Regulation and control method and system for biomass hydrothermal liquefaction nitrogen-containing heterocyclic compound

Abstract

The invention discloses a regulation and control method and system for biomass hydrothermal liquefaction of a nitrogen-containing heterocyclic ring compound, the regulation and control method for the content of the nitrogen-containing heterocyclic ring of the biomass comprises the steps that a first data set and a second data set are obtained, the first data set comprises element composition, hydrothermal liquefaction conditions and the content of the nitrogen-containing heterocyclic ring of the detected first biomass, and the second data set comprises element composition, hydrothermal liquefaction conditions and the content of the nitrogen-containing heterocyclic ring of the detected second biomass; the second data set comprises the element composition of the second biomass to be regulated and controlled; based on the first data set and a pre-constructed initial machine learning model, obtaining a trained machine learning model, the highest nitrogen-containing heterocyclic ring content output by the trained machine learning model, a characteristic value corresponding to the highest nitrogen-containing heterocyclic ring content and a characteristic weight value corresponding to the highest nitrogen-containing heterocyclic ring content; and the second biomass is regulated and controlled based on the second data set, the characteristic value corresponding to the highest nitrogen-containing heterocyclic ring content and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic ring content, the nitrogen-containing heterocyclic ring content of the regulated and controlled second biomass is obtained, and the prediction accuracy and the regulation and control effect of the nitrogen-containing heterocyclic ring content are improved.

Description

Technical Field

[0001] The present invention relates to the technical field related to the regulation of nitrogen-containing heterocyclic compounds in biomass, and in particular to a method and system for regulating nitrogen-containing heterocyclic compounds in hydrothermal liquefaction of biomass. Background Art

[0002] Due to the shortage of fossil resources and the increasingly prominent climate and environmental problems caused by fossil resources, the replacement of fossil resources is imperative to achieve the "dual carbon" goals. High-value nitrogen-containing heterocyclic chemicals (hereinafter referred to as NH) in the fields of medicine, solvents, etc. are generally processed from fossil resources. It is of great significance to use biomass raw materials to replace fossil resources to produce high-value NH.

[0003] Affected by factors such as the composition of biomass raw materials and hydrothermal reaction parameters, the type and content of NH in the liquefied product vary greatly. Traditional experimental research mainly measures the properties of the prepared nitrogen-containing heterocyclic products, mainly exploring the influence of reaction conditions on nitrogen-containing heterocyclics. At present, attempts are mainly made to predict the content of nitrogen-containing heterocyclics by using the elemental composition of biomass and hydrothermal liquefaction conditions. However, the elemental composition detection method has a large workload and high error in systematically analyzing the description of biomass composition, which leads to poor prediction of the reaction substrates of biomass hydrothermal liquefaction reaction due to insufficient information. Summary of the invention

[0004] The present invention aims to at least solve the technical problems existing in the prior art. To this end, the present invention provides a method and system for regulating nitrogen-containing heterocyclic compounds in biomass hydrothermal liquefaction, which can improve the prediction accuracy and regulation effect of nitrogen-containing heterocyclic content.

[0005] The first aspect of the present invention provides a method for regulating biomass hydrothermal liquefaction of nitrogen-containing heterocyclic compounds, comprising the following steps:

[0006] Acquire a first data set and a second data set, wherein the first data set includes the elemental composition, hydrothermal liquefaction conditions, and nitrogen-containing heterocyclic content of the measured first biomass, and the second data set includes the elemental composition of the second biomass to be regulated;

[0007] Based on the first data set and the pre-built initial machine learning model, a trained machine learning model is obtained, and the maximum nitrogen-containing heterocyclic content, the characteristic value corresponding to the maximum nitrogen-containing heterocyclic content, and the characteristic weight value corresponding to the maximum nitrogen-containing heterocyclic content are output by the model;

[0008] The second biomass is regulated based on the second data set, the characteristic value corresponding to the highest nitrogen-containing heterocyclic content, and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content to obtain the regulated nitrogen-containing heterocyclic content of the second biomass.

[0009] The control method according to the embodiment of the present invention has at least the following beneficial effects:

[0010] The method comprises obtaining a first data set and a second data set, wherein the first data set comprises the elemental composition, hydrothermal liquefaction conditions and nitrogen-containing heterocyclic content of the measured first biomass, and the second data set comprises the elemental composition of the second biomass to be regulated; based on the first data set and a pre-constructed initial machine learning model, a trained machine learning model and its output of the highest nitrogen-containing heterocyclic content, a characteristic value corresponding to the highest nitrogen-containing heterocyclic content and a characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content are obtained; based on the second data set, the characteristic value corresponding to the highest nitrogen-containing heterocyclic content and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content, the second biomass is regulated to obtain the nitrogen-containing heterocyclic content of the regulated second biomass, thereby improving the prediction accuracy and regulation effect of the nitrogen-containing heterocyclic content.

[0011] According to some embodiments of the present invention, the trained machine learning model based on the first data set and the pre-built initial machine learning model is obtained, and the maximum nitrogen-containing heterocyclic content, the characteristic value corresponding to the maximum nitrogen-containing heterocyclic content, and the characteristic weight value corresponding to the maximum nitrogen-containing heterocyclic content output by the model include:

[0012] Calculating an elemental composition derived descriptor based on the elemental composition of the first biomass and the molecular structure characteristics of the main components of the biomass to obtain a first biomass elemental composition derived descriptor;

[0013] The first biomass elemental composition derived descriptor, the hydrothermal liquefaction conditions and the nitrogen-containing heterocyclic content are input into the initial machine learning model to obtain the trained machine learning model and its output of the highest nitrogen-containing heterocyclic content, the characteristic value corresponding to the highest nitrogen-containing heterocyclic content and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content, wherein the characteristics include the first biomass elemental composition derived descriptor and the hydrothermal liquefaction conditions, and the hydrothermal liquefaction conditions include temperature, time and solid content.

