Carbon reduction method and system for producing green fuel from various biomasses

Abstract

The application discloses a carbon emission reduction method and system for preparing green fuel from multiple biomasses, and the method comprises the following steps: obtaining characteristic data of to-be-processed biomass raw materials; obtaining the full life cycle carbon emission of unit mass of different biomass raw materials, and the methanol yield of unit mass of the biomass raw materials, and calculating the carbon consumption yield ratio of the biomass raw materials; classifying the performance of the different biomass raw materials for preparing methanol according to the above results; analyzing the complementarity of the biomass raw materials based on the above results; selecting target biomass raw materials according to the classification results of the biomass raw materials; using the target biomass raw materials as the biomass raw materials for preparing methanol according to the classification results of the target biomass raw materials; or selecting one or more kinds of biomass raw materials which are complementary to the target biomass raw materials according to the complementarity of the target biomass raw materials, adjusting the mixing ratio of the biomass raw materials, and improving the methanol yield and carbon emission reduction effect of the biomass raw materials.

Description

Technical Field

[0001] This application relates to the field of carbon emission reduction technology, and more specifically, to a carbon emission reduction method and system for preparing green fuels from various biomass. Background Technology

[0002] With global emphasis on carbon emission reduction, the production of green methanol from biomass feedstocks has become a hot topic in academia and industry. However, while some biomass feedstocks reduce carbon emissions during the production of green methanol, their methanol yield is also correspondingly low, making them unsuitable for large-scale industrial production.

[0003] Current carbon reduction strategies for biomass-based methanol production primarily focus on process optimization and carbon emission management throughout the entire lifecycle of biomass feedstocks, neglecting the natural complementary advantages of various biomass feedstocks in terms of chemical composition, calorific value, moisture content, growth cycle, and geographical distribution. Therefore, there is room for improvement and enhancement in increasing biomass methanol production, reducing carbon emissions, and optimizing feedstock allocation. Summary of the Invention

[0004] This application systematically evaluates the carbon emissions, methanol production capacity, and complementarity of raw material characteristics of different biomass feedstocks in the methanol production process, thereby adjusting feedstock selection and ratio, optimizing biomass methanol production, and achieving carbon emission reduction. The technical solution is as follows:

[0005] In a first aspect, embodiments of this application provide a method for carbon emission reduction in the production of green fuels from various biomass sources, the method comprising:

[0006] Step 1: Obtain the characteristic data of the biomass raw material to be processed;

[0007] Step 2: Obtain the life-cycle carbon emissions per unit mass of different biomass feedstocks. And the methanol yield produced per unit mass of biomass feedstock Based on the obtained life-cycle carbon emissions and methanol production The carbon consumption-to-yield ratio for methanol production from biomass feedstock was calculated. = / ;

[0008] Where i represents any type of biomass feedstock; and the total life cycle carbon emissions... The total life-cycle carbon emissions during the production of methanol from different biomass feedstocks per unit mass, at each stage of planting, collection, transportation, and processing into methanol;

[0009] Step 3: Based on the characteristic data of the biomass feedstock to be processed and the methanol yield per unit mass of biomass feedstock. Carbon consumption and output ratio in methanol production The performance of methanol production from different biomass feedstocks was graded.

[0010] Step 4: Based on the characteristic data of the biomass feedstock to be processed and the obtained life cycle carbon emissions. Methanol production Carbon consumption-to-output ratio of methanol production from biomass feedstock The results of the grading of biomass raw materials were used to analyze the complementarity of biomass raw materials.

[0011] Step 5: Select the target biomass raw material based on the grading results of biomass raw materials in Step 3;

[0012] Based on the grade classification of the target biomass raw material, it is used alone as a biomass raw material for methanol production;

[0013] Alternatively, based on the complementarity of the target biomass feedstock, one or more biomass feedstocks that are complementary to the target biomass feedstock can be selected, and the mixing ratio of the biomass feedstocks can be adjusted to prepare methanol.

[0014] Furthermore, the grading of biomass raw materials in step 3 is based on the following scoring model:

[0015] =α +β ;

[0016] in, The grade of biomass feedstock is determined by α, where α represents methanol yield. The weight of β is the carbon consumption-to-yield ratio in methanol production. The weight.

[0017] Furthermore, step 3 includes:

[0018] Step 31: Obtain the grade rating of type i biomass raw material ;

[0019] Step 32: For The sorting process is as follows: > > >...> ;

[0020] Step 33: Set the grade rating for biomass raw materials Values ​​within the first range indicate superior biomass raw materials; biomass raw material grade rating. The value falls within the second range, indicating medium-grade biomass feedstock. (Biomass feedstock grade rating) The value is in the third range, indicating inferior biomass raw materials.

[0021] Furthermore, the target biomass raw material is either high-quality or medium-quality biomass raw material.

[0022] Furthermore, the methanol yield produced from different biomass feedstocks per unit mass... The numerical values ​​are used to classify the methanol production performance of different biomass raw materials. Step 3 further includes:

[0023] Step 301: Obtain the methanol yield of biomass feedstock i. ;

[0024] Step 302: For The sorting process is as follows: > > >...> ;

[0025] Step 303: Methanol Production If the value is in the fourth range, then the methanol production performance of biomass raw materials is excellent.

[0026] Furthermore, step 5 also includes:

[0027] Step 501: Calculate the grade score of high-quality biomass raw materials respectively. average Methanol production from biomass feedstocks with superior methanol production performance The average value M , where n is the quantity of biomass raw materials in the first range, and a is the quantity of biomass raw materials in the fourth range;

[0028] Step 502: Determine whether the grade score of the target biomass raw material is greater than the average value H. If yes, the target biomass raw material does not need to be mixed with other biomass raw materials to prepare methanol; if no, proceed to step 503.

[0029] Step 503: Determine whether the grade score of the target biomass raw material is greater than the maximum value of the inferior grade score. If so, based on the raw material database and grading model, analyze the main factors that cause the biomass raw material to have an insufficient grading score, match one or more complementary biomass raw materials from the raw material database, and adjust the selection and ratio of different biomass raw materials.

[0030] Furthermore, the characteristic data of biomass feedstock includes: the types of gases produced by the gasification of biomass feedstock and the amount of gas produced, wherein the types of gases are the reaction gases for the synthesis of methanol;

[0031] The complementarity analysis of biomass feedstocks includes the complementarity analysis of the production quantities of reaction gases from biomass gasification for methanol synthesis.

[0032] Furthermore, the reaction gases for methanol synthesis include at least a first reaction gas and a second reaction gas. If the output of the first reaction gas from the gasification of the target biomass feedstock is greater than the output of the second reaction gas, then the output of the second reaction gas from the gasification of the matched complementary biomass feedstock is greater than the output of the first reaction gas.

[0033] Furthermore, the characteristic data of biomass raw materials also include information on the chemical composition, calorific value, moisture content, and ash content of different types of biomass raw materials. By combining the complementary nature of the above data, a suitable ratio of biomass raw materials can be selected.

