Carbon thermal fuel efficient heat supply system based on intelligent control

Abstract

The invention discloses an efficient carbon thermal fuel heat supply system based on intelligent control, relates to the technical field of intelligent heat supply control, and accurately analyzes the actual energy conversion efficiency of the whole process of carbon thermal fuel combustion-heat exchange by counting the periodic heat demand quantity of an industrial park and combining an energy balance algorithm. The problems that traditional demand estimation is fuzzy and efficiency accounting is rough are solved, whether inventory early warning is triggered or not can be scientifically judged, core equipment is initialized based on the carbon thermal fuel type, the problem that equipment starting parameters are not matched with fuel characteristics is solved, and then the heat release amount in the monitoring period is obtained and compared with the historical period in the same period. Intelligent control of operation parameters of core equipment is realized, and efficiency loss caused by blind adjustment is avoided; the intelligent control process of core equipment operation parameters is monitored, an adjusting instruction is output immediately once load abnormity is recognized, the problem that heat supply stability is affected by load fluctuation is solved, the core equipment load stability is guaranteed, and efficient heat supply is guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent control of heat supply, in particular to a carbon heat fuel efficient heat supply system based on intelligent control. BACKGROUND

[0002] Under the current energy transformation and emission reduction demand, carbon heat fuel heat supply needs to solve the problems of traditional system fuel fluctuation, poor coordination, and lagging regulation, in order to improve efficiency and environmental protection. The current system process device core is as follows: fuel pretreatment stage, after metering sampling, moisture / ash monitoring modeling, stable calorific value into the furnace by proportioning feeder, crushing and screening, and low temperature drying device; combustion control link, ignition burner linkage air door, boiler body coordinated with air preheater, flue gas recirculation device with load, advanced process control to stabilize main steam parameters, plate heat exchanger and heat storage tank peak and valley regulation, low temperature coal economizer and flue gas condensing waste heat recovery; oxygen / carbon monoxide / nitrogen oxides closed loop control efficiency and emission, equipment performance monitoring system diagnoses coal blockage, ash deposition and other faults and interlock disposal, signal processing feedback deviation self-correction model and controller, finally realizing machine-furnace-grid coordinated efficient heat supply.

[0003] For example, the Chinese invention patent with publication number CN116068888B discloses a boiler turbine coordinated control optimization device and method based on heat supply load fluctuation, which comprises: a data acquisition and processing module for acquiring steam data of all heat users, at least acquiring operation data of the boiler and the turbine, and performing pretreatment; an optimization control solving module for determining the optimal operation state of the unit, analyzing the current boiler and turbine operation state, predicting the next stage operation mode and state, and giving pre-operation instructions; a unit control module for receiving the pre-operation instructions of the optimization control solving module; an operation execution module for completing the operation instructions of the unit control module; and a signal processing feedback module for feeding back the execution of the operation execution module to the unit control module and tracking and feeding back the real-time operation parameters and the execution results of the operation execution module.

[0004] For example, the Chinese invention patent with publication number CN112783115B discloses an online real-time optimization method and device for a steam power system, which comprises the following steps: step one, collecting device operation data, environmental data, price data and test analysis data and storing them into a real-time database, performing data preprocessing and steady state detection to obtain a data set of the steam power system; step two, performing Gaussian transformation on the data set of the steam power system; step three, establishing a steam power system optimization model with the lowest running cost of the steam power system as the objective function, and optimizing and solving the objective function within the constraint condition range; step four, judging the system steady state, if the system is in steady state, writing the optimization result of step three into the real-time database, and taking the deviation compensation of the boiler APC and the turbine DEH controlled variable set value, adjusting the process state to reach the optimization value.

[0005] The above-mentioned technology has the following technical problems: In the combustion process of carbothermic fuels, key parameters such as feed rate, damper opening, and ammonia injection rate need to be optimized in real time to obtain the optimal solution. However, current technology has obvious limitations. Its optimization process is relatively rough and fails to compare and analyze the historical operating data stored in the real-time database. It lacks comparison with the optimal operating parameters under the same load and fuel characteristics in the past, and does not refer to the fluctuation pattern of heating network demand in the same period in history. As a result, rolling optimization requires repeated trials within a wide range of parameter ranges, which significantly reduces the optimization efficiency.

[0006] Meanwhile, core objectives such as improving combustion efficiency and reducing pollutant emissions are mostly set empirically, often resulting in a disconnect between the objectives and reality. For example, the linkage parameters between the damper and the burner set based on fuzzy objectives may exceed the combined load-bearing capacity of the boiler body and the air preheater, or cause the closed-loop control of oxygen and carbon monoxide to fail to match the expected emission requirements. Ultimately, the optimization results are difficult to implement, and the synergistic effect of devices such as the proportioning feeder and the low-temperature economizer cannot be fully utilized. Summary of the Invention

[0007] To address the technical problems of low optimization efficiency and difficulty in implementing optimization results in existing technologies, this invention provides a high-efficiency carbon-fired fuel heating system based on intelligent control. The technical solution is as follows: A high-efficiency carbon-thermal fuel heating system based on intelligent control is provided. This system includes: a heat demand analysis module, used to statistically determine the periodic heat demand of an industrial park, and analyze the actual energy conversion efficiency of the carbon-thermal fuel in the entire combustion-heat exchange process using an energy balance algorithm, thereby determining whether to trigger an inventory warning signal; a core equipment intelligent control module, used to acquire and initialize the core equipment in the heating process based on the carbon-thermal fuel type, ensuring that the operating parameters of the core equipment during the start-up phase match the fuel characteristics, acquiring the heat release of the carbon-thermal fuel within the monitoring period, and comparing it with the historical heat release range for the same period, thereby intelligently controlling the operating parameters of the core equipment; and a high-efficiency heating guarantee module, which monitors the intelligent control process of the core equipment operating parameters, and when an abnormal load is detected in the core equipment load, immediately outputs adjustment commands to ensure that the load of the core equipment remains stable, guaranteeing efficient heating.

[0008] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: (1) This invention solves the problems of vague demand estimation and rough efficiency calculation in traditional methods by statistically analyzing the periodic heat demand of industrial parks and combining energy balance algorithms to accurately analyze the actual energy conversion efficiency of the entire process of carbon thermal fuel combustion-heat exchange. It can scientifically determine whether to trigger inventory warning and avoid fuel over- or under-supplied reserves. It initializes core equipment based on carbon thermal fuel type to solve the problem of mismatch between equipment start-up parameters and fuel characteristics. Then, by acquiring the heat release during the monitoring period and comparing it with the historical period, it realizes intelligent control of core equipment operating parameters and avoids efficiency loss caused by blind adjustment. The intelligent control process of monitoring core equipment operating parameters immediately outputs adjustment instructions once abnormal load is identified, which solves the problem of load fluctuation affecting heating stability, ensures stable load of core equipment, and guarantees efficient heating. The three work together to improve the accuracy and reliability of system operation, effectively solving the technical problems of low optimization efficiency and difficulty in implementing optimization results in traditional heating systems (such as the disconnect between optimization scheme and actual equipment conditions and fuel characteristics). It ensures that the entire process from demand analysis to parameter optimization to stable operation can be implemented efficiently, realizing the high efficiency and intelligence of carbon thermal fuel heating.

