A method and system for controlling the heat load of waste into the furnace by using a waste crane

Abstract

A method and system for controlling the heat load of waste into the furnace by intelligent waste crane, comprising: calculating the calorific value of the waste into the furnace by the heat load or power generation of the waste incineration boiler; back calculating the calorific value of the waste into the furnace according to the combustion residence time of the waste in the incinerator; calculating the waste heat load into the furnace according to the amount of waste into the furnace and the calorific value of the waste into the furnace; setting the optimal heat load of waste into the furnace; controlling the waste crane to feed waste and control the heat load of waste into the furnace according to the optimal heat load of waste into the furnace; correcting the combustion property of the waste according to the calorific value of the waste into the furnace, recording the original property and the combustion property of the waste; the method and system for controlling the heat load of waste into the furnace by intelligent waste crane calculates the calorific value of the waste into the furnace by the heat load or power generation of the waste incineration boiler, the waste heat load into the furnace is the product of the amount of waste into the furnace and the calorific value of the waste, the optimal heat load of waste into the furnace is set, the heat load of waste into the furnace is intelligently controlled by the waste crane, and stable combustion of the incinerator is ensured.

Description

Technical Field

[0001] This invention relates to a waste incineration technology, and in particular to a method and system for intelligently controlling the heat load of waste entering the furnace using a waste crane. Background Technology

[0002] Currently, waste from municipal solid waste incineration plants is transported to the plant's waste storage area via garbage trucks. In the storage area, the waste undergoes composting for 3-4 days in summer and 7-8 days in winter before being unloaded, mixed, and fed into the incinerator by a garbage crane for high-temperature combustion. The garbage crane control system is a separate system, operated manually or semi-automatically by the crane operator. Due to the complex composition and high moisture content of domestic waste, resulting in significant variations in calorific value, the garbage crane operator can only control the amount of waste fed into the furnace. They cannot control the quality of the waste fed into the furnace based on changes in calorific value to ensure a uniform heat load, directly or indirectly affecting the temperature and load stability of the waste incinerator. Summary of the Invention

[0003] Therefore, it is necessary to provide a method for effectively improving incineration performance by using intelligent control of the heat load of waste entering the furnace via a waste crane.

[0004] Another system is provided that utilizes a waste crane to intelligently control the heat load of the waste entering the furnace, thereby effectively improving incineration performance.

[0005] A method for intelligently controlling the heat load of waste entering the furnace using a waste crane includes:

[0006] Calorific value estimation: The calorific value of the waste fed into the furnace is estimated by the boiler heat load or power generation after waste incineration;

[0007] Reverse deduction: Based on the residence time of the waste in the incinerator, the calorific value of the waste entering the furnace at that time can be deduced;

[0008] Estimated heat load: The heat load of the waste entering the furnace is estimated based on the amount of waste and the calorific value of the waste.

[0009] Set the optimal heat load: Set the optimal heat load for the waste entering the incinerator based on the MCR calorific value of the waste incinerator and the amount of waste processed.

[0010] Heat load control: Control the heat load of the waste fed into the furnace by controlling the waste feeding process according to the optimal heat load of the waste.

[0011] Record: Adjust the combustion properties of the waste according to the calorific value of the waste entering the furnace, and record the original properties and combustion properties of the waste.

[0012] In a preferred embodiment, the step of calculating the calorific value of the corresponding waste fed into the boiler based on the boiler heat load includes: collecting and calculating the weight data of the waste fed into the boiler, and calculating the mechanical load of the corresponding waste fed into the boiler.

[0013] First, within the total time T, for each q...n′ The arithmetic mean L of the cumulative material input weight is calculated.

[0014]

[0015] In the above formula, where q is the value of each time. n′ The material input weights are discrete, non-continuous data. Therefore, a method of averaging multiple values ​​over a set time period was adopted.

[0016] Pushing process within time T The feeding stroke and mechanical load coefficient k are calculated based on the total weight of the waste entering the furnace.

[0017] First, the feeding stroke l is calculated based on the total time T. n′ The arithmetic mean of the total number of calculations is used as the denominator to obtain the initial mechanical load coefficient k. m′

[0018]

[0019] The mechanical load coefficient k is determined using a gradient iteration method based on machine learning. It iterates through data within the interval z1 to z2, continuously adjusting the mechanical load coefficient k parameter to approximate its optimal value. This is then converted into the real-time mechanical load L by multiplying the pushing stroke l by the k coefficient. SL ,

[0020] L SL =k×l (4)

[0021] Boiler heat load is generally characterized by boiler evaporation rate, i.e., main steam flow rate, as the target heat load f, combined with the aforementioned mechanical load L of the waste. SL The heat load is converted into the waste calorific value q, and the calculation formula is as follows:

[0022]

[0023] In the above formula, the calorific value q of the waste is the difference between the total energy generated and the total energy fed in, divided by the mechanical load L. SL That is, the boiler heat load f and the steam enthalpy H 汽 Energy of the product, slag loss Q 渣损 Energy, other boiler steam losses Q 汽损 Sum the results and then subtract the incoming water flow rate F. 水 With feedwater enthalpy H 水 Energy of the product, airflow rate F 风 enthalpy of air H 风 The energy of the product, divided by the mechanical load L of the incoming waste. SL .

[0024] In a preferred embodiment, the step of calculating the calorific value of the waste fed into the furnace based on the power generation includes:

[0025] The efficiency of waste incineration heat energy utilization is characterized by a ton-to-ton waste-to-energy generation table, and its relationship with the calorific value of the waste fed into the furnace is as follows:

[0026]

[0027] In the above formula, the unit of power generation per ton of waste is kWh / t of waste, and the calorific value of the waste fed into the furnace is (kJ / kg) × 1000 kg / t; therefore, the calorific value of the waste fed into the furnace is calculated as follows:

[0028]

[0029] In a preferred embodiment, the heat load control involves: controlling the garbage hoisting and mixing to achieve the optimal calorific value and feed amount of the garbage into the furnace based on the optimal heat load of the garbage entering the furnace; adjusting the mixing ratio according to the calorific value of the garbage entering the furnace; and controlling the garbage to be incinerated in the normal incineration zone. If the calorific value of the garbage in a certain zone is low, garbage from a zone with a high calorific value is grabbed, mixed, and then fed into the furnace; conversely, if the calorific value of the garbage in a certain zone is high, garbage from a zone with a low calorific value is grabbed, mixed, and then fed into the furnace.

[0030] In a preferred embodiment, the heat load control further includes: weighted averaging of the calorific value of the waste, including:

[0031] The horizontal plane of the garbage storage area is divided into an m×n grid, where m is the number of grids in the x-axis of the distance the garbage crane travels, and n is the number of grids in the y-axis of the distance the garbage crane travels. Each small square forms a matrix, as shown in the following formula:

[0032]

[0033] In the above formula, A is an m×n grid matrix. ij Let i and j be one of the elements, where i∈m and j∈n respectively. i, j, m, and n are all positive integers, and a span of 2 to 5m is generally considered as one cell.

[0034] When the grab bucket is in operation, its x and y coordinate data of the working point correspond to a certain gridded block A. ij , is the range of coordinates of an element in the matrix;

[0035] When waste is piled up or dumped in the storage facility, a certain A is used. ij Waste is stored in the grid area for natural fermentation, and then layer A is divided into grids at height h' based on the height of the z-coordinate. ij-h‘ Within the small cube, a new element is formed in the m×n×h three-dimensional matrix structure, namely an A. ij-h‘Small cubes, where h'∈h, are all positive integers, typically spanning 2-5m per cell. It is garbage A. ij The height h' layer below the grid area, starting from the stacking time, and using the natural progression of time as the increasing function of fermentation degree, represents the time-domain characteristics of waste fermentation at this point.

[0036] The time-domain function for waste fermentation is:

[0037] J(A ij-h‘ )=k t ×ξ×t k ×F(DT1-DT0) (7)

[0038] In the above formula, garbage A ij-h‘ The degree of fermentation of the block J(A) ij-h‘ ), related to its own waste properties ξ, and the ambient temperature t of the reservoir area. k Yes, there is a relationship. The natural composting time function F(DT1-DT0) is a positive correlation function between the current time DT1 and the difference between the initial composting time DT0. Waste fermentation is also affected by factors such as waste thickness, storage temperature, and internal microbial community. A fermentation coefficient k is used. t To characterize;

[0039] If the internal factors of waste fermentation cannot be calculated, then ξ*t k =1 is taken as a premise, that is, the relationship between relevant factors is combined to 1, and the required degree of waste fermentation J(A) is required. ij-h‘ If ) = 1 indicates normal fermentation, then it can be deduced that: in summer, the piling time is 3-4 days, then k t =0.25~0.33; In winter, the storage time is 7~8 days, then k t = 0.125~0.143. When the two-quarter coefficient k t When the change exceeds 1.8 times, the following method should be used;

[0040] The time-domain calculation method for natural fermentation of waste is applied to each waste A. ij-h‘ The block fermentation calculation uses the raw data as a baseline for calculation, and then calculates the waste fermentation coefficient k based on the calorific value in the waste combustion attributes. t The convolution calculation method and coefficient k in AI computation are adopted. t The iterative method, the calculation process is as follows:

[0041] Starting from the surface area of ​​the waste feeding zone, when the grid zoning is...

