4880 results about "Biomass" patented technology

The invention relates to the field of power equipment data processing, in particular to a biomass power generation combustion parameter deep learning method and system, and aims to solve the problem of phase mismatch caused by sampling frequency difference and clock reference offset of multi-source heterogeneous time sequence data. A parallel multi-scale convolution and bidirectional long-short-term memory network hybrid model is constructed, transient fluctuation and long-period trend features are extracted, and combustion stage feature weights are dynamically distributed through a gating attention mechanism. The optimization control module generates a multi-target constraint condition, an operation instruction is output in combination with a fuzzy inference engine, and a digital twin platform simulates an extreme working condition to enhance model robustness. A closed-loop feedback mechanism dynamically adjusts model parameters through combustion efficiency monitoring data and simulation results, and a two-stage fault-tolerant strategy realizes sensor abnormity compensation and historical control strategy backtracking. The problem of asynchronous data stream feature misalignment is effectively solved, and the combustion efficiency prediction precision and the control decision reliability are improved.

The invention relates to the technical field of biomass and carbon sink collaboration, in particular to a tropical rainforest biomass and carbon sink collaborative optimization decision-making system based on deep learning. According to the technical scheme, the system comprises a multi-modal data acquisition system, a space-time coupling preprocessing module, an ecological coupling deep learning model, a carbon sink dynamic evaluation system, a multi-objective decision optimization engine and an intelligent interaction execution platform. Data are comprehensively and safely acquired by means of a multi-modal data acquisition system, the data quality is optimized through a space-time coupling preprocessing module, biomass and carbon sink are accurately analyzed by means of an ecological coupling deep learning model and a carbon sink dynamic evaluation system, a scientific decision is made through a multi-objective decision optimization engine, execution is guaranteed, and the method is suitable for large-scale popularization and application. Convenient interaction and efficient execution are realized by means of the intelligent interaction execution platform, so that the accuracy, scientificity and execution efficiency of tropical rainforest biomass and carbon sink collaborative optimization decision making are remarkably improved, and ecological protection and sustainable development of tropical rainforests are powerfully promoted.

The invention provides a multi-fusion seaweed field ecosystem observation method, system, device and medium, and belongs to the technical field of ecological monitoring, the method comprises the following steps: obtaining chlorophyll a concentration, seaweed canopy spectrum and three-dimensional biomass point cloud; aligning the chlorophyll a concentration of the target sea area with the seaweed canopy spectrum, and fusing the three-dimensional biomass point cloud to generate a three-dimensional biomass model; constructing an in-situ sampling network to monitor water quality parameters, benthic organism video streams and eDNA metagenome sequencing data, calibrating a three-dimensional biomass model, executing anomaly detection through a lightweight LSTM model, and identifying benthic organism species in real time through an improved YOLOv5 model; constructing a graph neural network, outputting a carbon sink prediction value, generating a brown tide early warning signal when the carbon sink prediction value is lower than a dynamic threshold value, optimizing a patrol path of the unmanned aerial vehicle based on reinforcement learning, and improving the sampling frequency of the water quality sensor. According to the invention, multi-fusion monitoring of the seaweed field is realized, the ecological condition is accurately evaluated, and abnormity is warned in advance.

The invention provides a planting agriculture carbon sink metering method and system and computing equipment. The method comprises the following steps: establishing a dynamic farmland ecosystem carbon sink model according to biomass monitoring data, soil carbon content data, meteorological data and crop management measure records of a farmland ecosystem; based on the dynamic farmland ecosystem carbon sink model, analyzing carbon fixation efficiency under different crop types and different farming modes in a farmland ecosystem; and calculating the total carbon sink amount of the farmland ecosystem. According to the technical scheme provided by the invention, the dynamic farmland ecosystem carbon sink model is established, so that the carbon absorption capacity of the farmland ecosystem can be more accurately quantified, and the carbon sink metering precision is improved.

