Process capable of realizing uniform heating of kiln

Abstract

The invention discloses a process capable of realizing uniform heating of a kiln, and relates to the technical field of industrial kiln heating control, and the process comprises the following steps: constructing a furnace top mixed flow induction system, and realizing coordinated regulation and control of a temperature field and a concentration field through a multi-level mixed flow induction structure and dynamic flue gas circulation control; a pulse type multi-combustor alternate control system is constructed, and full-load efficient combustion is realized by adopting a pulse modulation and time sequence alternate combustion strategy; a model predictive control system is constructed, a multi-level predictive control framework and a rolling optimization mechanism are established, and temperature-fuel-air multivariable closed-loop coordination control is achieved; a multi-technology collaborative integrated system is constructed, and dynamic optimization control over the technological parameters of the whole process is achieved through partition control and multi-mode switching. According to the invention, the temperature distribution uniformity of the hearth is obviously improved, the combustion efficiency and the energy-saving performance are greatly improved, intelligent quick response control is realized, and a multi-technology collaborative optimization architecture is constructed.

Description

Technical Field

[0001] This invention relates to the field of industrial furnace heating control technology, and in particular to a process that can achieve uniform heating of the furnace. Background Technology

[0002] Industrial furnaces and kilns, as key thermal equipment in important industrial sectors such as steel, non-ferrous metals, building materials, and petrochemicals, undertake crucial production tasks such as metal heating, heat treatment, and calcination. Their operational efficiency and control level directly affect product quality, energy consumption, and environmental protection. With the deepening implementation of Industry 4.0 and green manufacturing concepts, increasingly higher demands are being placed on the temperature uniformity, combustion efficiency, and intelligent control levels of industrial furnaces and kilns. Under the strategic background of energy conservation, emission reduction, and carbon peaking and carbon neutrality, developing efficient, clean, and intelligent industrial furnace and kiln heating technologies has become an important direction for industry technological development.

[0003] Existing industrial furnace technologies suffer from the following main technical defects: First, uneven temperature distribution is a significant problem. Traditional burner arrangements and flue gas flow characteristics result in a pronounced temperature gradient within the furnace, with temperature differences reaching 50-100℃, severely impacting the consistency of product heating quality. Second, combustion efficiency is low. Existing burners mostly employ continuous combustion, operating under partial load for extended periods, resulting in combustion efficiencies typically only 75%-85%, and a lack of effective coordination control mechanisms among burners. Third, control systems exhibit lag and insufficient precision. Traditional PID control strategies face significant limitations when dealing with large time delays, strong nonlinearity, and strong coupling characteristics of multiple variables, with response times as long as 15-30 minutes, making it difficult to meet high-precision control requirements. Fourth, system integration is low. Each subsystem operates independently, lacking a collaborative optimization mechanism, resulting in suboptimal overall performance. Existing improvement technologies, such as increasing the number of burners, variable frequency power regulation, and passive flue gas recirculation, have alleviated some problems to a certain extent, but have failed to fundamentally solve key technical challenges such as temperature distribution mechanisms, burner collaborative control, and system integration optimization.

[0004] To address the aforementioned problems, this invention provides a process that enables uniform heating of kilns. Through deep integration and synergistic optimization of multiple technologies, it achieves the technical goals of uniform temperature distribution, efficient and clean combustion, and precise and intelligent control, providing an effective technical solution for the green, intelligent, and efficient development of industrial kilns. Summary of the Invention

[0005] To address the above problems, this invention provides a process that can achieve uniform heating of the kiln, thereby solving the problems of uneven temperature distribution, low combustion efficiency, delayed control response, and lack of coordinated optimization among subsystems in the prior art.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a process for achieving uniform heating of a kiln, comprising the following steps:

[0007] Step S1: Construct the furnace top mixing induction system and its synergistic combustion optimization mechanism, including setting up a multi-level mixing induction structure, establishing a dynamic flue gas circulation control system, and realizing synergistic regulation of temperature field and concentration field;

[0008] Step S2 involves constructing a pulse-type multi-burner alternating control system and its collaborative combustion optimization mechanism, including establishing a multi-burner pulse modulation control structure, constructing a time-sequential alternating combustion strategy execution system, and achieving collaborative control of full-load high-efficiency combustion and temperature field homogenization.

[0009] Step S3 involves constructing a model predictive control system and its multivariable coordinated optimization mechanism, including establishing a multi-level model predictive control architecture, constructing a rolling optimization and feedforward correction execution system, and realizing multivariable closed-loop coordinated control of temperature, fuel, and air.

[0010] Step S4 involves constructing a multi-technology collaborative integration system and its full-process optimization control mechanism, including establishing a partitioned control collaborative operation architecture, constructing a multi-mode switching control strategy execution system, and realizing dynamic optimization control of process parameters throughout the entire process.

[0011] The multi-level mixed flow induction structure in step S1 consists of a furnace top flow channel assembly, a controllable induction device assembly, and a flue gas distribution and adjustment assembly. The furnace top flow channel assembly includes three flow sub-channels of decreasing size, arranged in a spiral distribution with an angle of 120° between them, which are used to guide flue gas in different temperature ranges for differentiated reflux in a layered manner.

[0012] The controllable induction device assembly includes a series combination structure of a centrifugal induction fan module and an adjustable guide vane module. The centrifugal induction fan module is driven by a variable frequency speed control motor, and the adjustable guide vane module is composed of multiple arc-shaped guide vanes, the blade angle of which can be continuously adjusted.

[0013] The control function of the dynamic flue gas recirculation control system in step S1, wherein:

[0014] Minimizing temperature deviation is used as the primary control objective in this process and is included as a term in the control function, calculated as follows:

[0015]

[0016] Among them, F T For temperature control, T i Let T be the temperature at the i-th monitoring point inside the furnace. avg Let n be the target average temperature, and n be the total number of monitoring points.

