A mixed kitchen garbage energy processing method

Abstract

The application provides a mixed kitchen garbage energy processing method and relates to the field of kitchen garbage processing, which collects and codes characteristic parameters to form a G sequence by constructing a kitchen garbage collection grid unit, obtains a target grid unit suitable for pyrolysis by combining a multi-parameter fusion evaluation method, and performs fine grouping and mixed processing on the kitchen garbage, and finally performs resourceization path classification according to the pyrolysis residue characteristics to form an end-to-end energy processing system. While ensuring the pyrolysis efficiency, the adaptability of the processing process to the heterogeneity of raw materials is enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of kitchen waste treatment, and more particularly, to a mixed kitchen waste energy treatment method. BACKGROUND

[0002] Under the current guidance of the "double carbon" target, kitchen waste energy treatment technology has gradually become an important research direction in the field of solid waste resource utilization. Kitchen waste contains a large amount of organic matter, moisture, and a certain proportion of oil, cellulose, starch, and protein, and has high heat value potential and resource value. Traditional treatment methods mainly include landfill, composting, and anaerobic digestion, etc., but the above methods generally have limitations such as long treatment cycle, low energy recovery efficiency, and large greenhouse gas emissions. In recent years, pyrolysis technology has gradually become one of the effective paths for kitchen waste treatment due to its short process time, strong product diversity, and high energy concentration release efficiency. However, kitchen waste has complex composition and significant temporal and spatial volatility, and the key parameters such as moisture content, oil proportion, and carbon-hydrogen ratio of different batches differ greatly, so the energy efficiency under a single pyrolysis reaction condition is always limited. Therefore, it is urgent to explore an energy treatment method with better adaptability and process intelligent grouping capability.

[0003] The existing kitchen waste pyrolysis technology generally faces two key bottleneck problems: on the one hand, the raw material pretreatment lacks pertinence, and different properties of kitchen waste are usually treated uniformly, which is difficult to meet the requirements of the pyrolysis process for the stability of the raw material, resulting in large fluctuation of pyrolysis efficiency and unstable quality of carbonized residues; on the other hand, although some current researches try to use single indicators such as moisture content and oil content for sorting, they ignore the comprehensive evaluation of deep composite parameters such as carbon-hydrogen ratio and fiber proportion, and it is difficult to realize the scientific grouping of kitchen waste in the dimension of maximum energy utilization efficiency. In addition, the pyrolysis residue treatment still adopts a rough classification strategy, which fails to effectively distinguish the difference in its reuse value, thereby limiting the diversification of subsequent resource utilization paths.

[0004] Therefore, a mixed kitchen waste energy treatment scheme is needed. SUMMARY

[0005] In order to solve the above technical problems, the present application is proposed. The present application provides a mixed kitchen waste energy treatment method.

[0006] According to one aspect of the present invention, a method for treating mixed food waste for energy conversion is provided, comprising: constructing multiple food waste collection grid units within a treatment area; collecting characteristic parameters of the food waste in each grid unit and forming a digital code G sequence containing the characteristic parameters; evaluating the gasification calorific value of the food waste in each grid unit according to the G sequence, obtaining a target grid unit number sequence T suitable for efficient pyrolysis treatment, and outputting a corresponding food waste component identifier; and, based on the component identifier corresponding to the number sequence T, allocating the food waste from the multiple grid units to multiple mixed treatment batches B1 to B2, where the parameter differences within each group are controlled within a preset range. k In this context, k represents the number of batches in the mixed processing; the recombined mixed processing batches B1 to B2 are... k The residues are fed into pyrolysis reactors and subjected to pyrolysis reactions at corresponding reaction temperature and retention time parameters. Resource recycling pathways are then classified according to the characteristics of the pyrolysis residues.

[0007] Furthermore, the formation of the grid unit includes: dividing the processing area into multiple independent grid units and constructing a corresponding grid unit number sequence; setting up a kitchen waste collection device with automatic weighing and primary component detection in each grid unit; processing the collection feature parameters of the collection device according to a preset time period and converting them into structured coding parameters; and binding the coding parameters with the corresponding grid unit number to form a structured mapping pair.

[0008] Furthermore, the collected characteristic parameters include moisture content parameters, oil content parameters, carbon-to-hydrogen ratio parameters, and fiber content parameters.

[0009] Furthermore, the gasification calorific value assessment includes: extracting the water content parameter and C-H ratio parameter of each grid cell from the G sequence, constructing a binary correlation interval map, marking the gasification suitability level according to the relative position of each coordinate point in the map, initially screening grid cell numbers with a level higher than the preset standard level to form a candidate set; in the candidate set, extracting the oil content parameter and fiber content parameter of each grid cell, and calculating the pyrolysis contribution index Et.

[0010] Furthermore, the pyrolysis contribution index Et uses the oil content as the numerator; the fiber content is added by one and then raised to the power as the first part of the denominator, where the power exponent is the fiber inhibition coefficient α, which represents the nonlinear interference of fiber on the heat release of oil; multiplied by the moisture content and then multiplied by the moisture content correction factor β, and then added to 1 as the second part of the denominator, which represents the inhibitory effect of moisture on pyrolysis efficiency; the two inhibition terms are multiplied together as the overall denominator.

