catalyst, causes the mixed gas to undergo a cracking reaction, providing hydrogen-containing products for the subsequent product separation module. Simultaneously, its internal temperature field is adapted to adjust the temperature control requirements of the execution module. Generally, the catalyst packed inside the reactor module is... The reactor module exhibits good activity for both methanol cracking and water-gas shift reaction. In some embodiments, the main body of the reactor module adopts a cylindrical structure and is made of 316L stainless steel, which can adapt to the temperature and pressure environment during the reaction process and integrates sensors of the sensing and acquisition module. After the mixed gas enters, methanol cracking and water-gas shift reaction occur sequentially under the action of the catalyst. The heat during the reaction process is balanced by adjusting the execution module to ensure stable reaction.

[0055] Sensing and Acquisition Module: Used to collect three-dimensional temperature field, catalyst activity, inlet flow field and outlet component concentration within the reactor module, forming multi-dimensional data;

[0056] Furthermore, the three-dimensional temperature field includes the axial, radial, and circumferential temperatures of the reactor module.

[0057] Specifically, the sensing and acquisition module is used to acquire key operational data within the reactor module in real time, providing boundary conditions for the temperature analysis module to build a coupled model, and providing feedback for the regulation and control module. Generally, the acquisition of the three-dimensional temperature field needs to cover the axial, radial, and circumferential directions of the reactor module to comprehensively reflect the bed temperature distribution. Fiber optic sensors can be used for three-dimensional temperature field acquisition. In some embodiments, the sensors are arranged in three cross-sections along the axial direction, with each cross-section distributed at four radial positions and four circumferential angles to achieve full-dimensional temperature monitoring. Catalyst activity is mostly acquired using Raman spectroscopy probes, the inlet flow field can be detected using a laser Doppler velocimeter, and the outlet component concentration is analyzed using a quadrupole mass spectrometer.

[0058] Temperature analysis module: used to build and solve the reaction kinetics-heat transfer-catalyst activity coupling model based on multidimensional data, and output the real-time three-dimensional field distribution;

[0059] Furthermore, the construction and solution process specifically includes the following steps:

[0060] Based on multidimensional data, and combining the kinetic characteristics of methanol cracking reaction and water-gas shift reaction, the three-dimensional heat transfer law within the reactor module and the spatiotemporal characteristics of catalyst activity decay, a reaction kinetics-heat transfer-catalyst activity coupling model is established.

[0061] By inputting multidimensional data as boundary conditions into the coupled model, a set of equations to be solved, including the correlation between temperature, component concentration and catalyst activity, is obtained.

[0062] The finite element method is used to solve the system of equations. After dividing the reactor module, iterative calculations are performed to output the real-time three-dimensional field distribution.

[0063] Furthermore, the inlet flow field and outlet component concentration in the multidimensional data are used to correct reaction kinetic parameters, while the three-dimensional temperature field and catalyst activity are used to calibrate heat transfer coefficients and activity decay parameters. Kinetic characteristics include the rate variations of methanol cracking and water-gas shift reactions with temperature, component concentration, and catalyst activity. Three-dimensional heat transfer laws include the three-dimensional heat transfer laws of heat conduction, convection, and reaction heat coupling within the reactor module. The spatiotemporal characteristics of catalyst activity decay include the variation of the sintering rate of copper active sites in the catalyst with temperature and usage time. The three-dimensional field distribution includes the temperature field, concentration field, and catalyst activity field. The concentration field includes the concentration distribution of methanol, carbon monoxide, carbon dioxide, hydrogen, and water vapor. The catalyst activity field includes the distribution of catalyst activity factors at different locations within the reactor module. The activity factors are calculated by detecting the characteristic peak intensity of copper active sites in the catalyst.

[0064] Specifically, the temperature analysis module receives the multidimensional data output by the sensing and acquisition module. By constructing and solving the reaction kinetics-heat transfer-catalyst activity coupling model, it transforms the discrete acquired data into a continuous real-time three-dimensional field distribution, providing dynamic constraints for the temperature solution module to construct the objective functional, while ensuring that the model output can reflect the actual operating state of the reactor module.