[0014] According to some embodiments of the present invention, the first biomass elemental composition derived descriptor, the hydrothermal liquefaction condition and the nitrogen-containing heterocyclic ring content are input into the initial machine learning model to obtain the trained machine learning model and its output of the highest nitrogen-containing heterocyclic ring content, the characteristic value corresponding to the highest nitrogen-containing heterocyclic ring content and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic ring content, including:

[0015] Inputting the first biomass elemental composition derived descriptor, the hydrothermal liquefaction condition, and the nitrogen-containing heterocyclic content into the initial machine learning model, and calculating the loss value of the initial machine learning model by a random forest algorithm;

[0016] The initial machine learning model is reversely optimized based on the loss value to obtain the trained machine learning model and its output of the highest nitrogen-containing heterocyclic content, the characteristic value corresponding to the highest nitrogen-containing heterocyclic content, and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content.

[0017] According to some embodiments of the present invention, the first biomass elemental composition derived descriptor, the hydrothermal liquefaction condition and the nitrogen-containing heterocyclic content are input into the initial machine learning model, and the loss value of the initial machine learning model is calculated by the random forest algorithm. The calculation formula is:

[0018]

[0019] Among them, L is the loss value, y i is the true value of the i-th sample in the first data set, is the predicted value of the i-th sample in the first data set, and N is the total number of samples in the first data set.

[0020] According to some embodiments of the present invention, the second biomass is regulated based on the second data set, the characteristic value corresponding to the highest nitrogen-containing heterocyclic content, and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content to obtain the regulated nitrogen-containing heterocyclic content of the second biomass, including:

[0021] Calculate the elemental composition derived descriptor according to the elemental composition of the second biomass and the molecular structure characteristics of the main components of the biomass to obtain the elemental composition derived descriptor of the second biomass;

[0022] The second biomass is regulated according to the second biomass elemental composition derived descriptor, the characteristic value corresponding to the highest nitrogen-containing heterocyclic content, and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content to obtain the regulated nitrogen-containing heterocyclic content of the second biomass.

[0023] According to some embodiments of the present invention, the second biomass is regulated according to the second biomass elemental composition derived descriptor, the characteristic value corresponding to the highest nitrogen-containing heterocyclic content, and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content to obtain the regulated nitrogen-containing heterocyclic content of the second biomass, including:

[0024] Screening the second biomass element composition derived descriptor according to the feature weight value corresponding to the highest nitrogen-containing heterocyclic content and a preset derived descriptor ratio to obtain a second biomass screened derived descriptor;

[0025] The second biomass is regulated according to the characteristic value corresponding to the highest nitrogen-containing heterocyclic ring content and the descriptor derived after the screening of the second biomass to obtain the nitrogen-containing heterocyclic ring content of the second biomass after regulation.

[0026] According to some embodiments of the present invention, regulating the second biomass according to the characteristic value corresponding to the highest nitrogen-containing heterocyclic ring content and the descriptor derived after the second biomass screening to obtain the regulated nitrogen-containing heterocyclic ring content of the second biomass includes:

[0027] Based on the characteristic value corresponding to the highest nitrogen-containing heterocyclic content and the descriptor derived from the second biomass after screening, the second biomass is regulated by hydrogen peroxide coupled low-temperature baking pretreatment technology to obtain the regulated nitrogen-containing heterocyclic content of the second biomass.

[0028] In a second aspect of the present invention, a control system for hydrothermal liquefaction of nitrogen-containing heterocyclic compounds from biomass is provided. The control system for hydrothermal liquefaction of nitrogen-containing heterocyclic compounds from biomass comprises:

[0029] A data acquisition module, used to acquire a first data set and a second data set, wherein the first data set includes the elemental composition, hydrothermal liquefaction conditions and nitrogen-containing heterocyclic content of the measured first biomass, and the second data set includes the elemental composition of the second biomass to be regulated;

[0030] A model training module, for obtaining a trained machine learning model and its output of the highest nitrogen-containing heterocyclic content, a characteristic value corresponding to the highest nitrogen-containing heterocyclic content, and a characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content based on the first data set and a pre-constructed initial machine learning model;

[0031] A control module is used to control the second biomass based on the second data set, the characteristic value corresponding to the highest nitrogen-containing heterocyclic content and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content to obtain the nitrogen-containing heterocyclic content of the second biomass after control.

[0032] The system obtains a first data set and a second data set, wherein the first data set includes the measured elemental composition, hydrothermal liquefaction conditions and nitrogen-containing heterocyclic content of the first biomass, and the second data set includes the elemental composition of the second biomass to be regulated; based on the first data set and a pre-constructed initial machine learning model, a trained machine learning model and its output of the highest nitrogen-containing heterocyclic content, a characteristic value corresponding to the highest nitrogen-containing heterocyclic content, and a characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content are obtained; based on the second data set, the characteristic value corresponding to the highest nitrogen-containing heterocyclic content and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content, the second biomass is regulated to obtain the nitrogen-containing heterocyclic content of the second biomass after regulation, thereby improving the prediction accuracy and regulation effect of the nitrogen-containing heterocyclic content.

