Belt roasting desulfurization and denitrification intelligent advanced control system and control method thereof

Abstract

The application discloses a belt type roasting desulfurization and denitrification intelligent advanced control system and a control method thereof, and belongs to the technical field of flue gas purification in the steel industry. In view of the problems of poor multivariable coordination ability, high energy consumption and large emission fluctuation of PID control in the existing pellet production process, the system comprises a data collector, an APC server and a DCS controller. The data collector generates a dynamic working condition characteristic vector through multi-source data fusion; the APC server adopts a model predictive control algorithm, establishes a coupling relationship model of desulfurization and denitrification efficiency and resource consumption, and outputs an optimized target value; and the DCS controller realizes precise control based on a feedforward-feedback strategy. x Under the premise of ensuring that the concentrations of SO2 and NOx emissions are stable and meet the standards, the unit energy consumption of lime and ammonia is significantly reduced, and the anti-interference ability and operation stability of the system are improved.

Description

Technical Field

[0001] This invention relates to the field of flue gas purification technology in the steel industry. More specifically, this invention relates to an intelligent advanced control system and control method for belt roasting desulfurization and denitrification. Background Technology

[0002] In the pellet production process of the steel industry, the flue gas desulfurization and denitrification system is a key link in achieving pollutant emission standards. Current technologies generally employ conventional PID control combined with a DCS system to independently control the desulfurization tower and SCR reactor. While these technologies can meet basic emission and environmental protection requirements, they face the following technical bottlenecks in terms of economy and stability during actual operation: First, there is a strong coupling between the desulfurization and denitrification process variables. For example, the amount of quicklime fed not only affects SO2 removal efficiency but also indirectly changes the flue gas temperature at the desulfurization tower outlet through bed differential pressure fluctuations, thereby interfering with the denitrification efficiency of the downstream SCR reactor. Conventional PID control uses a single-loop independent adjustment method, which cannot effectively coordinate the dynamic correlation between multiple variables, leading to frequent manual intervention by operators, poor system anti-interference capability, and causing periodic fluctuations in emission concentrations and unstable operating conditions. Secondly, the optimization of the dosage of desulfurizing agent (hydrated lime) and reducing agent (ammonia) lacks a dynamic coordination mechanism. Traditional control strategies set the dosage based on fixed empirical thresholds, which cannot adapt to dynamic operating conditions such as fluctuations in raw material sulfur content and changes in combustion load. This often leads to excessive dosage and waste of resources, or insufficient dosage requiring manual adjustment, making it difficult to achieve optimal economic control. Thirdly, the existing control system has insufficient response capability to abnormal operating conditions (such as sensor failure and communication delay). When critical process parameters (such as bed differential pressure and SCR temperature) are detected as abnormal, emergency shutdown or switching to a fixed manual mode is usually adopted, which can easily cause production interruption or secondary pollution risks, affecting the stability of continuous production.

[0003] The core reasons for the above problems are as follows: First, the pellet flue gas purification system has complex characteristics such as multiple variables, large time lag, and nonlinearity. Traditional PID control methods are difficult to establish an accurate coupling relationship model, resulting in insufficient coordination between feedforward compensation and feedback regulation. Second, the dynamic balance between desulfurization and denitrification efficiency and reagent consumption is affected by multiple factors such as raw material composition and flue gas flow rate. Fixed control parameters cannot cover the entire operating range, while real-time optimization requires processing high-dimensional data and complex constraints. Conventional DCS systems have limited computing power and lack intelligent optimization algorithm support.

[0004] Furthermore, in the field of flue gas treatment in the steel industry, especially in the pellet roasting process, the flue gas is characterized by complex composition, large fluctuations in flow rate and temperature, and high instantaneous peak concentrations of sulfur dioxide and nitrogen oxides, making its treatment significantly more difficult than in areas with relatively stable operating conditions such as coal-fired power plants. Currently, although there is the concept of "ultra-low emissions" (such as SO2 ≤ 35 mg / m³),... 3NO x ≤50mg / m 3 The proposal includes even lower emission limits, but existing conventional PID-based control systems struggle to reliably achieve these standards under the dynamic conditions of pelletizing flue gas. More commonly, a conservative strategy of excessive reagent dosage is employed to ensure instantaneous compliance, leading to high operating costs. Therefore, true progress in this field lies not simply in pursuing lower emission limits, but in how to economically, reliably, and stably achieve the current stringent emission standards (such as SO2 ≤ 35 mg / m³) under the complex and fluctuating conditions of pelletizing production. 3 NO x ≤50mg / m 3 This invention provides a solution to this core pain point, and it is expected to be further optimized to an even better level. Summary of the Invention

[0005] One objective of this invention is to provide an intelligent advanced control system and control method for belt roasting desulfurization and denitrification, which, while ensuring stable compliance of flue gas emissions, focuses on solving the problems of high resource consumption, poor anti-interference ability, and weak adaptability to operating conditions of existing control methods, thereby achieving a balance between environmental protection and economy.

[0006] To achieve these objectives and other advantages of the present invention, according to one aspect of the present invention, an intelligent advanced control system for belt roasting desulfurization and denitrification is provided, comprising:

[0007] Data Acquisition Unit: Features a built-in multi-source data fusion module, enabling real-time acquisition of data from flue gas flow sensors, temperature sensors, and SO2 / NO3 sensors via industrial Ethernet. x The measurement data from the concentration analyzer, the slaked lime flow meter, and the ammonia gas inlet evaporator regulating valve are fused with the fluctuation signal output from the raw material composition analyzer and the load change signal fed back by the combustion load monitoring device to generate a dynamic operating condition feature vector.

[0008] APC Server: Communicates with the data acquisition unit and DCS controller via the OPC interface, integrating an adaptive prediction model module and an energy consumption-emission real-time optimization module. The adaptive prediction model module employs a model predictive control algorithm, updating the coupling relationship model between desulfurization efficiency, denitrification efficiency, and quicklime and ammonia consumption online based on historical operating data and dynamic operating condition feature vectors, outputting target values ​​for quicklime feed rate and ammonia inlet evaporator regulating valve opening. The energy consumption-emission real-time optimization module uses a dynamic constraint algorithm to optimize SO2 / NO... x Under the premise that the emission concentration meets the national standards, the priority weight of the control commands of the desulfurization optimization controller and the denitrification optimization controller is dynamically adjusted with the goal of minimizing the unit energy consumption of quicklime and ammonia.

[0009] DCS Controller: Built-in hierarchical fault-tolerant control unit, used to automatically switch from APC control mode to local safety control mode when a communication interruption or sensor failure is detected. It is connected to the desulfurization absorption tower bed differential pressure sensor, outlet temperature sensor, SCR reactor temperature sensor and actuators via hard wiring. The actuators include the lime feed frequency converter and the ammonia gas inlet evaporator regulating valve.

[0010] The advanced process control (APC) method of the above system, based on the multivariable model predictive control (MPC) architecture, achieves precise SO2 control and equipment stability, including the following steps:

[0011] Variable definition and model building:

[0012] Controlled variable (CV): The output variable that needs to be strictly controlled, including the SO2 equivalent value (Y1(s), a core environmental protection indicator, with a set value ≤35 mg / Nm³). 3 ), Calculated ash hopper level (Y2(s), equipment status, baseline value 2.98~2.99), Absorber tower pressure difference (Y3(s), slurry resistance, effective upper limit 2000 Pa), Denitrification outlet NO X Concentration (Y4(s), synergistic environmental protection indicator), flue gas emission temperature (Y5(s), equipment protection indicator);

[0013] Control Variables (MV): Adjustable execution variables, including quicklime feed rate (U1(s), raw material supply, baseline 78), circulating damper opening (U2(s), flue gas recirculation, baseline 25.4067%), ammonia inlet evaporator regulating valve opening (U3(s), denitrification raw material, baseline 40), forced draft fan speed (U4(s), air supply, baseline 1500 rpm), induced draft fan speed (U5(s), flue gas emission, baseline 1200 rpm);

[0014] Disturbance (DV): External variables that affect the system, including inlet flue gas volume (high-priority disturbance) and changes in fuel calorific value (minor disturbance).

[0015] Transfer function model: A 5×5 dimensional transfer function matrix is ​​used to describe the dynamic relationship between the control variable (U1(s)~U5(s)) and the controlled variable (Y1(s)~Y5(s)), and the matrix form is as follows:

[0016] ;

[0017] Among them, G ij (s) represents the j-th control variable (U) j (s) for the i-th controlled variable (Y) i The dynamic transfer function (e.g., G(s)) of (s) 11(s) is the transfer function of the hydrated lime feed rate (U1(s)) with respect to the SO2 equivalent value (Y1(s)), describing its dynamic response relationship; G 21 (s) is the transfer function of the hydrated lime feed rate (U1(s)) on the ash hopper level (Y2(s)), describing its coupling effect.

[0018] Model parameters: The sampling time of the transfer function matrix is ​​consistent with the DCS data update frequency. The prediction time domain is 120 steps (30 seconds per step, covering 1 hour of slow dynamics) to ensure that the model can accurately predict the future trend of the controlled quantity.

[0019] The APC model parameters are constructed as follows:

[0020] Each transfer function G in the 5×5 dimensional transfer function matrix ij The specific form of (s) is determined by combining mechanistic analysis with historical data fitting. First, based on the physicochemical principles of desulfurization and denitrification processes (such as mass conservation, energy conservation, and reaction kinetics), a nonlinear mechanistic model is established between the control variable (MV) and the controlled variable (CV). Then, under stable system operation conditions, a large amount of historical operating data (such as step response test data) is collected through the DCS system. For each transfer function G... ij (s) uses system identification methods (such as least squares) to perform parameter fitting on typical first-order or second-order transfer functions with pure time delay. Its general form can be expressed as: or .

[0021] Among them, K ij For process gain, τ ij (or τ) ij1 , τ ij2 ) is the time constant, θ ij For pure time delay. These parameters (K, τ, θ) are determined by fitting historical data so that the model output best matches the actual process response. For example, G 11 (s) describes the dynamic effect of the hydrated lime feed rate (U1) on the outlet SO2 concentration (Y1), with its gain K 11 A negative value (increasing the feed rate reduces SO2), time constant τ 11 This reflects the reaction and residence time within the desulfurization tower. The online parameter update mechanism uses recursive least squares method to fine-tune these parameters with real-time data to adapt to slow time-varying characteristics such as changes in catalyst activity. State estimation and feedback correction:

[0022] Kalman filtering is used to correct the predicted values ​​of the transfer function model, and the compensation gain factor is set to 1 to improve the adaptability to disturbances (such as changes in the inlet flue gas volume) and ensure accurate state estimation (such as the real-time state of SO2 concentration in the absorption tower).

[0023] Constrained optimization solution:

[0024] The multi-objective problem is solved using the quadratic programming (QP) optimization algorithm. The objectives include:

[0025] Environmental protection first: Minimize the deviation of SO2 conversion value (≤35 mg / Nm 3 );

[0026] Economic objective: Minimize lime slurry consumption (adjust the feed rate of hydrated lime).

