Cross-industry multi-body linkage regenerated sludge resource scheduling method

Abstract

The invention discloses a cross-industry multi-body linkage regenerated sludge resource scheduling method, and belongs to the field of resource circulation and industry collaboration. The implementation method comprises the following steps: in a determined area range m, counting main body distribution conditions of a sludge resource provider and a sludge resource disposer; a monthly-plant-level high-temporal-spatial-resolution sludge resource allocation path and an optimal overall scheduling scheme are formulated by establishing a cross-industry multi-main-body linkage regenerated sludge resource scheduling model based on an anti-fact theory and coupling methods such as a full life cycle method and a Monte Carlo simulation method. The uncertainty of key economic factors such as technical cost and interference capacity is brought into a scheduling model, and a dynamic robust scheduling strategy with high temporal-spatial resolution is constructed; and generating a sludge resource scheduling scheme according to the scheduling feasible interval, namely, realizing cross-industry multi-main-body linked regenerated sludge resource scheduling. According to the method, the waste resource utilization potential and the resource management and control precision can be improved, and cross-industry effective linkage is promoted.

Description

Technical Field

[0001] This invention belongs to the field of resource recycling and industry collaboration, and specifically relates to a method for scheduling recycled sludge resources that involves cross-industry multi-entity collaboration. Background Technology

[0002] With the acceleration of urbanization and the improvement of sewage treatment rates, my country's sludge production has continued to surge. Improperly disposed sludge contains heavy metals, pathogens, and persistent organic pollutants; direct landfilling or dumping will lead to soil pollution, water deterioration, greenhouse gas emissions, and health hazards. Existing mainstream disposal technologies face problems such as land scarcity, secondary pollution, and high investment costs. Using sludge as an alternative fuel, co-firing it with coal and municipal solid waste, has become an emerging direction with both pollution reduction and carbon reduction benefits. Since 2018, my country has promoted coal-fired power generation coupled with sludge, with the first batch of 29 pilot projects covering 12 provinces. However, the actual promotion effect has fallen far short of expectations. Key bottlenecks include: an immature sludge fuel utilization industrial chain; significant differences in plant-level characteristics such as co-firing technology, transportation distance, and existing sludge disposal technologies leading to high barriers to cross-industry collaboration; large seasonal fluctuations in sludge production and power plant fuel demand, making spatial and temporal resource matching difficult; and fluctuations in technological transformation costs and by-product prices, resulting in strong uncertainty in economic benefits. Therefore, it is urgent to systematically solve the problem of collaboration between "technology-entity-industry" with high-resolution data, and promote the cross-industry resource utilization of sludge from "pilot demonstration" to "large-scale promotion".

[0003] Current research largely focuses on optimizing single aspects, such as improving co-firing processes or planning transportation routes. Furthermore, much of this research is limited to specific types of generating units, lacking comprehensive decision support methods that cover all stages. This makes it difficult to address the dynamic matching problem between multiple sludge suppliers and diverse treatment entities. Simultaneously, existing models for co-firing biomass resources in thermal power plants primarily propose sludge or biomass resource supply plans on an interannual scale, failing to respond to seasonal fluctuations in power plant fuel demand. Therefore, they cannot precisely control and balance sludge production supply and power plant fuel demand on a monthly scale, overcoming the barriers of cross-industry resource mismatch in time and space. More importantly, recent studies on sludge resource utilization in the thermal power industry have not incorporated the volatility of the economic benefits of treatment technologies, making it difficult to guarantee the economic feasibility of cross-industry sludge resource utilization. Therefore, there is still a lack of dynamic and robust scheduling methods based on high spatiotemporal resolution data from Chinese wastewater treatment plants and thermal power plants to achieve precise cross-industry sludge resource matching. Summary of the Invention

[0004] To address the problems of insufficient accuracy, weak coordination, and difficulty in characterizing uncertainties in existing cross-industry resource scheduling, the purpose of this invention is to provide a cross-industry, multi-entity collaborative method for scheduling recycled sludge resources. By establishing a cross-industry, multi-entity collaborative recycled sludge resource scheduling model based on counterfactual theory and coupling it with methods such as full life cycle and Monte Carlo simulation, a high spatiotemporal resolution sludge resource allocation path and optimal overall scheduling scheme at the monthly-plant level are formulated to improve the potential for waste resource utilization, the accuracy of resource management, and promote effective cross-industry collaboration.

[0005] The objective of this invention is achieved through the following technical solution:

[0006] This invention discloses a cross-industry, multi-entity collaborative method for scheduling recycled sludge resources. Within a defined region, it statistically analyzes the distribution of sludge resource providers and disposers; it uses pre-determined upper limits for total sludge resource supply, lower limits for total treatment, maximum co-firing capacity, and lower limits for total economic profit as constraints; it takes maximizing the total greenhouse gas emission reduction between stages A and B as the linkage objective; and it constructs a cross-industry, multi-entity collaborative recycled sludge resource scheduling model based on counterfactual theory; it applies a method to independently uniformly distributed recycled sludge resources. , , Multiple Monte Carlo random samplings were conducted, and the unit profit of the sludge treatment technology was calculated for each sampling. The calculation of this sampling Sludge treatment technology Adopt existing sludge treatment technologies The amount of sludge disposed of sludge moisture content Energy supply The input is fed into the scheduling model to obtain the optimal sludge resource scheduling quantity for the directional disposal party within m under this sampling. ; Statistical analysis of the percentage of times sludge resource allocation cooperation was reached between suppliers and disposers within a range of m; Calculation of the probability of linkage. The path is selected as the priority scheduling path, 50%. The path is used as a supplementary scheduling path; the path corresponds to The median is used as the scheduling reference value, corresponding to The interval formed by the 25%-75% quantile is used as the feasible scheduling interval; a sludge resource scheduling plan is generated based on the feasible scheduling interval, that is, the scheduling of recycled sludge resources is realized through cross-industry multi-entity linkage.

[0007] This invention discloses a cross-industry, multi-entity collaborative method for scheduling recycled sludge resources, comprising the following steps:

[0008] Step 1: Within a defined area, statistically analyze the distribution of sludge resource providers and sludge resource disposal providers; statistically analyze the existing sludge disposal technologies of the providers within a time frame of m. Adopt existing sludge treatment technologies The amount of sludge disposed of sludge moisture content ; Statistics on the energy supply of the disposal party within m ;

[0009] Step 2: Based on Step 1, predict the upper limit of the total amount of sludge resources that the supplier can supply within m; the lower limit of the amount of sludge resources that the disposal party can process within m when applying sludge dry mixing disposal technology; the maximum amount of sludge resources that the disposal party can co-fire within m; and the lower limit of the total economic profit of the consortium within the entire time period M. The consortium refers to the sludge disposal cooperation group consisting of the disposal party and all its affiliated suppliers; the entire time period M refers to the sum of all m.

[0010] Step 3: Using the upper limit of the total sludge resource supply, the lower limit of the total treatment volume, the maximum co-firing volume, and the lower limit of the total economic profit value mentioned in Step 2 as constraints; taking the maximization of the total greenhouse gas emission reduction of A and B stages as the linkage objective; constructing a cross-industry multi-entity linkage regenerated sludge resource scheduling model based on counterfactual theory; A stage refers to the sludge disposal stage of the provider; B stage refers to the power generation operation, fuel extraction and processing, sludge deep dewatering, and sludge transportation stages of the disposal party;

[0011] Step 4: Collecting the unit investment cost of sludge treatment technology Unit maintenance cost and unit income The scope of the sludge treatment technology includes the provider's existing sludge treatment technology. and sludge dry mixing treatment technology;

[0012] Step 5: For those in Step 4 that follow an independent uniform distribution , , Multiple Monte Carlo random samplings were conducted, and the unit profit of the sludge treatment technology was calculated for each sampling. The calculation of this sampling With step one , , , The input is fed into the scheduling model in step three to obtain the optimal sludge resource scheduling amount for the directional disposal party within m under this sampling. ;

[0013] Step Six: Extract the results of multiple iterations in Step Five. The percentage of times that the provider and the disposal party reach cooperation on sludge resource allocation within a certain range (m) is used as the probability of linkage along this path. ;

[0014] Step 7: Adjust the linkage probability described in Step 6. The path is selected as the priority scheduling path, 50%. The path is used as a supplementary scheduling path; the path corresponds to The median is used as the scheduling reference value, corresponding to The interval formed by the 25%-75% quantile is used as the feasible interval for scheduling; when the sludge resource supply of the priority scheduling path is insufficient, the resource quantity of the supplementary scheduling path is activated to meet the lower limit of the treatment capacity of the disposal party; a sludge resource scheduling plan is generated, that is, the scheduling of recycled sludge resources through cross-industry multi-entity linkage is realized.