[0033] The third aspect of the present invention provides an electronic device for regulating the nitrogen-containing heterocyclic content of biomass, comprising at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions that can be executed by the at least one control processor, and the instructions are executed by the at least one control processor so that the at least one control processor can execute the above-mentioned method for regulating the nitrogen-containing heterocyclic compounds in the hydrothermal liquefaction of biomass.

[0034] In a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the above-mentioned method for regulating the hydrothermal liquefaction of nitrogen-containing heterocyclic compounds from biomass.

[0035] It should be noted that the beneficial effects between the second to fourth aspects of the present invention and the prior art are the same as the beneficial effects between the above-mentioned biomass hydrothermal liquefaction nitrogen-containing heterocyclic compound control system and the prior art, and will not be described in detail here.

[0036] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0038] Figure 1 It is a flow chart of a method for regulating biomass hydrothermal liquefaction of nitrogen-containing heterocyclic compounds according to one embodiment of the present invention;

[0039] Figure 2 It is a schematic diagram of the predicted results of the control method for hydrothermal liquefaction of nitrogen-containing heterocyclic compounds from biomass provided in the present application;

[0040] Figure 3 It is a schematic diagram of the data of the ranking results of the characteristic importance of the control method of biomass hydrothermal liquefaction of nitrogen-containing heterocyclic compounds provided in the present application;

[0041] Figure 4 It is a schematic diagram of the characteristic influence relationship result data of the regulation method of biomass hydrothermal liquefaction of nitrogen-containing heterocyclic compounds provided in the present application;

[0042] Figure 5 It is a schematic structural diagram of an embodiment of a control system for hydrothermal liquefaction of nitrogen-containing heterocyclic compounds from biomass provided in the present application;

[0043] Figure 6 It is a schematic diagram of the structure of an embodiment of the electronic device provided by the present application. DETAILED DESCRIPTION

[0044] Embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0045] In the description of the present invention, if there is a description of first, second, etc., it is only for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0046] In the description of the present invention, it should be understood that descriptions involving orientation, such as orientation or positional relationship indicated as up, down, etc., are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0047] In the description of the present invention, it should be noted that, unless otherwise clearly defined, terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention in combination with the specific content of the technical solution.

[0048] Due to the shortage of fossil resources and the increasingly prominent climate and environmental problems caused by fossil resources, the replacement of fossil resources is imperative to achieve the "dual carbon" goals. High-value nitrogen-containing heterocyclic chemicals (hereinafter referred to as NH) in the fields of medicine, solvents, etc. are generally processed from fossil resources. It is of great significance to use biomass raw materials to replace fossil resources to produce high-value NH.

[0049] Affected by factors such as the composition of biomass raw materials and hydrothermal reaction parameters, the type and content of NH in the liquefied product vary greatly. Traditional experimental research mainly measures the properties of the prepared nitrogen-containing heterocyclic products, mainly exploring the influence of reaction conditions on nitrogen-containing heterocyclics. At present, attempts are mainly made to predict the content of nitrogen-containing heterocyclics by using the elemental composition of biomass and hydrothermal liquefaction conditions. However, the elemental composition detection method has a large workload and high error in systematically analyzing the description of biomass composition, which leads to poor prediction of the reaction substrates of biomass hydrothermal liquefaction reaction due to insufficient information.

[0050] In order to solve the above technical defects, the embodiments of the present application provide a method and system for regulating the hydrothermal liquefaction of nitrogen-containing heterocyclic compounds from biomass.

[0051] See also Figure 1, is a schematic flow chart of a method for regulating biomass hydrothermal liquefaction of nitrogen-containing heterocyclic compounds provided in an embodiment of the present application, the method is applied to electronic devices, and the electronic devices may be servers, etc. Figure 1 As shown, the control method of biomass hydrothermal liquefaction of nitrogen-containing heterocyclic compounds includes:

[0052] Step S101, obtaining a first data set and a second data set, wherein the first data set includes the elemental composition, hydrothermal liquefaction conditions and nitrogen-containing heterocyclic content of the measured first biomass, and the second data set includes the elemental composition of the second biomass to be regulated;

[0053] Step S102, based on the first data set and the pre-constructed initial machine learning model, obtaining a trained machine learning model and the maximum nitrogen-containing heterocyclic content, the characteristic value corresponding to the maximum nitrogen-containing heterocyclic content, and the characteristic weight value corresponding to the maximum nitrogen-containing heterocyclic content output by the model;

[0054] Step S103, regulating the second biomass based on the second data set, the characteristic value corresponding to the highest nitrogen-containing heterocyclic content, and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content to obtain the regulated nitrogen-containing heterocyclic content of the second biomass.

[0055] The method comprises obtaining a first data set and a second data set, wherein the first data set comprises the elemental composition, hydrothermal liquefaction conditions and nitrogen-containing heterocyclic content of the measured first biomass, and the second data set comprises the elemental composition of the second biomass to be regulated; based on the first data set and a pre-constructed initial machine learning model, a trained machine learning model and its output of the highest nitrogen-containing heterocyclic content, a characteristic value corresponding to the highest nitrogen-containing heterocyclic content and a characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content are obtained; based on the second data set, the characteristic value corresponding to the highest nitrogen-containing heterocyclic content and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content, the second biomass is regulated to obtain the nitrogen-containing heterocyclic content of the regulated second biomass, thereby improving the prediction accuracy and regulation effect of the nitrogen-containing heterocyclic content.