[0027] Equipment constraints: stable ash hopper level (≤3.0), absorption tower pressure differential ≤2000 Pa;

[0028] Output control quantity adjustment instructions (such as increasing the target value of quicklime feed rate from the reference value).

[0029] Closed-loop control integration with DCS:

[0030] The control commands are sent to the DCS system to drive the field actuators (such as the lime feed frequency converter).

[0031] The actual values ​​of DCS (such as the actual opening of the hydrated lime feed rate and the SO2 equivalent value) are read in real time and fed back to the Kalman filter to update the model state, realizing the cycle of "model prediction → state estimation → optimization solution → closed-loop correction".

[0032] Preferably, the DCS controller includes:

[0033] The system includes a desulfurization optimization controller and a denitrification optimization controller. The desulfurization optimization controller, based on the target value of the hydrated lime feed rate output by the APC server, employs a feedforward-feedback composite control strategy. It uses the product of the inlet flue gas flow rate and SO2 concentration as the feedforward disturbance variable, and the bed differential pressure deviation and outlet temperature deviation as feedback variables to adjust the hydrated lime feed rate, achieving SO2 emission control at the margin, ensuring the outlet SO2 concentration does not exceed 35 mg / m³. 3 The denitrification optimization controller, based on the target value of the ammonia inlet evaporator regulating valve opening output by the APC server, uses a feedforward-feedback composite control strategy. Combustion load and flue gas oxygen content are used as feedforward disturbance variables, while SCR reactor temperature deviation and ammonia slip concentration are used as feedback variables to adjust the opening of the ammonia inlet evaporator regulating valve, thereby achieving NO reduction. x Emissions control, export NO x Concentration not exceeding 50 mg / m 3 .

[0034] Preferably, the generation of the dynamic operating condition feature vector includes the following steps:

[0035] Based on the fluctuation characteristics of process parameters, high-frequency and low-frequency components are separated. High-frequency components include instantaneous fluctuations in flue gas flow rate, while low-frequency components include trend changes in combustion load.

[0036] Based on the spectral characteristics of raw material composition fluctuation signals and combustion load change signals, process parameters with strong correlation coefficients greater than 0.8 with hydrated lime feed rate and ammonia gas inlet evaporator regulating valve opening were screened, including the sulfur content frequency band and NO. x Generation rate;

[0037] The correlation between the differential pressure of the desulfurization tower bed, the temperature of the SCR reactor and pollutant emissions was extracted by statistical analysis methods, and a dynamic operating condition feature vector was generated and transmitted to the adaptive prediction model module of the APC server.

[0038] Preferably, the method for constructing the coupling relationship model includes:

[0039] Outliers in historical operating data are filtered out using a statistical extremum removal method, and real-time process parameters are processed by moving average. Based on the processed historical operating data and real-time process parameters, a mathematical model is established for desulfurization efficiency, denitrification efficiency, and consumption of quicklime and ammonia. The input of the model is a dynamic operating condition feature vector, and the output is the target value of quicklime feed rate and the opening of the ammonia inlet evaporator regulating valve.

[0040] The allowable fluctuation range of differential pressure in the desulfurization absorption tower bed is defined as ±10%, and the active range threshold of SCR reactor temperature is defined as 180~420℃.

[0041] The model parameters are dynamically adjusted based on the residual amount of hydrated lime, catalyst agglomeration signal, and real-time deviations in desulfurization efficiency / denitrification efficiency, with a correction cycle of every 15 minutes. Specifically, when the absolute value of the desulfurization efficiency deviation exceeds ±5% or the absolute value of the denitrification efficiency deviation exceeds ±5%, the corresponding parameter adjustment weight is increased to 70%, and the weight of the basic historical data is reduced to 30%. When the absolute value of the deviation is less than ±2%, the parameter adjustment weight is reduced to 30%, and the weight of the basic historical data is increased to 70%.

[0042] The target values ​​for the feed rate of quicklime and the opening of the regulating valve for ammonia entering the evaporator are updated using a rolling optimization algorithm, and are updated once in each regulation cycle.

[0043] Preferably, the target value of the hydrated lime feed rate is calculated as follows:

[0044] The desulfurization optimization controller monitors the SO2 concentration at the desulfurization tower outlet in real time. Combining the product of the SO2 concentration in the inlet flue gas and the flue gas flow rate, the target value of the hydrated lime feed rate is generated through the model predictive control algorithm of the APC server.

[0045] Preferably, the target value for the opening of the ammonia gas inlet evaporator regulating valve is calculated as follows:

[0046] The denitrification optimization controller monitors the NO at the SCR reactor outlet in real time. x Concentration, combined with NO in inlet flue gas x The product of concentration and flue gas flow rate is used to generate the target value for the opening of the ammonia gas inlet evaporator regulating valve through the model predictive control algorithm of the APC server.

[0047] Preferably, the feedforward-feedback composite control strategy of the desulfurization optimization controller includes:

[0048] Real-time data collection of inlet flue gas flow rate, temperature, and SO2 concentration is performed. The model predictive control algorithm of the APC server calculates the SO2 increment for the next 3-5 minutes, generates feedforward compensation instructions, and writes them to the DCS controller's setpoint register via the OPC interface.

[0049] Based on the bed differential pressure deviation ΔP, outlet temperature deviation ΔT, inlet SO2 fluctuation amplitude, and final control effect from the historical operation database, the feedforward weight coefficient K is trained online using the least squares method. ff With feedback weight coefficient K fb The mapping relationship model;

[0050] When the differential pressure deviation ΔP in the bed exceeds ±10%, the feedback control weight is increased to 80%, and the feedforward weight is reduced to 20%; simultaneously, K is finely adjusted in real time according to the mapping relationship model. fb With K ff The specific values ​​are determined to make the weight allocation more closely match the characteristics of the current working conditions.

[0051] When the outlet temperature deviation ΔT exceeds ±5℃, the feedforward control weight is increased to 70%, and the feedback weight is reduced to 30%; simultaneously, K is fine-tuned in real time according to the mapping relationship model. ff With K fb The specific value;

[0052] If both ΔP and ΔT exceed the limit, the weights are allocated according to priority ΔP>ΔT to ensure that the total is 100%; at the same time, the specific weight coefficients under each priority are fine-tuned in real time according to the mapping relationship model.

[0053] Based on the process coupling relationship, decoupling is achieved to eliminate the mutual interference between quicklime feed rate, bed differential pressure and outlet temperature;

[0054] The weight coefficient update cycle is synchronized with the APC server's rolling optimization cycle, meaning it is updated once per adjustment cycle.

[0055] Preferably, the feedforward-feedback composite control strategy of the denitrification optimization controller includes:

[0056] Real-time data collection of combustion load and flue gas oxygen content is used to calculate NO levels for the next 3-5 minutes using the model predictive control algorithm on the APC server. x Generate trends, generate feedforward compensation instructions, and write them to the DCS controller's setpoint register via the OPC interface;

[0057] Based on historical operational database data including SCR reactor temperature deviation, ammonia slip concentration deviation, combustion load change rate, and final control performance, the feedforward priority coefficient P is trained online using the least squares method. ff With feedback priority coefficient P fb The mapping relationship model;

[0058] Based on the deviation between the SCR reactor temperature and the ammonia slip concentration, and combined with the activity index output by the real-time catalyst activity evaluation algorithm, feedforward and feedback priorities are assigned:

[0059] When the SCR reactor temperature deviates from the active range of 180~420℃, the feedback control priority is increased to 90%, and the feedforward priority is reduced to 10%; simultaneously, P is finely adjusted in real time according to the mapping relationship model. fb With P ff The specific value;

[0060] When the ammonia slip concentration approaches the 2 ppm limit, the feedforward control priority is increased to 80%, and the feedback priority is reduced to 20%; simultaneously, P is fine-tuned in real time according to the mapping relationship model. ff With P fb The specific value;

[0061] When the catalyst activity index is below 0.8, the feedback control priority is increased by an additional 10%, that is, the total priority is increased to 100%, the feedforward priority is reduced to 0%, and the basic setting value of the ammonia gas inlet evaporator regulating valve is reduced proportionally according to the activity index.

[0062] Based on the process coupling relationship, decoupling is achieved to eliminate the mutual interference between the opening of the regulating valve for ammonia gas entering the evaporator, the SCR temperature, and the flue gas flow rate.

[0063] The priority coefficient update cycle is synchronized with the APC server's rolling optimization cycle, meaning it is updated once per adjustment cycle.

[0064] Preferably, the optimization method of the real-time energy consumption-emission optimization module includes the following steps:

[0065] Real-time monitoring of SO2 and NOx If the emission concentration is lower than 85% of the limit, the desulfurization and denitrification controller is allowed to reduce the dosage of quicklime and ammonia according to the optimization instructions of the APC server.

[0066] Calculate the unit energy consumption index based on the real-time consumption of quicklime and ammonia and the amount of flue gas treated.

[0067] Based on the current operating condition feature vector and historical energy consumption data, an energy consumption prediction model is constructed through regression analysis to predict the unit energy consumption trend in the next 5-10 minutes; when the prediction shows that the unit energy consumption is about to exceed the process set threshold, the priority weight of the corresponding controller is adjusted in advance.

[0068] When SO2 or NO x When the concentration approaches the emission limit, the priority weight of the corresponding controller is increased to 80% through a dynamic constraint algorithm to ensure that emissions meet the standards.

[0069] When the unit energy consumption exceeds the process-set threshold, the weight of the high-energy-consumption controller is reduced to 60%, prioritizing the reduction of resource consumption;

[0070] The system dynamically corrects the slaked lime feed rate command of the desulfurization optimization controller and the ammonia gas inlet evaporator regulating valve opening command of the denitrification optimization controller. It is corrected once in each regulation cycle and synchronized with the rolling optimization algorithm cycle to balance emission compliance and resource consumption.

[0071] This invention also provides a control method for an intelligent advanced control system for belt roasting desulfurization and denitrification, including a dynamic adjustment step based on the catalyst activity index (CAI):

[0072] Real-time collection of NO at the inlet and outlet of the SCR reactor x Concentration, ammonia slip concentration, reactor temperature, flue gas flow rate, and bed pressure differential data;

[0073] The theoretical denitrification efficiency was calculated based on a new catalyst benchmark model.

[0074] Based on the ratio of actual denitrification efficiency to theoretical denitrification efficiency, combined with the degree of ammonia slip concentration deviating from the set value and the bed pressure difference change rate, the catalyst activity index (CAI) is calculated by weighted fusion.

[0075] Based on the CAI value, dynamically adjust the basic setting value of the ammonia gas inlet evaporator regulating valve opening;

[0076] The formula for calculating the catalyst activity index (CAI) is:

[0077] CAI=(η 实 / η 理 )×0.7+[1 / (1+|NH 3逃逸-2|)]×0.2+[1-min(1, |dP / dt| / 0.5)]×0.1;

[0078] Where, η 实 For the actual denitrification efficiency, η 理 To achieve the theoretically expected denitrification efficiency, NH 3逃逸 dP / dt represents the actual ammonia slip concentration, and dP / dt represents the rate of change of bed pressure difference.

