Production and preparation process of energy-saving synergistic raw material catalyst

Abstract

The invention discloses a production and preparation process of an energy-saving synergistic raw material catalyst, particularly relates to the field of carbon-based raw material catalysts, and aims to optimize the formation condition of a pore structure, accurately regulate and control the type and concentration distribution of surface functional groups and introduce a defect detection and quantitative evaluation method based on image recognition. The catalytic activity and stability of the catalyst are improved; according to the process, through the stage steps of drying, acid pickling, pyrolysis, surface modification and the like, and in combination with a surface characterization means and quantitative indexes, collaborative optimization of the structure and chemical performance of the catalyst is ensured. The catalyst is comprehensively improved from a microstructure to chemical characteristics, and is widely applied to the fields of energy conversion, pollution treatment and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon-based raw meal catalysts, and more particularly to a production and preparation process of an energy-saving and efficiency-enhancing raw meal catalyst. Background Art

[0002] Carbon-based raw meal catalysts are catalysts with carbon-based substances as their main components. Their precursors are usually derived from carbon-containing organic matter, such as plant residues or biomass waste. Through carbonization, activation and modification, catalytic materials with porous structures and high surface areas are formed. Such catalysts have important applications in energy conversion, environmental governance and other fields. The surface functional groups of carbon-based raw meal catalysts refer to specific chemical groups distributed on the surface of the catalyst, such as carboxyl, hydroxyl, aldehyde, etc. These functional groups directly affect the chemical reaction activity and selectivity of the catalyst. By regulating the type and density of functional groups, the surface acidity and electronic properties can be changed, thereby optimizing the performance of the catalyst and adapting to specific reaction requirements. The uniform distribution of functional groups and precise control of concentration are crucial to improving the efficiency of the catalyst.

[0003] In the existing technology, there is often a problem of coordinated regulation of the catalyst pore structure and surface functional groups. For example, when the pore structure does not meet the requirements of molecular diffusion, it may lead to insufficient utilization of the catalyst active sites. At the same time, the uneven distribution of surface functional groups will reduce the selectivity and stability of the reaction. Traditional methods are difficult to solve. Therefore, a production and preparation process for energy-saving and efficiency-enhancing raw material catalysts is needed for practical use. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a production and preparation process for energy-saving and efficiency-enhancing raw material catalysts, which improves the performance of the catalyst by optimizing the conditions for pore structure formation, regulating the types and distribution density of surface functional groups, and using comprehensive analysis technology to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above-mentioned object, the present invention provides the following technical solution: a production and preparation process of an energy-saving and efficiency-enhancing raw material catalyst, comprising:

[0006] Carbon-containing organic matter is selected as raw material, and is screened and dried to remove moisture and some impurities; then, metal impurities in the raw material are removed by pickling; the pickling solution includes sulfuric acid or hydrogen chloride solution;

[0007] The processed raw materials are placed in a heating furnace and pyrolyzed in an inert atmosphere; by adjusting the air flow, temperature, and holding time parameters, the raw materials are converted into porous carbon-based substances;

[0008] Using acidic or alkaline solutions to modify the surface of carbon-based substances to control the types and concentrations of their surface functional groups;

[0009] The surface-modified carbon-based material is placed in a heating furnace and activated in a preset atmosphere. By selecting a preset gas and adjusting the gas flow and temperature, the pore structure and surface properties of the catalyst are changed to form active sites.

[0010] The pore size distribution peak of the carbon-based raw meal catalyst is measured by a pore size distribution tester, and the specific surface area of ​​the carbon-based raw meal catalyst is confirmed by a BET test; and a judgment condition 1 is established based on the pore size distribution peak and the specific surface area;

[0011] Determine the surface carboxyl concentration of the carbon-based raw meal catalyst by acid-base titration or elemental analyzer, and determine the surface hydroxyl concentration of the carbon-based raw meal catalyst by infrared spectroscopy; establish judgment condition 2 based on the surface carboxyl concentration and surface hydroxyl concentration;

[0012] If the first or second judgment condition is judged as unqualified, image recognition processing is performed;

[0013] During image recognition processing, the catalyst surface is imaged using a scanning electron microscope to obtain surface image data; the catalyst surface characteristics are analyzed using an image recognition algorithm. The analysis steps of the image recognition algorithm include image feature extraction, functional group and defect identification, and quantitative evaluation;

[0014] Quantitative evaluation includes assessment of functional group density and surface defect uniformity.

[0015] In a preferred embodiment, the first judgment condition is: when the measured pore size distribution peak is lower than a preset pore size distribution peak threshold, and the measured specific surface area is lower than a preset specific surface area threshold;

[0016] The second judgment condition is: when the measured surface carboxyl concentration is lower than a preset surface carboxyl concentration threshold, and the measured surface hydroxyl concentration is lower than a preset surface hydroxyl concentration threshold.

[0017] In a preferred embodiment, carbon-containing organic matter is selected as raw material and subjected to screening and drying treatment; during the drying process, the drying temperature T is controlled. dry and time t dry , to remove moisture and some impurities;

[0018] M dry =M initial (1-k dry ·T dry ·t dry )

[0019] Among them, Mdry is the mass after drying; M initial is the initial mass of the raw material; k dry is the drying efficiency constant, which indicates the ratio of water and impurities lost by the raw material per unit time;

[0020] The metal impurities in the raw materials are removed by pickling treatment. The pickling reaction rate r acid Acid pickling solution concentration C acid and pickling treatment time t acid the impact of;

[0021] r acid =k acid ·C acid ·t acid ·A acid

[0022] The pickling reaction rate r acid Used to indicate the amount of metal impurities removed per unit time; k acid is the pickling reaction rate constant; the pickling solution concentration C acid Used to affect the reaction rate between acid and impurities; pickling treatment time t acid Used to indicate the duration of a reaction; A acid is the reaction surface area.

