A resource recycling method based on straw-biochar-biogas

Abstract

The present invention provides a resource recycling method based on straw-biochar-biogas, basic data collection, construction of a circular agricultural model based on the combination of biochar, biogas and derivatives, construction of a GRMAR model with biochar and biogas as the core, and construction of four evaluation indicators to reflect the superiority of the GRMAR model: (1) economic benefit index; (2) greenhouse gas emission GGE index; (3) non-point source pollution NPS; (4) water use efficiency WUE. The present invention establishes a green recycling model of agricultural resources (GRMAR) that can replace related energy sources and reduce the negative environmental impact of agricultural activities, while increasing production and generating income; the construction of a recycling model based on straw-biochar-biogas resources can promote sustainable agricultural development; in GRMAR, straw is prepared into biochar and biogas and their derivatives, and recycled into the farmland ecosystem, which can balance the relationship between food production increase, energy utilization and environmental protection.

Description

Technical Field

[0001] The invention belongs to the field of sustainable agriculture, and specifically relates to a resource recycling method based on straw-biochar-biogas. Background Art

[0002] Agriculture is the most fundamental issue for national development, and food production is the primary issue for national economic growth and social development. In the process of agricultural production, two products are produced: one is food, and the other is agricultural waste (such as straw). Straw is one of the main raw materials for producing biomass energy. As the population increases, the demand for food also increases. There are two possible paths to achieve the goal of increasing food production: one is to increase the area of ​​arable land, and the other is to increase the yield per unit of arable land. However, on a global scale, agricultural activities and farmland expansion have brought extensive environmental burdens to natural ecosystems. Therefore, increasing the yield per unit of arable land can alleviate land expansion and coordinate the balance between production and nature conservation. However, increasing production also means that the output of straw waste will increase. At present, the common method of straw waste treatment is on-site incineration, but this will lead to greenhouse gas emissions (GGE), non-point source pollution (NPS) and water pollution, which will put agricultural development into a vicious circle. In order to achieve sustainable development of output, energy and environment, building a green recycling model of agricultural resources (GRMAR) is expected to replace related energy and reduce the negative environmental impact of agricultural activities, while increasing production and generating income.

[0003] Establishing a mechanism framework in GRMAR is an important part. It is an effective solution to prepare straw into biochar and biogas and its derivatives to become biomass energy and recycle them to the farmland ecosystem to balance the synergistic relationship among food production increase, energy utilization and environment. It is very important for the GRMAR model to continuously recycle agricultural resources into the agricultural economic system for reasonable and sustainable maximum utilization, so economic benefits and environmental effects are one of the evaluation indicators of the GRMAR model. Food production is inseparable from water. Water use efficiency (WUE) is an important indicator to measure the relationship between food production increase and water conservation, which directly affects the synergy between economy and environment under the GRMAR model. However, in the process of producing biochar and biogas from straw, the synergistic effects of economic benefits, GGE, NPS and WUE on the agricultural system should be considered to create a resource recycling model that takes several into account at the same time.

[0004] In the GRMAR model, the different uses and treatment methods of biomass energy produced by straw-biochar-biogas cycle (SBBB) play an important role in alleviating the imbalance between energy supply and environment. The application of SBBB has positive significance for protecting the ecological environment and coping with global climate change, and is an important part of achieving global sustainable energy transition. Biochar and biogas prepared from straw and their derivatives are widely regarded as a model of "closed loop" system in the field of sustainable energy. As a new carbon management technology, biochar can improve soil structure and physical and chemical properties, reduce greenhouse gas emissions, increase food production, and play a positive role in NPS emissions and water resource utilization after being applied to the soil. Biogas and its derivatives are produced by anaerobic digestion (AD) technology, which has the performance advantage of producing renewable energy and has broad prospects for development and application. Biogas fermentation residues are high-quality organic fertilizers that can improve soil. The development and utilization of biogas can solve the energy problem of farmers and achieve the production of clean energy, waste control and ecological agriculture. The straw is used to prepare biochar and biogas and their derivatives, and returned to the field to promote crop growth, forming a benign "closed loop" system. However, the development of SBBB still faces many challenges, such as how to produce and utilize biomass energy in a green and efficient way through straw? Can and how can this new model develop agriculture and create economic benefits in a sustainable way within the cycle? How will this model change in the context of complex social and economic changes in the future? Summary of the invention

[0005] The purpose of the invention is to propose a practical method to address the problems and challenges of the above-mentioned prior art. The present invention takes biochar and biogas as the core to develop a green agricultural resource recycling model (GRMAR) that can balance the coordinated development of economy, energy and environment. It uses straw to produce and utilize green and efficient biomass energy to achieve the recycling of agricultural resources with the comprehensive purpose of improving resource utilization efficiency, reducing emissions and increasing income.

[0006] "Economy": The established GRMAR model, the GRMAR-Ⅰ model obtains economic benefits through the planting and sale of crops, while converting agricultural waste into valuable biochar and biogas, providing farmers with an additional source of income. The GRMAR-Ⅱ model can provide farmers with an additional source of income by using agricultural waste to prepare biochar and biogas. Part of the prepared biochar can be used in the AD process of biogas, improving biogas production and quality, and increasing the economic benefits of biogas. At the same time, the mixture of biochar and biogas residue can also be used as a soil conditioner to improve soil quality, increase crop yield and quality, and thus promote the development of the agricultural economy;

[0007] "Energy": The GRMAR model established, GRMAR-Ⅰ model uses biogas as renewable energy to meet the energy needs of agricultural machinery and reduce dependence on traditional energy. At the same time, the byproduct of biogas, biogas residue, can be used as organic fertilizer to provide nutrients and promote crop growth; GRMAR-Ⅱ model uses agricultural waste to prepare biogas to provide renewable energy, reduce dependence on traditional energy, and by adding biochar to the AD process of biogas preparation, it can increase biogas production and quality and increase energy utilization efficiency. This recycling model can promote energy supply in rural areas and improve energy sustainability.