[0079] The present invention has at least the following beneficial effects:

[0080] First, the SO2 content set in this invention is ≤35mg / m³. 3 NO x ≤50mg / m 3 The emission targets are designed to address the complex dynamics and multivariate coupling characteristics of flue gas from steel pelletizing plants, aiming to achieve stable and economical "edge-limit" control to ensure consistent compliance during long-term continuous operation. This standard is already considered advanced in current flue gas purification practices for pelletizing plants. More importantly, this invention is based on Model Predictive Control (MPC) and dynamic optimization algorithms. Its core architecture allows for the achievement of even lower emission limits (e.g., SO2 ≤ 20 mg / m³) through model parameter optimization and setpoint adjustment. 3 NO x ≤30mg / m 3 The system's capability allows it to adapt to stricter environmental regulations in the future, demonstrating its forward-looking and progressive nature.

[0081] Secondly, this invention achieves a significant improvement in overall system performance through a three-layer intelligent collaborative architecture of data acquisition unit, APC server, and DCS controller, combined with multi-source data fusion, model predictive control (MPC), and dynamic weight allocation mechanism. This ensures effective control of SO2 and NO. x Emission concentrations consistently meet standards (≤35mg / m³). 3 and ≤50mg / m 3 At the same time, it significantly reduces the unit consumption of quicklime and ammonia, effectively reducing operating costs; by introducing feedforward-feedback self-learning weight adjustment and hierarchical fault-tolerant strategies, it significantly enhances the system's adaptability to raw material fluctuations, load changes and abnormal operating conditions, improving production continuity and stability; and based on multi-objective optimization algorithms, it overcomes the problem of strong coupling control of multiple variables, achieves "edge-limiting" control, reduces emission fluctuations, and ultimately achieves comprehensive optimization results in three aspects: environmental compliance, economic operation and reliable control.

[0082] Other advantages, objectives and features of the present invention will become apparent in part from the following description, and in part from those skilled in the art through study and practice of the invention. Attached Figure Description

[0083] Figure 1 Flowchart of belt roasting desulfurization and denitrification process;

[0084] Figure 2 A block diagram for determining the communication status between the APC server and the DCS controller;

[0085] Figure 3 This is a block diagram of the switching logic for the DCS controller.

[0086] Figure 4 This is a block diagram of the DCS controller's intermediate point anti-miswrite logic. Detailed Implementation

[0087] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can implement it based on the description.

[0088] It should be understood that terms such as “having,” “comprising,” and “including” as used herein do not exclude the presence or addition of one or more other elements or combinations thereof.

[0089] This invention provides an intelligent advanced control system for belt roasting desulfurization and denitrification, comprising:

[0090] Data Acquisition Unit: Features a built-in multi-source data fusion module, enabling real-time acquisition of data from flue gas flow sensors, temperature sensors, and SO2 / NO3 sensors via industrial Ethernet. x The measurement data from the concentration analyzer, the slaked lime flow meter, and the ammonia gas inlet evaporator regulating valve are fused with the fluctuation signal output from the raw material composition analyzer and the load change signal fed back by the combustion load monitoring device to generate a dynamic operating condition feature vector.

[0091] APC Server: Communicates with the data acquisition unit and DCS controller via the OPC interface, integrating an adaptive prediction model module and an energy consumption-emission real-time optimization module. The adaptive prediction model module employs a model predictive control algorithm, updating the coupling relationship model between desulfurization efficiency, denitrification efficiency, and quicklime and ammonia consumption online based on historical operating data and dynamic operating condition feature vectors, outputting target values ​​for quicklime feed rate and ammonia inlet evaporator regulating valve opening. The energy consumption-emission real-time optimization module uses a dynamic constraint algorithm to optimize SO2 / NO... x Under the premise that the emission concentration meets the national standards, the priority weight of the control commands of the desulfurization optimization controller and the denitrification optimization controller is dynamically adjusted with the goal of minimizing the unit energy consumption of quicklime and ammonia.

[0092] DCS Controller: Built-in hierarchical fault-tolerant control unit, which is connected to the desulfurization absorption tower bed differential pressure sensor, outlet temperature sensor, SCR reactor temperature sensor and actuators via hard wiring. The actuators include the lime feed frequency converter and the ammonia gas inlet evaporator regulating valve.

[0093] In this technical solution, the data acquisition unit can be an industrial-grade embedded controller as the core hardware, integrating a multi-source data fusion module. The flue gas flow sensor can be a thermal mass flow meter with a measurement range of 0-50m. 3 / s, installed on the inlet pipe of the desulfurization tower; the temperature sensor can be a type K thermocouple with an accuracy of ±1℃, installed at the outlet of the desulfurization tower and in the middle of the SCR reactor; SO2 / NO x A non-dispersive infrared gas analyzer can be selected as the concentration analyzer and installed at the front end of the chimney. A Coriolis mass flow meter with a range of 0-10 t / h can be selected as the slaked lime flow meter and installed at the outlet of the slaked lime silo. An electric regulating valve with a nominal diameter of DN50 can be selected as the ammonia gas inlet regulating valve and installed on the ammonia gas supply pipeline. The data fusion module collects the above sensor data in real time via the industrial Ethernet protocol (Modbus / TCP) and performs weighted fusion with the signals from the raw material composition analyzer (installed at the batching chamber outlet) and the combustion load monitoring device (installed at the rotary kiln drive end) to generate a dynamic operating condition feature vector. The transmission cycle is 1 second.

[0094] The APC server can be equipped with an Intel Xeon processor to run model predictive control algorithms. The adaptive predictive model module uses a multivariate state-space model, with inputs being dynamic operating condition feature vectors and outputs being the hydrated lime feed rate (target range 2-8 t / h) and the ammonia gas inlet evaporator regulating valve opening (target range 20-80%). Model parameters are trained using historical operating data, with an online update cycle of 5 minutes. Historical operating data is filtered for outliers using statistical extremum removal methods (e.g., the 3σ criterion for outlier removal), and real-time process parameters are processed using a moving average with a window width of 10 to improve the stability of the model input data. The energy consumption-emission real-time optimization module uses a dynamic constraint algorithm, setting the SO2 emission threshold to 35 mg / m³. 3 NO x The emission threshold is 50 mg / m³ 3When the emission concentration is below 85% of the threshold, the dosage of the reagent can be reduced; when it approaches the threshold, the priority weight is automatically increased to 80%. The optimization results are written to the register of the DCS controller via the OPC UA protocol, with a communication latency of less than 200ms. Based on the current operating condition feature vector and historical energy consumption data, a unit energy consumption prediction model is constructed through linear regression to predict the energy consumption trend in the next 5-10 minutes. When the predicted value exceeds the threshold of 1.5 kg / ton of flue gas, the weight of the high-energy-consuming controller is reduced to 60% in advance. In the specific implementation of this system, the control variable "quicklime feed rate" is adjusted through the quicklime feed frequency converter, which controls the speed of the screw feeder by changing the frequency of the frequency converter, thereby achieving precise control of the feed rate. The control variable "ammonia gas inlet evaporator regulating valve opening" directly corresponds to the ammonia gas inlet evaporator regulating valve, which adjusts the ammonia supply by changing the valve opening.

[0095] The DCS controller can use a redundant PLC architecture with a built-in hierarchical fault-tolerant control unit. The desulfurization optimization controller adopts a feedforward-feedback composite algorithm, where the feedforward quantity is calculated as the inlet flue gas flow rate (m³ / s). 3 / s) × Inlet SO2 concentration (mg / m³) 3 The feedback parameters are the deviations between the bed differential pressure (allowable fluctuation ±10%) and the outlet temperature (set value 150±5℃). Based on the bed differential pressure deviation ΔP, outlet temperature deviation ΔT, and inlet SO2 fluctuation amplitude from the historical operation database, the feedforward weight coefficients (K) are trained online using the least squares method. ff ) and feedback weight coefficient (K fb The mapping model is used. When ΔP exceeds the limit, K is finely adjusted in real time according to the mapping model based on the fixed weight allocation. fb / K ff Value (e.g., when ΔP = +12%, K) fb (Fine-tuned from 0.8 to 0.85). The feedforward of the denitrification optimization controller is the combustion load (MW) × flue gas oxygen content (vol%), and the feedback is the deviation between the SCR reactor temperature (set range 280-420℃) and the ammonia slip concentration (threshold 2ppm). The actuators include a quicklime feed frequency converter (installed at the screw feeder drive end) and an ammonia inlet evaporator regulating valve (installed at the jet pump outlet). The hard-wired signal uses a 4-20mA analog signal.

[0096] The implementation of the hierarchical fault-tolerant control unit includes the following logic:

[0097] Communication status heartbeat detection logic: Every second, the APC server writes a heartbeat flag (hexadecimal value `0xAA55`) to a specific register of the DCS controller (e.g., `%DB100.DBW0`) via the OPC interface. Upon receiving this value, the DCS controller immediately resets it to `0x0000`. If the DCS controller detects that the register value has not been updated or has not changed to `0xAA55` for two consecutive cycles (i.e., 2 seconds), it determines that the communication with the APC server has timed out.

[0098] Sensor fault diagnosis logic: For critical sensors (such as SO2 concentration analyzers and bed differential pressure sensors), the DCS controller monitors their 4-20mA analog signals in real time. If the signal is consistently below 3.8mA or above 20.5mA, or if the signal value remains unchanged for 5 seconds (dead-line detection), the sensor is determined to be faulty.

[0099] Control strategy when a fault occurs:

[0100] Once a communication timeout or critical sensor failure is detected, the hierarchical fault-tolerant control unit immediately and automatically switches the relevant control loop (such as lime feed control) seamlessly from "APC remote automatic mode" to "DCS local manual mode".

[0101] In local manual mode, the DCS controller freezes the actuator's output value at the valid value at the last moment before the fault occurred, or switches to a preset safe output value (such as maintaining the lime feed frequency at 45Hz) to maintain basic production operation and prevent equipment shutdown.

[0102] At the same time, the corresponding alarm indicator light on the DCS operator station turns red and flashes, triggering an audible and visual alarm, and the event log records the fault type and the time of occurrence.

[0103] Recovery and Activation Logic: When the communication link returns to normal (heartbeat signal is received stably for more than 10 seconds) or the sensor signal returns to normal and remains so for 30 seconds, the alarm indicator light turns steady yellow, indicating that the fault has been resolved. At this time, the system will not automatically switch back to APC mode. The operator must manually click the "Confirm and Activate APC" button after confirming the process conditions are stable on-site before the control loop can re-activate APC optimized control. This design avoids frequent mode switching due to intermittent faults, ensuring system stability.