[0042] matrix

[0043] With convolution matrix or The mean of the product of , where B is a matrix with (2N+1) rows and (2N+1) columns, and N = 1, 2, 3..., leads to the formula for the average calorific value:

[0044]

[0045] Where A ij Waste fermentation coefficient k at height h' under the grid t For A ij The average value of the convolution of the B matrix centered at A ij The calorific value of the waste is continuously updated with multiple feeding and return cycles, and the calculation process iterates continuously to improve the waste fermentation coefficient k. t For greater accuracy, the corresponding data is transmitted to the grid-based management information system in the storage area and stored. It is calculated using multiple coefficients based on monthly changes or changes in ambient temperature of 15-20°C, resulting in the waste fermentation coefficient k. t1 k t2 k t3 ...represent the fermentation coefficients under different month, external atmospheric conditions, or internal environmental factors in the waste storage area, and are retrieved according to the corresponding database storage location.

[0046] In a preferred embodiment, the heat load control further includes: a comprehensive evaluation based on a deviation calculation and comparison of the calorific value corresponding to the degree of fermentation and the calorific value of the waste returned after incineration; the residence time from the time the waste is put into the incinerator to the completion of combustion is 1.5-2 hours; when calculating the arithmetic mean L, the time period used is q every 3.5-4.5 hours. n′ The average of multiple values ​​of the material input weight is calculated.

[0047] In a preferred embodiment, a comprehensive evaluation is performed by comparing the deviation between the calorific value corresponding to the degree of fermentation and the calorific value of the waste returned after incineration. The specific comparison method uses a formula...

[0048]

[0049] To calculate the same A ij The deviation between the degree of fermentation of the waste in the grid and the calorific value returned by waste incineration is that the calorific value returned by waste incineration is used as a reference value, and the calculated degree of fermentation of waste is used as the evaluation object. When the ratio is within a certain range, it is considered to be a reasonable value. Generally, 0.7 to 1.3 can be regarded as normal data. Otherwise, the degree of fermentation coefficient should be corrected.

[0050] In a preferred embodiment, setting the optimal heat load further includes: setting the optimal heat load of the waste entering the furnace through a supervised learning regression algorithm in artificial intelligence machine learning, setting the waste input weight and waste calorific value as input objects, taking the input waste heat load as the expected output value, analyzing training data, and generating inferences.

[0051] In a preferred embodiment, the record includes: in the gridded management information of the reservoir area, data is stored using an m×n matrix as the object, i.e., by Aij A block is considered a data storage unit of this matrix; in the database, it is considered a unit with A. ij The data information bar of the identification tag stores the original attributes and combustion attributes of the waste; when the grab bucket of the garbage crane moves, including the following states: first, grabbing material, the grabbing time, weight, height, and action information are recorded in the relevant A. ij In the grid; secondly, in the interval material relocation work, then A ij The original attributes and action information of the grid garbage are transferred to B. i′j′ In the grid corresponding to the material feeding point, all information about the waste at point A (material grabbing point) is transferred to point B (material feeding point), meaning the original attributes and action information are transferred accordingly; thirdly, in the feeding operation, A... ij The original attributes and action information of the grid waste are stored in a historical database with time and action tags, i.e., material grabbing A. ij Collect all information about the waste, plus historical data on the calorific value after combustion time.

[0052] In a preferred embodiment, the original attributes of the waste include one or more of the following: waste entry time, source, and type; the combustion attributes of the waste include: degree of fermentation during natural fermentation, waste calorific value information, and waste feeding and collection area information; the waste calorific value information includes the calorific value of the waste returned from combustion, SO2, and HCl; and the waste feeding and collection area information includes one or more of the following: time, source point, coordinate system of the collection area, and feeding amount.

[0053] A system for intelligently controlling the heat load of waste entering the furnace using a waste crane includes:

[0054] Calorific value calculation module: Calculates the calorific value of the corresponding waste fed into the furnace based on the boiler heat load or power generation after waste incineration;

[0055] Reverse calculation module: Based on the residence time of the waste in the incinerator, the calorific value of the waste entering the furnace at that time is deduced;

[0056] Heat load calculation module: Calculates the heat load of waste entering the furnace based on the amount of waste and the calorific value of the waste.

[0057] Optimal heat load setting module: Sets the optimal heat load for the waste entering the incinerator based on the MCR calorific value of the waste incinerator and the amount of waste processed.

[0058] Heat load control module: Controls the feeding of waste into the furnace based on the optimal heat load of the waste, and controls the heat load of the waste entering the furnace;

[0059] Recording module: Corrects the combustion properties of the waste based on the calorific value of the waste entering the furnace, and records the original properties and combustion properties of the waste.

[0060] The aforementioned method and system for intelligently controlling the heat load of waste entering the incinerator using a waste crane, based on thermodynamic calculations, can estimate the calorific value of the waste entering the incinerator through the heat load or power generation after waste incineration. The heat load of waste entering the incinerator is the product of the amount of waste entering the incinerator and the calorific value of the waste. When the calorific value of the waste increases, the amount of waste entering the incinerator decreases, and when the calorific value of the waste decreases, the amount of waste entering the incinerator increases. By employing supervised learning regression algorithms in artificial intelligence machine learning, the optimal heat load of waste entering the incinerator is set according to the target calorific value of the waste incinerator's MCR and the amount of waste processed. This guides the waste crane to learn operating skills and optimize the mixing of calorific value and the weight of waste fed in, thus intelligently controlling the heat load of waste entering the incinerator, ensuring stable combustion in the incinerator, and helping to improve the boiler's heat load and the overall power generation of the plant. Attached Figure Description

[0061] Figure 1 This is a flowchart illustrating a method for intelligently controlling the heat load of waste entering the furnace using a waste crane, according to an embodiment of the present invention.

[0062] Figure 2 This is a schematic diagram illustrating the principle of a preferred embodiment of the present invention, which utilizes a garbage crane to intelligently control the heat load of garbage entering the furnace.

[0063] Figure 3 This is a waste combustion attribute calculation process according to an embodiment of the present invention;

[0064] Figure 4 This is a coordinate diagram of the garbage crane position according to an embodiment of the present invention;

[0065] Figure 5 This is a schematic diagram of a gridded waste storage system according to an embodiment of the present invention;

[0066] Figure 6 This is a three-dimensional schematic diagram of a waste storage grid according to an embodiment of the present invention;

[0067] Figure 7 This is a combustion load diagram of an incinerator according to an embodiment of the present invention. Detailed Implementation

[0068] like Figures 1 to 2 As shown, a method for intelligently controlling the heat load of waste entering the furnace using a waste crane according to the present invention includes:

[0069] Step S101, Calorific value calculation: Calculate the calorific value of the corresponding waste fed into the furnace based on the boiler heat load or power generation after waste incineration;

[0070] Step S103, reverse calculation: Based on the residence time of the waste in the incinerator, deduce the calorific value of the waste entering the furnace at that time;

[0071] Step S105, calculate heat load: Calculate the heat load of the waste entering the furnace based on the amount of waste and the calorific value of the waste.

[0072] Step S107, Set the optimal heat load: Set the optimal heat load of the waste entering the furnace based on the MCR calorific value of the waste incinerator and the amount of waste processed;

[0073] Step S109, Heat load control: Control the feeding of waste into the furnace according to the optimal heat load of the waste, and control the heat load of the waste entering the furnace;

[0074] Step S111, Record: Correct the combustion properties of the waste according to the calorific value of the waste entering the furnace, and record the combustion properties of the waste.

[0075] In this embodiment, the MCR calorific value refers to the calorific value of the waste incinerator under the maximum continuous rating (MCR) condition.

[0076] Furthermore, in step S101 of this embodiment, the calorific value calculation step, the calculation of the calorific value of the corresponding waste fed into the furnace based on the boiler heat load and mechanical load includes: collecting and calculating the weight data of the waste fed into the furnace, and calculating the mechanical load of the corresponding waste fed into the furnace.

[0077] First, within the total time T, for each q... n′ The arithmetic mean L of the cumulative material input weight is calculated.