The invention relates to the technical field of boiler engineering and artificial intelligence modeling, in particular to a coal-fired boiler biomass blending combustion working condition intelligent optimization method, which comprises the following steps: constructing a three-dimensional mathematical model of coal-fired boiler biomass blending combustion based on experimental test data; performing multi-working-condition numerical simulation according to boiler combustion working condition fluctuation in the three-dimensional mathematical model; according to the numerical simulation data, constructing a time-space coupling model for co-combustion of biomass in the coal-fired boiler; the modeling accuracy is improved through a numerical simulation technology, the boiler operation condition is dynamically optimized in combination with space-time modeling, and theoretical support is provided for energy conservation and emission reduction and blending combustion proportion optimization; the method is suitable for environment-friendly transformation and operation optimization of the coal-fired boiler, and carbon emission and pollutant emission can be effectively reduced.

The invention relates to the field of power equipment data processing, in particular to a biomass power generation efficiency optimization method based on data driving, which comprises the following steps: collecting fuel multi-source heterogeneous data through a laser-induced breakdown spectroscopy and a temperature sensor, and carrying out space-time alignment processing by adopting a redundancy sensing fusion and dynamic time warping algorithm; and generating a fuel characteristic tensor for representing the fuel heat value, the water content and the ash content. A combustion digital twin dynamic model is constructed based on the tensor, the weight of the model is dynamically updated through cross validation of the CO / CO concentration ratio and the volatilization analysis rate, the combustion efficiency and the control stability are improved, and the problems of insufficient model adaptation and reduced heat transfer efficiency caused by fuel heterogeneity are solved.

The invention provides a heteroatom-doped porous carbon material for carbon dioxide adsorption as well as a preparation method and application thereof. The preparation method comprises the following steps: S1, drying and crushing waste biomass to obtain raw material powder; s2, mixing the raw material powder with an activating agent, and fully grinding to obtain a mixture of the raw material powder and the activating agent powder; s3, dissolving one or more heteroatom sources in hot deionized water to obtain a hot solution, pouring the mixture of the raw materials and the activator powder in the step S2 into the hot solution, and heating and stirring until water is evaporated; s4, performing high-temperature carbonization on the mixture finally obtained in the step S3 in an anoxic environment; and S5, cleaning and drying the product in the step S4 to obtain the heteroatom-doped porous carbon material. The heteroatom-doped porous carbon material disclosed by the invention has a highly developed pore structure, and the porous carbon prepared by taking the waste biomass as a precursor is used as a carbon dioxide adsorbent, so that high-value utilization of waste biomass resources is realized.

A method of preparing an enhanced oil recovery composition is described. The method includes carbonizing (200) a biomass waste material (202) to provide carbon microparticles (208). The method includes functionalizing (300) the carbon microparticles (208) through an acid treatment such that the carbon microparticles have a hydrophilic surface (306). The method further includes grinding (400) the carbon microparticles to provide carbon nanoparticles (406), where the carbon nanoparticles have a hydrophilic surface (306) and a hydrophobic surface (404). A method of enhanced oil recovery is also described.

The invention discloses a regional scale crop nitrogen dynamic regulation and control method and device. The method comprises the following steps of obtaining multi-source data and constructing a space-time database; selecting a crop growth model, and calibrating and verifying the crop growth model based on the space-time database to obtain the crop growth model and generate a localized model parameter set; dividing a target area into space units, inputting soil data, meteorological data and a localized model parameter set of each space unit to a crop growth model, simulating crop growth processes of different nitrogen gradients, extracting optimal biomass and critical nitrogen concentration data point pairs of each space unit, and performing fitting to generate a spatialized pCNDC parameter map layer; inverting an actual biomass and nitrogen concentration spatial distribution map based on remote sensing data, calculating critical nitrogen concentration and nitrogen nutrition index of each spatial unit in combination with a pCNDC parameter map layer, and generating a spatial distribution map; when the nitrogen nutrition index is lower than a preset threshold value, the nitrogen deficiency amount is calculated, and a variable fertilization prescription map is generated.