[0023] In a preferred embodiment, the raw material is converted into a carbon-based substance by high-temperature carbonization treatment. The raw material is pyrolyzed in an inert atmosphere. The reaction rate is affected by air flow, temperature, and holding time. The rate of change of the raw material mass is proposed to be

[0024]

[0025] in k is the rate of change of raw material mass per unit time; carbon is the carbonization reaction rate constant; E activation is the activation energy of the carbonization reaction; R is the gas constant; T carbon is the reaction temperature; M is the mass of the carbon-based substance, which indicates the remaining mass of the carbon-based substance during the reaction; f flow (Q gas ) is the airflow factor; airflow Q gas is the volume of gas passing through the reactor per unit time; τ soak For the insulation time.

[0026] In a preferred embodiment, the type and concentration of the surface functional groups determine the surface chemical properties of the catalyst; the surface of the carbon-based material is modified by an acidic solution or an alkaline solution to generate different types of functional groups; the amount of functional groups generated is N group;

[0027] N group =k group ·C solution ·t treat ·A treat

[0028] where k group is the rate constant for the formation of functional groups; C solution is the concentration of the solution; t treat is the processing time; A treat is the surface area of ​​the catalyst;

[0029] During the catalyst activation treatment, the pore structure and surface properties of the catalyst change, and the formation rate of active sites is affected by the airflow rate, temperature and treatment time;

[0030]

[0031] Among them A active is the number of active sites formed; k activate is the constant of the activation reaction; Q gas-1 is the gas flow rate; T activate is the activation temperature; t activate is the activation treatment time.

[0032] In a preferred embodiment, a first judgment condition is established based on the pore size distribution peak and the specific surface area, and the first judgment condition is used to judge whether the pore structure of the carbon-based raw meal catalyst meets the target requirements;

[0033]

[0034] Among them, P d (R) is the pore size distribution function; R is the pore size; V ads is the adsorption volume; is the rate of change of adsorption volume under pore size R;

[0035] The peak value of the pore size distribution is proposed to be R peak , pore size distribution peak R peak It's P d The maximum point of (R) is determined by the following conditions:

[0036] and

[0037] in represents the pore size distribution function P d (R) first derivative with respect to aperture R; represents the pore size distribution function P d (R) the second derivative with respect to the aperture R;

[0038] The specific surface area of ​​carbon-based raw meal catalyst was calculated by BET equation;

[0039]

[0040] Among them S BET is the specific surface area; V ads is the adsorption volume; N A is Avogadro's constant; σ is the cross-sectional area of ​​the adsorbed molecules; m is the mass of the catalyst;

[0041] Draft R threshold is the preset pore size distribution peak threshold; S threshold is the preset specific surface area threshold;

[0042] If R peak <R threshold And S BET threshold , it is judged as unqualified.

[0043] In a preferred embodiment, a second judgment condition is established based on the surface carboxyl concentration and the surface hydroxyl concentration; the second judgment condition is used to determine whether the surface functional groups of the carbon-based raw material catalyst meet the target requirements;

[0044] The acid-base titration expression for the surface carboxyl concentration is:

[0045]

[0046] Among them C COOH is the surface carboxyl concentration; V NaOH is the volume of sodium hydroxide solution consumed in titration; C NaOH is the concentration of sodium hydroxide solution; V HCI is the volume of hydrochloric acid solution consumed by back titration; C HCl is the concentration of hydrochloric acid solution; m is the mass of the catalyst;

[0047] The infrared spectrum expression of surface hydroxyl concentration is:

[0048] A OH =∈ OH ·C OH ·l

[0049] Among them A OH is the infrared absorption intensity; ∈ OH is the molar absorption coefficient of hydroxyl; C OH is the surface hydroxyl concentration; l is the optical path length; according to the absorption intensity A OH and the known ∈ OH and l, the surface hydroxyl concentration can be inferred; ​

[0050] Proposed C COOH,threshold is the preset surface carboxyl concentration threshold; C OH,threshold is the preset surface hydroxyl concentration threshold;

[0051] If C COOH <C COOH,threshold And C OH <C OH,threshold , it is judged as unqualified.

[0052] In a preferred embodiment, a scanning electron microscope is used to obtain an image of the catalyst surface, and each pixel of the image is i,j For an area on the surface, record the grayscale value of the area;

[0053]

[0054] Among them I normalized (x, y) is the normalized pixel value; I(x, y) is the grayscale value of the pixel with coordinates (x, y) in the original image; I min and I max are the minimum grayscale value and maximum grayscale value of the image respectively;

[0055] Surface features are extracted through edge detection and texture analysis, and periodic information is extracted using Fourier transform to quantify the distribution of surface functional groups.

[0056] Edge detection formula:

[0057]

[0058] Where G(x,y) is the gradient value at the coordinate (x,y), which is used to detect edge features; and are the gradients of the image in the x and y directions respectively;

[0059] Fourier transform analysis functional group distribution formula:

[0060]

[0061] Where F(u,v) is the amplitude distribution of the image in the frequency space; M and N are the number of horizontal and vertical pixels of the image, respectively, which are used to define the boundary of the frequency domain resolution; j is the imaginary unit; Represents the Fourier basis function in complex exponential form, the exponential part of which Used to describe the sine and cosine components corresponding to the frequency (u, v); u, v are the frequency domain coordinates of the image.

[0062] In a preferred embodiment, a convolutional neural network is used to extract deep features from images to detect the location and type of surface defects;

[0063]

[0064] Among them, P class (c) is the probability that the image belongs to category c; z c Output the score of the corresponding category c for the last layer of the convolutional neural network; k is the category index; the surface features are classified into specific types through deep learning algorithms;

[0065] Quantitative evaluation based on functional group density calculation and surface defect uniformity results;

[0066] Functional group density calculation formula:

[0067]

[0068] Among them D functional is the functional group density per unit area; N functional is the total number of functional groups detected; A total is the total surface area covered by the image;

[0069] Surface defect uniformity formula:

[0070]

[0071] Among them U defect is the defect uniformity index; D local,i is the defect density of the i-th local area; D mean is the average defect density of all local areas; n is the total number of divided areas; by quantifying the functional group density and defect uniformity, it is evaluated whether the performance of the catalyst surface meets expectations.