[0008] "Environment": The GRMAR model established reduces the pollution of agricultural waste to the environment by effectively collecting and utilizing it. Returning the mixture of biochar and digestate to the field can repair and improve the soil, improve the fertility and water retention capacity of the soil. At the same time, biochar can absorb harmful substances such as heavy metals in the soil, reducing the impact on the crop growth environment. This model can reduce the burning and stacking of agricultural waste, reduce environmental pollution, and reduce greenhouse gas emissions.

[0009] The present invention has the following advantages: (1) establishing a green agricultural resource recycling model (GRMAR) can replace related energy sources and reduce the negative environmental impact of agricultural activities, while increasing production and generating income; (2) the construction of a straw-biochar-biogas resource recycling model can promote the sustainable development of agriculture; (3) in GRMAR, straw is prepared into biochar and biogas and their derivatives, and recycled into the farmland ecosystem, which can balance the relationship between food production increase, energy utilization and environmental protection.

[0010] The technical solution created by the present invention is as follows:

[0011] A resource recycling method based on straw-biochar-biogas comprises the following steps:

[0012] Step 1: Basic data collection, including: NPS related data; socio-economic data; hydrological data; energy related data;

[0013] Step 2: Build a circular agriculture model based on the combination of biochar and biogas and their derivatives

[0014] With biochar and biogas as the core, the GRMAR model is constructed, including two recycling schemes:

[0015] GRMAR-Ⅰ: The crops planted in the first year are harvested, the fruits obtained are sold, and the agricultural waste obtained is collected for the preparation of biochar, biogas and derivatives. The amount of biochar prepared is determined according to the type of crops planted and the planting area. The amount of biogas and derivatives prepared is determined by the type of crops and the planting area to be applied. The amount of biogas residue and biogas liquid organic fertilizers to be applied is determined, and the biogas yield is calculated based on the amount of biogas derivatives. The obtained biogas derivatives and biochar are mixed and applied to the growth period of crops in the second year to promote the growth of crops. Part of the obtained biogas is used for energy use of agricultural machinery in the agricultural planting process, and the rest is sold.

[0016] GRMAR-Ⅱ: GRMAR-Ⅱ is the same as the first part of GRMAR-Ⅰ. Part of the agricultural waste obtained in the first year is used to prepare biochar, and the other part is used to prepare biogas and derivatives. However, the biochar prepared from agricultural waste is added to the AD process of biogas preparation. The biochar is mixed with sludge and liquid biogas and returned to the field to repair the soil and improve the impact of heavy metals in the sludge on the crop growth environment when applied to the field. Part of the sludge produced by AD technology is used to prepare biochar, and the biochar prepared from the sludge is mixed with the remaining sludge, and then applied to the field together to promote crop growth.

[0017] Step 3: Construct four evaluation indicators to reflect the superiority of the GRMAR model.

[0018] (1) Economic benefit index: It is expressed as the economic income and cost of planting. The final economic benefit is obtained by quantifying the benefits of selling the harvested crops minus the costs of the planting process:

[0019] EB=IN-CO

[0020] In the formula, EB is the economic benefit, IN is the economic income of crops, and CO is the cost.

[0021] The economic income of crops includes income from crops, straw, power generation and biogas.

[0022] IN=BC·X·Y+RB+RS

[0023]

[0024]

[0025] Where RB represents biogas income; RS represents straw income; Y represents GRMAR yield increment; X represents crop planting area, hectares; Cg represents biogas production of a single biogas digester, m 3; F is the amount of digestate required by the crop, kg / ha; Bs is the price of straw, yuan / kg; α is the crop grass-grain ratio; β is the crop collectability coefficient; YB is the preparation of biochar straw feed, kg / ha;

[0026] The production increment under the GRMAR model is expressed as:

[0027] Y=(1+ε)YT

[0028] Where ε represents the crop yield increase coefficient, YT represents the yield under the TSU mode, kg / ha;

[0029] The cost includes material cost, electricity cost and water cost. The material cost per unit area is calculated as follows:

[0030] CO=X(Cm+Cw)+CTg+CCg+CGp

[0031] Cm=Cs·Ds+Cp·Dp+Cf·Df+Cl+YB×Cb

[0032] Cw=xs·Pws+xg·Pwg

[0033] Where CO represents cost, Cm represents material cost, Cw represents water cost, Cs represents crop seed price, kg / ha; Ds represents crop seed usage per unit area, kg / ha; Cp represents crop pesticide price, RMB / kg; Dp represents crop pesticide usage per unit area, kg / ha; Cf represents agricultural film price, RMB / kg; Df represents agricultural film usage per unit area of ​​crop, kg / ha; Cl represents labor cost per unit area, RMB / ha; YB represents biochar usage per unit area, kg / ha; Cb represents biochar preparation price, RMB / kg; CTg represents biogas transportation cost, RMB; CCg represents biogas preparation cost, RMB; xs represents surface water consumption per unit area of ​​crop, m 3 / ha; Pws represents the surface water charging standard, RMB / m 3 .

[0034] (2) Greenhouse gas emissions (GGE): refers to minimizing greenhouse gas emissions from crop production. Greenhouse gases during planting activities include CO 2 , CH 4 and N 2 O.CO 2 Emissions resulting from the use of materials. 4 Emissions mainly come from rice fields. 2 O emissions mainly include soil emissions and emissions from fertilizer application.