The invention discloses a saline-alkaline tolerant growth-promoting bacillus velezensis AH216 and application thereof, and belongs to the technical field of microbiology and biologica.Bacillus velezensis AH216 is preserved in the China Center for Type Culture Collection (CCTCC), and the preservation number is CCTCC No: M 20241496. The saline-alkaline tolerant growth-promoting bacillus velezensis AH216 has the advantages that the saline-alkaline tolerant growth-promoting bacillus velezensis AH216 can be used for preparing saline-alkaline tolerant growth-promoting bacillus velezensis AH216; the bacillus velezensis AH216 provided by the invention can inhibit the growth of pathogenic bacteria of cotton fusarium wilt, and has the growth promoting functions of dissolving potassium, generating indoleacetic acid, cellulase, protease, siderophore and the like; meanwhile, the bacillus velezensis AH216 can tolerate a highly saline-alkaline environment, can improve agronomic characters such as the plant height, the biomass and the leaf area in the corn seedling stage in common soil and saline-alkaline soil, and can promote the development of a corn root system.

The invention relates to the field of unmanned aerial vehicle multispectral remote sensing image analysis and inversion model construction based on a machine learning algorithm, in particular to a temperate zone typical grassland surface biomass estimation method. A multi-rotor unmanned aerial vehicle is used for carrying a multi-spectral sensor, multi-band spectral reflection information of a pasture is obtained from a near low altitude, a physical spectral reflection model is constructed to calculate real and accurate reflectivity data of a same-band spectrum, vegetation coverage, soil attributes and pasture canopy information of a sampling area are recorded, and the earth surface biomass is measured by cutting a sample; according to the method, a plurality of vegetation indexes and a 3D space model are combined with a multi-modal input structure, data enhancement and small sample learning strategies are adopted, a biomass inversion model is constructed through an MANBA machine learning framework, the precision of grassland biomass estimation is improved, and effective technical support is provided for grassland ecological monitoring and management.

The invention provides a bio-oil yield prediction method based on interpretable machine learning, and relates to the field of biomass pyrolysis technology and machine learning, and the method comprises the following steps: 1, obtaining a biomass pyrolysis original data set, and carrying out the preprocessing; 2, enhancing the preprocessed data by applying a condition table generative adversarial network CTGAN; 3, dividing the enhanced data set into a training set and a test set, performing training by adopting an extreme gradient enhanced XGB model, optimizing hyper-parameters of a BO model through Bayesian, and constructing a CTGAN-XGB-BO prediction model; and 4, performing model interpretation on the trained and optimized bio-oil yield prediction model. Compared with the prior art, the machine learning algorithm based on the CTGAN-XGB-BO model provided by the invention has the advantages that the prediction precision of the bio-oil yield is improved, the time and the cost of experimental research are reduced, and researchers can better understand and optimize the production process and improve the process control level due to explanation and analysis of the model.

A method and system for fish identification based on body surface texture features and geometric features are provided. The method employs an improved Resnet network and a deep learning Yolov8 network to extract body surface texture features and geometric features of a fish on the basis of considering influences of fish tail swing and an oxygen concentration on a fish body form based on a small sample learning framework, and then realizes identity recognition of a fish individual by coupled analysis of the body surface texture features and the geometric features. The method can realize high-accuracy fish identification with few training samples of a fish to be identified from the perspective of actual application, provides theoretical basis and technical support for accurate fish stock assessment and accurate estimation of industrially farmed fish biomass, and meets the development requirements of modern agriculture.

The invention provides a special charcoal-based slow-release fertilizer for sandy soil as well as a preparation method and application thereof, and belongs to the technical field of fertilizers. The special charcoal-based slow-release fertilizer for the sandy soil is prepared from amino modified biomass charcoal, urea, urea formaldehyde, monoammonium phosphate, potassium sulfate, polyglutamic acid, fulvic acid, clay, attapulgite, a compound microbial agent and sodium alginate. The compound microbial agent is prepared from halomonas (ACCC 11681), bacillus subtilis (ACCC 60429) and bacillus mucilaginosus (ACCC 10094), and the compound microbial agent is prepared from bacillus subtilis (ACCC 60429) and bacillus mucilaginosus (ACCC 10094). The invention further provides a preparation method and application of the special charcoal-based slow-release fertilizer for the sandy soil. The special charcoal-based slow-release fertilizer for the sandy soil can remarkably improve the water and fertilizer retention capacity of the sandy soil, improve the farmland productivity of the sandy soil, save fertilizer and water input and improve the yield and quality of crops.

