Energy treatment method for mixed kitchen garbage

Abstract

The invention provides a mixed kitchen waste energy treatment method, which relates to the field of kitchen waste treatment, and comprises the following steps: constructing a kitchen waste collection grid unit, collecting and coding characteristic parameters to form a G sequence, and obtaining a target grid unit suitable for pyrolysis in combination with a multi-parameter fusion evaluation method; and the kitchen garbage is finely grouped and mixed, and finally resource path classification is executed according to the characteristics of the pyrolysis residues, so that a set of end-to-end energy treatment system is formed. And the adaptability of the treatment process to the heterogeneity of the raw materials is enhanced while the pyrolysis efficiency is ensured.

Description

Technical Field

[0001] The present invention relates to the field of kitchen waste treatment, and more specifically, to a method for energy treatment of mixed kitchen waste. Background Art

[0002] Guided by the current "dual carbon" goals, food waste energy processing technology has gradually become an important research direction in the field of solid waste resource utilization. Food waste contains large amounts of organic matter, water, and a certain proportion of oil, cellulose, starch, and protein, with high calorific value potential and resource value. Traditional treatment methods mainly include landfill, composting, and anaerobic digestion. However, these methods generally have limitations such as long treatment cycles, low energy recovery efficiency, and high greenhouse gas emissions. In recent years, pyrolysis technology has gradually become an effective method for food waste treatment due to its advantages such as short process time, high product diversity, and high concentrated energy release efficiency. However, the composition of food waste is complex and fluctuates significantly in time and space. Key parameters such as moisture content, oil content, and carbon-hydrogen ratio vary greatly between batches, which limits the energy efficiency of single pyrolysis reaction conditions. Therefore, it is urgent to explore energy-based treatment methods with greater differentiated adaptability and the ability to intelligently group processes.

[0003] Existing food waste pyrolysis technologies generally face two key bottlenecks: First, raw material pretreatment lacks specificity, often treating food waste of varying properties uniformly. This makes it difficult to meet the stability requirements of the pyrolysis process, leading to large fluctuations in pyrolysis efficiency and unstable carbonized residue quality. Second, while some current studies attempt to use single indicators such as moisture content and oil content for sorting, they ignore the comprehensive assessment of deeper, complex parameters such as the carbon-to-hydrogen ratio and fiber ratio, making it difficult to achieve scientific grouping of food waste to maximize energy efficiency. Furthermore, pyrolysis residue treatment still mostly relies on a coarse classification strategy, failing to effectively distinguish differences in their reuse value, thus limiting the diverse options for subsequent resource recovery pathways.

[0004] Therefore, a mixed kitchen waste energy treatment solution is needed. Summary of the Invention

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

[0006] According to one aspect of the present invention, a method for energy-based treatment of mixed kitchen waste is provided, which comprises: constructing a plurality of kitchen waste collection grid units within a treatment area, collecting characteristic parameters of the kitchen waste in each grid unit, and forming a digital code G sequence containing the characteristic parameters; evaluating the gasification calorific value of the kitchen 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 corresponding kitchen waste component identifications; and allocating the kitchen waste in the plurality of grid units to a plurality of mixed treatment batches B1 to B1 according to the component identifications corresponding to the number sequence T, wherein the intra-group parameter differences are controlled within a preset range. k Where k is the number of mixed processing batches; the reorganized mixed processing batches B1 to B k They are respectively sent to the pyrolysis reaction device and undergo pyrolysis reaction under the corresponding reaction temperature parameters and retention time parameters; resource recycling path classification is performed according to the characteristics of the pyrolysis residue.

[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; performing data processing on the collection characteristic parameters of the collection device according to a preset time period and converting them into structured coding parameters; binding the coding parameters with the corresponding grid unit number to form a structured mapping pair.

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

[0009] Furthermore, the gasification calorific value assessment includes: extracting the moisture content parameter and the carbon-hydrogen ratio parameter of each grid unit 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, preliminarily screening the grid unit numbers with levels higher than the preset standard level, and forming a candidate set; in the candidate set, extracting the oil content parameter and the fiber content parameter of each grid unit, 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 exponentially obtained as the denominator of the first part, where the exponent is the fiber inhibition coefficient α, which represents the nonlinear interference of the fiber on the heat release of the oil; the second part denominator is multiplied by the moisture content and the moisture correction factor β and then added by 1, which represents the inhibitory effect of moisture on the pyrolysis efficiency; the two inhibition terms are multiplied together to form the overall denominator.

[0011] Furthermore, the grid cells with Et higher than the set critical value are screened and updated into the numbering sequence T; and the corresponding component parameters of each grid cell in the numbering sequence T in the G sequence are combined in sequence to generate a kitchen waste component identification.