[0035] GE=(-SC+GEg)·X

[0036] In the formula, GE is greenhouse gas emission, SC is soil carbon sequestration efficiency, GEg represents greenhouse gas emissions per unit area; X represents the planting area of ​​crops, hectares.

[0037] The soil carbon sequestration efficiency is calculated as follows:

[0038]

[0039] Where, YB represents the amount of biochar used per unit area, kg / ha; Y represents the yield increment under the GRMAR model, kg / ha; represents the carbon absorption coefficient.

[0040] Greenhouse gas emissions are calculated as follows:

[0041]

[0042]

[0043]

[0044]

[0045]

[0046] In the formula, represents the carbon dioxide emissions per unit area, represents the impact of GRMAR on CO2 emissions, represents the impact of GRMAR on methane emissions, represents the impact of GRMAR on nitrous oxide emissions. Dfu represents the fuel consumption per unit area of ​​biogas conversion, kg / ha; δfu represents the carbon dioxide emission factor of fuel, kgCO2 / kg; δf represents the carbon dioxide emission factor of agricultural film, kgCO2 / kg; δe represents the carbon dioxide emission factor of electricity, kgCO2 / kg; Ee represents the electricity consumption per unit area of ​​biogas conversion, kW·h / ha; BC represents the crop sales price, yuan / kg; κ represents the methane emission per unit area, kgCH4 / ha; μ represents the nitrous oxide emission factor under the crop background, kgN2O / ha; ν represents the crop nitrous oxide emission factor, kgN2O / ha; represents the crop CO2 emission reduction factor; represents the rice methane emission reduction factor; It represents the crop nitrous oxide emission reduction factor.

[0047] (3) Non-point source pollution (NPS): including soil sediment particles, nutrients, pesticides, and various atmospheric particles, which enter the water, soil, or atmospheric environment through surface runoff, soil erosion, and farmland drainage. This indicator is expressed through an output coefficient model. Based on Johnes' classic output coefficient model, it is improved by introducing runoff factors, terrain factors, soil erosion factors, and vegetation interception factors that may affect non-point source pollution.

[0048] LN=σ·E·X

[0049] Where LN represents nonpoint source pollution; E represents crop yield coefficient, kg / (ha·year); σ represents watershed production factor, topographic factor, vegetation conservation factor and soil erosion factor.

[0050] (4) Water use efficiency WUE:

[0051]

[0052] Where WUE represents water use efficiency and βw represents the coefficient for improving water use efficiency.

[0053] Also includes:

[0054] Step 4: Explore the development prospects and value benefits of the two GRMAR models.

[0055] Two scenarios are proposed for each option:

[0056] Scenario 1: Calculation of the economic benefits of the traditional planting structure and the circular agriculture framework under the current market price conditions, the results of the four evaluation indicators GGE, NPS and WUE

[0057] The second scenario: Through historical economic data, a deep learning time series prediction model is used to analyze the changing patterns of economic data, predict the values ​​and changing trends of future economic data, couple it to the GRMAR model, and calculate the four evaluation indicators again. Finally, the superiority of the two constructed GRMAR models in system operation under two scenarios is analyzed.

[0058] (1) Meta-analysis to study the response of the GRMAR model to multidimensional measurement indicators - Scenario 1

[0059] Meta-method was used to analyze the effects of biochar and biogas residue application on crop yield, GGE, NPS and WUE, and the effects of adding biochar in the AD process on biogas production and NPS; the operation process was to test heterogeneity by using statistical tests, and the Q test was used to analyze the heterogeneity among the included studies. The 95% confidence interval (95% CI) represents the degree of dispersion of the effect size, P is the standardized weighted sum of squares, and the I2 statistic is the proportion of observed inter-study variation. Using I2 as a statistic, if P>0.1, I2≤50%, the fixed effect model was used for analysis; if P≤0.1, I2>50%, the source of heterogeneity was analyzed, and the random effect model was used for analysis, and the combined effect value Z, MD and 95% CI of the literature were analyzed.

[0060] (2) Predicting the impact of economic changes on GRMAR using LSTM networks - Scenario 2

[0061] The second scenario constructed is to conduct deep learning on historical economic data to explore the changing patterns of economic data over time, couple the obtained forecast data into the GRMAR framework, and further evaluate the superiority of the GRMAR model.

[0062] Use the long short-term memory network (LSTM) model to explore how economic data changes over time.

[0063] Step 5: Analysis of sustainability results of the GRMAR model

[0064]

[0065] The sustainability is evaluated using the utility function.

[0066] Assume that the order parameter of the crop planting system is U i (i=1,2...,n), the effect of the system order parameter on the order of the system can be expressed as follows.

[0067] Where, X i is the value of each indicator variable, U i For variable X i The effect of order on the system; α i ,β i are the upper and lower limits of the order parameter of the system's critical point of stability.

[0068] When calculating the comprehensive sustainability of crop planting systems, the linear weighted sum calculation model of the utility function method of system sustainability is applied. The formula is as follows:

[0069]

[0070] Where W i is the weight of the i-th evaluation index; Ui For variable X i The effect of system order. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 is a flow chart of the GRMAR mode circulation system of the present invention;

[0072] Figure 2 It is the Meta-analysis process of the present invention;

[0073] Figure 3 is a prediction flow chart of the LSTMS of the present invention;

[0074] Figure 4a It is one of the results of the sustainability analysis of the GRMAR model of the present invention;

[0075] Figure 4b This is the second result of the sustainability analysis of the GRMAR model of the present invention. DETAILED DESCRIPTION

[0076] The specific technical solution of the present invention is described in conjunction with the accompanying drawings.