The invention discloses a storage medium, a microwave flash pyrolysis process optimization method, a microwave flash pyrolysis process optimization device and microwave flash pyrolysis process optimization equipment. The microwave flash pyrolysis process optimization method comprises the following steps: generating a bio-oil yield prediction model: acquiring historical data including a plurality of parameter items of biomass microwave flash pyrolysis equipment; the parameter items comprise raw material data type parameter items, process data type parameter items and bio-oil yield type parameter items; generating modeling data for machine learning according to the time correspondence of the historical data; the modeling data is divided into a training set and a test set according to a preset division proportion; based on a random forest regression model, generating a bio-oil yield prediction model for predicting a bio-oil yield prediction value according to the modeling data; according to the method, the bio-oil yield can be rapidly and accurately predicted according to the current working condition of the biomass microwave flash pyrolysis equipment, and then effective data support is provided for production work such as process optimization and equipment maintenance of the biomass microwave flash pyrolysis equipment.

The invention relates to the technical field of intelligent agriculture, in particular to an intelligent disease and pest monitoring and prevention system which comprises an environment self-adaption module, a spectrum collection module, a dynamic threshold value analysis module, a prevention decision module, an execution terminal and a data storage module. A polarization filter is adopted to suppress reflection of light on the blade surface to obtain a spectrum fusion image; the dynamic threshold value analysis module is used for establishing a dynamic proportion threshold value of the insect spot area and the medicinal material biomass by adopting a U-Net network in combination with a growth cycle recognition algorithm so as to obtain graded insect pest evaluation parameters; and the prevention and control decision module is used for combining the graded insect pest evaluation parameters with the environmental parameter set and generating a prevention and control scheme by adopting a pesticide application decision tree model based on northern climate characteristics. According to the invention, through dynamic environment adaptive adjustment and northern climate optimization pesticide application decision, efficient and accurate prevention and control of diseases and insect pests in a complex agricultural environment are realized.

The invention relates to the technical field of forestry, and discloses an earth-rock mountainous area drought and barren difficult site partition coupling vegetation recovery method. According to the method, a composite fuzzy matter element-entropy weight method is adopted for calculation, and vegetation growth influence indexes on subdivided land parcels serve as independent variables; a similar drought and barren difficult site of a formed stable vegetation community area is used as a reference system, so that the type of an apical community formed by a certain subarea after vegetation is fully recovered is judged, and the European fitting degree is obtained when the plant biomass on a subdivided plot of the formed stable vegetation community area is used as a dependent variable; and measuring potential plant biomass of different partitions of the difficult site according to the Euclidean fitting degree. And judging which plants are suitable for growing in the partition according to the type of the apical community, and formulating a manual intervention means in a targeted manner, so as to ensure that the manual intervention means are suitable at each position. And then a specific plant combination and mixing mode is adopted to ensure that the mode of adopting different manual intervention means in different subareas is feasible in actual operation and mutual promotion among the subareas is realized.

The invention belongs to the technical field of microorganisms, and provides a growth-promoting P.adami M6 and application thereof, the P.adami M6 is preserved in the China Center for Type Culture Collection, the preservation number is CCTCC No: M20242835, the preservation date is December 18, 2024, the preservation number is CCTCC No: M20242835, the preservation number is CCTCC No: M20242835, the preservation number is CCTCC No: M20242835, and the preservation number is CCTCC No: M20242835. The strain is separated from rhizosphere soil of field planting foxtail millet in the Inner Mongolia red peak area, and is obtained through artificial separation and purification; the strain has the growth promoting characteristics of nitrogen fixation, phosphorus dissolution, IAA production and siderophore production; the M6 fungicide is used for inoculating millet, so that the growth of millet seedlings can be obviously promoted, and the plant height, the root length and the overground part and underground part biomass of the millet are obviously increased; m6 is used as a microbial agent to soak seeds during sowing and irrigate a bacterial liquid once 7 days after sowing, so that the growth effect of millet is best.