[0011] Furthermore, grid cells with Et values ​​higher than a set threshold are updated to number sequence T; and the corresponding component parameters of each grid cell in number sequence T in sequence G are combined sequentially to generate a food waste component identifier.

[0012] Further, the allocation of the mixed processing batches comprises: extracting the composite component identifiers corresponding to each grid cell in the number sequence T, and classifying the cells with carbon-hydrogen ratio parameters higher than a set boundary value into a high-heat group, and the rest into a general-heat group; respectively, for the grid cells in the high-heat group and the general-heat group, statistics the mean and range of the moisture content parameters and fiber content parameters, and according to the closeness, allocate to the preliminary mixed groups to form a plurality of mixed processing candidate groups with the parameter difference within a preset tolerance range Δ.

[0013] Further, the allocation of the mixed processing batches further comprises: in each mixed processing candidate group, further balancing the oil content parameters, so that the final oil content parameter fluctuation rate in each group does not exceed a set proportion, and finally forming a plurality of target mixed processing batches.

[0014] Further, the resource recycling path classification comprises: collecting each mixed processing batch B1 to B k After the pyrolysis reaction, the residue image and the weighing data are used to construct a residue characteristic index set; each data unit in the characteristic index set is marked as three types of recycling target types according to a preset classification rule; according to the recycling target types, the residues are guided to a carbon-based material processing line, an organic soil improvement line or a secondary treatment storage line, respectively, to complete the output mapping of the residue resource recycling path.

[0015] Further, the three types of recycling target types include carbon-making type, fertilizer-applicable type and non-directly resourceable type.

[0016] Compared with the prior art, the mixed kitchen waste energy processing method provided by the present application constructs a kitchen waste collection grid cell, collects and encodes feature parameters to form a G sequence, combines a multi-parameter fusion evaluation method to obtain target grid cells suitable for pyrolysis, and performs fine grouping and mixed processing on the kitchen waste, and finally performs resource classification according to the pyrolysis residue characteristics to form an end-to-end energy processing system. The method ensures the pyrolysis efficiency while enhancing the adaptability of the processing process to the heterogeneity of the raw materials. Compared with the existing technology, the present application realizes the whole-link optimization from front-end identification to back-end utilization. It is of great significance to improve the energy recycling efficiency and resource utilization level of kitchen waste, and to promote the development of green solid waste treatment technology. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to make the technical solutions in the embodiments of the present application or the prior art clearer, the accompanying drawings needed in the embodiments or prior art description will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort. In the drawings:

[0018] Figure 1 Flow chart of the mixed kitchen waste energy processing method according to the embodiment of the present application.

[0019] Figure 2 Flow chart of the formation of the grid unit in the mixed kitchen waste energy processing method according to the embodiment of the present application.

[0020] Figure 3 Flow chart of the distribution of the mixed processing batch in the mixed kitchen waste energy processing method according to the embodiment of the present application. DETAILED DESCRIPTION

[0021] In the following, the example embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein.

[0022] As described in the above background, the existing kitchen waste pyrolysis technology generally faces two key bottleneck problems: on the one hand, the pretreatment of raw materials lacks pertinence, and different properties of kitchen waste are usually treated uniformly, which is difficult to meet the requirements of the pyrolysis process on the stability of raw materials, resulting in large fluctuation of pyrolysis efficiency and unstable quality of carbonization residues; on the other hand, although some current researches try to use single indicators such as water content and oil content for sorting, they ignore the comprehensive evaluation of deep composite parameters such as carbon-hydrogen ratio and fiber proportion, and it is difficult to realize the scientific grouping of kitchen waste in the dimension of maximizing energy utilization efficiency. In addition, the pyrolysis residue treatment still adopts the rough classification strategy, which fails to effectively distinguish the difference in recycling value, thereby limiting the diversified selection of subsequent resourceization path. Therefore, a mixed kitchen waste energy processing scheme is needed.

[0023] Figure 1 Flow chart of the mixed kitchen waste energy processing method according to the embodiment of the present application. As Figure 1As shown, in the mixed kitchen garbage energy processing method, S1: a plurality of kitchen garbage collection grid units are constructed in a processing area, characteristic parameters of kitchen garbage in each grid unit are collected respectively, and a digital code G sequence containing the characteristic parameters is formed; S2: the kitchen garbage in each grid unit is evaluated according to the G sequence, a target grid unit number sequence T suitable for efficient pyrolysis processing is obtained, and the corresponding kitchen garbage component identifier is output; S3: according to the component identifier corresponding to the number sequence T, the kitchen garbage in the plurality of grid units is distributed to a plurality of groups, and the parameter difference of the mixed processing batch B1 to B k k is the number of mixed processing batches; S4: the reorganized mixed processing batches B1 to B k are respectively sent into a pyrolysis reaction device for pyrolysis reaction under corresponding reaction temperature parameters and retention time parameters; and S5: resource recycling path classification is performed according to the pyrolysis residue characteristics.

[0024] In an embodiment of the present application, S1 specifically includes: a plurality of kitchen garbage collection grid units are constructed in a processing area, and the moisture content parameter, the oil content parameter, the carbon-hydrogen ratio parameter and the fiber content parameter of the kitchen garbage in each grid unit are collected respectively to form a digital code G sequence containing the above four characteristic parameters.

