Biomass steam upgrading process optimization method based on comprehensive upgrading intensity index

Abstract

The application provides a biomass steam upgrading process optimization method based on comprehensive upgrading intensity index, and belongs to the technical field of biomass steam upgrading. The method comprises the following steps: providing a comprehensive upgrading intensity index and a quality index; establishing a coupling prediction model of the comprehensive upgrading intensity index and the quality index; solving a target comprehensive upgrading intensity index corresponding to a target quality index according to the coupling prediction model; and inversely deriving an optimized upgrading process parameter combination according to the target comprehensive upgrading intensity. The application integrates factors such as upgrading temperature, upgrading time, steam pressure and raw material characteristics to construct a comprehensive upgrading intensity index, and establishes a coupling prediction model between the comprehensive upgrading intensity index and the quality index, thereby realizing a significant change from experience trial and error to precise prediction and control of the steam upgrading process, significantly improving the process optimization efficiency and the quality controllability of the upgraded product, and having a good practical application prospect.

Description

Technical Field

[0001] This invention relates to the field of biomass steam upgrading technology, specifically to a method for optimizing biomass steam upgrading process based on a comprehensive upgrading intensity index. Background Technology

[0002] Biomass steam upgrading is a commonly used biomass pretreatment technology. It involves partially pyrolyzing and reorganizing biomass under high temperature and high pressure steam to obtain biochar products with higher energy density and better combustion characteristics.

[0003] Existing optimization methods for biomass steam upgrading mainly rely on trial and error or single-factor experiments. However, due to the significant differences in activation energy among different types of biomass, the upgrading effect fluctuates greatly under the same process conditions. Existing optimization methods are inefficient and it is difficult to form effective universal optimization standards, which can easily lead to unstable quality of the upgraded products obtained after optimization.

[0004] In view of this, it is necessary to provide an improved method for optimizing biomass steam upgrading process to solve the above problems. Summary of the Invention

[0005] In view of the technical problems existing in the background art, this application provides a biomass steam upgrading process optimization method based on the comprehensive upgrading intensity index, which aims to realize the transformation of steam upgrading process optimization from experience trial and error to precise prediction and control, significantly improve the efficiency of process optimization, and ensure the quality of upgraded products.

[0006] This application provides an optimization method for biomass steam upgrading process based on a comprehensive upgrading intensity index, comprising the following steps: S1. Provide a comprehensive quality improvement intensity index and quality indicators; the comprehensive quality improvement intensity index is calculated according to the following formula: R0=k1·exp(-E a,mix / RT)·t n1 ·P steam n2 ·f(M)·f(D); Where R0 is the comprehensive quality improvement intensity index, k1 is the pre-exponential factor, and E a,mix R is the comprehensive apparent activation energy of the biomass material to be processed, T is the upgrading temperature, t is the upgrading time, n1 is the time exponent, and P is the average apparent activation energy of the biomass material to be processed. steam Where n is the vapor partial pressure, n2 is the pressure exponent, f(M) is the moisture correction function, and f(D) is the particle size correction function. S2. Establish a coupled prediction model between the comprehensive quality improvement intensity index and the quality index; S3. Based on the coupled prediction model, solve for the target comprehensive quality improvement intensity index corresponding to the target quality index; S4. Based on the target comprehensive quality improvement intensity, deduce the optimized combination of quality improvement process parameters.

[0007] In some embodiments, in step S2, the coupling prediction model includes: Q j =a j ·R0 bj ·exp(c j ·R0)+d j ; Among them, Q j Let a be the j-th quality indicator. j Let bj be the scaling factor corresponding to the j-th quality indicator, bj be the power law exponent corresponding to the j-th quality indicator, and c be the scaling factor. j Let d be the coefficient of the exponential term of the j-th quality indicator. j Let a be the initial value corresponding to the j-th quality indicator; j bj c j Obtained through fitting.

[0008] In some embodiments, a j bj c j The result was obtained by fitting using a nonlinear least squares regression algorithm.

[0009] In some embodiments, the formula for calculating the comprehensive apparent activation energy of the biomass material to be treated is: E a,mix =∑(x k ·E a,k ); Where, x k Let ∑x be the mass fraction of the k-th type of biomass in the biomass material to be processed. k =1;E a,k Let be the apparent activation energy of the k-th type of biomass in the biomass material to be treated; k is an integer ≥ 1.