[0056] In some embodiments, in step S102, based on the first data set and the pre-built initial machine learning model, a trained machine learning model and its output of the highest nitrogen-containing heterocyclic content, the characteristic value corresponding to the highest nitrogen-containing heterocyclic content, and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content are obtained, including:

[0057] Step S201, calculating an elemental composition derived descriptor based on the elemental composition of the first biomass and the molecular structure characteristics of the main components of the biomass to obtain the first biomass elemental composition derived descriptor;

[0058] Step S202: input the first biomass elemental composition derived descriptor, hydrothermal liquefaction conditions and nitrogen-containing heterocyclic content into the initial machine learning model to obtain the trained machine learning model and its output of the highest nitrogen-containing heterocyclic content, the characteristic value corresponding to the highest nitrogen-containing heterocyclic content and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content, wherein the characteristics include the first biomass elemental composition derived descriptor and the hydrothermal liquefaction conditions, and the hydrothermal liquefaction conditions include temperature, time and solid content.

[0059] Specifically, in some embodiments, the first biomass elemental composition (except Ash) is subjected to elemental composition derived descriptor calculation based on the molecular structure characteristics of the main components of the biomass.

[0060] The calculated descriptors are in elemental molar ratios and include, but are not limited to, H / C, O / C, N / C, H / (0.5C+N+O+S), N / O, H / N, H / 3O, H / O, (H-2C) / (N+O), (CON) / (HN), (H+O+N+S) / C, (H-2N) / C, 2N / C, (HN) / C, (O+N) / C, S / H, S / N(HO) / C, 2O / C, 2H / C, 2H / 3C, S / C, H / (0.5C+N+O), (HNOS) / C, H / (C+O+N+S), (HNO) / C.

[0061] Hydrothermal liquefaction conditions include but are not limited to temperature, time and solid content.

[0062] This embodiment trains the initial machine learning model by using the first biomass element composition derived descriptor, hydrothermal liquefaction conditions, and nitrogen-containing heterocyclic ring content, thereby improving the accuracy of model training.

[0063] In some embodiments, in step S202, the first biomass elemental composition derived descriptor, the hydrothermal liquefaction conditions, and the nitrogen-containing heterocyclic content are input into an initial machine learning model to obtain a trained machine learning model and its output of the highest nitrogen-containing heterocyclic content, the characteristic value corresponding to the highest nitrogen-containing heterocyclic content, and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content, including:

[0064] Step S301, inputting the first biomass element composition derived descriptor, hydrothermal liquefaction conditions and nitrogen-containing heterocyclic content into an initial machine learning model, and calculating the loss value of the initial machine learning model by a random forest algorithm;

[0065] Step S302: reversely optimize the initial machine learning model based on the loss value to obtain a trained machine learning model and its output of the highest nitrogen-containing heterocyclic content, the characteristic value corresponding to the highest nitrogen-containing heterocyclic content, and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content.

[0066] Specifically, the hyperparameters of the initial machine learning model for random forest are adjusted through cross-validation errors; the hyperparameters include the number of trees and the depth of the trees;

[0067] Input the training set into the initial machine learning model, and calculate the loss function of the initial machine learning model through the random forest algorithm;

[0068] The initial machine learning model is trained through the supervision of the target loss function to obtain a trained machine learning model.

[0069] Specifically, in some embodiments, the random forest algorithm is used to analyze the nitrogen-containing heterocyclic content prediction model to determine the impact of each input feature variable on the dependent variable, and obtain the importance ranking of the input feature variables.

[0070] The VIM is defined as follows: In each tree of the random forest, a randomly drawn training bootstrap sample is used to build the tree, and the prediction error rate of the out-of-bag data (OOB) is calculated, and then the variable X is randomly permuted. j After the observation value is obtained, the tree is built again and the OOB prediction error rate is calculated. Finally, the difference between the two OOB error rates is calculated and the average value in all trees after standardization is the variable X. j The importance of replacement

[0071]

[0072] in, is the number of observations of the O0B data of the i-th tree, I is the indicator function, that is, it takes 1 when the two values ​​are equal and takes 0 when they are not equal; Y p ∈{0,1} is the true result of the p-th observation, is the prediction result of the i-th tree for the p-th observation of OOB data before random permutation, is the prediction result of the i-th tree for the p-th observation of OOB data after random permutation.

[0073] When the variable X j When it does not appear in the i-th tree,

[0074] VARIABLE X j The permutation importance in RF is defined as:

[0075]

[0076] Where n is the number of classification trees in RF.

[0077] The nitrogen-containing heterocyclic content prediction model uses partial dependence analysis to obtain the influence relationship between key features and output features.s The partial dependencies can be defined as:

[0078]

[0079] Where x={x1,x1,…,x p} represents the predictor in the model, and the prediction function is z s is the characteristic sum of the partial dependence function that should be plotted, z c are the other features used; p c Yes c The boundary probability density of is:

[0080] p c (z c )=∫p(x)dz s

[0081]

[0082] Among them, z i,c (i=1,2,…,n) is z found in the training sample c value.

[0083] In some embodiments, in step S301, the first biomass elemental composition derived descriptor, the hydrothermal liquefaction conditions, and the nitrogen-containing heterocyclic content are input into the initial machine learning model, and the loss value of the initial machine learning model is calculated by the random forest algorithm as follows:

[0084]

[0085] Among them, L is the loss value, y i is the true value of the i-th sample in the first data set, is the predicted value of the i-th sample in the first data set, and N is the total number of samples in the first data set.