[0025] From the perspective of technical architecture, the design of step S1 is not only a physical division of the garbage area, but more importantly, the area, data and processing path are organically linked through information means. The traditional kitchen garbage processing method is mainly based on centralized collection and extensive classification, which lacks detailed identification of garbage distribution characteristics and component composition, resulting in difficulty in accurately matching the characteristics of raw materials in subsequent processing methods, causing low energy efficiency or resource waste. The present application introduces the concept of "grid unit", which is essentially a regional management technology that combines spatial geographic information and heterogeneous characteristics of garbage components. This technology divides the spatial grid in the processing area reasonably, and configures a terminal device with component collection capability for each grid unit, so that each grid becomes a logical unit that binds information and raw material attributes, realizing high-precision data expression of raw material characteristics.

[0026] As shown in Figure 2 , the formation of the grid unit includes: dividing the processing area into a plurality of independent grid units, constructing a corresponding grid unit number sequence; setting a kitchen garbage collection device with automatic weighing and primary component detection in each grid unit; data processing of the collected characteristic parameters of the collection device according to a preset time period, converting to structured coding parameters; binding the coding parameters and the corresponding grid unit number to form a structured mapping pair.

[0027] Specifically, according to the density of kitchen waste sources, transportation paths and geographical distribution and other parameters, the processing area is divided into multiple independent grid units. It should be noted that priority is given to areas with large amounts of garbage, ensuring that each grid unit after division has representativeness for data sampling and component difference analysis, and avoiding data deviation or resource allocation imbalance due to unreasonable regional division. In the process of constructing the grid, GIS (Geographic Information System) or intelligent zoning algorithm is also introduced to assist in division, in order to realize the scientificity and practicality of spatial division, that is, pre-zoning according to yield, and then GIS refinement. At the same time, each grid unit is assigned a unique number, that is, the corresponding grid unit number sequence N1 to N n , for binding and tracking of garbage attribute data, where n is the number of grid units. This number not only facilitates data tracking and management, but also provides an identification basis for subsequent establishment of grid data-processing path mapping relationships.

[0028] After completing the grid division, kitchen waste collection devices with automatic weighing and primary component detection functions are deployed in each grid unit. Conventional garbage collection equipment is mostly mechanical or centralized transmission and processing devices, which can only collect total amount information and cannot obtain component characteristics. However, the collection devices deployed in the present application integrate multiple sensing modules, such as near-infrared spectroscopy-based moisture content detection, optical density-based preliminary identification of grease, and conductivity and organic reaction ratio-based estimation of carbon-hydrogen ratio, to quickly collect key parameter values in kitchen waste. At the same time, these collection devices combine with automatic weighing modules to record the mass of the garbage put in, ensuring the accuracy of subsequent parameter normalization and unit mass estimation. In terms of parameter selection, moisture content, grease content, carbon-hydrogen ratio and fiber content are set as the four types of key indicators that can efficiently reflect the energy potential of kitchen waste selected by the present application. Among them, moisture content directly determines the energy loss and reaction efficiency in the pyrolysis process, grease content affects gas production efficiency and oil product ratio, carbon-hydrogen ratio is the core chemical indicator for determining gasification heat value, and fiber content is related to solid residue and thermal stability.

[0029] The above four types of parameters are subjected to sampling normalization, unit conversion and time alignment processing by the data preprocessing module, which not only represents the average physical and chemical properties of kitchen waste in the current time period, but also serves as a basis for judging the selection of garbage processing methods for the grid. In actual application, due to the time dynamics and source heterogeneity of garbage components, parameter collection needs to set a reasonable time period, such as every hour or every two hours as a sampling window, and combine with a sliding window mechanism to dynamically smooth the data within a short period, in order to reduce the interference of accidental factors on the overall evaluation results.

[0030] According to the preset time period, the parameters collected by the grid collection device are subjected to data processing and converted into structured coded parameters G1 to Gn , to express the corresponding grid cell N1 to N n of kitchen garbage characteristics; the coding parameters G1 to G n and its corresponding grid cell number N1 to N n binding, forming a structured mapping pair M1 to M n , for subsequent partition processing strategy selection and energy path planning. It is known that the structured mapping can not only be used for the selection of pyrolysis adaptive grid cells in the subsequent steps, but also provide quantitative support for the scheduling optimization of regional heat treatment devices. Traditional kitchen garbage distribution is mainly based on transportation convenience and equipment load balancing, ignoring the direct impact of raw material components on treatment reaction effect. The mapping system of the present application can realize the scheduling logic change from "garbage characteristics to treatment path" instead of "garbage location to path", which is a key information bridge for garbage energy utilization.

[0031] Further, in the data storage structure, the mapping pair can be stored in the form of a database table or a structured object, and an update strategy is set, such as automatic overwrite update after each data sampling or time series data set construction according to time axis accumulation, to facilitate subsequent trend analysis, training or energy efficiency estimation. It is worth noting that the generation of the feature parameter G sequence is not limited to numerical data records, but can be further extended to composite structures such as graph type expression, symbol type label, etc., to provide a structural basis for introducing graph neural networks, spatial modeling and other advanced analysis in the future.