[0034] On the other hand, embodiments of this application also provide a carbon emission reduction system for various biomass-based green fuel production methods, used to achieve the aforementioned carbon emission reduction methods for various biomass-based green fuel production methods. The system includes:

[0035] The acquisition module acquires the characteristic data of the biomass raw materials to be processed;

[0036] The calculation module obtains the life-cycle carbon emissions per unit mass of different biomass feedstocks. And the methanol yield produced per unit mass of biomass feedstock Based on the obtained life-cycle carbon emissions and methanol production The carbon consumption-to-yield ratio for methanol production from biomass feedstock was calculated. = / ;

[0037] Where i represents any type of biomass feedstock; and the total life cycle carbon emissions... The total life-cycle carbon emissions during the production of methanol from different biomass feedstocks per unit mass, at each stage of planting, collection, transportation, and processing into methanol;

[0038] The grading module is based on the characteristic data of the biomass feedstock to be processed and the methanol yield per unit mass of biomass feedstock. Carbon consumption and output ratio in methanol production The performance of methanol production from different biomass feedstocks was graded.

[0039] The analysis module, based on the characteristic data of the biomass feedstock to be processed and the obtained life-cycle carbon emissions, Methanol production Carbon consumption-to-output ratio of methanol production from biomass feedstock The results of the grading of biomass raw materials were used to analyze the complementarity of biomass raw materials.

[0040] In the selection module, based on the grade classification results of biomass raw materials, select the target biomass raw material;

[0041] Based on the grade classification of the target biomass raw material, it is used alone as a biomass raw material for methanol production;

[0042] Alternatively, based on the complementarity of the target biomass feedstock, one or more biomass feedstocks that are complementary to the target biomass feedstock can be selected, and the mixing ratio of the biomass feedstocks can be adjusted to prepare methanol.

[0043] The solution provided in this application has the following beneficial effects:

[0044] 1. The carbon emission reduction methods for biomass-based green fuel production provided in this application, by acquiring characteristic data of biomass raw materials, the total life-cycle carbon emissions of different biomass raw materials per unit mass, the methanol production per unit mass of biomass raw materials, and the calculated carbon consumption-output ratio of biomass raw materials for methanol production, can clarify the properties of different biomass raw materials and the relationship between methanol production and carbon emissions per unit mass of biomass raw materials. This allows for targeted selection and matching of biomass raw materials when choosing mixed raw materials, and through reasonable proportioning, fully leveraging the advantages of different biomass raw materials, thereby increasing overall methanol production while reducing carbon emissions and achieving optimized utilization of biomass raw materials.

[0045] 2. The carbon emission reduction methods for preparing green fuels from biomass provided in this application can comprehensively evaluate the different carbon emissions of different biomass raw materials at different life cycles by collecting the carbon emissions of different biomass raw materials throughout their entire life cycle. This allows for the selection of biomass raw material combinations with lower carbon emissions throughout their entire life cycle, effectively reducing the carbon emissions in the entire methanol preparation process. Alternatively, based on the stage where the target biomass raw material has a high carbon emission, biomass raw materials with lower carbon emissions at the corresponding stage can be selected for mixing and preparing methanol to reduce carbon emissions.

[0046] 3. The carbon emission reduction methods for biomass-based green fuel production provided in this application scientifically classify biomass raw materials based on their characteristics, methanol yield, and carbon consumption-output ratio in methanol production. This allows for the rapid screening of high-performance biomass raw materials and the rational utilization of lower-performance biomass raw materials alongside high-performance ones, thereby reducing their adverse effects on the overall methanol production process.

[0047] 4. The carbon emission reduction methods for preparing green fuels from biomass provided in this application, through complementary analysis of biomass raw materials and selection and ratio adjustment of biomass raw materials in combination with grade classification results, can effectively reduce carbon emissions in the methanol preparation process while giving full play to the advantages of different raw materials and increasing methanol production, thereby achieving the goal of carbon emission reduction and realizing green production.

[0048] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0049] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a flowchart illustrating a method for carbon emission reduction in the preparation of green fuels from biomass, provided in one embodiment of this application. Detailed Implementation

[0051] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0052] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0053] Please see Figure 1 , Figure 1 Flowcharts of various carbon emission reduction methods for preparing green fuels from biomass provided in embodiments of this application. Figure 1 As shown, this method has the following five steps:

[0054] Step 1: Obtain the characteristic data of the biomass raw material to be processed.

[0055] In this step, biomass raw materials refer to substances derived from biological organisms. These substances are typically renewable, such as crop straw, wood processing residues, energy crops, livestock manure, and some aquatic plants. After appropriate processing, these raw materials can be converted into energy, materials, or other useful products.

[0056] Specifically, the characteristic data of biomass raw materials includes information such as chemical composition, calorific value, moisture content, and ash content of different types of biomass raw materials. This step also includes analyzing the acquired characteristic data of biomass raw materials, specifically referring to a systematic study and evaluation of various physical and chemical properties of the biomass raw materials. This includes at least the following types:

[0057] 1. Physical property analysis: The shape, size, density, moisture content, and bulk density of biomass raw materials are analyzed. These physical properties affect the storage, transportation, and processing of biomass raw materials.

[0058] 2. Chemical property analysis: This includes data on the elemental composition, chemical composition, ash content, and ash composition of biomass raw materials. The elemental composition and chemical composition determine the energy content and chemical reactivity of biomass raw materials, while the ash content and ash composition determine the combustion performance of biomass raw materials.

[0059] 3. Biological characteristics analysis: This includes data on the biodegradability, microbial composition and activity of biomass raw materials. Some biomass raw materials are processed through biotransformation, so understanding their biological characteristics is crucial.

[0060] There are many ways to obtain characteristic data of biomass feedstocks. The following are some common methods for obtaining characteristic data of biomass feedstocks:

[0061] 1. Experimental Analysis

[0062] Experimental analysis is the most direct and reliable method for obtaining characteristic data of biomass raw materials. By analyzing biomass samples using laboratory equipment, detailed characteristic data of biomass raw materials can be obtained.

[0063] Specifically, physical property testing can be performed using specialized instruments to determine the shape, size, density, moisture content, and bulk density of biomass raw materials; chemical property testing can be performed using chemical analysis methods to determine the elemental composition, chemical composition, ash content, and other information of biomass raw materials. Elemental composition can be determined using an elemental analyzer, chemical composition can be determined using chemical extraction and quantitative analysis methods, ash content can be calculated by burning the biomass raw materials at high temperature to constant weight and then calculating based on the mass of the remaining residue, and ash composition can be analyzed using equipment such as X-ray fluorescence spectrometers.

[0064] 2. Field survey and sampling

[0065] For biomass feedstocks originating from specific regions or planting areas, on-site investigations and sampling are conducted to obtain their characteristic data. Information on the growing environment of the biomass feedstock, such as soil type, climate conditions, fertilization and irrigation, as well as the growth cycle and harvesting method, is gathered, as these factors affect the characteristics of the biomass feedstock. For example, corn stalks grown in different regions may have different chemical compositions and physical properties due to differences in soil fertility and climate. On-site sampling allows for the acquisition of representative samples for subsequent testing and analysis, thereby providing a more accurate understanding of the feedstock's characteristics.

[0066] 3. Monitoring during the production process

[0067] In actual production processes, real-time monitoring of biomass feedstocks is crucial. For example, in the process of biomass gasification to produce methanol, online monitoring equipment is installed to measure parameters such as temperature, pressure, and flow rate of the feedstock entering the reactor, as well as changes in the composition of the biomass feedstock. Intermediate and final products can also be analyzed during the production process to indirectly understand the impact of biomass feedstock characteristics on the production process. Monitoring data during production allows for timely adjustments to the production process, optimization of feedstock usage, and the accumulation of empirical data on the relationship between biomass feedstock characteristics and production performance.