[0086] Specifically, in some embodiments, a test set is obtained by dividing the tested sample data set, and the test set is input into a trained machine learning model to obtain a prediction result output by the trained machine learning model;

[0087] The determination coefficient, root mean square error and mean absolute error corresponding to the trained machine learning model are calculated through the prediction results; the calculation formulas for the determination coefficient, root mean square error and mean absolute error include:

[0088]

[0089] Among them, R 2 represents the coefficient of determination, RMSE represents the root mean square error, MAE represents the mean absolute error, N2 represents the number of samples in the test set, and Respectively represent the i2th experimental value and the corresponding predicted value, represents the average of N2 experimental values;

[0090] The evaluation results of the trained machine learning model are calculated based on the coefficient of determination and root mean square error.

[0091] In some embodiments, in step S103, the second biomass is regulated based on the second data set, the characteristic value corresponding to the highest nitrogen-containing heterocyclic content, and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content to obtain the regulated nitrogen-containing heterocyclic content of the second biomass, including:

[0092] Step S401, calculating the elemental composition derived descriptor according to the elemental composition of the second biomass and the molecular structure characteristics of the main components of the biomass, to obtain the elemental composition derived descriptor of the second biomass;

[0093] Step S402, regulating the second biomass according to the second biomass element composition derived descriptor, the characteristic value corresponding to the highest nitrogen-containing heterocyclic content, and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content to obtain the nitrogen-containing heterocyclic content of the second biomass after regulation.

[0094] In some embodiments, in step S402, the second biomass is regulated according to the second biomass elemental composition derived descriptor, the characteristic value corresponding to the highest nitrogen-containing heterocyclic content, and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content to obtain the regulated nitrogen-containing heterocyclic content of the second biomass, including:

[0095] Step S501, screening the second biomass element composition derived descriptor according to the feature weight value corresponding to the highest nitrogen-containing heterocyclic content and the preset derived descriptor ratio to obtain the second biomass screened derived descriptor;

[0096] Step S502: regulating the second biomass according to the characteristic value corresponding to the highest nitrogen-containing heterocyclic content and the descriptor derived after the second biomass screening to obtain the nitrogen-containing heterocyclic content of the second biomass after regulation.

[0097] In some embodiments, in step S502, the second biomass is regulated according to the characteristic value corresponding to the highest nitrogen-containing heterocyclic content and the descriptor derived after the second biomass screening to obtain the regulated nitrogen-containing heterocyclic content of the second biomass, including:

[0098] Step S601: Based on the characteristic value corresponding to the highest nitrogen-containing heterocyclic content and the descriptor derived after the second biomass screening, the second biomass is regulated by hydrogen peroxide coupled low-temperature baking pretreatment technology to obtain the regulated nitrogen-containing heterocyclic content of the second biomass.

[0099] Specifically, in some embodiments, the second biomass is stirred and mixed with a hydrogen peroxide solution, filtered, and then subjected to a low-temperature baking treatment to obtain the second biomass raw material.

[0100] The second biomass raw material is subjected to a hydrothermal liquefaction experiment to obtain a product with a high nitrogen heterocyclic content.

[0101] In some embodiments, the mass concentration of hydrogen peroxide ranges from 5% to 20%, the mass ratio of the second biomass to hydrogen peroxide is 1:30, and the stirring time is 1 hour to 3 hours.

[0102] In some embodiments, the baking temperature ranges from 100 to 150° C., and the baking time ranges from 30 to 90 min.

[0103] Specifically, refer to Figures 2 to 4 In some embodiments, the biomass element composition, hydrothermal liquefaction conditions and nitrogen-containing heterocyclic content characteristics of the 116 groups of samples tested are obtained. The biomass element composition is unified, and the calculation formula includes:

[0104] C+H+O+N+S+Ash=100

[0105] The elements were derived descriptors calculated according to the chemical molecular structure characteristics, as shown in Table 1 Table 1 shows the input features of element composition and its derived descriptors and hydrothermal liquefaction conditions, with a total of 35 input features and 1 output variable.

[0106] Table 1

[0107]

[0108]

[0109] A variety of descriptors derived from the elemental composition of biomass and the hydrothermal liquefaction conditions were taken as input features, and the nitrogen heterocyclic content of the product was taken as the output feature to obtain the measured sample data set.

[0110] The tested sample data set is divided into a training set, and the preset target prediction model is trained with the training set to obtain the final target prediction model.

[0111] The random forest algorithm was used to analyze the prediction model of nitrogen-containing heterocyclic content, determine the impact of each input feature variable on the dependent variable, and obtain the importance ranking of the input feature variables. Partial dependency analysis was also used on the prediction model of nitrogen-containing heterocyclic content to obtain the influence relationship between key features and output features.

[0112] Specifically, in order to facilitate understanding by those skilled in the art, a set of optimal embodiments is provided below:

[0113] 1. Data acquisition:

[0114] A first data set and a second data set are obtained, wherein the first data set includes the elemental composition, hydrothermal liquefaction conditions, and nitrogen-containing heterocyclic content of the measured first biomass, and the second data set includes the elemental composition of the second biomass to be regulated.