[0072] The technical effects and advantages of the present invention are as follows:

[0073] 1. This solution enhances the catalytic activity and selectivity of carbon-based raw meal catalysts by optimizing the pore structure and surface functional group distribution. Specifically, the formation of pore structure during pyrolysis, surface modification, and activation is controlled to ensure that the peak pore size distribution meets the design target. Furthermore, the types and concentrations of surface carboxyl and hydroxyl groups are regulated by acid-base titration and infrared spectroscopy, giving the catalyst higher surface reactivity and adaptability.

[0074] 2. This solution improves the raw material conversion efficiency and reduces energy consumption by regulating parameters in the drying, pickling and activation steps. At the same time, it screens out samples that do not meet the standards through judgment conditions 1 and 2, avoiding waste of resources.

[0075] 3. This solution utilizes scanning electron microscopy combined with edge detection, Fourier transform, and convolutional neural network technologies to analyze the catalyst surface characteristics layer by layer. Normalization eliminates image brightness deviations, edge detection identifies defect boundaries, Fourier transform extracts surface periodicity, and CNN deep learning enables surface property classification and automatic identification of defect types, enabling quantitative evaluation of catalyst surface performance and providing a clear basis for subsequent process improvements. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0077] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0078] Refer to the instruction manual Figure 1 A production and preparation process of an energy-saving and efficiency-enhancing raw material catalyst according to one embodiment of the present invention comprises:

[0079] Carbon-containing organic matter is selected as raw material, and is screened and dried to remove moisture and some impurities; then, metal impurities in the raw material are removed by pickling; the pickling solution includes sulfuric acid or hydrogen chloride solution;

[0080] The processed raw materials are placed in a heating furnace and pyrolyzed in an inert atmosphere; by adjusting the air flow, temperature, and holding time parameters, the raw materials are converted into porous carbon-based substances;

[0081] Using acidic or alkaline solutions to modify the surface of carbon-based substances to control the types and concentrations of their surface functional groups;

[0082] The surface-modified carbon-based material is placed in a heating furnace and activated in a preset atmosphere. By selecting a preset gas and adjusting the gas flow and temperature, the pore structure and surface properties of the catalyst are changed to form active sites.

[0083] The pore size distribution peak of the carbon-based raw meal catalyst is measured by a pore size distribution tester, and the specific surface area of ​​the carbon-based raw meal catalyst is confirmed by a BET test; and a judgment condition 1 is established based on the pore size distribution peak and the specific surface area;

[0084] Determine the surface carboxyl concentration of the carbon-based raw meal catalyst by acid-base titration or elemental analyzer, and determine the surface hydroxyl concentration of the carbon-based raw meal catalyst by infrared spectroscopy; establish judgment condition 2 based on the surface carboxyl concentration and surface hydroxyl concentration;

[0085] If the first or second judgment condition is judged as unqualified, image recognition processing is performed;

[0086] During image recognition processing, the catalyst surface is imaged using a scanning electron microscope to obtain surface image data; the catalyst surface characteristics are analyzed using an image recognition algorithm. The analysis steps of the image recognition algorithm include image feature extraction, functional group and defect identification, and quantitative evaluation;

[0087] Quantitative evaluation includes assessment of functional group density and surface defect uniformity;

[0088] It should be noted that the catalytic activity of carbon-based raw material catalysts is improved by optimizing their structure and surface properties. The scheme first selects carbon-containing organic matter as raw material, and carries out screening, drying and acid washing. The purpose of this step is to remove moisture and impurities, especially metal impurities, to provide pure raw materials, thereby avoiding the interference of impurities on subsequent reactions. Subsequently, the raw materials are converted into porous carbon-based substances through a pyrolysis process under a high-temperature inert atmosphere. This process is achieved by adjusting the airflow, temperature and holding time to ensure the formation of pore structure and carbonization of the material. Then, through surface modification treatment with acidic or alkaline solutions, the types and concentrations of functional groups on the catalyst surface are introduced or adjusted to give the catalyst better surface chemical properties. The treated carbon-based substance is then activated, and the gas flow and temperature are adjusted to optimize the pore structure and form more active sites, which provides more reactive centers for the catalyst in practical applications; then, pore size distribution tests and specific surface area measurements are used to verify whether the pore structure meets expectations, while acid-base titration and infrared spectroscopy are used to quantify the surface functional group concentration, and judgment is made based on preset standards; if the result is unsatisfactory, a scanning electron microscope combined with an image recognition algorithm is used for in-depth analysis to evaluate the surface defects and functional group distribution of the catalyst, and the process conditions are corrected through quantitative evaluation feedback; the logic of this design is to optimize in stages, with the purpose and results of each link closely linked, and through precise control and testing, the excellent performance of the catalyst in catalytic performance, stability and consistency is ensured; the necessity of this solution lies in that it comprehensively solves key problems such as raw material purification, pore structure optimization, surface functional group control and defect correction, thereby achieving high-performance catalyst preparation;

[0089] The four parameters of peak pore size distribution, specific surface area, surface carboxyl concentration, and surface hydroxyl concentration were selected because they collectively constitute the core performance indicators for carbon-based raw meal catalysts, covering two key aspects of the catalyst's microstructure and surface chemical properties. The peak pore size distribution reflects the catalyst's ability to diffuse and adsorb reactant molecules by characterizing the pore size distribution, while the specific surface area directly determines the catalyst's contact efficiency with the reactants and the number of active sites. These two parameters are quantified using a pore size distribution analyzer and BET test, accurately determining whether the pore structure meets the expected functional requirements. Meanwhile, the surface carboxyl concentration and surface hydroxyl concentration represent the acidity and polarity of the catalyst surface, respectively. The type and concentration of these functional groups play a decisive role in the selectivity and activity of the catalytic reaction. Acid-base titration and infrared spectroscopy are used to determine the carboxyl and hydroxyl concentrations, respectively, and the judgment criteria are established by comparison with preset standards. The combination of these four parameters not only comprehensively covers the physical and chemical properties of the catalyst but also provides clear direction for process optimization through experimental data feedback, thereby ensuring the catalyst's efficient and stable performance. This selection is scientific and targeted, and is a key step in improving the quality of catalyst preparation.