[0065] Specifically, the coupling model mainly includes three types of equations: the first is the energy conservation equation, which reflects the coupling relationship between heat transfer and reaction heat release / absorption within the reactor module, enabling a quantitative description of temperature changes. Its expression is: ,in, This represents the catalyst bed density, which is taken as 1800 kg / m³ in this embodiment; This represents the specific heat capacity of the catalyst bed, which is taken as a value in this embodiment. ; This indicates the temperature at a specific point within the reactor module; Indicates time; This represents the effective thermal conductivity of the catalyst bed, which is taken as a value in this embodiment. ; The value represents the unit heat released by the methanol cracking reaction. Since the reaction is endothermic, the value is taken as 90700 J / mol in this example. The value represents the unit heat released by the water-gas shift reaction; since this reaction is exothermic, it is taken as -41200 J / mol in this embodiment. Indicates the methanol cracking reaction rate; This indicates the rate of the water-gas shift reaction.

[0066] Second, there are component diffusion equations, which describe the relationship between the diffusion and reaction consumption / generation of each component in a gas mixture, enabling dynamic simulation of the concentration field. Examples include methanol, carbon monoxide, carbon dioxide, hydrogen, and water vapor. The expression for these equations is: ,in, Indicates the first Concentration of each component ( (These correspond to methanol, carbon monoxide, carbon dioxide, hydrogen, and water vapor, respectively). Indicates time; Indicates the first The diffusion coefficients of the components, in this embodiment, are that of methanol. Values m² / s, carbon monoxide is m² / s, carbon dioxide is m² / s, hydrogen is m² / s, water vapor is m² / s; Represents the Laplace operator; Indicates the first In the first reaction The stoichiometric coefficients of various components, such as methanol in the cracking reaction. The value is -1, representing the role of carbon monoxide in the cracking reaction. The value is +1; Indicates the first The rate of each reaction, Corresponding to the cleavage reaction, Corresponding transformation reaction.

[0067] Thirdly, the catalyst activity decay equation reflects the change in the sintering rate of copper active sites in the catalyst with temperature and usage time, enabling quantitative calculation of the active field. Its expression is: ,in, This represents the catalyst activity factor, with an initial value of 1, determined by detecting copper active sites 211. The characteristic peak intensity is obtained through calculation; Indicates time; This represents the catalyst deactivation rate constant, whose value varies with temperature, and is expressed as follows: ,in Values , The value is 150 kJ / mol. Here is the gas constant, with a value of [value missing]. ; This indicates the reaction order, which is set to 2 in this embodiment.

[0068] In the above equation, and The values ​​need to be determined in conjunction with kinetic characteristics, and their values ​​are adjusted according to changes in temperature, component concentration, and catalyst activity. Inlet flow field and outlet component concentration from multidimensional data can be used to correct reaction kinetic parameters, such as... The rate constant in Three-dimensional temperature field and catalyst activity can be used to calibrate heat transfer coefficient. With activity decay parameter This ensures that the equation parameters match the actual operating conditions of the reactor.

[0069] As an option, when using multidimensional data as boundary conditions input to the coupled model, the three-dimensional temperature measurement data, catalyst activity measurement data, inlet flow field velocity data, and outlet component concentration data collected by sensing need to be mapped to the boundary positions of the model, such as using the outlet component concentration as the concentration boundary of the model outlet section and the inlet flow field velocity as the velocity boundary of the model inlet section, thereby obtaining a set of equations to be solved that include the correlation between temperature, component concentration, and catalyst activity. Generally, the finite element method is used to solve the set of equations to be solved. During the solution process, the reactor module needs to be spatially discretized first. As an option, hexahedral elements can be used for partitioning, which can better adapt to the structure of a cylindrical reactor and reduce geometric discretization errors. Specifically, the time step of the iterative calculation needs to be set to ensure that the calculation results reflect real-time performance and avoid calculation errors caused by excessive step size. In some embodiments, GPU parallel computing can be used to improve the solution efficiency.

[0070] In this embodiment, the reactor module is divided into 10,000 hexahedral elements when using the finite element method. The time step for iterative calculation is set to 10 seconds. The coupled model is solved by secondary development of COMSOL Multiphysics software. During the solution process, multidimensional data is called in real time to update the boundary conditions. The output real-time three-dimensional field distribution includes temperature field, concentration field and catalyst activity field. The concentration field covers the spatial concentration distribution of methanol, carbon monoxide, carbon dioxide, hydrogen and water vapor. The catalyst activity field covers the distribution of catalyst activity factors at different locations within the reactor module, ensuring that the output results can provide comprehensive and accurate dynamic constraints for the subsequent temperature solution module.

