A method for optimizing the processing capacity of urban household garbage facilities based on mixed integer programming

Abstract

This invention discloses a method for optimizing the treatment capacity of urban solid waste facilities based on mixed integer programming. First, a grey system model is used to predict future urban solid waste collection volume. Then, based on the prediction results, an urban solid waste treatment capacity planning model is used to provide a long-term planning scheme. Next, a short-term decision is made on the planning scheme, and finally, the practical benefits of the decision are given. This process is repeated until the end of the entire planning period. This invention combines high efficiency and foresight: the model design is compact and lightweight, requiring only 0.01 seconds to complete calculations on a standard computer, greatly improving decision-making efficiency; through the innovative mechanism of "long-term planning guiding short-term decision-making," the model demonstrates excellent robustness in a 20-year long-term simulation; this invention can effectively avoid the chain reaction caused by the concentrated decommissioning of waste treatment plants and assist relevant departments in preparing funds in advance, achieving a smooth transition and sustainable development of the waste treatment system.

Description

Technical Field

[0001] This invention belongs to the field of urban domestic waste management and relates to a method for optimizing the processing capacity of urban domestic waste facilities, specifically a method for optimizing the processing capacity of urban domestic waste facilities based on mixed integer programming. Background Technology

[0002] Scientific management of urban solid waste treatment capacity is a crucial link in the solid waste planning system. Reasonable capacity planning not only helps reduce the overall cost of waste treatment but also provides fundamental support for promoting waste sorting and the effective implementation of various waste reduction policies. However, my country's current waste treatment system faces a significant overcapacity problem. Data shows that as of 2023, the national solid waste incineration capacity had increased 1.36 times compared to 2018, but the actual average utilization rate was only 66.7%, and it is showing a declining trend year by year. This phenomenon reflects a significant disconnect between the construction of treatment facilities and actual demand. Currently, the planning of urban solid waste treatment capacity in my country relies heavily on experience-based judgments or spontaneous market choices, lacking systematic forecasting, multi-scenario analysis, and dynamic adjustment mechanisms. This results in many construction plans being insufficient in terms of foresight, scientific rigor, practicality, and remediability. Therefore, it is urgent to construct a more scientific, dynamic, and adaptable urban solid waste treatment facility capacity planning method to achieve efficient resource allocation and sustainable development. Summary of the Invention

[0003] To address the capacity planning problem of municipal solid waste treatment facilities, including waste incineration plants, food waste treatment plants, and landfills, this invention provides a forward-looking, real-time-adjustable method for optimizing the treatment capacity of municipal solid waste facilities based on mixed integer programming. This method not only provides scientific, specific, and long-term measures regarding when, how many, and how many types of municipal solid waste treatment facilities to construct, but also calculates the environmental and economic impacts of the municipal solid waste treatment process. The environmental impacts include greenhouse gas emissions from municipal solid waste treatment and the volume reduction effect of municipal solid waste treatment.

[0004] The objective of this invention is achieved through the following technical solution:

[0005] A method for optimizing the processing capacity of urban solid waste facilities based on mixed integer programming includes the following steps:

[0006] Step 1: Predict the amount of urban domestic waste collected during the entire planning period using a grey system model, where:

[0007] The grey differential equation of the grey system model is in the form of:

[0008]

[0009] In the formula: The development coefficient; This is the gray action quantity; For the first The nth moment, that is, the nth moment of the year corresponding to the initial value of the time series. Year; for Real-time urban household waste collection volume; The background value is the nearest neighbor mean generation sequence of the accumulated generation sequence;

[0010] The formula for predicting the amount of municipal solid waste collected is shown below:

[0011]

[0012] In the formula, for Predicted values ​​of household waste collection volume at any given time; The amount of urban household waste collected at the first moment;

[0013] Step 2: Establish an environmental impact and economic benefit accounting model for four types of municipal solid waste treatment facilities with different capacity scales: waste incineration, landfill, anaerobic digestion of kitchen waste, and recycling.