[0012] Furthermore, allocating the mixed processing batch includes: extracting the composite component identifier corresponding to each grid unit in the numbering sequence T, and classifying the units whose carbon-hydrogen ratio parameters are higher than the set threshold value into the high heat group, and the rest into the general heat group; for the grid units in the high heat group and the general heat group, respectively, counting the mean and range of their moisture content parameters and fiber content parameters, and allocating them to preliminary mixed groups according to their proximity, to form multiple mixed processing candidate groups whose intra-group parameter differences are less than the preset tolerance range Δ.

[0013] Furthermore, the allocation of the mixed processing batches also includes: further balancing the oil content parameters within each mixed processing candidate group so that the final oil content parameter fluctuation rate within each group does not exceed a set ratio, and finally forming multiple target mixed processing batches.

[0014] Furthermore, the resource recycling path classification includes: collecting each mixed processing batch B1 to B k A residue characteristic index set is constructed based on the residue image and weighing data produced after the pyrolysis reaction; each data unit in the characteristic index set is marked as one of three types of reuse targets according to preset classification rules; and according to the reuse target type, the residue is directed to the carbon-based material processing line, the organic soil improvement line or the secondary processing reserve line, completing the output mapping of the residue resource reuse path.

[0015] Furthermore, the three types of recycling targets include charcoal-making type, fertilizer-making type and non-direct resource-recycling type.

[0016] Compared with the existing technology, the mixed kitchen waste energy treatment method provided by the present invention constructs a kitchen waste collection grid unit, collects and encodes characteristic parameters to form a G sequence, combines a multi-parameter fusion evaluation method to obtain a target grid unit suitable for pyrolysis, and performs fine grouping and mixed treatment on the kitchen waste, and finally performs resource path classification according to the characteristics of the pyrolysis residue to form an end-to-end energy treatment system. While ensuring the pyrolysis efficiency, this method enhances the adaptability of the treatment process to the heterogeneity of raw materials. Compared with the problems of extensive raw material processing, single reaction process, and monotonous residue recycling path in the existing technology, the present invention realizes the optimization of the entire link 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 promote the development of green solid waste treatment technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0018] Figure 1 Flowchart of a method for processing mixed kitchen waste into energy according to an embodiment of the present invention.

[0019] Figure 2 The figure is a flow chart of forming a grid unit in a method for processing mixed kitchen waste into energy according to an embodiment of the present invention.

[0020] Figure 3 This is a flow chart of allocating mixed processing batches in the mixed kitchen waste energy processing method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] Below, the exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described herein.

[0022] As mentioned in the above background technology, existing kitchen waste pyrolysis technologies generally face two key bottleneck problems: on the one hand, the raw material pretreatment lacks specificity, and kitchen waste of different properties is usually treated uniformly, which makes it difficult to meet the requirements of the pyrolysis process for raw material stability, resulting in large fluctuations in pyrolysis efficiency and unstable quality of carbonized residues; on the other hand, although some current studies have attempted to use single indicators such as moisture content and oil content for sorting, they have ignored the comprehensive evaluation of deep composite parameters such as carbon-hydrogen ratio and fiber ratio, making it difficult to achieve scientific grouping of kitchen waste in the dimension of maximizing energy utilization efficiency. In addition, the treatment of pyrolysis residues still mostly adopts a coarse classification strategy, which fails to effectively distinguish the differences in their reuse value, thereby limiting the diversified selection of subsequent resource recovery paths. Therefore, a mixed kitchen waste energy treatment solution is needed.

[0023] Figure 1 Flowchart of the mixed kitchen waste energy treatment method according to an embodiment of the present invention. Figure 1As shown, in the mixed kitchen waste energy treatment method, the following steps are included: S1: constructing multiple kitchen waste collection grid units in the treatment area, collecting characteristic parameters of the kitchen waste in each grid unit, and forming a digital code G sequence containing the characteristic parameters; S2: evaluating the gasification calorific value of the kitchen waste in each grid unit according to the G sequence, obtaining a target grid unit numbering sequence T suitable for efficient pyrolysis treatment, and outputting the corresponding kitchen waste component identification; S3: according to the component identification corresponding to the numbering sequence T, allocating the kitchen waste in the multiple grid units to multiple mixed treatment batches B1 to B1 with the intra-group parameter differences controlled within a preset range. k Where k is the number of mixed processing batches; S4: the reorganized mixed processing batches B1 to B k The residues are respectively sent to the pyrolysis reaction device to undergo pyrolysis reaction under corresponding reaction temperature parameters and retention time parameters; S5: resource recycling path classification is performed according to the characteristics of the pyrolysis residues.