[0078]

[0079] In formula (1), each q is calculated over the total time T. n′ The arithmetic mean L is calculated by cumulatively calculating the weight of materials fed. Where each q... n′ The material input weights are discrete, non-continuous data. Therefore, a method of averaging multiple values ​​over a specific time period, preferably 3.5-4.5 hours, is adopted. Preferably, a method of averaging multiple values ​​collected over a 4-hour period is used.

[0080] Due to the long time involved, which is not conducive to continuous calculation of waste calorific value, this embodiment adopts an artificial intelligence data processing method, which calculates the material pushing stroke over a period of time T. The relationship between the pushing stroke and the mechanical load, represented by the total weight of the waste entering the furnace, is calculated using a coefficient (k) to enable machine self-learning and real-time calculation. The specific details are as follows:

[0081]

[0082] Formula (2) is based on the total time T in Formula (1) for the pushing stroke l n′ The arithmetic mean of the total number of calculations is used as the denominator to obtain the initial mechanical load coefficient k. m′ .

[0083]

[0084] The mechanical load coefficient k is obtained by using the machine self-learning gradient iteration method of formula (3), continuously iterating the data within the interval from z1 to z2, and continuously adjusting the mechanical load coefficient k parameter to make the k value approach the optimal value, thereby obtaining the optimal formula (4). The pushing stroke l multiplied by the k coefficient is directly converted into the real-time mechanical load L. SL Model.

[0085] L SL =k×l (4)

[0086] Boiler heat load is generally characterized by boiler evaporation rate, i.e., main steam flow rate, as the target heat load f, combined with the aforementioned mechanical load L of the waste. SL The heat load is converted into the waste calorific value q, and the calculation formula is as follows:

[0087] In formula (5), the calorific value q of the waste is the difference between the total energy generated and the total energy supplied, divided by the weight of the waste, i.e., the boiler heat load f and the enthalpy H of the steam. 汽 Energy of the product, slag loss Q 渣损 Energy, other boiler steam losses Q 汽损 Sum the results and then subtract the incoming water flow rate F. 水 With feedwater enthalpy H 水 Energy of the product, airflow rate F 风 enthalpy of air H 风 The energy of the product, divided by the mechanical load L of the amount of waste fed in. SL All calculations are based on unit time. Calculations based on the aforementioned data can be performed by the waste incineration control system or by this system directly calculating and outputting relevant data collected during boiler combustion.

[0088] Furthermore, in step S101 of this embodiment, the calorific value calculation step, calculating the calorific value of the corresponding waste fed into the furnace based on the power generation includes:

[0089] The efficiency of waste incineration heat energy utilization is characterized by a ton-to-ton waste-to-energy generation table, and its relationship with the calorific value of the waste fed into the furnace is as follows:

[0090]

[0091] In formula (9), the unit of power generation per ton of waste is kWh / t of waste, and the calorific value of the waste fed into the furnace is (kJ / kg) × 1000kg / t; therefore, the calorific value of the waste fed into the furnace is calculated as follows:

[0092]

[0093] According to formula (10), the real-time calorific value can be calculated using an empirical method.

[0094] Furthermore, step S109 of this embodiment, heat load control, also includes: controlling the garbage hoisting and mixing to achieve the optimal calorific value and feeding amount of the garbage into the furnace based on the optimal heat load of the garbage entering the furnace; adjusting the mixing ratio according to the calorific value of the garbage entering the furnace; and controlling the garbage to be incinerated in the normal incineration zone. If the calorific value of the garbage in a certain zone is low, garbage from a zone with a high calorific value is grabbed, mixed with it, and then fed into the furnace; if the calorific value of the garbage in a certain zone is high, garbage from a zone with a low calorific value is grabbed, mixed with it, and then fed into the furnace.

[0095] like Figure 7 As shown, the specific calorific value level can be determined by the q1 to q3 lines in the figure. If it is closer to the q1 line, the calorific value is higher, and if it is higher, the calorific value is even higher. If it is closer to the q3 line, the calorific value is lower, and if it is lower, the calorific value is poor or low. The q2 line is the best, and anything closer to it is also good.

[0096] according to Figure 7 The calorific value of the waste can be evaluated by fitting the calorific value after incineration to this graph and determining the region where the calorific value point is located.

[0097] Waste calorific value mixing involves mixing the waste in the upper calorific value zone (Q2) with the waste in the lower zone, bringing the calorific value line as close to the Q2 line as possible. It could also involve mixing waste in the Q4 line with waste in the zone corresponding to the Q1 line, placing it within the normal combustion zone. Alternatively, it could involve mixing waste in the Q4 line with waste in the zone corresponding to the Q1 line, also placing it within the normal combustion zone. Different calorific values ​​correspond to different waste storage zones, and the mixing of these zones is carried out using a waste crane to grab and mix the waste.

[0098] The waste storage area can be divided into multiple functional zones, such as Zone A for fermentation, Zone B for feeding pre-fermented waste, Zone C for new waste stockpiling, and Zone D for other industrial materials, etc., for zoned management. The waste in these zones has different calorific values ​​and qualities.

[0099] After the feeding area in Zone B is full, it transitions to the stockpiling area. If the stockpiling area is full, it transitions to the fermentation area. After fermentation is complete, it becomes the feeding area again, operating in a cyclical manner. Areas storing new and aged waste are generally considered low-calorific-value areas; fermented and industrial waste are considered high-calorific-value areas. When a zone completes a specific function, its role can be automatically changed through waste storage management or manually modified. In the automatic control system of the waste crane, work tasks can be automatically assigned based on the role of a given zone.

[0100] When the calorific value of waste in a certain area is low, it is necessary to take waste from areas with high calorific value, mix it thoroughly, and then add it back into the system. Only after mixing the good and bad waste can the combustion requirements be met. This process is called mixing.

[0101] The waste ratio for mixing is determined by taking into account both the calorific value of fermentation and the calorific value returned from combustion.

[0102] Furthermore, in step S109 of this embodiment, the heat load control further includes: weighted averaging of the calorific value of the waste, including:

[0103] The horizontal plane of the garbage storage area is divided into an m×n grid, where m is the number of grids in the x-axis (coordinate of the distance traveled by the garbage crane) and n is the number of grids in the y-axis (coordinate of the distance traveled by the garbage crane). Each small square forms a matrix, as shown in the following formula:

[0104]

[0105] Formula (6), m×n grid matrix A, A ij Let i be one of the elements, and j be i∈m and j∈n respectively. i, j, m, and n are all positive integers. The division standard is that each grid is sufficient to define the state of its storage area. Generally, a span of 2 to 5m is appropriate for one grid. If it is too small, the amount of data will increase exponentially. If it is too large, it will not be sufficient to represent the local state.

[0106] When the grab bucket is in operation, its x and y coordinate data of the working point correspond to a certain gridded block A. ij , is the range of coordinates of an element in the matrix. For example... Figures 4 to 5 As shown.

[0107] In the grid-based management information of the reservoir area, an m×n matrix is ​​used as the data storage object, i.e., the above-mentioned A ij A block is considered a data storage unit of this matrix, and in a database, it can be considered a unit with A. ij The data information bar of the identity tag stores new field information about the original attributes and combustion attributes of the waste.

[0108] When the grab bucket of the garbage crane moves, there are mainly these states: First, it grabs material, then the grabbing time, weight, height, and movement information are recorded to the relevant A. ij In the grid; secondly, in the interval material transfer operation, then A ij The original attributes and action information of the grid garbage are transferred to B. i′j′ In the grid corresponding to the material feeding point, all information about the waste at point A (material grabbing point) is transferred to point B (material feeding point), meaning the original attributes and action information are transferred accordingly; thirdly, in the feeding operation, A... ij The original attributes and action information of the grid waste are stored in a historical database with time and action tags, i.e., material grabbing A. ij It includes all information about the waste, plus historical data on the calorific value (including SO2 and HCl) returned after a combustion time of 1.5 to 2 hours.

[0109] like Figure 6 As shown, when waste is piled up or dumped in the storage facility, a certain A... ijThe waste stored in the grid area undergoes natural fermentation, and then the A layer of the h' layer, which is divided by the height of the z-coordinate, is used. ij-h′ Within the small cube, a new element is formed in the m×n×h three-dimensional matrix structure, namely an A. ij-h′ Small cubes, where h'∈h, are all positive integers, typically spanning 2-5m per cell. It is garbage A. ij The height h' layer below the grid area, starting from the stacking time and using the natural time progression as the increasing function of fermentation degree, is used as the time-domain characteristic of waste fermentation at this point, which can be used as the characteristic of expert experience data. A ij-h′ Small cubes, such as Figure 6 As shown.