[0009] (2) This invention initializes the core equipment of the heating process through the historical equipment parameter setting set, avoiding the parameter trial and error cost of traditional manual debugging, and ensuring that the equipment is adapted to the fuel characteristics as soon as it starts up; at the same time, it obtains the heat release of carbon thermal fuel in real time during the monitoring period and accurately compares it with the heat release range of the same period in history, and outputs targeted adjustment instructions for different operating conditions: when the current heat release is greater than the upper limit of the range, the efficiency loss caused by heat redundancy is avoided by increasing the waste treatment rate; when it is within the range, the secondary damper opening is slightly increased to optimize the combustion efficiency; when it is less than the lower limit of the range, the fuel feed amount or burner power can be adjusted to make up for the heat output. The whole process does not require repeated manual exploration. Through the closed-loop logic of historical parameter initialization - real-time data comparison - targeted adjustment of operating conditions, it replaces the traditional inefficient optimization mode, greatly shortens the parameter optimization cycle, effectively solves the technical problem of low optimization efficiency, and ensures that the adjustment measures are accurately implemented, thereby improving the operating efficiency and stability of the heating system.

[0010] (3) This invention implements dynamic adjustment for different operating conditions of carbon thermal fuel heat release. At the same time, during the adjustment process, the load anomaly coefficient is used as a constraint condition to monitor the load stability of the core equipment in real time. If the coefficient exceeds the threshold, the adjustment range is immediately limited to prevent the adjustment measures from being out of sync with the equipment load. This design avoids the problem of the adjustment scheme being out of sync with the actual operating conditions in traditional optimization. It also ensures that each step of adjustment is in line with the equipment operating capacity through the load anomaly coefficient, so that the optimization strategy is transformed from theoretically feasible to practically effective. It effectively solves the technical pain point that the optimization results are difficult to implement and ensures that the heating system maintains stable operation in efficient adjustment. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a schematic diagram of a high-efficiency carbon-thermal fuel heating system based on intelligent control, provided in an embodiment of the present invention. Figure 2 This is a flowchart of the intelligent control process for efficient heating using carbon-fired fuels provided in an embodiment of the present invention. Detailed Implementation

[0013] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0014] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0015] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0016] This invention provides a high-efficiency carbon-fired fuel heating system based on intelligent control. For example... Figure 1 The diagram shows a high-efficiency carbon-fired fuel heating system based on intelligent control. The system includes a heat demand analysis module, a core equipment intelligent control module, a high-efficiency heating guarantee module, and a database.

[0017] The heat demand analysis module is connected to the core equipment intelligent control module, which in turn is connected to the high-efficiency heating guarantee module. All three modules—heat demand analysis, core equipment intelligent control, and high-efficiency heating guarantee—are connected to a database.

[0018] The database supports dynamic adjustment operations and is designed around the adaptability to operating conditions and the validity of data: core parameters (various definition values, abnormal increment mapping relationships, etc.) are comprehensively determined by combining historical operating data of core equipment, design thresholds, and long-term adjustment experience values ​​to ensure adaptability to actual operating conditions; data is stored in a classified and structured manner, with different indexes established to distinguish between static and dynamic parameter storage formats and add data source labels, while regular calibration ensures data timeliness, which facilitates quick parameter retrieval and provides reliable support for dynamic adjustment.

[0019] The heat demand analysis module is used to calculate the periodic heat demand of the industrial park. It analyzes the actual energy conversion efficiency of carbon-thermal fuel in the entire combustion-heat exchange process through an energy balance algorithm, thereby determining whether to trigger an inventory warning signal.

[0020] By incorporating calendar factors such as weekdays / weekends, shift schedules, and process start / stop times for segmented statistics, and utilizing seasonal decomposition (such as seasonal-trend decomposition using local weighted regression) to extract annual and weekly cycle components, the cyclical heat demand of the industrial park can be statistically determined. For example, after adjusting for data from the previous 12 months, the park's average daily heat demand is approximately 180 MWh on weekdays and approximately 95 MWh on weekends, with a total peak demand of approximately 1520 MWh in winter and approximately 640 MWh in summer.

[0021] The core equipment for the heating process is initialized based on the type of carbon-thermal fuel. Specifically, the initialization process involves: using the carbon-thermal fuel type as an index, querying the database for the corresponding historical equipment parameter settings, and then initializing the core equipment accordingly. Using the carbon-thermal fuel type as the primary key index, the system retrieves the verified equipment parameter settings for that type from the database and downloads them to the DCS / PLC in one go to complete the initialization of the core heating equipment. For example, when the fuel type is biochar-B2, the system automatically retrieves the optimal settings for the same load range over the past three years: feeder base frequency 32.5Hz, primary air / secondary air ratio... 0.78 / 1.00, initial opening of main damper 45% / secondary damper 38%, target temperature of circulating fluidized bed 845℃, opening of return valve 62%, excess air coefficient λ=1.18, initial value of SCR ammonia injection 6.2kg / h, target feedwater temperature at economizer outlet 138℃, heating network temperature setting 85℃, circulating pump VFD 41%, and actuator dead zone and rate limit are attached; after initialization, the boiler burner, fan / damper actuator, coal feeder, return valve, SCR metering pump and heating network pump valve will enter controlled operation according to this historical optimal set, providing a convergence starting point and reachable boundary for subsequent online optimization.

[0022] Historical equipment parameter setting set refers to the full set of operating data of core equipment under stable operating conditions, which is the optimal combination of core equipment parameters determined through carbon-thermal fuel efficiency verification and multiple rounds of optimization screening.

[0023] After the core equipment of the heating process is initialized, the first batch of carbon thermal fuel is metered by the proportioning feeder and delivered to the furnace of the boiler combustion system. The core equipment then enters a coordinated operation state, thereby acquiring the heat release of the carbon thermal fuel within the monitoring period and intelligently controlling the operating parameters of the core equipment. Within this period, the heat release is calculated based on the actual measurement on the output side: if it is steam supply, the difference in heat content between steam and feedwater is obtained using the main steam flow meter and pressure / temperature sensor, and the heat release is obtained by accumulating the instantaneous steam volume × (main steam heat content − feedwater heat content) within the period; if it is hot water network, the heat release is obtained by accumulating the instantaneous flow rate × specific heat × (supply and return water temperature difference) within the period using the ultrasonic heat meter / flow meter and supply and return water temperatures. At the same time, the consistency of dry flue gas heat loss, moisture heat loss and incomplete combustion loss is checked using a flue gas O2 / CO analyzer, flue gas temperature and flue gas flow meter.

[0024] The boiler combustion system is the core unit for the energy conversion of carbonaceous fuel in heating, consisting of a furnace, burner, and air distribution system. It receives metered carbonaceous fuel, controls the mixing ratio and timing through the burner, and adjusts the excess air coefficient through the air distribution system to achieve stable combustion within the furnace, converting the fuel's chemical energy into thermal energy and providing a stable heat source for subsequent heat transfer processes.

[0025] The monitoring cycle refers to the complete energy transfer time window that starts from the initialization of the core heating equipment and covers the feeding, combustion, heat absorption of the heating surface, and output of the heating network / steam supply.

[0026] Core equipment refers to the collective term for key equipment operating collaboratively in the heating process. In an example embodiment, core equipment includes: a proportioning feeder and conveying / crushing unit, a boiler combustion system (furnace, burner or circulating fluidized bed and return valve), primary / secondary air fans and damper actuators, flue gas recirculation and air preheater, boiler heating surfaces (economizer, superheater, reheater), flue gas treatment unit (denitrification SCR / desulfurization / dust removal), feedwater and main steam system (pumps, valves, pressure and temperature measuring points), heat exchange and distribution on the heating network side (plate heat exchanger, circulating pump, regulating valve, hot water storage tank), and a monitoring and control layer (DCS / PLC, APC / MPC optimization and edge computing, online instruments for O2 / CO / NOx and flow, temperature and pressure, etc.). The collaborative operation mechanism is as follows: The optimization / scheduling layer generates feed and air distribution references based on load and fuel characteristics, which are synchronously sent to the feeder and dampers by the DCS to maintain the excess air coefficient and furnace temperature field stability. The heat generated by combustion is transferred to the steam / feed water side through the heating surface. The APC fine-tunes the air-coal ratio, FGR and SCR ammonia injection according to the main steam pressure / temperature closed loop to achieve efficiency-emission boundary control. The turbine or heating network side adjusts the extraction back pressure, supply temperature and flow rate according to the output demand and return water temperature through the DEH / pump valve VFD linkage. The hot water storage tank is used for peak and valley filling. The entire process is completed by online instruments and real-time database to complete clock alignment and deviation monitoring. Feedback is fed back to the MPC for parameter self-correction and rolling optimization, thereby forming a stable, fast and traceable collaborative control closed loop in the turbine-furnace-grid link.