[0077] A resource recycling method based on straw-biochar-biogas under the coordinated development of agricultural economy-energy-environment, such as Figure 1 As shown, the following steps are included:

[0078] Step 1: Basic data collection. The data involved in the process mainly include: NPS related data; socio-economic data; hydrological data; energy related data, etc.

[0079] Step 2: Build a new circular agriculture model based on the combination of biochar and biogas and their derivatives.

[0080] Biogas production is also considered to be an effective example of circular economy to a large extent. In order to solve the problem of straw waste and heating burning in the traditional straw utilization model (Traditional straw utilization TSU, which uses planting-selling food-using straw for heating / making feed, etc.), the present invention constructs a new circulation model. The present invention takes biochar and biogas as the core, constructs a GRMAR model, and promotes a green resource recycling model that coordinates output, energy and environment. It includes two circulation schemes:

[0081] GRMAR-Ⅰ: The crops planted in the first year are harvested, the fruits obtained are sold, and the agricultural waste such as straw is collected for the preparation of biochar, biogas and derivatives. The amount of biochar preparation is determined according to the type of crops planted and the planting area. The amount of biogas and derivatives prepared is determined by the type of crops and the planting area to be applied. The amount of biogas residue and biogas liquid organic fertilizer is determined, and the biogas yield is calculated based on the amount of biogas derivatives. The obtained biogas derivatives and biochar are mixed and applied to the growth period of crops in the second year to promote the growth of crops. Part of the obtained biogas is used for energy use of agricultural machinery in the agricultural planting process, and the rest is sold.

[0082] GRMAR-Ⅱ: GRMAR-Ⅱ is the same as the first part of GRMAR-Ⅰ. Part of the agricultural waste obtained in the first year is used to prepare biochar, and the other part is used to prepare biogas and derivatives. However, the biochar prepared from agricultural waste is not returned to the field, but added to the AD process of biogas preparation. The biochar and sludge are mixed together and returned to the field because biochar is used as a soil conditioner to repair the soil, improve the impact of heavy metals in the sludge on the crop growth environment when applied to the field, and further improve the utilization rate of resources. Prepare biochar from part of the sludge produced by AD technology, mix the biochar prepared from the sludge with the remaining sludge, and then apply them together to the field to promote crop growth.

[0083] Among the two different recycling schemes proposed, GRMAR-Ⅰ is simple to operate, mature in technology, low in cost, and has high economic benefits; although GRMAR-Ⅱ adds the process of preparing biochar from sludge, which increases the production cost, the addition of biochar as an additive to the AD process can reduce the generation of pollutants in the AD process and promote the production of methane, which improves soil protection and environmental effects. The four evaluation indicators of GGE, NPS, WUE and economic benefits reflect the superiority of the GRMAR model over traditional straw utilization methods.

[0084] Step 3: Construct four evaluation indicators to reflect the superiority of the GRMAR model.

[0085] (1) Economic benefit index: It is expressed as the economic income and cost of planting. The final economic benefit is obtained by quantifying the benefits of selling the harvested crops minus the costs of the planting process:

[0086] EB=IN-CO

[0087] In the formula, EB is the economic benefit, IN is the economic income of crops, and CO is the cost.

[0088] The economic income of crops includes income from crops, straw, power generation and biogas.

[0089] IN=BC·X·Y+RB+RS

[0090]

[0091]

[0092] Where RB represents biogas income; RS represents straw income; Y represents GRMAR yield increment; X represents crop planting area, hectares; Cg represents biogas production of a single biogas digester, m 3 ; F is the amount of digestate required by the crop, kg / ha; Bs is the price of straw, yuan / kg; α is the crop grass-grain ratio; β is the crop collectability coefficient; YB is the preparation of biochar straw feed, kg / ha;

[0093] The production increment under the GRMAR model is expressed as:

[0094] Y=(1+ε)YT

[0095] Where ε represents the crop yield increase coefficient, YT represents the yield under the TSU mode, kg / ha;

[0096] The costs include material costs (fertilizers, pesticides, seeds, agricultural fuels, agricultural films, biochar preparation and biogas digester construction management costs), electricity costs (water extraction, pumping and drainage), and water costs (surface water and groundwater irrigation). The material cost per unit area is calculated as follows:

[0097] CO=X(Cm+Cw)+CTg+CCg+CGp

[0098] Cm=Cs·Ds+Cp·Dp+Cf·Df+Cl+YB×Cb

[0099] Cw=xs·Pws+xg·Pwg

[0100] Where CO represents cost, Cm represents material cost, Cw represents water cost, Cs represents crop seed price, kg / ha; Ds represents crop seed usage per unit area, kg / ha; Cp represents crop pesticide price, RMB / kg; Dp represents crop pesticide usage per unit area, kg / ha; Cf represents agricultural film price, RMB / kg; Df represents agricultural film usage per unit area of ​​crop, kg / ha; Cl represents labor cost per unit area, RMB / ha; YB represents biochar usage per unit area, kg / ha; Cb represents biochar preparation price, RMB / kg; CTg represents biogas transportation cost, RMB; CCg represents biogas preparation cost, RMB; xs represents surface water consumption per unit area of ​​crop, m 3 / ha; Pws represents the surface water charging standard, RMB / m 3 .