[0017] Meanwhile, in order to regularize the flue gas recirculation system, concentration distribution uniformity is used as another term in the control function:

[0018]

[0019] Among them, F C C is the concentration control item. i Let C be the flue gas concentration at the i-th monitoring point. avg The target average concentration;

[0020] The overall control function for this process is:

[0021] F total =α·F T +β·F C

[0022] Among them, F total Let α be the overall control function, and β be the weighting coefficients.

[0023] The multi-burner pulse modulation control structure in step S2 includes a pulse width modulation execution unit, a burner load control unit, and an air-fuel ratio constant maintenance unit. The pulse width modulation execution unit adopts a hierarchical control method, which includes two control levels: coarse adjustment and fine adjustment.

[0024] In step S2, the timing alternating combustion strategy executes the system's optimization function, where:

[0025] To ensure that the alternating operation of the burners matches the heat load distribution requirements as closely as possible, temperature uniformity is taken as the primary optimization objective and incorporated into the optimization function, as shown in the following expression:

[0026] F temp =(T i -T avg ) 2

[0027] Among them, F temp To optimize temperature uniformity, T i Let T be the temperature at the i-th monitoring point surrounding product 3. avg Set the target temperature, where n is the total number of monitoring points;

[0028] Meanwhile, in order to maximize the energy-saving effect of the alternating combustion system, fuel consumption minimization is used as another term in the optimization function:

[0029]

[0030] Among them, F fuel For energy consumption optimization, Let t be the fuel consumption rate of the k-th burner. kThis corresponds to the combustion duration, where m is the total number of burners;

[0031] The overall optimization function of the system is a weighted sum of the above two items, specifically:

[0032] F opt =γ·F temp +δ·F fuel

[0033] Among them, F opt Let be the overall optimization function, and γ and δ be the weighting coefficients.

[0034] The multi-level model predictive control architecture in step S3 includes a furnace thermal prediction model unit, a product heating kinetics model unit, and a multi-objective optimization solution unit. The furnace thermal prediction model unit adopts a multi-timescale prediction structure, which includes a macroscopic prediction layer and a microscopic prediction layer.

[0035] In step S3, the control objective function of the rolling optimization and feedforward correction execution system is:

[0036] To ensure that the model predictive control effect matches the coordination requirements of the multivariable system as closely as possible, the optimization of the comprehensive performance index is used as the main control objective in this process and is included as a term in the control objective function. The calculation method is as follows:

[0037]

[0038] Where J1 is the temperature tracking performance index, y(k+i|k) is the predicted temperature at time k+i from time k, r(k+i) is the corresponding reference temperature, Q is the temperature tracking weight matrix, and N... p To predict the time domain length; simultaneously, to optimize both the energy consumption and emissions of the control system, multi-objective constraint optimization is used as another term in the control objective function:

[0039]

[0040] Where J2 is the control and emission optimization index, Δu(k+i) is the control increment, R is the control weight matrix, λ is the emission penalty factor, and N c To control the time domain length, E(k+i) is the predicted emission concentration at time k+i.

[0041] The overall control objective function for this process is:

[0042] J = J1 + ρ·J2

[0043] Where J is the overall control objective function and ρ is the balance weight coefficient.

[0044] The zoned control collaborative operation architecture in step S4 divides the furnace into multiple control zones, including a product preheating zone, a product heating zone, and a product soaking zone. Each zone has different temperature control ranges and control accuracy requirements.

[0045] The multi-mode switching control strategy includes heating mode, heat preservation mode and temperature control standby mode. In heating mode, the burner operates in high-power pulse mode, in heat preservation mode the burner operates in low-power pulse mode and maintains a constant temperature, and in temperature control standby mode fuel consumption is significantly reduced.

[0046] Compared with the prior art, the beneficial effects of the present invention are:

[0047] This invention, through the multi-level flow channel design and controllable induction device of the furnace top mixed flow induction system, combined with the pulsed multi-burner alternating control strategy, significantly improves the uniformity of the furnace thermal field, effectively controls the temperature difference between the upper and lower parts of the furnace, and achieves high-precision temperature control in each control zone, meeting stringent process requirements. It effectively solves problems such as inconsistent product heating quality and local overheating and burn-off, providing a reliable guarantee for the production of high-quality products.

[0048] This invention employs pulsed alternating combustion technology to ensure that each burner operates efficiently under rated load. Through the forward-looking optimization of the model predictive control system and the coordinated control of multiple burners, the overall energy consumption of the system is significantly reduced, and fuel consumption is significantly decreased. At the same time, through flue gas circulation dilution and combustion temperature control, the generation of pollutants such as NOx is effectively suppressed, and the emission concentration meets the stringent environmental protection standards, achieving the goal of clean and efficient combustion.

[0049] The model predictive control system constructed in this invention possesses powerful forward-looking predictive capabilities, enabling it to anticipate changes in operating conditions and proactively adjust accordingly. Through rolling optimization and feedforward correction mechanisms, it achieves precise control of systems with large time lags, strong nonlinearity, and multivariable coupling. Furnace pressure fluctuations are effectively suppressed, and temperature tracking accuracy and dynamic response performance are significantly superior to existing technologies. The adaptability and robustness of the control system are greatly improved, effectively coping with frequent changes in production conditions.

[0050] This invention innovatively integrates three advanced technologies: furnace top mixed flow induction, pulse alternating combustion, and model predictive control, forming an organic whole with complementary functions and synergistic effects. Through zoned control and collaborative operation and multi-mode switching control strategies, the control mode can be intelligently adjusted according to the product location and process stage, realizing seamless switching between multiple operating modes such as heating, heat preservation, and temperature control standby. Attached Figure Description

[0051] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. The following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained from these drawings without creative effort.