[0010] In some embodiments, the formula for calculating the apparent activation energy of the k-th type of biomass in the biomass material to be treated is: E a,k =W 1,k ·Ea1+W 2,k ·Ea2+W 3,k Ea3; Among them, W 1,k Let W be the mass fraction of cellulose in the k-th type of biomass. 2,k W represents the mass fraction of hemicellulose in the k-th biomass. 3,kW represents the mass fraction of lignin in the k-th type of biomass. 1,k +W 2,k +W 3,k =1; Ea1 is the activation energy of cellulose, ranging from 180 to 220 kJ / mol; Ea2 is the activation energy of hemicellulose, ranging from 80 to 120 kJ / mol; Ea3 is the activation energy of lignin, ranging from 40 to 80 kJ / mol.

[0011] In some embodiments, the formula for calculating the moisture correction function is: f(M) = 1 + α·(M) ad -M ref ); Wherein, α is the moisture sensitivity coefficient. When the biomass material to be treated is woody, the value of α ranges from 0.01 to 0.02; when the biomass material to be treated is herbaceous, the value of α ranges from 0.02 to 0.04; and when the biomass material to be treated is fruit shell, the value of α ranges from 0.03 to 0.05. ad M represents the measured moisture content of the biomass material to be processed. ref This is the preset reference moisture content.

[0012] In some embodiments, the formula for calculating the particle size correction function is: f(D) = (D / D) ref ) β ; Where D is the measured average particle size of the biomass material to be treated, D ref The preset reference average particle size is β, which is the particle size index, ranging from -0.3 to -0.1.

[0013] In some embodiments, step S3, the step of solving the target comprehensive quality improvement intensity index corresponding to the target quality index according to the coupled prediction model, includes: A multi-objective optimization algorithm is used to solve for the threshold range of the comprehensive quality improvement intensity index corresponding to the target quality index; The median value of the threshold range is used as the target comprehensive quality improvement intensity index.

[0014] In some embodiments, in step S4, the inversion method includes: using the target comprehensive quality improvement intensity as a constraint, solving for the combination of quality improvement process parameters.

[0015] In some embodiments, the optimized process parameters obtained through reverse engineering are combined and then subjected to closed-loop control; the closed-loop control steps include: S51. Monitor the upgrading temperature and steam partial pressure in real time, and calculate the real-time comprehensive upgrading intensity index. S52. Determine whether the real-time comprehensive quality improvement intensity index is within the threshold range of the comprehensive quality improvement intensity index; if the determination result is "yes", then maintain the current quality improvement process parameters unchanged; if the determination result is "no", then adjust the current quality improvement process parameters. S53. Repeat steps S51-S52.

[0016] The beneficial effects of this application include: This application provides a method for optimizing biomass steam upgrading processes based on a comprehensive upgrading intensity index. It constructs a comprehensive upgrading intensity index integrating factors such as upgrading temperature, upgrading time, steam pressure, and raw material characteristics, achieving quantification of a single indicator in the steam upgrading process. Based on this, this application establishes a coupled prediction model between the comprehensive upgrading intensity index and quality indicators. This model can solve for the corresponding target comprehensive upgrading intensity index based on the target quality indicators, and then deduce the corresponding upgrading process parameters, achieving a significant shift in steam upgrading processes from experience-based trial and error to precise predictive control. Furthermore, the method provided in this application is applicable to the optimization of steam upgrading processes for various types of biomass, significantly improving process optimization efficiency and the quality controllability of upgraded products, and has good practical application prospects.

[0017] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of this application, the accompanying drawings used in this application will be briefly described below. Obviously, the drawings described below are merely some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without any creative effort.

[0019] Figure 1 This is a schematic flowchart illustrating the optimization method for biomass steam upgrading process based on the comprehensive upgrading intensity index provided in this application embodiment. Detailed Implementation

[0020] The embodiments of the technical solution of this application will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of this application and are therefore merely examples, and should not be used to limit the scope of protection of this application.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0022] In the description of the embodiments in this application, "a plurality of" means two or more, unless otherwise expressly specified. The reference to "embodiment" herein means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0023] Existing optimization methods for biomass steam upgrading processes mainly rely on trial-and-error or single-factor experiments, which are not only inefficient but also prone to causing instability in the quality of the upgraded products. Therefore, this application provides a biomass steam upgrading process optimization method based on a comprehensive upgrading intensity index. By constructing a comprehensive upgrading intensity index that integrates factors such as upgrading temperature, upgrading time, steam pressure, and raw material characteristics, and establishing a coupled predictive model between the comprehensive upgrading intensity index and quality indicators, the optimization of the steam upgrading process shifts from empirical trial-and-error to precise predictive control, significantly improving process optimization efficiency and ensuring the quality of the upgraded products.