[0024] In one embodiment of the present invention, S1 specifically includes: constructing multiple kitchen waste collection grid units in the processing area, collecting moisture content parameters, oil content parameters, carbon-hydrogen ratio parameters and fiber content parameters of the kitchen waste in each grid unit, and forming 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 to physically divide the garbage area, but more importantly, to organically link the area, data and processing path through information technology. Traditional kitchen waste treatment methods are mainly based on centralized collection and extensive classification, lacking detailed identification of garbage distribution characteristics and component composition, resulting in subsequent treatment methods being difficult to accurately match raw material characteristics, resulting in low energy efficiency or waste of resources. The present invention introduces the concept of "grid unit", which is essentially a regional management technology that integrates spatial geographic information with the heterogeneous characteristics of garbage components. This technology reasonably divides the spatial grid within the processing area and configures each grid unit with a terminal device with component collection capabilities, so that each grid becomes a logical unit that binds information and raw material attributes, thereby achieving high-precision digital expression of raw material characteristics.

[0026] like Figure 2 As shown, 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 a kitchen waste collection device with automatic weighing and primary component detection in each grid unit; performing data processing on the collection characteristic parameters of the collection device according to a preset time period and converting them into structured coding parameters; binding the coding parameters with the corresponding grid unit number to form a structured mapping pair.

[0027] Specifically, the processing area is divided into multiple independent grid units based on parameters such as the density of kitchen waste sources, transportation routes, and geographical distribution. It should be noted that priority should be given to areas with large amounts of garbage generated to ensure that each grid unit after division is representative of data sampling and component difference analysis, so as to avoid data offset or imbalance in resource allocation due to unreasonable regional division. In the process of constructing the grid, it is also necessary to introduce GIS (geographic information system) or intelligent partitioning algorithm for auxiliary division to achieve scientific and practical spatial division, that is, pre-partition by output first, and then refine by GIS. At the same time, each grid unit is given a unique number, that is, a corresponding grid unit number sequence N1 to N is constructed. n , used to bind and track garbage attribute data, where n is the number of grid cells. This number not only facilitates data tracking and management but also provides an identification basis for the subsequent establishment of a mapping relationship between grid data and processing paths.

[0028] After gridding is complete, a food waste collection device equipped with automatic weighing and primary composition detection is deployed in each grid cell. Conventional waste collection equipment is mostly mechanical or centralized conveyor processing, which only collects total volume information but cannot identify component characteristics. The collection device deployed in this invention integrates multiple sensor modules, such as near-infrared spectroscopy to detect moisture content, optical density detection to initially identify oil and fat, and conductivity and organic reaction ratio detection to estimate the carbon-to-hydrogen ratio. These sensors rapidly collect key parameters of food waste through physical and chemical detection methods. Simultaneously, these collection devices, combined with automatic weighing modules, record the mass of the waste input, ensuring the accuracy of subsequent parameter normalization and unit mass estimation. Regarding parameter selection, moisture content, oil content, carbon-to-hydrogen ratio, and fiber content are selected as four key indicators that effectively reflect the energy potential of food waste. Moisture content directly determines energy loss and reaction efficiency during pyrolysis, oil content affects gas production efficiency and the ratio of oil-phase products, carbon-to-hydrogen ratio is a core chemical indicator that determines the calorific value of gasification, and fiber content is related to solid residue and thermal stability.

[0029] The above four parameters undergo sampling normalization, unit conversion, and time alignment in the data preprocessing module. These parameters not only represent the average physical and chemical properties of food waste within the current time period but also serve as a basis for determining the appropriate waste treatment method for that grid. In practical applications, due to the temporal dynamics and heterogeneous sources of waste composition, parameter collection requires a reasonable time period, such as an hourly or bi-hourly sampling window. A sliding window mechanism is then used to dynamically smooth data within short time periods to reduce the impact of random factors on the overall assessment results.

[0030] The parameters collected by the grid collection device are processed according to a preset time period and converted into structured coding parameters G1 to Gn , to express the corresponding grid units N1 to N n The characteristics of kitchen waste; the encoding parameters G1 to G n The corresponding grid units are numbered N1 to N n Bind to form a structured mapping pair M1 to M n , used for subsequent zoning treatment strategy selection and energy quantification path planning. It can be seen that this structured mapping can not only be used for the selection of pyrolysis adaptation grid units in subsequent steps, but also provide quantitative support for the scheduling optimization of regional thermal treatment equipment. Traditional kitchen waste distribution is mostly based on transportation convenience and equipment load balance, while ignoring the direct impact of raw material components on the treatment reaction effect. The mapping system of the present invention can realize the scheduling logic transformation based on "garbage characteristics → treatment path" rather than "garbage location → path", and is a key information bridge for waste energy utilization.

[0031] Furthermore, within the data storage structure, the mapping pairs can be stored as database tables or structured objects, with a defined update strategy. For example, the data can be automatically overwritten and updated after each sampling, or a time series dataset can be constructed by accumulating data along the time axis to facilitate subsequent trend analysis, training, or energy efficiency estimation. It is worth noting that the generation of the characteristic parameter G sequence is not limited to numerical data records but can be further expanded to include composite structures such as graph-based expressions and symbolic labels, providing a structural foundation for the future introduction of advanced analytics such as graph neural networks and spatial modeling.