[0101] (2) Greenhouse gas emissions (GGE) indicator: refers to minimizing greenhouse gas emissions from crop production. The main greenhouse gases during planting activities include CO 2 , CH 4 and N 2 O.CO 2 Emissions resulting from the use of materials. 4 Emissions mainly come from rice fields. 2 O emissions mainly include soil emissions and emissions from fertilizer application.

[0102] GE=(-SC+GEg)·X

[0103] In the formula, GE is greenhouse gas emission, SC is soil carbon sequestration efficiency, GEg represents greenhouse gas emissions per unit area; X represents the planting area of ​​crops, hectares.

[0104] The soil carbon sequestration efficiency is calculated as follows:

[0105]

[0106] Where, YB represents the amount of biochar used per unit area, kg / ha; Y represents the yield increment under the GRMAR model, kg / ha; represents the carbon absorption coefficient.

[0107] Greenhouse gas emissions are calculated as follows:

[0108]

[0109] ce CO2 =δfe·F+δp·Dp+δfu·Dfu+δf·Df+δe·Ee

[0110]

[0111]

[0112]

[0113] In the formula, represents the carbon dioxide emissions per unit area, represents the impact of GRMAR on CO2 emissions, represents the impact of GRMAR on methane emissions, represents the impact of GRMAR on nitrous oxide emissions. Dfu represents the fuel consumption per unit area of ​​biogas conversion, kg / ha; δfu represents the carbon dioxide emission factor of fuel, kgCO2 / kg; δf represents the carbon dioxide emission factor of agricultural film, kgCO2 / kg; δe represents the carbon dioxide emission factor of electricity, kgCO2 / kg; Ee represents the electricity consumption per unit area of ​​biogas conversion, kW·h / ha; BC represents the crop sales price, yuan / kg; κ represents the methane emission per unit area, kgCH4 / ha; μ represents the nitrous oxide emission factor under the crop background, kgN2O / ha; ν represents the crop nitrous oxide emission factor, kgN2O / ha; represents the crop CO2 emission reduction factor; represents the rice methane emission reduction factor; It represents the crop nitrous oxide emission reduction factor.

[0114] (3) Non-point source pollution (NPS): mainly composed of soil sediment particles, nutrients such as nitrogen and phosphorus, pesticides, various atmospheric particulate matter, etc., which enter the water, soil or atmospheric environment through surface runoff, soil erosion, farmland drainage, etc. This indicator is expressed by the output coefficient model. The present invention improves on the basis of Johnes' classic output coefficient model and introduces runoff generation factors, terrain factors, soil erosion factors and vegetation interception factors that may affect non-point source pollution.

[0115] LN=σ·E·X

[0116] Where LN represents nonpoint source pollution; E represents crop yield coefficient, kg / (ha·year); σ represents watershed production factor, topographic factor, vegetation conservation factor and soil erosion factor.

[0117] (4) Water use efficiency (WUE): It is an indicator to measure the relationship between crop yield and water consumption. It is also one of the comprehensive indicators for evaluating the suitability of plant growth under water deficit. This indicator is expressed by dividing the yield by the water consumption. WUE has a direct impact on the amount of irrigation water during the crop growth period, thereby affecting the crop yield, and further has a greater impact on the GRMAR model. Therefore, WUE is selected as one of the so-called evaluation indicators.

[0118]

[0119] Where WUE represents water use efficiency and βw represents the coefficient for improving water use efficiency.

[0120] Step 4: Explore the development prospects and value benefits of the two GRMAR models.

[0121] In order to explore the development research prospects and the income value created by the two constructed GRMAR models under the changes in future economic conditions, the present invention proposes two scenarios for each scheme. The first scenario is: under the current market price conditions, the economic benefits in the traditional planting structure and the circular agriculture framework are calculated, and the results of the four evaluation indicators of GGE, NPS and WUE. However, the future economy is full of changes and challenges, and it seems subjective and one-sided to evaluate only from the first scenario. For this reason, the present invention proposes a second scenario: through historical economic data, a deep learning time series prediction model is used to analyze the changing laws of economic data, predict the values ​​and changing trends of future economic data, couple it to the GRMAR model, and calculate the four evaluation indicators again, and finally analyze the superiority of the system operation of the two constructed GRMAR models under the two scenarios.

[0122] (1) Meta-analysis to study the response of the GRMAR model to multidimensional measurement indicators - Scenario 1

[0123] Meta-analysis is rigorously designed and can provide a mechanism for quantitatively estimating the degree of effect of evaluation indicators based on the research content. Through statistical concepts and methods, it can collect, organize and analyze numerous empirical studies conducted by experts and scholars on a certain issue, and seek out clear relationship patterns between the issue or variables of concern, thereby providing a more objective conclusion.

[0124] In order to explore the effects of biochar and biogas residue organic fertilizer on the multidimensional objectives (economic benefits, GGE, NPS and WUE) of the GRMAR model, the present invention adopts the Meta-analysis method. Figure 2 As shown in the figure, based on the analysis of a large number of literature, the Meta method was used to analyze the effects of biochar and biogas residue application on crop yield, GGE, NPS and WUE, and the effects of adding biochar in the AD process on biogas production and NPS; the operation process was to test heterogeneity by using statistical tests, and the Q test was used to analyze the heterogeneity between the included studies. The 95% confidence interval (95% CI) represents the degree of dispersion of the effect size, P is the standardized weighted sum of squares, and the I2 statistic is the proportion of observed inter-study variation. Using I2 as a statistic, if P>0.1, I2≤50%, the fixed effect model was used for analysis; if P≤0.1, I2>50%, the source of heterogeneity was analyzed, and the random effect model was used for analysis, and the combined effect value Z, MD and 95% CI of the literature were analyzed.