[0024] This application provides an optimization method for biomass steam upgrading process based on a comprehensive upgrading intensity index, the process flow diagram of which is shown below. Figure 1 As shown, the specific steps include the following: S1. Provides a comprehensive quality improvement intensity index and quality indicators; the comprehensive quality improvement intensity index is calculated according to the following formula: R0=k1·exp(-E a,mix / RT)·t n1 ·P steam n2 ·f(M)·f(D); Where R0 is the comprehensive quality improvement intensity index, k1 is the pre-exponential factor, and E a,mix R is the comprehensive apparent activation energy of the biomass material to be processed, T is the upgrading temperature, t is the upgrading time, n1 is the time exponent, and P is the average apparent activation energy of the biomass material to be processed. steam Where n is the vapor partial pressure, n2 is the pressure exponent, f(M) is the moisture correction function, and f(D) is the particle size correction function. S2. Establish a coupled prediction model of comprehensive quality improvement intensity index and quality indicators; S3. Based on the coupled prediction model, solve for the target comprehensive quality improvement intensity index corresponding to the target quality index; S4. Based on the target comprehensive quality improvement intensity, deduce the optimized combination of quality improvement process parameters.

[0025] In the technical solution of this application embodiment, a comprehensive upgrading intensity index integrating factors such as upgrading temperature, upgrading time, steam pressure, and raw material characteristics is constructed, thereby achieving the quantification of a single index in the steam upgrading process. Based on this, this application establishes a coupled prediction model between the comprehensive upgrading intensity index and quality indicators. This model can solve for the corresponding target comprehensive upgrading intensity index based on the target quality indicators, and then deduce the corresponding upgrading process parameters, achieving a significant shift in the steam upgrading process from experience-based trial and error to precise predictive control. Furthermore, the method provided in this application is applicable to the optimization of steam upgrading processes for various types of biomass, significantly improving process optimization efficiency and the quality controllability of upgraded products, and has good practical application prospects.

[0026] The following examples illustrate the specific implementation of the biomass steam upgrading process optimization method based on the comprehensive upgrading intensity index provided in this application.

[0027] In some embodiments, in order to calculate the overall quality improvement index, it is necessary to first determine the characteristic parameters of the biomass material to be treated; these characteristic parameters include industrial analysis parameters, fiber composition, and physicochemical properties. More specifically, in some embodiments, the industrial analysis parameters include moisture content and fixed carbon content; the fiber composition includes the mass fraction of cellulose, the mass fraction of hemicellulose, and the mass fraction of lignin; and the physicochemical properties include average particle size and higher heating value.

[0028] In the technical solution of this application embodiment, the measured moisture content and average particle size can be used to calculate the moisture correction function and particle size correction function, respectively. The fixed carbon content and higher heating value can be used as initial quality indicators, and the fiber composition can be used to calculate the comprehensive apparent activation energy.

[0029] Furthermore, in some embodiments, the pre-exponential factor k1 in the formula for calculating the comprehensive quality improvement intensity index ranges from 10. 8 ~10 12 min - ¹, specifically determined based on the reactivity level of the biomass material to be processed. Specifically, for woody raw materials (such as sawdust and branches), which have a dense structure and low reactivity, k1 should be a higher value, preferably within the range of 10. 10 ~10 12 min -¹; For herbaceous raw materials (such as straw and rice husks), which have a loose structure and high reactivity, k1 should be taken as a lower value, preferably within the range of 10. 8 ~10 10 min - ¹; For fruit shell materials (such as coconut shells, walnut shells, and camellia shells): they have a hard structure and high lignin content, but the particles are small. k1 should be taken as an intermediate value, preferably within the range of 10. 9 ~10 11 min -1 When the biomass material to be processed contains multiple different types of raw materials, the pre-exponential factor of the mixture can be calculated based on the proportion of each type of raw material. For example, if the biomass material to be processed contains 30 wt% lignocellulosic raw materials and 70 wt% herbaceous raw materials, then the pre-exponential factor of the lignocellulosic raw materials should be used. Pre-treatment factor with 30%+ herbal ingredients 70% is used as the pre-exponential factor for the mixture.