[0027] The process for determining whether an inventory warning signal has been triggered is as follows: based on the cyclical heat demand and actual energy conversion efficiency of the industrial park, the expected demand for carbon-thermal fuel is derived and compared with the actual demand for carbon-thermal fuel.

[0028] The actual energy conversion efficiency is calculated using the inverse balance method. This involves collecting parameters such as boiler flue gas temperature, flue gas composition (oxygen and carbon monoxide concentrations), and fuel ash content, calculating various energy losses including flue gas heat loss and heat loss from incomplete chemical / mechanical combustion, and subtracting the total loss percentage from 100% to obtain the actual energy conversion efficiency (reflecting the proportion of fuel energy converted into effective heating energy). Based on this, and considering the periodic heat demand of the industrial park, the expected demand for carbon-thermal fuel is derived using the energy conservation principle: the formula is: Expected demand for carbon-thermal fuel = Periodic heat demand of the industrial park ÷ (Lower heating value of carbon-thermal fuel × Actual energy conversion efficiency), where the lower heating value of carbon-thermal fuel is an inherent property of the fuel (obtained through a calorific value meter).

[0029] If the expected demand for carbon thermal fuel exceeds the actual demand, an inventory warning signal is triggered, for example, by issuing a low inventory warning notice to inventory management personnel.

[0030] If the expected demand for carbon thermal fuel is less than or equal to the actual demand for carbon thermal fuel, it is determined that the inventory warning signal will not be triggered, thereby obtaining and initializing the core equipment of the heating process based on the carbon thermal fuel type.

[0031] The core equipment intelligent control module is used to acquire and initialize the core equipment of the heating process based on the type of carbon thermal fuel, ensuring that the operating parameters of the core equipment during the start-up phase match the fuel characteristics, acquiring the heat release of the carbon thermal fuel within the monitoring period, and comparing it with the historical heat release range for the same period, thereby intelligently controlling the operating parameters of the core equipment.

[0032] The intelligent control of the core equipment's operating parameters involves comparing the heat release of carbon-thermal fuel during the monitoring period with the historical heat release range for the same period.

[0033] If the heat release of the carbothermic fuel during the monitoring period exceeds the upper limit of the historical heat release range for the same period, it indicates that the actual lower heating value of the current carbothermic fuel is higher than the level of the same type of fuel in the same period in history. This may be due to factors such as optimization of fuel composition (e.g., fixed carbon content, volatile matter ratio) and reduction of impurity content, causing the heat released per unit mass of fuel combustion to exceed the historical limit. This is marked as a high heat release condition, requiring an increase in the waste treatment rate. This is mainly due to solid waste (e.g., slag, fly ash) generated after the combustion of carbothermic fuel and by-products formed during flue gas purification (e.g., desulfurization gypsum). When the heat release is high, fuel consumption may increase simultaneously, or the combustion temperature may rise, leading to changes in ash and slag formation characteristics. Targeted measures are needed to improve the treatment rate. The heat release of the carbothermic fuel during the monitoring period should be compared with the defined heat release. If the heat release of the carbothermic fuel during the monitoring period is greater than or equal to the defined heat release, the furnace is already in a high-temperature state. If the heat release is excessive and exceeds the safety threshold set based on furnace performance, the opening of the secondary air damper will be reduced to ensure that the heat release of the carbothermic fuel in the next monitoring cycle is less than the defined heat release. If it is maintained at around 95% of the defined heat release, there is no need to adjust it excessively. This is because: firstly, it reduces the amount of combustion air, weakens the degree of complete combustion of the fuel, reduces the chemical energy conversion efficiency of the carbothermic fuel per unit time, and directly reduces heat output; secondly, it moderately reduces the furnace combustion temperature to avoid further heat accumulation in a high-temperature environment, and at the same time prevents overheating damage to the furnace lining and heating surfaces due to excessive heat load. Through this adjustment, the combustion intensity of the fuel in the next monitoring cycle can be precisely controlled to ensure that the heat release is reduced to below the defined heat release, which not only ensures the safe operation of the furnace, but also avoids energy waste or equipment risks caused by heat redundancy. If the heat release of the carbothermic fuel in the monitoring cycle is less than the defined heat release, the current coordinated operation of the core equipment will be maintained.

[0034] The heat release is defined as the maximum allowable heat release stored in the database. It is determined based on the furnace performance, combined with the furnace design heat resistance limit (such as the maximum temperature that the lining material can withstand) and the rated heat load capacity (to avoid damage to the furnace heating surface due to overheating). The maximum heat input that the furnace can withstand is derived by relevant technical personnel. Then, combined with the combustion characteristics of carbothermic fuel, it is converted into the maximum allowable heat release of the corresponding fuel within the monitoring period. Finally, it is determined and stored in the database as the benchmark for judging whether the heat release exceeds the standard.

[0035] The core of increasing the waste treatment rate follows a dynamic matching logic that different treatment rates correspond to different heat release ranges. The waste treatment rate is continuously updated synchronously with changes in heat release during operation. The dynamic matching logic is pre-determined by professional technicians based on the characteristics of the heating system, equipment operating parameters, and fuel combustion patterns, and stored in the system database in the form of a key-value mapping table. For example, in the initial operation phase of the system, the baseline heat release (such as the average heat release during the same period in history) in the historical equipment parameter setting set is used as a reference to set the corresponding baseline waste treatment rate. This includes the baseline speed of the slag conveyor (such as v0=0.6m / s) and the baseline dust removal interval of the dust collector (such as t0=90 minutes), etc., to ensure that the waste treatment rate and the generation rate are fully matched under the baseline heat release conditions, thus avoiding initial accumulation.

[0036] When the system detects that the current actual heat release is higher than the upper limit of the historical heat release range for the same period, it updates the parameters of the waste treatment equipment based on the principle that the processing rate is positively correlated with the heat release. For example, the speed of the slag conveyor is synchronously increased to 0.69 m / s, and the dust collector cleaning interval is shortened to 78 minutes, so that the waste treatment rate matches the current generation in real time.

[0037] This dynamic adjustment method can avoid the accumulation of waste due to increased heat release, preventing it from affecting the combustion stability of the boiler furnace or the compliance of environmental emissions. At the same time, it forms a closed loop with the heat load control of core equipment, ensuring the safety and efficiency of the overall operation of the heating system.

[0038] If the heat release of the carbothermic fuel within the monitoring period falls within the historical heat release range for the same period, and the current heat release is basically consistent with the historical operating benchmark under this condition, the system stability has been verified, then it is marked as a heat release matching condition. The secondary damper opening increment is added to the current secondary damper opening according to the preset secondary damper opening increment in the database, thereby increasing the secondary damper opening. Under the premise of ensuring stable operating conditions, the fuel combustion efficiency is tentatively improved to pursue higher heat release. The secondary air volume entering the furnace is moderately increased to optimize the mixing uniformity of fuel and combustion air and enhance the completeness of the combustion reaction. Theoretically, without exceeding the upper limit of the historical heat release range, the energy release potential of the carbothermic fuel can be explored, a better heat output level can be investigated, and the effect of intelligent control can be verified at the same time.