[0014] Step 3: Using the planning model for the construction of various municipal solid waste treatment facilities, the planning period is divided into different planning stages, with a 5-year time interval as the planning duration. A mixed integer programming method is employed, combined with the prediction results of the grey system model, to determine the optimal treatment capacity within each planning stage. The formula for the planning model is expressed as follows:

[0015]

[0016] In the formula: For economic effect, The objective function for greenhouse gas emission reduction is... The objective function is to reduce the amount of urban household waste. , , for , , The economic value of , , , Represent stage The quality of household waste entering the corresponding treatment facilities, including: These represent recyclable waste, other waste, and kitchen waste, respectively. The number of planning periods; for The stage is based on the quality of recyclable waste; for Other waste quality at different stages; for The quality of kitchen waste at different stages; , , These represent the number of newly constructed processing facilities for each processing scale; To the minimum tolerable waste incineration capacity utilization rate; To the minimum tolerable utilization rate of food waste processing capacity; and They are respectively The initial waste incineration capacity and kitchen waste anaerobic digestion capacity that were built before the planning period; and They are respectively The processing capacity of newly built incineration plants and anaerobic digestion facilities for kitchen waste; Garbage density; for The capacity of the landfill during the period; For slag generation function; The remaining waste from slag resource utilization; This represents the initial capacity of the landfill. For the construction of new storage capacity for landfills; for Constraints on the recycling rate of recyclable waste during the period; for Constraints on the recycling rate of kitchen waste during the period; and They are respectively Lifespan of waste incineration plants and anaerobic digestion plants for food waste. and They represent New facilities for large-scale waste incineration plants and anaerobic digestion plants for kitchen waste are under construction. The number of moments;

[0017] Step 4: Adopt a long-term planning and short-term decision-making approach. Each decision uses only the planning scheme from the first planning period. The planning model is updated and revised based on data collected over time, ultimately yielding all decision results. Specifically, given that the planning scheme for the first planning period and the volume of municipal solid waste collected are determined, the actual revenue for that period is calculated using the practical revenue model, as shown in the following formula:

[0018]

[0019] In the formula: The decision has already been made regarding waste incineration capacity; The decision has been made regarding the anaerobic digestion capacity for kitchen waste; The decision already made regarding landfill capacity; , , This represents the actual volume of corresponding components of domestic waste collected during that period.

[0020] Compared with the prior art, the present invention has the following advantages:

[0021] This invention combines high efficiency with forward-looking vision. First, the model is compact and lightweight, requiring only 0.01 seconds to complete calculations on a standard computer, significantly improving decision-making efficiency. Second, by employing an innovative mechanism of "long-term planning guiding short-term decisions," the model demonstrates exceptional robustness in a 20-year long-term simulation, with an average relative decision loss rate of only 3.81% even in the face of drastic changes in waste volume. Finally, this invention addresses both the present and the future, effectively mitigating the chain reaction caused by the concentrated decommissioning of waste treatment plants and assisting relevant departments in preparing funds in advance, thus achieving a smooth transition and sustainable development of the waste treatment system. Attached Figure Description

[0022] Figure 1 This is a flowchart of a method for optimizing the processing capacity of urban solid waste facilities based on mixed integer programming.

[0023] Figure 2 This is a histogram of the frequency distribution of decision loss obtained after computer simulation.

[0024] Figure 3 This is a histogram of the frequency distribution of the relative loss rate of decisions, obtained after computer simulation. Detailed Implementation

[0025] The technical solution of the present invention will be further described below with reference to the accompanying drawings, but it is not limited thereto. Any modifications or equivalent substitutions to the technical solution of the present invention that do not depart from the spirit and scope of the technical solution of the present invention should be covered within the protection scope of the present invention.

[0026] This invention uses mixed-integer programming to find the optimal processing capacity of urban solid waste treatment facilities, formulates a forward-looking and sustainable construction plan for these facilities, and provides decision-making strategies. First, a grey system model is used to predict future urban solid waste collection volumes. Then, based on the prediction results, an urban solid waste treatment capacity planning model is used to provide a long-term planning scheme. Next, short-term decisions are made regarding the planning scheme, and finally, the practical benefits of the decisions are presented. This process is repeated until the entire planning period ends. Figure 1 As shown, the specific steps include the following:

[0027] Step 1: Predict the amount of urban domestic waste collected during the entire planning period using a grey system model. The specific steps are as follows: First, input the time series of past urban domestic waste collection volume. Then, use the least squares method to optimize the model parameters based on this time series to complete the model training. Finally, use this model to predict the future amount of urban domestic waste collected.

[0028] Urban solid waste treatment facilities are divided into four types: waste incineration, landfill, anaerobic digestion and recycling facilities for kitchen waste. Among them, waste incineration, landfill, and anaerobic digestion facilities for kitchen waste are divided into four typical capacity scales. For detailed parameters, please refer to Tables 2, 3 and 4.