[0125] The present invention uses Meta-analysis to search Pubmed, EBSCO, Web of Science, and CNKI, and initially selects 121 documents for analysis, and finally 37 documents meet the standards. The various factors of returning biochar and biogas residue to the field are quantified. Returning biochar and biogas residue to the field instead of fertilizer will reduce CH4 emissions by 15.2%, CO2 by 17.3%, and NO2 by 7.9% compared with fertilizer. Meta-analysis also determined that returning biochar and biogas residue to the field increases crop yields, reduces NPS emissions, and increases WUE percentages.

[0126] (2) Predicting the impact of economic changes on GRMAR using LSTM networks - Scenario 2

[0127] Economic forecasting can reveal the development laws of economic phenomena, point out the future development trends and possible levels of economic phenomena, and provide guidance for future changes. In order to evaluate the impact of economic change laws on the GRMAR model, the second scenario constructed by this invention is to conduct deep learning on historical economic data to explore the laws of economic data changes over time, couple the obtained forecast data into the GRMAR framework, and further evaluate the superiority of the GRMAR model.

[0128] In modern forecasting applications, traditional forecasting models are relatively inadequate in their ability to infer intelligent results from similar time series data sets, which results in heavy computing tasks and a large amount of manual labor requirements. Deep learning models have come into people's view with their ability to extract high-order features. Recurrent neural network (RNN) models in deep learning models are often used for time series forecasting and can achieve higher forecasting results, but RNN is not suitable for processing long data and cannot solve the dependency problem of long data. In order to solve the long-term dependency problem of RNN, the present invention uses a long short-term memory network (LSTMs) model to explore the changing patterns of economic data over time. LSTM can selectively forget or retain past information and make judgments on long information. Therefore, LSTM can handle large amounts of data very well. The present invention uses LSTM to predict the future sales price of crops. The operating process combined with the content of the present invention is as follows. Figure 3 .

[0129] Step 5: Analysis of sustainability results of the GRMAR model.

[0130]

[0131] In order to evaluate the sustainability of the constructed GRMAR model, the present invention uses the utility function to evaluate the sustainability. The utility function evaluation method, when the system is in a stable state, the state equation is linear, the extreme point of the function is the critical point of the system stable region, and the variable also has a quantitative change when the system is in a stable state. This quantitative change has two effects on the order of the system: one is a positive effect, that is, as the variable increases, the system order trend increases, and the other is a negative effect, that is, as the variable increases, the system order trend decreases. Assume that the order parameter of the crop planting system is U i (i=1,2...,n), the effect of the system order parameter on the order of the system can be expressed as follows.

[0132] Where, X i is the value of each indicator variable, U i For variable X i The effect of order on the system; α i ,β i are the upper and lower limits of the order parameter of the system's critical point of stability.

[0133] However, in the analysis of crop planting sustainability system, the weights of various indicators are not completely consistent. When calculating the comprehensive sustainability of crop planting system, the linear weighted sum calculation model of the utility function method of system sustainability is applied. The formula is as follows:

[0134]

[0135] Where W i is the weight of the i-th evaluation index; U i For variable X i The effect of system order.

[0136] The sustainability result varies between 0 and 1. The closer it is to 1, the better the sustainability function and the better the system benefits. Figure 4a and Figure 4b As shown, the ratios 1-7 represent different proportions of weights. Figure 4a The effects of the four evaluation indicators of economic benefit, GGE, WUE and NPS are shown. The larger the area of ​​the graph, the better the sustainability. The effects of the cycle GRMAR-Ⅰ and GRMAR-Ⅱ are significantly better than the TSU mode. The economic effect of GRMAR-Ⅰ is better than that of GRMAR-Ⅱ, while the NPS effect of GRMAR-Ⅱ is better than that of GRMAR-Ⅰ.

[0137] The calculation of the efficacy function involves the determination of weights. For this purpose, the present invention sets 7 different weight ratios. Since both GGE and NPS belong to the environmental protection category, their weight ratios are the same, such as Figure 4b. According to the order of economic benefits, GGE, WUE and NPS, ratio 1 (1:1:1:1) is the result of equal weights of various indicators. It can be seen that the sustainability ranking is GRMAR-Ⅱ>GRMAR-Ⅰ>TSU mode. However, such a weight ratio is too one-sided. From the perspective of growers, the economic weight may account for a larger proportion, while from the perspective of national strategy, the environmental weight is higher than that from the economic perspective only. Ratios 5-7 (2:1.5:1:1.5, 3:0.5:2:0.5, 3:1:1:1) are cases where the economic weight accounts for a larger proportion. When the economic weight accounts for a larger proportion, the sustainable result will tend to choose GRMAR-Ⅰ. Choosing GRMAR-Ⅰ will maximize the overall sustainability, thereby satisfying the decision makers' pursuit of maximizing economic benefits in the green circular development model. Ratios 2-4 (1:1:3:1, 1:1.5:2:1.5, 2:0.5:3:0.5) are cases where the environmental weight accounts for a larger proportion. When the environmental weight accounts for a larger proportion, GRMAR-Ⅱ will be more inclined to be selected. Compared with GRMAR-Ⅰ, GRMAR-Ⅱ mode will focus more on environmental protection instead of only considering the appeal of interests. However, no matter which trend, the two schemes of GRMAR mode are better than TSU mode.