[0090] The above scheme optimizes catalytic efficiency, reduces resource waste and energy consumption by regulating the pore structure and surface functional group distribution, thereby realizing energy conservation and efficiency improvement.

[0091] The first judgment condition is: when the measured pore size distribution peak is lower than the preset pore size distribution peak threshold, and the measured specific surface area is lower than the preset specific surface area threshold;

[0092] The second judgment condition is: when the measured surface carboxyl concentration is lower than a preset surface carboxyl concentration threshold, and the measured surface hydroxyl concentration is lower than a preset surface hydroxyl concentration threshold.

[0093] Carbon-containing organic matter is selected as raw material and subjected to screening and drying treatment; during the drying process, the drying temperature T dry and time t dry , to remove moisture and some impurities;

[0094] M dry =M initial (1-k dry ·T dry ·t dry )

[0095] Among them, M dry M is the mass after drying, which means the residual mass of the raw material after the drying process is completed; initial is the initial mass of the raw material, i.e. the mass of the raw material before treatment; k dryis the drying efficiency constant, which indicates the ratio of water and impurities lost by the raw material per unit time; T dry is the drying temperature, which is used to affect the water volatilization rate; t dry is the drying time, which is used to control the duration of water removal;

[0096] The metal impurities in the raw materials are removed by pickling treatment. The pickling reaction rate r acid Acid pickling solution concentration C acid and pickling treatment time t acid the impact of;

[0097] r acid =k acid ·C acid ·t acid ·A acid

[0098] The pickling reaction rate r acid Used to indicate the amount of metal impurities removed per unit time; k acid is the pickling reaction rate constant, which depends on the type of acid, temperature and reaction mechanism; the pickling solution concentration C acid Used to affect the reaction rate between acid and impurities; pickling treatment time t acid Used to indicate the duration of a reaction; A acid is the reaction surface area, that is, the surface area of ​​the catalyst in contact with the solution, which affects the extent of the reaction;

[0099] In the above scheme, the material purification and pre-treatment effects are optimized by regulating parameters such as drying temperature, time, and pickling solution concentration. During the drying process, the residual mass of the raw material after drying is calculated by adjusting the drying temperature (which affects the water volatilization rate) and time (which determines the thoroughness of water removal). The relationship between the drying efficiency constant, temperature, and time in the formula illustrates the adaptability of the process to different raw material moisture and impurities. The purpose of this design is to reduce interference factors in subsequent reactions and ensure the uniformity and high purity of the raw materials. The pickling process adopts the joint control of solution concentration, reaction surface area, and treatment time. The removal efficiency of metal impurities is accurately evaluated through the quantitative formula of the reaction rate between acid and impurities. The pickling reaction rate constant is related to the type of acid, reaction temperature, and surface area, ensuring the controllability and consistency of the reaction conditions. This design logic embodies the precise quantification of process data, which not only optimizes energy consumption and resource utilization, but also lays a stable foundation for the subsequent performance of the catalyst, thereby enhancing the ultimate activity and efficiency of the catalyst.

[0100] The raw materials are converted into carbon-based substances through high-temperature carbonization treatment. The raw materials are pyrolyzed in an inert atmosphere. The reaction rate is affected by air flow, temperature, and holding time. The rate of change of the raw material mass is proposed to be

[0101]

[0102] in k is the rate of change of raw material mass per unit time, indicating the conversion rate of the substance at high temperature; carbon is the carbonization reaction rate constant, which determines the speed of the reaction; E activation is the activation energy of the carbonization reaction, which affects the temperature dependence of the reaction rate; R is the gas constant, which is used to describe the gas properties; T carbon is the reaction temperature, which affects the pyrolysis reaction rate; M is the mass of the carbon-based substance, which indicates the remaining mass of the carbon-based substance during the reaction; f flow (Q gas ) is the airflow factor, which indicates the effect of airflow on the pyrolysis process; airflow Q gas It is the volume of gas passing through the reactor per unit time, which affects the heat conduction and reaction uniformity of the pyrolysis reaction; when the gas flow rate Q gas When the airflow factor f increases, the heat conduction efficiency in the reactor is improved, which can accelerate the reaction process. flow It is usually a function that increases with the increase of airflow; where Q gas is the air flow rate during the pyrolysis process, used to calculate Q in other formulas. gas-1 Make a distinction; τ soak The holding time indicates the time the sample is kept at a certain temperature in the heating furnace. The longer the holding time, the more complete the pyrolysis reaction is and the higher the degree of conversion of carbon-based substances. The holding time is a multiplicative factor, indicating the total time the reaction occurs. The exponential function reflects the effect of temperature on the reaction rate. It is used to describe how temperature affects the reaction rate. Specifically, as the temperature increases, the exponential term becomes smaller, resulting in the reaction rate constant k carbon As the temperature increases, the reaction rate increases, and conversely, when the temperature decreases, the reaction rate slows down. In addition, the reason for using a negative sign in the reaction rate formula is that the raw material gradually loses mass during the pyrolysis process, and the rate of mass loss is proportional to the existing mass M of the raw material. Therefore, the change in mass is negative, indicating that the mass is decreasing over time.