[0104] Catalyst Activity Index (CAI) is calculated through multi-source data fusion:

[0105] CAI = (η 实 / η 理)×0.7 + [1 / (1+|NH3 escape-2|)]×0.2 + [1-min(1, |dP / dt| / 0.5)]×0.1;

[0106] Where η 理 Calculated using the new catalyst baseline model. When CAI < 0.8, the feedback priority is increased by an additional 10%, and the basic setting value of the ammonia gas inlet evaporator regulating valve opening is reduced proportionally (e.g., 15% reduction when CAI = 0.7).

[0107] The weighted fusion coefficients (0.7, 0.2, 0.1) in the Catalyst Activity Index (CAI) calculation formula were determined based on multiple regression analysis of a large amount of historical operating data combined with expert experience. The specific process is as follows: Data samples were collected under different catalyst conditions during long-term system operation, including actual denitrification efficiency (η). 实 Theoretical efficiency (η) 理 The actual efficiency was measured using the ammonia slip concentration (NH3 slip) and the bed pressure difference change rate (dP / dt), with the catalyst activity measured offline in the laboratory as the baseline true value. Multiple linear regression analysis was used to determine the significance of each variable's influence on the catalyst activity. The analysis results showed that the ratio of actual efficiency to theoretical efficiency (η) was significant. 实 / η 理 The CAI index has the highest explanatory power for changes in catalyst activity (approximately 70% contribution). The deviation of ammonia slip concentration from the setpoint directly reflects a decrease in catalyst selectivity, contributing approximately 20%, while the bed pressure difference rate reflects potential blockage, contributing approximately 10%. This weighting ensures that the CAI index comprehensively and accurately reflects the overall health of the catalyst. Verified by actual field data, its output value is highly correlated with the actual catalyst activity (correlation coefficient R0). 2 >0.9).

[0108] like Figure 1 As shown, the specific implementation process of the intelligent advanced control system for belt roasting desulfurization and denitrification in this technical solution is as follows:

[0109] 1. Flue gas collection and pretreatment

[0110] Flue gas enters the system from the outlet of the pellet roasting furnace through the flue, and flows sequentially through the desulfurization tower and the SCR reactor.

[0111] A flue gas flow sensor, a temperature sensor, and an SO2 concentration analyzer are installed at the inlet pipe of the desulfurization tower to monitor the inlet flue gas parameters (flow rate, temperature, and SO2 concentration) in real time.

[0112] SCR reactor inlet pipe installation NO x Concentration analyzer and zirconia oxygen analyzer for detecting NO x Concentration and oxygen content.

[0113] 2. Core technologies for desulfurization and denitrification

[0114] Desulfurization process: Flue gas enters the desulfurization tower and comes into countercurrent contact with slaked lime (Ca(OH)2), which is quantitatively injected as a desulfurizing agent via a screw feeder, resulting in a chemical reaction (SO2 + Ca(OH)2 → CaSO3 + H2O). A bed differential pressure sensor monitors the bed pressure difference in the desulfurization tower in real time, and an outlet temperature sensor detects the temperature of the purified flue gas.

[0115] Denitrification process: After desulfurization, the flue gas enters the SCR reactor. Ammonia is precisely injected through a jet pump and regulating valve, and under the action of a catalyst, it reacts with NO. x The reaction is (4NO + 4NH3 + O2 → 4N2 + 6H2O). Temperature and differential pressure sensors are installed in the middle of the SCR reactor to monitor catalyst activity and reaction temperature.

[0116] 3. Data Acquisition and Optimization Control

[0117] Data acquisition unit: Real-time acquisition of flue gas flow rate, temperature, SO2 / NO3 ratio via industrial Ethernet. x Signals such as concentration, quicklime flow rate, and ammonia gas inlet valve opening are fused with data from the raw material composition analyzer and combustion load monitoring device to generate a dynamic operating condition feature vector.

[0118] APC server: Based on model predictive control algorithm, it outputs target values ​​for quicklime feed rate and ammonia gas inlet evaporator regulating valve opening, and transmits the instructions to DCS controller through OPC interface.

[0119] DCS controller: Performs hierarchical fault-tolerant control and adjusts the dosage of quicklime and ammonia through a feedforward-feedback strategy.

[0120] 4. Emission and safety interlock

[0121] After purification, the flue gas is discharged through a chimney, with an SO2 / NO2 control device installed at the front of the chimney. x Concentration analyzers and ammonia slip detectors ensure emission concentrations (SO2 ≤ 35 mg / m³). 3 NO x ≤50mg / m 3 (Meets the standard)

[0122] When the SCR reactor temperature exceeds the limit (>420℃ or <280℃) or the ammonia escape concentration is >2ppm, the interlock protection logic is triggered, freezing the opening of the ammonia gas inlet evaporator regulating valve and triggering an alarm.

[0123] 5. Process Coupling and Decoupling Control

[0124] The differential pressure of the desulfurization tower bed and the outlet temperature are decoupled using a decoupling matrix to eliminate the interference of quicklime dosage on the downstream SCR temperature.

[0125] The SCR reactor temperature and ammonia slip concentration are dynamically weighted to balance denitrification efficiency and secondary pollution risk.

[0126] This technical solution achieves dynamic and coordinated control of the desulfurization and denitrification system through multi-source data fusion and model prediction optimization, reducing emission fluctuations and reagent waste; the hierarchical fault-tolerant design improves stability under abnormal operating conditions and reduces the risk of production interruption; the hardware selection and assembly location design take into account both measurement accuracy and process adaptability, ensuring long-term operational reliability.

[0127] In another technical solution, the DCS controller includes:

[0128] The system includes a desulfurization optimization controller and a denitrification optimization controller. The desulfurization optimization controller, based on the target value of the hydrated lime feed rate output by the APC server, employs a feedforward-feedback composite control strategy. It uses the product of the inlet flue gas flow rate and SO2 concentration as the feedforward disturbance variable, and the bed differential pressure deviation and outlet temperature deviation as feedback variables to adjust the hydrated lime feed rate, achieving SO2 emission control at the margin, ensuring the outlet SO2 concentration does not exceed 35 mg / m³. 3 The denitrification optimization controller, based on the target value of the ammonia inlet evaporator regulating valve opening output by the APC server, uses a feedforward-feedback composite control strategy. Combustion load and flue gas oxygen content are used as feedforward disturbance variables, while SCR reactor temperature deviation and ammonia slip concentration are used as feedback variables to adjust the opening of the ammonia inlet evaporator regulating valve, thereby achieving NO reduction. x Emissions control, export NO x Concentration not exceeding 50 mg / m 3 .

[0129] In this technical solution, the DCS controller can be a redundant DCS control module. The desulfurization optimization controller receives the target value of the quicklime feed rate (range 2-8 t / h) output by the APC server. The feedforward disturbance variable is the inlet flue gas flow rate (range 0-50 m³ / h). 3 / s) and SO2 concentration (range 0-1000 mg / m 3The product of the combustion load (range 0-50MW) and the flue gas oxygen content (range 0-15vol%) is used as the feedback variables, and the outlet temperature deviation (set value 150±5℃) is used as the feedback variables. The controller output signal is transmitted to the lime feed frequency converter (installed at the screw feeder drive end) via a 4-20mA analog signal, with an adjustment cycle of 1 second. The denitrification optimization controller receives the target value of the ammonia inlet evaporator regulating valve opening (range 20-80%), the feedforward disturbance variable is the product of the combustion load (range 0-50MW) and the flue gas oxygen content (range 0-15vol%), and the feedback variables are the SCR reactor temperature deviation (set range 280-420℃) and the ammonia slip concentration (threshold 2ppm). The control command is transmitted to the ammonia inlet evaporator regulating valve (installed at the ejector pump outlet) via hardwiring, with an adjustment cycle of 1 second.

[0130] like Figure 2 As shown, the communication status between the APC server and the DCS controller can be detected via a heartbeat signal. The APC server writes a flag bit (e.g., hexadecimal value 0xAA55) to the DCS controller every second, and the DCS controller resets immediately upon receiving the flag bit. If the DCS controller does not detect a valid flag bit within two consecutive control cycles (i.e., 2 seconds), a communication timeout is determined. At this time, the DCS controller automatically switches the APC control mode to local manual mode and triggers a red alarm indicator on the operating interface. After communication is restored, the operator needs to manually confirm and re-engage APC control. The communication link can use industrial Ethernet (such as Profinet or EtherNet / IP), with a transmission latency of no more than 200ms to ensure real-time performance.

[0131] like Figure 3 As shown, the switching logic of the DCS controller is embedded in the DCS program. Under normal operating conditions, it operates in predictive control mode. When a sensor failure is detected (such as the loss of SO2 concentration analyzer signal for 5 seconds) or APC communication timeout (no heartbeat signal received for 2 consecutive control cycles), the DCS logic automatically triggers the switching command.

[0132] like Figure 4 As shown, to prevent abnormal values ​​from the APC output from being written into the DCS control loop, an intermediate point register is set up as a buffer. The target value of the hydrated lime feed rate calculated by the APC (e.g., 6.5 t / h) is first written into the intermediate point register. The DCS logic compares the deviation of the intermediate point value with the current actual value in real time. If the deviation exceeds the allowable range (e.g., ±10%), the write operation is rejected and a yellow warning is triggered, while the current setpoint remains unchanged. For example, if the current hydrated lime feed rate is 6.0 t / h, and the APC output value is 7.5 t / h (deviation 25%), the DCS locks the setpoint and records the event log. The detection cycle of the intermediate point logic is 500 ms, and the deviation threshold can be adjusted according to process requirements.

[0133] In this technical solution, the feedforward-feedback strategy of the DCS controller dynamically coordinates the relationship between multiple variables to achieve SO2 and NO x Stable control of emission concentrations; hardware selection and parameter settings adapted to industrial site requirements, improving the reliability and maintainability of the control system.

[0134] In another technical solution, the generation of the dynamic operating condition feature vector includes the following steps:

[0135] Based on the fluctuation characteristics of process parameters, high-frequency and low-frequency components are separated. High-frequency components include instantaneous fluctuations in flue gas flow rate, while low-frequency components include trend changes in combustion load.

[0136] Based on the spectral characteristics of raw material composition fluctuation signals and combustion load change signals, process parameters strongly correlated with the feed rate of hydrated lime and the opening of the ammonia gas regulating valve in the evaporator were screened, including the sulfur content frequency band and NO. x Generation rate;

[0137] The correlation between the differential pressure of the desulfurization tower bed, the temperature of the SCR reactor and pollutant emissions was extracted by statistical analysis methods, and a dynamic operating condition feature vector was generated and transmitted to the adaptive prediction model module of the APC server.

[0138] In this technical solution, the dynamic operating condition feature vector can be generated using a digital signal processing module to perform multi-scale decomposition of process parameters such as flue gas flow rate and combustion load. High-frequency components are defined as signals with a fluctuation period of less than 10 seconds (such as instantaneous fluctuations in flue gas flow rate), extracted using a high-pass filter (cutoff frequency 0.1Hz); low-frequency components are defined as signals with a fluctuation period of more than 5 minutes (such as trends in combustion load), separated using a low-pass filter (cutoff frequency 0.003Hz). The signal processing module can be integrated into the embedded processor of the data acquisition unit (installed in the control cabinet) to process raw sensor data in real time, with a sampling frequency of 1Hz. After separation, the high-frequency and low-frequency components are stored in independent buffers for subsequent feature selection.