[0052] Figure 1 This is a schematic diagram of the process flow for achieving uniform heating in a kiln, provided by an embodiment of the present invention.

[0053] Figure 2 This is a schematic diagram of the burner arrangement that enables uniform heating of the kiln, provided by an embodiment of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but is merely a selection of embodiments of the present invention.

[0055] Please refer to Figure 1 and Figure 2 , Figure 1 This is a schematic diagram of a process flow that enables uniform heating in a kiln, provided by an embodiment of the present invention. Figure 2 This is a schematic diagram of the burner arrangement for achieving uniform heating in a kiln, provided by an embodiment of the present invention, including the following steps:

[0056] Step S1: Construct the furnace top mixing-induction system and its synergistic combustion optimization mechanism, including setting up a multi-level mixing-induction structure, establishing a dynamic flue gas circulation control system, and realizing synergistic regulation of temperature field and concentration field.

[0057] S1-1, Construct a multi-level mixed-flow induction structure. The multi-level mixed-flow induction structure consists of a furnace top flow channel assembly, a controllable induction device assembly, and a flue gas distribution and regulation assembly, as detailed below:

[0058] The furnace top diversion channel assembly is located in an annular area at a radial distance of 0.3-0.8 times the furnace diameter from the flue gas outlet path 5. This diversion channel assembly includes three diversion sub-channels with decreasing dimensions: the first diversion sub-channel has a cross-sectional area of ​​15%-25% of the flue gas outlet cross-sectional area and an opening angle of 15°-30° downward relative to the furnace top horizontal surface; the second diversion sub-channel has a cross-sectional area of ​​60%-80% of the first diversion sub-channel and an opening angle of 30°-45° downward relative to the furnace top horizontal surface; the third diversion sub-channel has a cross-sectional area of ​​40%-60% of the second diversion sub-channel and an opening angle of 45°-60° downward relative to the furnace top horizontal surface. The three diversion sub-channels are arranged in a spiral distribution with an included angle of 120° between them, forming a stepped diversion geometry, which is used to guide flue gas in different temperature ranges for differentiated recirculation in stages.

[0059] The controllable induction device assembly comprises a series combination of a centrifugal induction fan module and an adjustable guide vane module. The centrifugal induction fan module is driven by a variable frequency speed-regulating motor with an impeller diameter of 300mm-600mm, a speed range of 500-2000rpm, and an airflow adjustment range of 1000-8000m³ / h. 3 / h, used to provide adjustable negative pressure induction force; the adjustable guide vane module consists of 12-24 arc-shaped guide vanes, each guide vane is 150mm-300mm long and 80mm-150mm wide, the vane angle can be continuously adjusted within the range of 0°-90°, the vane material is high-temperature resistant stainless steel 316L, and the surface is provided with guide grooves to enhance the airflow guiding effect. Through precise control of the guide vane angle, the direction and velocity of the return flue gas flow can be precisely controlled.

[0060] The flue gas distribution and regulation component adopts a hierarchical control architecture, including a main control regulation module and an auxiliary regulation module:

[0061] The main control module is located at the main flue gas inlet and is responsible for overall flue gas distribution control. Its flow rate regulation range is 40%-60% of the total flue gas volume, and its temperature monitoring range is 800℃-1200℃. Auxiliary control modules are located at the inlets of each diversion channel and are responsible for fine-tuning the flue gas flow in each branch. Their flow rate regulation range covers 5%-20% of the total flue gas volume, and their temperature monitoring range adapts to the temperature characteristics of different return paths.

[0062] In addition, at the connection nodes between each flow channel and the furnace interior, a swirl-enhanced mixing device and a concentration homogenizing distributor are added for optimized connection, further enhancing the mixing effect of flue gas and fresh combustion products and achieving spatial homogenization of component concentration. The swirl-enhanced mixing device includes a combination structure of tangential air inlets and axial guide vanes. The number of tangential air inlets is 4-8, the diameter of each air inlet is 50mm-120mm, the length of the axial guide vanes is 200mm-500mm, and the spiral angle is 30°-60°, which is used to enhance the turbulent mixing intensity of the return flue gas and the airflow in the furnace.

[0063] In addition, the concentration homogenizing distributor structure includes a porous distribution plate, a mixing enhancement cavity, and a concentration detection sensor array. The pore size and porosity of the porous distribution plate can be reasonably set according to actual needs. The volume of the mixing enhancement cavity is adapted to the system scale configuration. The concentration detection sensor array has multiple detection points and has high detection accuracy. The uniform concentration field generated by this structure is coupled with the swirling mixing effect to further optimize and obtain a highly efficient mixing effect.

[0064] S1-2, A dynamic flue gas recirculation control system is established. During operation, the high-temperature flue gas generated by burner 1 is input into the improved mixed-flow induction system. Due to the excellent flow field regulation capability of the controllable induction device components and the superior flow distribution performance of the flue gas distribution adjustment components in the mixed-flow induction system, a dynamically adjustable flue gas recirculation loop is constructed. The recirculation characteristics are represented by a multi-dimensional parameter matrix C1, and the recirculation intensity is optimized in real time through various adjustment components. In order to make the flue gas recirculation effect match the combustion conditions as much as possible, the minimization of temperature deviation is used as the main control objective in this process and is included as a term in the control function. The calculation method is as follows:

[0065]

[0066] Among them, F T For temperature control, T i Let T be the temperature at the i-th monitoring point inside the furnace. avg Let n be the target average temperature, and n be the total number of monitoring points.

[0067] Meanwhile, in order to regularize the flue gas recirculation system, concentration distribution uniformity is used as another term in the control function:

[0068]

[0069] Among them, F C C is the concentration control item. i Let C be the flue gas concentration at the i-th monitoring point. avg The target average concentration.

[0070] The overall control function for this process is:

[0071] F total =α·F T +β·F C

[0072] Among them, F total The overall control function is defined by α and β, which are weighting coefficients that are dynamically adjusted according to specific operating conditions.