[0138] At present, a variety of different modes of comprehensive utilization of biogas have been developed. AD of agricultural waste produces biogas, biogas slurry and biogas residue, and then the biogas is used for lighting and cooking. The biogas slurry and biogas residue are then used as biogas fertilizer, and agricultural waste is recycled, but there are also certain limitations. For example, the role of biogas is only used for lighting and cooking, and other uses have not been developed. In addition, biogas fertilizer is directly applied to the soil, which has a weak effect on soil improvement and causes heavy metal pollution. Therefore, in order to solve the above problems, it is necessary to optimize and improve the biogas utilization mode. The innovative circulation mode constructed by the present invention divides biomass utilization into two levels. First, the scheme of returning biochar and biogas residue to the field together; returning biochar and biogas residue to the field together can repair the soil and improve the crop growth environment. Second, the application of biochar in AD, adding biochar to AD can increase biogas production. Third, biochar is produced by biogas residue, and the biochar prepared from biogas residue is superior to the biochar prepared directly from biomass waste in terms of pH value, hydrophobicity, carbon yield and its adsorption performance.

[0139] In the GRMAR model, GRMAR-Ⅱ adds a step of preparing biochar from part of the sludge compared to GRMAR-Ⅰ, and the biochar prepared from the original straw is added to the biogas preparation process. This is because adding biochar to the AD process can also directly reduce CO2 emissions. In addition, biochar can absorb excess nutrients from the AF process, and biochar-modified AD can promote the removal of CO2 and produce high-purity, low-H2S biomethane. These analyses show that innovative circular agricultural models can solve the problems of low utilization efficiency and secondary pollution of sludge and liquid biogas. As global GGE is becoming increasingly serious, it is of great significance to promote the use of biochar in in-situ purification of biogas.

[0140] Since a part of biogas construction is added to circular agriculture, it will correspondingly drive some people to engage in the construction and maintenance of biogas pools, which will provide more employment opportunities to a certain extent. It is a way to understand the impact of the GRMAR model on employment. The GRMAR model will also drive more transportation industries, whether it is the transportation of straw or the transportation of increased grain production, it will promote the circular development of the economy to a certain extent. In view of the above-mentioned problems, these challenges need to be solved in order to effectively apply the innovative circular agriculture model proposed in the present invention.

Claims

1. A resource recycling method based on straw-biochar-biogas, characterized in that: The following steps are involved: Step 1: Basic data collection, including: NPS related data; socio-economic data; hydrological data; energy related data; Step 2: Build a circular agriculture model based on the combination of biochar, biogas and their derivatives; With biochar and biogas as the core, the GRMAR model is constructed, including two recycling schemes: GRMAR-Ⅰ: The crops planted in the first year are harvested, the fruits obtained are sold, and the agricultural waste obtained is collected for the preparation of biochar, biogas and derivatives; the amount of biochar prepared is determined according to the types of crops planted and the planting area; the amount of biogas and derivatives prepared is determined by the type of crops and the planting area to be applied. The amount of biogas residue and biogas liquid organic fertilizers to be applied is determined, and the biogas output is calculated based on the amount of biogas derivatives; the obtained biogas derivatives and biochar are mixed and applied to the growth period of crops in the second year to promote the growth of crops; part of the obtained biogas is used as energy for agricultural machinery in the agricultural planting process, and the rest is sold; GRMAR-Ⅱ: GRMAR-Ⅱ is the same as the first part of GRMAR-Ⅰ. Both use part of the agricultural waste obtained in the first year to prepare biochar, and the other part to prepare biogas and its derivatives; however, the biochar prepared from agricultural waste is added to the AD process of biogas preparation; the biochar and biogas residue and liquid are mixed together and returned to the field to repair the soil and improve the impact of heavy metals in the biogas residue on the crop growth environment when applied to the field; part of the biogas residue produced by AD technology is used to prepare biochar, and the biochar prepared from the biogas residue is mixed with the remaining biogas residue, and then applied to the field together to promote crop growth; Step 3: Construct four evaluation indicators to reflect the superiority of the GRMAR model; (1) Economic benefit indicators; (2) Greenhouse gas emissions (GGE) indicator; (3) non-point source pollution NPS; (4) Water use efficiency (WUE).