[0029] Step 2: Establish an accounting model for the environmental impact and economic benefits of four types of municipal solid waste treatment facilities with different capacity scales: waste incineration, landfill, anaerobic digestion of kitchen waste, and recycling.

[0030] The environmental impacts are the reduction in greenhouse gas emissions and the reduction in municipal solid waste. The reduction in municipal solid waste is the difference in mass between the municipal solid waste entering the corresponding treatment facility and the municipal solid waste leaving the treatment facility. The reduction in municipal solid waste in landfills is 0.

[0031] Greenhouse gas emission reductions include emissions from wastewater treatment facilities, emissions from facility operation, emissions from purchased electricity, and emission reductions from production products. The emission reduction from production products is the average greenhouse gas emission from the production of that product nationwide. The greenhouse gas emission from that facility is calculated by subtracting the greenhouse gas emission reductions from the emission reductions from each other.

[0032] Economic benefits refer to the total profit from the treatment of urban domestic waste during the planning period. The costs of urban domestic waste treatment include labor costs, maintenance costs, material costs, sewage treatment costs, waste gas treatment costs, energy usage costs, waste residue treatment costs, and initial investment costs, as well as national waste treatment subsidies and product sales revenue.

[0033] Step 3: Establish an optimization method for the treatment capacity of urban domestic waste facilities. Using the planning model for the construction of various urban domestic waste treatment facilities, the planning period is divided into different planning stages, with a 5-year time interval as the planning duration, i.e., one period. The optimal treatment capacity within each planning stage is determined by the mixed integer programming method.

[0034] The reduction in municipal solid waste is the sum of all waste reductions over the entire planning period. Different treatment facilities have different reduction effects on different components of waste. The objective function for reducing municipal solid waste is as follows:

[0035]

[0036] In the formula: Representing each planning stage; functions , , , These represent reductions in municipal solid waste through recycling, waste incineration, landfill, and anaerobic digestion of kitchen waste. , , , Represent stage The quality of household waste entering the corresponding treatment facilities, including: These represent recyclable waste, other waste, and kitchen waste, respectively. The number of planning periods.

[0037] Similarly, the objective function for greenhouse gas emission reduction in municipal solid waste treatment is as follows:

[0038]

[0039] In the formula: function , , , These represent greenhouse gas emission reductions from recycling, waste incineration, landfill, and anaerobic digestion of kitchen waste, respectively.

[0040] The calculation of the economic effects of waste disposal is quite complex, and the formula is shown below:

[0041]

[0042] In the formula: function , , , The economic benefits of recycling, waste incineration, landfill, and anaerobic digestion of kitchen waste are respectively. , , These represent the number of newly constructed processing facilities for each processing scale. These represent different capacity scales of the processing facilities.

[0043] This invention defines the last planning period as The economic benefits include the residual value of planned and constructed facilities, the return on waste treatment, the variable costs of waste treatment, the fixed costs of waste treatment, and the investment in waste treatment facilities. The process of establishing the economic benefit function of waste incineration is as follows:

[0044]

[0045] In the formula: Variable costs for waste incineration plants; The fixed costs of waste incineration are determined by the number of employees at the waste treatment facility; Investment in the construction of waste incineration plants; The returns for waste incineration consist of treatment benefits, subsidies, and waste disposal fees. To process the residual value of the facility, multiply by This indicates that the residual value will be recovered in a lump sum. For incineration plant The planned lifespan of a large-scale processing plant For the planning period The residual value of the treatment facility is the percentage of its remaining lifespan in its original lifespan multiplied by the initial investment. This is the net present value discount function; The number of planning periods. yes The number of waste incineration plants already built at the beginning of the period is a predictable constant; yes period The number of planned waste incineration plants of a certain scale is determined by... The variables that make up the composition; for Fixed costs of large-scale waste incineration plants; for Investment in large-scale waste incineration plants.

[0046] The economic benefits of waste recycling are directly proportional to the amount of waste recycled. The economic benefit calculation for landfills and food waste treatment plants is similar to that for waste incineration plants, but the residual value calculation for landfills is as follows:

[0047]

[0048] In the formula: The residual value of landfill facilities; The remaining capacity of the landfill in the last planning phase; The value of a landfill per unit volume.