[0073] S1-3 achieves coordinated control of temperature and concentration fields. Through the synergistic effect of the furnace top mixing and induction system, a composite circulating flow field with multi-scale vortex characteristics is formed inside the furnace. The flue gas flows back and forth in an orderly manner along the designed multi-path 5, and the heat released by combustion is evenly transferred to the product 3 and the furnace wall 4 according to the preset distribution pattern.

[0074] Based on this, the recirculated inert flue gas reduces the oxygen content in the fresh air through dilution and suppresses the peak temperature of the combustion process through the thermal absorption effect, thereby effectively reducing the generation of thermal pollutants such as NOx. Through the organic synergy mechanism between the multi-stage furnace top induced recirculation system and the combustion system, the uniformity of the spatial distribution of the temperature field in the furnace and the dynamic balance of flue gas composition in time and space are achieved, creating an ideal thermal environment for efficient, low-emission clean combustion processes.

[0075] Step S2 involves constructing a pulse-type multi-burner alternating control system and its collaborative combustion optimization mechanism, including establishing a multi-burner pulse modulation control structure, constructing a time-sequential alternating combustion strategy execution system, and achieving collaborative control of full-load high-efficiency combustion and temperature field homogenization.

[0076] S2-1, the multi-burner pulse modulation control structure consists of a pulse width modulation execution unit, a burner load control unit, and an air-fuel ratio constant maintenance unit: the pulse width modulation execution unit is set in the control loop of each burner 1, and the execution unit adopts a hierarchical control method, including two control levels: coarse adjustment and fine adjustment.

[0077] Coarse control, with a pulse period set to 60s-180s, a duty cycle adjustment range of 20%-80%, and a pulse accuracy of ±1s, is suitable for rapid heat load adjustment. Fine control, with a pulse period set to 10s-60s, a duty cycle adjustment range of 40%-60%, and a pulse accuracy of ±0.2s, is suitable for precise and stable temperature control. The two control levels automatically switch control modes according to the size of the furnace temperature deviation to achieve precise proportional adjustment of the burner's combustion time and pause time under different operating conditions.

[0078] The burner load control unit comprises a parallel control structure of a constant flow supply module and a full-load operation guarantee module. The constant flow supply module uses a mass flow controller to regulate fuel supply, ensuring that the fuel and combustion air flow rates of each burner are always maintained at the design rated values ​​during ignition and combustion. The full-load operation guarantee module consists of a combustion status monitor and a load optimization regulator. The combustion status monitor monitors flame temperature, flame length, and combustion completeness in real time, while the load optimization regulator automatically adjusts the burner operating parameters based on the monitoring data to ensure that the burner can operate at the rated load state after each ignition.

[0079] The constant air-fuel ratio maintenance unit adopts a key node monitoring architecture, which includes a core monitoring module and a supplementary monitoring module: The core monitoring module is located in the main gas pipeline and the main combustion air pipeline, and is responsible for the main air-fuel ratio control, with an oxygen content monitoring range of 19%-23% and an air-fuel ratio adjustment range of 1.00-1.15; The supplementary monitoring module is located in the burner outlet flue and the monitoring point inside the furnace, and is responsible for combustion effect verification and feedback adjustment, with an oxygen content monitoring range of 2%-10% and an air-fuel ratio adjustment range of 0.95-1.12.

[0080] In addition, flame morphology stabilization devices and combustion completion enhancers are added at the interface between each burner and the flame flow path in the furnace to optimize combustion, improve flame stability during pulse combustion, and achieve complete fuel combustion. The flame morphology stabilization device includes a combination structure of a swirling flame stabilizer and a flame detector. The swirling flame stabilizer is equipped with 8-16 tangential air inlets, each with a diameter of 15mm-30mm and a swirling intensity number of 0.6-1.2, which is used to enhance the stability and combustion intensity of the flame root.

[0081] Meanwhile, the combustion completeness enhancer structure includes a series combination of a secondary air injection system, a combustion chamber temperature homogenization device, and an exhaust gas composition detection device. The secondary air injection quantity is 8%-15% of the main combustion air quantity, and the injection pressure is 0.1-0.3MPa. The combustion chamber temperature homogenization device controls the temperature distribution non-uniformity within ±15℃. The exhaust gas composition detection device monitors the concentration of pollutants such as CO, NOx, and SO2 in real time, generates the obtained high-efficiency complete combustion characteristic parameters, and couples and optimizes them with the previous flame stabilization effect to obtain the final pulse combustion high-efficiency effect.

[0082] S2-2, Construct a time-sequential alternating combustion strategy execution system. During the operation phase, multiple burners 1 are input into an improved pulse modulation control system. Due to the excellent timing control capability of the pulse width modulation execution unit in the pulse modulation control system and the superior full-load operation performance of the burner load control unit, a multi-burner time-sequential alternating combustion strategy is established. This alternating combustion characteristic is represented by a timing matrix C2, and the combustion timing is precisely optimized through each control unit. To ensure that the alternating operation effect of the burners matches the heat load distribution requirements as closely as possible, temperature uniformity is taken as the main optimization objective and incorporated into the optimization function, the expression of which is as follows:

[0083] F temp =(T i -T avg ) 2

[0084] Among them, F temp To optimize temperature uniformity, T i Let T be the temperature at the i-th monitoring point surrounding product 3. avg Set the target temperature, where n is the total number of monitoring points;

[0085] Meanwhile, in order to maximize the energy-saving effect of the alternating combustion system, fuel consumption minimization is used as another term in the optimization function:

[0086]

[0087] Among them, F fuel For energy consumption optimization, Let t be the fuel consumption rate of the k-th burner. k Let m be the corresponding combustion duration, and m be the total number of burners.