[0032] In summary, step S1 builds regional grid cells, configures component collection devices, collects four types of feature parameters including moisture content, oil content, carbon-hydrogen ratio and fiber content, and performs digital coding to form a digital coding G sequence. The sequence and the numbered grid cells establish a structured mapping relationship, thereby realizing accurate expression of the spatial distribution and physical and chemical properties of kitchen garbage at the source stage. This scheme not only builds a quantifiable data foundation system, but also provides support for subsequent efficient calorific value evaluation and mixed processing path selection, which is significantly better than the existing technology of "first collection and then measurement" and "region-based delivery". It is an important basic step to realize the intelligent processing of garbage energy, data and fine processing.

[0033] In an embodiment of the present application, S2 specifically comprises: according to the G sequence, evaluating the gasification calorific value of the kitchen garbage of each grid cell, obtaining a target grid cell number sequence T suitable for efficient pyrolysis treatment, and outputting the corresponding kitchen garbage component identifier.

[0034] The gasification heat value evaluation includes: extracting the water content parameter and the carbon-hydrogen ratio parameter of each grid cell from the G sequence, constructing a binary correlation interval atlas, and marking the gasification suitability level according to the relative position of each coordinate point in the atlas, preliminarily screening the grid cell numbers with a level higher than a preset standard level to form a candidate set; in the candidate set, the oil content parameter and the fiber content parameter of each grid cell are extracted, and a pyrolysis contribution index Et is calculated.

[0035] The coordinate point is the coordinate point corresponding to the water content parameter Mh of each grid cell as the horizontal axis and the carbon-hydrogen ratio parameter Ch as the vertical axis on a two-dimensional plane. In other words: each grid cell occupies a unique position in the two-dimensional atlas composed of (Mh, Ch), and the relative position of the point (such as falling in the high Ch and low Mh area) reflects its potential heat value characteristics under the gasification condition.

[0036] Illustratively, the division of the atlas is based on the research results of the pyrolysis behavior of kitchen waste - the heat value is positively affected by the carbon-hydrogen ratio and negatively affected by the water content, forming a spatial mapping relationship of gasification suitability. Therefore, in the two-dimensional atlas, the area with high Ch and low Mh usually corresponds to better gasification performance, and the atlas is divided into three level blocks A (high suitability), B (medium suitability), and C (low suitability) according to the present application, which represent the gradient distribution of the pyrolysis potential.

[0037] It should be noted that the atlas division is not an equal interval or a simple clustering method, but a non-uniform partitioning of the atlas by combining experimental data from the past years of kitchen waste thermochemical treatment process and using an empirical boundary function, in order to better meet the requirements of actual pyrolysis reaction conditions. This method breaks out of the traditional linear scoring or fuzzy logic weighting method, and maps the material gasification tendency in the atlas space geometry distribution, which is a more intuitive and recognizable gasification evaluation method. After marking is completed, the system screens all grid cell numbers with level A to form a candidate set, which is the range of kitchen waste units that can be preliminarily accepted for pyrolysis.

[0038] Then, in the candidate set, the corresponding oil content parameter Fg and fiber content parameter Fb of each grid cell are further extracted for calculating the pyrolysis contribution index Et.

[0039] The pyrolysis contribution index Et takes the oil content as a molecular term; the fiber content plus one is taken as the first part of the denominator, where the power index is the fiber inhibition coefficient α, representing the nonlinear interference of fiber on oil heat release; the water content multiplied by the water content correction factor β and then plus 1 as the second part of the denominator, representing the inhibition effect of water on pyrolysis efficiency; and the multiplication of the two inhibition terms as the overall denominator.

[0040] Exemplarily, the design of the pyrolysis contribution index aims to quantify the potential energy release capacity of kitchen waste in the pyrolysis reaction, and a nonlinear coupling relationship of oil content, fiber content and water content is used for quantification, so as to comprehensively characterize the potential energy release capacity of kitchen waste in the pyrolysis process. Specifically, the following combination relationship is used:

[0041]

[0042] It should be noted that in the calculation formula, the molecular part represents the heat release potential of the combustible oil component; the first layer inhibition term (Fb+1) α The simulation of the interference of the fiber structure "wrapping retardation" phenomenon on the heat release of oil, +1 guarantees the numerical stability of the formula, and alpha strengthens the nonlinear effect; the second layer inhibition term (1+βMh) introduces the water content as a heat value dilution factor, simulates the negative effect of the endothermic evaporation effect of water in the pyrolysis process on the oil pyrolysis efficiency. The index structure has more practical pertinence and differential expression ability compared with the traditional heat value evaluation model, avoids the "fuzzification" problem of the physical property characteristics caused by the simple weighted average of oil, fiber and water, and instead models the pyrolysis feasibility in the form of "release potential / retardation factor", which is suitable for fine screening and batch optimization of multi-component mixed kitchen waste.

[0043] Further, the grid cells with Et higher than the set critical value are updated as the target grid cell number sequence T; and the corresponding component parameters of each grid cell in the sequence T in the sequence G are combined in order to generate the kitchen waste component identifier.

[0044] Exemplarily, the critical value threshold of Et is set as the threshold for entering the final target set. The numerical value of the critical value threshold can be obtained by training historical experimental data, or dynamically adjusted according to the production capacity demand of the target processing device. When a certain grid cell has Et≥critical value threshold, it is considered that the cell has excellent pyrolysis contribution potential, and the system will include the number in the final target grid cell number sequence T. The sequence not only represents the distribution number of each to-be-processed unit in space, but also directly serves as the basic input for subsequent mixed grouping and pyrolysis equipment scheduling.