[0049] After determining the objective function of the urban solid waste facility treatment capacity optimization model, it is necessary to determine the constraints that limit the model optimization. The constraints determined by this invention include flow balance constraints, treatment capacity constraints, storage capacity change constraints, new construction constraints, recycling rate constraints, and implementation life constraints.

[0050] The flow balance constraint ensures that the collected waste can be fully processed by various treatment facilities. Assuming that all household waste collected in the city can be completely processed, the total amount of each type of waste flowing to each treatment facility should be consistent with the total amount of waste collected. Therefore, the flow balance constraint is as follows:

[0051]

[0052] In the formula: for The stage is based on the quality of recyclable waste; for Other waste quality at different stages; for Quality of kitchen waste at different stages.

[0053] The processing capacity constraint ensures that the amount of waste processed is lower than the processing capacity, while the utilization rate of the processing capacity of each facility can be higher than a certain level, thus guaranteeing the basic profitability of the municipal solid waste treatment facility. The processing capacity constraint of this invention is as follows:

[0054]

[0055] In the formula: To the minimum tolerable waste incineration capacity utilization rate; To the minimum tolerable utilization rate of food waste processing capacity; and They are respectively The initial waste incineration capacity and anaerobic digestion capacity of kitchen waste that were built before the planning period are predictable and constant. and They are respectively The capacity to construct new incineration plants and anaerobic digestion facilities for kitchen waste.

[0056] Municipal solid waste landfills are responsible for processing recalcitrant municipal solid waste. Since the landfill capacity is continuously depleted, the following constraints apply to capacity changes:

[0057]

[0058] In the formula: Garbage density; for The capacity of the landfill during the period; Let be the slag generation function, and be a linear function. The remaining waste from slag resource utilization; This represents the initial capacity of the landfill. For the construction of new storage capacity for landfills.

[0059] New constraints are introduced to ensure that the number of newly built waste treatment facilities does not exceed a certain limit within a certain period, preventing an unreasonable increase in the number of waste treatment facilities and reducing the computational burden on the model. The new constraints are as follows:

[0060]

[0061] This constraint ensures that no more than two of each type of waste treatment facility can be built within each five-year period, i.e., each phase.

[0062] Because the recycling of municipal solid waste is limited by waste sorting and the inherent properties of the waste itself, complete recycling is difficult. Therefore, it is necessary to set a constraint on the recycling rate of municipal solid waste. The formula for the recycling rate constraint is as follows:

[0063]

[0064] In the formula: Since the amount of recyclable waste actually recycled in a city is limited, and the recycling cost of some recyclable waste is relatively high, therefore, for Constraints on the recycling rate of recyclable waste during the period; for Constraints on the recycling rate of kitchen waste during the period.

[0065] Each waste treatment facility has a certain operational lifespan. The lifespan of a landfill depends on its capacity, while the lifespan of waste incineration and anaerobic digestion facilities depends on their planned lifespan at the time of construction. The number of waste treatment facilities in each planning period for waste incineration plants and anaerobic digestion plants is shown below:

[0066]

[0067] In the formula: and They are respectively Lifespan of waste incineration plants and anaerobic digestion plants for food waste. and They represent New facilities for large-scale waste incineration plants and anaerobic digestion plants for kitchen waste are under construction. The number of moments.

[0068] This planning decision-making approach adopts a long-term planning and short-term decision-making method, that is, each decision only uses the planning scheme of the first planning period, and the model is corrected by data updates as time goes on, so as to finally obtain all the decision results.

[0069] Example:

[0070] Taking Harbin as an example, the entire usage process of this invention will be described.

[0071] First, data on the amount of municipal solid waste collected in Harbin from 2000 to 2025 was obtained through a survey. This data was then used to train a grey system model to predict the amount of municipal solid waste collected over the next 20 years. The training process for the grey system model is as follows:

[0072] The time series data of Harbin's municipal solid waste collection volume is as follows:

[0073]

[0074] In the formula: This is the original time series data; The amount of raw household waste collected each year.

[0075] By accumulating the data to reduce the randomness of the model, a new time series is obtained, as shown in the following formula:

[0076]

[0077] In the formula: This is a time series data of accumulated household waste collection volume; Total amount of household waste collected for each year prior; for The nearest neighbor mean of the internal data generates a sequence (background value).