[0015] Furthermore, in step one, the energy supply of the disposal unit within m... The statistical method is as follows:

[0016] Energy supply for disposal within m It consists of two parts: power supply within m and heat supply within m; based on the power load curve of the sub-regions within the region and the total power supply, the power supply time characteristics of the treatment unit within m are characterized; based on the heating duration of the sub-regions within the region and the total heat supply of the thermal power plant, the heat supply time characteristics of the treatment unit within m are characterized; calculation ;

[0017]

[0018]

[0019] in, Represents a sub-region within a given region; Indicates power generation technology; This indicates all regions within the sub-region that employ power generation technologies. The total power supply provided by the disposal party within M, in kWh; Indicates the use of power generation technology The installed capacity of the disposal party; This indicates all regions within the sub-region that employ power generation technologies. The total installed capacity of the disposal party; The power supply allocation coefficient within m represents the proportion of the power supply provided by the disposal party within the sub-region within m to the total power supply within M. Its value can be obtained from the power load curve. Indicates the heating capacity provided by the handling party; This indicates the total heating capacity of the thermal power plants within the sub-region where the disposal party is located; This represents the heat supply of the thermal power plant within a sub-region of m, expressed in kJ. This represents the set of months for the centralized heating season within a sub-region; This represents the total heat supply from the thermal power plant within sub-region M; This represents the total centralized heating supply from the thermal power plant within sub-region M; This indicates that M contains m numbers; This indicates the actual number of heating days within the sub-region m; This represents the total number of heating days in the sub-region within M.

[0020] Furthermore, in step two, the method for predicting the upper limit of the total amount of sludge resources that can be supplied by provider m is as follows:

[0021] The upper limit of the total amount of sludge resources that can be supplied within m is obtained by calculating the equivalent dry sludge volume under the target moisture content condition.

[0022]

[0023] in, This indicates the upper limit of the total amount of sludge resources that the provider m can supply; Indicates existing sludge treatment technologies A set; This represents the total amount of sludge treated by the provider using various existing sludge treatment technologies within the range of m. The moisture content of the sludge within the main body m provided by the supplier; The upper limit requirement for the moisture content of sludge co-incineration by the disposal party.

[0024] Furthermore, in step two, the method for predicting the lower limit of sludge resource treatment volume within m when the disposal party applies the sludge dry mixing treatment technology is as follows:

[0025] Introducing a minimum sludge supply ratio indicator , represents the minimum mass percentage of sludge in the mixture; this index value is based on the total sludge output within M and the fuel requirements of different types of disposal parties within M, to obtain the minimum suitable blending ratio that sludge resources can achieve; thus, the lower limit of sludge resource treatment volume within m when the disposal party applies sludge dry mixing disposal technology is calculated.

[0026]

[0027]

[0028]

[0029]

[0030] in, These respectively represent wastewater treatment plants, coal-fired power plants, and municipal solid waste incineration power plants; coal-fired power plants and municipal solid waste incineration power plants are different types of disposal entities. These represent the sets of all wastewater treatment plants, all coal-fired power plants, and all municipal solid waste incineration power plants, respectively. Represents the sum of all m; This represents the amount of coal consumed by a coal-fired power plant within a range of m, consisting of two parts: coal consumption for power generation and coal consumption for heating. This indicates the coal consumption for power generation within the sub-region where the coal-fired power plant is located; This indicates the coal consumption for heating within the sub-region where the coal-fired power plant is located; This indicates the minimum amount of sludge that can be disposed of within m when the dry mixing sludge disposal technology is applied in a coal-fired power plant. This indicates the minimum amount of sludge that can be disposed of within m when the sludge dry mixing treatment technology is applied in a municipal solid waste incineration power plant. The energy supply of a municipal solid waste incineration power plant within m, i.e. the energy supply of a waste disposal unit using municipal solid waste incineration power generation technology within m; This represents the total energy supply of municipal solid waste incineration power plants within a sub-region m where the municipal solid waste incineration power plant is located; This indicates the total amount of municipal solid waste incinerated in the sub-region where the municipal solid waste incineration power plant is located; This indicates the rounding up operation.

[0031] Furthermore, in step three, the scheduling model is constructed based on counterfactual theory, and the target optimization is achieved by comparing the greenhouse gas emission difference between the linked scenario and the non-linked scenario.

[0032] Furthermore, in step three, the method for constructing a cross-industry, multi-entity collaborative model for the scheduling of recycled sludge resources includes:

[0033] (1) Set the objective function:

[0034]

[0035] Wastewater treatment plants are providers of sludge resources; coal-fired power plants and municipal solid waste incineration power plants are different sludge resource disposal providers. This indicates the change in greenhouse gas emissions from coal-fired power plants under a coordinated scenario (application of sludge dry mixing treatment technology) compared to a scenario without coordinated treatment (no application). This indicates the change in greenhouse gas emissions from municipal solid waste incineration power plants under a coordinated scenario (using sludge dry mixing treatment technology) compared to a scenario without coordination (without application).

[0036]

[0037]

[0038] in, These respectively represent wastewater treatment plants, coal-fired power plants, municipal solid waste incineration power plants, time ranges, and existing sludge treatment technologies; This represents the collection of all wastewater treatment plants, all coal-fired power plants, and all municipal solid waste incineration power plants. This represents the sum of all m's; The amount of sludge supplied by the wastewater treatment plant to the coal-fired power plant within m; The amount of sludge supplied by the wastewater treatment plant to the municipal solid waste incineration power plant within m; These represent greenhouse gas emission factors from sludge incineration, coal combustion, wastewater treatment plants using existing sludge disposal technologies within m, sludge transportation, and coal mining, processing, and treatment, respectively. These represent the average emission factors of the power grids where the coal-fired power plant and the municipal solid waste incineration power plant are located, respectively. The lower heating value of coal consumed by coal-fired power plants; Moisture content is The average lower heating value of dried sludge; This refers to the electricity consumption for deep dewatering of one ton of sludge. This indicates the road transport distance between a wastewater treatment plant and a coal-fired power plant; This indicates the road transport distance between wastewater treatment plants and municipal solid waste incineration power plants.

[0039] (2) Set constraints:

[0040] (2-1). Upper limit constraint on the total amount of sludge resources that can be supplied by provider m.

[0041] The total amount of sludge supplied by each wastewater treatment plant to coal-fired power plants and municipal solid waste incineration power plants within a given m shall not exceed the upper limit of the total sludge resources that the wastewater treatment plant can supply within a given m:

[0042]

[0043] in, This represents the total amount of sludge supplied by the wastewater treatment plant to all coal-fired power plants within a given m. The total amount of sludge supplied by the wastewater treatment plant to all municipal solid waste incineration power plants within a range of m. This represents the upper limit of the total amount of sludge resources that can be supplied by the provider m, and the calculation method is as described in claim 4;

[0044] (2-2). Minimum constraint on sludge resource treatment volume within m when the disposal party applies sludge dry mixing disposal technology.