The invention relates to the technical field of coal gangue treatment, in particular to a soil conditioner based on coal gangue and application of the soil conditioner. The soil conditioner is prepared by the following steps: putting calcined and modified coal gangue into domestic sewage, adsorbing organic matters, mineral nutrient elements and microorganisms in the sewage into a coal gangue material through domestic sewage reinforcement treatment, and then carrying out standing and aging treatment to change a microbial community structure in the coal gangue, so that the soil conditioner is prepared. The bio-availability of organic matters and mineral nutrient elements in the gangue is improved, and standing and aging are performed for 15-45 days to obtain the soil conditioner. The soil conditioner provided by the invention is rich in nitrogen, phosphorus, potassium, organic matters and abundant microbial communities, the saline-alkali soil quality can be improved by applying the soil conditioner provided by the invention to saline-alkali soil, the crop germination rate and biomass are further improved, and the goal of integrating ecological gradient utilization of coal gangue and saline-alkali soil improvement is achieved.

According to the invention, biomass rice hull ash and a sodium hydroxide solution are reacted to obtain water glass and carbon mud. And reacting the water glass with CO2 generated by calcining the limestone to produce a white carbon black product and a mixed waste liquid of sodium carbonate and sodium bicarbonate. Meanwhile, quick lime obtained by calcining limestone is digested and then reacts with sodium carbonate and sodium bicarbonate mixed waste liquid to generate a light calcium carbonate product and dilute sodium hydroxide waste liquid. And finally, the dilute sodium hydroxide waste liquid is concentrated and reused for water glass production, so that the waste liquid is recycled, and a complete process closed loop is formed. According to the process method disclosed by the invention, two leading-edge innovative processes, namely production of white carbon black by renewable resource biomass rice hull ash and production of white carbon black by a carbonization method, are organically combined and are coupled with production of light calcium carbonate by a limestone calcination process, so that the problem that a large amount of sodium sulfate-containing wastewater is discharged in a traditional process is solved; meanwhile, the carbon footprint of white carbon black and calcium carbonate products is greatly reduced.

A method of estimating an amount of a sequestered carbon-based constituent in a volume of soil that includes taking a core soil sample of a sample volume of soil proximate at and below a root system associated with carbon soil accumulation from a biomass. The core sample is taken at a depth extending from an earthen surface, the depth having a depth range. The core sample is tested to provide a data set.

The invention discloses a carbon sink dynamic prediction method, device and equipment based on multi-source remote sensing space-time fusion and three-dimensional point cloud deep learning and a storage medium, and relates to the technical field of remote sensing information processing and ecological environment monitoring, and the method comprises the steps: obtaining space-time fusion data, a vegetation point cloud inversion algorithm and a carbon sink prediction hybrid model; extracting three-dimensional point cloud biomass based on the vegetation point cloud inversion algorithm and the space-time fusion data, and determining vegetation parameter information; and predicting a carbon sink change trend based on the vegetation parameter information and the carbon sink prediction hybrid model, determining a carbon sink prediction result, and controlling a system to complete disaster assessment and risk early warning based on the carbon sink prediction result. According to the method, multi-source remote sensing space-time fusion is carried out to eliminate the space-time resolution difference, the three-dimensional point cloud biomass is extracted, the carbon sink change trend is predicted to realize disaster assessment and risk early warning, the multi-source data fusion precision and the vegetation parameter inversion accuracy are effectively improved, and the method has remarkable environmental benefits and social values.