[0071] Temperature solution module: used to construct the target functional based on the three-dimensional field distribution and solve for the optimized real-time temperature field under preset constraints;

[0072] Furthermore, constructing the objective functional specifically includes the following steps:

[0073] The parameters of the objective functional are defined with the goal of maximizing the comprehensive benefits of hydrogen yield, heating energy consumption and catalyst lifetime across the entire reactor module.

[0074] The parameters are integrated into an integrand, and the objective functional containing the integrand is constructed with the global space of the reactor module as the integration domain.

[0075] The three-dimensional field distribution is used as the dynamic constraint input to the target functional. Combined with the preset constraint conditions, the variational method-finite element hybrid algorithm is used to discretize the target functional, dividing the whole domain into elements and transforming it into a discrete extremum problem.

[0076] The discrete extremum problem is solved iteratively using a sequential quadratic programming algorithm to obtain an optimized real-time temperature field.

[0077] Furthermore, the parameters include hydrogen yield, heating energy consumption, catalyst lifetime, and catalyst replacement cost. The hydrogen yield is determined based on the ratio of outlet hydrogen concentration to inlet methanol concentration. The heating energy consumption is determined based on the cumulative power of temperature regulation. The catalyst lifetime is determined based on the decay trend of the active factor. The numerator of the integrand is the product of hydrogen yield and catalyst lifetime, and the denominator is the sum of heating energy consumption and catalyst replacement cost. The preset constraints include the temperature within the reactor module, methanol conversion rate, and the volume concentration of carbon monoxide in the outlet gas.

[0078] Specifically, the temperature solution module receives the real-time three-dimensional field distribution output by the temperature analysis module. By constructing an objective functional with the goal of maximizing comprehensive benefits and solving for the extreme values ​​under preset constraints, an optimized real-time temperature field adapted to the actual operating conditions of the reactor module is obtained.

[0079] Specifically, constructing the objective functional first requires defining core parameters. Generally, these core parameters include hydrogen yield, heating energy consumption, catalyst lifetime, and catalyst replacement cost. Hydrogen yield characterizes reaction efficiency and is determined based on the ratio of outlet hydrogen concentration to inlet methanol concentration. Heating energy consumption reflects the energy consumption for temperature regulation and is determined based on the cumulative power of each temperature control mechanism in the regulation execution module. Catalyst lifetime reflects long-term operational economy and is determined based on the decay trend of catalyst activity factors in the three-dimensional field distribution. Catalyst replacement cost is positively correlated with the decay of activity factors and is used to quantify the economic cost of catalyst loss.

[0080] The above parameters are integrated into an integrand, with the numerator being the product of hydrogen yield and catalyst lifetime, and the denominator being the sum of heating energy consumption and catalyst replacement cost, thus achieving a comprehensive trade-off between efficiency, energy consumption, and lifetime. Then, using the entire space of the reactor module as the integration domain, an objective functional is constructed, the expression of which is: ,in, The objective functional characterizes the overall benefits across the entire reactor module. The global spatial integration domain of the reactor module; The hydrogen yield is taken as 1 / 3 of the ratio of the outlet hydrogen concentration to the inlet methanol concentration (based on the stoichiometric relationship of methanol completely cracking to produce 3 mol H2). The catalyst lifetime is the cumulative time taken for the activity factor to increase from its initial value of 1 to 0.5. The heating energy consumption is taken as the integral of the power of the axial, radial, and circumferential temperature control mechanisms of the regulating module over time. The catalyst replacement cost is the product of the initial replacement cost and (1 - activity factor). Let be the temperature at a certain point in space within the reactor module, and be the variable to be optimized in the functional.