[0039] If the heat release of the carbide fuel during the monitoring period is less than the lower limit of the historical heat release range for the same period, it reflects that the actual quality of the current batch of carbide fuel is lower than the historical level, which is a judgment of insufficient current energy output. It is then marked as a low heat release condition. The fuel residence time is increased by adding the current fuel residence time increment according to the preset fuel residence time increment in the database. At the same time, the primary air temperature is increased by adding the current primary air temperature according to the preset primary air temperature increment in the database, thereby increasing the primary air temperature and verifying the effect of intelligent control.

[0040] The residence time of fuel in the furnace directly determines the sufficiency of the combustion reaction. If the current batch of fuel is of low quality (e.g., low fixed carbon content, high impurity ratio), the combustion reaction rate will be slower than that of high-quality fuel. If the residence time remains unchanged, the fuel may be discharged before complete combustion, resulting in wasted heat. The core function of primary air is to carry fuel into the furnace and provide initial combustion air. Its temperature directly affects the ignition efficiency and combustion intensity of the fuel. When the fuel quality is low, its ignition point may be higher or the rate of heat release during combustion may be slower. If the primary air temperature remains unchanged, it may lead to delayed fuel ignition and lower local furnace temperatures, further inhibiting heat release. Increasing the residence time can prolong the combustion process. The reaction cycle of the fuel in the high-temperature furnace allows the combustible components (such as fixed carbon and volatiles) in the fuel more time to come into contact with the combustion air and burn completely, reducing heat loss due to incomplete combustion and indirectly increasing the actual heat release per unit of fuel. By increasing the primary air temperature, on the one hand, the fuel can be preheated in advance, reducing the energy consumption required for ignition and accelerating the fuel ignition speed; on the other hand, it can directly increase the initial temperature of the furnace, enhance the intensity of the combustion reaction, promote the rapid release of heat from the fuel, and alleviate the problem of low heat release caused by insufficient fuel quality. The primary air temperature regulation provides an environment for efficient start-up and reaction of combustion, and the fuel residence time regulation provides sufficient time support for combustion.

[0041] The preset increments for secondary damper opening, fuel residence time, and primary air temperature in the database are determined by technicians based on historical heat release data, boiler furnace rated parameters (such as heat resistance temperature and rated heat load), and the combustion characteristics of carbide fuels. First, multiple sets of operating condition tests are conducted to record the equipment's operational stability and heat release changes under different increments. Then, an increment range is selected where the adjusted heat release accurately meets the standards without causing equipment overheating or incomplete combustion. The median value within this range is then used as the preset increment. This approach avoids excessive parameter fluctuations during adjustment, ensuring system stability, while also shortening the adjustment response time and improving control efficiency.

[0042] To verify the effectiveness of intelligent control, the specific verification process is as follows: obtain the heat release of the carbothermic fuel during the verification period and compare it with the heat release of the carbothermic fuel during the monitoring period; the verification period is the next period adjacent to the detection period, and the duration of the verification period is equal to the duration of the monitoring period, so as to accurately verify the actual effect of the adjustment measures (such as the opening of the secondary damper) within the monitoring period.

[0043] Under heat release matching conditions, if there are no heat release optimization conditions, the secondary damper opening is restored and the current collaborative operation state of the core equipment is maintained. If there are heat release optimization conditions, the secondary damper opening is continuously increased according to the preset secondary damper opening increment. During the continuous increase, the intelligent control process of monitoring the operating parameters of the core equipment is carried out until the control termination condition is met.

[0044] If the heat release is low and there are no conditions to optimize the heat release, the primary air temperature will be restored and the current collaborative operation of the core equipment will be maintained. If there are conditions to optimize the heat release, the primary air temperature will be continuously increased according to the preset primary air temperature increment. During the continuous increase, the intelligent control process of monitoring the operating parameters of the core equipment will continue until the control termination condition is met.

[0045] The optimal heat release condition refers to a condition where the heat release of the carbothermic fuel during the verification period is greater than that during the monitoring period, and the increase in heat release exceeds the defined increase range. The increase in heat release refers to the difference between the heat release of the carbothermic fuel during the verification period and the heat release during the monitoring period. The defined increase range refers to the minimum allowable increase in heat release stored in the database. The achievement of this condition indicates that the intelligent control measures implemented during the monitoring period (such as secondary damper opening adjustment and fuel residence time adjustment) are significant and effective. On the one hand, the positive increase in heat release confirms the optimization effect of the control strategy on the combustion conditions, meeting the expected goal of improving energy output. On the other hand, the increase exceeding the defined value indicates that the adjustment strength and effect of the control measures have reached the preset standard and are not accidental fluctuations. Based on this, it can be determined that the current intelligent control logic is suitable for the current system operating conditions and fuel characteristics, and has the feasibility of continuous application. Subsequent adjustments can be made according to this control strategy to stably maintain the optimized heat release state, while providing a valid reference for the iteration of control parameters under similar operating conditions.

[0046] The control termination condition refers to either the rate of increase in heat release being less than the defined rate of increase in heat release, or the heat release being equal to the defined heat release. The defined rate of increase in heat release refers to the minimum allowable rate of increase in heat release stored in the database. Based on actual operational results, the determination that the heat release equals the defined heat release in the control termination condition does not require a strict requirement of perfect numerical consistency; it is sufficient to allow the heat release to stabilize at approximately 95% of the defined heat release. The achievement of this condition indicates that the current heat release has approached or reached the maximum reasonable level that the system can handle. A slower rate of increase in heat release means that further control will have very limited gain in increasing heat output, and the space for further heat release growth is extremely small. Reaching the vicinity of the defined heat release directly matches the maximum safe heat threshold set based on furnace performance. Satisfying either of these conditions indicates that the heat release has reached the maximum reasonable value under the current operating conditions. Terminating control at this point avoids the risk of excessive heat due to over-adjustment, while ensuring the system operates stably with optimal heat output.

[0047] The historical heat release range is set by obtaining the set of operating parameters of the core equipment during the monitoring period from the operation log, and comparing it with the historical operating parameter sets stored in the database using a similarity algorithm (such as Euclidean distance algorithm) to obtain the similarity between the operating parameter set and each historical operating parameter set.

[0048] Operating parameter set refers to the collection of operating parameters of core equipment (such as boilers, waste treatment equipment, etc.) during operation, such as furnace temperature, slag conveyor speed, etc.

[0049] The similarity scores are sorted in descending order. The historical operating condition parameter set with the highest similarity score is marked as the target comparison operating condition parameter set. The historical energy efficiency ratio of carbon-thermal fuel under the target comparison operating condition parameter set is obtained. The energy efficiency ratio of carbon-thermal fuel during the monitoring period is obtained and compared with the historical energy efficiency ratio.

[0050] The carbothermic fuel energy efficiency ratio (CFR) is a core indicator for measuring the energy utilization efficiency during the combustion of carbothermic fuels. Specifically, it refers to the proportion of effective heat energy actually converted from the combustion of carbothermic fuels by core equipment (such as a boiler). In other words, it is the ratio of the effective heat released during combustion to the total chemical energy contained in the fuel itself. A higher ratio indicates less fuel energy waste and higher utilization efficiency. This ratio needs to be obtained based on actual operating parameters. First, the total chemical energy of the carbothermic fuel is collected (calculated by multiplying the actual fuel mass consumed by the core equipment during the monitoring period by the inherent lower heating value of the fuel (obtained through laboratory testing)). The first is the net calorific value (kg). The second is the effective heat release after combustion (calculated from data such as furnace outlet heat medium temperature, flow rate, and return water temperature in the operating parameter set, or by using the carbon thermal fuel heat release monitored by the system and eliminating ineffective losses such as furnace heat dissipation and heat carried away by flue gas, in kJ). The result is then calculated using the formula: Carbon thermal fuel energy efficiency ratio = (effective heat release ÷ total chemical energy of fuel) × 100% (usually expressed as a percentage). For example, if 100 kg of carbon thermal fuel with a net calorific value of 18000 kJ / kg is consumed during the monitoring period, the effective heat release is 1.5 × 10⁻⁶ kJ / kg. 6 At kJ, the energy efficiency ratio is approximately 83.3%, meaning that 83.3% of the fuel's energy is converted into effective heating energy during this cycle.