[0030] Furthermore, in some embodiments, the formula for calculating the overall apparent activation energy of the biomass material to be treated is as follows: E a,mix =∑(x k ·E a,k ); Where, x k Let ∑x be the mass fraction of the k-th type of biomass in the biomass material to be processed. k =1;E a,k Let k be the apparent activation energy of the k-th type of biomass in the biomass material to be treated; k is an integer ≥ 1. When k = 1, it indicates that the biomass material to be treated is a single type of biomass; when k > 1, it indicates that the biomass material to be treated is a mixture of multiple types of biomass.

[0031] Furthermore, in some embodiments, the formula for calculating the apparent activation energy of the kth type of biomass in the biomass material to be treated is as follows: E a,k =W 1,k ·Ea1+W 2,k ·Ea2+W 3,k Ea3; Among them, W 1,k Let W be the mass fraction of cellulose in the k-th type of biomass. 2,k W represents the mass fraction of hemicellulose in the k-th biomass. 3,k W represents the mass fraction of lignin in the k-th type of biomass. 1,k +W 2,k +W 3,k=1; Ea1 is the activation energy of cellulose, ranging from 180 to 220 kJ / mol, preferably 200 kJ / mol; Ea2 is the activation energy of hemicellulose, ranging from 80 to 120 kJ / mol, preferably 100 kJ / mol; Ea3 is the activation energy of lignin, ranging from 40 to 80 kJ / mol, preferably 60 kJ / mol.

[0032] Further, in some embodiments, the time index n1 is preferably in the range of 0.5 to 1.5. If the reaction in the upgrading process is mainly surface-based, the time index n1 is further preferably in the range of 0.5 to 0.8; if the reaction in the upgrading process is mainly internal diffusion, the time index n1 is further preferably in the range of 1.0 to 1.5. In some embodiments, the pressure index n2 is preferably in the range of 0.2 to 0.8. If the steam in the reaction vessel is low-pressure steam (partial pressure less than 0.4 MPa), the pressure index n2 is further preferably in the range of 0.2 to 0.5; if the steam in the reaction vessel is high-pressure steam (partial pressure greater than 0.4 MPa), the pressure index n2 is further preferably in the range of 0.5 to 0.8.

[0033] Furthermore, in some embodiments, the formula for calculating the moisture correction function is: f(M) = 1 + α·(M) ad -M ref ); Wherein, α is the moisture sensitivity coefficient. When the biomass material to be treated is woody, the value of α ranges from 0.01 to 0.02; when the biomass material to be treated is herbaceous, the value of α ranges from 0.02 to 0.04; and when the biomass material to be treated is fruit shell, the value of α ranges from 0.03 to 0.05. ad M represents the measured moisture content of the biomass material to be processed. ref This is a preset reference moisture content. Specifically, in some embodiments, M ref The preferred value is 10%.

[0034] In the technical solution of this application embodiment, through the above method, when M ad >M ref When f(M) > 1, it indicates that the moisture content is too high and the quality improvement intensity needs to be strengthened; when M ad =M ref When M is constant, f(M) = 1, which is the standard state; when M is constant... ad <M ref When f(M) < 1, it indicates that low moisture content can reduce the quality improvement intensity.

[0035] Furthermore, in some embodiments, the formula for calculating the particle size correction function is: f(D) = (D / D) ref ) β ; Where D is the measured average particle size of the biomass material to be treated, D ref The preset reference average particle size is β, which is the particle size index, ranging from -0.3 to -0.1. More specifically, in some embodiments, if the ratio of the maximum particle size to the minimum particle size of the biomass material to be treated is <1.5, the particle size distribution is highly uniform, and β is -0.1; if the ratio is 1.5 to 2.5, the particle size distribution is relatively uniform, and β is -0.2; if the ratio is >2.5, the particle size distribution is non-uniform, and β is -0.3.