[0081] After constructing the functional, the three-dimensional field distribution output by the temperature analysis module needs to be used as the dynamic constraint input. The concentration field in the three-dimensional field distribution provides real-time component data for hydrogen yield calculation, the activity field provides a basis for catalyst lifetime and replacement cost calculation, and the temperature field provides an initial temperature distribution reference for functional solution. At the same time, combined with preset constraints, which generally include the temperature at any location within the reactor module, methanol conversion rate, and carbon monoxide volume concentration in the outlet gas, the optimization results are ensured to meet the basic reaction performance requirements.

[0082] Next, a variational-finite element hybrid algorithm is used to discretize the objective functional, transforming the continuous global optimization problem into a discrete extremum problem. During discretization, the entire reactor module is first divided into several elements. As an option, hexahedral elements can be used, as they better fit the geometry of a cylindrical reactor, reducing discretization errors. The discretized functional expression is: ,in, The total number of units; , , The first Hydrogen yield, catalyst life, heating energy consumption, and replacement cost of each unit; For the first The volume of each unit; For the first The average temperature of each unit is the discrete variable to be optimized.

[0083] After discretization, the discrete extremum problem is solved iteratively using a sequential quadratic programming algorithm. This can be achieved by calling the optimization toolbox of the numerical computation software. At the same time, the constraint satisfaction status during the iteration process is fed back in real time. If a certain iteration step does not meet the preset constraints, such as the temperature exceeding the upper limit, the search direction is adjusted and the calculation is repeated until the optimal solution that satisfies all constraints is obtained.

[0084] In this embodiment, when using the variational-finite element hybrid algorithm, the entire reactor module is divided into 10,000 hexahedral elements, consistent with the element division of the temperature analysis module, to reduce data matching errors. The discrete extremum problem is solved using the sequential quadratic programming toolbox in MATLAB software, with the iterative convergence threshold set to a change in the functional value of less than... The preset constraints are set as follows: the temperature at any location within the reactor module does not exceed 300℃, the methanol conversion rate is not less than 95%, and the carbon monoxide volume concentration in the outlet gas does not exceed 1%. The optimized real-time temperature field obtained by solving is output in the form of the average temperature of each unit, and then transmitted to the regulation and control module as the benchmark for temperature deviation calculation, ensuring that the action of the regulation and execution module can accurately guide the optimal temperature state.

[0085] Adjustment and control module: Used to output the temperature control quantity of the reactor module through the Actor-Critic neural network based on the deviation between the three-dimensional temperature field and the real-time temperature field and the amount of catalyst activity decay;

[0086] Furthermore, the temperature control parameters of the output reactor module specifically include the following steps:

[0087] The measurement point deviation between the three-dimensional temperature field and the real-time temperature field, as well as the catalyst activity decay, are integrated into state variables.

[0088] Axial, radial, and circumferential heating power and outlet water cooling flow rate are defined as control variables;

[0089] An Actor-Critic neural network is constructed based on the state variables and control variables. Through iterative training, the Actor-Critic neural network is made to satisfy the control criteria, thereby obtaining the temperature control quantity of the reactor module.

[0090] Furthermore, the Actor-Critic neural network uses the ReLU function as the activation function. The Critic network takes the state variable as input and the value function reflecting the control effect as output, while the Actor network takes the state variable as input and the control variable as output. During iterative training, the network parameters are optimized by minimizing the weighted sum of control deviation and energy consumption. The control deviation is the deviation between the three-dimensional temperature field and the real-time temperature field, and the control energy consumption is the consumption of axial, radial, and circumferential heating power and outlet water cooling flow rate. The control criterion is to evaluate the control effect through the Critic network and guide the Actor network to adjust the output until the three-dimensional temperature field reaches the optimized real-time temperature field.

[0091] Specifically, the adjustment and control module receives the three-dimensional temperature field output by the sensing and acquisition module and the optimized real-time temperature field output by the temperature solution module. Combined with the catalyst activity decay, it generates precise temperature control values ​​through the Actor-Critic neural network, providing the action basis for the adjustment and execution module. This ensures that the temperature field of the reactor module can dynamically approach the optimization target, while balancing temperature adjustment accuracy and energy consumption.

[0092] Specifically, the first step is to define state variables. These state variables must encompass information on temperature deviation and catalyst activity decay to ensure the network can fully perceive the current state of the system. Their expression is: ,in, for The state variables at any given time have 21 dimensions. To determine the deviation between the actual temperature of the 16 temperature measurement points in the sensing and acquisition module and the corresponding measurement points in the optimized real-time temperature field, ; The decay rate is measured at 5 catalyst activity points. for Time of the first Catalyst activity factor at each measuring point.