[0045] In order to further improve the operation efficiency and data processing consistency of the system, the present application extracts the complete parameter feature vector of each grid cell in the target sequence T from the corresponding G sequence parameters, for example, G i =(Ch i ,Mh i ,Fg i ,Fb i), that is, containing Mh, Fg, Ch, Fb four parameters, and splicing in the order of numbering to generate a unified kitchen waste composition identifier. This composition identifier is not only used for subsequent steps to control the component balance of the mixed processing batch, but also can be used as a unique code to track the calorific value characteristics of the raw material in the overall operation of the system, effectively improving the process closed loop and controllability from evaluation to processing of the entire system.

[0046] In summary, this step introduces a two-dimensional map partitioning strategy combined with a nonlinear pyrolysis contribution index construction method, discarding the rough algorithm in the prior art that simply relies on weighted average or classification discrimination, effectively fusing the coupling relationship between different parameters of kitchen waste, and realizing a more fine and adaptive identification mechanism for gasification calorific value characteristics, providing an engineering applicable support foundation for subsequent batch reorganization and pyrolysis processing, and having strong engineering practical value and industrial promotion potential.

[0047] S3: According to the composition identifier corresponding to the numbering sequence T, the kitchen waste in the plurality of grid units is allocated to the mixed processing batches B1 to B k , wherein k is the number of mixed processing batches.

[0048] Preferably, the present application adopts a three-stage mixed strategy of stratification-clustering-balance when allocating mixed processing batches, ensuring that the thermal reaction potential and physical homogeneity of the group parameters are considered.

[0049] As shown in Figure 3 , allocating the mixed processing batches includes: extracting the composite composition identifier corresponding to each grid unit in the numbering sequence T, and grouping the units with a carbon-hydrogen ratio parameter higher than the set boundary value into a high-heat group, and the rest into a general heat group; respectively, for the grid units in the high-heat group and the general heat group, statistics the mean and range of the moisture content parameter and the fiber content parameter, and allocate them to the preliminary mixed group according to their closeness, forming a plurality of mixed processing candidate groups with the difference between the group parameters less than the preset tolerance range Δ; in each mixed processing candidate group, further balance the oil content parameter, so that the final oil content parameter fluctuation rate in each group does not exceed the set proportion, and finally form a plurality of target mixed processing batches.

[0050] For example, the first stage: initial grouping of the heat value potential based on the carbon-hydrogen ratio parameter Ch value: the present application constructs a high heat value grouping strategy, that is, taking the carbon-hydrogen ratio parameter Ch as the main control factor to perform initial grouping of the kitchen waste thermal reaction activity. This is because the Ch value directly affects the flammability and reaction heat release of organic components in the thermal cracking process, and has high classification sensitivity. The specific implementation is: set an empirical or model-derived carbon-hydrogen ratio threshold value, automatically traverse all grid cells in the T sequence, and when the Ch value is greater than or equal to the carbon-hydrogen ratio threshold value, it is classified into the "high heat group" H1; the remaining grid cells are classified into the "general heat group" H2. The advantage of this grouping strategy is to exclude mixed situations with different thermal reaction potentials in advance, ensuring the stability of the group. For example, if the carbon-hydrogen ratio threshold value is set to 1.8, all cells with a carbon-hydrogen ratio higher than this value can be considered to have strong thermal cracking potential, and are suitable for reaction under high thermal efficiency conditions to avoid low Ch samples dragging the overall heat value efficiency.

[0051] Further, to avoid the problem of too large difference in reaction potential within the same heat group, the present application introduces an intra-group carbon-hydrogen ratio fluctuation constraint mechanism: limit the maximum difference of carbon-hydrogen ratio of all cells in any preliminary mixed candidate group to be less than the set limit threshold, control the consistency of thermal reaction intensity within the group. This strategy effectively avoids the "group imbalance" problem caused by traditional threshold hard division, and enhances the balance and controllability of subsequent pyrolysis reaction.

[0052] The second stage: intra-group balanced clustering according to the joint difference of water content Mh and fiber content Fb: after the preliminary formation of the high heat group and the general heat group, the grid cells in the two heat groups are evaluated for parameter difference to realize the precision operation of intra-group balancing and batch mixing. The core of this operation is to perform statistical analysis of the mean and range of the water content Mh and the fiber content Fb. In particular, for all G i =(Ch i ,Mh i ,Fg i ,Fb i ), the system calculates the mean and range of Mh and Fb, and then according to the closeness of each cell to the mean, uses K-neighbor clustering or adaptive distribution algorithm to divide it into several preliminary mixed candidate groups with parameter difference less than the preset tolerance Δ. Where Δ is a threshold value set by the system, representing the maximum allowed deviation of water content and fiber content within the same group. This step can effectively suppress the phenomenon of uneven pyrolysis rate caused by different water content and fiber composition, and optimize the uniformity of gasification reaction heat distribution. For example, if Δ is set to (±3%, ±2%), it means that Mh and Fb in the same batch should not exceed this range, thereby ensuring the stability of the subsequent treatment equipment operation and the consistency of residue composition control. It should be noted that S3 grouping is only for T sequence cells.