The invention discloses a Ca / Mg-based modified biochar material and a preparation method and application thereof, and the preparation method comprises the following steps: pretreatment: drying a biomass material shaddock peel, crushing, sieving, washing and drying, then carrying out acid leaching activation, washing again and drying to obtain high-activity biomass powder for later use; dipping modification: fully mixing the high-activity biomass powder obtained in the pretreatment step with CaO and MgO, adding distilled water, dipping under heating and stirring conditions, then carrying out solid-liquid separation, washing a filter cake for multiple times, freeze-drying, and sieving to obtain an intermediate product; the intermediate product is calcined, naturally cooled, washed and dried, and the Ca / Mg-based modified bimetallic modified charcoal material is obtained and can be applied to adsorption of phosphate in a wastewater solution as an adsorbent. The preparation method is simple, the raw materials are easy to obtain, the cost is low, the material stability is good, the pH adaptability is good, the adsorption effect is good, and the method is suitable for water environments with more complex pollution factors.

The invention relates to a forest cultivation dynamic monitoring method and system based on a remote sensing technology, and the method comprises the steps: collecting point cloud and multi-source topographic data of a complex topographic region through the fusion of an airborne laser radar and a satellite radar technology, and generating a high-precision point cloud and topographic parameter matrix through denoising and registration; a segmentation threshold value is dynamically optimized in combination with topographic features, the canopy point cloud penetration rate of abrupt slope and valley areas is improved, and the problem of canopy missegmentation caused by uneven point cloud density under a complex terrain is solved; constructing a penetration rate compensation model driven by terrain influence factors, correcting laser radar point cloud deviation and inverting high-precision tree height, crown breadth and biomass parameters; and based on fusion analysis of the multi-temporal vegetation index and a machine learning model, early warning of diseases and insect pests and fire hazards and dynamic evaluation of a man-made forest cultivation effect are realized. According to the method, the bottleneck of complex terrain monitoring is broken through, the forest parameter inversion precision and the real-time response capability are remarkably improved, and technical support is provided for precise management of forestry resources.

The invention discloses a Burkholderia gladioli (Burkholderia gladioli) CT-5 strain and an application of the Burkholderia gladioli CT-5 strain, and relates to the technical field of microorganisms. The strain is preserved in the China Center for Type Culture Collection, the preservation number is CCTCC NO: M20242732, and the preservation date is December 05, 2024. The Burkholderia gladioli (Burkholderia gladioli) CT-5 provided by the invention has a remarkable prevention and treatment effect (Plt, Plt) on idesia root rot caused by Fusarium solani (Fusarium solani), and the Burkholderia gladioli (Burkholderia gladioli) CT-5 has a remarkable prevention and treatment effect on idesia root rot caused by Fusarium solani (Busarium solani). The compound has the advantages that the compound can be used for inhibiting pathogenic bacteria of various plants, and the compound can be used for inhibiting pathogenic bacteria of various plants, such as Schizophyllum commune, Diaporthe erens, Colletotrichum acutatum and the like, and has a remarkable inhibiting effect on other various plant pathogenic bacteria such as the Schizophyllum commune, the Diaporthe erens, the Colletotrichum acutatum and the like. In a pot experiment, the biomass, soil nutrients, SOD, POD and CAT enzyme activities of idesia polycarpa seedlings in a CT-5 treatment group are remarkably improved compared with those of a pathogenic bacteria control group (FP) (Plt; 0.05), which indicates that the strain can promote plant growth and activate the activity of defensive enzyme in the body, so that effective control of idesia root rot is realized, and powerful theoretical basis and technical support are provided for sustainable development of idesia.

The invention relates to a habitat restoration-oriented sediment elution critical threshold evaluation method. The method comprises the following steps: introducing form classification including an exchange state, a carbonate binding state, an iron and manganese oxide binding state, an organic binding state and a residue state; a dynamic disturbance response mechanism is fused, and the elutable proportion is re-evaluated under the conditions of flow velocity, pH, oxidation reduction potential Eh and change; calculating a potential active flux index PAFI to quantify a real releasable part of the pollutants in the bottom mud; establishing pollution flux and biological response time sequence data; depicting ecological response sensitive inflection points by using nonlinear fitting; extracting an ecological function critical damage point EBCP as an ecological constraint upper limit of the elution threshold; constructing a habitat bearing factor set comprising a water body volume ratio, submerged vegetation density and benthic organism biomass; introducing a bearing feedback coefficient CFC, and dynamically correcting a pollutant critical flux value; and outputting a loadable elution flux interval for judging whether the ecological risk red line is touched or not.