[0036] In the technical solution of this application embodiment, through the above method, when D <D ref When f(D) > 1, it indicates that small particles have high heat transfer efficiency and the actual quality improvement intensity is enhanced; when D = D ref When f(D) = 1, it represents the standard state; when D > D ref When f(D) < 1, it indicates that the heat transfer efficiency of large particles is low and the actual quality improvement intensity is weakened.

[0037] Furthermore, in some embodiments, in step S2, the coupled prediction model includes: Q j =a j ·R0 bj ·exp(c j ·R0)+d j ; Among them, Q j Let a be the j-th quality indicator. j Let bj be the scaling factor corresponding to the j-th quality indicator, bj be the power law exponent corresponding to the j-th quality indicator, and c be the scaling factor. j Let d be the coefficient of the exponential term of the j-th quality indicator. j Let a be the initial value corresponding to the j-th quality indicator; j bj c j Obtained through fitting.

[0038] Furthermore, in some embodiments, a j bj c j The result was obtained by fitting using a nonlinear least squares regression algorithm.

[0039] More specifically, in some embodiments, a j bj c j Determined in the following ways: Conduct experiments and record the R0 value and the measured quality index value Q at each experimental point. j,exp Perform nonlinear least squares regression with the objective function min∑(Q j,model -Qj,exp ) 2 Q j,model Let Q be the model predicted value of the j-th quality indicator. j,exp For the experimentally measured value of the j-th quality index, the fitting parameters are optimized by minimizing the sum of squares of the deviations between the two values; the coefficient of determination Rj is used as the setpoint. 2 To perform fitting, R is required. 2 >0.85.

[0040] The formula for calculating the coefficient of determination is as follows: , is the measured arithmetic mean of all experimental samples for the j-th quality indicator.

[0041] Furthermore, in some embodiments, the quality indicators include fixed carbon content and higher heating value, with fixed carbon content as the first quality indicator and higher heating value as the second quality indicator. The calculation formulas for fixed carbon content and higher heating value are as follows: Q1=a1·R0 b1 ·exp(c1·R0)+d1; Q2=a2·R0 b2 ·exp(c2·R0)+d2; Where Q1 and Q2 are the fixed carbon content and higher heating value, respectively; a1, b1, and c1 are the correlation coefficients corresponding to the fixed carbon content; a2, b2, and c2 are the correlation coefficients corresponding to the higher heating value; d1 is the fixed carbon content when R0=0 (i.e., the initial fixed carbon content of the biomass material to be treated before upgrading); and d2 is the higher heating value when R0=0 (i.e., the initial higher heating value of the biomass material to be treated before upgrading).

[0042] More specifically, the aforementioned correlation coefficients are related to the type of raw material. For woody raw materials, the preferred range for a1 is 40-50, the preferred range for b1 is 0.7-0.9, the preferred range for c1 is -0.4 to -0.2, the preferred range for a2 is 7-9, the preferred range for b2 is 0.5-0.7, and the preferred range for c2 is -0.3 to -0.1; for herbaceous raw materials, the preferred range for a1 is 35-45, the preferred range for b1 is 0.8-1.0, and the preferred range for c1 is -0.5 to -0.3. The preferred values ​​for 2 are 6 to 8, b2 is 0.6 to 0.8, and c2 is -0.4 to -0.2. For nutshell raw materials, the preferred values ​​for a1 are 38 to 48, b1 is 0.75 to 0.95, c1 is -0.45 to -0.25, a2 is 6.5 to 8.5, b2 is 0.55 to 0.75, and c2 is -0.35 to -0.15.

[0043] Furthermore, in some embodiments, step S3, which involves solving for the target comprehensive quality improvement intensity index corresponding to the target quality index based on the coupled prediction model, includes: A multi-objective optimization algorithm is used to solve for the threshold range of the comprehensive quality improvement intensity index corresponding to the target quality index; The midpoint of the threshold range is used as the target comprehensive quality improvement intensity index.