[0115] 2. Model training:

[0116] Based on the first data set and the pre-built initial machine learning model, the trained machine learning model and its output of the highest nitrogen-containing heterocyclic content, the characteristic value corresponding to the highest nitrogen-containing heterocyclic content, and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content are obtained, specifically:

[0117] Calculating an elemental composition derived descriptor based on the elemental composition of the first biomass and the molecular structure characteristics of the main components of the biomass to obtain an elemental composition derived descriptor of the first biomass;

[0118] The first biomass elemental composition derived descriptor, hydrothermal liquefaction conditions, and nitrogen-containing heterocyclic content are input into the initial machine learning model, and the loss value of the initial machine learning model is calculated by the random forest algorithm, wherein the first biomass elemental composition derived descriptor, hydrothermal liquefaction conditions, and nitrogen-containing heterocyclic content are input into the initial machine learning model, and the loss value of the initial machine learning model is calculated by the random forest algorithm. The calculation formula is:

[0119]

[0120] Among them, L is the loss value, y i is the true value of the i-th sample in the first data set, is the predicted value of the i-th sample in the first data set, and N is the total number of samples in the first data set;

[0121] The initial machine learning model is reversely optimized based on the loss value to obtain a trained machine learning model and its output of the highest nitrogen-containing heterocyclic content, the characteristic value corresponding to the highest nitrogen-containing heterocyclic content, and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content, wherein the characteristics include the first biomass element composition derived descriptor and hydrothermal liquefaction conditions, and the hydrothermal liquefaction conditions include temperature, time and solid content.

[0122] 3. Biomass Regulation:

[0123] The second biomass is regulated based on the second data set, the characteristic value corresponding to the highest nitrogen-containing heterocyclic content, and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content to obtain the nitrogen-containing heterocyclic content of the second biomass after regulation, specifically:

[0124] Calculate the elemental composition derived descriptor according to the elemental composition of the second biomass and the molecular structure characteristics of the main components of the biomass to obtain the elemental composition derived descriptor of the second biomass;

[0125] The second biomass element composition derived descriptor is screened according to the feature weight value corresponding to the highest nitrogen-containing heterocyclic content and the preset derived descriptor ratio to obtain the second biomass screened derived descriptor;

[0126] Based on the characteristic value corresponding to the highest nitrogen-containing heterocyclic content and the descriptor derived after the second biomass screening, the second biomass is regulated by hydrogen peroxide-coupled low-temperature baking pretreatment technology to obtain the nitrogen-containing heterocyclic content of the regulated second biomass.

[0127] In addition, refer to Figure 5 One embodiment of the present invention provides a control system for hydrothermal liquefaction of nitrogen-containing heterocyclic compounds from biomass, comprising a data acquisition module 1100, a model training module 1200 and a control module 1300, wherein:

[0128] The data acquisition module 1100 is used to acquire a first data set and a second data set, wherein the first data set includes the elemental composition, hydrothermal liquefaction conditions and nitrogen-containing heterocyclic content of the measured first biomass, and the second data set includes the elemental composition of the second biomass to be regulated;

[0129] The model training module 1200 is used to obtain a trained machine learning model and its output of the highest nitrogen-containing heterocyclic content, the characteristic value corresponding to the highest nitrogen-containing heterocyclic content, and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content based on the first data set and the pre-built initial machine learning model;

[0130] The control module 1300 is used to control the second biomass based on the second data set, the characteristic value corresponding to the highest nitrogen-containing heterocyclic content and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content to obtain the nitrogen-containing heterocyclic content of the second biomass after control.

[0131] The system obtains a first data set and a second data set, wherein the first data set includes the measured elemental composition, hydrothermal liquefaction conditions and nitrogen-containing heterocyclic content of the first biomass, and the second data set includes the elemental composition of the second biomass to be regulated; based on the first data set and a pre-constructed initial machine learning model, a trained machine learning model and its output of the highest nitrogen-containing heterocyclic content, a characteristic value corresponding to the highest nitrogen-containing heterocyclic content, and a characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content are obtained; based on the second data set, the characteristic value corresponding to the highest nitrogen-containing heterocyclic content and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content, the second biomass is regulated to obtain the nitrogen-containing heterocyclic content of the second biomass after regulation, thereby improving the prediction accuracy and regulation effect of the nitrogen-containing heterocyclic content.

[0132] In some implementations, the model training module is further specifically used to:

[0133] Calculating an elemental composition derived descriptor based on the elemental composition of the first biomass and the molecular structure characteristics of the main components of the biomass to obtain an elemental composition derived descriptor of the first biomass;

[0134] The first biomass elemental composition derived descriptor, hydrothermal liquefaction conditions and nitrogen-containing heterocyclic content are input into the initial machine learning model to obtain a trained machine learning model and its output of the highest nitrogen-containing heterocyclic content, the characteristic value corresponding to the highest nitrogen-containing heterocyclic content and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content, wherein the characteristics include the first biomass elemental composition derived descriptor and hydrothermal liquefaction conditions, and the hydrothermal liquefaction conditions include temperature, time and solid content.

[0135] In some implementations, the model training module is further specifically used to:

[0136] inputting the first biomass elemental composition derived descriptor, hydrothermal liquefaction conditions, and nitrogen-containing heterocyclic ring content into the initial machine learning model, and calculating the loss value of the initial machine learning model by a random forest algorithm;

[0137] The initial machine learning model is reversely optimized based on the loss value to obtain a trained machine learning model and its output of the highest nitrogen-containing heterocyclic content, the characteristic value corresponding to the highest nitrogen-containing heterocyclic content, and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content.