[0068] In this step, by acquiring and analyzing the characteristic data of biomass raw materials, the properties of different biomass raw materials can be clarified, providing a basis for the selection of subsequent processing technologies for different biomass materials. This avoids problems such as low production efficiency and unstable quality caused by improper process selection, and improves the feasibility and reliability of the processing process. Furthermore, it allows for the targeted selection of biomass raw materials in subsequent steps and their reasonable proportioning, fully leveraging the advantages of different biomass raw materials.

[0069] Step 2: Obtain the life-cycle carbon emissions per unit mass of different biomass feedstocks. And the methanol yield produced per unit mass of biomass feedstock Based on the obtained life-cycle carbon emissions and methanol production The carbon consumption-to-yield ratio for methanol production from biomass feedstock was calculated. = / Where i represents any type of biomass feedstock; and the total life cycle carbon emissions are... The total life-cycle carbon emissions during the production of methanol from different biomass feedstocks per unit mass, at each stage of planting, collection, transportation, and processing into methanol.

[0070] Specifically, unit mass refers to common units of mass such as 1 ton, and life-cycle carbon emissions cover the greenhouse gas emissions generated throughout the entire process of biomass feedstock production, from planting to final processing into methanol. This includes:

[0071] 1. Planting stage: This includes carbon emissions generated during land preparation, seed sowing, fertilization, irrigation, pest and disease control, and the use of agricultural machinery during the planting process.

[0072] 2. Collection stage: This includes the harvesting, baling, and storage of biomass raw materials, as well as the operation of agricultural machinery and the carbon emissions generated by the input of manpower and materials during the collection process.

[0073] 3. Transportation stage: The collected biomass raw materials are transported to the processing site. The fuel consumption of the transportation vehicles during the transportation process will generate carbon emissions.

[0074] 4. Processing stage: This includes carbon emissions generated from processes such as pretreatment of biomass raw materials, gasification, gas purification, methanol synthesis, equipment use, and energy consumption.

[0075] Based on the collected carbon emissions throughout the entire life cycle of different biomass raw materials And the methanol yield that can be produced Calculate the carbon consumption-to-yield ratio for methanol production from biomass feedstock. It can be seen that The higher the value, the more methanol the biomass feedstock can produce during the methanol production process, meaning the higher the efficiency of the biomass feedstock in methanol production and the more suitable it is for large-scale methanol production. The higher the value, the more methanol can be obtained from the biomass feedstock with lower carbon emissions, resulting in greater environmental benefits.

[0076] In this step, by collecting the life-cycle carbon emissions and methanol yield of different biomass feedstocks per unit mass, and calculating the carbon consumption-output ratio of methanol production, a comprehensive evaluation of the performance of different biomass feedstocks in methanol production can be conducted from two key dimensions: energy output and environmental impact. This helps to screen out high-yield and environmentally friendly high-quality biomass feedstocks, providing a scientific basis for the selection of methanol production feedstocks. Through detailed analysis of the life-cycle carbon emissions at each stage, it helps to identify the links with high carbon emissions in the entire production process, enabling continuous optimization of the methanol production process and technology, and improving overall production efficiency and sustainability.

[0077] Step 3: Based on the characteristic data of the biomass feedstock to be processed and the methanol yield per unit mass of biomass feedstock. Carbon consumption and output ratio in methanol production The performance of methanol produced from different biomass feedstocks was classified into different grades.

[0078] In this step, the grading of biomass raw materials is based on the following scoring model: =α +β ;in, The grade of biomass feedstock is determined by α, where α represents methanol yield. The weight of β is the carbon consumption-to-yield ratio in methanol production. The weights reflect the relative importance of methanol production and the carbon consumption-to-output ratio in the rating. Their specific values ​​can be set according to actual production needs and the degree of emphasis placed on methanol production and the carbon consumption-to-output ratio. For example, if more emphasis is placed on methanol production, the value of α can be appropriately increased; if more emphasis is placed on energy efficiency and environmental protection, the value of β can be increased.

[0079] By constructing mathematical models =α +β To quantify the grade of biomass feedstock, for each biomass feedstock, combine its methanol yield... Carbon consumption and output ratio in methanol production And the weights, which can be used to calculate the corresponding rating. Furthermore, different biomass raw materials can be classified into grades based on their grade scores, providing a quantitative standard for the selection and blending of subsequent biomass raw materials.

[0080] Furthermore, step 3 includes:

[0081] Step 31: Obtain the grade rating of type i biomass raw material Step 32: For The sorting process is as follows: > > >...> >...> >...> .

[0082] Obtain the grade rating of each of the i biomass raw materials. Next, these scores need to be sorted in descending order. That is, for this type of biomass raw material, the corresponding grade score is... Biomass feedstocks showed the best performance in methanol production. Next, and so on, until... The lowest grade among all biomass feedstocks indicates the worst performance in methanol production. This ranking clearly shows the order of superiority or inferiority of different biomass feedstocks in methanol production.

[0083] Step 33: Set the grade rating for biomass raw materials Values ​​within the first range indicate superior biomass raw materials; biomass raw material grade rating. The value falls within the second range, indicating medium-grade biomass feedstock. (Biomass feedstock grade rating) The value is in the third range, indicating inferior biomass raw materials.

[0084] Specifically, the first range is [ , The second range is [], , The third range is [] , ], where n=0.3i, q=0.8i, and n<q. That is, high-quality biomass raw materials are graded. The value is in the rating level. The top 30% of medium-grade biomass raw materials are graded. The value is in the rating level. The middle 50% consists of inferior biomass raw materials, which are graded according to quality. The value is in the rating level. The last 20%.

[0085] Specifically, high-quality biomass raw materials include the following:

[0086] 1. Methanol production High, and the carbon consumption-to-output ratio of methanol production is high. High indicates high methanol production per unit mass of biomass feedstock with low carbon emissions; methanol production High means that, per unit mass, biomass feedstock can produce a relatively large amount of methanol; the carbon consumption-to-yield ratio in methanol production. A high carbon emission rate indicates that for every unit of carbon emission generated during methanol production, the biomass feedstock can produce more methanol, meaning that the carbon emission per unit mass of biomass feedstock is low when producing methanol.

[0087] 2. Methanol production The carbon consumption-to-output ratio in methanol production is moderate, but... High; although the methanol yield of this type of biomass feedstock is high. It is at a moderate level, meaning the amount of methanol produced per unit mass is not particularly outstanding, but its carbon consumption-to-yield ratio in methanol production is... The high level indicates its excellent performance in carbon emission control, even with high methanol production. While the carbon emissions are moderate, this biomass raw material still has high value from the perspectives of energy efficiency and environmental protection due to its low carbon emissions.

[0088] 3. Methanol production High, but the carbon consumption-to-output ratio in methanol production is high. Medium; methanol yield of this biomass feedstock High efficiency ensures a high methanol output per unit mass, meeting the yield requirements in methanol production, and its methanol production carbon consumption-to-output ratio is high. It is only at a moderate level, meaning that the carbon emissions are not optimal, but due to the obvious advantage in production volume, it still has high value when considering the overall benefits of methanol production.

[0089] Medium-grade biomass feedstocks include the following:

[0090] 1. Methanol production Carbon consumption and output ratio in methanol production At a moderate level, these biomass feedstocks exhibit relatively balanced performance across various indicators; they are neither exceptionally high in yield nor exceptionally low in carbon emissions, falling within the middle range in all aspects. In actual production, they can serve as a stable feedstock source, lacking both outstanding advantages and significant disadvantages. When the supply of high-quality biomass feedstocks is insufficient or their cost is too high, moderate-level feedstocks can serve as an alternative to maintain stable methanol production.