[0139] Based on the raw material composition fluctuation signal (sulfur content variation range 0.5%-3%) and the combustion load signal (fluctuation amplitude ±15%), a Fast Fourier Transform (FFT) was used for spectral analysis. The sulfur content frequency band can be selected as 0.01-0.05Hz (corresponding to the periodic fluctuation of raw material transportation), NO x The generation rate frequency band was selected as 0.02-0.1Hz (corresponding to a step change in combustion load). The spectrum analysis module can use an embedded DSP chip (installed in the signal processing unit of the data acquisition unit) to screen parameters with strong correlation coefficients greater than 0.8 to the lime feed rate and the opening of the ammonia gas inlet evaporator regulating valve. The screening results are transmitted to the APC server via industrial Ethernet, with a transmission cycle of 2 seconds.

[0140] Principal component analysis (PCA) was used to extract the differential pressure of the desulfurization tower bed (range 0-10 kPa), the SCR reactor temperature (range 200-450℃), and pollutant emissions (SO2 concentration 0-50 mg / m³). 3 NO x Concentration 0-60 mg / m 3 The statistical analysis module can be integrated into the APC server's software platform to calculate the covariance matrix of each parameter, retain principal components with a cumulative contribution rate exceeding 85%, and generate dynamic working condition feature vectors (such as 8-dimensional vectors) with compressed dimensions. The feature vectors are updated once per adjustment cycle (one adjustment cycle is 60 seconds) and transmitted to the adaptive prediction model module via the OPC UA protocol for model training and optimization.

[0141] In this technical solution, the separation of high-frequency and low-frequency components improves the identification of operating conditions and reduces noise interference with model predictions; the spectrum filtering mechanism strengthens the correlation analysis of key process parameters and optimizes the targeting of control commands; statistical correlation extraction reduces data dimensionality and improves model computation efficiency. Hardware and algorithm selection are adapted to the real-time requirements of industrial sites, ensuring the reliability and practicality of feature vectors.

[0142] In another technical solution, the method for constructing the coupling relationship model includes:

[0143] Outliers in historical operating data are filtered out using a statistical extremum removal method, and real-time process parameters are processed by moving average. Based on the processed historical operating data and real-time process parameters, a mathematical model is established for desulfurization efficiency, denitrification efficiency, and consumption of quicklime and ammonia. The input of the model is a dynamic operating condition feature vector, and the output is the target value of quicklime feed rate and the opening of the ammonia inlet evaporator regulating valve.

[0144] The allowable fluctuation range of differential pressure in the desulfurization absorption tower bed is defined as ±10%, and the active range threshold of SCR reactor temperature is defined as 180~420℃.

[0145] The model parameters are dynamically adjusted based on the residual amount of hydrated lime, catalyst agglomeration signal, and real-time deviations in desulfurization efficiency / denitrification efficiency, with a correction cycle of every 15 minutes. Specifically, when the absolute value of the desulfurization efficiency deviation exceeds ±5% or the absolute value of the denitrification efficiency deviation exceeds ±5%, the corresponding parameter adjustment weight is increased to 70%, and the weight of the basic historical data is reduced to 30%. When the absolute value of the deviation is less than ±2%, the parameter adjustment weight is reduced to 30%, and the weight of the basic historical data is increased to 70%.

[0146] The target values ​​for the feed rate of quicklime and the opening of the regulating valve for ammonia entering the evaporator are updated using a rolling optimization algorithm, and are updated once in each regulation cycle.

[0147] In this technical solution, the coupling relationship model can be constructed using multiple linear regression or neural network algorithms, based on historical operating data (such as desulfurization efficiency of 90-95% and denitrification efficiency of 85-92% over the past 3 months) and real-time process parameters (dynamic operating condition feature vectors). The model input includes an 8-dimensional dynamic operating condition feature vector (such as average flue gas flow rate, sulfur content frequency band energy, bed differential pressure trend, etc.), and the output is the target value for quicklime feed rate (range 2-8 t / h) and the target value for the opening of the ammonia gas inlet evaporator regulating valve (range 20-80%). Historical data can be stored in a relational database on an APC server, while real-time data is collected via an OPC interface at a sampling frequency of 1 Hz. Model training combines offline batch learning with online incremental learning, with an initial training cycle of 24 hours and an online update cycle of 15 minutes. A statistical extremum removal method based on the 3σ criterion is used to filter out outliers in the historical operating data: the mean and standard deviation of a certain process parameter (such as inlet SO2 concentration) over a period of time are calculated, and data points deviating from the mean by more than 3 times the standard deviation are removed.

[0148] The allowable fluctuation range of the differential pressure in the desulfurization absorption tower bed is set to ±10% (based on the design value of 5 kPa, the allowable range is 4.5-5.5 kPa). A capacitive pressure transmitter can be used as the differential pressure sensor and installed at the bottom of the desulfurization tower. The active range threshold for the SCR reactor temperature is set to 280-420℃. A type K thermocouple can be used as the temperature sensor and installed in the middle of the SCR reactor. When the bed differential pressure exceeds ±10% or the SCR temperature exceeds 280-420℃, the model automatically triggers constraint conditions to limit the adjustment range of the target values ​​for slaked lime or ammonia.

[0149] The residual amount of hydrated lime is detected by a level gauge (installed on the side wall of the hydrated lime silo, range 0-20t). Model correction is triggered when the residual amount is below 15%. Catalyst agglomeration signal is detected by a differential pressure sensor (installed at the inlet and outlet of the SCR reactor, range 0-10kPa). Agglomeration is determined when the differential pressure exceeds 5kPa. The correction module uses recursive least squares method, updating model parameters every 15 minutes. The rolling optimization algorithm can use model predictive control (MPC), with an optimization cycle of 5 minutes. Each optimization calculates the target value sequence for the next 30 minutes, selecting the first value as the current command. The optimization objective function includes emission deviation, reagent consumption, and process constraint penalty terms, with weight coefficients determined through offline debugging.

[0150] In this technical solution, the mathematical model is constructed by fusing historical and real-time data to improve the prediction accuracy of desulfurization and denitrification efficiency and reagent consumption; the defined thresholds for process parameters clearly define the system's safety boundaries to prevent operational variables from exceeding limits; dynamic correction and rolling optimization mechanisms enhance the model's adaptability to operating condition fluctuations, ensuring long-term operational stability and reliability. Hardware selection and parameter settings are adapted to industrial site requirements, reducing maintenance complexity.

[0151] In another technical solution, the target value of the hydrated lime feed rate is calculated as follows:

[0152] The desulfurization optimization controller monitors the SO2 concentration at the desulfurization tower outlet in real time. Combining the product of the SO2 concentration in the inlet flue gas and the flue gas flow rate, the target value of the hydrated lime feed rate is generated through the model predictive control algorithm of the APC server.

[0153] In this technical solution, the desulfurization optimization controller can be a DCS control module to monitor the SO2 concentration at the desulfurization tower outlet in real time (range 0-50 mg / m³). 3 (Installed at the flue outlet). Inlet flue gas SO2 concentration (range 0-1000 mg / m³). 3 The flue gas flow rate (range 0-50m) was detected by a non-dispersive infrared analyzer (installed on the inlet pipe of the desulfurization tower). 3 The flow rate ( / s) is measured by a thermal mass flow meter (installed on the inlet pipe). The APC server uses a model predictive control algorithm, taking the product of the inlet SO2 concentration and the flue gas flow rate (unit: kg / h) as the feedforward, and combining it with the outlet SO2 concentration feedback, to generate a target value for the hydrated lime feed rate (range: 2-8 t / h). The target value is updated once every adjustment cycle (one cycle is 60s) and transmitted to the hydrated lime feed frequency converter (installed on the screw feeder) via a 4-20mA signal.

[0154] In another technical solution, the target value of the ammonia gas inlet evaporator regulating valve opening is calculated as follows:

[0155] The denitrification optimization controller monitors the NO at the SCR reactor outlet in real time. x Concentration, combined with NO in inlet flue gas x The product of ammonia concentration and flue gas flow rate is used to generate the target value for the opening of the ammonia inlet evaporator regulating valve via the model predictive control algorithm on the APC server. In this technical solution, the denitrification optimization controller can be a DCS control module to monitor the NO at the SCR reactor outlet in real time. x Concentration (range 0-50 mg / m³) 3 (Installed at the front of the chimney). Inlet flue gas NO x Concentration (range 0-1000 mg / m³) 3 The flue gas flow rate (range 0-50m) was detected by a non-dispersive infrared analyzer (installed on the inlet pipe of the SCR reactor).3 The flow rate (NO / s) is measured by a thermal mass flow meter (installed on the SCR inlet pipe). The APC server uses a model predictive control algorithm to measure the inlet NO. x The product of concentration and flue gas flow rate (in kg / h) is the feedforward quantity, combined with the outlet NO. x Concentration feedback generates a target value (range 20-80%) for the opening of the ammonia gas inlet evaporator regulating valve. The target value is updated once every regulation cycle (one regulation cycle is 60s) and transmitted to the electric regulating valve (installed at the outlet of the ammonia gas injection pump) via a 4-20mA signal.

[0156] In another technical solution, the feedforward-feedback composite control strategy of the desulfurization optimization controller includes:

[0157] Real-time data collection of inlet flue gas flow rate, temperature, and SO2 concentration is performed. The model predictive control algorithm of the APC server calculates the SO2 increment for the next 3-5 minutes, generates feedforward compensation instructions, and writes them to the DCS controller's setpoint register via the OPC interface.

[0158] Based on the bed differential pressure deviation ΔP, outlet temperature deviation ΔT, inlet SO2 fluctuation amplitude, and final control effect from the historical operation database, the feedforward weight coefficient K is trained online using the least squares method. ff With feedback weight coefficient K fb The mapping relationship model;

[0159] When the differential pressure deviation ΔP in the bed exceeds ±10%, the feedback control weight is increased to 80%, and the feedforward weight is reduced to 20%; simultaneously, K is finely adjusted in real time according to the mapping relationship model. fb With K ff The specific values ​​are determined to make the weight allocation more closely match the characteristics of the current working conditions.

[0160] When the outlet temperature deviation ΔT exceeds ±5℃, the feedforward control weight is increased to 70%, and the feedback weight is reduced to 30%; simultaneously, K is fine-tuned in real time according to the mapping relationship model. ff With K fb The specific value;

[0161] If both ΔP and ΔT exceed the limit, the weights are allocated according to priority ΔP>ΔT to ensure that the total is 100%; at the same time, the specific weight coefficients under each priority are fine-tuned in real time according to the mapping relationship model.