[0078] The grey differential equation of the model is in the form of the following formula:

[0079]

[0080] In the formula: The development coefficient (reflecting the development trend of the system); This is the grey effect (reflecting the relationship of data changes).

[0081] Parameters are estimated using the least squares method. and The formula is shown below:

[0082]

[0083] In the formula: For vectors .

[0084] At time The formula for predicting time is as follows:

[0085]

[0086] In the formula for Predicted values ​​for the amount of household waste collected at any given time.

[0087] After the forecast is completed, a planning model for the construction of various municipal solid waste treatment facilities is used to divide the 20-year treatment period into four decision periods, each with a duration of five years. The model determines the type, scale, and number of municipal solid waste treatment facilities to be built in each period, helping to determine the optimal annual waste treatment capacity for Harbin.

[0088] The planning models for the construction of various municipal solid waste treatment facilities have been detailed previously, and the formulas for these models are expressed below:

[0089]

[0090] In the formula: , , The economic value of three indicators, among which It is 1 yuan / yuan. It is 78 yuan / ton. It is 16 yuan / ton.

[0091] This model can return the optimal capacity construction plan under the prediction results. However, since the prediction model predicts a certain range, the plan may produce a large error. Therefore, a decision principle is needed to reduce the losses caused by the decision.

[0092] This model adopts the principle of long-term planning and short-term decision-making, using the planning scheme of the first period as the decision scheme of the first period. The decision parameters and prediction results are updated as time progresses until the end of the planning period, so as to reduce the decision loss of the model.

[0093] Given that the initial decision-making plan and parameters such as the amount of municipal solid waste collected are determined, the actual revenue model for this period is shown in the following formula:

[0094]

[0095] In the formula: The decision has already been made regarding waste incineration capacity; The decision has been made regarding the anaerobic digestion capacity for kitchen waste; The decision already made regarding landfill capacity; , , This represents the actual volume of corresponding components of domestic waste collected during that period.

[0096] Model validation:

[0097] To verify the planning effect of the model on real-world problems, this invention investigated the annual growth rate of domestic waste in Harbin and obtained the optimal probability density curve of these data through kernel density estimation. Based on this probability density curve, 100 sets of time series data on the possible domestic waste collection volume in Harbin for the next 20 years were generated. Using this time series data as the actual domestic waste collection volume value, the decision loss of this invention was simulated when the domestic waste collection volume was unknown.

[0098] First, simulated household waste collection data is fed into the planning model, which will design an optimal planning scheme based on the data and derive the optimal revenue value under the planning scheme.

[0099] The model will then gradually arrive at a decision-making plan by adopting a long-term planning and short-term decision-making approach when the future volume of municipal solid waste collection is unknown. The process is as follows: each prediction covers 4 periods, or 20 years, of the future volume of municipal solid waste collection, and provides the construction decision for the first period.

[0100] After the decision is made, the waste treatment capacity of each facility in Harbin during that period can be determined, and this capacity, along with the simulated amount of domestic waste collected, can be fed into the practical benefit model. The model will then calculate the first-period benefit under the simulated scenario of this decision.

[0101] As time progresses, unknown data and parameters become known, and are then fed back into the model for updates. This process is repeated until all decisions are made and the sum of all periodic returns is obtained, which is the decision return.

[0102] The optimal benefit minus the decision benefit equals the decision loss.

[0103] Figure 2 and Figure 3 The histogram of the decision loss probability distribution of the model is given, where the average relative loss of the model is 3.81%.

[0104] The environmental benefits and economic returns of each treatment facility are determined as follows:

[0105] The model has various important parameters that need to be calculated and determined, including the economic returns and environmental benefits of recycling, waste incineration plants, anaerobic digestion treatment plants for kitchen waste, and landfills.

[0106] Calculation of relevant benefit parameters for recycling:

[0107] The economic returns from recycling are calculated using the following formula:

[0108]

[0109] In the formula: In return for recycling; The average market value of recycled products is set at 752 yuan / ton.

[0110] The emission reduction from recycling of various types of waste is shown in Table 1. The average greenhouse gas emissions from waste recycling are calculated by weighting the proportion of each type of recyclable waste in the total recycled waste in Harbin. The calculation formula is shown below:

[0111]

[0112] In the formula: Emission reductions due to recycling; The emission reduction factor for waste recycling is calculated from the weighted average of recycled waste, and the calculated value is 2.36tco2 / t.