[0110] The time-domain function for waste fermentation is:

[0111] J(A ij-h′ )=k t ×ξ×t k ×F(DT1-DT0) (7)

[0112] In formula (7), A ij-h′ The degree of fermentation of the block of waste J(A) ij-h′ ), and its own waste attributes (such as type) ξ, and the ambient temperature t of the storage area. k There is a direct relationship; the natural composting time function F(DT1-DT0) is a positive correlation function representing the difference between the current time DT1 and the initial composting time DT0. However, waste fermentation is also directly related to factors such as waste thickness, storage temperature, and internal microbial community. Therefore, a fermentation coefficient k is used. t Characterization. Fermentation coefficient k t This can be estimated based on empirical values. If the intrinsic factors of waste fermentation cannot be calculated, then ξ×t k =1 is taken as a premise, that is, the relationship between relevant factors is combined to 1, and the required degree of waste fermentation J(A) is required. ij-h′ If ) = 1 indicates normal fermentation, then it can be deduced that: in summer, the piling time is 3-4 days, then k t =0.25~0.33; In winter, the storage time is 7~8 days, then k t = 0.125~0.143. When the two-quarter coefficient k t If the change exceeds 1.8 times, such as reaching nearly 2 times, it can only be theoretically extrapolated and does not meet the requirements for precise calculation. Therefore, the following method is adopted.

[0113] The time-domain calculation method for natural fermentation of waste is applied to each waste A. ij-h′ The raw data used for fermentation calculations can be used as a baseline for calculations, and its accuracy is mainly determined by the fermentation coefficient k. tIn this embodiment, the calorific value from the waste combustion attributes is used to inversely calculate the waste fermentation data. The convolution calculation method and coefficient iteration method in AI calculation are employed to ensure the balance of the calculation while continuously updating and refining the fermentation coefficients for greater accuracy. The calculation process is as follows:

[0114] Starting from the surface area of ​​the waste feeding zone, when the grid zoning is as described in the aforementioned formula.

[0115] matrix

[0116] With convolution matrix or The mean of the product of , where B is a matrix with (2N+1) rows and (2N+1) columns, and N = 1, 2, 3..., leads to the formula for the average calorific value:

[0117]

[0118] Where A ij Waste fermentation coefficient k at height h' under the grid t For A ij The average value of the convolution of the B matrix centered at A. ij The calorific value of the waste is continuously updated based on multiple feeding and return cycles, and the calculation process iterates continuously, thus determining the waste fermentation coefficient k. t This would be even more accurate. AI machine learning capabilities can be employed. The corresponding data is transmitted to the grid-based management information system within the storage area for storage, enabling the intelligent waste crane to automatically control the calorific value based on this data.

[0119] To prevent the waste fermentation coefficient k t If iterative data from different seasons is included, then in actual operation, multiple coefficients are used for calculation, i.e., the waste fermentation coefficient k, which is calculated monthly or for every 15-20°C change in ambient temperature. t1 k t2 k t3 ...represent the fermentation coefficients under different month, external atmospheric conditions, or internal environmental factors in the waste storage area, respectively. These can be retrieved from the corresponding database storage location.

[0120] The fermentation coefficient, used in digital information systems to represent the changing trend of the natural fermentation process of waste, is related to ambient temperature. The degree of waste fermentation corresponds to the calorific value of the waste. The fermentation coefficient can be corrected based on the calorific value returned after waste combustion. The waste fermentation coefficient k... tThe natural composting time is related to the ambient temperature, and there is a positive correlation between the time and the actual temperature of the fermentation zone. The fermentation coefficient is calculated by returning a calorific value once, then returning again for further calculation, with each calculation based on corrections from the previous one. The more sample data used in the calculation, the higher the accuracy. The fermentation coefficient is used to predict the calorific value of waste, and the waste mixing ratio is determined by considering both the calorific value of the fermented waste and the returned calorific value. A higher accuracy fermentation coefficient results in a more accurate prediction of the calorific value of waste in the fermentation zone; this includes waste that was not incinerated and therefore did not have a returned calorific value.

[0121] The weighted average method for calculating the calorific value of waste primarily addresses potential calculation deviations due to factors such as uneven mixing of waste within each grid cell, weighing errors during waste collection, and unpredictable factors during combustion. A convolutional matrix can be used to achieve this. To perform secondary calculations on its surrounding blocks and obtain the relevant average values ​​is beneficial for the following comprehensive evaluation.

[0122] A comprehensive evaluation is conducted based on the deviation between the calorific value corresponding to the degree of fermentation and the calorific value of the waste returned after incineration. The specific comparison method uses the formula...

[0123]

[0124] To calculate the same A ij The deviation between the degree of fermentation of the waste in the grid and the calorific value returned by waste incineration is defined by using the calorific value returned by waste incineration as a reference value and the calculated degree of fermentation as the evaluation object. When the ratio is within a certain range, it is considered a reasonable value, generally 0.7 to 1.3 can be considered normal data; otherwise, the fermentation degree coefficient should be corrected. The combustion attributes of waste can be defined by the waste composting time, with different levels represented by good, average, and poor fermentation.

[0125] Furthermore, in this embodiment, the waste mixing ratio is determined by taking into account both the calorific value corresponding to the degree of fermentation and the return calorific value.

[0126] The garbage crane controls the mixing of garbage to achieve the desired calorific value for the furnace, typically using ratios such as 4:1, 3:1, 2:1, 1:1, and N:1. For example, it might grab a portion from the high or low calorific value zone, scatter it in the feeding area, grab it again, scatter it once or twice more, and then grab N+1 portions for the furnace. The garbage crane's control, based on the specific characteristics of the garbage in each zone, is managed using a grid-based inventory management system. For instance, it might be positioned at point B... i′j′ Take one portion and place it in A. ij Mix well in the middle, then take N+1 portions and put them into the oven; then from B i′j′ Take one portion and place it in A ij Mix well, then add the ingredients.

[0127] The waste ratio should be within the normal incineration zone. Generally, it is considered that the calorific value line q1 to q3 is sufficient, which is the intersection zone of the mechanical load and thermal load. The ratio process is as follows: fermented waste → incinerated and returns calorific value → stored at the feeding point → waste ratio → incinerated again and returned → ratio again → (and so on...).

[0128] Furthermore, setting the optimal heat load in this embodiment also includes: setting the optimal heat load of the waste entering the furnace through a supervised learning regression algorithm in artificial intelligence machine learning, setting the amount of waste input and the calorific value of the waste as input objects, taking the input waste heat load as the expected output value, analyzing training data, and generating inferences.

[0129] In this embodiment, the calorific value of the waste fed into the incinerator can be approximately calculated using the ratio of boiler heat load (i.e., boiler evaporation rate, also known as main steam flow rate) to the amount of waste fed into the incinerator. Based on this calorific value, the position of the boiler operating state within the incinerator combustion diagram is calculated using thermodynamics. The waste crane then adjusts the proportions of the waste based on the calculated calorific value of the waste fed into the incinerator to achieve stability in the calorific value of the waste.

[0130] By employing supervised learning regression algorithms from artificial intelligence machine learning, the optimal heat load value of the waste fed into the incinerator is set. This guides the waste crane to learn its operational skills and optimize the amount of waste fed, intelligently controlling the heat load of the waste fed into the incinerator to ensure stable combustion. The incinerator combustion load diagram is shown below. Figure 6 As shown.

[0131] Furthermore, step S111 of this embodiment also includes: correcting the calorific value of the waste based on the weighted average calorific value of the waste, correcting the combustion properties of the waste, and recording the combustion properties of the waste.

[0132] Furthermore, step S111 of this embodiment records: in the gridded management information of the reservoir area, data is stored using an m×n matrix as the object, that is, the above-mentioned data storage is performed by A. ij A block is considered a data storage unit of this matrix; in the database, it is considered a unit with A. ij The data information bar of the identification tag stores the original attributes and combustion attributes of the waste; when the grab bucket of the garbage crane moves, including the following states: first, grabbing material, the grabbing time, weight, height, and action information are recorded in the relevant A. ij In the grid; secondly, in the interval material relocation work, then A ij The original attributes and action information of the grid garbage are transferred to B. i′j′ In the grid corresponding to the material feeding point, all information about the waste at point A (material grabbing point) is transferred to point B (material feeding point), meaning the original attributes and action information are transferred accordingly; thirdly, in the feeding operation, A... ij The original attributes and action information of the grid waste are stored in a historical database with time and action tags, namely, material grabbing A.ij Collect all information about the waste, plus historical data on the calorific value after combustion time.

[0133] The original attributes of the waste in this embodiment include: waste arrival time, source, and type (one or more of these). The combustion attributes of the waste in this embodiment include: degree of fermentation during natural fermentation, waste calorific value information, and waste collection point information. Waste collection point information includes: time, source point, coordinate system of the collection area, and amount of waste collected. Waste calorific value information includes: calorific value of the waste returned from combustion, SO2 content, and HCl content.