[0091] 2. Methanol production Low, but the carbon consumption-to-yield ratio in methanol production is low. High indicates low carbon emissions from biomass feedstocks; this type of biomass feedstock also has high methanol production. The yield is relatively low, meaning the amount of methanol produced per unit mass of biomass feedstock is small, indicating a deficiency in output. However, its carbon consumption-to-output ratio in methanol production is relatively high. A high yield indicates good performance in terms of carbon emissions, with low carbon emissions. In situations with high environmental protection requirements, or where insufficient yield can be compensated for through other means, this type of biomass feedstock still has some value. For example, in certain production processes, the utilization rate of the feedstock can be improved by optimizing the process, or it can be mixed with other high-yield feedstocks to compensate for its low yield, thus it is classified as a medium-yield biomass feedstock.

[0092] 3. Methanol production High, but the carbon consumption-to-output ratio in methanol production is high. A low value indicates that the biomass feedstock has a high methanol yield but also a high carbon emission. This type of biomass feedstock has a high methanol yield, capable of producing a large amount of methanol per unit mass, meeting the basic production requirements. However, its methanol production carbon consumption-to-output ratio is low. The term "low" means that it generates relatively high carbon emissions during the methanol production process. With increasingly stringent environmental regulations, higher carbon emissions may increase production costs and potentially necessitate additional carbon mitigation measures. However, due to its yield advantage, it still has some application potential in production scenarios with relatively relaxed cost and environmental requirements, or where output is more important, and is therefore classified as a medium-yield biomass feedstock.

[0093] Inferior biomass feedstocks include: methanol production Low carbon consumption and yield ratio in methanol production "Low" means that, under the same mass conditions, this type of biomass feedstock can produce less methanol than other biomass feedstocks, and has higher carbon emissions. From an energy utilization perspective, this type of biomass feedstock is neither efficient in converting into methanol nor does it impose a significant environmental burden during production, which does not align with the principles of efficient and environmentally friendly production. In actual methanol production, using this type of biomass feedstock may increase production costs, as more biomass feedstock is required to achieve a certain methanol yield, and additional costs may be needed to address the higher carbon emissions. Therefore, based on these disadvantages, this type of biomass feedstock is classified as inferior and its use is usually minimized during feedstock selection, or considered only after its performance has been improved and optimized.

[0094] Furthermore, the target biomass feedstock is either high-grade or medium-grade biomass feedstock. Target biomass feedstock refers to biomass feedstock that exhibits high performance and is suitable for methanol production. Based on the grading results, the target biomass feedstock is limited to either high-grade or medium-grade biomass feedstock. High-grade biomass feedstock yields high methanol production and has low carbon emissions, significantly improving the economic and environmental benefits of methanol production. While medium-grade biomass feedstock is not as high-grade in performance, it also has certain advantages. For example, it is more adaptable; in some cases, medium-grade biomass feedstock may be more suitable for specific production processes or regional conditions. It may also have a cost advantage, reducing production costs while ensuring methanol production output.

[0095] In practical applications, the following factors need to be considered when selecting target biomass raw materials:

[0096] 1. Production Requirements: Select appropriate biomass feedstock based on the specific requirements of methanol production, such as output targets and carbon emission restrictions. If there are high requirements for methanol output, high-quality biomass feedstock can be selected; if there are strict restrictions on carbon emissions, high-quality biomass feedstock can also be selected.

[0097] 2. Cost Factors: Consider the acquisition and processing costs of biomass feedstocks. Medium-grade biomass feedstocks may have a cost advantage, especially when resources are abundant and prices are low.

[0098] 3. Sustainability: Selecting sustainable biomass feedstocks ensures the long-term stability and environmental friendliness of the production process. High- and medium-grade biomass feedstocks typically offer better sustainability, aligning with the development direction of the biomass energy industry.

[0099] 4. Technological adaptability: Consider the adaptability of biomass feedstocks to existing production processes. Some biomass feedstocks may be more suitable for specific processing technologies, and selection needs to be based on the actual situation.

[0100] The target biomass feedstock is either high-grade or medium-grade biomass feedstock. This selection criterion is based on two key indicators: methanol yield and carbon consumption-to-yield ratio in methanol production. High-grade biomass feedstock performs excellently in terms of methanol yield and low carbon emissions, making it an ideal feedstock choice. While medium-grade biomass feedstock has generally lower performance, it still offers certain advantages in some situations. By selecting high-grade or medium-grade biomass feedstock, the economic and environmental benefits of methanol production can be optimized, thus achieving sustainable development goals.

[0101] Furthermore, the methanol yield produced from different biomass feedstocks per unit mass... The numerical values ​​are used to classify the methanol production performance of different biomass raw materials. Step 3 also includes:

[0102] Step 301: Obtain the methanol yield of biomass feedstock i. For each biomass feedstock, the methanol yield that can be produced per unit mass of that feedstock is obtained through experimental measurements, production records, or other reliable methods, and recorded as follows: Here This represents the methanol yield of the i-th biomass feedstock, with each feedstock corresponding to a specific... The value reflects the production capacity of the raw material in methanol production.

[0103] Step 302: For The sorting process is as follows: > > >...> >...> Where a = 0.3i; the methanol yield of each of the i biomass feedstocks was obtained. Next, these production figures need to be sorted. The sorting method is to arrange them in descending order, i.e., the corresponding methanol production is... Biomass feedstocks have the strongest ability to produce methanol per unit mass; Next, and so on, until... It has the lowest methanol yield among all biomass feedstocks, and its methanol production capacity is relatively weak. By ranking and identifying key positions in this way, the differences and order of methanol yield among different feedstocks can be clearly seen.

[0104] Step 303: Methanol Production If the value is in the fourth range, then the methanol production performance from biomass feedstock is excellent; specifically, the fourth range is […]. , When the methanol production of a certain biomass feedstock... When a biomass feedstock falls within this range, it is considered to have a stronger methanol production capacity compared to most other biomass feedstocks. In actual production, these superior feedstocks may be given priority in use to improve methanol production efficiency and yield.

[0105] In this step, by classifying different biomass raw materials into grades, the performance differences of different biomass raw materials can be clearly identified, providing guidance for the mixed processing of biomass raw materials and a basis for the subsequent mixed use of different biomass raw materials. This approach can fully leverage the advantages of high-quality biomass raw materials while making reasonable use of inferior ones, ensuring methanol production while reducing costs and carbon emissions, thus achieving optimized utilization of biomass raw materials. An objective scoring model is used to quantitatively score the biomass raw materials, allowing the performance of different raw materials to be compared and ranked through specific numerical values. This quantitative evaluation method is more scientific and objective, avoiding the ambiguity and uncertainty of subjective judgment, and helping to accurately assess the performance of different biomass raw materials in methanol production.

[0106] Step 4: Based on the characteristic data of the biomass feedstock to be processed and the obtained life cycle carbon emissions. Methanol production Carbon consumption-to-output ratio of methanol production from biomass feedstock The results of the grading of biomass raw materials were used to analyze the complementarity of biomass raw materials.