[0044] More specifically, in some embodiments, the specific steps of using a multi-objective optimization algorithm to solve for the threshold range of the comprehensive quality improvement intensity index corresponding to the target quality include: Step 1: Set a fixed lower limit constraint Q for carbon C and the lower limit constraint of high calorific value Q HHV and equipment constraint R0, min ≤R0≤R0, max (Based on device capabilities and security, where R0, min For the operating conditions of upgrading temperature 200℃, upgrading time 10min, and steam partial pressure 0.1MPa, R0, max (This corresponds to the working conditions of a upgrading temperature of 300℃, an upgrading time of 120min, and a steam partial pressure of 1.0MPa). Step 2: Based on the coupled prediction model, solve for the R0 interval [R0, ..., ...] that satisfies the fixed carbon constraint. C1 ,R0, C2 and the R0 interval [R0, satisfying the high calorific value constraint] HHV1 R0, HHV2 ]; Step 3: Take the intersection of each constraint interval to obtain the feasible region of the comprehensive quality improvement intensity index; Step 4: Use the median value of the feasible region as the target comprehensive quality improvement intensity index.

[0045] Furthermore, in some embodiments, step S4 includes the following inversion method: solving for the combination of upgrading process parameters using the target comprehensive upgrading intensity as a constraint. Specifically, the combination of upgrading process parameters includes upgrading temperature, upgrading time, and steam partial pressure.

[0046] More specifically, in some embodiments, the method for solving the combination of quality improvement process parameters is as follows: Substituting R0=k1·exp(-E) under the constraints a,mix / RT)·t n1 ·P steam n2 ·f(M)·f(D), solve for the combination of process parameters for quality improvement.

[0047] Furthermore, in some embodiments, after reversing the optimized combination of quality improvement process parameters, closed-loop control is then performed; the steps of this closed-loop control include: S51. Monitor the upgrading temperature and steam partial pressure in real time, and calculate the real-time comprehensive upgrading intensity index. S52. Determine whether the real-time comprehensive quality improvement intensity index is within the threshold range of the comprehensive quality improvement intensity index; if the determination result is "yes", then maintain the current quality improvement process parameters unchanged; if the determination result is "no", then adjust the current quality improvement process parameters. S53. Repeat steps S51-S52.

[0048] More specifically, in some embodiments, the approach to adjusting the upgrading process parameters includes: When the real-time comprehensive quality improvement intensity index is lower than the lower limit of the threshold range, the quality improvement temperature is increased first, by 5~10℃ each time. If the temperature has reached the upper limit, the quality improvement time is extended, by 5~10 minutes each time. When the real-time comprehensive quality improvement intensity index is higher than the upper limit of the threshold range, the quality improvement temperature is reduced first, by 5~10℃ each time. If the temperature has reached the lower limit, the quality improvement time is shortened, by 5~10 minutes each time.

[0049] The following is a specific embodiment. It should be noted that the embodiment described below is exemplary and is only used to explain this application, and should not be construed as limiting this application. Where specific techniques or conditions are not specified in the embodiment, they shall be performed in accordance with the techniques or conditions described in the literature in this field or in accordance with the product manual.

[0050] Example 1 This embodiment provides an optimization method for biomass steam upgrading process based on a comprehensive upgrading intensity index. In this embodiment, the biomass material to be treated is pine sawdust. The characteristic parameters of the biomass material to be treated are detected, and the results are as follows: For the aforementioned biomass materials, the method provided in this embodiment specifically includes the following steps: S1 provides a comprehensive quality improvement intensity index and quality indicators.

[0051] The comprehensive quality improvement intensity index R0 is calculated according to the following formula: R0=k1·exp(-E a,mix / RT)·t n1 ·P steam n2 ·f(M)·f(D); In the above formula, k1 is the pre-exponential factor, with a value of 8 × 10. 10 min -1 R is the ideal gas constant, specifically 8.314; T is the upgrading temperature; t is the upgrading time; n1 is the time exponent, with a value of 0.7; P steam n is the vapor partial pressure; n2 is the pressure exponent, with a value of 0.4; f(M) is the moisture correction function, specifically 0.9775; f(D) is the particle size correction function, specifically 0.956.

[0052] E a,mix The apparent activation energy of the biomass material to be treated is calculated using the following formula: E a,mix =0.42×200+0.28×100+0.26×60=84+28+15.6=127.6kJ / mol.

[0053] S2. Establish a coupled prediction model of comprehensive quality improvement intensity index and quality indicators.

[0054] The coupled prediction model includes: Q1 = 45 × R0 0.8 ×exp(-0.3×R0)+18.4; Q2=8×R0 0.6 ×exp(-0.2×R0)+19.2.

[0055] S3. Based on the coupled prediction model, solve for the target comprehensive quality improvement intensity index corresponding to the target quality index.