[0138] In some embodiments, the control module is further specifically used for:

[0139] Calculate the elemental composition derived descriptor according to the elemental composition of the second biomass and the molecular structure characteristics of the main components of the biomass to obtain the elemental composition derived descriptor of the second biomass;

[0140] The second biomass is regulated according to the second biomass element composition derived descriptor, the characteristic value corresponding to the highest nitrogen-containing heterocyclic content, and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content to obtain the regulated nitrogen-containing heterocyclic content of the second biomass.

[0141] In some embodiments, the control module is further specifically used for:

[0142] The second biomass element composition derived descriptor is screened according to the feature weight value corresponding to the highest nitrogen-containing heterocyclic content and the preset derived descriptor ratio to obtain the second biomass screened derived descriptor;

[0143] The second biomass is regulated according to the characteristic value corresponding to the highest nitrogen-containing heterocyclic ring content and the descriptor derived after the second biomass screening to obtain the nitrogen-containing heterocyclic ring content of the regulated second biomass.

[0144] In some embodiments, the control module is further specifically used for:

[0145] Based on the characteristic value corresponding to the highest nitrogen-containing heterocyclic content and the descriptor derived after the second biomass screening, the second biomass is regulated by hydrogen peroxide-coupled low-temperature baking pretreatment technology to obtain the nitrogen-containing heterocyclic content of the regulated second biomass.

[0146] It should be noted that the present system embodiment and the above-mentioned method embodiment are based on the same inventive concept, so the relevant contents of the above-mentioned method embodiment are also applicable to the present system embodiment and will not be repeated here.

[0147] Figure 6 A schematic diagram of the hardware structure for regulating the nitrogen-containing heterocyclic content of biomass provided in an embodiment of the present application is shown.

[0148] The device for regulating the content of nitrogen-containing heterocyclic rings in biomass may include a processor 501 and a memory 502 storing computer program instructions.

[0149] Specifically, the processor 501 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0150] The memory 502 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 502 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 502 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 502 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 502 is a non-volatile solid-state memory.

[0151] In some embodiments, the memory 502 may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical or other physical / tangible memory storage device. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present disclosure.

[0152] The processor 501 reads and executes the computer program instructions stored in the memory 502 to implement any one of the control methods for hydrothermal liquefaction of nitrogen-containing heterocyclic compounds from biomass in the above embodiments.

[0153] In one example, the biomass nitrogen heterocyclic content control device may further include a communication interface 503 and a bus 510. Figure 6 As shown, the processor 501, the memory 502, and the communication interface 503 are connected via a bus 510 and communicate with each other.

[0154] The communication interface 503 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0155] Bus 510 includes hardware, software or both, and the parts of the regulating and controlling equipment of biomass nitrogen-containing heterocyclic content are coupled to each other. For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front-side bus (FSB), hypertransmission (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In suitable cases, bus 510 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the application considers any suitable bus or interconnection.

[0156] The biomass nitrogen-containing heterocyclic content control device can perform the biomass hydrothermal liquefaction nitrogen-containing heterocyclic compound control method in the embodiment of the present application based on the three-dimensional design model, thereby achieving the combination of Figure 1 and Figure 5 A method and system for regulating the hydrothermal liquefaction of biomass into nitrogen-containing heterocyclic compounds is described.

[0157] In addition, in combination with the control method of hydrothermal liquefaction of nitrogen-containing heterocyclic compounds by biomass in the above embodiments, the embodiments of the present application may provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by the processor, any of the control methods of hydrothermal liquefaction of nitrogen-containing heterocyclic compounds by biomass in the above embodiments is implemented.

[0158] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0159] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0160] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0161] Aspects of the present disclosure are described above with reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to the embodiment of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs a specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0162] The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

Claims

1. A method for regulating biomass hydrothermal liquefaction of nitrogen-containing heterocyclic compounds, characterized in that: The method for regulating and controlling the hydrothermal liquefaction of nitrogen-containing heterocyclic compounds from biomass comprises: Acquire a first data set and a second data set, wherein the first data set includes the elemental composition, hydrothermal liquefaction conditions, and nitrogen-containing heterocyclic content of the measured first biomass, and the second data set includes the elemental composition of the second biomass to be regulated; Based on the first data set and the pre-built initial machine learning model, a trained machine learning model is obtained, and the maximum nitrogen-containing heterocyclic content, the characteristic value corresponding to the maximum nitrogen-containing heterocyclic content, and the characteristic weight value corresponding to the maximum nitrogen-containing heterocyclic content are output by the model; The second biomass is regulated based on the second data set, the characteristic value corresponding to the highest nitrogen-containing heterocyclic content, and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content to obtain the regulated nitrogen-containing heterocyclic content of the second biomass.

2. The method for controlling the hydrothermal liquefaction of nitrogen-containing heterocyclic compounds from biomass according to claim 1, characterized in that: The method of obtaining a trained machine learning model based on the first data set and the pre-built initial machine learning model and the maximum nitrogen-containing heterocyclic content, the characteristic value corresponding to the maximum nitrogen-containing heterocyclic content, and the characteristic weight value corresponding to the maximum nitrogen-containing heterocyclic content output by the model includes: Calculating an elemental composition derived descriptor based on the elemental composition of the first biomass and the molecular structure characteristics of the main components of the biomass to obtain a first biomass elemental composition derived descriptor; The first biomass elemental composition derived descriptor, the hydrothermal liquefaction conditions and the nitrogen-containing heterocyclic content are input into the initial machine learning model to obtain the trained machine learning model and its output of the highest nitrogen-containing heterocyclic content, the characteristic value corresponding to the highest nitrogen-containing heterocyclic content and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content, wherein the characteristics include the first biomass elemental composition derived descriptor and the hydrothermal liquefaction conditions, and the hydrothermal liquefaction conditions include temperature, time and solid content.