[0107] In this step, the complementarity analysis of biomass feedstocks includes a complementary analysis of data such as the gas production of different feedstocks and the chemical composition, calorific value, moisture content, and ash content of different types of biomass feedstocks. Complementarity analysis refers to combining the characteristics of different biomass feedstocks to leverage their respective advantages and compensate for each other's shortcomings, thereby improving overall methanol production efficiency and reducing carbon emissions. Specifically, complementarity analysis includes at least the following types:

[0108] 1. Complementary Calorific Value and Ash Fusion: Some biomass feedstocks have high calorific value but poor ash fusion, making them prone to slagging; while others have low calorific value but good ash fusion, making them less prone to slagging. Mixing these biomass feedstocks can improve combustion efficiency, reduce slagging, and thus increase methanol synthesis efficiency. For example, corn stalks and rice husk powder can be mixed; the high softening temperature of rice husk powder can reduce coking during the combustion of corn stalks.

[0109] 2. Complementary Chemical Composition: Different biomass feedstocks have different chemical compositions. By mixing them, the chemical composition can be optimized, thereby increasing methanol production. For example, mixing biomass feedstocks with high cellulose content and those with high lignin content can improve the quality of combustible gases during biomass gasification, thus increasing methanol production.

[0110] 3. Carbon emission complementarity: Different biomass feedstocks have different carbon emissions throughout their entire life cycle. By using a mixture of low-carbon-emission biomass feedstocks and high-carbon-emission biomass feedstocks, the overall carbon emission can be reduced.

[0111] Step 5: Based on the grading results of biomass raw materials in Step 3, select the target biomass raw material; based on the grading results of the target biomass raw material, use it alone as a biomass raw material for methanol preparation; or, based on the complementarity of the target biomass raw material, select one or more biomass raw materials that are complementary to the target biomass raw material, and adjust the mixing ratio of the biomass raw materials to prepare methanol.

[0112] Furthermore, step 5 also includes:

[0113] Step 501: Calculate the average grade score Z of each high-quality biomass raw material. The average methanol yield (M) of biomass feedstock with excellent methanol production performance (X). , where n is the quantity of biomass raw materials in the first range, and a is the quantity of biomass raw materials in the fourth range.

[0114] Specifically, all biomass raw materials are first graded. The values ​​are sorted from largest to smallest to calculate the grade score of superior biomass raw materials. The average value H, H represents the average level of the grade rating for superior biomass raw materials. Similarly, M M represents the average production capacity of high-quality biomass feedstock in terms of methanol yield; N Among them, the carbon consumption-to-output ratios of methanol production from biomass feedstocks were ranked from highest to lowest. t represents the carbon consumption-to-output ratio of methanol production from the top 30% of biomass feedstocks, t represents the number of biomass feedstocks in the top 30% of the biomass feedstocks in terms of carbon consumption-to-output ratio of methanol production, and N represents the average level of the top 30% of biomass feedstocks in terms of carbon consumption-to-output ratio of methanol production.

[0115] Step 502: Determine whether the grade score of the target biomass feedstock is greater than the average value H. If yes, the target biomass feedstock does not need to be mixed with other biomass feedstocks to prepare methanol; if not, proceed to step 503. For a specific target biomass feedstock A, first determine its grade score. Is it greater than the average value H? H indicates that biomass feedstock A has a grade score higher than the average of superior biomass feedstocks, indicating good performance. It can be used independently and does not need to be mixed with other biomass feedstocks to produce methanol. If If the rating is H, it indicates that its performance has not reached the average level of high-quality biomass raw materials, and further analysis is needed to determine whether it can be used independently.

[0116] Step 503: Determine whether the grade score of the target biomass raw material is greater than the maximum value of the inferior grade score. If so, that is, the target biomass raw material is a high-grade or medium-grade biomass raw material, then based on the biomass raw material database and grading model, the main factors that cause the biomass raw material to have an insufficient grade rating are analyzed, and one or more complementary biomass raw materials are matched from the biomass raw material database to adjust the selection and ratio of different biomass raw materials.

[0117] Specifically, the grade of target biomass feedstock A is determined. Is it higher than the maximum value of the inferior rating level? ,like This indicates that although the performance of the biomass feedstock has not reached the average level of high-grade biomass feedstock, it is still superior to that of low-grade biomass feedstock. Further analysis of the main factors leading to its insufficient performance is needed, and based on the analysis results, one or more complementary biomass feedstocks should be matched from the feedstock database for mixed use. If If the result is negative, it indicates that its performance is poor and it is not suitable for methanol production. Other better biomass raw materials need to be considered.

[0118] Specifically, if the grade of biomass raw material A is rated... The maximum value of the inferior rating level However, if the value is less than the average value H, then analysis needs to be conducted from the following two main factors:

[0119] 1. Methanol production Assess the methanol yield of target biomass feedstock A Is it below the average value M? The low yield indicates poor performance in methanol production, which may be due to the unsuitability of the chemical composition of biomass feedstock for efficient gasification or methanol synthesis.

[0120] 2. Carbon consumption to output ratio in methanol production To evaluate the methanol production efficiency of biomass feedstock A Is it below the average value N? A lower value indicates a higher carbon emission level over its entire life cycle, which may be due to high energy consumption or high carbon emissions during planting, harvesting, transportation, or processing.

[0121] Furthermore, based on the above analysis results, we searched the biomass feedstock database for biomass feedstocks that could complement biomass feedstock A. If the methanol production of biomass feedstock A... If the methanol production efficiency of biomass feedstock A is low, other biomass feedstocks with high methanol yields can be selected for blending to increase the overall methanol production; if the methanol production efficiency of biomass feedstock A is low... The carbon emissions are relatively low, so we can choose to use biomass raw materials with lower carbon emissions in combination with it, thereby reducing the overall carbon emissions throughout the entire life cycle.

[0122] Specifically, the selection of target biomass feedstocks and complementary biomass feedstocks involves at least the following steps:

[0123] 1. Data Analysis: Through big data analysis and machine learning algorithms, the data in the biomass raw material database is mined and analyzed to identify the carbon emission characteristics of different biomass raw materials at different stages and the complementarity of different carbon emission characteristics.

[0124] The complementarity of different carbon emission characteristics refers to whether different biomass raw materials can complement each other at different production stages in the methanol production process to reduce overall carbon emissions. For example, there is complementarity between wood and crop straw. Wood has a high carbon content and high calorific value, but a long growth cycle; crop straw has a high moisture content, but a short growth cycle and is widely available. By using these raw materials in combination, their complementarity can be fully utilized to improve raw material utilization.

[0125] 2. Define Production Needs and Targets: Clearly define the specific needs for methanol production, including expected methanol output, product quality standards, production cost budget, and environmental requirements. Different production needs will lead to different biomass feedstock choices. For example, if the goal is to achieve high-purity methanol and the budget is sufficient, a biomass feedstock with low impurity content may be preferred; if cost reduction is the primary objective, the price and availability of the feedstock will be more important.

[0126] Setting clear goals helps in targeted screening and evaluation during the subsequent selection process, providing guidance for choosing suitable biomass raw materials.

[0127] 3. Selection of biomass raw materials: Based on data analysis results and production needs, determine the selection and ratio of essential mixed biomass raw materials and complementary biomass.

[0128] The AI-based intelligent screening system for biomass raw materials complements the biomass raw materials themselves. Through big data analysis and machine learning algorithms, it analyzes and comprehensively evaluates key indicators such as chemical composition, carbon content, water content, and carbon emissions of various biomass raw materials, and automatically selects the optimal combination.