[0113]

[0114] This invention assumes that recycled waste produces no byproducts once it is utilized, i.e., 100% utilization. Therefore, the formula for calculating the reduction in waste generated through recycling is as follows:

[0115]

[0116] In the formula: Reduced waste production through recycling.

[0117] Calculation of benefits related to waste incineration:

[0118] The economic returns of waste incineration are calculated as follows:

[0119] The economic returns of waste incineration include subsidies for waste-to-energy generation, waste disposal fees, and, if combined heat and power (CHP) is implemented, heating revenue. In addition to these revenues, waste incineration plants incur various operating costs, including fly ash treatment costs, purchased electricity usage costs, leachate treatment costs, filter residue treatment costs, and flue gas treatment costs. The economic returns of waste incineration are calculated as follows:

[0120]

[0121] In the formula: Waste incineration disposal fee; For the revenue generated from electricity generation; For heating revenue; Cost of fly ash disposal; Cost of purchased electricity; Cost of landfill leachate treatment; Cost of slag disposal; Cost of flue gas treatment.

[0122] Through calculation .

[0123] The calculation of waste incineration power generation and the profit from power generation is as follows:

[0124]

[0125] In the formula: for Calorific value of solid waste; For the boiler's thermal efficiency; The thermoelectric conversion efficiency of the boiler; For the return of electricity generation; The plant's power consumption rate is set at 18%. Subsidies for power generation.

[0126] The calculations for heating supply and heating returns are as follows:

[0127]

[0128] In the formula: For providing heat; for Calorific value of solid waste; For the boiler's thermal efficiency; For boiler cogeneration efficiency; The total heating duration is measured in months. In return for heating services; For heating subsidies.

[0129] The costs of incineration fly ash disposal and purchased electricity usage are shown below:

[0130]

[0131] In the formula: Cost of fly ash disposal; The fly ash generation rate is set to 0.02. Costs for fly ash disposal; Cost of purchased electricity; For electricity generation; The external power consumption rate is 2% of the plant's electricity consumption. Cost of fly ash disposal.

[0132] The costs of treating incineration leachate are shown below:

[0133]

[0134] In the formula: For landfill leachate production; Moisture content before entering the factory; This refers to the moisture content after composting. The cost of treating landfill leachate; The cost of treating each ton of landfill leachate.

[0135] The slag disposal costs are shown below:

[0136]

[0137] In the formula: For slag production; It is ash content; Cost of slag disposal; The remaining rate of slag resource utilization; Costs for landfilling slag.

[0138] The costs for flue gas treatment are as follows:

[0139]

[0140] In the formula: For flue gas production; for The amount of flue gas produced by different types of waste is determined by the chemical composition of the waste. Costs for flue gas treatment; The cost of treating flue gas is set at 0.02 yuan / m³. 3 .

[0141] The greenhouse gas emission reduction from waste incineration is calculated as follows:

[0142]

[0143] In the formula: Greenhouse gas emissions avoided through waste-to-energy generation; Greenhouse gas emissions avoided for heating purposes; Greenhouse gas emissions from the incineration of waste mineral components; Greenhouse gas emissions from landfill leachate treatment; Greenhouse gas emissions from the use of purchased electricity.

[0144] Calculated: .

[0145] The greenhouse gas emissions from the incineration of mineral components in municipal solid waste are calculated as follows:

[0146]

[0147] In the formula: Greenhouse gas emissions from the incineration of waste mineral components; For the first The dry matter mass of the waste; For the first The carbon content of each substance; for The carbon content of each type of waste element and each type of mineral carbon; for Oxidizing factors in waste; For a fixed period of time.

[0148] Before entering the incinerator, waste typically undergoes composting to reduce its moisture content, which generates leachate. The treatment of leachate produces greenhouse gases such as N2O and CH4. The calculation of greenhouse gas emissions from leachate in this invention is as follows:

[0149]

[0150] In the formula: To treat greenhouse gas emissions from landfill leachate; per ton Leachate production factor for waste types; Leachate discharge factor; Moisture content before entering the factory; This represents the moisture content after composting.

[0151] When businesses need to purchase electricity from external sources for maintenance or other reasons, they generate greenhouse gas emissions. The formula for calculating emissions from purchased electricity is shown below:

[0152]

[0153] In the formula: Greenhouse gas emissions from the use of purchased electricity; This refers to the amount of electricity used from external purchases. The emission factor is the emission factor for purchased electricity, and the national average carbon emissions per unit of electricity generation. For electricity generation; The proportion of purchased electricity to total power generation is taken as 0.36%.