[0134] SO2 and HCl can be obtained by measuring and transmitting data through a CEMS (Continuous Emission Monitoring System) device for incinerated flue gas. This allows us to obtain the flue gas emission characteristics of the waste in this area, enabling us to deduce the original characteristics of the waste through historical data analysis. Furthermore, the correlation between these data points can guide the proactive control of the flue gas control system.

[0135] Source information generally refers to where the garbage comes from (e.g., XX street, XX factory, etc.), the type of garbage (e.g., household waste, industrial waste, office waste, etc.), and the weight of the garbage (to facilitate volume estimation).

[0136] In this embodiment, the combustion properties of the waste are mainly composed of information from the waste collection point and information related to the calorific value of the waste after combustion in the furnace. For example... Figure 3 As shown, field 1 contains the waste feeding collection point information obtained from the intelligent waste crane control system. After time recursion t, it is combined with field 2, the calorific value returned from waste combustion, to form a data set including time, source point, coordinate system of the collection area, feeding amount, and calorific value (which may also include other data).

[0137] A new field containing complete information such as SO2 and HCl will be added. This new field will be stored in the corresponding waste collection point data information within the gridded zoning management of the waste repository.

[0138] The recording steps of this invention can be corrected once the calorific value of the waste is obtained. The order of the steps is not limited and can be adjusted after the calorific value is calculated. The order of the steps is only for convenience of explanation and is not intended to limit the scope of the invention.

[0139] The calorific value of the corresponding waste can be calculated by the boiler heat load or power generation. Since the residence time of waste from the time it is put into the incinerator until it is burned in the incinerator is about 1.5 to 2 hours, the calorific value of the waste put into the furnace at that time can be deduced accordingly. The calorific value of the waste in the waste storage area can be obtained by using the database records of the grid area where the waste is fed 1.5 to 2 hours in advance by the waste crane.

[0140] When the data from the grid partitions are combined to form a data group covering the surface of the waste storage area, it maps to form the calorific value distribution curves or functional color change display diagrams of each waste storage area. Based on the waste incinerator combustion diagram, ensuring stable and efficient combustion of waste is achieved within the normal combustion zone between the calorific value lines q1-q3. The optimal operating condition is around the calorific value line q2. When the heat load of waste entering the furnace is constant, the amount of waste entering the furnace is inversely proportional to its calorific value. By collecting and analyzing operating parameters such as the operation of the waste crane, the amount of waste entering the furnace, the boiler heat load, power generation, and the combustion conditions of the waste incinerator, and establishing an intelligent control model for the waste crane around the optimal calorific value line q2 on the combustion diagram, and through extensive data training, comparison and correction, and model optimization, the relationships and trends of various variables are discovered. This allows for an intelligent control method that enables the waste crane to control the heat load of waste entering the furnace to achieve optimal control conditions.

[0141] The quality of combustion conditions is mainly reflected in unsuitable heat load, furnace temperature, and oxygen levels, all of which can be measured directly online. Poor conditions generally include low heat load, decreased or low furnace temperature, and high oxygen levels; these are characteristics of boiler operation. The calorific value of the waste directly affects the incinerator's operating conditions. It is generally believed that a poor or low calorific value of the waste is a direct cause of poor operating conditions, often referred to as "the waste is difficult to burn."

[0142] The entire system of this invention integrates waste incineration technology with waste storage management into a digital and intelligent management method, and applies artificial intelligence supervised learning algorithms, representing an innovative technology in the industry. Leveraging the powerful computing power and data storage advantages of modern computers, it improves the predictability, efficiency, and accuracy of control, while simplifying the system's hardware requirements. Based on the predicted calorific value of waste incineration, the system can control the amount of waste fed into the furnace using a waste crane, achieving stable calorific value and assisting in stable combustion, thus further improving waste incineration efficiency and power generation.

[0143] This invention proposes a system for intelligently controlling the heat load of waste entering the furnace using a waste crane, comprising:

[0144] Calorific value calculation module: Calculates the calorific value of the waste fed into the furnace based on the boiler heat load or power generation after waste incineration;

[0145] Reverse calculation module: Based on the residence time of the waste in the incinerator, the calorific value of the waste entering the furnace at that time is deduced;

[0146] Heat load calculation module: Calculates the heat load of waste entering the furnace based on the amount of waste and the calorific value of the waste.

[0147] Optimal heat load setting module: Sets the optimal heat load for the waste entering the incinerator based on the MCR calorific value of the waste incinerator and the amount of waste processed.

[0148] Heat load control module: Controls the heat load of waste fed into the furnace based on the optimal heat load of the waste;

[0149] Recording module: Corrects the combustion properties of the waste based on the calorific value of the waste entering the furnace, and records the combustion properties of the waste.

[0150] In this embodiment, the MCR calorific value refers to the calorific value of the waste incinerator under the maximum continuous rating (MCR) state; controlling the heat load of the waste entering the furnace refers to controlling the heat load of the boiler by automatically mixing the waste in the fermented waste storage area and controlling the calorific value of the waste entering the furnace.

[0151] Furthermore, in the calorific value calculation module of this embodiment, the calculation of the calorific value of the corresponding waste fed into the boiler based on the boiler heat load includes: collecting and calculating the weight data of the waste fed into the boiler, and calculating the mechanical load of the corresponding waste fed into the boiler.

[0152] First, within the total time T, for each q... n′ The arithmetic mean L of the cumulative material input weight is calculated.

[0153]

[0154] In formula (1), each q is calculated over the total time T. n′ The arithmetic mean L is calculated by cumulatively calculating the weight of materials fed. Where each q... n′ The material input weights are discrete, non-continuous data. Therefore, a method of averaging multiple values ​​over a specific time period, preferably 3.5-4.5 hours, is adopted. Preferably, a method of averaging multiple values ​​collected over a 4-hour period is used.

[0155] Due to the long time involved, which is not conducive to continuous calculation of waste calorific value, this embodiment adopts an artificial intelligence data processing method, which calculates the material pushing stroke over a period of time T. The relationship between the pushing stroke and the mechanical load, represented by the total weight of the waste entering the furnace, is calculated using a coefficient (k) to enable machine self-learning and real-time calculation. The specific details are as follows:

[0156]

[0157] Formula (2) is based on the total time T in Formula (1) for the pushing stroke l n′ The arithmetic mean of the total number of calculations is used as the denominator to obtain the initial mechanical load coefficient k. m′ .

[0158]

[0159] The mechanical load coefficient k is obtained by using the machine self-learning gradient iteration method of formula (3), continuously iterating the data within the interval from z1 to z2, and continuously adjusting the mechanical load coefficient k parameter to make the k value approach the optimal value, thereby obtaining the optimal formula (4). The pushing stroke l multiplied by the k coefficient is directly converted into the real-time mechanical load L. SL Model.

[0160] L SL =k×l (4)

[0161] Boiler heat load is generally characterized by boiler evaporation rate, i.e., main steam flow rate, as the target heat load f, combined with the aforementioned mechanical load L of the waste. SL The heat load is converted into the waste calorific value q, and the calculation formula is as follows:

[0162] In formula (5), the calorific value q of the waste is the difference between the total energy generated and the total energy supplied, divided by the weight of the waste, i.e., the boiler heat load f and the enthalpy H of the steam. 汽 Energy of the product, slag loss Q 渣损 Energy, other boiler steam losses Q 汽损 Sum the results and then subtract the incoming water flow rate F. 水 With feedwater enthalpy H 水 Energy of the product, airflow rate F 风 enthalpy of air H 风 The energy of the product, divided by the mechanical load L of the amount of waste fed in. SL All calculations are based on unit time. Calculations based on the aforementioned data can be performed by the waste incineration control system or by this system directly calculating and outputting relevant data collected during boiler combustion.

[0163] Furthermore, in the calorific value calculation module of this embodiment, the calculation of the calorific value of the corresponding waste fed into the furnace based on the power generation includes:

[0164] The efficiency of waste incineration heat energy utilization is characterized by a ton-to-ton waste-to-energy generation table, and its relationship with the calorific value of the waste fed into the furnace is as follows:

[0165]

[0166] In formula (9), the unit of power generation per ton of waste is kWh / t of waste, and the calorific value of the waste fed into the furnace is (kJ / kg) × 1000kg / t; therefore, the calorific value of the waste fed into the furnace is calculated as follows:

[0167]

[0168] According to formula (10), the real-time calorific value can be calculated using an empirical method.

[0169] Furthermore, the heat load control module in this embodiment also includes: controlling the garbage hoisting and mixing to achieve the optimal calorific value and feeding amount of the garbage into the furnace based on the optimal heat load of the garbage entering the furnace; adjusting the mixing ratio according to the calorific value of the garbage entering the furnace; and controlling the garbage to be incinerated in the normal incineration zone. If the calorific value of the garbage in a certain zone is low, garbage from a zone with a high calorific value is grabbed, mixed, and then fed into the furnace; if the calorific value of the garbage in a certain zone is high, garbage from a zone with a low calorific value is grabbed, mixed, and then fed into the furnace.