[0162] Based on the process coupling relationship, decoupling is achieved to eliminate the mutual interference between quicklime feed rate, bed differential pressure and outlet temperature;

[0163] The weight coefficient update cycle is synchronized with the APC server's rolling optimization cycle, meaning it is updated once per adjustment cycle.

[0164] In this technical solution, a thermal mass flow meter (range 0-50m) can be selected for the inlet flue gas flow rate. 3 / s, installed on the inlet pipe of the desulfurization tower), the temperature can be measured by a type K thermocouple (range 0-400℃, installed between the inlet and outlet of the desulfurization tower), and the SO2 concentration can be measured by a non-dispersive infrared analyzer (range 0-1000mg / m³). 3 (Installed in the inlet flue). The model predictive control algorithm of the APC server calculates the SO2 increment for the next 3-5 minutes every 5 minutes. The input to the predictive model is the average value of the inlet flow rate, temperature, and SO2 concentration within a sliding window (window length 10 minutes). The output is a feedforward compensation instruction for the hydrated lime feed rate (adjustment range ±20%). The feedforward instruction is written to the setpoint register of the DCS controller via the OPC interface (address offset 0x1000), with a transmission cycle of 1 second.

[0165] The differential pressure deviation threshold for the bed is set to ±10% (reference value 5 kPa), and the outlet temperature deviation threshold is set to ±5℃ (set value 150℃). When the differential pressure deviation exceeds ±10%, the PID module of the DCS controller increases the feedback control weight to 80% and decreases the feedforward weight to 20%, meaning the feedback correction accounts for 80% and the feedforward compensation accounts for 20%. When the temperature deviation exceeds ±5℃, the feedforward weight increases to 70% and the feedback weight decreases to 30%. Weight allocation is implemented through DCS logic blocks, and the control command calculation formula is: final command = feedforward quantity × feedforward weight + feedback quantity × feedback weight. The weight switching response time is less than 2 seconds to ensure control continuity.

[0166] A decoupling matrix is ​​established based on the coupling relationship of the desulfurization tower process, including the influence coefficients of hydrated lime feed rate on bed differential pressure (0.8 kPa / t·h), hydrated lime feed rate on outlet temperature (-2℃ / t·h), and bed differential pressure on outlet temperature (0.5℃ / kPa). The decoupling algorithm can employ matrix inverse operation or feedforward compensation, integrated into the multivariable control module of the DCS controller. The decoupled control commands are transmitted to the hydrated lime feed frequency converter (installed on the screw feeder) and the desulfurization tower induced draft fan (installed in the outlet flue), with an adjustment cycle of 500 ms. The decoupling parameters are updated every 24 hours using historical data training to ensure the model matches actual operating conditions.

[0167] Based on the most recent 500 sets of historical data (including bed differential pressure deviation ΔP, outlet temperature deviation ΔT, inlet SO2 fluctuation amplitude, and corresponding final control effects), the feedforward weight coefficient K is trained online and updated in real time using the recursive least squares (RLS) method. ff With feedback weight coefficient K fb The mapping model is updated in sync with the APC rolling optimization cycle.

[0168] In this technical solution, the feedforward compensation command uses model prediction to respond in advance to SO2 load changes, reducing outlet concentration fluctuations; the dynamic weight adjustment mechanism balances the priorities of differential pressure and temperature control, improving system stability; multivariable decoupling control effectively eliminates mutual interference between process parameters, reduces the frequency of equipment adjustments, and extends the service life of key components. Hardware selection and algorithm design are adapted to the real-time requirements of industrial sites, ensuring long-term operational reliability.

[0169] As a specific embodiment of the present invention, the dynamic weight allocation logic in the feedforward-feedback composite control strategy of the desulfurization optimization controller is as follows: Based on the mapping relationship model obtained by training historical operation data, the system presets key weight switching conditions. For example, when the bed differential pressure deviation ΔP exceeds its allowable fluctuation range (e.g., ±10%), the control system increases the weight of feedback control to a higher level (e.g., 80%), while correspondingly reducing the weight of feedforward control (e.g., 20%) to prioritize stabilizing the equipment state; when the outlet temperature deviation ΔT exceeds its allowable range (e.g., ±5℃), the weight of feedforward control is increased to a higher level (e.g., 70%), and the weight of feedback control is reduced (e.g., 30%) to quickly respond to the impact of thermal inertia. If both the bed differential pressure and outlet temperature deviations exceed the limits, the weights are allocated according to the preset priority (e.g., equipment safety priority, setting the priority of ΔP higher than ΔT) to ensure that the total control weight is 100%. The specific percentage values ​​mentioned above can be adjusted according to actual process requirements. The core of the present invention lies in the logic of the dynamic weight allocation, rather than the fixed values ​​themselves.

[0170] In another technical solution, the feedforward-feedback composite control strategy of the denitrification optimization controller includes:

[0171] Real-time data collection of combustion load and flue gas oxygen content is used to calculate NO levels for the next 3-5 minutes using the model predictive control algorithm on the APC server. x Generate trends, generate feedforward compensation instructions, and write them to the DCS controller's setpoint register via the OPC interface;

[0172] Based on historical operational database data including SCR reactor temperature deviation, ammonia slip concentration deviation, combustion load change rate, and final control performance, the feedforward priority coefficient P is trained online using the least squares method. ff With feedback priority coefficient P fb The mapping relationship model;

[0173] Based on the deviation between the SCR reactor temperature and the ammonia slip concentration, and combined with the activity index output by the real-time catalyst activity evaluation algorithm, feedforward and feedback priorities are assigned:

[0174] When the SCR reactor temperature deviates from the active range of 180~420℃, the feedback control priority is increased to 90%, and the feedforward priority is reduced to 10%; simultaneously, P is finely adjusted in real time according to the mapping relationship model. fb With P ff The specific value;

[0175] When the ammonia slip concentration approaches the 2 ppm limit, the feedforward control priority is increased to 80%, and the feedback priority is reduced to 20%; simultaneously, P is fine-tuned in real time according to the mapping relationship model. ff With P fb The specific value;

[0176] When the catalyst activity index is below 0.8, the feedback control priority is increased by an additional 10%, that is, the total priority is increased to 100%, the feedforward priority is reduced to 0%, and the basic setting value of the ammonia gas inlet evaporator regulating valve is reduced proportionally according to the activity index.

[0177] Based on the process coupling relationship, decoupling is achieved to eliminate the mutual interference between the opening of the regulating valve for ammonia gas entering the evaporator, the SCR temperature, and the flue gas flow rate.

[0178] The priority coefficient update cycle is synchronized with the APC server's rolling optimization cycle, meaning it is updated once per adjustment cycle.

[0179] In this technical solution, the combustion load can be monitored using a motor torque sensor (range 0-50 kN·m, installed at the drive end of the rotary kiln), and the flue gas oxygen content can be monitored using a zirconia oxygen analyzer (range 0-25 vol%, installed at the inlet flue of the SCR reactor). The model predictive control algorithm of the APC server calculates the NO content for the next 3-5 minutes every 5 minutes. x The trend generation and prediction model takes the sliding average of combustion load (window length 10 minutes) and the instantaneous value of flue gas oxygen as input, and outputs a feedforward compensation command (adjustment range ±15%) for the opening of the ammonia inlet evaporator regulating valve. The feedforward command is written to the DCS controller's setpoint register (address offset 0x2000) via the OPC interface, with a transmission cycle of 1 second.

[0180] The SCR reactor temperature activity range is set at 280-420℃, and the ammonia slip concentration threshold is set at 2ppm. When the temperature deviates from the activity range (e.g., reaching 430℃ or 270℃), the feedback control priority increases to 90%, and the feedforward priority decreases to 10%, meaning the feedback correction accounts for 90%. When the ammonia slip concentration approaches 2ppm (e.g., reaching 1.8ppm), the feedforward priority increases to 80%, and the feedback priority decreases to 20%. Priority allocation is implemented through DCS logic blocks, and the calculation formula is: Final command = Feedforward quantity × Feedforward weight + Feedback quantity × Feedback weight. The weight switching response time is less than 2 seconds to ensure control continuity.

[0181] A decoupling matrix was established based on the coupling relationship of the SCR process, including the influence coefficient of the ammonia gas inlet evaporator regulating valve opening on the SCR temperature (-3℃ / %) and the influence coefficient of flue gas flow rate on the SCR temperature (0.5℃ / m³). 3 The influence coefficient of the ammonia slip on the opening of the ammonia inlet evaporator regulating valve (0.2 ppm / %) was calculated. The decoupling algorithm can use a feedforward compensation method, integrated into the multivariable control module of the DCS controller. The decoupled control commands are transmitted to the ammonia inlet evaporator regulating valve (installed at the ejector pump outlet) and the SCR induced draft fan (installed at the outlet flue), with an adjustment cycle of 500 ms. The decoupling parameters are updated every 24 hours using historical data training to ensure the model matches actual operating conditions.

[0182] The real-time catalyst activity evaluation algorithm is implemented according to the following steps:

[0183] Data Acquisition: Real-time acquisition of NO at the inlet and outlet of the SCR reactor. x Concentration value (unit: mg / m³) 3 Ammonia slip concentration (ppm), reactor mid-section temperature (°C), and flue gas flow rate (m³ / s). 3 / s) and SCR reactor bed pressure difference (unit: Pa).

[0184] Efficiency calculation: based on inlet and outlet NO x Concentration, calculate the actual denitrification efficiency η 实际 = (entry NO x -Export NO x ) / entranceNO x ×100%.

[0185] Theoretical efficiency calculation: Based on the baseline performance curve of the new catalyst (the curve is calculated using flue gas flow rate, reactor temperature, and inlet NO). x (The concentration is taken as input and stored in the database of the APC server). The query retrieves the theoretical expected denitrification efficiency η under the current operating conditions. 理论 .

[0186] Catalyst Activity Index (CAI) Generation: The catalyst activity index (CAI) is calculated in real time using the following weighted fusion formula:

[0187] CAI=(η 实际 / η 理论 )×0.7 + [1 / (1+|C 氨逃逸 - 2|)]×0.2 + [1-min(1,|ΔP / Δt| / 0.5)]×0.1;

[0188] Among them, C 氨逃逸The actual measured ammonia slip concentration is given, and |ΔP / Δt| is the absolute value of the rate of change of bed pressure difference (Pa / min).

[0189] Dynamic application: The real-time calculated CAI value is transmitted to the denitrification optimization controller via the OPC interface. When CAI < 0.8, it is determined that the catalyst activity has decreased. At this time, the system automatically reduces the basic setting value of the ammonia gas inlet evaporator regulating valve by (1-CAI) ​​(for example, when CAI = 0.7, the opening setting value is reduced by 30%), and prioritizes ensuring that the ammonia slip concentration does not exceed the standard.

[0190] In this technical solution, the feedforward compensation command is predicted by the model to respond to NO in advance. x The system generates trends and reduces outlet concentration fluctuations; a priority dynamic allocation mechanism balances the conflict between SCR temperature and ammonia slip control, improving system stability; multivariate decoupling control effectively eliminates mutual interference between process parameters, reduces the frequency of equipment adjustments, and extends catalyst lifespan. Hardware selection and algorithm design are adapted to industrial control requirements, ensuring long-term operational reliability.