[0088] The overall optimization function of the system is a weighted sum of the above two items, specifically:

[0089] F opt =γ·F temp +δ·F fuel

[0090] Among them, F opt The overall optimization function is defined by γ and δ, which are weighting coefficients that are dynamically adjusted according to production process requirements. Typically, γ is set to 0.6-0.8 and δ is set to 0.2-0.4.

[0091] The optimization strategy for another set of burner control parameters C3 is completely coordinated with that of parameter C2. However, its timing arrangement adopts a phase difference control algorithm, which is set differently according to the spatial position of the burner and the heat transfer path. This allows each burner to form a tiered start-up working mode in the time sequence. The start-up phase difference between adjacent burners is set to 60°-120° to ensure that an appropriate number of burners are in working condition at any given time. This effectively maintains the continuity and uniformity of the heat load inside the furnace during the detection phase.

[0092] S2-3, achieving full-load high-efficiency combustion and temperature field homogenization coordinated control. Through the synergistic effect of the above-mentioned pulsed multi-burner alternating control system and the furnace top mixing induction system in step S1, a dynamic thermal field distribution with spatiotemporal coupling is formed in the furnace. Each burner 1 performs full-load pulse combustion according to the optimized timing strategy. With the precise separation and guidance of the flame flow by the fire baffle 2, the high-temperature flame jet generated by alternating combustion flows orderly in the furnace along the preset circulation path.

[0093] It should be noted that by precisely arranging the timing of multiple burners through the control system, burner 1 no longer adopts the traditional mode of synchronous ignition, but instead alternates to burn in turn according to the optimal sequence determined by thermal calculations. Each area in the furnace takes turns to be heated by the flame according to the design cycle, thereby effectively eliminating the temperature gradient problem caused by fixed combustion positions.

[0094] Based on making full use of the existing burner 1 and fire baffle 2 arrangement, the newly constructed pulsed alternating combustion control strategy forms a highly integrated synergistic optimization mechanism with the original combustion system, realizing the spatiotemporal uniform distribution of the furnace heat field and the efficient and clean combustion process. Compared with the traditional continuous proportional combustion method, it has a significantly superior comprehensive energy-saving and emission-reduction effect, providing important technical support for the green and efficient operation of industrial furnaces.

[0095] Step S3 involves constructing a model predictive control system and its multivariate coordinated optimization mechanism, including: establishing a multi-level model predictive control architecture, constructing a rolling optimization and feedforward correction execution system, and realizing multivariate closed-loop coordinated control of temperature, fuel, and air.

[0096] S3-1, Establish a multi-level model predictive control architecture, which consists of a furnace thermal prediction model unit, a product heating kinetics model unit, and a multi-objective optimization solution unit:

[0097] The furnace thermal prediction model unit is located in the system control core layer. This prediction model unit adopts a multi-timescale prediction structure, including both macroscopic and microscopic prediction levels:

[0098] The macroscopic prediction layer has a prediction time domain of 300s-600s, a prediction step size of 30s-60s, and a prediction accuracy of ±15℃, suitable for long-term prediction of the overall temperature trend of the furnace. The microscopic prediction layer has a prediction time domain of 60s-180s, a prediction step size of 5s-15s, and a prediction accuracy of ±3℃, suitable for short-term accurate prediction of temperature changes in the micro-regions surrounding product 3. The two prediction layers adopt a multi-timescale nested prediction method, automatically switching the prediction scale according to the control accuracy requirements, to achieve accurate prediction and calculation of the evolution law of the furnace temperature field at different time scales.

[0099] The product heating kinetics model unit includes a cascaded calculation structure of a heat transfer coefficient identification module and a heating curve prediction module. The heat transfer coefficient identification module uses the recursive least squares method to identify parameters online. The heating curve prediction module uses the finite element numerical solution method to ensure that the temperature evolution trajectory during the product heating process can accurately follow the process setting curve.

[0100] The multi-objective optimization solution unit adopts a comprehensive optimization architecture, which includes main control objectives and constraint objectives: the main control objectives are to maximize temperature uniformity and minimize energy consumption, and multi-objective fusion is performed by weighted coefficient method; the constraint objectives are to meet emission standards, ensure product quality and ensure stable equipment operation, which serve as optimization boundary conditions.

[0101] The objective function is:

[0102]

[0103] The constraint is that fuel consumption ≤ design value × 110%; the optimization objectives are fused using a weighted coefficient method, and the weight coefficients are dynamically allocated according to the importance of the production process.

[0104] In addition, at the interface between each prediction model and the actual control execution, a rolling time-domain optimizer and a robust compensator are added to enhance predictive control, thereby improving the model prediction accuracy and effectively compensating for model uncertainties. The rolling time-domain optimizer includes a combination structure of a prediction time-domain setter and a control time-domain setter. The prediction time-domain length is set to 20-60 sampling periods, the control time-domain length is set to 3-10 sampling periods, and the rolling optimization period is 30s-120s, which is used to realize online rolling optimization calculation of model predictive control.

[0105] Similarly, the robust compensator structure includes a parallel combination of a model mismatch detector, a parameter adaptive regulator, and a disturbance observation compensator. The model mismatch detector uses statistical testing methods to detect model prediction errors, the parameter adaptive regulator adjusts model parameters based on the detection results, and the disturbance observation compensator estimates and compensates for external disturbances to generate the obtained high-precision predictive control effect. This effect is then fused with the previous rolling optimization results to obtain the final robust predictive control performance.