[0103] High-temperature carbonization treatment achieves efficient conversion of raw materials into carbon-based substances through precise control of multiple key parameters; the reaction rate is determined by the reaction temperature and activation energy. The higher the temperature, the more complete the energy exchange between molecules and the faster the reaction rate, but too high a temperature may destroy the target pore structure, so a precise balance is required; secondly, the gas flow rate affects the heat conduction in the reactor and the degree of contact between the material and the gas. An appropriate airflow rate can accelerate heat transfer while maintaining a uniform reaction environment to ensure the uniformity of the carbonization process; the holding time determines the sufficiency of the reaction. Too short a time will lead to incomplete reaction, while too long a time may lead to excessive carbonization of the structure or energy waste; finally, the mass loss rate of the raw material during the carbonization process reflects the degree of carbonization conversion, and the formation of the carbon structure is proportional to the current residual mass; these parameters are interrelated and jointly guarantee the conversion efficiency of carbon-based substances and the consistency of the final pore structure, ensuring that the material properties meet the expected application requirements.

[0104] The type and concentration of surface functional groups determine the surface chemical properties of the catalyst. The surface of carbon-based materials is modified by acidic or alkaline solutions to generate different types of functional groups. The amount of functional groups generated is N group ;

[0105] N group =k group ·C solution ·t treat ·A treat

[0106] The amount of functional group generated N group Used to determine the chemical properties of the catalyst surface; k group is the rate constant for the formation of functional groups, which depends on factors such as the type of treatment solution and temperature; C solution is the concentration of the solution, which is used to affect the rate of formation of functional groups; t treat is the processing time, which is used to control the generation time of functional groups; A treat is the surface area of ​​the catalyst, which is used to affect the area in contact with the solution;

[0107] When the catalyst is activated in a high-temperature atmosphere, the pore structure and surface properties of the catalyst change, and the formation rate of active sites is affected by the gas flow rate, temperature, and treatment time;

[0108]

[0109] Among them A active is the number of active sites formed, which is used to determine the catalytic activity of the catalyst; k activate is the constant of the activation reaction, which determines the reaction rate; Q gas-1 is the gas flow rate, which is used to affect the contact efficiency between the catalyst and the gas; Q gas-1is the air flow rate during the activation process; T activate is the activation temperature, which is used to affect the reaction rate and the formation of active sites; t activate is the activation treatment time;

[0110] The type and concentration of surface functional groups directly determine the surface chemical properties and reactivity of the catalyst, so it is crucial to generate an appropriate amount of evenly distributed functional groups through a surface modification process. During the surface modification process, the type and concentration of the solution determine the rate and type of functional group generation, while the treatment time and surface area control the sufficiency and uniformity of the reaction, ensuring that the surface properties of the catalyst meet the expected requirements. The activation treatment further optimizes the pore structure and surface properties of the catalyst, forming more active sites by adjusting the gas flow, temperature and time. These active sites are the core of the catalyst's catalytic reaction, and their number directly affects the catalyst's reaction activity. The gas flow rate controls the contact efficiency between the reaction gas and the catalyst surface, and the temperature and time ensure the sufficient generation of active sites but avoid excessive damage to the catalyst surface. The overall design aims to achieve synergistic optimization of the catalyst surface functional groups and active sites through precise control of the modification and activation conditions, thereby improving the selectivity and activity of the catalyst.

[0111] Establishing a judgment condition 1 based on the pore size distribution peak and specific surface area, the judgment condition 1 is used to judge whether the pore structure of the carbon-based raw meal catalyst meets the target requirements;

[0112]

[0113] Among them, P d (R) is the pore size distribution function, which indicates the density of the adsorption volume change when the pore size is R; R is the pore diameter, which indicates the radius of the pore; V ads is the adsorption volume; is the rate of change of adsorption volume under pore size R; represents partial derivative;

[0114] The peak value of the pore size distribution is proposed to be R peak , pore size distribution peak R peak It's P d The maximum point of (R) is determined by the following conditions:

[0115] and

[0116] in represents the pore size distribution function P d (R) first derivative with respect to aperture R; Indicates P d (R) in R peak It reaches a steady state, that is, the slope of the function is zero; represents the pore size distribution function P d (R) the second derivative with respect to the aperture R; Indicates that in R peak At this point, the pore size distribution function reaches its maximum value, that is, the function has a downward curvature;

[0117] in Determine the stationary point of the function, indicating that this point may be an extreme point; Further judge the stationary point to be a maximum point, rather than a minimum or inflection point;

[0118] The specific surface area of ​​carbon-based raw meal catalyst was calculated by BET equation;

[0119]

[0120] Among them S BET is the specific surface area; V ads is the adsorption volume; N A is Avogadro's constant, which is 6.022×10 23 ;σ is the cross-sectional area of ​​the adsorbed molecules; m is the mass of the catalyst;

[0121] Draft R threshold is the preset pore size distribution peak threshold; S threshold is the preset specific surface area threshold;

[0122] If R peak <R threshold And S BET threshold , it is judged as unqualified.

[0123] The second judgment condition is established based on the surface carboxyl concentration and the surface hydroxyl concentration; the second judgment condition is used to determine whether the surface functional groups of the carbon-based raw material catalyst meet the target requirements;

[0124] The acid-base titration expression for the surface carboxyl concentration is:

[0125]

[0126] Among them C COOH is the surface carboxyl concentration; V NaOH is the volume of sodium hydroxide solution consumed in titration; C NaOH is the concentration of sodium hydroxide solution; V HCI is the volume of hydrochloric acid solution consumed by back titration; C HCl is the concentration of hydrochloric acid solution; m is the mass of the catalyst;

[0127] The infrared spectrum expression of surface hydroxyl concentration is:

[0128] A OH =∈​OH ·C OH ·l

[0129] Among them A OH is the infrared absorption intensity; ∈ OH is the molar absorption coefficient of hydroxyl; C OH is the surface hydroxyl concentration; l is the optical path length; according to the absorption intensity A OH and the known ∈ OH and l, the surface hydroxyl concentration can be inferred;