[0093] Next, define the control variables. Each control variable must correspond one-to-one with the temperature control mechanism of the regulating module to ensure that the control quantity can directly drive the execution action. Its expression is: ,in, for The control variable for time has 8 dimensions; The three sections of the axial temperature control mechanism of the actuator module are respectively: an inlet section annular electric heating tube, a middle section spiral heating rod, and an outlet section auxiliary heating. These represent the flow rates of the radial two-ring temperature control mechanism, the inner ring heat transfer oil jacket, and the outer ring cooling medium, respectively. These are the power ratings of the infrared heating modules in the circumferential zone 2, respectively. This refers to the flow rate of the outlet water-cooled coil.

[0094] Subsequently, an Actor-Critic neural network is constructed, comprising a Critic network and an Actor network, which work together to achieve optimal control. The Critic network evaluates the matching effect between the current state and the control input, outputting a value function to reflect the long-term benefits of the control strategy. Its expression is: ,in, The value function output by the Critic network reflects the overall effectiveness of the control strategy under the current state; ReLU activation function is used to introduce nonlinear mapping and improve the network's adaptability to complex working conditions; , These are the weight matrices from the input layer to the hidden layer and from the hidden layer to the output layer of the Critic network, respectively. , These are the bias vectors for the corresponding layers; the number of neurons in the hidden layer is set to 64 to ensure that the network can capture the correlation between variables while avoiding overfitting.

[0095] Actor networks are used to output control variables based on the current state; their expression is: ,in, The control input output by the Actor network; , These are the weight matrices from the input layer to the hidden layer and from the hidden layer to the output layer of the Actor network, respectively. , These are the bias vectors for the corresponding layers; the activation function and the number of hidden layer neurons are consistent with the Critic network to ensure that the feature extraction capabilities of the two networks are matched.

[0096] During iterative training, the network parameters need to be optimized by minimizing the loss function. The loss function is set as a weighted sum of control bias and energy consumption to balance adjustment accuracy and energy consumption. Its expression is: ,in, The loss function; To control the weight of the deviation, a value of 1 is set to ensure that temperature deviation is corrected first. The weight for energy consumption is set to 0.1 to avoid excessive energy consumption. The square of the L2 norm of the state variable characterizes the combined effect of temperature deviation and activity decay. The L2 norm squared value of the control variable characterizes the energy consumption level of the regulating execution module. As an alternative, and The value can be dynamically adjusted according to the actual operating conditions. For example, when the catalyst activity decays rapidly, it can be appropriately increased. Prioritize maintaining temperature stability.

[0097] The training process follows an evaluation-optimization loop: the Critic network outputs a value function based on the current state and control input to evaluate the control effect; the Actor network adjusts the control input output based on the feedback from the value function; then, the error is calculated using a loss function, and backpropagation is used to update the weights and biases of both networks until the loss function converges. In some embodiments, GPUs can be used to accelerate training to improve iteration efficiency.

[0098] In this embodiment, the Actor-Critic neural network is built based on the PyTorch framework. Both the Critic and Actor networks contain two hidden layers, with 64 neurons in each layer. The training iteration period is set to 0.1 seconds, consistent with the sampling frequency of the sensing and acquisition module, to ensure data synchronization. The convergence threshold is set to a value less than 1 / 2 * 10 consecutive iterations of the loss function. The control output from the network needs to be mapped to the action range of the regulating execution module, such as heating power 0-10kW and flow rate 0-5L / min, and then transmitted to the corresponding temperature control mechanism to ensure that the execution action accurately matches the control target.

[0099] Regulation execution module: used to adjust the axial, radial and circumferential temperature of the reactor module according to the temperature control value;

[0100] Furthermore, the axial, radial, and circumferential temperatures of the reactor module are adjusted according to the temperature control parameters. The axial temperature gradient is adjusted by the power of the annular electric heating tube, the spiral heating rod, the water-cooled coil, and the water-cooled jacket. The radial temperature difference is balanced by the inner ring heat-conducting oil jacket and the outer ring semiconductor cooling chip. The temperature of the corresponding circumferential region is adjusted by the side-mounted infrared heater to correct circumferential temperature fluctuations.