[0053] It should be noted that the above tolerance parameters are not arbitrarily set, but are adjusted based on the operating condition adaptation window of the pyrolysis device used, combined with the sensitivity of the actual thermal reactor to the effects of moisture fluctuations and fiber content on the heat transfer rate.

[0054] Phase 3: Oil Content Balance Control: After completing the initial mixing group division, the system further introduces a balance control mechanism for the oil content parameter Fg within each candidate mixing group to ensure that the thermal reaction efficiency of the final mixed batch fluctuates within a controllable range. Specifically, for each candidate group, the oil content parameter of each unit is extracted, and its standard deviation σ is calculated. j and volatility ρ j Wherein, volatility ρ j It can be calculated using the following formula: ρ j =σ j / μ j , where μ j This represents the average oil content of the group, and is compared with the system's set upper limit for fluctuation, ρ. m Compare them. If ρ j >ρ m In such cases, fine-tuning is required within the group, prioritizing the removal or replacement of grid cells with significant deviations, or introducing neutralizing parameter cells to balance the overall oil content. This strategy effectively controls local overheating or undercooling during the thermal reaction release process, and is particularly suitable for industrial scenarios with high requirements for reactor input continuity.

[0055] Finally, provided that the differences in all parameters are within the tolerance threshold, each optimized candidate mixture group is designated as the target mixture processing batch B1 to B2. k A unique batch number is assigned for tracking and management. Here, k is defined as the target mixed processing batch size, which depends on the number of effective groups obtained after parameter balancing. It is worth noting that this invention employs a dynamic batching mechanism, meaning k is not a fixed value, but rather depends on the size of the T sequence, parameter distribution characteristics, Δ, and ρ. m The system settings are closely related. In practice, the value of k can be determined through simulation or adaptively adjusted according to the equipment's processing capacity and energy-saving goals. For example, when processing highly variable food waste samples, the system may set k to a larger value to maintain processing stability, while when the sample homogeneity is high, some groups may be merged to improve processing efficiency.

[0056] To sum up, step S3 realizes the intelligent matching of kitchen waste from component identification to reaction batch by introducing multi-parameter collaborative clustering, difference tolerance control and grease balance mechanism. Compared with the existing coarse-grained division method based on source place, the present application realizes fine grouping based on pyrolysis response characteristics, which not only improves the energy conversion efficiency, but also effectively reduces the reactor thermal load fluctuation rate, and has a significant promoting effect on promoting the stability and resource recovery rate of the kitchen waste energy processing system.

[0057] S4: the recombined mixed treatment batches B1 to Bn are sent into the pyrolysis reaction device to perform pyrolysis reaction under corresponding reaction temperature parameters and retention time parameters, to produce combustible gas and pyrolysis residue products. k

[0058] Firstly, the core premise of this step is to dynamically match each mixed treatment batch B i with the pyrolysis reactor operating parameters, to ensure that the pyrolysis conditions and the physicochemical characteristics of the garbage components in the treatment process work together, to maximize the pyrolysis gas production efficiency and residue quality control. For this purpose, the present application introduces a "two-parameter matching" mechanism, that is, for each batch, the optimal pyrolysis temperature and retention time are not fixed values, but are inversely fitted by the comprehensive parameters (carbon-hydrogen ratio, moisture content, oil and fiber content) of each batch in step S3.

[0059] Specifically, for each batch B i The system first statistically models the key parameters of the samples in the batch. Suppose that there are n i grid units in the batch, and the component parameter set is Based on the following empirical model, the optimal pyrolysis temperature and retention time are jointly predicted: the higher the carbon-hydrogen ratio, the higher the recommended reaction temperature; the higher the moisture content, the temperature needs to be moderately reduced to reduce the energy consumption caused by water evaporation; the higher the oil content, the more intense the reaction, and the temperature can be appropriately increased to speed up the gas production rate; the higher the fiber content, the retention time needs to be extended to complete the complete cracking reaction; the greater the moisture content fluctuation, the more the retention time needs to be increased to maintain the thermal field balance. After completing the optimal pyrolysis temperature and retention time matching, the pyrolysis reaction device enters the preheating and parameter setting stage. Before each batch B i is sent into the reaction chamber, it will be intelligently preheated according to its recommended temperature, using a step-up or constant temperature adjustment strategy to ensure that the furnace temperature zone reaches a steady state before the actual reaction. For example, if the pyrolysis temperature is 480 degrees Celsius, the temperature rising curve can be set as: the first 3 minutes rise by 20 degrees Celsius per minute, the 4th to 5th minutes maintain a slow rise to the target temperature, and then maintain stability to avoid rapid temperature rise causing residue coking or uneven gas generation.

[0060] ​During the pyrolysis reaction process, the furnace temperature, material retention state, combustible gas yield and other core indicators will be continuously monitored and fed back in real time, and according to the deviation between the monitoring value and the predicted value, the combustion auxiliary gas flow or the feeding rate will be dynamically adjusted to realize closed-loop control. For example, if the combustible gas output rate is much lower than expected, the system can moderately increase the blast rate or extend the reaction time to make up for the delay in reaction rate. Such adjustment means constitutes the core mechanism of the "pyrolysis adaptive feedback adjustment system" of the present application.