[0130] Proposed C COOH,threshold is the preset surface carboxyl concentration threshold, indicating the minimum acceptable carboxyl concentration; C OH,threshold is the preset surface hydroxyl concentration threshold, indicating the minimum acceptable hydroxyl concentration;

[0131] If C COOH <C COOH,threshold And C OH <C OH,threshold , it is judged as unqualified;

[0132] The scheme's pore size distribution and specific surface area determination criteria (a) and surface carboxyl concentration and surface hydroxyl concentration determination criteria (b) are dual-constraint indicators designed to accurately evaluate the catalyst's microstructure and chemical properties. The pore size distribution peak reflects the catalyst's adaptability to molecular diffusion, and determining whether it reaches the peak range ensures the functional design of the pore structure. The specific surface area indirectly measures the number of active sites on the catalyst by measuring the adsorption amount, thereby ensuring reaction efficiency. For chemical properties, the carboxyl concentration and hydroxyl concentration reflect the distribution of acidic and polar functional groups, respectively, which are crucial to the catalyst's selective reactivity. The carboxyl concentration is quantified by acid-base titration, derived from the relationship between consumption and concentration in the solution, while the hydroxyl concentration is reflected by measuring absorbance changes using infrared spectroscopy. These determination criteria ensure that the catalyst's structure meets diffusion and reaction requirements while also ensuring the stability and adaptability of the chemical properties, thereby achieving multi-dimensional performance optimization of the catalyst. Ultimately, unqualified materials are screened through quantitative evaluation and parameter comparison, ensuring standardization and efficiency in the production process.

[0133] The catalyst surface image was acquired using a scanning electron microscope. Each pixel of the image I i,j Corresponding to a tiny area on the surface, record the grayscale value of the area;

[0134]

[0135] Among them I normalized (x, y) is the normalized pixel value, ranging from [0, 1]; I(x, y) is the grayscale value of the pixel with coordinates (x, y) in the original image; Imin and I max are the minimum and maximum grayscale values ​​of the image respectively; the normalized image data in the above formula is used to eliminate the influence of brightness fluctuation in the subsequent algorithm;

[0136] Surface features are extracted through edge detection and texture analysis, and periodic information is extracted using Fourier transform to quantify the distribution of surface functional groups.

[0137] Edge detection formula:

[0138]

[0139] Where G(x,y) is the gradient value at the coordinate (x,y), which is used to detect edge features; and are the gradients of the image in the x and y directions, respectively, which can be calculated using the Sobel operator or other differential methods; Represents the gradient of the image in the x-direction, that is, the rate of change of the grayscale value in the horizontal direction, which is achieved by calculating the grayscale difference between adjacent pixels in the horizontal direction. The unit is grayscale value / pixel; Represents the gradient of the image in the y direction, that is, the rate of change of the grayscale value in the vertical direction, which is achieved by calculating the grayscale difference between adjacent pixels in the vertical direction, and the unit is grayscale value / pixel; the comprehensive value of the gradient in the x and y directions is calculated by the Pythagorean theorem, that is, the gradient intensity in two-dimensional space, which is used to quantify the edge properties of the pixel;

[0140] Fourier transform analysis functional group distribution formula:

[0141]

[0142] Where F(u,v) is the amplitude distribution of the image in the frequency space, reflecting the periodic characteristics and functional group distribution; M and N are the number of horizontal and vertical pixels of the image, respectively, which are used to define the boundary of the frequency domain resolution; j is the imaginary unit, j 2 =-1;I normalized (x,y) is at the spatial coordinate (x,y), and is used as the input signal to participate in the calculation of the frequency component; Represents the Fourier basis function in complex exponential form, the exponential part of which Used to describe the sine and cosine components corresponding to the frequency (u, v); u and v are the frequency domain coordinates of the image, representing the frequency components in the horizontal and vertical directions respectively, and the unit is cycle / pixel; the surface periodic structure, such as the regular distribution of certain functional groups, is extracted through frequency domain features.

[0143] Use convolutional neural network (CNN) to extract deep features from images and detect the location and type of surface defects;

[0144]

[0145] Among them, P class (c) is the probability that the image belongs to category c; z c The last layer of the convolutional neural network outputs the score of the corresponding category c, z c reflects the network's confidence score that the input image belongs to category c, score z c The larger the value, the more confident the network is that the input belongs to category c; category c is a predefined classification label, such as "functional group", "crack", "hole" or "no defect", where the probability value range is [0, 1], and the sum of the probabilities of all categories is 1, indicating that the image completely belongs to some known category; e is the base of the natural logarithm, with an approximate value of 2.718. It is a constant in mathematics and is often used in the calculation of exponential growth or decay; k is the category index, representing all possible categories, for example, the categories may include "defect type 1", "defect type 2", "no defect", etc. It represents the sum of the index scores of all possible categories, normalized to ensure that the sum of the probabilities of all categories is 1; deep learning algorithms are used to classify surface features into specific types, such as specific functional groups, cracks, holes, etc.

[0146] The process includes:

[0147] The input image passes through the convolutional neural network (CNN) to extract low-level features (such as edges) and high-level features (such as defect shapes);

[0148] In the last layer of CNN, the fully connected neurons output the scores z for each category. c ;

[0149] Apply the Softmax function to score z c Convert to P class (c), calculate the probability that the image belongs to category c;

[0150] According to the category with the highest probability, the defect type of the pixel or area (such as crack, hole, functional group, etc.) is determined.