[0170] like Figure 7 As shown, the specific calorific value level can be determined by the q1 to q3 lines in the figure. If it is closer to the q1 line, the calorific value is higher, and if it is higher, it is higher. If it is closer to the q3 line, the calorific value is lower, and if it is lower, it is lower. The q2 line is the best, and anything closer to it is good.

[0171] according to Figure 7 The calorific value of the waste can be evaluated by fitting the calorific value after incineration to this graph and determining the region where the calorific value point is located.

[0172] Waste calorific value mixing involves mixing the waste in the upper calorific value zone (Q2) with the waste in the lower zone, bringing the calorific value line as close to the Q2 line as possible. It could also involve mixing waste in the Q4 line with waste in the zone corresponding to the Q1 line, placing it within the normal combustion zone. Alternatively, it could involve mixing waste in the Q4 line with waste in the zone corresponding to the Q1 line, also placing it within the normal combustion zone. Different calorific values ​​correspond to different waste storage zones, and the mixing of these zones is carried out using a waste crane to grab and mix the waste.

[0173] The waste storage area can be divided into multiple functional zones, such as Zone A for fermentation, Zone B for feeding pre-fermented waste, Zone C for new waste stockpiling, and Zone D for other industrial materials, etc., for zoned management. The waste in these zones has different calorific values ​​and qualities.

[0174] After the feeding area in Zone B is full, it transitions to the stockpiling area. If the stockpiling area is full, it transitions to the fermentation area. After fermentation is complete, it becomes the feeding area again, operating in a cyclical manner. Areas storing new and aged waste are generally considered low-calorific-value areas; fermented and industrial waste are considered high-calorific-value areas. When a zone completes a specific function, its role can be automatically changed through waste storage management or manually modified. In the automatic control system of the waste crane, work tasks can be automatically assigned based on the role of a given zone.

[0175] When the calorific value of waste in a certain area is low, it is necessary to take waste from areas with high calorific value, mix it thoroughly, and then add it back into the system. Only after mixing the good and bad waste can the combustion requirements be met. This process is called mixing.

[0176] The waste ratio for mixing is determined by taking into account both the calorific value of fermentation and the calorific value returned from combustion.

[0177] Furthermore, the heat load control module in this embodiment also includes: weighted averaging of the calorific value of the waste, including:

[0178] The horizontal plane of the garbage storage area is divided into an m×n grid, where m is the number of grids in the x-axis (coordinate of the distance traveled by the garbage crane) and n is the number of grids in the y-axis (coordinate of the distance traveled by the garbage crane). Each small square forms a matrix, as shown in the following formula:

[0179]

[0180] Formula (6), m×n grid matrix A, A ij Let i be one of the elements, and j be i∈m and j∈n respectively. i, j, m, and n are all positive integers. The division standard is that each grid is sufficient to define the state of its storage area. Generally, a span of 2 to 5m is appropriate for one grid. If it is too small, the amount of data will increase exponentially. If it is too large, it will not be sufficient to represent the local state.

[0181] When the grab bucket is in operation, its x and y coordinate data of the working point correspond to a certain gridded block A. ij , is the range of coordinates of an element in the matrix. For example... Figures 4 to 5 As shown.

[0182] In the grid-based management information of the reservoir area, an m×n matrix is ​​used as the data storage object, i.e., the above-mentioned A ij A block is considered a data storage unit of this matrix, and in a database, it can be considered a unit with A. ij The data information bar of the identity tag stores new field information about the original attributes and combustion attributes of the waste.

[0183] When the grab bucket of the garbage crane moves, there are mainly these states: First, it grabs material, then the grabbing time, weight, height, and movement information are recorded to the relevant A. ij In the grid; secondly, in the interval material relocation work, then A ij The original attributes and action information of the grid waste are transferred to the corresponding grid at the discharge point. That is, all information about the waste at point A (grabbing point) is transferred to the information at point B (discharging point), meaning the original attributes and action information are transferred accordingly. Thirdly, during the feeding operation, A... ij The original attributes and action information of the grid waste are stored in a historical database with time and action tags, i.e., material grabbing A. ij It includes all information about the waste, plus historical data on the calorific value (including SO2 and HCl) returned after a combustion time of 1.5 to 2 hours.

[0184] like Figure 6 As shown, when waste is piled up or dumped in the storage facility, a certain A... ij The waste stored in the grid area undergoes natural fermentation, and then the A layer of the h' layer, which is divided by the height of the z-coordinate, is used. ij-h′Within the small cube, a new element is formed in the m×n×h three-dimensional matrix structure, namely an A. ij-h′ Small cubes, where h'∈h, are all positive integers, and the grid is generally divided into cells with a span of 2-5m. It is garbage A. ij The height h' layer below the grid area, starting from the stacking time and using the natural time progression as the increasing function of fermentation degree, is used as the time-domain characteristic of waste fermentation at this point, which can be used as the characteristic of expert experience data. A ij-h′ Small cubes, such as Figure 6 As shown.

[0185] The time-domain function for waste fermentation is:

[0186] J(A ij-h′ )=k t ×ξ×t k ×F(DT1-DT0) (7)

[0187] In formula (7), A ij-h′ The degree of fermentation of the block of waste J(A) ij-h′ ), and its own waste attributes (such as type) ξ, and the ambient temperature t of the storage area. k There is a direct relationship; the natural composting time function F(DT1-DT0) is a positive correlation function representing the difference between the current time DT1 and the initial composting time DT0. However, waste fermentation is also directly related to factors such as waste thickness, storage temperature, and internal microbial community. Therefore, a fermentation coefficient k is used. t Characterization. Fermentation coefficient k t This can be estimated based on empirical values. If the intrinsic factors of waste fermentation cannot be calculated, then ξ×t k =1 is taken as a premise, that is, the relationship between relevant factors is combined to 1, and the required degree of waste fermentation J(A) is required. ij-h′ If ) = 1 indicates normal fermentation, then it can be deduced that: in summer, the piling time is 3-4 days, then k t =0.25~0.33; In winter, the storage time is 7~8 days, then k t = 0.125~0.143. When the two-quarter coefficient k t If the change exceeds 1.8 times, such as reaching nearly 2 times, it can only be theoretically extrapolated and does not meet the requirements for precise calculation. Therefore, the following method is adopted.

[0188] The time-domain calculation method for natural fermentation of waste is applied to each waste A. ij-h′ The raw data used for fermentation calculations can be used as a baseline for calculations, and its accuracy is mainly determined by the fermentation coefficient k. t In this embodiment, the calorific value from the waste combustion attributes is used to inversely calculate the waste fermentation data. The convolution calculation method and coefficient iteration method in AI calculation are employed to ensure the balance of the calculation while continuously updating and refining the fermentation coefficients for greater accuracy. The calculation process is as follows:

[0189] Starting from the surface area of ​​the waste feeding zone, when the grid zoning is as described in the aforementioned formula.

[0190] matrix

[0191] With convolution matrix or The mean of the product of , where B is a matrix with (2N+1) rows and (2N+1) columns, and N = 1, 2, 3..., leads to the formula for the average calorific value:

[0192]

[0193] Where A ij Waste fermentation coefficient k at height h' under the grid t For A ij The average value of the convolution of the B matrix centered at A. ij The calorific value of the waste is continuously updated based on multiple feeding and return cycles, and the calculation process iterates continuously, thus determining the waste fermentation coefficient k. t This would be even more accurate. AI machine learning capabilities can be employed. The corresponding data is transmitted to the grid-based management information system within the storage area for storage, enabling the intelligent waste crane to automatically control the calorific value based on this data.

[0194] To prevent the waste fermentation coefficient k t If iterative data from different seasons is included, then in actual operation, multiple coefficients are used for calculation, i.e., the waste fermentation coefficient k, which is calculated monthly or for every 15-20°C change in ambient temperature. t1 k t2 k t3 ...represent the fermentation coefficients under different month, external atmospheric conditions, or internal environmental factors in the waste storage area, respectively. These can be retrieved from the corresponding database storage location.

[0195] The fermentation coefficient, used in digital information systems to represent the changing trend of the natural fermentation process of waste, is related to ambient temperature. The degree of waste fermentation corresponds to the calorific value of the waste. The fermentation coefficient can be corrected based on the calorific value returned after waste combustion. The waste fermentation coefficient k... t The fermentation coefficient is related to ambient temperature; the natural composting time and the actual temperature of the fermentation zone show a positive correlation. The fermentation coefficient is calculated by returning a calorific value once, then returning again for further calculation, with each calculation based on corrections from the previous one. The more sample data used in the calculation, the higher the accuracy. The fermentation coefficient is used to predict the calorific value of waste, and the waste mixing ratio is determined by considering both the calorific value of the fermented waste and the returned calorific value. A higher accuracy fermentation coefficient leads to a more accurate prediction of the calorific value of waste in the fermentation zone; this includes waste that was not incinerated and therefore did not have a returned calorific value.