[0101] Specifically, the regulating execution module receives the temperature control output from the regulating control module and adjusts the axial, radial, and circumferential temperatures of the reactor module through a zoned temperature regulation mechanism. This ensures that the actual temperature field approximates the optimized real-time temperature field, guaranteeing the stability of the pyrolysis reaction. Axial temperature control is achieved through zoned temperature control using an annular electric heating tube, a spiral heating rod, a water-cooled coil, and a water-cooled jacket. Radial temperature control relies on the inner ring heat-conducting oil jacket and the outer ring semiconductor cooling chip. Circumferential temperature fluctuations are compensated for by a side-mounted infrared heater. In some embodiments, the temperature regulation mechanism employs servo control to ensure accuracy. In this embodiment, the annular electric heating tube has a power of 0-5kW, the spiral heating rod 0-10kW, the water cooling flow rate 0-5L / min and 0-3L / min, the inner ring heat-conducting oil flow rate 0-3L / min, the outer ring cooling chip power 0-2kW, and the infrared heaters are divided into two groups covering the circumference. Each mechanism responds promptly, maintaining a stable temperature gradient.

[0102] Product separation module: Used to collect the reaction products after the pyrolysis reaction of the reactor module, and to separate and purify the reaction products to complete hydrogen production.

[0103] Specifically, the product separation module receives the reaction products output from the reactor module. Through staged processing, it collects and purifies the products, ultimately obtaining high-purity hydrogen and completing the hydrogen production closed loop. Generally, liquid components in the products must be removed first, followed by the separation of gaseous impurities to ensure the hydrogen purity meets standards. The products are first cooled by a condenser, then separated into gas and liquid phases by a gas-liquid separator, and finally purified by a pressure swing adsorption (PSA) device. Molecular sieves can be used as the adsorbent in the PSA. In this embodiment, the condenser outlet temperature is set to 40°C, and the PSA uses 13X molecular sieves, which can adsorb impurities to obtain high-purity hydrogen.

[0104] Example 2:

[0105] A distributed fuel cell hydrogen refueling station has a medium daily hydrogen supply capacity, requiring high-purity hydrogen for fuel cell vehicles. However, during peak refueling periods, the load surges. Traditional methanol cracking hydrogen production units only perform axial single-point temperature measurement, leading to excessive CO concentrations due to radial heat transfer lag and circumferential temperature differences caused by flow field deviation. Furthermore, the lack of optimization for energy consumption and catalyst lifespan results in poor hydrogen supply stability and high operating costs. To address these issues, this invention employs a highly efficient methanol cracking hydrogen production method with adjustable temperature gradients, the steps of which are as follows: Figure 2 As shown. The specific implementation process of this method is as follows:

[0106] Pretreatment: The methanol stored in the hydrogen refueling station is mixed with the purified deionized water in the station according to a preset ratio. After stirring evenly, the mixture is vaporized using the residual heat of the subsequent reaction to obtain a stable mixed gas that meets the continuous hydrogen supply requirements of the hydrogen refueling station.

[0107] Reactor: A compact cylindrical reactor is used to receive the mixed gas and is filled with a catalyst suitable for methanol cracking and conversion reactions, allowing the mixed gas to react under suitable conditions, which is suitable for the limited space of the hydrogen refueling station.

[0108] Sensing and data acquisition: Collect three-dimensional temperature field, catalyst activity, inlet flow field and outlet component concentration in the reactor to form multi-dimensional data and capture fluctuations in operating conditions in real time.

[0109] Temperature analysis: Based on multidimensional data, a coupled model is constructed and solved to output a real-time three-dimensional field distribution that reflects the current working conditions.

[0110] Temperature solution: Based on the three-dimensional field distribution, an objective functional is constructed, and constraints are set in combination with hydrogen supply stability, energy consumption and catalyst lifetime requirements to solve and optimize the real-time temperature field.

[0111] Regulation and control: Based on the temperature deviation and catalyst activity decay, the Actor-Critic neural network outputs the temperature control quantity.

[0112] Adjustment execution: Adjust the temperature of each dimension of the reactor according to the control quantity, quickly correct the temperature difference, and ensure the stability of the reaction.