[0191] As another specific embodiment of the present invention, the priority dynamic allocation logic in the feedforward-feedback composite control strategy of the denitrification optimization controller is as follows: The system dynamically adjusts the control priority according to the degree of deviation of key process parameters. For example, when the SCR reactor temperature deviates from the optimal activity range of the catalyst (e.g., 180~420℃), it indicates that the reaction environment has deteriorated. At this time, the priority of feedback control is significantly increased (e.g., increased to 90%), and the priority of feedforward control is correspondingly reduced (e.g., 10%) to prioritize the restoration of a suitable reaction temperature. When the ammonia slip concentration is close to the environmental limit (e.g., 2ppm), in order to avoid secondary pollution, the priority of feedforward control is increased (e.g., increased to 80%) to quickly suppress the upward trend of ammonia slip. When the catalyst activity index (CAI) is lower than a specific threshold (e.g., 0.8), it indicates that the catalyst performance has deteriorated. At this time, the priority of feedback control is further increased (e.g., increased by an additional 10%, with a total priority of 100%), and the basic setting value of ammonia injection is reduced proportionally to protect the catalyst while ensuring that emissions meet standards. These specific percentages and thresholds are configurable, and the scope of protection of the present invention is defined by the logic method based on the dynamic adjustment of priority using multiple parameters.

[0192] In another technical solution, the optimization method of the real-time energy consumption-emission optimization module includes the following steps:

[0193] Real-time monitoring of SO2 and NO x If the emission concentration is lower than 85% of the limit, the desulfurization and denitrification controller is allowed to reduce the dosage of quicklime and ammonia according to the optimization instructions of the APC server.

[0194] Calculate the unit energy consumption index based on the real-time consumption of quicklime and ammonia and the amount of flue gas treated.

[0195] Based on the current operating condition feature vector and historical energy consumption data, an energy consumption prediction model is constructed through regression analysis to predict the unit energy consumption trend in the next 5-10 minutes; when the prediction shows that the unit energy consumption is about to exceed the process set threshold, the priority weight of the corresponding controller is adjusted in advance.

[0196] When SO2 or NO x When the concentration approaches the emission limit, the priority weight of the corresponding controller is increased to 80% through a dynamic constraint algorithm to ensure that emissions meet the standards.

[0197] When the unit energy consumption exceeds the process-set threshold, the weight of the high-energy-consumption controller is reduced to 60%, prioritizing the reduction of resource consumption;

[0198] The system dynamically corrects the slaked lime feed rate command of the desulfurization optimization controller and the ammonia gas inlet evaporator regulating valve opening command of the denitrification optimization controller. The system updates and corrects the commands once per regulation cycle and synchronizes with the rolling optimization algorithm cycle to balance emission compliance and resource consumption.

[0199] In this technical solution, SO2 and NO x Emission concentrations can be monitored in real time using a non-dispersive infrared analyzer (installed at the front of the chimney) and a chemiluminescence analyzer (installed at the SCR reactor outlet), with measurement ranges of 0-50 mg / m³, respectively. 3 and 0-60mg / m 3 When the SO2 concentration is below 30 mg / m³ 3 (Limit 35 mg / m²) 3 85% or NO x Concentration below 42.5 mg / m³ 3 (Limit 50 mg / m²) 3 When the flow rate is 85% (or below 85%), the APC server allows the desulfurization and denitrification controllers to reduce the quicklime dosage (down to a minimum of 2 t / h) and the ammonia inlet evaporator regulating valve opening (down to a minimum of 20%) according to optimization instructions. The optimization instructions are written to the DCS controller via the OPC interface, with an execution cycle of 1 second.

[0200] The algorithm design of the energy consumption-emission real-time optimization module not only ensures that the system meets the set emission limits (SO2≤35mg / m³), but also ensures that the system can achieve the desired energy consumption-emission optimization. 3 NO x ≤50mg / m 3 Economic operation under these conditions. This is achieved by adjusting emission constraints in the optimization algorithm (e.g., changing the target setpoint for SO2 from 35 mg / m³). 3 Reduced to 20 mg / m 3This module can automatically reallocate control weights, prioritizing the fulfillment of more stringent emission requirements within acceptable energy consumption ranges. This demonstrates that the system described in this invention is not fixed to the current emission level; its intelligent control framework itself supports a smooth upgrade to ultra-low emission targets, exhibiting significant technological scalability.

[0201] The formula for calculating unit energy consumption is: Unit energy consumption (kg / ton of flue gas) = ​​(Slaked lime consumption + Ammonia consumption) / Flue gas treatment volume. Slaked lime consumption is measured using a Coriolis mass flow meter (installed at the outlet of the slaked lime silo), ammonia consumption is measured using an electromagnetic flow meter (installed on the ammonia supply pipeline), and flue gas treatment volume is obtained using a thermal mass flow meter (installed at the inlet of the desulfurization tower). When SO2 or NO... x Concentration close to the limit (e.g., SO2 ≥ 33 mg / m³) 3 or NO x ≥47mg / m 3 When the energy consumption exceeds the process threshold (e.g., 1.5 kg / ton of flue gas), the dynamic constraint algorithm increases the priority weight of the corresponding controller to 80%, while reducing the weight of other controllers to 20%. When the energy consumption per unit exceeds the process threshold (e.g., 1.5 kg / ton of flue gas), the weight of the high-energy-consuming controller is reduced to 60%. The weight adjustment is executed in real time through the DCS logic module, with a response time of less than 2 seconds.

[0202] The quicklime feed rate command from the desulfurization optimization controller and the ammonia inlet evaporator regulating valve opening command from the denitrification optimization controller are dynamically corrected every 10 minutes. The correction process is based on a rolling optimization algorithm, comprehensively considering emission data, energy consumption indicators, and process constraints (such as bed differential pressure ±10%, SCR temperature 280-420℃) over the past 30 minutes. The correction results are updated to the DCS controller via the APC server, with the quicklime feed rate adjustment step not exceeding ±0.5 t / h and the ammonia inlet evaporator regulating valve opening adjustment step not exceeding ±5%. The correction logic is integrated into the optimization module of the DCS controller (installed in the control cabinet) to ensure smooth command transition.

[0203] In this technical solution, real-time monitoring and dynamic weight allocation mechanisms balance the conflict between emission compliance and resource consumption, reducing reagent waste; quantitative assessment of unit energy consumption provides data support for process optimization; periodic instruction correction enhances the system's adaptability to operating condition fluctuations and ensures long-term operational stability. Hardware selection and algorithm design are adapted to industrial control needs, enhancing environmental compliance and economic efficiency.

[0204] The core of the "dynamic constraint algorithm" is a constrained multi-objective optimization problem, and its mathematical principle is as follows: In each optimization cycle, the algorithm constructs an objective function J, which aims to minimize the total cost while satisfying emission constraints.

[0205] 1. Objective function:

[0206] ;

[0207] in, and Emission setpoint (e.g., 35 mg / m³) 3 and 50mg / m 3 ), C 石灰 and C 氨 F is the unit cost coefficient. 石灰 and F 氨 The consumption amount is represented by w1, w2, and w3, which are dynamic weighting coefficients, i.e., the "priority weights" described in this invention.

[0208] 2. Inequality Constraints: The primary constraint is that emissions must meet standards, i.e. and (δ represents the safety margin). Secondly, there are equipment safety constraints, such as bed differential pressure ΔP ≤ ΔP. max The SCR temperature T is within the active range, etc.

[0209] 3. Solution Method: Within each MPC rolling optimization cycle, this optimization problem is transformed into a quadratic programming (QP) problem. The algorithm uses the Lagrange multiplier method to handle inequality constraints. When the emission concentration approaches the limit, the corresponding constraint is activated, forcing the algorithm to prioritize adjusting the weights (increasing w1 or w2) to meet environmental protection requirements. Dynamic weight adjustment is manifested in the process of solving the QP problem online and updating w1, w2, and w3 based on real-time emission concentration and energy consumption predictions.

[0210] In another technical solution, the real-time catalyst activity evaluation algorithm includes the following steps:

[0211] Multi-source data fusion: Real-time acquisition of NO at the SCR reactor inlet / outlet x Concentration, ammonia slip concentration, reactor temperature distribution, flue gas flow rate, ammonia dosage, and bed pressure difference data;

[0212] Benchmark efficiency calculation: Based on inlet NO x Using a pre-defined new catalyst activity benchmark model, the theoretical expected denitrification efficiency is calculated based on the concentration, flue gas flow rate, reactor temperature, and ammonia dosage.

[0213] Actual efficiency calculation: Based on inlet / outlet NO x Concentration calculation for actual denitrification efficiency;

[0214] Catalyst Activity Index (CAI) is calculated by weighting the ratio of actual denitrification efficiency to theoretically expected denitrification efficiency, combined with the deviation of ammonia slip concentration (actual value / set value) and the bed pressure difference change rate (dP / dt). The weighting is as follows: efficiency ratio 70%, ammonia slip deviation 20%, and pressure difference change rate 10%. The formula is:

[0215] CAI=(η 实 / η 理 )×0.7+[1 / (1+|C 氨 -2|)]×0.2+[1-min(1,|V ΔP | / 0.5) ]×0.1;

[0216] Where η 实 For the actual denitrification efficiency, η 理 To achieve the theoretically expected denitrification efficiency, C 氨 V represents the actual ammonia slip concentration. ΔP This represents the rate of change of bed pressure differential;

[0217] Dynamic calibration: The coefficients of the baseline model and weighted fusion formula are calibrated quarterly using catalyst activity sample data from offline laboratory testing.

[0218] Output and Application: The real-time calculated CAI is transmitted to the denitrification optimization controller for dynamic adjustment of control strategies and operating limits.

[0219] This invention also provides a control method for an intelligent advanced control system for belt roasting desulfurization and denitrification, including a dynamic adjustment step based on the catalyst activity index (CAI):

[0220] Real-time collection of NO at the inlet and outlet of the SCR reactor x Concentration, ammonia slip concentration, reactor temperature, flue gas flow rate, and bed pressure differential data;

[0221] The theoretical denitrification efficiency was calculated based on a new catalyst benchmark model.

[0222] Based on the ratio of actual denitrification efficiency to theoretical denitrification efficiency, combined with the degree of ammonia slip concentration deviating from the set value and the bed pressure difference change rate, the catalyst activity index (CAI) is calculated by weighted fusion.

[0223] Based on the CAI value, dynamically adjust the basic setting value of the ammonia gas inlet evaporator regulating valve opening;

[0224] The formula for calculating the catalyst activity index (CAI) is:

[0225] CAI=(η 实 / η 理 )×0.7+[1 / (1+|NH3逃逸 -2|)]×0.2+[1-min(1,|dP / dt| / 0.5)]×0.1;

[0226] Where, η 实 For the actual denitrification efficiency, η 理 To achieve the theoretically expected denitrification efficiency, NH 3逃逸 dP / dt represents the actual ammonia slip concentration, and dP / dt represents the rate of change of bed pressure difference.