[0106] S3-2, a rolling optimization and feedforward correction execution system is constructed. During the operation phase, data from multiple sensors arranged inside the furnace are input into the improved model predictive control system. Due to the excellent dynamic prediction capability of the furnace thermal prediction model unit and the superior coordinated optimization performance of the multi-objective optimization solution unit in the model predictive control system, a forward-looking control strategy based on future information is effectively established. This control characteristic can be represented by the predictive control parameter matrix C4, and the rolling optimization update of the control output is achieved through each execution unit. In order to match the model predictive control effect with the coordination requirements of the multivariable system as much as possible, the optimization of the comprehensive performance index is used as the main control objective in this process and is included as a term in the control objective function. The calculation method is as follows:

[0107]

[0108] Where J1 is the temperature tracking performance index, y(k+i|k) is the predicted temperature at time k+i from time k, r(k+i) is the corresponding reference temperature, Q is the temperature tracking weight matrix, and N... p To predict the time domain length; simultaneously, to optimize both the energy consumption and emissions of the control system, multi-objective constraint optimization is used as another term in the control objective function:

[0109]

[0110] Where J2 is the control and emission optimization index, Δu(k+i) is the control increment, R is the control weight matrix, λ is the emission penalty factor, and N c To control the time domain length, E(k+i) is the predicted emission concentration at time k+i.

[0111] The overall control objective function for this process is:

[0112] J = J1 + ρ·J2

[0113] Where J is the overall control objective function, and ρ is the balance weight coefficient, which is usually taken as 0.1-0.3 and is dynamically adjusted according to the control priority.

[0114] Specifically, the model predictive controller automatically generates switching timing adjustment instructions, pulse parameter optimization instructions, and furnace top induced fan speed adjustment instructions or flue damper opening adjustment instructions for each burner 1 based on the prediction deviation calculation results, so as to simultaneously meet multiple control indicators such as furnace temperature uniformity target, product heating rate requirements and emission constraints.

[0115] S3-3 achieves multi-variable closed-loop coordinated control of temperature, fuel, and air. Through the deep integration and collaboration between the above-mentioned model predictive control system and the aforementioned steps S1 and S2, the entire combustion control system forms a forward-looking and adaptive intelligent temperature-fuel-air multi-variable closed-loop coordinated control architecture.

[0116] When a new product 3 is detected entering the furnace or when the operating conditions change, the model predictive controller can predict its dynamic impact on the furnace temperature distribution and combustion state 120s-300s in advance, and accordingly shorten the pulse combustion cycle from the standard value of 120s to 90s-60s, while increasing the top flue gas induction amount from the rated value by 15%-30% to enhance the directional heating of this area; conversely, when the product is about to be discharged from the furnace or the heat load is reduced, the controller reduces the fuel supply power to 70%-85% of the rated value 60s-180s in advance, and extends the combustion interval time to 120%-150% of the standard value to effectively avoid furnace temperature overshoot.

[0117] It should be noted that many technical coordination challenges in the practical application of the pulse combustion control strategy have been systematically resolved under the model predictive control framework in this step: the controller comprehensively considers the impact effect of alternating burner start-up and shutdown on the total gas flow, and adopts a time-sharing staggered ignition strategy to avoid drastic fluctuations in gas supply pressure caused by the simultaneous start-up and shutdown of a large number of burners in a short period of time; at the same time, dynamic constraints on furnace pressure are applied, and when the pressure deviation from the set value is predicted, the flue damper opening or the frequency of the induced fan is adjusted 15s-45s in advance for active compensation to ensure that the furnace pressure is always maintained in a stable and controlled state, with a pressure fluctuation standard deviation ≤15Pa.

[0118] More importantly, through rolling optimization and feedforward correction mechanisms in model predictive control, the system overcomes the control limitations of traditional PID control when facing complex characteristics such as large time lag, strong nonlinearity, and multivariable coupling, achieving high-precision tracking control and dynamic optimization performance of furnace temperature. The furnace temperature curves of each segment strictly conform to the process setting requirements, while avoiding energy waste and increased harmful emissions, achieving an organic unity of high dynamic control performance and high-precision temperature control.

[0119] Step S4, Constructing a Multi-Technology Collaborative Integration System and Its Full-Process Optimization Control Mechanism. S4 includes: establishing a partitioned control collaborative operation architecture, constructing a multi-mode switching control strategy execution system, and realizing dynamic optimization control of process parameters throughout the entire process.

[0120] S4-1, Establish a zoned control collaborative operation architecture, which consists of a furnace zone control unit, a product tracking and positioning unit, and a technology collaborative integration unit. The furnace zone control unit divides the entire furnace into three functionally progressive control zones according to the process flow.

[0121] First control zone (product preheating zone): Length set at 25%-35% of the total furnace length, temperature control range at 200℃-600℃, control accuracy at ±8℃, and burner quantity at 30%-40% of the total; Second control zone (product heating zone): Length set at 40%-50% of the total furnace length, temperature control range at 600℃-1000℃, control accuracy at ±5℃, and burner quantity at 45%-55% of the total; Third control zone (product soaking zone): Length set at 20%-30% of the total furnace length, temperature control range at 900℃-1200℃, control accuracy at ±3℃, and burner quantity at 15%-25% of the total.

[0122] The three control zones operate using a combination of independent control and coordinated linkage. The control focus is automatically switched according to the real-time position of product 3 in the furnace to achieve precise temperature control of the product heating process at different stages of the process.

[0123] The product tracking and positioning unit includes a real-time monitoring structure comprising a position detection module and a temperature tracking module. The position detection module employs a fusion technology of laser ranging and machine vision. The product temperature tracking module uses an infrared thermal imager array to ensure that the model predictive control system can acquire the precise position information and temperature distribution status of product 3 in real time, providing accurate feedback data for dynamic control strategy adjustments.

[0124] The technology collaboration and integration unit adopts a core collaboration architecture, comprising a main collaboration module and auxiliary collaboration modules: the main collaboration module is responsible for coordinating mixed-flow induction and combustion control, and pulse combustion and temperature control, with a coordination cycle of 30s-120s and a coordination accuracy of ≥90%; the auxiliary collaboration module is responsible for coordinating model prediction and equipment execution, and coordinating zone control and global optimization, with a coordination response time of ≤15s and a coordination effect of ≥85%.