3. The method for controlling the hydrothermal liquefaction of nitrogen-containing heterocyclic compounds from biomass according to claim 2, characterized in that: The step of inputting the first biomass elemental composition derived descriptor, the hydrothermal liquefaction condition and the nitrogen-containing heterocyclic ring content into the initial machine learning model to obtain the trained machine learning model and its output of the highest nitrogen-containing heterocyclic ring content, the characteristic value corresponding to the highest nitrogen-containing heterocyclic ring content and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic ring content, comprises: Inputting the first biomass elemental composition derived descriptor, the hydrothermal liquefaction condition, and the nitrogen-containing heterocyclic content into the initial machine learning model, and calculating the loss value of the initial machine learning model by a random forest algorithm; The initial machine learning model is reversely optimized based on the loss value to obtain the trained machine learning model and its output of the highest nitrogen-containing heterocyclic content, the characteristic value corresponding to the highest nitrogen-containing heterocyclic content, and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content.

4. The method for controlling the hydrothermal liquefaction of nitrogen-containing heterocyclic compounds from biomass according to claim 3, characterized in that: The formula for inputting the first biomass elemental composition derived descriptor, the hydrothermal liquefaction condition and the nitrogen-containing heterocyclic content into the initial machine learning model and calculating the loss value of the initial machine learning model by the random forest algorithm is: Among them, L is the loss value, y i is the true value of the i-th sample in the first data set, is the predicted value of the i-th sample in the first data set, and N is the total number of samples in the first data set.

5. The method for controlling the hydrothermal liquefaction of nitrogen-containing heterocyclic compounds from biomass according to claim 1, characterized in that: The method of regulating the second biomass based on the second data set, the characteristic value corresponding to the highest nitrogen-containing heterocyclic content, and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content to obtain the regulated nitrogen-containing heterocyclic content of the second biomass includes: Calculate the elemental composition derived descriptor according to the elemental composition of the second biomass and the molecular structure characteristics of the main components of the biomass to obtain the elemental composition derived descriptor of the second biomass; The second biomass is regulated according to the second biomass elemental composition derived descriptor, the characteristic value corresponding to the highest nitrogen-containing heterocyclic content, and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content to obtain the regulated nitrogen-containing heterocyclic content of the second biomass.

6. The method for controlling the hydrothermal liquefaction of nitrogen-containing heterocyclic compounds from biomass according to claim 5, characterized in that: The method of regulating the second biomass according to the second biomass elemental composition derived descriptor, the characteristic value corresponding to the highest nitrogen-containing heterocyclic content, and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content to obtain the regulated nitrogen-containing heterocyclic content of the second biomass includes: Screening the second biomass element composition derived descriptor according to the feature weight value corresponding to the highest nitrogen-containing heterocyclic content and a preset derived descriptor ratio to obtain a second biomass screened derived descriptor; The second biomass is regulated according to the characteristic value corresponding to the highest nitrogen-containing heterocyclic ring content and the descriptor derived after the screening of the second biomass to obtain the nitrogen-containing heterocyclic ring content of the second biomass after regulation.

7. The method for controlling the hydrothermal liquefaction of nitrogen-containing heterocyclic compounds from biomass according to claim 6, characterized in that: The step of regulating the second biomass according to the characteristic value corresponding to the highest nitrogen-containing heterocyclic ring content and the descriptor derived after the second biomass screening to obtain the regulated nitrogen-containing heterocyclic ring content of the second biomass includes: Based on the characteristic value corresponding to the highest nitrogen-containing heterocyclic content and the descriptor derived from the second biomass after screening, the second biomass is regulated by hydrogen peroxide coupled low-temperature baking pretreatment technology to obtain the regulated nitrogen-containing heterocyclic content of the second biomass.

8. A control system for hydrothermal liquefaction of nitrogen-containing heterocyclic compounds from biomass, characterized in that: The control system for hydrothermal liquefaction of nitrogen-containing heterocyclic compounds from biomass comprises: A data acquisition module, used to acquire a first data set and a second data set, wherein the first data set includes the elemental composition, hydrothermal liquefaction conditions and nitrogen-containing heterocyclic content of the measured first biomass, and the second data set includes the elemental composition of the second biomass to be regulated; A model training module, for obtaining a trained machine learning model and its output of the highest nitrogen-containing heterocyclic content, a characteristic value corresponding to the highest nitrogen-containing heterocyclic content, and a characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content based on the first data set and a pre-constructed initial machine learning model; A control module is used to control the second biomass based on the second data set, the characteristic value corresponding to the highest nitrogen-containing heterocyclic content and the characteristic weight value corresponding to the highest nitrogen-containing heterocyclic content to obtain the nitrogen-containing heterocyclic content of the second biomass after control.

9. A device for regulating the content of nitrogen-containing heterocyclic rings in biomass, characterized in that: It includes at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions that can be executed by the at least one control processor, and the instructions are executed by the at least one control processor so that the at least one control processor can execute a method for regulating biomass hydrothermal liquefaction of nitrogen-containing heterocyclic compounds as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the method for regulating the hydrothermal liquefaction of nitrogen-containing heterocyclic compounds from biomass as described in any one of claims 1 to 7.

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