[0032] In summary, step S1 constructs regional grid units, configures component collection devices, collects four characteristic parameters: moisture content, oil content, carbon-hydrogen ratio, and fiber content, and digitally encodes them to form a digitally encoded G sequence. This sequence is then mapped to the numbered grid units to establish a structured mapping relationship, thereby achieving an accurate expression of the spatial distribution and physical and chemical properties of kitchen waste at the source stage. This solution not only builds a quantifiable data foundation system, but also provides support for subsequent efficient calorific value assessment and mixed treatment path selection. It is significantly superior to the existing "collect first, then measure" and "deliver by area" treatment methods, and is an important basic step in realizing the process-based, data-based, and refined intelligent processing of waste energy.

[0033] In one embodiment of the present invention, S2 specifically includes: evaluating the gasification calorific value of the kitchen 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 the corresponding kitchen waste component identification.

[0034] The gasification calorific value assessment includes: extracting the moisture content parameter and the carbon-hydrogen 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, preliminarily screening the grid cell numbers with levels higher than the preset standard level, and forming a candidate set; in the candidate set, extracting the oil content parameter and the fiber content parameter of each grid cell, and calculating the pyrolysis contribution index Et.

[0035] The coordinate points are the coordinates of each grid cell on a two-dimensional plane, with its moisture content parameter Mh as the horizontal axis and its carbon-to-hydrogen ratio parameter Ch as the vertical axis. In other words, each grid cell occupies a unique position in the two-dimensional (Mh, Ch) map, and the relative position of that point (for example, falling in a high Ch, low Mh region) reflects its potential heating value under gasification conditions.

[0036] For example, the division of this map is based on research findings on the pyrolysis behavior of food waste—calorific value is positively affected by the carbon-hydrogen ratio and negatively affected by moisture content, forming a spatial mapping relationship of gasification suitability. Therefore, in this two-dimensional map, regions with high Ch and low Mh generally correspond to better gasification performance. Based on this, the present invention divides the map into three graded blocks: A (high suitability), B (medium suitability), and C (low suitability), representing the gradient distribution of their pyrolysis potential.

[0037] It should be noted that the division of the spectrum is not an equidistant or simple clustering method, but is based on the experimental data from the thermochemical treatment of food waste over the years, and uses empirical boundary functions to perform non-uniform partitioning of the spectrum to better meet the requirements of actual pyrolysis reaction conditions. This method breaks away from the traditional linear scoring or fuzzy logic weighting method, and maps the gasification tendency of the material with the geometric distribution of the spectrum space. It is a more intuitive and recognizable gasification assessment method. After the marking is completed, the system screens all grid unit numbers with a grade of A to form a candidate set as the preliminary range of food waste units that are acceptable for pyrolysis.

[0038] Next, in the candidate set, the corresponding oil content parameter Fg and fiber content parameter Fb in each grid cell are further extracted to calculate the pyrolysis contribution index Et.

[0039] The pyrolysis contribution index Et uses the oil content as the numerator; the fiber content is added to one and then exponentially used as the denominator of the first part, where the exponent is the fiber inhibition coefficient α, which represents the nonlinear interference of the fiber on the heat release of the oil; multiplied by the moisture content and the moisture correction factor β and then added to 1 as the denominator of the second part, representing the inhibitory effect of moisture on the pyrolysis efficiency; the two inhibition terms are multiplied together to form the overall denominator.

[0040] For example, the pyrolysis contribution index is designed to quantify the potential energy release capacity of food waste during pyrolysis. It uses a nonlinear coupling relationship between fat content, fiber content, and moisture content to comprehensively characterize the potential energy release capacity of food waste during pyrolysis. Specifically, the following combination relationship is used:

[0041]

[0042] It should be noted that in this calculation formula, the molecular part represents the heat release potential of the combustible oil component; the first-level inhibition term (Fb+1) α This model simulates the interference of the "wrapping retardation" phenomenon of the fiber structure on the thermal release of oil. The +1 factor ensures numerical stability, while the α factor enhances nonlinear effects. The second-level inhibition term (1+βMh) introduces moisture content as a calorific value dilution factor to simulate the negative impact of the endothermic evaporation of water during pyrolysis on the pyrolysis efficiency of oil. Compared to traditional calorific value assessment models, this index structure is more practical and differentiated. It avoids the "fuzzy" physical property characteristics caused by a simple weighted average of oil, fiber, and moisture. Instead, it models pyrolysis feasibility using a "release potential / retardation factor" structure, making it suitable for the precise screening and batch optimization of multi-component mixed kitchen waste.