[0056] In this embodiment, the target quality indicators include fixed carbon and higher calorific value; Specific solution methods include: Target quality indicators: FCad ≥ 40%; HHV ≥ 21 MJ / kg; Equipment constraints: 200℃≤T≤300℃, 10min≤t≤120min, 0.1MPa≤Psteam≤1.0MPa, corresponding R0 range: [0.35,8.5]; Solving for the constraint-satisfying interval table Feasible region intersection: [0.45, 8.5] Target value R0 (interval midpoint): R0 = (0.45 + 2.0) / 2 = 1.23 (Note: The upper limit is set at 2.0 instead of 8.5 to avoid excessive carbonization leading to too low energy yield.) Validate the predicted values Q1 = 45 × 1.23 0.8 ×exp(-0.3×1.23)+18.4=43.2%; Q2 = 8 × 1.23 0.6 ×exp(-0.2×1.23)+19.2=22.8MJ / kg.

[0057] S4. Based on the target comprehensive quality improvement intensity, deduce the optimized combination of quality improvement process parameters.

[0058] Specific inversion methods include: Objective: Solve for (T,t,Psteam) under constraints such that R0(T,t,Psteam)=1.23 With the steam partial pressure Psteam = 0.2 MPa, the solution is simplified as follows: 1.23 = 8 × 10 10 ×exp(-127600 / 8.314T)×t 0.7 ×0.2 0.4 ×0.934.

[0059] Multiple feasible solutions can be obtained: Option A (moderate temperature, reasonable time) is selected: Experimental verification results As can be seen from the above results, the method provided in this embodiment can effectively optimize the biomass steam upgrading process.

[0060] In summary, this application provides a method for optimizing biomass steam upgrading processes based on a comprehensive upgrading intensity index, belonging to the field of biomass steam upgrading technology. The method includes: providing a comprehensive upgrading intensity index and quality indicators; establishing a coupled prediction model between the comprehensive upgrading intensity index and quality indicators; solving for the target comprehensive upgrading intensity index corresponding to the target quality indicators based on the coupled prediction model; and retrieving the optimized combination of upgrading process parameters based on the target comprehensive upgrading intensity. This application, by constructing a comprehensive upgrading intensity index integrating factors such as upgrading temperature, upgrading time, steam pressure, and raw material characteristics, and establishing a coupled prediction model between the comprehensive upgrading intensity index and quality indicators, achieves a significant transformation in steam upgrading processes from experience-based trial and error to precise predictive control. This significantly improves process optimization efficiency and the quality controllability of upgraded products, demonstrating promising practical application prospects.

[0061] It should be noted that this application is not limited to the above-described embodiments. The above embodiments are merely examples, and any embodiments with the same structure and effect as the technical concept within the scope of this application are included in the technical scope of this application. Furthermore, various modifications that can be conceived by those skilled in the art to the embodiments, and other ways of constructing by combining some of the constituent elements of the embodiments, without departing from the spirit of this application, are also included in the scope of this application.

Claims

1. A method for optimizing biomass steam upgrading process based on a comprehensive upgrading intensity index, characterized in that, Includes the following steps: S1. Provide a comprehensive quality improvement intensity index and quality indicators; the comprehensive quality improvement intensity index is calculated according to the following formula: R0=k1·exp(-E a,mix / RT)·t n1 ·P steam n2 ·f(M)·f(D); Where R0 is the comprehensive quality improvement intensity index, k1 is the pre-exponential factor, and E a,mix R is the comprehensive apparent activation energy of the biomass material to be processed, T is the upgrading temperature, t is the upgrading time, n1 is the time exponent, and P is the average apparent activation energy of the biomass material to be processed. steam Where n is the vapor partial pressure, n2 is the pressure exponent, f(M) is the moisture correction function, and f(D) is the particle size correction function. S2. Establish a coupled prediction model between the comprehensive quality improvement intensity index and the quality index; S3. Based on the coupled prediction model, solve for the target comprehensive quality improvement intensity index corresponding to the target quality index; S4. Based on the target comprehensive quality improvement intensity, deduce the optimized combination of quality improvement process parameters.