[0151] Quantitative evaluation based on functional group density calculation and surface defect uniformity results;

[0152] Functional group density calculation formula:

[0153]

[0154] Among them D functional is the functional group density per unit area; N functional is the total number of functional groups detected; A total is the total surface area covered by the image;

[0155] Surface defect uniformity formula:

[0156]

[0157] Among them U defect is the defect uniformity index, ranging from [0, 1], and the closer the value is to 1, the more uniform the surface is; D local，i is the defect density of the i-th local area; D mean is the average defect density of all local regions; n is the total number of divided regions; by quantifying the functional group density and defect uniformity, we can evaluate whether the performance of the catalyst surface meets expectations;

[0158] It should be noted that the above scheme achieves a comprehensive analysis and quantitative evaluation of catalyst surface characteristics. First, the scheme is based on normalization processing, which standardizes the image grayscale value to a unified range of 0 to 1 to eliminate brightness differences caused by factors such as equipment and lighting during the acquisition process, ensuring input consistency for subsequent analysis. This link solves the problem that data deviation in traditional detection may lead to feature misjudgment. Subsequently, the edge detection algorithm is used to identify the boundary areas of grayscale changes in the image, and surface features such as the edge morphology and position distribution of defects such as cracks and holes are accurately extracted. These defects are usually the root cause of the deterioration of catalyst surface performance, and edge detection achieves accurate positioning and quantification of these features through gradient calculation, providing reliable input data for subsequent classification. In addition, the Fourier transform converts the image from the spatial domain to the frequency domain, and extracts the periodicity and macroscopic laws of the distribution of surface functional groups by analyzing the frequency domain characteristics. For example, the functional groups on the catalyst surface may be distributed at a certain fixed interval. The Fourier transform can verify whether these intervals meet the design requirements, thereby judging the rationality of the distribution. These steps comprehensively analyze the surface characteristics from the micro to the macro level.

[0159] Based on feature extraction, the solution introduces a convolutional neural network (CNN) for deep classification. By learning the features of a large number of labeled samples, the network can identify surface areas belonging to categories such as "cracks," "holes," or "functional groups," and quantify the classification results in the form of probability to ensure the accuracy and stability of the output. For example, by training the model to identify the characteristic differences between carboxyl-containing regions and non-functional group regions, the distribution of surface chemical properties of samples can be automatically annotated in industrial inspections. The final solution achieves performance evaluation by quantifying the functional group density and surface defect uniformity index, where the functional group density reflects the number of chemically active areas on the catalyst surface, while the defect uniformity measures the completeness of the surface distribution. This series of designs aims to simplify the complex surface characteristics of the catalyst into quantifiable indicators, provide an optimization basis for production, and rely on mature equipment and tools such as scanning electron microscopes (SEM), MATLAB, and Python to achieve industrial application, solve the standardization and consistency problems in the catalyst preparation process, and thus ensure the high efficiency and stable performance of the catalyst.

[0160] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A production and preparation process for energy-saving and efficiency-enhancing raw material catalyst, characterized in that: include: Select carbon-containing organic matter as raw material, and perform screening and drying on it to remove moisture and some impurities; The metal impurities in the raw materials are then removed by pickling treatment; the pickling solution includes sulfuric acid or hydrogen chloride solution; The processed raw materials are placed in a heating furnace and pyrolyzed in an inert atmosphere; by adjusting the air flow, temperature, and holding time parameters, the raw materials are converted into porous carbon-based substances; Using acidic or alkaline solutions to modify the surface of carbon-based substances to control the types and concentrations of their surface functional groups; The surface-modified carbon-based material is placed in a heating furnace and activated in a preset atmosphere. By selecting a preset gas and adjusting the gas flow and temperature, the pore structure and surface properties of the catalyst are changed to form active sites. The pore size distribution peak of the carbon-based raw meal catalyst was measured by a pore size distribution tester, and the specific surface area of ​​the carbon-based raw meal catalyst was confirmed by a BET test; Establish judgment condition 1 based on pore size distribution peak and specific surface area; Determine the surface carboxyl concentration of the carbon-based raw meal catalyst by acid-base titration or elemental analyzer, and determine the surface hydroxyl concentration of the carbon-based raw meal catalyst by infrared spectroscopy; establish judgment condition 2 based on the surface carboxyl concentration and surface hydroxyl concentration; If the first or second judgment condition is judged as unqualified, image recognition processing is performed; During image recognition processing, the catalyst surface is imaged using a scanning electron microscope to obtain surface image data; the catalyst surface characteristics are analyzed using an image recognition algorithm. The analysis steps of the image recognition algorithm include image feature extraction, functional group and defect identification, and quantitative evaluation; Quantitative evaluation includes assessment of functional group density and surface defect uniformity.

2. The production and preparation process of an energy-saving and efficiency-enhancing raw material catalyst according to claim 1, characterized in that: The first judgment condition is: when the measured pore size distribution peak is lower than the preset pore size distribution peak threshold, and the measured specific surface area is lower than the preset specific surface area threshold; The second judgment condition is: when the measured surface carboxyl concentration is lower than a preset surface carboxyl concentration threshold, and the measured surface hydroxyl concentration is lower than a preset surface hydroxyl concentration threshold.

3. The production and preparation process of an energy-saving and efficiency-enhancing raw material catalyst according to claim 2, characterized in that: Carbon-containing organic matter is selected as raw material and subjected to screening and drying treatment; during the drying process, the drying temperature T dry and time t dry , to remove moisture and some impurities; M dry =M initial ·(1-k dry ·T dry ·t dry ) Among them, M dry is the mass after drying; M initial is the initial mass of the raw material; k dry is the drying efficiency constant, which indicates the ratio of water and impurities lost by the raw material per unit time; The metal impurities in the raw materials are removed by pickling treatment. The pickling reaction rate r acid Acid pickling solution concentration C acid and pickling treatment time t acid the impact of; r acid =k acid ·C acid ·t acid ·A acid The pickling reaction rate r acid Used to indicate the amount of metal impurities removed per unit time; k acid is the pickling reaction rate constant; the pickling solution concentration C acid Used to affect the reaction rate between acid and impurities; pickling treatment time t acid Used to indicate the duration of a reaction; A acid is the reaction surface area.