[0043] Furthermore, the grid cells whose Et is higher than the set critical value are screened and updated as the target grid cell number sequence T; and the corresponding component parameters of each grid cell in the target grid cell number sequence T in the G sequence are combined in sequence to generate a kitchen waste component identification.

[0044] Exemplarily, the present invention sets a critical value threshold of Et as the threshold for entering the final target set. The value of the critical value threshold can be obtained by training historical experimental data, or dynamically adjusted according to the production capacity requirements of the target processing device. When the Et of a grid cell ≥ the critical value threshold, it can be considered that the cell has excellent pyrolysis contribution potential, and the system incorporates this number into the final target grid cell number sequence T. This sequence not only represents the distribution number of each unit to be processed 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 operating efficiency and data processing consistency of the system, the present invention extracts the complete parameter feature vector of the G sequence parameters corresponding to each grid unit in the target sequence T, for example, G i =(Ch i ,Mh i ,Fg i ,Fb i), which includes four parameters: Mh, Fg, Ch, and Fb, and is concatenated in numerical order to generate a unified food waste composition identifier. This composition identifier is not only used to control the composition balance of mixed processing batches in subsequent steps, but also serves as a unique code to track the calorific value characteristics of raw materials throughout the system's operation, effectively improving the closed-loop and controllable process from assessment to processing.

[0046] In summary, this step abandons the crude algorithm of existing technology that simply relies on weighted average or classification discrimination by introducing a two-dimensional map partitioning strategy combined with a nonlinear pyrolysis contribution index construction method, effectively integrates the coupling relationship between different parameters of kitchen waste, and realizes a more refined and adaptive identification mechanism for the gasification calorific value characteristics, providing a support foundation with engineering applicability for subsequent batch reorganization and pyrolysis treatment, and has strong engineering practical value and industrial promotion potential.

[0047] S3: Based on the component identification corresponding to the number sequence T, the kitchen waste in multiple grid cells is allocated to multiple mixed processing batches B1 to B1 with the parameter differences within the group controlled within the preset range. k Where k is the number of batches for mixed processing.

[0048] Preferably, the present invention adopts a three-stage mixing strategy of stratification-clustering-balancing when allocating mixing treatment batches to ensure that the thermal reaction potential and physical homogeneity of the parameters within the group are taken into account.

[0049] like Figure 3 As shown, allocating the mixed processing batches includes: extracting the composite component identifier corresponding to each grid unit in the numbering sequence T, and classifying the units whose carbon-hydrogen ratio parameters are higher than the set demarcation value into the high heat group, and the rest into the general heat group; for the grid units in the high heat group and the general heat group, respectively, the mean and range of their moisture content parameters and fiber content parameters are counted, and they are allocated to the preliminary mixed group according to their proximity, to form multiple mixed processing candidate groups whose intra-group parameter differences are less than the preset tolerance range Δ; within each mixed processing candidate group, the oil content parameters are further balanced so that the oil content parameter fluctuation rate in each group does not exceed the set ratio, and finally multiple target mixed processing batches are formed.

[0050] For example, the first stage involves initial grouping based on the calorific value potential of the carbon-to-hydrogen ratio parameter, Ch. The present invention constructs a high calorific value grouping strategy, using the carbon-to-hydrogen ratio parameter, Ch, as the primary control factor to perform initial grouping of kitchen waste thermal reactivity. This is because the Ch value directly affects the flammability and heat release of organic components during thermal cracking, and thus has a high classification sensitivity. A specific implementation involves setting an empirically or model-derived carbon-to-hydrogen ratio threshold, automatically traversing all grid cells in the T sequence, and automatically assigning cells with a Ch value greater than or equal to the carbon-to-hydrogen ratio threshold to the "high calorific value group" H1; the remaining cells are assigned to the "normal calorific value group" H2. The advantage of this grouping strategy is that it preemptively eliminates the mixing of cells with different thermal reactivity potentials, ensuring intra-group processing stability. For example, if the carbon-to-hydrogen ratio threshold is set to 1.8, all cells with a carbon-to-hydrogen ratio above this value can be considered to have strong thermal cracking potential and are suitable for reactions under high thermal efficiency conditions, thereby preventing low Ch samples from dragging down overall calorific value efficiency.

[0051] Furthermore, to avoid significant disparity in reaction potential within the same thermal group, the present invention introduces a mechanism to constrain the carbon-hydrogen ratio fluctuation within the group: within any preliminary candidate mixing group, the maximum carbon-hydrogen ratio range of all units must be less than a set threshold, thereby controlling the consistency of thermal reaction intensity within the group. This strategy effectively avoids the "intra-group imbalance" problem caused by traditional hard-cutting based on thresholds, enhancing the balance and controllability of subsequent pyrolysis reactions.