The invention discloses a biomass pyrolysis product prediction method based on a machine learning model, and the method comprises the steps: collecting data under different biomass pyrolysis conditions, and carrying out the processing of the data, and obtaining target data; constructing a machine learning model, and training the machine learning model based on the target data to obtain a target machine learning model; according to the target machine learning model, predicting the yield of the biochar and the content of nitrogen, phosphorus and potassium, and obtaining a prediction result of the yield of the biochar and the composition of nitrogen, phosphorus and potassium elements. By introducing the machine learning model and combining the data enhancement technology, high-precision prediction can be performed on the yield of the biochar and the contents of nitrogen, phosphorus and potassium of the biochar based on the element composition and pyrolysis process parameters of the biomass raw material, and technical support is provided for optimization design of the biomass pyrolysis process and application of the biochar fertilizer.

The invention relates to the technical field of mesoporous carbon material preparation, in particular to a high-temperature mesoporous carbon preparation method which comprises the following steps: S1, mixing a metal halide and an inorganic hydrogen bond donor, and heating and stirring until a solution is clear to obtain an inorganic eutectic solvent; s2, mixing the inorganic eutectic solvent and a biomass raw material for reaction; s3, filtering and washing the material obtained in the step S2; and S4, carbonizing the material obtained in the step S3 in a high-temperature inert atmosphere to obtain the high-temperature mesoporous carbon. According to the method, the wood fiber biomass such as the lowest-value straw, the lowest-value bamboo and the lowest-value wood dust is directly used as raw materials, component separation does not need to be conducted on the wood fiber biomass, and high-value utilization of a large amount of low-value wood fiber biomass can be achieved. The method is relatively simple in process, relatively mild in condition and relatively high in product yield, and an activating agent does not need to be used. The biomass mesoporous carbon prepared by the method also has a high specific surface area in a huge mesoporous volume state, and the specific capacitance in an alkaline electrolyte exceeds 260F / g.

The invention relates to a method for efficiently separating three components of lignocellulose biomass by using an alcohol-based ternary eutectic solvent, which comprises the following steps: uniformly mixing choline chloride, 4-chlorobenzenesulfonic acid and an organic alcohol solvent, heating in an oil bath, and stirring to obtain the alcohol-based eutectic solvent; the preparation method comprises the following steps: mixing an alcohol-based eutectic solvent with agriculture and forestry solid waste powder, putting the mixture into a reaction kettle, introducing carbon dioxide gas until the carbon dioxide gas forms subcritical carbon dioxide, and carrying out stepped progressive increase reaction under the reaction pressure of 2-4MPa to obtain an intermediate product; mixing the intermediate product with a reaction stopping agent, and performing suction filtration, cleaning, vacuum drying and grinding to obtain dry cellulose solid residual powder; adding deionized water of which the volume is 10 times that of the filtrate into the filtrate, standing, precipitating and centrifuging to obtain a lignin-enriched component; and adding a 5-30% ethanol solution, continuously stirring, centrifuging and drying to obtain the purified lignin. The lignin removal rate and the cellulose retention rate of the lignocellulose in the agricultural and forestry solid wastes are effectively improved.

The invention discloses a preparation process of a soil conditioner and application of the soil conditioner in soil improvement, and the preparation process comprises the following steps: S1, preparation of pretreated biomass; s2, preparation of composite biochar; s3, preparation of sulfhydrylated biochar; s4, preparing the modified composite biochar; and S5, uniformly mixing the modified composite charcoal, kaolin, humic acid, fly ash, slaked lime and vermiculite to obtain the soil conditioner. According to the soil conditioner, modified composite biochar prepared through specific functionalization is blended with kaolin, humic acid, fly ash, slaked lime and vermiculite according to a certain proportion, and the prepared soil conditioner has unique advantages in the aspects of improving soil organic matter, fixing carbon, providing microelements and especially regulating and controlling the activity of soil microorganisms; advanced complementation and synergistic interaction of all the components are achieved, and the comprehensive treatment requirement of complex degraded soil can be better met.