[0113] Product separation: Collect the reaction products, separate the liquid components first, and then purify them by adsorption to obtain high-purity hydrogen, thus completing the hydrogen production process.

[0114] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A methanol cracking hydrogen production unit with high efficiency for regulating temperature gradient, characterized in that, include: Pretreatment module: used to mix methanol and deionized water in a preset ratio and vaporize them to obtain a mixed gas; Reactor module: Used to receive the gas mixture and cause it to undergo a cracking reaction via a catalyst; Sensing and Acquisition Module: Used to collect three-dimensional temperature field, catalyst activity, inlet flow field and outlet component concentration within the reactor module, forming multi-dimensional data; Temperature analysis module: used to build and solve the reaction kinetics-heat transfer-catalyst activity coupling model based on multidimensional data, and output the real-time three-dimensional field distribution; Temperature solution module: used to construct the target functional based on the three-dimensional field distribution and solve for the optimized real-time temperature field under preset constraints; Adjustment and control module: Used to output the temperature control quantity of the reactor module through the Actor-Critic neural network based on the deviation between the three-dimensional temperature field and the real-time temperature field and the amount of catalyst activity decay; Regulation execution module: used to adjust the axial, radial and circumferential temperature of the reactor module according to the temperature control value; Product separation module: Used to collect the reaction products after the pyrolysis reaction of the reactor module, and to separate and purify the reaction products to complete hydrogen production.

2. The methanol cracking hydrogen production unit with high efficiency temperature gradient regulation according to claim 1, characterized in that: The preset ratio is 1:1.2-1:1.5, the mixing rate is 60-100 rpm, the mixing time is not less than 10 minutes, the vaporization is carried out through a vaporizer, the temperature is 150-180℃, and the three-dimensional temperature field includes the axial, radial and circumferential temperatures of the reactor module.

3. The methanol cracking hydrogen production unit with high efficiency temperature gradient regulation according to claim 1, characterized in that: The construction and solution process specifically includes the following steps: Based on multidimensional data, and combining the kinetic characteristics of methanol cracking reaction and water-gas shift reaction, the three-dimensional heat transfer law within the reactor module and the spatiotemporal characteristics of catalyst activity decay, a reaction kinetics-heat transfer-catalyst activity coupling model is established. By inputting multidimensional data as boundary conditions into the coupled model, a set of equations to be solved, including the correlation between temperature, component concentration and catalyst activity, is obtained. The finite element method is used to solve the system of equations. After dividing the reactor module, iterative calculations are performed to output the real-time three-dimensional field distribution.

4. The methanol cracking hydrogen production unit with efficient temperature gradient regulation according to claim 3, characterized in that: The inlet flow field and outlet component concentration in the multidimensional data are used to correct reaction kinetic parameters, while the three-dimensional temperature field and catalyst activity are used to calibrate heat transfer coefficients and activity decay parameters. The kinetic characteristics include the rate variations of methanol cracking and water-gas shift reactions with temperature, component concentration, and catalyst activity. The three-dimensional heat transfer characteristics include the three-dimensional heat transfer patterns of conduction, convection, and reaction thermal coupling within the reactor module. The spatiotemporal characteristics of catalyst activity decay include the sintering rate of copper active sites in the catalyst with temperature and usage time. The three-dimensional field distribution includes a temperature field, a concentration field, and a catalyst activity field. The concentration field includes the concentration distribution of methanol, carbon monoxide, carbon dioxide, hydrogen, and water vapor. The catalyst activity field includes the distribution of catalyst activity factors at different locations within the reactor module. The activity factors are calculated by detecting the characteristic peak intensity of copper active sites in the catalyst.

5. The methanol cracking hydrogen production unit with efficient temperature gradient regulation according to claim 1, characterized in that: The construction of the target functional specifically includes the following steps: The parameters of the objective functional are defined with the goal of maximizing the comprehensive benefits of hydrogen yield, heating energy consumption and catalyst lifetime across the entire reactor module. The parameters are integrated into an integrand, and an objective functional containing the integrand is constructed with the global space of the reactor module as the integration domain. The three-dimensional field distribution is used as the dynamic constraint input to the target functional. Combined with the preset constraint conditions, the variational method-finite element hybrid algorithm is used to discretize the target functional, dividing the whole domain into elements and transforming it into a discrete extremum problem. The discrete extremum problem is solved iteratively using a sequential quadratic programming algorithm to obtain an optimized real-time temperature field.