[0052] The second stage: perform balanced clustering within the group based on the combined difference of moisture content Mh and fiber content Fb: After initially forming the high heat group and the general heat group, the parameter difference evaluation will be performed on the grid cells in the two heat groups to achieve precise operation of group balance and batch mixing. The core of this operation is to perform statistical analysis of the mean and range of the two parameters of moisture content Mh and fiber content Fb. In particular, for all G in each heat group H1 and H2 i =(Ch i ,Mh i ,Fg i ,Fb i ), the system calculates the mean and range of its Mh and Fb, and then uses K-nearest neighbor clustering or adaptive distribution algorithm based on the degree of proximity of each unit to the mean to divide it into several preliminary mixed candidate groups with parameter differences less than the preset tolerance Δ. Among them, Δ is the threshold vector set by the system, which represents the maximum allowable deviation of moisture content and fiber content in the same group. This step can effectively suppress the uneven pyrolysis rate caused by different moisture and fiber compositions, and optimize the uniformity of heat distribution of gasification reaction. 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 stable operation of subsequent processing equipment and the consistency of residue composition control. It should be noted that S3 grouping is only for T sequence units.

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

[0054] Phase 3: Balanced control of oil content: After completing the above preliminary division of mixed groups, the system further introduces a balanced control mechanism for the oil content parameter Fg in each candidate mixed 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 , where volatility ρ j It can be calculated by the following formula: j =σ j / μ j , where μ j is the average oil content of the group and is equal to the upper limit of fluctuation ρ set by the system. m Compare. If ρ j >ρ m , 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 can effectively control local overheating or overcooling during the thermal reaction release process and is particularly suitable for industrial scenarios where reactor input continuity is critical.

[0055] Finally, under the premise that the differences in each parameter are within the tolerance threshold, each optimized candidate mixed group is marked as the target mixed processing batch B1 to B k , and assign a unique batch number for tracking management. The definition of k is the number of target mixed processing batches, which depends on the number of valid groups obtained after the final parameter balance screening. It is worth noting that the present invention adopts a dynamic batching mechanism, that is, k is not a fixed value, but is related to the size of the T sequence, the parameter distribution characteristics, Δ and ρ m The value of k is closely tied to system settings such as [number of samples] and [number of samples]. In practice, the value of k can be determined through simulation or adaptively adjusted based on the equipment's processing capacity and energy-saving goals. For example, when processing highly variable food waste samples, the system might set k to a larger value to maintain processing stability. However, when the samples are highly uniform, some groups might be merged to improve processing efficiency.

[0056] In summary, step S3 achieves intelligent matching of kitchen waste from component identification to reaction batching by introducing multi-parameter collaborative clustering, difference tolerance control, and oil balance mechanisms. Compared to existing coarse-grained grouping methods based on origin, this method achieves fine-grained grouping based on pyrolysis response characteristics, which not only improves energy conversion efficiency but also effectively reduces reactor thermal load fluctuations, significantly promoting the stability of kitchen waste energy treatment systems and improving resource recovery rates.

[0057] S4: Mix the recombined batches B1 to B k They are respectively fed into a pyrolysis reaction device, and undergo pyrolysis reaction under corresponding reaction temperature parameters and retention time parameters to produce combustible gas and pyrolysis residue products.

[0058] First of all, the core premise of this step is to mix each batch B i Dynamic matching is achieved between the pyrolysis conditions and the physicochemical properties of the waste components during the treatment process, ensuring a synergistic effect between the pyrolysis conditions and the waste components, maximizing pyrolysis gas production efficiency and residue quality control. To this end, this step introduces a "dual parameter matching" mechanism, whereby the optimal pyrolysis temperature and retention time are determined for each batch separately. These two parameters are not fixed values, but are instead inferred and fitted from the comprehensive parameters of each batch in step S3 (carbon-hydrogen ratio, moisture content, oil and fiber content).

[0059] Specifically, for each batch B i The system first performs statistical modeling on the key parameters of the samples in the group. Assume that there are n i grid cells, whose component parameter set is The optimal pyrolysis temperature and retention time are jointly predicted based on the following empirical model: the higher the carbon-hydrogen ratio, the higher the recommended reaction temperature; the higher the moisture content, the more the temperature needs to be lowered to slow down 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 more the retention time needs to be extended to complete the cracking reaction; the greater the fluctuation in moisture content, the more the retention time needs to be increased to maintain thermal field balance. After completing the matching of the optimal pyrolysis temperature and retention time, the pyrolysis reaction device is deployed to enter the preheating and parameter setting stage. Each batch B i Before being sent into the reaction chamber, it will be intelligently preheated according to the recommended temperature, using a step-by-step temperature increase or constant temperature regulation strategy to ensure that the temperature zone in the furnace reaches a stable state before the actual reaction. For example, if the pyrolysis temperature is 480 degrees Celsius, the temperature rise curve can be set as follows: increase by 20 degrees per minute for the first three minutes, maintain a slow increase to the target temperature for the fourth to fifth minutes, and then maintain stability to avoid rapid temperature increase that may cause residue coking or uneven gas generation.