[0125] Furthermore, at the functional boundaries of each technical module, synergy enhancers and performance evaluation monitors are integrated and optimized to further amplify the synergistic effects of various technologies and achieve continuous improvement in the overall system performance. The synergy enhancer comprises a combination of a technology coupling analyzer and a synergy gain optimizer. The technology coupling analyzer calculates the correlation strength between each technical module in real time, while the synergy gain optimizer adjusts the operating parameters of each module based on the correlation analysis results to maximize the performance improvement brought about by technological synergy.

[0126] Similarly, the performance evaluation monitor structure includes a serial combination of a key performance indicator monitor, a synergy effect evaluator, and an improvement strategy generator. The key performance indicator monitor tracks core indicators such as temperature control accuracy, energy efficiency, and emission levels. The synergy effect evaluator quantifies the performance improvement brought about by the technology integration. The improvement strategy generator automatically generates system optimization suggestions based on the evaluation results, generates the obtained continuous improvement mechanism, and iteratively optimizes it with the previous synergy enhancement effects to obtain the final high-efficiency synergistic integrated system performance.

[0127] S4-2, a multi-mode switching control strategy execution system is constructed. During the operation phase, product 3, input from the front section of the furnace, is gradually heated according to process requirements. Due to the excellent real-time monitoring capability of the product tracking and positioning unit and the superior coordinated control performance of the technology integration unit in the multi-mode switching control system, a dynamic control mode switching strategy based on product position and process stage can be effectively established. The characteristics of this control mode can be represented by a multi-dimensional control state matrix C5, and intelligent switching optimization of the control mode is achieved through a model predictive control system. To ensure that the multi-mode control switching effect matches the specific needs of different process stages as closely as possible, maximizing process compliance is used as the main control objective and included as a term in the mode switching objective function. The calculation method is as follows:

[0128]

[0129] Among them, T i T represents the actual temperature at the i-th position of the product. target,i For the corresponding process target temperature, W i This refers to the positional weighting coefficient.

[0130] Meanwhile, in order to optimize both system energy consumption and operational stability, a comprehensive efficiency index is used as another term in the objective function:

[0131] F efficiency =η heat ·η combustion ·η control

[0132] Where, ηheat For heat transfer efficiency, η combustion For combustion efficiency, η control To improve system efficiency, the overall mode-switching objective function for this process is:

[0133]

[0134] in, ψ and ψ are the mode weight coefficients, usually The value is 0.7-0.8, and the value of ψ is 0.2-0.3, which are dynamically adjusted according to production priority.

[0135] Specifically, the model predictive control system intelligently switches between multiple control modes based on the specific needs of different process stages:

[0136] Heating mode: When the product enters the preheating and heating zones, the system focuses on increasing the combustion intensity to 85%-100% of the rated power, setting the burner pulse duty cycle to 60%-80%, and shortening the pulse cycle to 60s-90s; at the same time, the flue gas circulation volume is increased to 20%-30% by using the furnace top induced reflux device, and the induced fan speed is increased to 1200rpm-1600rpm to quickly and uniformly distribute the furnace temperature, with the temperature rise rate controlled within the range of 8℃ / min-15℃ / min.

[0137] Uniform heat preservation mode: When the product enters the uniform heat preservation zone, the system adjusts the pulse combustion duty cycle to 40%-60%, extends the pulse cycle to 90s-150s, reduces the induced air volume to 60%-80% of the rated value, and reduces the induced fan speed to 800rpm-1200rpm. This maintains a constant temperature within ±3℃ and reduces airflow disturbance in the furnace to reduce the product oxidation loss rate to ≤2%.

[0138] Temperature-controlled standby mode: During production breaks or cooling phases, the control system automatically adjusts to a lean air-fuel ratio combustion mode and adopts an intermittent ventilation strategy, reducing fuel consumption to 40%-60% of normal operation, achieving significant energy-saving effects.

[0139] S4-3 enables dynamic optimization and control of process parameters throughout the entire process. Through the coordinated action of the aforementioned multi-technology integrated system, the various components within the furnace form an organically integrated operating mechanism that is functionally complementary and synergistically efficient. The furnace top mixed-flow induction system improves the uniformity of the thermal field in the furnace space to over 95%, effectively solving the technical problem of large temperature differences between the upper and lower parts of the traditional furnace. The pulse alternating combustion system ensures that each burner operates in a highly efficient combustion state and can flexibly respond to load changes, overcoming the problems of difficulty in timely adjustment and low combustion efficiency in the previous continuous combustion mode. The model predictive control system acts as the intelligent coordination "brain" of the entire system, unifying and coordinating the induced flow and the collaborative work of the burner group, significantly improving the foresight and accuracy of control, and effectively making up for the technical deficiencies of conventional feedback control strategies such as lag and parameter fixation.

[0140] Compared with existing technologies, this invention innovatively integrates three advanced technologies—furnace top mixed flow induction technology, pulsed alternating combustion technology, and model predictive control technology—into an industrial heating furnace system. This achieves intelligent control of the furnace thermal process and a comprehensive improvement in the overall system performance, fully demonstrating the invention's significant technological advancement and engineering applicability. It provides an important technical solution and industrial application platform for the green, intelligent, and efficient development of modern industrial furnaces.