2. A resource recycling method based on straw-biochar-biogas according to claim 1, characterized in that: The evaluation indicators in step 3 are: (1) Economic benefit indicators: expressed as planting economic income and planting costs; quantify the benefits of selling harvested crops minus the costs of the planting process to obtain the final economic benefits: EB=IN-CO In the formula, EB is the economic benefit, IN is the economic income of crops, and CO is the cost; The economic income of crops includes income from crops, straw, electricity generation and biogas; IN=BC·X·Y+RB+RS Where RB represents biogas income; RS represents straw income; Y represents GRMAR yield increment; X represents crop planting area, hectares; Cg represents biogas production of a single biogas digester, m 3 ; F is the amount of digestate required by the crop, kg / ha; Bs is the price of straw, yuan / kg; α is the crop grass-grain ratio; β is the crop collectable coefficient; YB is the amount of biochar used per unit area, kg / ha; The production increment under the GRMAR model is expressed as: Y=(1+ε)YT Where ε represents the crop yield increase coefficient, YT represents the yield under the TSU mode, kg / ha; The cost includes material cost, electricity cost and water cost; the material cost per unit area is calculated as follows: CO=X(Cm+Cw)+CTg+CCg+CGp Cm=Cs·Ds+Cp·Dp+Cf·Df+Cl+YB×Cb Cw=xs·Pws+xg·Pwg Where CO represents cost, Cm represents material cost, Cw represents water cost, Cs represents crop seed price, kg / ha; Ds represents crop seed usage per unit area, kg / ha; Cp represents crop pesticide price, RMB / kg; Dp represents crop pesticide usage per unit area, kg / ha; Cf represents agricultural film price, RMB / kg; Df represents agricultural film usage per unit area of ​​crop, kg / ha; Cl represents labor cost per unit area, RMB / ha; YB represents biochar usage per unit area, kg / ha; Cb represents biochar preparation price, RMB / kg; CTg represents biogas transportation cost, RMB; CCg represents biogas preparation cost, RMB; xs represents surface water consumption per unit area of ​​crop, m 3 / ha; Pws represents the surface water charging standard, RMB / m 3 ; (2) Greenhouse gas emissions (GGE): refers to minimizing greenhouse gas emissions from crop production; greenhouse gases during planting activities include CO2, CH4 and N2O; CO2 emissions are generated by the use of materials; CH4 emissions mainly come from rice fields; N2O emissions mainly include soil emissions and emissions from fertilizer application; GE=(-SC+GEg)·X In the formula, GE is greenhouse gas emission, SC is soil carbon sequestration efficiency, GEg represents greenhouse gas emission per unit area; X represents the planting area of ​​crops, hectares; The soil carbon sequestration efficiency is calculated as follows: Where, YB represents the amount of biochar used per unit area, kg / ha; Y represents the yield increment under the GRMAR model, kg / ha; represents the carbon absorption coefficient; Greenhouse gas emissions are calculated as follows: In the formula, represents the carbon dioxide emissions per unit area, represents the impact of GRMAR on CO2 emissions, represents the impact of GRMAR on methane emissions, represents the impact of GRMAR on nitrous oxide emissions; Dfu represents the fuel consumption per unit area of ​​biogas conversion, kg / ha; δfu represents the carbon dioxide emission factor of fuel, kgCO2 / kg; δf represents the carbon dioxide emission factor of agricultural film, kgCO2 / kg; δe represents the carbon dioxide emission factor of electricity, kgCO2 / kg; Ee represents the electricity consumption per unit area of ​​biogas conversion, kW·h / ha; BC represents the crop sales price, yuan / kg; κ represents the methane emission per unit area, kgCH4 / ha; μ represents the nitrous oxide emission factor under the crop background, kgN2O / ha; ν represents the crop nitrous oxide emission factor, kgN2O / ha; represents the crop CO2 emission reduction factor; represents the rice methane emission reduction factor; represents the crop nitrous oxide emission reduction factor; (3) Non-point source pollution (NPS): includes soil sediment particles, nutrients, pesticides, and various atmospheric particles that enter the water, soil, or atmospheric environment through surface runoff, soil erosion, and farmland drainage. This indicator is expressed through an output coefficient model; it is improved on the basis of Johnes' classic output coefficient model, introducing runoff factors, terrain factors, soil erosion factors, and vegetation interception factors that affect non-point source pollution; LN=σ·E·X Where, LN represents non-point source pollution; E represents crop yield coefficient, kg / (ha·year); σ represents watershed production factor, topographic factor, vegetation conservation factor and soil erosion factor; (4) Water use efficiency WUE: Where WUE represents water use efficiency; βw represents the coefficient for improving water use efficiency.

3. The resource recycling method based on straw-biochar-biogas according to claim 1, characterized in that: It also includes: Step 4: Explore the development prospects and value benefits of the two GRMAR models; Two scenarios are proposed for each option: Scenario 1: Under the current market price conditions, the economic benefits of the traditional planting structure and the circular agriculture framework are calculated, and the results of the four evaluation indicators of GGE, NPS and WUE are calculated; The second scenario: Through historical economic data, a deep learning time series forecasting model is used to analyze the changing patterns of economic data, predict the value and changing trend of future economic data, couple it to the GRMAR model, calculate the four evaluation indicators again, and finally analyze the superiority of the two constructed GRMAR models in the system operation under two scenarios; (1) Meta-analysis to study the response of the GRMAR model to multidimensional measurement indicators - Scenario 1 Meta-method was used to analyze the effects of biochar and biogas residue application on crop yield, GGE, NPS and WUE, and the effects of adding biochar in the AD process on biogas production and NPS. The operation process was to test heterogeneity by using statistical tests, and the Q test was used to analyze the heterogeneity among the included studies. The 95% confidence interval (95% CI) indicated the degree of dispersion of the effect size, P was the standardized weighted sum of squares, and the I2 statistic was the proportion of the observed inter-study variation. I2 was used as a statistic. If P>0.1 and I2≤50%, a fixed effect model was used for analysis. If P≤0.1 and I2>50%, the source of heterogeneity was analyzed, and a random effect model was used for analysis. The combined effect value Z, MD and 95% CI of the literature were analyzed. (2) Predicting the impact of economic changes on GRMAR using LSTM networks - Scenario 2 The second scenario constructed is to conduct deep learning on historical economic data to explore the changing patterns of economic data over time, and couple the obtained forecast data into the GRMAR framework to further evaluate the superiority of the GRMAR model; Use the long short-term memory network (LSTM) model to explore how economic data changes over time.

4. The resource recycling method based on straw-biochar-biogas according to claim 1, characterized in that: Also included: Step 5: Analysis of sustainability results of the GRMAR model Sustainability is evaluated using utility functions; Assume that the order parameter of the crop planting system is U i (i=1,2...,n), the effect of the system order parameter on the order of the system is expressed as follows; In the formula, X i is the value of each indicator variable, U i For variable X i The effect of order on the system; α i ,β i are the upper and lower limits of the order parameter of the system's critical point of stability; When calculating the comprehensive sustainability of crop planting systems, the linear weighted sum calculation model of the utility function method of system sustainability is applied. The formula is as follows: Where W i is the weight of the i-th evaluation index; U i For variable X i The effect of system order.

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