[0045] Once a thermal power plant implements sludge co-firing retrofit, it must ensure a stable sludge supply within each m. The lower limit constraint on the total amount of sludge resources to be processed by the disposal party is used to ensure that the sludge supply within each m within M can stably meet the fuel demand of the power plant in a predetermined proportion.

[0046]

[0047]

[0048] in, This indicates the minimum amount of sludge that can be disposed of within m when the dry mixing sludge disposal technology is applied in a coal-fired power plant. This indicates the minimum amount of sludge that can be disposed of within m when the sludge dry mixing treatment technology is applied in a municipal solid waste incineration power plant. , The calculation method is as described in claim 5;

[0049] (2-3). The maximum co-firing limit of sludge resources within m when the disposal party applies the sludge dry-mixing disposal technology.

[0050] Due to the influence of pollutant emission concentration and the mixing ratio of sludge and raw materials on the combustion efficiency and operating efficiency of thermal power unit boilers, the mixing ratio of sludge and raw materials needs to be lower than the maximum sludge co-firing ratio; the amount of sludge co-firing by the disposal party within m should not exceed the maximum co-firing amount.

[0051]

[0052]

[0053] in, The total amount of sludge supplied by all wastewater treatment plants to the coal-fired power plant within m; The variable is a binary variable, representing whether a coal-fired power plant uses the dry mixing sludge treatment technology. 0 indicates no, and 1 indicates that the technology is used and the sludge is treated. This represents the upper limit of the mixing ratio of sludge and raw materials / fuels, expressed as the mass percentage of sludge in the mixture; The total amount of sludge supplied by all wastewater treatment plants to the municipal solid waste incineration power plant within m; This is a binary variable representing whether a municipal solid waste incineration power plant uses the dry sludge treatment technology, where 0 indicates no and 1 indicates that the technology is used and the sludge is treated.

[0054] (2-4). Lower bound constraint on the total economic profit of the consortium over the entire time period M.

[0055] Compared to the current situation without the adoption of sludge dry mixing treatment technology, the total economic profit of the complex consisting of a thermal power plant and all its associated wastewater treatment plants over the entire time period M should not be less than To ensure the economic viability of the cooperation;

[0056]

[0057]

[0058] in, The unit profit from co-firing sludge from coal-fired power plants; The unit profit for co-firing sludge from municipal solid waste incineration power plants; The unit profit of the wastewater treatment plant for existing sludge disposal methods within m; , , These represent the unit profit of different sludge treatment technologies, and are considered unit profits. Different categories; Unit fuel cost for coal-fired power plants; For sludge transportation costs; This represents the minimum additional profit that the consortium needs to achieve within M, calculated based on the original profit level without the adoption of sludge dry mixing technology.

[0059] Furthermore, in step five, unit profit The calculation method is as follows:

[0060] When the disposal party originally used multiple technologies to dispose of sludge, the unit profit... This is the weighted average of the profits of all existing sludge treatment technology units;

[0061]

[0062] in, , , These represent existing sludge treatment technologies. The unit revenue, unit investment cost, and unit operation and maintenance cost.

[0063] Furthermore, in step five, the total number of Monte Carlo random samplings should be no less than 1000.

[0064] Beneficial effects:

[0065] 1. The present invention discloses a cross-industry, multi-entity collaborative method for scheduling recycled sludge resources. By establishing a full-process decision support model covering multiple sludge supply entities and multiple sludge co-processing entities, it simulates the cross-industry resource reuse behavior of each entity and their interaction, thereby achieving monthly dynamic overall scheduling of sludge resources across industries, improving the potential for waste resource utilization and the accuracy of resource management.

[0066] 2. The present invention discloses a cross-industry multi-entity collaborative method for scheduling recycled sludge resources. By establishing a cross-industry multi-entity collaborative recycled sludge resource scheduling model based on counterfactual theory and coupling it with a full life cycle assessment method, a high spatiotemporal resolution sludge resource allocation path with "monthly-plant level" is formulated. This method overcomes the limitations of the current model technology chain, such as missing links, low temporal resolution, and neglect of seasonal characteristics of energy supply, and promotes effective cross-industry collaboration.

[0067] 3. The present invention discloses a cross-industry, multi-entity collaborative method for scheduling recycled sludge resources. By coupling Monte Carlo simulation technology into the scheduling model, the uncertainty of key economic factors such as technology cost and profitability is incorporated into the scheduling scheme formulation process. This enables the construction of a dynamic and robust scheduling strategy with high spatiotemporal resolution, in order to address the current predicament of low reliability in the economic feasibility assessment of sludge dry mixing treatment technology, insufficient characterization of competitive relationships among sludge treatment technologies, and obstruction of cross-industry inter-entity collaboration. Attached Figure Description

[0068] Figure 1 A schematic diagram of a cross-industry, multi-entity collaborative method for scheduling recycled sludge resources according to the present invention;

[0069] Figure 2 The example shows a statistical chart of monthly sludge production and maximum co-firing capacity of thermal power plant sludge resources by province across the country.

[0070] Figure 3 A statistical chart showing the path linkage probability and optimal sludge resource scheduling amount for all loop results in the example;

[0071] Figure 4 The monthly dynamic evolution of the total sludge resource allocation in the example. Detailed Implementation

[0072] To better illustrate the purpose and advantages of the present invention, the invention will be further described below in conjunction with the accompanying drawings and examples.

[0073] Example: This example uses 5218 urban wastewater treatment plants and 1990 power plants operating in the national wastewater treatment industry and thermal power generation industry in 2020 as examples to provide a detailed description of a cross-industry, multi-entity collaborative method for scheduling recycled sludge resources. Figure 1 As shown, the specific implementation steps are as follows:

[0074] This study statistically analyzed the distribution of 5,218 sludge resource providers and 1,990 sludge resource disposal entities nationwide; it used the predicted upper limit of total sludge resource supply, lower limit of total treatment volume, maximum co-firing capacity, and lower limit of total economic profit as constraints; it took maximizing the total greenhouse gas emission reduction between stages A and B as the linkage objective; and it constructed a cross-industry, multi-entity linkage model for reclaimed sludge resource scheduling based on counterfactual theory; and it considered the sludge resources following an independent uniform distribution. , , Multiple Monte Carlo random samplings were conducted, and the unit profit of the sludge treatment technology was calculated for each sampling. The calculation of this sampling Sludge treatment technology Adopt existing sludge treatment technologies The amount of sludge disposed of sludge moisture content Energy supply The input is fed into the scheduling model to obtain the optimal sludge resource scheduling quantity for the directional disposal party within m under this sampling. ; Statistical analysis of the percentage of times sludge resource allocation cooperation was reached between suppliers and disposers within a range of m; Calculation of the probability of linkage. The path is selected as the priority scheduling path, 50%. The path is used as a supplementary scheduling path; the path corresponds to The median is used as the scheduling reference value, corresponding to The interval formed by the 25%-75% quantile is used as the feasible scheduling interval; a sludge resource scheduling plan is generated based on the feasible scheduling interval, that is, the scheduling of recycled sludge resources is realized through cross-industry multi-entity linkage.

[0075] S1 Define the region and compile statistics on the main entities within the region.

[0076] This embodiment takes China as the study area; the sludge resource providers are the main urban domestic sewage treatment plants in operation in 2020, which are included in the sewage treatment industry nationwide; the sludge resource disposers are the main coal-fired power plants and municipal solid waste incineration power plants in operation in 2020, which are included in the thermal power generation industry nationwide.