[0129] By comprehensively and accurately identifying the carbon emission characteristics and complementarities of different biomass feedstocks, the optimal selection and proportioning of biomass feedstocks can be achieved, thereby reducing overall carbon emissions. Furthermore, this method possesses strong flexibility and dynamic adjustment capabilities, enabling it to adapt to changes in different production stages and market demands.

[0130] In this embodiment, by classifying biomass feedstocks into grades, the target biomass feedstock can be precisely selected from numerous feedstocks based on clear standards. Feedstocks with high grade scores can be used alone to produce methanol, reducing the complexity and uncertainty caused by feedstock mixing, improving the stability and efficiency of the production process, and contributing to efficient methanol production. Selecting other feedstocks based on the complementarity of the target biomass feedstock and adjusting the mixing ratio can fully leverage the advantages of different feedstocks and achieve optimal resource allocation. In this way, some low-cost but advantageous feedstocks can be used to compensate for the deficiencies of the target feedstock, thereby reducing production costs while ensuring methanol yield and quality. The average grade score of superior biomass feedstocks, the average methanol yield of excellent biomass feedstocks, and the average yield of the top 30% of feedstocks in terms of carbon consumption-to-yield ratio in methanol production are calculated. The data-driven approach provides a quantitative reference standard for evaluating target biomass feedstocks. By comparing these average values, it is possible to scientifically determine whether and how to mix the target feedstock with other feedstocks. This data-driven decision-making method avoids the blindness of subjective judgment and improves the scientificity and accuracy of decision-making. By analyzing two key factors—methanol yield and carbon consumption-to-output ratio—of the target biomass feedstock, the causes of its performance deficiencies can be accurately identified. Based on this, complementary feedstocks can be matched from the feedstock database to improve the overall performance of the feedstocks in a targeted manner. For example, for feedstocks with low methanol yield, mixing them with feedstocks with high yield can effectively increase the overall methanol yield. For feedstocks with low carbon consumption-to-output ratio, pairing them with feedstocks with low carbon emissions can reduce the total life-cycle carbon emissions, thereby improving the performance and environmental benefits of the entire methanol production process.

[0131] Furthermore, the characteristic data of biomass feedstock includes: the types and quantities of gases produced by biomass feedstock gasification, wherein the gas types are reaction gases for methanol synthesis; the complementarity analysis of biomass feedstock includes: the complementarity analysis between the quantities of reaction gases for methanol synthesis produced by biomass feedstock gasification.

[0132] Furthermore, the reaction gases for methanol synthesis include at least a first reaction gas and a second reaction gas. If the output of the first reaction gas from the gasification of the target biomass feedstock is greater than the output of the second reaction gas, then the output of the second reaction gas from the gasification of the matched complementary biomass feedstock is greater than the output of the first reaction gas.

[0133] Methanol production typically requires specific feedstock gases to participate in a chemical reaction. A common method uses syngas as a feedstock, whose main components are carbon monoxide and hydrogen. These react under specific temperature, pressure, and catalyst conditions to produce methanol. If a biomass feedstock, after specific conversion processes such as pyrolysis or gasification, can produce a large amount of hydrogen while generating relatively less carbon monoxide, then this biomass feedstock has an advantage in hydrogen production. In the methanol production process, it can provide a sufficient hydrogen source for the reaction. Corresponding to the biomass feedstock that produces a large amount of hydrogen, there exists another biomass feedstock that, after similar processing, can produce a large amount of carbon monoxide. This biomass feedstock has an advantage in carbon monoxide production and can provide a sufficient carbon monoxide source for the methanol production reaction.

[0134] In methanol production, using a single biomass feedstock may result in high carbon emissions due to its inherent characteristics. For example, if only a biomass feedstock that produces large amounts of hydrogen is used, additional, high-carbon-emission processes may be needed to supplement carbon monoxide to meet the required proportions for the methanol production reaction. However, when two complementary biomass feedstocks are mixed, their respective feedstock gases can complement each other, thus meeting the required feedstock gas ratios for methanol production while reducing reliance on additional high-carbon-emission processes or feedstocks. Based on this approach, from the perspective of the entire lifecycle of methanol production, mixing the two biomass feedstocks allows for the direct generation of a suitable ratio of carbon monoxide and hydrogen during the gasification stage, avoiding subsequent high-energy-consuming and high-carbon-emission operations to adjust the gas ratios, thereby reducing carbon emissions and achieving a more environmentally friendly and sustainable production process.

[0135] In this embodiment, by adjusting the ratio of different biomass raw materials, the ratio of hydrogen to carbon monoxide in the syngas can be optimized, thereby improving the methanol synthesis efficiency. Selecting lower-priced biomass raw materials (such as agricultural and forestry waste) for mixed use can reduce production costs while ensuring methanol production. By optimizing the raw material ratio and reducing the use of high-carbon emission raw materials, the carbon emissions throughout the entire life cycle of methanol production can be significantly reduced.

[0136] Furthermore, the characteristic data of biomass raw materials also include information on the chemical composition, calorific value, moisture content, and ash content of different types of biomass raw materials. By combining the complementary nature of the above data, a suitable ratio of biomass raw materials can be selected.

[0137] 1. Chemical Composition: The chemical composition of biomass feedstocks is complex and diverse, mainly including cellulose, hemicellulose, lignin, protein, fat, and various minerals. The proportions and contents of these chemical components vary depending on the type, source, and growth environment of the feedstock. For example, wood-based biomass feedstocks typically contain higher levels of cellulose and lignin, while crop straw has a relatively higher hemicellulose content. Understanding the chemical composition is crucial for assessing the reactivity, conversion pathways, and potential byproducts of biomass feedstocks in methanol production. Different chemical components exhibit different behaviors in methanol production processes such as gasification and fermentation, thus affecting the final methanol yield and quality.

[0138] 2. Calorific Value: Calorific value refers to the amount of heat released when a unit mass of biomass feedstock is completely burned, reflecting the energy content of the feedstock. Different types of biomass feedstocks have significantly different calorific values. Generally, feedstocks with higher carbon content and lower moisture content have relatively higher calorific values. For example, dry wood has a high calorific value, while aquatic plants with high water content have a relatively low calorific value. In the methanol production process, feedstocks with high calorific values ​​can provide more energy, helping to maintain the temperature required for the reaction and reducing the consumption of external energy. However, this may also affect the utilization efficiency and cost of the feedstock.

[0139] 3. Moisture Content: Moisture content refers to the mass fraction of water contained in biomass feedstocks. Moisture content significantly impacts the storage, transportation, and processing of biomass feedstocks. Feedstocks with high moisture content are prone to mold and spoilage during storage, reducing feedstock quality and increasing transportation costs. In methanol production processes, excessively high moisture content increases the difficulty and energy consumption of pretreatment, such as requiring drying to reduce moisture content, thereby increasing production costs. Therefore, understanding the moisture content of biomass feedstocks is crucial for rationally planning storage, transportation, and processing, as well as optimizing methanol production processes.

[0140] 4. Ash Content: Ash refers to the inorganic substances remaining after biomass feedstocks are burned at high temperatures, and its content reflects the mineral content of the feedstock. Different types of biomass feedstocks have different ash contents. For example, some agricultural wastes have relatively high ash contents, while some wood-based feedstocks have low ash contents. Ash can cause wear and tear on equipment during methanol production, affecting the reaction process, and may also lead to catalyst poisoning, reducing catalyst activity and lifespan. Therefore, ash content is also one of the important indicators for evaluating the quality and suitability of biomass feedstocks.