[0154] Waste incineration is an environmentally friendly energy source, as the recovered energy avoids greenhouse gas emissions from industries such as thermal power and heating. The calculations are as follows:

[0155]

[0156] In the formula: To generate electricity while avoiding greenhouse gas emissions; To avoid greenhouse gas emissions during heating; The plant's power consumption rate is set at 18%. This represents the national average greenhouse gas emissions per unit of electricity generated. For providing heat; This represents the national average greenhouse gas emissions from heating.

[0157] The reduction in waste by incineration is calculated as follows:

[0158] .

[0159] In the formula: For a specific time period; the coefficient is the volume reduction rate of each type of waste under incineration treatment.

[0160] The relevant benefit parameters for anaerobic digestion of kitchen waste are calculated as follows:

[0161] The economic return of anaerobic digestion of kitchen waste includes kitchen waste disposal fees, profits from biogas production, and operating costs for kitchen waste treatment, including purchased electricity costs, leachate treatment costs, electricity costs, and material costs. The economic return of anaerobic digestion of kitchen waste is calculated as follows:

[0162]

[0163] In the formula: The cost of food waste disposal is obtained from the local government. The economic returns from biogas power generation; This refers to the operating costs of food waste treatment.

[0164] Calculated: .

[0165] The economic return on biogas power generation is calculated as follows:

[0166]

[0167] In the formula: The gas production rate of kitchen waste; Biogas density; Biogas collection rate; The calorific value of biogas; For biogas power generation thermal efficiency; Subsidies for power generation.

[0168] The operating cost of kitchen waste includes many items, which are difficult to explain in detail. Therefore, an average value of 318.2 yuan / ton is taken.

[0169] The greenhouse gas emissions from food waste treatment are calculated as follows:

[0170]

[0171] In the formula: To avoid greenhouse gas emissions for biogas power generation, the calculations are similar to those for waste incineration. The calculation of greenhouse gas emissions from the use of purchased electricity is similar to that for waste incineration. Greenhouse gas emissions from kitchen waste wastewater treatment; The wastewater production factor is calculated from changes in moisture content, similar to that of waste incineration. Gas production from kitchen waste; The percentage of methane in biogas; The density of methane; Biogas collection rate; The waste reduction rate for composting; It is a greenhouse gas emission factor for aerobic composting.

[0172] Calculated: .

[0173] The reduction in waste volume due to anaerobic digestion of kitchen waste is calculated as follows:

[0174]

[0175] In the formula: the coefficient is the reduction rate of anaerobic digestion in landfill, and it is the product of the reduction rate of anaerobic digestion and the reduction rate of subsequent aerobic composting.

[0176] The environmental benefits, i.e., the economic returns, of landfilling are calculated as follows:

[0177] The economic returns of landfill are calculated as follows:

[0178]

[0179] In the formula: Waste disposal fees for landfills; The operating costs of landfills include electricity, materials, and leachate treatment.

[0180] Calculated: .

[0181] Greenhouse gas emissions from landfill are calculated as follows:

[0182]

[0183] In the formula: for The percentage of biodegradable organic matter in waste; for The proportion of anaerobic degradable organic matter in waste; The correction factor is set to 1. The percentage of methane in biogas; For landfill biogas collection rate; It is the greenhouse gas equivalent of methane.

[0184] Calculated: .

[0185] This invention assumes that landfilling cannot reduce the amount of waste, and the amount of waste avoided from landfilling is 0.

[0186] In addition to the parameters mentioned above, there are some additional parameters of this invention that are not specified, as shown in Tables 2, 3, and 4.

[0187]

[0188]

[0189]

[0190] The fixed costs, investment, and residual value of municipal solid waste treatment can be calculated using the parameters in Tables 2, 3, and 4.