[0077] S1-1 Main body distribution

[0078] The wastewater treatment industry encompasses 5,218 urban domestic wastewater treatment plants, accounting for approximately 84% of the national treatment capacity. The wastewater treatment plant data collected in this embodiment mainly comes from the Ministry of Housing and Urban-Rural Development of China, including project name, construction location, and operation period, involving information on 6,228 wastewater treatment plants. After removing 752 wastewater treatment plants that did not have an operation period field record in 2020 and 258 wastewater treatment plants with incomplete information, the dataset ultimately retains information on 5,218 wastewater treatment plants.

[0079] The thermal power generation industry encompasses 1,425 coal-fired power plants and 565 municipal solid waste incineration power plants, accounting for approximately 98% and 95% of the national installed capacity, respectively. This embodiment retrieved the power plant names, unit numbers, latitude and longitude information, installed capacity, and commissioning / decommissioning years of coal-fired units from the Global Energy Monitor database, identifying 3,242 coal-fired units in operation in 2020. These were then aggregated into 1,425 plant-level information entries based on the power plant name field. The address information and commissioning dates of the municipal solid waste incineration power plants were obtained from the China Circular Economy Association.

[0080] For urban sewage treatment plants and municipal solid waste incineration power plants for which latitude and longitude information was not directly collected, latitude and longitude information was obtained using the geocoding service of Baidu Maps Open Platform based on the address field of the construction location, and each plant was given a unique project code based on its province code.

[0081] The data on the road transport distance between the urban sewage treatment plant and the power plant was calculated using the OSRM package in R language and the latitude and longitude coordinates of the urban sewage treatment plant and the power plant. The distance is approximately the same as the driving distance shown on Baidu Maps.

[0082] S1-2 Provider Information

[0083] In this embodiment, the time range m is the natural month, and the time period M is the whole year of 2020. In addition to geographic information, this embodiment further collects information from the Ministry of Housing and Urban-Rural Development of China on the design treatment capacity, monthly sewage treatment volume, sludge production volume, sludge moisture content, sludge disposal technology, sludge disposal volume, land use volume, building material use volume, separate incineration volume, sanitary landfill volume, and other disposal volume of all urban domestic sewage treatment plants.

[0084] Existing sludge treatment technologies include land application, building material utilization, separate incineration, sanitary landfill, and others; if the land application quantity in the field is not 0, it indicates that land application treatment technology has been adopted; subsequently, the existing sludge treatment technologies of the town's domestic sewage treatment plant are recorded. For land use, the land use volume field represents the use of existing sludge treatment technologies. The amount of sludge disposed of .

[0085] S1-3 Disposal Party Information

[0086] In addition to geographic information and physical attribute information, this embodiment further collected data on provincial coal consumption for power generation, provincial coal consumption for heating, calorific value of raw coal, annual power generation of provincial coal-fired power plants, typical power load curves of each provincial power grid, annual heat supply of provincial thermal power plants, annual centralized heating volume of provincial thermal power plants, centralized heating time of each province in 2020, total installed capacity of provincial coal-fired power plants, total installed capacity of provincial municipal solid waste incineration power plants, total installed capacity of provincial thermal power plants, power generation capacity of provincial municipal solid waste incineration power plants, annual municipal solid waste incineration power generation, and annual municipal solid waste incineration volume in 2020. Data sources include the Global Energy Monitor database, the "Statistical Compilation of Electric Power Industry", the "China Urban and Rural Construction Statistical Yearbook 2020", and the National Development and Reform Commission.

[0087] This embodiment uses provincial administrative regions of my country as sub-regions within the geographical area, and according to claim 3, the energy supply of the disposal party within m (monthly). The statistical method is shown in the formula below to calculate the monthly energy supply of coal-fired power plants and municipal solid waste incineration power plants.

[0088]

[0089]

[0090] The energy supply within m (monthly) of coal-fired power plants and municipal solid waste incineration power plants consists of two parts: electricity supply within m (monthly) and heat supply within m (monthly). The time characteristics of electricity supply within m (monthly) of coal-fired power plants and municipal solid waste incineration power plants are characterized based on the typical power load curves and total power supply of each provincial power grid. The time characteristics of heat supply within m (monthly) of coal-fired power plants and municipal solid waste incineration power plants are characterized based on the heating duration of each province and the total heat supply of cogeneration plants. Due to data availability, all power plants in the examples are considered as cogeneration plants.

[0091] in, This refers to a sub-region within a given area, which in this example is a province in my country. This refers to power generation technologies, including coal-fired power generation and municipal solid waste incineration power generation. This indicates that all provinces using power generation technology Total annual power supply by the disposal party, in kWh; Indicates the use of power generation technology The installed capacity of the disposal party; This indicates that all provinces using power generation technology The total installed capacity of the disposal party; The power supply allocation coefficient within m (monthly) represents the proportion of the power supply within m (monthly) of each province to the total power supply within M (annual). Its value can be obtained based on the typical power load curve of each province. Indicates the heating capacity provided by the handling party; This indicates the total heating capacity of the thermal power plants in the province where the disposal party is located; This represents the monthly heat supply within a given m range of the thermal power plant in the sub-region, expressed in kJ. This represents the collection of months for the centralized heating season in each province; This indicates the total heat supply from thermal power plants in each province; This represents the total amount of centralized heating supplied by thermal power plants within area M in each province (throughout the year); This indicates that M contains m numbers, which is the number of months in the whole year, 12. This indicates the actual number of heating days in each province within a given month (m); This indicates the total number of heating days in each province within the year (M).

[0092] S2 Predicts the Boundaries of Cross-Industry Collaboration Constraints

[0093] According to S1, the upper limit of the total monthly sludge resources that the provider can supply is predicted; the lower limit of the total monthly sludge resources that the disposer can process when applying the sludge dry mixing treatment technology; the maximum monthly sludge resource co-firing amount; and the lower limit of the total annual economic profit of the consortium. The consortium refers to the sludge treatment cooperation group consisting of the disposer and all its affiliated providers.

[0094] S2-1 Upper limit of available sludge resources from the supplier

[0095] Because the moisture content of the sludge supplied by different providers varies, direct scheduling based on absolute output may lead to excessive moisture content in the feed to thermal power plants, thus affecting combustion efficiency and system stability. To meet the control requirements of sludge moisture content for co-incineration by disposal parties, namely coal-fired power plants and municipal solid waste incineration power plants, the sludge supply to each municipal wastewater treatment plant needs to be adjusted. By calculating the equivalent dry sludge volume under the target moisture content condition, the upper limit of the total monthly sludge resource that municipal wastewater treatment plants can supply is obtained.

[0096]

[0097] in, This indicates the maximum total amount of sludge resources that the provider can supply within a given month (m). Indicates existing sludge treatment technologies A set; This represents the total amount of sludge treated by the provider using each existing sludge treatment technology within m (monthly). The sludge moisture content within m (monthly) of the main body provided by the supplier; The upper limit of moisture content required for the co-incineration of sludge by the disposal party is set at 45% according to research literature.

[0098] S2-2 Minimum capacity for sludge resource treatment by the disposal party

[0099] When applying sludge dry mixing treatment technology, sludge needs to be mixed with coal or municipal solid waste in a certain proportion to ensure stable combustion performance of the mixture. If the sludge supply is interrupted, not only will the co-treatment effect be lost, but the energy efficiency of the combustion system may also be reduced due to frequent adjustments to the feeding system or unstable incineration parameters. Therefore, a lower limit ratio of sludge supply is introduced. This indicates the minimum mass percentage of sludge in the mixture; this indicator value is based on the total national sludge output in 2020, the total raw coal demand of national coal-fired power plants in 2020, and the total demand of national municipal solid waste incineration power plants in 2020, to obtain the minimum suitable blending ratio of sludge resources that can be achieved as 1%; thus, the lower limit of the monthly sludge resource treatment volume of the disposal party is calculated.