6. The methanol cracking hydrogen production apparatus for high-efficiency temperature gradient regulation according to claim 5, characterized in that: The parameters include hydrogen yield, heating energy consumption, catalyst lifetime, and catalyst replacement cost. The hydrogen yield is determined based on the ratio of outlet hydrogen concentration to inlet methanol concentration. The heating energy consumption is determined based on the cumulative power of temperature regulation. The catalyst lifetime is determined based on the decay trend of the active factor. The numerator of the integrand is the product of hydrogen yield and catalyst lifetime, and the denominator is the sum of heating energy consumption and catalyst replacement cost. The preset constraints include the temperature within the reactor module, methanol conversion rate, and the volume concentration of carbon monoxide in the outlet gas.

7. The methanol cracking hydrogen production apparatus for high-efficiency temperature gradient regulation according to claim 1, characterized in that: The temperature control of the output reactor module specifically includes the following steps: The measurement point deviation between the three-dimensional temperature field and the real-time temperature field, as well as the catalyst activity decay, are integrated into state variables. Axial, radial, and circumferential heating power and outlet water cooling flow rate are defined as control variables; An Actor-Critic neural network is constructed based on the state variables and control variables. Through iterative training, the Actor-Critic neural network is made to satisfy the control criteria, thereby obtaining the temperature control quantity of the reactor module.

8. A methanol cracking hydrogen production apparatus for efficient temperature gradient regulation according to claim 7, characterized in that: The Actor-Critic neural network uses the ReLU function as the activation function. The Critic network takes state variables as input and a value function reflecting the control effect as output, while the Actor network takes state variables as input and control variables as output. During the iterative training process, the network parameters are optimized by minimizing the weighted sum of control deviation and energy consumption. The control deviation is the deviation between the three-dimensional temperature field and the real-time temperature field, and the control energy consumption is the consumption of axial, radial, and circumferential heating power and outlet water cooling flow rate. The control criterion is to evaluate the control effect through the Critic network and guide the Actor network to adjust the output until the three-dimensional temperature field reaches the optimized real-time temperature field.

9. A methanol cracking hydrogen production apparatus for efficient temperature gradient regulation according to claim 1, characterized in that: The axial, radial, and circumferential temperatures of the regulating reactor module are controlled by adjusting the axial temperature gradient through the power of the annular electric heating tube, the spiral heating rod, the water-cooled coil, and the water-cooled jacket. The radial temperature difference is balanced by the inner ring heat-conducting oil jacket and the outer ring semiconductor cooling chip. The temperature of the corresponding circumferential region is adjusted by the side-mounted infrared heater to correct circumferential temperature fluctuations.

10. A method for efficiently regulating the temperature gradient in methanol cracking to produce hydrogen, characterized in that: A methanol cracking hydrogen production apparatus for efficient temperature gradient regulation as described in any one of claims 1-9, comprising the following steps: Pretreatment: Methanol and deionized water are mixed in a preset ratio and vaporized to obtain a mixed gas; Reactor: Receives the mixed gas and causes it to undergo a cracking reaction through a catalyst; Sensing and data acquisition: The three-dimensional temperature field, catalyst activity, inlet flow field, and outlet component concentration within the reactor module are collected to form multi-dimensional data; Temperature analysis: Based on multidimensional data, a coupled reaction kinetics-heat transfer-catalyst activity model is constructed and solved to output the real-time three-dimensional field distribution; Temperature solution: Based on the three-dimensional field distribution, an objective functional is constructed, and the optimized real-time temperature field is obtained by solving under preset constraints. Regulation and control: Based on the deviation between the three-dimensional temperature field and the real-time temperature field and the amount of catalyst activity decay, the temperature control quantity of the reactor module is output through the Actor-Critic neural network; Adjustment execution: Adjust the axial, radial, and circumferential temperatures of the reactor module according to the temperature control parameters; Product separation: Collect the reaction products after the pyrolysis reaction of the reactor module, and separate and purify the reaction products to complete hydrogen production.

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