[0141] Because different biomass feedstocks differ in chemical composition, calorific value, moisture content, and ash content, these differences can create complementary relationships. For example, one biomass feedstock may have a high calorific value but also a high moisture content, while another feedstock may have a low calorific value but a low moisture content. Mixing these two feedstocks can reduce the overall moisture content and decrease the energy consumption of the drying process while ensuring a certain energy supply. Similarly, if one feedstock has a low content of a certain component while another feedstock has a high content of that component, mixing them can make the chemical composition of the feedstocks more favorable for the methanol production reaction. By analyzing these characteristic data and identifying the complementary relationships between different feedstocks, it is possible to select a more suitable feedstock combination for methanol production.

[0142] After identifying complementary biomass feedstocks, it is necessary to further determine their optimal ratio. A suitable ratio ensures that the mixed feedstocks achieve optimal chemical composition, calorific value, moisture content, and ash content, thereby improving the efficiency and quality of methanol production. For example, by adjusting the proportions of different feedstocks, the calorific value of the mixed feedstock can meet the energy requirements of the methanol production process, while simultaneously controlling the moisture and ash content within a reasonable range, reducing adverse effects on equipment and the reaction. Determining the appropriate ratio requires comprehensive consideration of multiple factors and continuous optimization through experiments and data analysis to maximize the utilization of biomass feedstocks and achieve optimal methanol production.

[0143] In summary, by combining various characteristic data of biomass raw materials for selection and proportioning, the characteristics of different biomass raw materials can be rationally utilized to improve the efficiency of methanol production, reduce costs, and minimize the impact on equipment and the environment.

[0144] On the other hand, embodiments of this application provide a carbon emission reduction system for various biomass-based green fuel production methods, used to achieve the aforementioned carbon emission reduction methods for various biomass-based green fuel production methods. The system includes:

[0145] The acquisition module obtains characteristic data of the biomass feedstock to be processed. It collects data on the chemical composition, calorific value, moisture content, ash content, and yield of the feedstock gas used in methanol production. The collected data is then systematically studied and evaluated to provide fundamental support for subsequent methanol production efficiency analysis, biomass feedstock blending, and carbon emission assessment.

[0146] The calculation module obtains the life-cycle carbon emissions per unit mass of different biomass feedstocks. And the methanol yield produced per unit mass of biomass feedstock Based on the obtained life-cycle carbon emissions and methanol production The carbon consumption-to-yield ratio for methanol production from biomass feedstock was calculated. = / Where i represents any type of biomass feedstock; and the total life cycle carbon emissions are... This study calculates the total life-cycle carbon emissions (CCO) of different biomass feedstocks during the methanol production process, considering each stage from planting, harvesting, transportation, and processing. A comprehensive evaluation of the performance of biomass feedstocks in methanol production provides a scientific basis for their selection and optimization. Methanol yield is also included. This directly reflects the conversion efficiency of biomass feedstocks, which is affected by factors such as the chemical composition of the biomass feedstocks, gasification processes, and reaction conditions for methanol synthesis. By analyzing the carbon consumption ratio in methanol production from biomass feedstocks, we can evaluate the performance of different biomass feedstocks, screen out biomass feedstocks with high methanol yield and low carbon emissions, and provide a scientific basis for subsequent biomass feedstock selection and blending strategies, achieving a balance between resource utilization and environmental protection.

[0147] The grading module is based on the characteristic data of the biomass feedstock to be processed and the methanol yield per unit mass of biomass feedstock. Carbon consumption and output ratio in methanol production The performance of methanol production from different biomass feedstocks is graded; further, the grading of biomass feedstocks is based on the following scoring model: =α +β ;in, The grade of biomass feedstock is determined by α, where α represents methanol yield. The weight of β is the carbon consumption-to-yield ratio in methanol production. The weighting of different biomass feedstocks is assessed. Based on the characteristics of the biomass feedstock, the methanol yield per unit mass of biomass feedstock, and the methanol production efficiency of the biomass feedstock, the performance of different biomass feedstocks in methanol production is comprehensively evaluated and classified into different levels. This module aims to provide a scientific basis for the subsequent blending of biomass feedstocks, optimize resource utilization efficiency, reduce carbon emissions in the process of producing methanol from biomass feedstocks, and improve the overall efficiency of methanol production.

[0148] The analysis module, based on the characteristic data of the biomass feedstock to be processed and the obtained life-cycle carbon emissions, Methanol production Carbon consumption-to-output ratio of methanol production from biomass feedstock This study analyzes the complementarity of biomass feedstocks based on their grading results. The aim is to assess the complementarity between different biomass feedstocks based on their characteristic data, life-cycle carbon emissions, methanol production, carbon consumption-to-yield ratio in methanol production, and grading results. Through analysis, it identifies which biomass feedstocks can be combined and used to optimize performance in methanol production, improve resource utilization efficiency, reduce carbon emissions, and enhance overall economic benefits.

[0149] Specifically, complementarity analysis includes at least the following types:

[0150] 1. Complementarity of chemical components:

[0151] 1.1 Balance between cellulose and lignin: Biomass feedstocks with high cellulose content, such as corn stalks, will produce more syngas during gasification, making them suitable for methanol synthesis; biomass feedstocks with high lignin content, such as sawdust, have high calorific value but low gasification efficiency. By using them together, the high reactivity of cellulose and the high calorific value of lignin can be utilized to optimize the quality of syngas and methanol production.

[0152] 1.2 Balance of Hydrogen and Carbon Monoxide: Some biomass feedstocks may produce more carbon monoxide but less hydrogen during gasification; while other biomass feedstocks may produce more hydrogen but less carbon monoxide. By using a mixture, the ratio of hydrogen to carbon monoxide in the syngas can be adjusted to be closer to the ideal ratio for methanol synthesis.

[0153] 2. Carbon emission complementarity: High-carbon-emission biomass feedstocks can be mixed with low-carbon-emission biomass feedstocks to reduce overall carbon emissions.

[0154] In the selection module, select the target biomass raw material based on the grade classification results.

[0155] Based on the grading results of the target biomass feedstock, it can be used alone as a biomass feedstock for methanol production; or, based on the complementarity of the target biomass feedstock, one or more complementary biomass feedstocks can be selected, and the mixing ratio of the biomass feedstocks can be adjusted to produce methanol. Based on the grading and complementarity analysis results of the biomass feedstocks, suitable biomass feedstocks are selected for mixing, and their mixing ratio is optimized. By adjusting the selection and ratio of biomass feedstocks, the methanol yield and carbon emission reduction effect of the biomass feedstocks can be improved, achieving optimal resource utilization and maximizing environmental benefits.

[0156] Example 1

[0157] Complementary characteristics of biomass feedstocks:

[0158] Biomass feedstock A is corn stalks, and its complementary biomass feedstock B is soybean stalks, which have shorter plant heights. Intercropping these two biomass feedstocks reduces carbon emissions from the planting stage. Intercropping refers to the agricultural technique of simultaneously planting two or more crops on the same plot of land according to a certain ratio of row spacing, plant spacing, and land area. On one hand, intercropping corn and soybeans can improve land use efficiency. Corn and soybeans have different growth cycles and space requirements; corn is a tall crop, while soybeans are short-stalked. Through intercropping, soybeans can grow between corn rows, making full use of land space and improving land use efficiency. Furthermore, intercropping can increase biomass yield per unit area, thereby increasing the total yield of corn and soybeans.