[0100]

[0101]

[0102]

[0103]

[0104] in, These respectively represent wastewater treatment plants, coal-fired power plants, and municipal solid waste incineration power plants; coal-fired power plants and municipal solid waste incineration power plants are different types of disposal entities. These represent the sets of all wastewater treatment plants, all coal-fired power plants, and all municipal solid waste incineration power plants, respectively. Represents the sum of all m; This represents the monthly coal consumption of a coal-fired power plant within a given m period, consisting of two parts: coal consumption for power generation and coal consumption for heating. This indicates the coal consumption for power generation within the sub-region where the coal-fired power plant is located; This indicates the coal consumption for heating within the sub-region where the coal-fired power plant is located; This indicates the minimum monthly sludge disposal volume when using dry sludge treatment technology in coal-fired power plants. This indicates the minimum sludge disposal volume per month (m) when the sludge dry mixing treatment technology is applied in a municipal solid waste incineration power plant. This refers to the energy supply of a municipal solid waste incineration power plant within m (monthly), which is the energy supply of a disposal party using municipal solid waste incineration power generation technology within m (monthly). This represents the total energy supply of municipal solid waste incineration power plants within a sub-region m (monthly). This indicates the total amount of municipal solid waste incinerated in the sub-region where the municipal solid waste incineration power plant is located; This indicates the rounding up operation.

[0105] S2-3 Maximum co-firing capacity of sludge resources for disposal

[0106] Due to the influence of pollutant emission concentration and the mixing ratio of sludge and raw materials on the combustion efficiency and operating efficiency of thermal power unit boilers, the mixing ratio of sludge and raw materials needs to be lower than the maximum sludge co-firing ratio: the monthly sludge co-firing amount of the disposal party should not exceed the maximum co-firing amount.

[0107]

[0108]

[0109] in, , These represent the maximum monthly co-firing amount of sludge resources within m for coal-fired power plants and municipal solid waste incineration power plants, respectively. The upper limit of the mixing ratio of sludge and raw materials is expressed as the mass percentage of sludge in the mixture. According to literature research, its value is set at 5%.

[0110] S2-4 Lower limit of total economic profit of the consortium

[0111] Compared to the current situation without the adoption of sludge dry mixing treatment technology, the total annual economic profit of the consortium consisting of a thermal power plant and all its associated municipal wastewater treatment plants should not be less than [amount missing]. To ensure the economic viability of the cooperation; in the embodiment, it is set If the total annual economic profit of the consortium is not lower than the total profit level of the unlinked situation, then it is economically feasible.

[0112] S3. A cross-industry, multi-stakeholder collaborative resource scheduling model for recycled sludge is constructed based on counterfactual theory.

[0113] The constraints are defined in S2: the upper limit of the total sludge resource supply, the lower limit of the total treatment volume, the maximum co-firing volume, and the lower limit of the total economic profit. The linkage objective is to maximize the total greenhouse gas emission reduction between stages A and B. A cross-industry, multi-entity linkage sludge resource scheduling model is constructed based on counterfactual theory. Stage A refers to the sludge disposal stage of the provider; Stage B refers to the power generation operation, fuel extraction and processing, sludge deep dewatering, and sludge transportation stages of the disposing party. The scheduling model, based on counterfactual theory, optimizes the objective by comparing the greenhouse gas emission differences between linked and non-linked scenarios. After adaptive adjustments, this scheduling model framework can serve cross-industry circular strategy design for different regions (urban clusters, provinces, prefecture-level cities) and different resources (such as industrial by-products and agricultural and forestry waste) within China.

[0114] S3-1 Establish the objective function

[0115]

[0116] Wastewater treatment plants are providers of sludge resources; coal-fired power plants and municipal solid waste incineration power plants are different sludge resource disposal providers. This indicates the change in greenhouse gas emissions from coal-fired power plants under a coordinated scenario (application of sludge dry mixing treatment technology) compared to a scenario without coordinated treatment (no application). This indicates the change in greenhouse gas emissions from municipal solid waste incineration power plants under a coordinated scenario (using sludge dry mixing treatment technology) compared to a scenario without coordination (without application).

[0117]

[0118]

[0119] in, The amount of sludge supplied by urban sewage treatment plants to coal-fired power plants within m (monthly); The amount of sludge supplied by the wastewater treatment plant to the municipal solid waste incineration power plant within m (monthly); The greenhouse gas emission factors for sludge incineration, coal combustion, sludge transportation, and coal mining, processing, and treatment are respectively 0.0798 tCO. 2eq / t、1.99tCO 2eq / t、0.123kgCO 2eq / (t km), 0.18tCO 2eq / t, data sourced from literature, World Resources Institute, and China Life Cycle Basic Database; This represents the greenhouse gas emission factors of a wastewater treatment plant using existing sludge disposal technologies for sludge treatment over a period of time (m, month by month). The emission factors for land application, building material application, separate incineration, sanitary landfill, and other technologies are 212.46, -170.06, -313.22, 867.22, and 89.10 kg CO2, respectively. 2eq / t, data sourced from research literature; The values ​​represent the average emission factors of the power grids where coal-fired power plants and municipal solid waste incineration power plants are located. The average emission factors per kilowatt-hour of electricity for the North China, Northeast, Northwest, East China, Central China and Southern power grids are 1.25, 1.34, 0.963, 0.948, 0.774 and 0.784 kgCO2eq / kWh, respectively. The data are from the China Life Cycle Basic Database. The lower heating value of the coal consumed by the coal-fired power plant is 20.91 GJ / t. Moisture content is The average lower heating value of the dried sludge is 10 GJ / t; The power consumption per ton of sludge for deep dewatering is 28 kWh / t (calculated at a moisture content of 45%). This indicates the road transport distance between a wastewater treatment plant and a coal-fired power plant; This indicates the road transport distance between wastewater treatment plants and municipal solid waste incineration power plants.

[0120] S3-2 Establishing Constraints

[0121] S3-2-1 Monthly Upper Limit Constraint on the Total Amount of Sludge Resources Available for Supply by the Provider

[0122] The total amount of sludge supplied by each urban wastewater treatment plant to coal-fired power plants and municipal solid waste incineration power plants within m (monthly) shall not exceed the upper limit of the total sludge resources that the urban wastewater treatment plant can supply within m (monthly):

[0123]

[0124] in, The total amount of sludge supplied by urban wastewater treatment plants to all coal-fired power plants within m (monthly); The total amount of sludge supplied by urban wastewater treatment plants to all municipal solid waste incineration power plants within m (monthly); This represents the upper limit of the total amount of sludge resources that the provider can supply within m (monthly), and the calculation method is shown in S2-1;

[0125] S3-2-2 Monthly minimum sludge resource treatment capacity constraints when the disposal party applies sludge dry mixing disposal technology

[0126] Once a thermal power plant implements sludge co-firing retrofit, it must ensure a stable sludge supply within each m (monthly). The lower limit constraint on the total amount of sludge resources to be processed by the disposal party is used to ensure that the sludge supply within M (year) can stably meet the power plant's fuel demand in a predetermined proportion.

[0127]

[0128]

[0129] in, This indicates the minimum sludge disposal volume per month (m) when the dry sludge treatment technology is applied in a coal-fired power plant. This indicates the minimum sludge disposal volume per month (m) when the sludge dry mixing treatment technology is applied in a municipal solid waste incineration power plant. , The calculation method is shown in S2-2;

[0130] S3-2-3 Monthly Maximum Co-firing Limits for Sludge Resources When Applying Dry-Mix Sludge Treatment Technology

[0131] Due to the influence of pollutant emission concentration and the mixing ratio of sludge and raw materials on the combustion efficiency and operating efficiency of thermal power unit boilers, the mixing ratio of sludge and raw materials needs to be lower than the maximum sludge co-firing ratio: the amount of sludge co-firing by the disposal party within m (monthly) should not exceed the maximum co-firing amount.