[0061] After the pyrolysis reaction reaches the retention time, the reactor starts to switch to the product collection phase. The combustible gas is first purified through a multi-stage gas purification system, including condensation, de-tar, acid washing and other steps, and finally the combustible gas product is output. In the present application, due to the high component balance of the previous treatment batch, the residue structure has good consistency, high biochar conversion efficiency and low harmful ingredient residue, which is significantly better than the residue quality of traditional whole batch mixed investment treatment.

[0062] In addition, in order to further improve the thermal efficiency and energy saving ability of the system operation, the "pyrolysis heat feedback recycling" mechanism is introduced in this step. That is, part of the heat value in the output gas (recycled by combustion) is used to heat the reaction temperature required for the next batch, forming a closed-loop heat energy recycling path. For example, in a multi-reactor parallel system, when batch B i After the batch reaction is completed, its fuel gas can be directly guided to heat the next batch B i+1 , significantly reducing the load of external heaters and achieving energy self-balancing.

[0063] It is particularly worth pointing out that the present application also introduces the "batch pyrolysis archive" mechanism. That is, the component parameters, recommended treatment parameters, actual treatment curve, output material quantity ratio and quality information of each mixed treatment batch are recorded in the system database. Subsequently, similar component batches will be fine-tuned or directly called optimal solutions according to the historical batch performance, improving response efficiency and continuously optimizing control parameters.

[0064] S5: The pyrolysis residue output is classified according to the carbon content and ash content to determine whether it is backfilled to the original grid unit as soil improvement material or transferred to the biochar preparation unit, and the processing information is written back to the original G sequence database for subsequent optimization.

[0065] The resource recycling path classification includes: collecting the carbon content and ash content of each mixed treatment batch B1 to B kThe residue image and the weighing data generated after the pyrolysis reaction are used to construct a residue characteristic index set. Each data unit in the characteristic index set is marked as one of three types of recycling target types according to a preset classification rule. The residue is guided to a carbon-based material processing line, an organic soil improvement line, or a secondary treatment storage line according to the recycling target type, and the output mapping of the residue resource recycling path is completed. The three types of recycling target types include a carbon production type, a fertilizer application type, and a type that cannot be directly recycled.

[0066] For example, first, after each mixed processing batch completes the pyrolysis reaction in step S4, the system automatically collects and preliminarily characterizes the pyrolysis residue generated thereby. Standardized image analysis and weighing detection processes are performed on the pyrolysis residue generated by each batch to extract three key characteristic parameters, which are used to construct a residue characteristic index set and achieve high-dimensional expression of the residue recycling characteristics. Specifically, each residue sample is processed and the following three core indexes are extracted: apparent carbonization ratio, particle size distribution coefficient, and inorganic residue density. The apparent carbonization ratio refers to the ratio of the number of pixels with a gray value below a set carbonization threshold to the total number of all effective pixels in the residue image, which reflects the carbonization degree of the residue. The particle size distribution coefficient is used to measure the consistency of the particle size of the residue particles, that is, the ratio of the standard deviation of the particle size to the average particle size. The inorganic residue density represents the content of the inorganic components remaining in the unit mass of the pyrolysis input material, reflecting the "mineral density" of the ash residue, which is a composite index obtained by multiplying the mass ratio of the pyrolysis residue to the input raw material by the ash content.

[0067] Further, to avoid the problem of misjudgment caused by the dependence of the traditional classification scheme on a single parameter of heat value (or carbon content), the present application proposes a three-classification rule based on combined physical-chemical attributes, which labels each residue sample as one of the following three types of recycling targets: for example, the applicable range of the carbonizable type includes: the apparent carbonization ratio is greater than 0.75, the dark color proportion in the image is high, and the carbonization degree is good; the particle size distribution coefficient is less than 0.3, the particle size is concentrated, and the processing performance is good; the inorganic residue density is less than 0.2, the inorganic residue is less, and the impurities are low, which is suitable for entering the deep processing path of carbon-based materials, such as activated carbon and biochar bricks; the applicable range of the fertilizer type includes a carbonization ratio between 0.4 and 0.75, i.e. partial carbonization, containing a certain carbon source; the particle size distribution coefficient is less than 0.4, i.e. the particle size is moderate, which can be directly used for farmland application; the inorganic residue density is between 0.2 and 0.5, i.e. containing a certain mineral substance, which has the potential to adjust acid and alkali, and is suitable for entering the organic fertilizer mixing or soil improvement path; the non-direct recyclable type meets any of the following conditions: the apparent carbonization ratio is less than 0.4, the particle size distribution coefficient is greater than 0.5, or the inorganic residue density is greater than 0.5, which needs to be sent to the secondary treatment line (repyrolysis, compounding or concentrated combustion). It should be noted that the above data is only case data, and the actual conditions should be combined for judgment. The three-classification mechanism combines the three-dimensional indicators of appearance, structure and composition to replace the two-dimensional rough judgment of "heat value-ash content"; it does not depend on machine learning models, and the logic is transparent and the rules are adjustable, which is suitable for different processing scales and equipment conditions.

[0068] After the residue classification is completed, each batch of residue is guided into the corresponding processing path, and a type-path mapping is constructed. After the path is executed, the actual flow, residue conversion rate, final output and other data are written back to the processing database to form a full-process traceability closed loop, which is used to optimize the grid unit combination and pyrolysis parameter configuration of subsequent batches.