[0159] On the other hand, corn and soybeans have different root systems. Corn has a well-developed root system that mainly absorbs nutrients from the lower soil layers and has a high demand for nitrogen fertilizer. Soybean roots contain rhizobia, which can fix nitrogen from the air and convert it into nitrogen-containing compounds that plants can absorb and utilize. Soybeans can not only meet their own needs but also release some nitrogen into the soil for corn to absorb and utilize. This complementary effect can reduce the use of nitrogen fertilizer, thereby reducing carbon emissions in the fertilizer production process.

[0160] Example 2

[0161] Complementarity of gasification and syngas:

[0162] The key feedstocks for methanol synthesis are carbon monoxide and hydrogen. Biomass feedstock A primarily produces carbon monoxide during its gasification process. However, the syngas produced solely from biomass feedstock A may lack sufficient hydrogen, leading to an imbalance in the hydrogen-to-carbon monoxide ratio and affecting methanol synthesis efficiency. Biomass feedstock B primarily produces hydrogen during its gasification process. These two biomass feedstocks complement each other in the composition of the methanol syngas produced, and their combination can provide a more comprehensive range of feedstock gases for methanol production.

[0163] Optimizing the syngas composition and reducing the need for added hydrogen or carbon monoxide can lower energy consumption during gasification and synthesis. Reduced energy consumption directly leads to lower carbon emissions. Optimizing the syngas ratio can reduce side reactions such as methanation, which not only reduce methanol production but also increase carbon emissions. Using a mixture of biomass feedstock A and biomass feedstock B can reduce these side reactions, thereby lowering carbon emissions.

[0164] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application. It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0165] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for carbon emission reduction in the preparation of green fuels from various biomass sources, characterized in that, The method includes: Step 1: Obtain the characteristic data of the biomass raw material to be processed; Step 2: Obtain the life-cycle carbon emissions per unit mass of different biomass feedstocks. And the methanol yield produced per unit mass of biomass feedstock Based on the obtained life-cycle carbon emissions and methanol production The carbon consumption-to-yield ratio for methanol production from biomass feedstock was calculated. = / ; Where i represents any type of biomass feedstock; and the total life cycle carbon emissions... The total life-cycle carbon emissions during the production of methanol from different biomass feedstocks per unit mass, at each stage of planting, collection, transportation, and processing into methanol; Step 3: Based on the characteristic data of the biomass feedstock to be processed and the methanol yield per unit mass of biomass feedstock. Carbon consumption and output ratio in methanol production The performance of methanol production from different biomass feedstocks was graded. The biomass raw material grading in step 3 is based on the following scoring model: =α +β ； in, The grade of biomass feedstock is determined by α, where α represents methanol yield. The weight of β is the carbon consumption-to-yield ratio in methanol production. The weights; Step 4: Based on the characteristic data of the biomass feedstock to be processed and the obtained life cycle carbon emissions. Methanol production Carbon consumption-to-output ratio of methanol production from biomass feedstock The results of the grading of biomass raw materials were used to analyze the complementarity of biomass raw materials. Step 5: Select the target biomass raw material based on the grading results of biomass raw materials in Step 3; Based on the complementarity of the target biomass feedstock, one or more biomass feedstocks that are complementary to the target biomass feedstock are selected, and the mixing ratio of the biomass feedstocks is adjusted to prepare methanol; the complementarity analysis includes at least: complementary calorific value and ash fusion properties, complementary chemical composition, and complementary carbon emissions.

2. The carbon emission reduction method for preparing green fuels from various biomass sources according to claim 1, characterized in that, Step 3 includes: Step 31: Obtain the grade rating of type i biomass raw material ; Step 32: Grading of biomass raw materials Sort the values ​​from largest to smallest; Step 33: Set the grade rating for biomass raw materials Values ​​within the first range indicate superior biomass raw materials; biomass raw material grade rating. The value falls within the second range, indicating medium-grade biomass feedstock. (Biomass feedstock grade rating) The value is in the third range, indicating inferior biomass raw materials.

3. The carbon emission reduction method for preparing green fuels from various biomass sources according to claim 2, characterized in that, The target biomass feedstock is either high-quality or medium-quality biomass feedstock.

4. The carbon emission reduction method for preparing green fuels from various biomass sources according to claim 2, characterized in that, Based on the methanol yield per unit mass of different biomass feedstocks, the methanol production performance of different biomass feedstocks is classified. Step 3 further includes: Step 301: Obtain the methanol yield of biomass feedstock i. ; Step 302: Methanol yield per unit mass of different biomass feedstocks Sort the values ​​from largest to smallest; Step 303: Methanol Production If the value is in the fourth range, then the methanol production performance of biomass raw materials is excellent.

5. The carbon emission reduction method for preparing green fuels from various biomass sources according to claim 1, characterized in that, The characteristic data of biomass feedstock includes: the types of gases produced by the gasification of biomass feedstock and the amount of gas produced, wherein the types of gases are the reaction gases for the synthesis of methanol; The complementarity analysis of biomass feedstocks includes the complementarity analysis of the production quantities of reaction gases for methanol synthesis generated from biomass feedstock gasification.

6. The carbon emission reduction method for preparing green fuels from various biomass sources according to claim 5, characterized in that, The reaction gases for methanol synthesis include at least a first reaction gas and a second reaction gas. If the output of the first reaction gas from the gasification of the target biomass feedstock is greater than the output of the second reaction gas, then the output of the second reaction gas from the gasification of the matched complementary biomass feedstock is greater than the output of the first reaction gas.

7. The carbon emission reduction method for preparing green fuels from various biomass according to claim 1, characterized in that, The characteristic data of biomass raw materials also include information on the chemical composition, calorific value, moisture content, and ash content of different types of biomass raw materials. By combining the complementary nature of the above data, a suitable ratio of biomass raw materials can be selected.

8. A carbon emission reduction system for producing green fuel from multiple biomass sources, used to achieve the carbon emission reduction method for producing green fuel from multiple biomass sources as described in any one of claims 1-7, characterized in that, The system includes: The acquisition module acquires the characteristic data of the biomass raw materials to be processed; The calculation module obtains the life-cycle carbon emissions per unit mass of different biomass feedstocks. And the methanol yield produced per unit mass of biomass feedstock Based on the obtained life-cycle carbon emissions and methanol production The carbon consumption-to-yield ratio for methanol production from biomass feedstock was calculated. = / ; Where i represents any type of biomass feedstock; and the total life cycle carbon emissions... The total life-cycle carbon emissions during the production of methanol from different biomass feedstocks per unit mass, at each stage of planting, collection, transportation, and processing into methanol; The grading module is based on the characteristic data of the biomass feedstock to be processed and the methanol yield per unit mass of biomass feedstock. Carbon consumption and output ratio in methanol production The performance of methanol production from different biomass feedstocks was graded. The analysis module, based on the characteristic data of the biomass feedstock to be processed and the obtained life-cycle carbon emissions, Methanol production Carbon consumption-to-output ratio of methanol production from biomass feedstock The results of the grading of biomass raw materials were used to analyze the complementarity of biomass raw materials. In the selection module, based on the grade classification results of biomass raw materials, select the target biomass raw material; Based on the grade classification of the target biomass raw material, it is used alone as a biomass raw material for methanol production; Alternatively, based on the complementarity of the target biomass feedstock, one or more biomass feedstocks that are complementary to the target biomass feedstock can be selected, and the mixing ratio of the biomass feedstocks can be adjusted to prepare methanol.

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