[0132]

[0133]

[0134] in, This represents the total amount of sludge supplied by all wastewater treatment plants to the coal-fired power plant within m (monthly). The variable is a binary variable, representing whether a coal-fired power plant uses the dry mixing sludge treatment technology. 0 indicates no, and 1 indicates that the technology is used and the sludge is treated. The total amount of sludge supplied by all wastewater treatment plants to the municipal solid waste incineration power plant within m (monthly); This is a binary variable representing whether a municipal solid waste incineration power plant uses the dry sludge treatment technology, where 0 indicates no and 1 indicates that the technology is used and the sludge is treated. , The values ​​represent the maximum co-firing amount of sludge resources within m (monthly) of coal-fired power plants and municipal solid waste incineration power plants, respectively, and the calculation method is shown in S2-3.

[0135] S3-2-4 Lower Limit Constraint on Total Annual Economic Profit of the Consortium

[0136] Compared to the current situation without the adoption of sludge dry mixing treatment technology, the total economic profit of the complex consisting of a thermal power plant and all its associated wastewater treatment plants should not be less than [amount missing] over the entire time period M (year). To ensure the economic viability of the cooperation:

[0137]

[0138]

[0139] in, The unit profit from co-firing sludge from coal-fired power plants; The unit profit for co-firing sludge from municipal solid waste incineration power plants; The unit profit of the wastewater treatment plant for existing sludge disposal methods within m (monthly); , , These represent the unit profit of different sludge treatment technologies, and are considered unit profits. The different categories are shown in Table 1; The unit fuel cost for coal-fired power plants is calculated by multiplying the annual average price of Qinhuangdao thermal coal (557 yuan / ton) in 2020 by the coal price adjustment coefficient of the province where the coal-fired power plant is located (0.38-1.20). The data comes from China Coal Market Network. The cost of sludge transportation is 0.65 yuan / (t). km); This represents the minimum additional profit that the consortium needs to achieve within M (the entire year). Its calculation basis is the original profit level without the use of sludge dry mixing technology, as shown in S2-4. In the example... .

[0140] S4 Collects technical and economic data on sludge treatment.

[0141] Unit investment cost of sludge collection and treatment technology Unit maintenance cost and unit income The scope is shown in Table 1. Data sources include the "Technical Guidelines for Sludge Treatment and Disposal of Urban Wastewater Treatment Plants (Implementation)," the "2024 China Sludge Treatment Industry Special Survey and In-depth Analysis Report," literature, web pages, and publicly available data from 29 coal-fired power plant-sludge co-firing pilot projects; among them, unit investment cost The calculation is based on construction cost / 30 / 365, where 30 represents the design life of the sludge treatment facility and 365 represents the number of days in a year; the sludge treatment technology includes the provider's existing sludge treatment technology. and sludge dry mixing treatment technology;

[0142]

[0143] S5 generates single-cycle scheduling results

[0144] S4 follows an independent uniform distribution , , 1000 Monte Carlo random samplings were conducted, and the unit profit of the sludge treatment technology was calculated for each sampling. The calculation of this sampling Compared with the statistics in S1 , , , The data are input into the S3 scheduling model to obtain the optimal sludge resource scheduling amount for the directional disposal party within m months (monthly) under this sampling. ;

[0145] When the disposal party originally used multiple technologies to dispose of sludge, the unit profit... This is the weighted average of the profits of all existing sludge treatment technology units;

[0146]

[0147] in, , , These represent existing sludge treatment technologies. Unit revenue, unit investment cost, and unit operation and maintenance cost; unit profit The range is shown in Table 1. The profit value of land use disposal technology is set according to the average profit of "anaerobic digestion + land use" and "aerobic composting + land use".

[0148] This embodiment employs the asymptotic distance iteration method to solve the scheduling model, thereby improving the solvability and applicability of the high spatiotemporal accuracy model. If all potential paths were directly input, the scheduling model in this embodiment would be a nonlinear optimization problem with 136 million decision variables and 110,000 constraints, requiring at least 1TB of computing resources to solve. The asymptotic distance iteration method finds the optimal solution by gradually expanding the distance range, thus enabling the solution of this embodiment with lower computing resources (less than 64GB). This model solution method initially sets a maximum distance threshold and starts optimization from a smaller distance range. In each iteration, the algorithm first selects all distance combinations that do not exceed the currently set threshold and calculates the corresponding objective function value. The threshold is gradually increased (e.g., by 100km each time), and the search for the optimal solution continues in the new distance combinations. The algorithm continues until the objective function value no longer improves significantly, at which point the iteration stops. The final threshold in this embodiment, i.e., the maximum distance, is 300km.

[0149] S6 Analyze all round-robin scheduling results

[0150] Extracting the results of 1000 iterations from S5 The percentage of times that the supplier and the disposal party reach cooperation on sludge resource allocation within a given period (m, month by month) is used as the probability of linkage along this path. The probability statistics of linkage across all paths are as follows: Figure 3 As shown in (a).

[0151] S7 Sludge Resource Scheduling Scheme

[0152] The linkage probability described in S6 The path is selected as the priority scheduling path, 50%. The path is used as a supplementary scheduling path; the path corresponds to The median is used as the scheduling reference value, corresponding to The interval formed by the 25%-75% quantile is used as the feasible scheduling interval; when the sludge resource supply of the priority scheduling path is insufficient, the resource quantity of the supplementary scheduling path is activated to meet the lower limit of the treatment capacity of the disposal party; a sludge resource scheduling plan is generated. In this embodiment, the scheduling reference values ​​and feasible scheduling intervals of the priority scheduling path and the supplementary scheduling path are summarized as follows: Figure 3-4 As shown.

[0153] Figure 2 (ab) respectively presents the statistical results of monthly sludge production by province nationwide and the highest co-firing capacity of sludge resources from thermal power plants (only covering the power generation technology in this embodiment). Figure 2 (a) Sludge production is greatly affected by influent water quality and wastewater treatment technology, resulting in significant monthly fluctuations and regional differences. For example... Figure 2 (b) The fluctuations in the maximum monthly co-firing capacity of sludge resources from thermal power plants are more significant than the fluctuations in sludge production, especially in northern regions such as Shandong, Jilin, and Liaoning. Since peak fuel demand from thermal power plants is concentrated in November-December, January, July, and August, winter heat supply further exacerbates fuel demand in Northwest, Northeast, North China, and Henan regions, with peak values ​​even exceeding summer peaks. In most provinces, the total sludge production is far lower than the maximum co-firing capacity of sludge resources from thermal power plants in that region. Except for Beijing, Qinghai, Shanghai, and Sichuan, the total monthly sludge production in all provinces is lower than the maximum co-firing capacity of sludge resources from thermal power plants in those provinces. Therefore, nationwide, not all thermal power plants are suitable for adopting sludge dry co-firing treatment technology.

[0154] According to S6, the statistical results of the number of paths with different linkage probabilities in the embodiment are as follows: Figure 3As shown in (a), among all the loop results, more than 230,000 paths have a linkage probability of less than 10%. The curve drops sharply in the range where the linkage probability is less than 20%, indicating that under conditions of strong economic uncertainty, a large number of paths cannot reliably guarantee the economic feasibility of linkage. As shown in S7, after screening out the paths with a linkage probability of not less than 50%, it can be seen that the overall number of paths still shows a downward trend and exhibits irregular fluctuations. These fluctuations reflect the nonlinear dynamic process of sludge resource allocation. Figure 3 (b) Further, it shows the trend of scheduling reference value and total scheduling feasible interval as the linkage probability changes. The scheduling feasible interval of the priority path (linkage probability not less than 70%) is narrower. Compared with the supplementary path (linkage probability between 50% and 70%), its scheduling reference value shows higher stability.