Claims

1. A method for optimizing the processing capacity of urban solid waste facilities based on mixed integer programming, characterized in that... The method includes the following steps: Step 1: Predict the amount of urban domestic waste collected during the entire planning period using a grey system model; Step 2: Establish an environmental impact and economic benefit accounting model for four types of municipal solid waste treatment facilities with different capacity scales: waste incineration, landfill, anaerobic digestion of kitchen waste, and recycling. Step 3: Using the planning model for the construction of various municipal solid waste treatment facilities, the planning period is divided into different planning stages, with a 5-year time interval as the planning duration. The mixed integer programming method is used, combined with the prediction results of the grey system model, to determine the optimal treatment capacity in each planning stage. Step 4: Adopt a long-term planning and short-term decision-making approach. That is, each decision only uses the planning scheme of the first planning period. The planning model is revised by data updates as time progresses, and finally all decision results are obtained.

2. The method for optimizing the processing capacity of urban domestic waste facilities based on mixed integer programming according to claim 1, characterized in that... In step 1, the grey differential equation of the grey system model is in the form of: In the formula: The development coefficient; This is the gray action quantity; for Real-time urban household waste collection volume; This is the background value.

3. The method for optimizing the processing capacity of urban domestic waste facilities based on mixed integer programming according to claim 1, characterized in that... In step 1, the formula for predicting the amount of urban domestic waste collected is as follows: In the formula, for Predicted values ​​of household waste collection volume at any given time; The development coefficient; This is the gray action quantity; This represents the amount of urban household waste collected at the first moment.

4. The method for optimizing the processing capacity of urban domestic waste facilities based on mixed integer programming according to claim 1, characterized in that... In step 3, the formula for the planning model is expressed as follows: In the formula: For economic effect, The objective function for greenhouse gas emission reduction is... The objective function is to reduce the amount of urban household waste. , , for , , The economic value of , , , Represent stage The quality of household waste entering the corresponding treatment facilities, including: These represent recyclable waste, other waste, and kitchen waste, respectively. The number of planning periods; for The stage is based on the quality of recyclable waste; for Other waste quality at different stages; for The quality of kitchen waste at different stages; , , These represent the number of newly constructed processing facilities for each processing scale; To the minimum tolerable waste incineration capacity utilization rate; To the minimum tolerable utilization rate of food waste processing capacity; and They are respectively The initial waste incineration capacity and kitchen waste anaerobic digestion capacity that were built before the planning period; and They are respectively The processing capacity of newly built incineration plants and anaerobic digestion facilities for kitchen waste; Garbage density; for The capacity of the landfill during the period; For slag generation function; The remaining waste from slag resource utilization; This represents the initial capacity of the landfill. For the construction of new storage capacity for landfills; for Constraints on the recycling rate of recyclable waste during the period; for Constraints on the recycling rate of kitchen waste during the period; and They are respectively Lifespan of waste incineration plants and anaerobic digestion plants for food waste. and They represent New facilities for large-scale waste incineration plants and anaerobic digestion plants for kitchen waste are under construction. The number of moments.

5. The method for optimizing the processing capacity of urban domestic waste facilities based on mixed integer programming according to claim 4, characterized in that... The objective function for reducing urban household waste is shown below: In the formula: Representing each planning stage; functions , , , These represent reductions in municipal solid waste through recycling, waste incineration, landfill, and anaerobic digestion of kitchen waste. , , , Represent stage The quality of household waste entering the corresponding treatment facilities, including: These represent recyclable waste, other waste, and kitchen waste, respectively. The number of planning periods.

6. The method for optimizing the processing capacity of urban solid waste facilities based on mixed integer programming according to claim 4, characterized in that... The objective function for the greenhouse gas emission reduction is shown below: In the formula: function , , , These represent greenhouse gas emission reductions from recycling, waste incineration, landfill, and anaerobic digestion of kitchen waste, respectively.

7. The method for optimizing the processing capacity of urban solid waste facilities based on mixed integer programming according to claim 4, characterized in that... The formula for calculating the economic effect is as follows: In the formula: function , , , The economic benefits of recycling, waste incineration, landfill, and anaerobic digestion of kitchen waste are respectively. , , These represent the number of newly constructed processing facilities for each processing scale. These represent different capacity scales of the processing facilities.

8. The method for optimizing the processing capacity of urban domestic waste facilities based on mixed integer programming according to claim 4, characterized in that... In step 4, given that the planning scheme and the amount of domestic waste collected in the first planning period are determined, the actual revenue for that period is calculated using the practical revenue model, as shown in the following formula: In the formula: The decision has already been made regarding waste incineration capacity; The decision has been made regarding the anaerobic digestion capacity for kitchen waste; The decision already made regarding landfill capacity; , , This represents the actual volume of corresponding components of domestic waste collected during that period.

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