[0196] The weighted average method for calculating the calorific value of waste primarily addresses potential calculation deviations due to factors such as uneven mixing of waste within each grid cell, weighing errors during waste collection, and unpredictable factors during combustion. A convolutional matrix can be used to achieve this. To perform secondary calculations on its surrounding blocks and obtain the relevant average values ​​is beneficial for the following comprehensive evaluation.

[0197] A comprehensive evaluation is conducted based on the deviation between the calorific value corresponding to the degree of fermentation and the calorific value of the waste returned after incineration. The specific comparison method uses the formula...

[0198]

[0199] To calculate the same A ij The deviation between the degree of fermentation of the waste in the grid and the calorific value returned by waste incineration is defined by using the calorific value returned by waste incineration as a reference value and the calculated degree of fermentation as the evaluation object. When the ratio is within a certain range, it is considered a reasonable value, generally 0.7 to 1.3 can be considered normal data; otherwise, the fermentation degree coefficient should be corrected. The combustion attributes of waste can be defined by the waste composting time, with different levels represented by good, average, and poor fermentation.

[0200] Furthermore, in this embodiment, the waste mixing ratio is determined by taking into account both the calorific value corresponding to the degree of fermentation and the return calorific value.

[0201] The garbage crane controls the mixing of garbage to achieve the desired calorific value for the furnace, typically using ratios such as 4:1, 3:1, 2:1, 1:1, and N:1. For example, it might grab a portion from the high or low calorific value zone, scatter it in the feeding area, grab it again, scatter it once or twice more, and then grab N+1 portions for the furnace. The garbage crane's control, based on the specific characteristics of the garbage in each zone, is managed using a grid-based inventory management system. For instance, it might be positioned at point B... i′j′ Take one portion from B and place it in Aij. Then take N+1 portions and put them into the oven. i′j′ Take one portion and place it in A ij Mix well, then add the ingredients.

[0202] The waste ratio should be within the normal incineration zone. Generally, it is considered that the calorific value line q1 to q3 is sufficient, which is the intersection zone of the mechanical load and thermal load. The ratio process is as follows: fermented waste → incinerated and returns calorific value → stored at the feeding point → waste ratio → incinerated again and returned → ratio again → (and so on...).

[0203] Furthermore, the optimal heat load setting module in this embodiment also includes: setting the optimal heat load of the waste entering the furnace through a supervised learning regression algorithm in artificial intelligence machine learning, setting the amount of waste input and the calorific value of the waste as input objects, taking the input waste heat load as the expected output value, analyzing training data, and generating inference functions.

[0204] Furthermore, the recording module in this embodiment also includes: correcting the calorific value of the waste based on the weighted average calorific value of the waste, correcting the combustion properties of the waste, and recording the combustion properties of the waste.

[0205] Furthermore, the recording module in this embodiment also includes: in the gridded management information of the reservoir area, data is stored using an m×n matrix as the object, i.e., the aforementioned A ij A block is considered a data storage unit of this matrix; in the database, it is considered a unit with A. ij The data information bar of the identification tag stores the original attributes and combustion attributes of the waste; when the grab bucket of the garbage crane moves, including the following states: first, grabbing material, the grabbing time, weight, height, and action information are recorded in the relevant A. ij In the grid; secondly, in the interval material transfer operation, then A ij The original attributes and action information of the grid garbage are transferred to B. i′j′ In the grid corresponding to the material feeding point, all information about the waste at point A (material grabbing point) is transferred to point B (material feeding point), meaning the original attributes and action information are transferred accordingly; thirdly, in the feeding operation, A... ij The original attributes and action information of the grid waste are stored in a historical database with time and action tags, namely, material grabbing A. ij Collect all information about the waste, plus historical data on the calorific value after combustion time.

[0206] The original attributes of the waste in this embodiment include: waste arrival time, source, and type (one or more of these). The combustion attributes of the waste in this embodiment include: degree of fermentation during natural fermentation, waste calorific value information, and waste collection point information. Waste collection point information includes: time, source point, coordinate system of the collection area, and amount of waste collected. Waste calorific value information includes: calorific value of the waste returned from combustion, SO2 content, and HCl content.

[0207] This invention employs an algorithm that uses a garbage crane to intelligently control the calorific value of the waste fed into the furnace to ensure stable heat load during waste incineration. The algorithm primarily utilizes a garbage crane as the executing device, employing an intelligent control system to control the amount and calorific value of the waste fed into the furnace, thereby stabilizing the heat load. The mechanical load and boiler heat load are then fed back to calculate the calorific value of the waste. The calorific value calculation results are combined with the feeding data to form the combustion attributes of the waste. These combustion attributes are then combined with a time-domain calculation method based on the natural fermentation of waste within the waste storage area, continuously learning and iterating. This information is then fed into a grid-based management information system for the waste storage area. This allows for the revision and correction of the combustion attributes of the waste at the initial garbage crane loading point, and finally, the combustion information is pushed to the garbage crane in the storage area to select or mix waste to achieve the optimal calorific value for the waste fed into the furnace—a closed-loop management system.

[0208] In the description of this invention, it should be understood that terms such as "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0209] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0210] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0211] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for intelligently controlling the heat load of waste entering the furnace using a waste crane, characterized in that, include: Calorific value estimation: The calorific value of the waste fed into the furnace is estimated by analyzing the boiler heat load and power generation after waste incineration. Reverse deduction: Based on the combustion residence time in the waste incinerator, the calorific value of the waste entering the furnace at that time can be deduced; Estimated heat load: The heat load of the waste entering the furnace is estimated based on the amount of waste and the calorific value of the waste. Set the optimal heat load: Set the optimal heat load for the waste entering the incinerator based on the MCR calorific value of the waste incinerator and the amount of waste processed. Heat load control: Control the heat load of the waste fed into the furnace by controlling the waste feeding process according to the optimal heat load of the waste. Record: Adjust the combustion properties of the waste according to the calorific value of the waste entering the furnace, and record the original properties and combustion properties of the waste; The setting of the optimal heat load also includes: setting the optimal heat load of the waste entering the furnace through a supervised learning regression algorithm in artificial intelligence machine learning, setting the waste input weight and waste calorific value as input objects, taking the input waste heat load as the expected output value, analyzing training data, and generating inferences; In the aforementioned heat load control, the comprehensive evaluation, based on the deviation calculation and comparison of the calorific value corresponding to the degree of fermentation and the calorific value of the waste returned after incineration, is as follows: Using... To calculate the same A ij The deviation between the degree of fermentation of the waste in the grid and the calorific value returned by waste incineration is used as the reference value, while the calculated degree of fermentation is used as the evaluation object. When the ratio is within the set range, it is considered normal data; otherwise, the degree of fermentation coefficient is corrected.

2. The method for intelligently controlling the heat load of waste entering the furnace using a waste crane according to claim 1, characterized in that, The calculation of the boiler heat load corresponding to the calorific value of the waste fed into the boiler includes: collecting and calculating the weight data of the waste fed into the boiler, and calculating the mechanical load of the waste fed into the boiler. First, within the total time T, for each q... n′ The arithmetic mean L of the cumulative material input weight is calculated. In the above formula, where q is the value of each time. n′ The material input weights are discrete, non-continuous data. Therefore, a method of averaging multiple values ​​over a set time period was adopted. Pushing process within time T The feeding stroke and mechanical load coefficient k are calculated based on the total weight of the waste entering the furnace. First, the feeding stroke l is calculated based on the total time T. n′ The arithmetic mean of the total number of calculations is used as the denominator to obtain the initial mechanical load coefficient k. m′ The mechanical load coefficient k is determined using a gradient iteration method based on machine learning. It iterates through data within the interval z1 to z2, continuously adjusting the mechanical load coefficient k parameter to approximate its optimal value. This is then converted into the real-time mechanical load L by multiplying the pushing stroke l by the k coefficient. SL , L SL =k×l (4) Boiler heat load, characterized by boiler evaporation rate (i.e., main steam flow rate), is used as the target heat load f, combined with the aforementioned mechanical load L of the waste. SL The heat load is converted into the waste calorific value q, and the calculation formula is as follows: In the above formula, the calorific value q of the waste is the difference between the total energy generated and the total energy fed in, divided by the mechanical load L. SL That is, the boiler heat load f and the steam enthalpy H 汽 Energy of the product, slag loss Q 渣损 Energy, other boiler steam losses Q 汽损 The sum, minus the incoming water flow rate F 水 With feedwater enthalpy H 水 Energy of the product, airflow rate F 风 enthalpy of air H 风 The energy of the product, divided by the mechanical load L of the incoming waste. SL .