[0155] Except for February, the total monthly sludge resource allocation for both the priority and supplementary scheduling paths was between 400 and 500 thousand tons. The resource allocation for the priority scheduling strategy remained at a relatively high level in most months; the resource allocation for the supplementary scheduling strategy fluctuated more, with significantly lower allocations in some months (February) and closer to the priority scheduling strategy in others (June and December). The fluctuations in the priority and supplementary scheduling strategies peaked in July and December, respectively. Ultimately, 1196 urban wastewater treatment plants and 163 coal-fired power plants formed cross-industry linkages, while no municipal solid waste incineration power plants participated in the linkage.

[0156] The above detailed description further illustrates the purpose, technical solution, and beneficial effects of the invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A cross-industry, multi-entity collaborative method for the scheduling of recycled sludge resources, characterized in that: Within a defined area, the main distribution of sludge resource providers and sludge resource disposers will be statistically analyzed; the upper limit of the predicted total sludge resource supply, the lower limit of the total treatment volume, the maximum co-firing volume, and the lower limit of the total economic profit will be used as constraints. The goal is to maximize the total greenhouse gas emission reduction between stages A and B. A cross-industry, multi-entity collaborative resource scheduling model for recycled sludge is constructed based on counterfactual theory. Multiple Monte Carlo random samplings are performed on IC, OM, and R, which follow independent and uniform distributions. The unit profit P of sludge treatment technology is calculated for each sampling. The calculated P is then compared with the sludge treatment technology t and the amount of sludge SP treated using the existing sludge treatment technology t. i,m,t sludge moisture content ω w,i,m Energy supply The data is input into the scheduling model to obtain the optimal sludge resource scheduling amount SW for the supplier and the disposal party within m under this sampling. The percentage of times the supplier and the disposal party reach a sludge resource scheduling cooperation within m is counted. Paths with a linkage probability γ≥70% are designated as priority scheduling paths, and paths with 50%≤γ<70% are designated as supplementary scheduling paths. The median of SW corresponding to the path is used as the scheduling reference value, and the interval formed by the 25%-75% quantile of the corresponding SW is designated as the scheduling feasible interval. Based on the feasible scheduling range, a sludge resource scheduling plan is generated, which realizes the scheduling of recycled sludge resources through cross-industry multi-entity linkage.

2. The scheduling method according to claim 1, characterized in that, Includes the following steps: Step 1: Within the defined area, statistically analyze the distribution of sludge resource providers and sludge resource disposal providers; statistically analyze the existing sludge disposal technologies (t) of the providers within a time frame (m), and the amount of sludge (SP) disposed of using these existing technologies (t). i,m,t sludge moisture content ω w,i,m ; Statistics on the energy supply of the disposal party within m Step 2: Based on Step 1, predict the upper limit of the total amount of sludge resources that the supplier can supply within m; the lower limit of the amount of sludge resources that the disposal party can process within m when applying sludge dry mixing disposal technology; the maximum amount of sludge resources that the disposal party can co-fire within m; and the lower limit of the total economic profit of the consortium within the entire time period M. The consortium refers to the sludge disposal cooperation group consisting of the disposal party and all its affiliated suppliers; the entire time period M refers to the sum of all m. Step 3: Use the upper limit of the total sludge resource supply, the lower limit of the total treatment volume, the maximum co-firing volume, and the lower limit of the total economic profit value mentioned in Step 2 as constraints; take maximizing the total greenhouse gas emission reduction of Step A and Step B as the linkage objective; A cross-industry, multi-entity collaborative model for the scheduling of recycled sludge resources is constructed based on counterfactual theory. The A stage is the sludge disposal stage of the provider; the B stage is the power generation operation, fuel extraction and processing, sludge deep dewatering, and sludge transportation stages of the disposal party. Step 4: Unit investment cost of sludge treatment technology IC∈[IC min IC max Unit maintenance cost OM∈[OM] min ,OM max and unit income R∈[R min ,R max The scope of the sludge treatment technology includes the provider's existing sludge treatment technology and sludge dry mixing treatment technology. Step 5: Perform multiple Monte Carlo random samplings on IC, OM, and R, which follow independent and uniform distributions from Step 4. Calculate the unit profit P of the sludge treatment technology for each sampling. Compare the P calculated from this sampling with the t and SP from Step 1. i,m,t ω w,i,m , Inputting the data into the scheduling model in step three yields the optimal sludge resource scheduling quantity SW within m of the sampling location for the directional disposal party. Step 6: Extract the SW from the results of multiple iterations in Step 5, and count the percentage of times the provider and the disposer reach cooperation on sludge resource scheduling within m, which is taken as the linkage probability γ of this path. Step 7: The paths with a linkage probability γ≥70% as described in Step 6 are designated as priority scheduling paths, and the paths with a linkage probability 50%≤γ<70% are designated as supplementary scheduling paths; the median of the SW corresponding to the path is used as the scheduling reference value, and the interval formed by the 25%-75% quantile of the corresponding SW is used as the scheduling feasible interval. When the sludge resource supply of the priority scheduling path is insufficient, the resource quantity of the supplementary scheduling path is activated to meet the lower limit of the treatment capacity of the disposal party; a sludge resource scheduling plan is generated, that is, the cross-industry multi-entity linkage of recycled sludge resource scheduling is realized.

3. The scheduling method according to claim 2, characterized in that, In step one, the energy supply of the disposal unit within m... The statistical method is as follows: Energy supply for disposal within m It consists of two parts: power supply within m and heat supply within m; based on the power load curve of the sub-regions within the region and the total power supply, the power supply time characteristics of the treatment unit within m are characterized; based on the heating duration of the sub-regions within the region and the total heat supply of the thermal power plant, the heat supply time characteristics of the treatment unit within m are characterized; calculation Where p represents a sub-region within the region; g represents the power generation technology; ES p This represents the total power supply within M from all disposal methods using power generation technology g, expressed in kWh. This indicates the installed capacity of the power generation technology used in the treatment process; This represents the total installed capacity of all power generation technologies used in the sub-region; p,m C is the power supply allocation coefficient within m, representing the proportion of the power supply from the handling unit within m to the total power supply within M. Its value can be obtained from the power load curve. h Indicates the heating capacity of the handling party; C p,e Indicates the total heating capacity of thermal power plants within the sub-region where the disposal party is located; HS p,m (m) represents the heat supply of the thermal power plant within the sub-region m, in kJ; This represents the collection of months for the centralized heating season within a sub-region; HS p This indicates the total heat supply from the thermal power plant within sub-region M; DHS p σ represents the total centralized heating supply from the thermal power plants within sub-region M; σ represents the number of m plants contained in M; D p,m D represents the actual number of heating days within the sub-region m; p This represents the total number of heating days in the sub-region within M.

4. The scheduling method according to claim 3, characterized in that, In step two, the method for predicting the upper limit of the total amount of sludge resources that can be supplied by provider m is as follows: The upper limit of the total amount of sludge resources that can be supplied within m is obtained by calculating the equivalent dry sludge volume under the target moisture content condition. in, This indicates the upper limit of the total amount of sludge resources that the provider m can supply; T represents the set of existing sludge treatment technologies t; ∑ t∈T SP i,m,t ω represents the total amount of sludge treated by the provider using various existing sludge treatment technologies within the range m; w,i,m ω represents the sludge moisture content within the main body m provided by the supplier. d The upper limit requirement for the moisture content of sludge co-incineration by the disposal party.