[0051] If the deviation between the energy efficiency ratio of carbon thermal fuel and the historical energy efficiency ratio during the monitoring period is less than or equal to the defined deviation, it indicates that the current energy efficiency ratio is at a similar level to the historical energy efficiency ratio, the energy utilization efficiency of the current carbon thermal fuel has not fluctuated significantly, and the combustion conditions of the core equipment are basically consistent with the historical stable conditions. In this case, the heat release range under the target comparison condition parameter set is directly marked as the historical heat release range of the same period. Using the historical heat release range of the same period as the benchmark, there is no need to explore the optimal conditions from scratch for each monitoring period, thus making the optimization efficiency higher.

[0052] The degree of deviation between the energy efficiency ratio of carbon-thermal fuel during the monitoring period and the historical energy efficiency ratio refers to the absolute value of the difference between the energy efficiency ratio of carbon-thermal fuel during the monitoring period and the historical energy efficiency ratio; the degree of deviation is defined as the maximum value allowed to represent the degree of deviation stored in the database.

[0053] If the deviation between the energy efficiency ratio (EER) of the carbon thermal fuel during the monitoring period and the historical EER is greater than the defined deviation, it indicates that there is a significant difference between the current energy utilization efficiency of the carbon thermal fuel and the historical stable operating conditions. The original historical heat release range can no longer accurately adapt to the current operating conditions. If it continues to be used, it is easy to cause adjustment deviation or efficiency waste. Therefore, if the EER of the carbon thermal fuel during the monitoring period is greater than the historical EER, the EER of the carbon thermal fuel during the monitoring period is divided by the historical EER. The result is multiplied by the upper and lower limits of the heat release range to complete the increase correction target comparison operating condition parameter set heat release range. After the increase correction, it is marked as the historical synchronous heat release range. If the EER of the carbon thermal fuel during the monitoring period is less than the historical EER, the EER of the carbon thermal fuel during the monitoring period is divided by the historical EER. The result is multiplied by the upper and lower limits of the heat release range heat release range to complete the decrease correction target comparison operating condition parameter set heat release range. After the decrease correction, it is marked as the historical synchronous heat release range.

[0054] After the correction is completed, it can not only dynamically adapt the historical heat release range to the current actual operating conditions, solving the problem of the original fixed range being out of sync with efficiency changes, but also provide a precise reference for subsequent heat release adjustment, ensuring the timeliness and effectiveness of the control strategy.

[0055] The high-efficiency heating guarantee module monitors the operating parameters of core equipment and performs intelligent control. When it detects an abnormal load on the core equipment, it immediately outputs adjustment commands to ensure that the load on the core equipment remains stable and to guarantee efficient heating.

[0056] The intelligent control process for monitoring the operating parameters of core equipment involves the following steps: First, several operating parameters of the core equipment are collected, specifically the actual parameter values ​​of key components during real-time operation. These include real-time furnace temperature, actual primary air flow rate, actual fuel consumption, heat medium outlet pressure, and actual secondary air damper opening. Then, these operating parameters are compared with their corresponding rated parameters (such as rated furnace operating temperature, rated primary air flow rate, and rated heat medium outlet pressure). The operating parameter margins of the core equipment are obtained by subtracting the corresponding operating parameter values ​​from the rated parameter values. For example, if the rated furnace operating temperature is 1200℃ and the real-time furnace temperature is 1050℃, the furnace temperature margin is 1200℃ - 1050℃ = 150℃; if the rated primary air flow rate is 500 m³ / h and the actual primary air flow rate is 420 m³ / h, the primary air flow rate margin is 500 m³ / h - 420 m³ / h = 80 m³ / h.

[0057] If the margin of an operating parameter exceeding the defined number is less than the corresponding defined margin, it indicates that several key parameters of the core equipment are approaching their rated operating state, and the overall load stability risk has increased significantly. In this case, the defined load anomaly coefficient is marked as the load anomaly coefficient. The defined number represents the maximum allowed number of operating parameter margins that are less than the corresponding defined margin. The defined margin refers to the minimum allowed value of the operating parameter margin. The defined load anomaly coefficient represents the maximum allowed value of the load anomaly coefficient. The defined values ​​of different parameters are all stored in the database.

[0058] Conversely, after normalizing the margins of several operating parameters, a weighted summation is performed, and the reciprocal of the summation result is marked as the load anomaly coefficient. The weights in the weighted summation range from 0 to 1, and the specific values ​​are determined by technicians based on the degree of influence of different parameters on the stability of equipment operation (i.e., parameter importance). For example, the weight of the furnace temperature margin, which plays a key role in equipment safety, can be set to 0.3.

[0059] The load anomaly coefficient within the data analysis period is statistically analyzed, and the growth rate of the load anomaly coefficient is obtained. In the intelligent control process of monitoring the operating parameters of core equipment, based on a preset fixed time window (i.e., the data analysis period, the duration of which is determined according to the parameter fluctuation characteristics and control accuracy), a sliding window mode is adopted (moving one period along the time axis each time to achieve continuous coverage). The load anomaly coefficient is acquired in real time within the data analysis period. By comparing adjacent load anomaly coefficients and combining them with the period duration, the average rate of the load anomaly coefficient within that period is calculated, which is marked as the growth rate of the load anomaly coefficient. This approach can avoid misjudgment of single data, track the load anomaly trend in real time, and ensure control accuracy.

[0060] Abnormal load conditions include mild load abnormalities and severe load abnormalities. A mild load abnormality is defined as a load abnormality coefficient that is less than the defined load abnormality coefficient, and the growth rate of the load abnormality coefficient is greater than or equal to the growth rate of the defined load abnormality coefficient. A severe load abnormality is defined as a load abnormality coefficient that is greater than or equal to the defined load abnormality coefficient.

[0061] Output adjustment commands, specifically: Under heat release matching conditions, if there is a severe load anomaly, indicating that the equipment load is close to or exceeds the safety threshold, further opening the dampers will further aggravate the load and increase the risk of anomalies (such as causing furnace overpressure or a surge in heat loss). In this case, the continuous increase of the secondary damper opening should be stopped, and the current coordinated operation of the core equipment should be maintained. This measure can quickly curb the deterioration of the load. If there is a slight load anomaly, the increment of the secondary damper opening should be reduced based on the load anomaly coefficient. The average load anomaly coefficient within the data analysis period is compared with the defined load anomaly coefficient. The result of the ratio is subtracted from 1, and the final result is multiplied by the increment of the secondary damper opening. After the reduction, it is determined whether a deep optimization adjustment is needed. This approach retains a certain opening increment to maintain the heat release matching requirements, while avoiding over-adjustment that could lead to load fluctuations through the attenuation coefficient associated with the load anomaly coefficient, ensuring more precise adjustment.

[0062] If there is a severe load anomaly under low heat output conditions, the continuous increase of primary air temperature will be stopped directly, and the current collaborative operation of the core equipment will be maintained. This measure can quickly curb the deterioration of the load. If there is a slight load anomaly, the primary air temperature increment will be reduced based on the load anomaly coefficient. The average load anomaly coefficient within the data analysis period is compared with the defined load anomaly coefficient. The result of the ratio processing is subtracted from 1, and the final result is multiplied by the primary air temperature increment. After the reduction is completed, it is determined whether deep optimization adjustment is needed.