[0060] During the pyrolysis reaction, core indicators such as furnace temperature, material retention status, and combustible gas yield are continuously monitored and provided real-time feedback. Based on the deviation between the monitored and predicted values, the combustion-assisted airflow or feed rate is dynamically adjusted to achieve closed-loop control. For example, if the combustible gas yield rate falls far short of expectations, the system can appropriately increase the blast rate or extend the reaction time to compensate for the delayed reaction rate. This type of regulation constitutes the core mechanism of the "pyrolysis adaptive feedback control system" of this invention.

[0061] After the pyrolysis reaction reaches its retention time, the reactor begins to switch to the product collection stage. The combustible gas first passes through a multi-stage gas purification system, including condensation, detarring, deacidification and washing, and finally outputs the finished combustible gas. In this invention, due to the high component balance of the early treatment batch, the residue structure is well-consistent, with high biochar conversion efficiency and low residual harmful components, significantly superior to the residue quality of traditional batch mixing treatment.

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

[0063] Of particular note, this invention also incorporates a "batch pyrolysis archive" mechanism. This records component parameters, recommended processing parameters, actual processing curves, output volume ratios, and quality for each mixing batch in a system database. This allows for subsequent fine-tuning of processing strategies for similar batches based on historical batch performance, or direct application of optimal solutions, improving response efficiency and continuously optimizing control parameters.

[0064] S5: The pyrolysis residues are classified into resource reuse paths according to their carbon content and ash content to determine whether they should be backfilled into the original grid unit as soil improvement material or transferred to the biochar preparation unit. The processing information is then written back to the original G sequence database for subsequent optimization.

[0065] The resource recycling path classification includes: collecting each mixed processing batch B1 to B kThe system uses images and weight data from the residue produced after the pyrolysis reaction to construct a residue characteristic index set. Each data unit in this characteristic index set is then labeled into one of three types of reuse targets according to pre-set classification rules. Based on these reuse targets, the residue is then directed to a carbon-based material processing line, an organic soil improvement line, or a secondary processing and storage line, completing the output mapping of the residue resource reuse pathway. The three reuse target types include those suitable for charcoal production, those suitable for fertilizer application, and those not directly recyclable.

[0066] For example, first, after each mixed processing batch completes the pyrolysis reaction in step S4, the system will automatically collect and preliminarily characterize the pyrolysis residues produced, perform standardized image analysis and weighing detection processes on the pyrolysis residues produced by each batch, extract three key characteristic parameters, and use them to construct a residue characteristic index set to achieve a high-dimensional expression of the residue resource characteristics. Specifically, each residue sample will be processed and the following three core indicators will be extracted: apparent carbonization ratio, particle size distribution coefficient, and inorganic residue density. Among them, the apparent carbonization ratio refers to the ratio of the number of pixels in the residue image whose grayscale value is lower than the set carbonization threshold to the total number of all valid pixels, which is used to reflect the degree of carbonization of the residue; the particle size distribution coefficient is used to measure the consistency of the particle size of the residue, 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 inorganic components remaining in the unit mass of the pyrolysis input material, reflecting the "mineral density" of the ash, which is a composite indicator obtained by multiplying the mass ratio of the pyrolysis residue to the mass of the input raw material by the ash content.

[0067] Furthermore, in order to avoid the problem of misjudgment caused by the traditional classification scheme relying on a single parameter of calorific value (or carbon content), the present invention proposes a three-classification rule based on the combined physical and chemical properties, which marks each residue sample into the following three target reuse types. For example, the applicable scope of the charcoal-making type includes: the apparent carbonization ratio is greater than 0.75, the dark color ratio 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 small, the impurities are low, and it is suitable for entering the deep processing path of carbon-based materials, such as activated carbon and biochar bricks; The applicable scope of the fertilization type includes a carbonization ratio between 0.4 and 0.75, that is, partial carbonization, containing a certain carbon source; a particle size distribution coefficient of less than 0.4, that is, the particle size is moderate and can be directly used for farmland spreading; an inorganic residue density between 0.2 and 0.5, that is, it contains certain minerals and has the potential to adjust acidity and alkali, suitable for entering the organic fertilizer mixing or soil improvement path; the non-direct resource type can meet any of the following conditions: such as 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, this type needs to be sent to the secondary processing line (re-pyrolysis, compounding or centralized combustion). It should be noted that the above data is only case data, and the specific judgment should be based on actual conditions. This three-classification mechanism combines the three-dimensional indicators of appearance, structure and composition, replacing the two-dimensional rough judgment of "calorific value-ash content"; it does not rely on machine learning models, has transparent logic, adjustable rules, and is adaptable to 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 direction, residue conversion rate, final output and other data are synchronously written back to the processing database, forming a full-process traceability closed loop, and used to optimize the grid unit combination and pyrolysis parameter configuration of subsequent batches.