5. The scheduling method according to claim 4, characterized in that, In step two, the method for predicting the lower limit of sludge resource treatment volume within m when applying sludge dry mixing treatment technology is as follows: The sludge supply lower limit ratio index (MSR) is introduced to represent the minimum mass proportion of sludge in the mixture. This index value is based on the total sludge output within M and the fuel requirements of different types of disposal parties within M to obtain the appropriate minimum blending ratio of sludge resources. Thus, the lower limit of sludge resource treatment volume within m is calculated when the disposal party applies the sludge dry mixing disposal technology. Where i, j, k represent wastewater treatment plants, coal-fired power plants, and municipal solid waste incineration power plants, respectively; coal-fired power plants and municipal solid waste incineration power plants represent different types of disposal methods; I, J, K represent the sets of all wastewater treatment plants, all coal-fired power plants, and all municipal solid waste incineration power plants, respectively; M represents the sum of all m; CC j,m This represents the coal consumption of a coal-fired power plant within a range of m, consisting of two parts: coal consumption for power generation and coal consumption for heating; CCE j∈P This indicates the coal consumption for power generation within the sub-region where the coal-fired power plant is located; CCH j∈P This indicates the coal consumption for heating within the sub-region where the coal-fired power plant is located; This indicates the minimum amount of sludge that can be disposed of within m when the dry mixing sludge disposal technology is applied in a coal-fired power plant. This indicates the minimum amount of sludge that can be disposed of within m when the sludge dry mixing treatment technology is applied in a municipal solid waste incineration power plant. EW represents the energy supply of a municipal solid waste incineration power plant within a given m, i.e., the energy supply of a waste disposal facility using municipal solid waste incineration power generation technology within a given m. m,k∈p WC represents the total energy supply of municipal solid waste incineration power plants within a sub-region (m) where the plant is located; k∈p This indicates the total amount of municipal solid waste incinerated in the sub-region where the municipal solid waste incineration power plant is located; [·] indicates the rounding up operation.

6. The scheduling method according to claim 4, characterized in that, In step three, the scheduling model is constructed based on counterfactual theory and achieves target optimization by comparing the difference in greenhouse gas emissions between linked scenarios and non-linked scenarios.

7. The scheduling method according to claim 6, characterized in that, In step three, the method for constructing a cross-industry, multi-entity collaborative model for the scheduling of recycled sludge resources includes: (1) Set the objective function: max-(ΔGHGE Coal +ΔGHGE Waste ) Wastewater treatment plants are the providers of sludge resources; coal-fired power plants and municipal solid waste incineration power plants are the different sludge resource disposers; ΔGHGE Coal This represents the change in greenhouse gas emissions from coal-fired power plants under a coordinated scenario (using sludge dry mixing treatment technology) compared to a scenario without coordinated treatment (without application); ΔGHGE Waste This indicates the change in greenhouse gas emissions from municipal solid waste incineration power plants under a coordinated scenario (using sludge dry mixing treatment technology) compared to a scenario without coordination (without application). Where i, j, k, m, t represent wastewater treatment plants, coal-fired power plants, municipal solid waste incineration power plants, time ranges, and existing sludge treatment technologies, respectively; I, J, K represent the set of all wastewater treatment plants, all coal-fired power plants, and all municipal solid waste incineration power plants; M represents the sum of all m; SW i,j,m SW represents the amount of sludge supplied by the wastewater treatment plant to the coal-fired power plant within a given m. i,k,m The amount of sludge supplied by the wastewater treatment plant to the municipal solid waste incineration power plant within m; ef SB ,ef CB ,ef i,t,m ,ef Trans ,ef CMP , respectively represent the greenhouse gas emission factors of sludge incineration, coal combustion, sludge treatment at wastewater treatment plants using existing sludge disposal technologies within m, sludge transportation, and coal mining, processing, and treatment; ef j∈Grid ,ef k∈Grid LHV represents the average emission factor of the power grid where the coal-fired power plant and the municipal solid waste incineration power plant are located, respectively. j The lower heating value of coal consumed by coal-fired power plants; The moisture content is ω d Average lower heating value of dried sludge; ECF DE This refers to the electricity consumption per ton of sludge for deep dewatering; d i,j This indicates the road transport distance between the wastewater treatment plant and the coal-fired power plant; d i,k This indicates the road transport distance between wastewater treatment plants and municipal solid waste incineration power plants. (2) Set constraints: (2-1). Upper limit constraint on the total amount of sludge resources that can be supplied by provider m. The total amount of sludge supplied by each wastewater treatment plant to coal-fired power plants and municipal solid waste incineration power plants within a given m shall not exceed the upper limit of the total sludge resources that the wastewater treatment plant can supply within a given m: Where, ∑ j∈J SW i,j,m ∑ is the total amount of sludge supplied by the wastewater treatment plant to all coal-fired power plants within a range of m; k∈K SW i,k,m The total amount of sludge supplied by the wastewater treatment plant to all municipal solid waste incineration power plants within a range of m. This represents the upper limit of the total amount of sludge resources that can be supplied by the provider m, and the calculation method is as described in claim 4; (2-2). Minimum constraint on sludge resource treatment volume within m when the disposal party applies sludge dry mixing disposal technology. Once a thermal power plant implements sludge co-firing retrofit, it must ensure a stable sludge supply within each m. The lower limit constraint on the total amount of sludge resources to be processed by the disposal party is used to ensure that the sludge supply within each m within M can stably meet the fuel demand of the power plant in a predetermined proportion. in, This indicates the minimum amount of sludge that can be disposed of within m when the dry mixing sludge disposal technology is applied in a coal-fired power plant. This indicates the minimum amount of sludge that can be disposed of within m when the sludge dry mixing treatment technology is applied in a municipal solid waste incineration power plant. The calculation method is as described in claim 5; (2-3). The maximum co-firing limit of sludge resources within m when the disposal party applies the sludge dry-mixing disposal technology. Due to the influence of pollutant emission concentration and the mixing ratio of sludge and raw materials on the combustion efficiency and operating efficiency of thermal power unit boilers, the mixing ratio of sludge and raw materials needs to be lower than the maximum sludge blending ratio; the amount of sludge blended by the disposal party within m should not exceed the maximum allowable blending amount; Where, ∑ i∈I SW i,j,m The total amount of sludge supplied by all wastewater treatment plants to the coal-fired power plant within a range of m; z j ∈ {0,1} is a binary variable representing whether a coal-fired power plant uses sludge dry mixing treatment technology, with 0 indicating no and 1 indicating the application of this technology and sludge treatment; MMR is the upper limit of the mixing ratio of sludge and raw materials / fuels, expressed as the mass percentage of sludge in the mixture; ∑ i∈I SW i,j,m The total amount of sludge supplied by all wastewater treatment plants to the municipal solid waste incineration power plant within a range of m; z k ∈{0,1} is a binary variable representing whether the municipal solid waste incineration power plant uses the sludge dry mixing treatment technology, where 0 indicates no and 1 indicates that the technology is used and the sludge is treated. (2-4). Lower limit constraint on the total economic profit of the consortium over the entire time period M. Compared to the current situation where sludge dry mixing treatment technology is not used, the total economic profit of the consortium consisting of the thermal power plant and all its associated wastewater treatment plants should not be less than τ over the entire time period M, in order to ensure the economic feasibility of the cooperation. Among them, P j The unit profit from co-firing sludge from coal-fired power plants; P k The unit profit from co-firing sludge in municipal solid waste incineration power plants; P i,t,m P represents the unit profit of the wastewater treatment plant for existing sludge disposal methods within a given m range; j P k P i,t,m These represent the unit profit of different sludge treatment technologies and belong to different categories of unit profit P; FC j τ represents the unit fuel cost of a coal-fired power plant; TC represents the sludge transportation cost; and τ represents the minimum additional profit that the consortium needs to achieve within M, calculated based on the original profit level without the adoption of sludge dry mixing technology.

8. The scheduling method according to claim 7, characterized in that, In step five, the unit profit P is calculated as follows: When the disposal party originally used multiple technologies to dispose of sludge, the unit profit P is the weighted average of the unit profits of each existing sludge disposal technology. Among them, R t IC t OM t These represent the unit revenue, unit investment cost, and unit operation and maintenance cost of the existing sludge treatment technology, respectively.

9. The scheduling method according to claim 8, characterized in that, In step five, the total number of Monte Carlo random samplings should be no less than 1000.

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