[0141] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations will be apparent to those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A process for achieving uniform heating in a kiln, characterized in that, The process includes the following steps: Step S1: Construct the furnace top mixing induction system and its synergistic combustion optimization mechanism, including setting up a multi-level mixing induction structure, establishing a dynamic flue gas circulation control system, and realizing synergistic regulation of temperature field and concentration field; Step S2 involves constructing a pulse-type multi-burner alternating control system and its collaborative combustion optimization mechanism, including establishing a multi-burner pulse modulation control structure, constructing a time-sequential alternating combustion strategy execution system, and achieving collaborative control of full-load high-efficiency combustion and temperature field homogenization. Step S3 involves constructing a model predictive control system and its multivariable coordinated optimization mechanism, including establishing a multi-level model predictive control architecture, constructing a rolling optimization and feedforward correction execution system, and realizing multivariable closed-loop coordinated control of temperature, fuel, and air. Step S4 involves constructing a multi-technology collaborative integration system and its full-process optimization control mechanism, including establishing a partitioned control collaborative operation architecture, constructing a multi-mode switching control strategy execution system, and realizing dynamic optimization control of process parameters throughout the entire process.

2. The process for achieving uniform heating of the kiln according to claim 1, characterized in that, The multi-level mixed flow induction structure in step S1 consists of a furnace top flow channel assembly, a controllable induction device assembly, and a flue gas distribution and adjustment assembly. The furnace top flow channel assembly includes three flow sub-channels of decreasing size, arranged in a spiral distribution with an included angle of 120° between them, which are used to guide flue gas in different temperature ranges for differentiated reflux in a layered manner.

3. The process for achieving uniform heating of the kiln according to claim 2, characterized in that, The controllable induction device assembly includes a series combination structure of a centrifugal induction fan module and an adjustable guide vane module. The centrifugal induction fan module is driven by a variable frequency speed control motor, and the adjustable guide vane module is composed of multiple arc-shaped guide vanes, the blade angle of which can be continuously adjusted.

4. The process for achieving uniform heating of the kiln according to claim 1, characterized in that, The control function of the dynamic flue gas recirculation control system in step S1 includes: Minimizing temperature deviation is used as the primary control objective in this process and is included as a term in the control function, calculated as follows: Among them, F T For temperature control, T i Let T be the temperature at the i-th monitoring point inside the furnace. avg Let n be the target average temperature, and n be the total number of monitoring points. Meanwhile, in order to regularize the flue gas recirculation system, concentration distribution uniformity is used as another term in the control function: Among them, F C C is the concentration control item. i Let C be the flue gas concentration at the i-th monitoring point. avg The target average concentration; The overall control function for this process is: F total =α·F T +β·F C Among them, F total Let α be the overall control function, and β be the weighting coefficients.

5. The process for achieving uniform heating of the kiln according to claim 1, characterized in that, The multi-burner pulse modulation control structure in step S2 includes a pulse width modulation execution unit, a burner load control unit, and an air-fuel ratio constant maintenance unit. The pulse width modulation execution unit adopts a hierarchical control method, which includes two control levels: coarse adjustment and fine adjustment.

6. The process for achieving uniform heating of the kiln according to claim 1, characterized in that, In step S2, the optimization function of the time-interleaved combustion strategy execution system is defined as follows: To ensure that the alternating operation of the burners matches the heat load distribution requirements as closely as possible, temperature uniformity is taken as the primary optimization objective and incorporated into the optimization function, as shown in the following expression: F temp =(T i -T avg ) 2 Among them, F temp To optimize temperature uniformity, T i Let T be the temperature at the i-th monitoring point surrounding product 3. avg Set the target temperature, where n is the total number of monitoring points; Meanwhile, in order to maximize the energy-saving effect of the alternating combustion system, fuel consumption minimization is used as another term in the optimization function: Among them, F fuel For energy consumption optimization, Let t be the fuel consumption rate of the k-th burner. k This corresponds to the combustion duration, where m is the total number of burners; The overall optimization function of the system is a weighted sum of the above two items, specifically: F opt =γ·F temp +δ·F fuel Among them, F opt Let be the overall optimization function, and γ and δ be the weighting coefficients.

7. The process for achieving uniform heating of the kiln according to claim 1, characterized in that, The multi-level model predictive control architecture in step S3 includes a furnace thermal prediction model unit, a product heating kinetics model unit, and a multi-objective optimization solution unit. The furnace thermal prediction model unit adopts a multi-timescale prediction structure, which includes a macroscopic prediction layer and a microscopic prediction layer.

8. The process for achieving uniform heating of the kiln according to claim 1, characterized in that, In step S3, the control objective function of the rolling optimization and feedforward correction execution system is: To ensure that the model predictive control effect matches the coordination requirements of the multivariable system as closely as possible, the optimization of the comprehensive performance index is used as the main control objective in this process and is included as a term in the control objective function. The calculation method is as follows: Where J1 is the temperature tracking performance index, y(k+i|k) is the predicted temperature at time k+i from time k, r(k+i) is the corresponding reference temperature, Q is the temperature tracking weight matrix, and N... p To predict the time domain length; simultaneously, to optimize both the energy consumption and emissions of the control system, multi-objective constraint optimization is used as another term in the control objective function: Where J2 is the control and emission optimization index, Δu(k+i) is the control increment, R is the control weight matrix, λ is the emission penalty factor, and N c To control the time domain length, E(k+i) is the predicted emission concentration at time k+i. The overall control objective function for this process is: J = J1 + ρ·J2 Where J is the overall control objective function and ρ is the balance weight coefficient.

9. The process for achieving uniform heating of a kiln according to claim 1, characterized in that, The partitioned control collaborative operation architecture in step S4 divides the furnace into multiple control partitions, including a product preheating zone, a product heating zone, and a product soaking zone. Each partition has different temperature control ranges and control accuracy requirements.

10. The process for achieving uniform heating of a kiln according to claim 9, characterized in that, The multi-mode switching control strategy includes a heating mode, a heat equalization and heat preservation mode, and a temperature control standby mode. In the heating mode, the burner operates in a high-power pulse mode, in the heat equalization and heat preservation mode, the burner operates in a low-power pulse mode and maintains a constant temperature, and in the temperature control standby mode, fuel consumption is significantly reduced.

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