[0063] In the intelligent control process of monitoring the operating parameters of core equipment, we focus only on monitoring the heat release matching condition and the low heat release condition. The core reason is that the adjustment actions in these two conditions will increase the equipment load: under the heat release matching condition, the secondary air damper opening needs to be increased by a preset increment; under the low heat release condition, the fuel residence time and primary air temperature need to be increased simultaneously. Such adjustments will increase the operating intensity of the equipment and may cause load fluctuations. On the other hand, the adjustment direction for the high heat release condition is to increase the waste treatment rate (reduce the secondary air damper opening as needed), which essentially reduces the energy input of the equipment to reduce the load. The load risk is relatively lower. Therefore, by specifically monitoring the first two conditions, we can concentrate resources to control the operational stability during the load increase process, accurately avoid the risks that may be caused by the load increase, and ensure control accuracy.

[0064] The aforementioned output adjustment commands for heat release matching and low heat release conditions focus on the synergy between dynamically adapting to operating conditions and risk control, unlike conventional technologies that simply pursue efficiency or stability. On the one hand, through a tiered strategy of immediately stopping adjustments in cases of severe anomalies and quantitatively reducing incremental adjustments in cases of mild anomalies, a balance is achieved between risk prevention and continued adjustment. This avoids equipment overload caused by continued adjustment under severe anomalies (such as furnace overpressure in heat release matching conditions or excessive fan load in low heat release conditions), while retaining necessary adjustment amounts to advance the heat release target in cases of mild anomalies (such as maintaining heat release matching in matching conditions and alleviating insufficient heat in low heat release conditions). This solves the problem in conventional technologies where either stopping adjustments affects the target. On the one hand, it emphasizes the contradiction of amplifying risks; on the other hand, when there are minor anomalies, the incremental calculation method based on the relationship between the average load anomaly coefficient and the defined load anomaly coefficient within the data analysis period directly transforms the load anomaly state into the basis for adjusting intensity correction. This upgrades the adjustment action from executing fixed parameters to adapting to operating conditions through feedback, avoiding the over- or under-adjustment problems that are prone to occur when adjusting according to preset increments in conventional technologies. At the same time, the step of judging in-depth optimization after adjustment further realizes the connection between real-time correction and root cause investigation. This not only ensures the stability of the current operating conditions but also accumulates adaptation experience for subsequent similar operating conditions. Overall, it improves the flexibility of intelligent control in responding to complex load changes and its risk prediction capabilities, rather than being limited to conventional energy efficiency improvement or parameter compliance.

[0065] After the reduction is complete, determine whether deep optimization adjustment is needed. The specific determination process is as follows: Under heat release matching conditions, if there is no slight load anomaly, it is determined that no deep optimization adjustment is needed. If there is still a slight load anomaly, it is determined that deep optimization adjustment is needed. Specifically, if there are conditions for slight adjustment effectiveness, indicating that only the abnormal load growth trend has been initially alleviated but the expected effective control effect has not been achieved, the secondary damper opening increment is directly set to the minimum increment of the secondary damper opening. If there are conditions for significant adjustment effectiveness, the abnormal increment is matched based on the decrease in the growth rate of the load anomaly coefficient, the corrected load anomaly coefficient is increased, and the secondary damper opening increment is reduced again. After the deep optimization adjustment is completed, if there is still a slight load anomaly, the continuous increase of the secondary damper opening is directly stopped, and the current coordinated operation status of the core equipment is maintained.

[0066] If there is no slight load anomaly under low heat output conditions, it is determined that no deep optimization adjustment is needed. If there is still a slight load anomaly, it is determined that deep optimization adjustment is needed. Specifically, if there are conditions for slight adjustment effectiveness, the primary air temperature increment is directly set to the minimum primary air temperature increment. If there are conditions for significant adjustment effectiveness, the abnormal increment is matched based on the decrease in the growth rate of the load anomaly coefficient, the correction load anomaly coefficient is increased, thereby reducing the primary air temperature increment again. After the deep optimization adjustment is completed, if there is still a slight load anomaly, the continuous increase of primary air temperature is directly stopped, and the current collaborative operation status of the core equipment is maintained.

[0067] It's important to explain that the minimum increment minimizes the additional disturbance to the load caused by the adjustment action, providing a buffer for stable equipment load. On the other hand, compared to direct shutdown (which easily interrupts the heat release matching requirement), the minimum increment maintains the continuity of damper opening adjustment, ensuring the basic target of heat release matching is not interrupted. It also allows sufficient time for subsequent in-depth optimization (such as investigating the root causes of fuel quality and equipment component wear) through extremely low-intensity adjustment—avoiding abnormal load rebounds due to adjustment stagnation during in-depth optimization. Furthermore, the load data under the minimum increment better reflects the true root cause of the anomaly (reducing adjustment interference), providing a reliable basis for accurate problem localization. In addition, this setting requires no complex parameter calculations and can be quickly implemented, solving the problem of ambiguous adjustment strategies before conventional in-depth optimization. It achieves seamless integration between immediate safety control and subsequent precise optimization, rather than being limited to conventional incremental reduction logic.

[0068] Based on the decrease in the growth rate of the load anomaly coefficient, the abnormal increment is matched and the load anomaly coefficient is increased and corrected. This refers to the abnormal increment corresponding to the decrease range of the growth rate of each load anomaly coefficient stored in the database. Using the decrease range of the current load anomaly coefficient growth rate as an index, the corresponding abnormal increment is queried from the database. The abnormal increment refers to the value that increases and corrects the load anomaly coefficient. For example, to further reduce the secondary damper opening increment, the average load anomaly coefficient within the data analysis period is added to the abnormal increment. The ratio between the accumulated result and the defined load anomaly coefficient is subtracted from 1, and the result is multiplied by the primary air temperature increment to complete the further reduction of the secondary damper opening increment. Similarly, the same principle applies.

[0069] Increasing the correction load anomaly coefficient and adjusting again indicates that although the adjustment has effectively curbed the abnormal load growth, the effect needs to be further consolidated through dynamic adaptation to avoid repeated load anomalies due to the redundancy of the original increment. The decrease reflects the current adjustment's improvement on the load. Matching the abnormal increment accordingly can accurately quantify the adjustment space that still needs optimization. Increasing the correction load anomaly coefficient can enhance the accurate characterization of the load state and provide a more realistic basis for the second reduction increment. Its advantages are as follows: On the one hand, compared with a fixed reduction increment, dynamic adjustment based on the reduction increment can avoid over-adjustment (such as blindly reducing the increment and causing insufficient heat recovery) or under-adjustment (such as residual increment causing load fluctuations), ensuring that the primary air temperature adjustment does not interrupt the heat increase target under low heat release conditions, and can continuously reduce the risk of abnormal load; On the other hand, through the correlation logic of reduction increment - abnormal increment - correction coefficient, the adjustment is upgraded from single effective adjustment to continuous optimization, forming a closed-loop control, which solves the problem of insufficient adaptability to operating conditions caused by static maintenance after the adjustment is effective in conventional technology, further improving the accuracy of intelligent control in balancing load and heat release, and accumulating more refined adjustment parameter experience for subsequent similar operating conditions.

[0070] Adjusting the condition for mild effectiveness means that the rate of decrease in the growth rate of the load anomaly coefficient is less than the preset limit in the database. This indicates that the abnormal load growth trend has only been initially alleviated, but the expected effective control effect has not been achieved.

[0071] The condition for significant adjustment effectiveness is that the decrease in the growth rate of the load anomaly coefficient is greater than or equal to the decrease in the defined growth rate of the load anomaly coefficient, indicating that the current quantitative reduction adjustment strategy has accurately adapted to the load anomaly state.

[0072] Define the minimum allowable decrease in the growth rate of the load anomaly coefficient. The decrease in the growth rate of the load anomaly coefficient refers to the difference between the historical adjacent growth rates of the load anomaly coefficient and the current growth rate of the load anomaly coefficient.