2. The method for optimizing biomass steam upgrading process based on comprehensive upgrading intensity index according to claim 1, characterized in that, In step S2, the coupled prediction model includes: Q j =a j ·R0 bj ·exp(c j ·R0)+d j ; Among them, Q j Let a be the j-th quality indicator. j Let bj be the scaling factor corresponding to the j-th quality indicator, bj be the power law exponent corresponding to the j-th quality indicator, and c be the scaling factor. j Let d be the coefficient of the exponential term of the j-th quality indicator. j Let a be the initial value corresponding to the j-th quality indicator; j bj c j Obtained through fitting.

3. The method for optimizing biomass steam upgrading process based on comprehensive upgrading intensity index according to claim 2, characterized in that, a j bj c j The result was obtained by fitting using a nonlinear least squares regression algorithm.

4. The method for optimizing biomass steam upgrading process based on comprehensive upgrading intensity index according to claim 1, characterized in that, The formula for calculating the comprehensive apparent activation energy of the biomass material to be treated is as follows: AND a,mix =∑(x k ·AND a,k ); Where, x k Let ∑x be the mass fraction of the k-th type of biomass in the biomass material to be processed. k =1;E a,k Let be the apparent activation energy of the k-th type of biomass in the biomass material to be treated; k is an integer ≥ 1.

5. The method for optimizing biomass steam upgrading process based on comprehensive upgrading intensity index according to claim 4, characterized in that, The formula for calculating the apparent activation energy of the k-th type of biomass in the biomass material to be processed is as follows: HAVE BEEN a,k =W 1,k ·Air1+W 2,k ·Air2+W 3,k ·Air3; Among them, W 1,k Let W be the mass fraction of cellulose in the k-th type of biomass. 2,k W represents the mass fraction of hemicellulose in the k-th biomass. 3,k W represents the mass fraction of lignin in the k-th type of biomass. 1,k +W 2,k +W 3,k =1; Ea1 is the activation energy of cellulose, ranging from 180 to 220 kJ / mol; Ea2 is the activation energy of hemicellulose, ranging from 80 to 120 kJ / mol; Ea3 is the activation energy of lignin, ranging from 40 to 80 kJ / mol.

6. The method for optimizing biomass steam upgrading process based on comprehensive upgrading intensity index according to claim 1, characterized in that, The formula for calculating the moisture correction function is as follows: f(M)=1+α·(M ad -M ref ); Wherein, α is the moisture sensitivity coefficient. When the biomass material to be treated is woody, the value of α ranges from 0.01 to 0.02; when the biomass material to be treated is herbaceous, the value of α ranges from 0.02 to 0.04; and when the biomass material to be treated is fruit shell, the value of α ranges from 0.03 to 0.

05. ad M represents the measured moisture content of the biomass material to be processed. ref This is the preset reference moisture content.

7. The method for optimizing biomass steam upgrading process based on comprehensive upgrading intensity index according to claim 1, characterized in that, The formula for calculating the particle size correction function is as follows: f(D)=(D / D ref ) β ; Where D is the measured average particle size of the biomass material to be treated, D ref The preset reference average particle size is β, which is the particle size index, ranging from -0.3 to -0.

1.

8. The method for optimizing biomass steam upgrading process based on comprehensive upgrading intensity index according to claim 1, characterized in that, In step S3, the step of solving the target comprehensive quality improvement intensity index corresponding to the target quality index based on the coupled prediction model includes: A multi-objective optimization algorithm is used to solve for the threshold range of the comprehensive quality improvement intensity index corresponding to the target quality index; The median value of the threshold range is used as the target comprehensive quality improvement intensity index.

9. The method for optimizing biomass steam upgrading process based on comprehensive upgrading intensity index according to claim 1, characterized in that, In step S4, the inversion method includes: using the target comprehensive quality improvement intensity as a constraint, solving for the combination of quality improvement process parameters.

10. The method for optimizing biomass steam upgrading process based on comprehensive upgrading intensity index according to claim 1, characterized in that, After the optimized process parameters are combined according to the reverse analysis, closed-loop control is then performed. The closed-loop control steps include: S51. Monitor the upgrading temperature and steam partial pressure in real time, and calculate the real-time comprehensive upgrading intensity index. S52. Determine whether the real-time comprehensive quality improvement intensity index is within the threshold range of the comprehensive quality improvement intensity index; if the determination result is "yes", then maintain the current quality improvement process parameters unchanged; if the determination result is "no", then adjust the current quality improvement process parameters. S53. Repeat steps S51-S52.

Images