[0069] In summary, the mixed kitchen waste energy processing method based on the embodiments of the present application is illustrated, which constructs a kitchen waste collection grid unit, collects and encodes characteristic parameters to form a G sequence, obtains a target grid unit suitable for pyrolysis by combining a multi-parameter fusion evaluation method, and performs fine grouping and mixed processing on the kitchen waste. Finally, according to the characteristics of the pyrolysis residue, the resource utilization path classification is performed to form an end-to-end energy processing system. This method ensures the pyrolysis efficiency while enhancing the adaptability of the processing process to the heterogeneity of the raw materials. Compared with the problems of extensive raw material processing, single reaction process and monotonous residue recycling path in the prior art, the present application realizes the full-link optimization from front-end identification to back-end utilization. It is of great significance to improve the energy recovery efficiency and resource utilization level of kitchen waste, and to promote the development of green solid waste treatment technology.

Claims

1. A method for treating mixed kitchen waste into energy, characterized in that, include: Multiple kitchen waste collection grid units are constructed within the processing area. Feature parameters are collected from the kitchen waste in each grid unit, and a digital code G sequence containing the feature parameters is formed. Based on the G sequence, the gasification calorific value of kitchen waste in each grid unit is evaluated, the target grid unit number sequence T suitable for efficient pyrolysis treatment is obtained, and the corresponding kitchen waste component identification is output. Based on the component identifier corresponding to the number sequence T, kitchen waste from multiple grid units is allocated to multiple mixed processing batches with parameter differences within preset ranges. to middle, This refers to the number of batches processed in the mixed batch; The recombined mixed batch to They were fed into pyrolysis reactors and pyrolyzed under the corresponding reaction temperature and retention time parameters. Based on the characteristics of pyrolysis residue, resource recycling pathways are classified. The gasification calorific value assessment includes: extracting the water content parameters and C-H ratio parameters of each grid cell from the G sequence, constructing a binary correlation interval map, marking the gasification suitability level according to the relative position of each coordinate point in the map, initially screening grid cell numbers with levels higher than the preset standard level to form a candidate set; in the candidate set, extracting the oil content parameters and fiber content parameters of each grid cell, and calculating the pyrolysis contribution index Et; The pyrolysis contribution index Et uses the oil content as the numerator; the fiber content is added to 1 and the result is raised to the power of the result as the first part of the denominator, where the power exponent is the fiber inhibition coefficient. This indicates the nonlinear interference of fibers on the heat release of oils; Multiply the moisture content by the moisture content correction factor. Adding 1 as the second denominator indicates the inhibitory effect of moisture on pyrolysis efficiency; the product of the two inhibitory terms is used as the overall denominator.

2. The method for treating mixed kitchen waste into energy according to claim 1, characterized in that, The formation of the grid unit includes: dividing the processing area into multiple independent grid units and constructing a corresponding grid unit number sequence; setting up a kitchen waste collection device with automatic weighing and primary component detection in each grid unit; processing the collection feature parameters of the collection device according to a preset time period and converting them into structured coding parameters; and binding the coding parameters with the corresponding grid unit number to form a structured mapping pair.

3. The method for treating mixed kitchen waste into energy according to claim 2, characterized in that, The collected characteristic parameters include moisture content, oil content, carbon-to-hydrogen ratio, and fiber content.

4. The method for treating mixed kitchen waste into energy according to claim 1, characterized in that, Grid cells with Et values ​​higher than a set threshold are updated to number sequence T; and the corresponding component parameters of each grid cell in number sequence T in sequence G are combined sequentially to generate food waste component identifiers.

5. The method for treating mixed kitchen waste into energy according to claim 1, characterized in that, The allocation of the mixed processing batch includes: extracting the component identifiers corresponding to each grid cell in the number sequence T, classifying the cells with a carbon-hydrogen ratio parameter higher than a set threshold value into the high-heat group, and classifying the rest into the general-heat group; calculating the mean and range of the moisture content parameter and fiber content parameter for the grid cells in the high-heat group and the general-heat group respectively, and allocating them to the preliminary mixing group according to their similarity, forming multiple mixed processing candidate groups with parameter differences within the group less than a preset tolerance range Δ.

6. The method for treating mixed kitchen waste into energy according to claim 5, characterized in that, The allocation of the mixed processing batches also includes: within each mixed processing candidate group, further balancing the oil content parameters so that the final oil content parameter fluctuation rate within each group does not exceed a set proportion, ultimately forming multiple target mixed processing batches.

7. The method for treating mixed kitchen waste into energy according to claim 1, characterized in that, The resource recycling pathways are categorized as follows: collecting samples from each mixed processing batch. to Images and weighing data of the residue generated after the pyrolysis reaction are used to construct a set of residue characteristic indicators. Each data unit in the set of characteristic indicators is marked as one of three types of reuse targets according to a preset classification rule. According to the reuse target type, the residue is guided to a carbon-based material processing line, an organic soil improvement line, or a secondary treatment reserve line, respectively, to complete the output mapping of the residue resource reuse path.

8. The method for treating mixed kitchen waste into energy according to claim 7, characterized in that, The three types of reuse targets include those that can be used for charcoal production, those that can be used for fertilizer, and those that cannot be directly recycled.

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