[0073] Ensuring that the load on core equipment remains stable also includes optimizing control parameters after intelligent control ends. The specific optimization process involves collecting several set parameters of the current core equipment, namely, the parameter values ​​that have been determined for each operating link of the core equipment when intelligent control ends (such as the final secondary damper opening, fuel residence time, primary air temperature, etc.), and the final load anomaly coefficient, namely, the last load anomaly coefficient analyzed after the end of intelligent control.

[0074] If the difference between the defined load anomaly coefficient and the final load anomaly coefficient is greater than the defined difference stored in the database, it indicates that the maximum allowable value of the difference between the defined load anomaly coefficient and the final load anomaly coefficient is reached. This means that the load anomaly level of the current core equipment is significantly lower than the allowable range, and the load operation stability is excellent. It also confirms that after the intelligent control is completed, several set parameters of the core equipment (such as the final secondary damper opening and fuel residence time) are adapted to the current working conditions and have practical application feasibility. Based on several set parameters of the current core equipment, updating the historical equipment parameter setting set can not only significantly shorten the initialization time, but also avoid the efficiency loss caused by repeated trial and error, significantly improve the optimization efficiency of subsequent working conditions, and at the same time ensure the matching degree between the initialization parameters and the actual working conditions, further reduce the risk of load fluctuation, and distribute the heat supply to the industrial park on demand.

[0075] If the difference between the defined load anomaly coefficient and the final load anomaly coefficient is less than or equal to the defined difference, it indicates that the current load anomaly of the core equipment is at or near the upper limit of the allowable range. Although it can meet the basic operating requirements, it also means that the equipment setting parameters (such as the opening of the secondary damper and the fuel residence time) after the intelligent control ends have low adaptability to the current operating conditions. If they are used again when the same batch of carbon thermal fuel is initialized, it may not be able to stably achieve low load anomaly operation. There may even be problems such as reduced optimization efficiency or increased risk of load fluctuation due to insufficient parameter adaptability. Therefore, there is no need to update the historical equipment parameter setting set, and the heat supply can be directly allocated to the industrial park on demand.

[0076] Figure 2 This is a flowchart of the intelligent control process for efficient carbon-thermal fuel heating provided in this embodiment of the invention. It calculates the periodic heat demand of the industrial park and determines whether to trigger an inventory warning signal. If the expected demand for carbon-thermal fuel is greater than the actual demand, the inventory warning signal is triggered. If the expected demand for carbon-thermal fuel is less than or equal to the actual demand, the inventory warning signal is not triggered. The core equipment of the heating process is then acquired and initialized based on the type of carbon-thermal fuel. The heat release of carbon-thermal fuel during the monitoring period is acquired and compared with the heat release range of the same period in history.

[0077] If the heat release of the carbothermic fuel during the monitoring period exceeds the upper limit of the historical heat release range for the same period, it is marked as a high heat release condition. The waste treatment rate is increased, and the heat release of the carbothermic fuel during the monitoring period is compared with a defined heat release threshold. If the heat release of the carbothermic fuel during the monitoring period is greater than or equal to the defined heat release threshold, the secondary air damper opening is reduced to ensure that the heat release of the carbothermic fuel in the next monitoring period is less than the defined heat release threshold. If the heat release of the carbothermic fuel during the monitoring period is less than the defined heat release threshold, the current coordinated operation of the core equipment is maintained. If the heat release of the carbothermic fuel during the monitoring period falls within the historical heat release range for the same period, it is marked as heat release matching. Under normal operating conditions, the secondary damper opening is increased according to the preset secondary damper opening increment, while verifying the effectiveness of intelligent control. If the heat release of the carbon-thermal fuel during the monitoring period is less than the lower limit of the historical heat release range for the same period, it is marked as a low heat release condition. The fuel residence time is increased according to the preset fuel residence time increment, and the primary air temperature is increased according to the preset primary air temperature increment, while verifying the effectiveness of intelligent control. The intelligent control process monitors the operating parameters of the core equipment. When an abnormal load is detected in the core equipment load, an adjustment command is immediately output to ensure that the load of the core equipment remains stable and to ensure efficient heating. After the intelligent control ends, the control parameters are optimized.

[0078] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A high-efficiency carbon-fired fuel heating system based on intelligent control, characterized in that, The system includes: The heat demand analysis module is used to calculate the periodic heat demand of the industrial park. It analyzes the actual energy conversion efficiency of carbon-thermal fuel in the entire combustion-heat exchange process through an energy balance algorithm, thereby determining whether to trigger an inventory warning signal. The core equipment intelligent control module is used to acquire and initialize the core equipment of the heating process based on the type of carbon thermal fuel, to ensure that the operating parameters of the core equipment during the start-up phase match the fuel characteristics, to acquire the heat release of the carbon thermal fuel during the monitoring period, and to compare it with the heat release range of the same period in history, thereby intelligently controlling the operating parameters of the core equipment. The high-efficiency heating guarantee module monitors the operating parameters of core equipment through intelligent control. When it detects an abnormal load on the core equipment, it immediately outputs adjustment commands to ensure that the load on the core equipment remains stable and to guarantee efficient heating.

2. The high-efficiency carbon-fired fuel heating system based on intelligent control according to claim 1, characterized in that, The specific process for determining whether an inventory warning signal has been triggered is as follows: Based on the periodic heat demand and actual energy conversion efficiency of the industrial park, the expected demand for carbon-thermal fuel is derived and compared with the actual demand for carbon-thermal fuel. If the expected demand for carbon thermal fuel exceeds the actual demand, an inventory warning signal will be triggered. If the expected demand for carbon thermal fuel is less than or equal to the actual demand for carbon thermal fuel, it is determined that the inventory warning signal will not be triggered, thereby obtaining and initializing the core equipment of the heating process based on the carbon thermal fuel type.

3. The high-efficiency carbon-fired fuel heating system based on intelligent control according to claim 1, characterized in that, The core equipment for initializing the heating process based on carbon-thermal fuel type undergoes the following initialization process: Using carbon-thermal fuel type as an index, the database is queried to retrieve the historical equipment parameter setting set corresponding to that type, thereby initializing the core equipment of the heating process; The historical equipment parameter setting set refers to the full operating data of the core equipment under stable operating conditions, and the optimal combination of core equipment parameters determined through carbon-thermal fuel efficiency verification and multiple rounds of optimization screening. After the core equipment of the heating process is initialized, the first batch of carbon thermal fuel is metered by the proportioning feeder and delivered to the furnace of the boiler combustion system. The core equipment enters a coordinated operation state, thereby obtaining the heat release of the carbon thermal fuel during the monitoring period and intelligently controlling the operating parameters of the core equipment.

4. The high-efficiency carbon-fired fuel heating system based on intelligent control according to claim 3, characterized in that, The operating parameters of the intelligent control core equipment, and the specific control process are as follows: The heat release of carbon-thermal fuel during the monitoring period is compared with the heat release range of the same period in history; If the heat release of the carbothermic fuel during the monitoring period is greater than the upper limit of the historical heat release range for the same period, it is marked as a high heat release condition. The waste treatment rate is increased, and the heat release of the carbothermic fuel during the monitoring period is compared with the defined heat release. If the heat release of the carbothermic fuel during the monitoring period is greater than or equal to the defined heat release, the opening of the secondary air damper is reduced so that the heat release of the carbothermic fuel in the next monitoring period is less than the defined heat release. If the heat release of the carbothermic fuel during the monitoring period is less than the defined heat release, the current collaborative operation status of the core equipment is maintained. If the heat release of the carbon thermal fuel during the monitoring period falls within the historical heat release range for the same period, it is marked as a heat release matching condition. The opening of the secondary damper is increased according to the preset secondary damper opening increment, while verifying the effect of intelligent control. If the heat release of the carbon thermal fuel during the monitoring period is less than the lower limit of the historical heat release range for the same period, it is marked as a low heat release condition. The fuel residence time is increased according to the preset fuel residence time increment, and the primary air temperature is increased according to the preset primary air temperature increment, while verifying the effect of intelligent control.