4. The production and preparation process of an energy-saving and efficiency-enhancing raw material catalyst according to claim 3, characterized in that: The raw materials are converted into carbon-based substances through high-temperature carbonization treatment. The raw materials are pyrolyzed in an inert atmosphere. The reaction rate is affected by air flow, temperature, and holding time. The rate of change of the raw material mass is proposed to be in k is the rate of change of raw material mass per unit time; carbon is the carbonization reaction rate constant; E activation is the activation energy of the carbonization reaction; R is the gas constant; T carbon is the reaction temperature; M is the mass of the carbon-based substance, which indicates the remaining mass of the carbon-based substance during the reaction; f flow (Q gas ) is the airflow factor; airflow Q gas is the volume of gas passing through the reactor per unit time; τ soak For the insulation time.

5. The production and preparation process of the energy-saving and efficiency-enhancing raw material catalyst according to claim 4, characterized in that: The type and concentration of surface functional groups determine the surface chemical properties of the catalyst. The surface of carbon-based materials is modified by acidic or alkaline solutions to generate different types of functional groups. The amount of functional groups generated is N group ; N group =k group ·C solution ·t treat ·A treat where k group is the rate constant for the formation of functional groups; C solution is the concentration of the solution; t treat is the processing time; A treat is the surface area of ​​the catalyst; During the catalyst activation treatment, the pore structure and surface properties of the catalyst change, and the formation rate of active sites is affected by the airflow rate, temperature and treatment time; Among them A active is the number of active sites formed; k activate is the constant of the activation reaction; Q gas-1 is the gas flow rate; T activate is the activation temperature; t activate is the activation treatment time.

6. The production and preparation process of the energy-saving and efficiency-enhancing raw material catalyst according to claim 5, characterized in that: Establishing a judgment condition 1 based on the pore size distribution peak and specific surface area, the judgment condition 1 is used to judge whether the pore structure of the carbon-based raw meal catalyst meets the target requirements; Among them, P d (R) is the pore size distribution function; R is the pore size; V ads is the adsorption volume; is the rate of change of adsorption volume under pore size R; The peak value of the pore size distribution is proposed to be R peak , pore size distribution peak R peak It's P d The maximum point of (R) is determined by the following conditions: and in represents the pore size distribution function P d (R) first derivative with respect to aperture R; represents the pore size distribution function P d (R) the second derivative with respect to the aperture R; The specific surface area of ​​carbon-based raw meal catalyst was calculated by BET equation; Among them S BET is the specific surface area; V ads is the adsorption volume; N A is Avogadro's constant; σ is the cross-sectional area of ​​the adsorbed molecules; m is the mass of the catalyst; Draft R threshold is the preset pore size distribution peak threshold; S threshold is the preset specific surface area threshold; If R peak <R threshold And S BET threshold , it is judged as unqualified.​ 7. The production and preparation process of the energy-saving and efficiency-enhancing raw material catalyst according to claim 6, characterized in that: The second judgment condition is established based on the surface carboxyl concentration and the surface hydroxyl concentration; the second judgment condition is used to determine whether the surface functional groups of the carbon-based raw material catalyst meet the target requirements; The acid-base titration expression for the surface carboxyl concentration is: Among them C COOH is the surface carboxyl concentration; V NaOH is the volume of sodium hydroxide solution consumed in titration; C NaOH is the concentration of sodium hydroxide solution; V HCI is the volume of hydrochloric acid solution consumed by back titration; C HCl is the concentration of hydrochloric acid solution; m is the mass of the catalyst; The infrared spectrum expression of surface hydroxyl concentration is: A OH =∈ OH ·C OH ·l Among them A OH is the infrared absorption intensity; ∈ OH is the molar absorption coefficient of hydroxyl; C OH is the surface hydroxyl concentration; l is the optical path length; according to the absorption intensity A OH and the known ∈ OH and l, the surface hydroxyl concentration can be inferred; Proposed C COOH,threshold is the preset surface carboxyl concentration threshold; C OH,threshold is the preset surface hydroxyl concentration threshold; If C COOH <C COOH,threshold And C OH <C OH,threshold , it is judged as unqualified.

8. The production and preparation process of the energy-saving and efficiency-enhancing raw material catalyst according to claim 7, characterized in that: The catalyst surface image was acquired using a scanning electron microscope. Each pixel of the image I i,j For an area on the surface, record the grayscale value of the area; Among them I normalized (x, y) is the normalized pixel value; I(x, y) is the grayscale value of the pixel with coordinates (x, y) in the original image; I min and I max are the minimum grayscale value and maximum grayscale value of the image respectively; Surface features are extracted through edge detection and texture analysis, and periodic information is extracted using Fourier transform to quantify the distribution of surface functional groups. Edge detection formula: Where G(x,y) is the gradient value at the coordinate (x,y), which is used to detect edge features; and are the gradients of the image in the x and y directions respectively; Fourier transform analysis functional group distribution formula: Where F(u,v) is the amplitude distribution of the image in the frequency space; M and N are the number of horizontal and vertical pixels of the image, respectively, which are used to define the boundary of the frequency domain resolution; j is the imaginary unit; Represents the Fourier basis function in complex exponential form, the exponential part of which Used to describe the sine and cosine components corresponding to the frequency (u, v); u, v are the frequency domain coordinates of the image.

9. The production and preparation process of the energy-saving and efficiency-enhancing raw material catalyst according to claim 8, characterized in that: Use convolutional neural networks to extract deep features from images and detect the location and type of surface defects; Among them, P class (c) is the probability that the image belongs to category c; z c Output the score of the corresponding category c for the last layer of the convolutional neural network; k is the category index; the surface features are classified into specific types through deep learning algorithms; Quantitative evaluation based on functional group density calculation and surface defect uniformity results; Functional group density calculation formula: Among them D functional is the functional group density per unit area; N functional is the total number of functional groups detected; A total is the total surface area covered by the image; Surface defect uniformity formula: Among them U defect is the defect uniformity index; D local,i is the defect density of the i-th local area; D mean is the average defect density of all local areas; n is the total number of divided areas; by quantifying the functional group density and defect uniformity, it is evaluated whether the performance of the catalyst surface meets expectations.

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