3. The method for intelligently controlling the heat load of waste entering the furnace using a waste crane according to claim 1, characterized in that, The calorific value of the waste fed into the furnace corresponding to the power generation calculation includes: The efficiency of waste incineration heat energy utilization is characterized by a ton-to-ton waste-to-energy generation table, and its relationship with the calorific value of the waste fed into the furnace is as follows: In the above formula, the unit of power generation per ton of waste is kWh / t of waste, and the calorific value of the waste fed into the furnace is (kJ / kg) × 1000 kg / t; therefore, the calorific value of the waste fed into the furnace is calculated as follows:

4. The method for intelligently controlling the heat load of waste entering the furnace using a waste crane as described in claim 1, characterized in that, The heat load control is as follows: the waste is controlled to achieve the optimal calorific value and feed amount based on the optimal heat load of the waste entering the furnace. The mixing ratio is determined according to the calorific value of the waste entering the furnace, and the waste is controlled to be burned in the normal incineration zone. If the calorific value of the waste in a certain zone is low, waste from a zone with a high calorific value is grabbed, mixed with the waste, and then fed into the furnace. If the calorific value of the waste in a certain zone is high, waste from a zone with a low calorific value is grabbed, mixed with the waste, and then fed into the furnace.

5. The method for intelligently controlling the heat load of waste entering the furnace using a waste crane according to claim 2, characterized in that, The heat load control also includes: weighted averaging of the calorific value of waste, including: The horizontal plane of the waste storage area is divided into an m×n grid, where m is the number of grids in the x-axis of the waste crane's movement distance, and n is the number of grids in the y-axis of the waste crane's movement distance. Each small square forms a matrix, as shown in the following formula: In the above formula, A is an m×n grid matrix. ij Let i be one of the elements, and j be i∈m and j∈n respectively, where i, j, m, and n are all positive integers, and a span of 2 to 5m is one cell. When the grab bucket is in operation, its x and y coordinate data of the working point correspond to a certain gridded block A. ij , is the range of coordinates of an element in the matrix; When waste is piled up or dumped in the storage facility, a certain A is used. ij Waste is stored in the grid area for natural fermentation, and then layer A is divided into grids at height h' based on the height of the z-coordinate. ij-h‘ Within the small cube, a new element is formed in the m×n×h three-dimensional matrix structure, namely an A. ij-h‘ Small cubes, where h'∈h, are all positive integers, with a span of 2 to 5m per cell, are garbage A. ij The layer at height h' below the grid area, starting from the stockpiling time, uses the natural time progression as the increasing function of fermentation degree to represent the time-domain characteristics of waste fermentation at this point. The waste fermentation time-domain function is: J(A ij-h‘ )=k t ×ξ×t k ×F(DT1-DT0) (7) In the above formula, garbage A ij-h‘ The degree of fermentation of the block J(A) ij-h‘ ), related to its own waste properties ξ, and the ambient temperature t of the reservoir area. k Yes, there is a relationship. The natural composting time function F(DT1-DT0) is a positive correlation function between the current time DT1 and the difference between the initial composting time DT0. Waste fermentation is also affected by factors such as waste thickness, storage temperature, and internal microbial community. A fermentation coefficient k is used. t To characterize; If the intrinsic factors of waste fermentation cannot be calculated, then ξ×t k =1 is taken as a premise, that is, the relationship between relevant factors is combined to 1, and the required degree of waste fermentation J(A) is required. ij-h‘ If ) = 1 indicates normal fermentation, then it can be deduced that: in summer, the piling time is 3-4 days, then k t =0.25~0.33; In winter, the storage time is 7~8 days, then k t = 0.125~0.143, when the two-season coefficient k t When the change exceeds 1.8 times, the following method should be used; The time-domain calculation method for natural fermentation of waste is applied to each waste A. ij-h‘ The block fermentation calculation uses the raw data as a baseline for calculation, and then calculates the waste fermentation coefficient k based on the calorific value in the waste combustion attributes. t The calculation process is as follows: Starting from the surface area of ​​the waste feeding zone, when the grid zoning is... matrix With convolution matrix or The mean of the product of , B is a matrix with (2N+1) rows and (2N+1) columns, N = 1, 2, 3, ..., from which the formula for average calorific value is derived: Where A ij Waste fermentation coefficient k at height h' under the grid t For A ij The average value of the convolution of the B matrix centered at A ij The calorific value of the waste is continuously updated with multiple feeding and return cycles, and the calculation process iterates continuously to improve the waste fermentation coefficient k. t For greater accuracy, the corresponding data is transmitted to the grid-based management information system in the storage area and stored. It is calculated using multiple coefficients based on monthly changes or changes in ambient temperature of 15-20°C, resulting in the waste fermentation coefficient k. t1 k t2 k t3 …represent the fermentation coefficients under different month, external atmospheric conditions, or internal environmental factors in the waste storage area, and are retrieved according to the corresponding database storage location.

6. The method for intelligently controlling the heat load of waste entering the furnace using a waste crane as described in claim 5, characterized in that, The heat load control also includes: a comprehensive evaluation based on the deviation between the calorific value corresponding to the degree of fermentation and the calorific value of the waste returned after incineration, wherein the residence time of the waste from the time of input to the completion of combustion in the incinerator is 1.5-2 hours; when calculating the arithmetic mean L, the time period used is q every 3.5-4.5 hours. n The average of the weights of the materials fed is calculated from multiple values.

7. The method for intelligently controlling the heat load of waste entering the furnace using a waste crane according to claim 5, characterized in that, The records include: in the grid-based management information of the reservoir area, data is stored using an m×n matrix as the object, i.e., by A ij A block is considered a data storage unit of this matrix, and in the database, it is considered a unit with A. ij The data information bar of the identification tag stores the original attributes and combustion attributes of the waste; when the grab bucket of the garbage crane moves, including the following states: first, grabbing material, the grabbing time, weight, height, and action information are recorded in the relevant A. ij In the grid; secondly, in the interval material transfer operation, then A ij The original attributes and action information of the grid garbage are transferred to B. i′j′ In the grid corresponding to the material feeding point, all information about the waste at point A (material grabbing point) is transferred to point B (material feeding point), meaning the original attributes and action information are transferred accordingly; thirdly, in the feeding operation, A... ij The original attributes and action information of the grid waste are stored in a historical database with time and action tags, i.e., material grabbing A. ij Collect all information about the waste, plus historical data on the calorific value after combustion time.

8. The method for intelligently controlling the heat load of waste entering the furnace using a waste crane according to any one of claims 1 to 7, characterized in that, The original attributes of the waste include one or more of the following: waste entry time, source, and type. The combustion attributes of the waste include: degree of fermentation during natural fermentation, calorific value information of the waste, and information on the waste feeding and collection area. The calorific value information of the waste includes the calorific value of the waste returned from combustion, SO2, and HCl. The information on the waste feeding and collection area includes one or more of the following: time, source point, coordinate system of the collection area, and feeding amount.

9. A system for intelligently controlling the heat load of waste entering the furnace using a waste crane, characterized in that, include: Calorific value calculation module: Calculates the calorific value of the waste fed into the furnace based on the boiler heat load and power generation after waste incineration; Reverse calculation module: Based on the residence time of the waste in the incinerator, the calorific value of the waste entering the furnace at that time is deduced; Heat load calculation module: Calculates the heat load of waste entering the furnace based on the amount of waste and the calorific value of the waste. Optimal heat load setting module: Sets the optimal heat load for the waste entering the incinerator based on the MCR calorific value of the waste incinerator and the amount of waste processed. Heat load control module: Controls the heat load of waste fed into the furnace based on the optimal heat load of the waste; Recording module: Corrects the combustion properties of the waste based on the calorific value of the waste entering the furnace, and records the original properties and combustion properties of the waste; The module for setting the optimal heat load also includes: setting the optimal heat load of the waste entering the furnace through a supervised learning regression algorithm in artificial intelligence machine learning, setting the waste input weight and waste calorific value as input objects, taking the input waste heat load as the expected output value, analyzing training data, and generating inferences; In the heat load control module, the comprehensive evaluation based on the deviation calculation and comparison between the calorific value corresponding to the degree of fermentation and the calorific value of the waste returned after incineration is as follows: Using... To calculate the same A ij The deviation between the degree of fermentation of the waste in the grid and the calorific value returned by waste incineration is used as the reference value, while the calculated degree of fermentation is used as the evaluation object. When the ratio is within the set range, it is considered normal data; otherwise, the degree of fermentation coefficient is corrected.

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