5. The high-efficiency carbon-fired fuel heating system based on intelligent control according to claim 4, characterized in that, The specific process for setting the historical heat release range for the same period is as follows: Obtain the set of operating condition parameters of the core equipment during the monitoring period, and compare the similarity with each historical set of operating condition parameters to obtain the similarity between the set of operating condition parameters and each historical set of operating condition parameters. The similarity scores are sorted in descending order. The historical operating condition parameter set with the highest similarity score is marked as the target comparison operating condition parameter set. The historical energy efficiency ratio of carbon-thermal fuel under the target comparison operating condition parameter set is obtained. Obtain the energy efficiency ratio of carbon-thermal fuel during the monitoring period and compare it with the historical energy efficiency ratio; If the deviation between the energy efficiency ratio of carbon thermal fuel during the monitoring period and the historical energy efficiency ratio is less than or equal to the defined deviation, then the heat release range under the target comparison condition parameter set will be directly marked as the historical heat release range for the same period. If the deviation between the energy efficiency ratio of carbon thermal fuel during the monitoring period and the historical energy efficiency ratio is greater than the defined deviation, then under the condition that the energy efficiency ratio of carbon thermal fuel during the monitoring period is greater than the historical energy efficiency ratio, the heat release range under the correction target comparison operating condition parameter set is increased, and the range marked as the historical heat release range after the correction is increased. Under the condition that the energy efficiency ratio of carbon thermal fuel during the monitoring period is less than the historical energy efficiency ratio, the heat release range under the correction target comparison operating condition parameter set is decreased, and the range marked as the historical heat release range after the correction is decreased.

6. The high-efficiency carbon-fired fuel heating system based on intelligent control according to claim 4, characterized in that, The verification process for the intelligent control effect is as follows: The heat release of the carbothermic fuel during the verification period is obtained and compared with the heat release of the carbothermic fuel during the monitoring period. Under heat release matching conditions, if there are no heat release optimization conditions, the secondary damper opening is restored and the current collaborative operation state of the core equipment is maintained. If there are heat release optimization conditions, the secondary damper opening is continuously increased according to the preset secondary damper opening increment. During the continuous increase, the intelligent control process of monitoring the operating parameters of the core equipment is carried out until the control termination condition is met. If the heat release is low and there are no conditions to optimize the heat release, the primary air temperature will be restored and the current collaborative operation of the core equipment will be maintained. If there are conditions to optimize the heat release, the primary air temperature will be continuously increased according to the preset primary air temperature increment. During the continuous increase, the intelligent control process of monitoring the operating parameters of the core equipment will continue until the control termination condition is met. The heat release optimization condition refers to the fact that the heat release of the carbothermic fuel during the verification period is greater than the heat release of the carbothermic fuel during the monitoring period, and the increase in heat release is greater than the defined increase. The control termination condition refers to the rate of increase of heat release being less than the defined rate of increase of heat release, or the heat release being equal to the defined heat release.

7. The high-efficiency carbon-fired fuel heating system based on intelligent control according to claim 6, characterized in that, The intelligent control process for monitoring the operating parameters of the core equipment is as follows: Collect several operating parameters of the core equipment and compare them with the corresponding rated parameters to obtain the margin of several operating parameters of the core equipment. If the margin of operating parameters exceeding the defined quantity is less than the corresponding defined margin, then the defined load anomaly coefficient will be marked as the load anomaly coefficient. Conversely, after normalizing the margins of several operating parameters, a weighted sum is taken, and the reciprocal of the summation result is marked as the load anomaly coefficient. The load anomaly coefficient within the data analysis period is statistically analyzed, and the growth rate of the load anomaly coefficient is obtained.

8. The high-efficiency carbon-fired fuel heating system based on intelligent control according to claim 1, characterized in that, The abnormal load conditions include mild load abnormalities and severe load abnormalities. The term "mild load abnormality" refers to a load abnormality coefficient that is less than the defined load abnormality coefficient, and a load abnormality coefficient growth rate that is greater than or equal to the defined load abnormality coefficient growth rate. The aforementioned severe load anomaly refers to a load anomaly coefficient that is greater than or equal to the defined load anomaly coefficient. The output adjustment command specifically refers to: Under heat release matching conditions, if there is a severe load anomaly, the continuous increase of the secondary damper opening will be stopped, and the current collaborative operation of the core equipment will be maintained. If there is a slight load anomaly, the increase of the secondary damper opening will be reduced based on the load anomaly coefficient. After the reduction is completed, it will be determined whether a deep optimization adjustment is needed. If there is a severe load anomaly when the heat output is low, the continuous increase of primary air temperature will be stopped and the current collaborative operation of the core equipment will be maintained. If there is a slight load anomaly, the primary air temperature increment will be reduced based on the load anomaly coefficient. After the reduction is completed, it will be determined whether a deep optimization adjustment is needed.

9. The high-efficiency carbon-fired fuel heating system based on intelligent control according to claim 8, characterized in that, After the reduction is completed, it is determined whether a depth optimization adjustment is needed. The specific determination process is as follows: Under heat dissipation matching conditions, if there is no slight load abnormality, it is determined that no deep optimization adjustment is needed. If there is still a slight load abnormality, it is determined that deep optimization adjustment is needed. Specifically, if there are conditions for slight adjustment effectiveness, the secondary damper opening increment is directly set to the minimum secondary damper opening increment. If there are conditions for significant adjustment effectiveness, the abnormal increment is matched based on the decrease in the growth rate of the load abnormality coefficient, the correction load abnormality coefficient is increased, and the secondary damper opening increment is reduced again. After the deep optimization adjustment is completed, if there is still a slight load abnormality, the continuous increase of the secondary damper opening is directly stopped, and the current collaborative operation status of the core equipment is maintained. If there is no slight load abnormality under low heat output conditions, it is determined that no deep optimization adjustment is needed. If there is still a slight load abnormality, it is determined that deep optimization adjustment is needed. Specifically, if there are conditions for slight adjustment effectiveness, the primary air temperature increment is directly set to the minimum primary air temperature increment. If there are conditions for significant adjustment effectiveness, the abnormal increment is matched based on the decrease in the growth rate of the load abnormality coefficient, the correction load abnormality coefficient is increased, and the primary air temperature increment is reduced again. After the deep optimization adjustment is completed, if there is still a slight load abnormality, the continuous increase of primary air temperature is directly stopped, and the current collaborative operation status of the core equipment is maintained. The condition for mild adjustment effectiveness refers to the fact that the decrease in the rate of decrease of the load anomaly coefficient growth is less than the defined decrease in the rate of decrease of the load anomaly coefficient growth. The condition for the adjustment to be significantly effective is that the decrease in the rate of increase of the load anomaly coefficient is greater than or equal to the decrease in the rate of increase of the load anomaly coefficient.

10. The high-efficiency carbon-fired fuel heating system based on intelligent control according to claim 1, characterized in that, Ensuring that the core equipment load remains stable also includes optimizing control parameters after the intelligent control process ends. The specific optimization process is as follows: Collect several setting parameters of the current core equipment and the final load anomaly coefficient; If the difference between the defined load anomaly coefficient and the final load anomaly coefficient is greater than the defined difference, then based on several set parameters of the current core equipment, the historical equipment parameter setting set is updated, and the heat supply is allocated to the industrial park as needed. If the difference between the defined load anomaly coefficient and the final load anomaly coefficient is less than or equal to the defined difference, the heat supply will be allocated to the industrial park as needed.

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