[0069] In summary, the energy treatment method for mixed kitchen waste based on the embodiment of the present invention is explained, which constructs a kitchen waste collection grid unit, collects and encodes characteristic parameters to form a G sequence, combines a multi-parameter fusion evaluation method to obtain a target grid unit suitable for pyrolysis, and performs fine grouping and mixed treatment on the kitchen waste, and finally performs resource path classification according to the characteristics of the pyrolysis residue to form an end-to-end energy treatment system. While ensuring the pyrolysis efficiency, this method enhances the adaptability of the treatment process to the heterogeneity of 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 invention realizes 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 promote the development of green solid waste treatment technology.

Claims

1. A method for processing mixed kitchen waste into energy, characterized in that: include: Constructing multiple grid cells for collecting food waste within the processing area, collecting characteristic parameters of the food waste in each grid cell, and forming a digital code G sequence containing the characteristic parameters; Evaluate the gasification calorific value of the kitchen waste in each grid unit according to the G sequence, obtain a target grid unit number sequence T suitable for efficient pyrolysis treatment, and output the corresponding kitchen waste component identifier; According to the component identification corresponding to the number sequence T, the kitchen waste in multiple grid units is allocated to multiple mixed processing batches B1 to B1 with the intra-group parameter differences controlled within the preset range. k In , k is the number of mixed processing batches; The recombined mixed processing batches B1 to B k They are respectively fed into a pyrolysis reaction device and subjected to pyrolysis reaction under corresponding reaction temperature parameters and retention time parameters; Resource recycling paths are classified according to the characteristics of pyrolysis residues.

2. The method for processing 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 a kitchen waste collection device with automatic weighing and primary component detection in each grid unit; performing data processing on the collection characteristic parameters of the collection device according to a preset time period and converting them into structured coding parameters; binding the coding parameters with the corresponding grid unit number to form a structured mapping pair.

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

4. The method for processing mixed kitchen waste into energy according to claim 1, characterized in that: The gasification calorific value assessment includes: extracting the moisture content parameter and the carbon-hydrogen 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, preliminarily screening the grid cell numbers with levels higher than the preset standard level, and forming a candidate set; in the candidate set, extracting the oil content parameter and the fiber content parameter of each grid cell, and calculating the pyrolysis contribution index Et.

5. The method for processing mixed kitchen waste into energy according to claim 4, characterized in that: The pyrolysis contribution index Et uses the oil content as the numerator; the fiber content is added to one and then exponentially used as the denominator of the first part, where the exponent is the fiber inhibition coefficient α, which represents the nonlinear interference of the fiber on the heat release of the oil; multiplied by the moisture content and the moisture correction factor β and then added to 1 as the denominator of the second part, representing the inhibitory effect of moisture on the pyrolysis efficiency; the two inhibition terms are multiplied together to form the overall denominator.

6. The method for processing mixed kitchen waste into energy according to claim 5, characterized in that: The grid cells with Et higher than the set critical value are screened and updated into the numbering sequence T; and the corresponding component parameters of each grid cell in the numbering sequence T in the G sequence are combined in sequence to generate the kitchen waste component identification.

7. The method for processing mixed kitchen waste into energy according to claim 1, characterized in that: Allocating the mixed processing batches includes: extracting the composite component identifier corresponding to each grid cell in the numbering sequence T, classifying the cells whose carbon-hydrogen ratio parameters are higher than the set cutoff value into the high heat group, and classifying the rest into the general heat group; for the grid cells in the high heat group and the general heat group, respectively, counting the mean and range of their moisture content parameters and fiber content parameters, and allocating them to preliminary mixed groups according to their proximity, to form multiple mixed processing candidate groups whose intra-group parameter differences are less than a preset tolerance range Δ.

8. The method for processing mixed kitchen waste into energy according to claim 7, characterized in that: The allocating the mixed processing batches also includes: further balancing the oil content parameter within each mixed processing candidate group so that the oil content parameter fluctuation rate within each group does not exceed a set ratio, and finally forming multiple target mixed processing batches.

9. The method for processing mixed kitchen waste into energy according to claim 1, characterized in that: The resource recycling path classification includes: collecting each mixed processing batch B1 to B k A residue characteristic index set is constructed based on the residue image and weighing data produced after the pyrolysis reaction; each data unit in the characteristic index set is marked as one of three types of reuse targets according to preset classification rules; and according to the reuse target type, the residue is directed to the carbon-based material processing line, the organic soil improvement line or the secondary processing reserve line, completing the output mapping of the residue resource reuse path.

10. The method for processing mixed kitchen waste into energy according to claim 9, characterized in that: The three types of recycling targets include charcoal-making type, fertilizer-making type and non-direct resource-recycling type.

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