[0227] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention. For those skilled in the art, other modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and illustrations shown and described herein.

Claims

1. A belt roasting desulfurization and denitrification intelligent advanced control system, characterized in that, include: Data Acquisition Unit: Features a built-in multi-source data fusion module, enabling real-time acquisition of data from flue gas flow sensors, temperature sensors, and SO2 / NO3 sensors via industrial Ethernet. x The measurement data from the concentration analyzer, the slaked lime flow meter, and the ammonia gas inlet evaporator regulating valve are fused with the fluctuation signal output from the raw material composition analyzer and the load change signal fed back by the combustion load monitoring device to generate a dynamic operating condition feature vector. APC Server: Communicates with the data acquisition unit and DCS controller via the OPC interface, integrating an adaptive prediction model module and an energy consumption-emission real-time optimization module. The adaptive prediction model module employs a model predictive control algorithm, updating the coupling relationship model between desulfurization efficiency, denitrification efficiency, and quicklime and ammonia consumption online based on historical operating data and dynamic operating condition feature vectors, outputting target values ​​for quicklime feed rate and ammonia inlet evaporator regulating valve opening. The energy consumption-emission real-time optimization module uses a dynamic constraint algorithm to optimize SO2 / NO... x Under the premise that the emission concentration meets the national standards, the priority weight of the control commands of the desulfurization optimization controller and the denitrification optimization controller is dynamically adjusted with the goal of minimizing the unit energy consumption of quicklime and ammonia. DCS Controller: Built-in hierarchical fault-tolerant control unit, used to automatically switch from APC control mode to local safety control mode when a communication interruption or sensor failure is detected. It is connected to the desulfurization absorption tower bed differential pressure sensor, outlet temperature sensor, SCR reactor temperature sensor and actuators via hard wiring. The actuators include the lime feed frequency converter and the ammonia gas inlet evaporator regulating valve.

2. The intelligent advanced control system for the belt roasting desulfurization and denitrification according to claim 1, characterized in that, The DCS controller includes: The system includes a desulfurization optimization controller and a denitrification optimization controller. The desulfurization optimization controller, based on the target value of the hydrated lime feed rate output by the APC server, employs a feedforward-feedback composite control strategy. It uses the product of the inlet flue gas flow rate and SO2 concentration as the feedforward disturbance variable, and the bed differential pressure deviation and outlet temperature deviation as feedback variables to adjust the hydrated lime feed rate, achieving SO2 emission control at the margin, ensuring that the outlet SO2 concentration does not exceed 35 mg / m³. 3 The denitrification optimization controller, based on the target value of the ammonia inlet evaporator regulating valve opening output by the APC server, uses a feedforward-feedback composite control strategy. Combustion load and flue gas oxygen content are used as feedforward disturbance variables, while SCR reactor temperature deviation and ammonia slip concentration are used as feedback variables to adjust the opening of the ammonia inlet evaporator regulating valve, thereby achieving NO reduction. x Emissions control measures ensure that NO emissions at the export level are controlled. x Concentration not exceeding 50 mg / m 3 .

3. The intelligent advanced control system for desulfurization and denitrification by belt roasting according to claim 1, wherein The generation of the dynamic operating condition feature vector includes the following steps: Based on the fluctuation characteristics of process parameters, high-frequency and low-frequency components are separated. High-frequency components include instantaneous fluctuations in flue gas flow rate, while low-frequency components include trend changes in combustion load. Based on the spectral characteristics of the raw material composition fluctuation signal and the combustion load change signal, the strong correlation process parameters related to the calcium oxide feed amount and the ammonia gas evaporator regulating valve opening degree are screened, including the sulfur content frequency band and the NO x generation rate; The correlation between the differential pressure of the desulfurization tower bed, the temperature of the SCR reactor and pollutant emissions was extracted by statistical analysis methods, and a dynamic operating condition feature vector was generated and transmitted to the adaptive prediction model module of the APC server.

4. The intelligent advanced control system for desulfurization and denitrification by belt roasting according to claim 1, wherein The method for constructing the coupling relationship model includes: Outliers in historical operating data are filtered out using a statistical extremum removal method, and real-time process parameters are processed by moving average. Based on the processed historical operating data and real-time process parameters, a mathematical model is established for desulfurization efficiency, denitrification efficiency, and consumption of quicklime and ammonia. The input of the model is a dynamic operating condition feature vector, and the output is the target value of quicklime feed rate and the opening of the ammonia inlet evaporator regulating valve. The model parameters are dynamically adjusted based on the residual amount of hydrated lime, catalyst agglomeration signal, and real-time deviations in desulfurization efficiency / denitrification efficiency. Specifically, when the absolute value of the desulfurization efficiency deviation exceeds ±5% or the absolute value of the denitrification efficiency deviation exceeds ±5%, the parameter adjustment weight of the corresponding model is increased to 70%, and the weight of the basic historical data is reduced to 30%. When the absolute value of the deviation is less than ±2%, the parameter adjustment weight is reduced to 30%, and the weight of the basic historical data is increased to 70%. The target values ​​for the feed rate of quicklime and the opening of the regulating valve for ammonia entering the evaporator are updated using a rolling optimization algorithm, and are updated once in each regulation cycle.

5. The intelligent advanced control system for desulfurization and denitrification by belt roasting according to claim 1, wherein The method for calculating the target value of hydrated lime feed rate is as follows: The desulfurization optimization controller monitors the SO2 concentration at the desulfurization tower outlet in real time. Combining the product of the SO2 concentration in the inlet flue gas and the flue gas flow rate, the target value of the hydrated lime feed rate is generated through the model predictive control algorithm of the APC server.

6. The intelligent advanced control system for desulfurization and denitrification by belt roasting according to claim 1, wherein The calculation method for the target value of the ammonia gas inlet evaporator regulating valve opening is as follows: The denitrification optimization controller monitors the NO at the SCR reactor outlet in real time. x Concentration, combined with NO in inlet flue gas x The product of concentration and flue gas flow rate is used to generate the target value for the opening of the ammonia gas inlet evaporator regulating valve through the model predictive control algorithm of the APC server.

7. The intelligent advanced control system for desulfurization and denitrification by belt roasting according to claim 2, wherein The feedforward-feedback composite control strategy of the desulfurization optimization controller includes: Real-time acquisition of inlet flue gas flow rate, temperature and SO2 concentration data; calculation of SO2 increment in the next 3-5 minutes using model predictive control algorithm of APC server; generation of feedforward compensation instruction and writing to the setpoint register of DCS controller through OPC interface. Based on the bed differential pressure deviation ΔP, outlet temperature deviation ΔT, inlet SO2 fluctuation amplitude, and final control effect from the historical operation database, the feedforward weight coefficient K is trained online using the least squares method. ff With feedback weight coefficient K fb The mapping relationship model; Based on the dynamic changes in bed differential pressure deviation ΔP and outlet temperature deviation ΔT, the weight allocation of feedforward and feedback control is dynamically adjusted; when ΔP and ΔT exceed the limits simultaneously, the weight allocation is performed according to the preset priority coefficient; based on the process coupling relationship, decoupling is achieved to eliminate the mutual interference between quicklime feed rate, bed differential pressure and outlet temperature. The weight coefficient update cycle is synchronized with the APC server's rolling optimization cycle, meaning it is updated once per adjustment cycle.

8. The intelligent advanced control system for desulfurization and denitrification by belt roasting according to claim 2, wherein The feedforward-feedback composite control strategy of the denitrification optimization controller includes: Real-time acquisition of combustion load and flue gas oxygen data, through the APC server model predictive control algorithm to calculate the future 3-5 minutes NO x Generate trends, generate feedforward compensation instructions and write into the DCS controller set value register through the OPC interface; Based on historical operational database data including SCR reactor temperature deviation, ammonia slip concentration deviation, combustion load change rate, and final control performance, the feedforward priority coefficient P is trained online using the least squares method. ff With feedback priority coefficient P fb The mapping relationship model; The priority of feedforward and feedback control is dynamically adjusted based on the temperature deviation of the SCR reactor, the ammonia slip concentration deviation, and the catalyst activity index. Based on the process coupling relationship, decoupling is achieved to eliminate the mutual interference between the opening of the regulating valve for ammonia gas entering the evaporator, the SCR temperature, and the flue gas flow rate. The priority coefficient update cycle is synchronized with the APC server's rolling optimization cycle, meaning it is updated once per adjustment cycle.

9. The intelligent advanced control system for belt roasting desulfurization and denitrification as described in claim 1, characterized in that, The optimization method of the real-time energy consumption-emission optimization module includes the following steps: Real-time monitoring of SO2 and NO x If the emission concentration is lower than 85% of the limit, the desulfurization and denitrification controller is allowed to reduce the dosage of quicklime and ammonia according to the optimization instructions of the APC server. Calculate the unit energy consumption index based on the real-time consumption of quicklime and ammonia and the amount of flue gas treated. Based on the current operating condition feature vector and historical energy consumption data, an energy consumption prediction model is constructed through regression analysis to predict the unit energy consumption trend in the next 5-10 minutes; when the prediction shows that the unit energy consumption is about to exceed the process set threshold, the priority weight of the corresponding controller is adjusted in advance. When SO2 or NO x When the concentration is close to the emission limit, the priority weight of the corresponding controller is raised to 80% through a dynamic constraint algorithm, and the emission standard is guaranteed. When the unit energy consumption exceeds the process-set threshold, the weight of the high-energy-consumption controller is reduced to 60%, prioritizing the reduction of resource consumption; The system dynamically corrects the slaked lime feed rate command of the desulfurization optimization controller and the ammonia gas inlet evaporator regulating valve opening command of the denitrification optimization controller. It is corrected once in each regulation cycle and synchronized with the rolling optimization algorithm cycle to balance emission compliance and resource consumption.

10. The control method of the intelligent advanced control system for the belt roasting desulfurization and denitrification according to any one of claims 1 to 9, characterized in that, This includes a dynamic adjustment step based on the catalyst activity index (CAI): NO concentrations at the inlet and outlet of the SCR reactor were collected in real time x NO concentration at the inlet and outlet of the SCR reactor, ammonia slip concentration, reactor temperature, flue gas flow and bed pressure drop data; The theoretical denitrification efficiency was calculated based on a new catalyst benchmark model. Based on the ratio of actual denitrification efficiency to theoretical denitrification efficiency, combined with the degree of ammonia slip concentration deviating from the set value and the bed pressure difference change rate, the catalyst activity index (CAI) is calculated by weighted fusion. Based on the catalyst activity index (CAI), the basic setting value of the ammonia gas inlet evaporator regulating valve opening is reduced proportionally according to the activity index. The formula for calculating the catalyst activity index (CAI) is: CAI = (η 实 / η 理 ) x 0.7 + [1 / (1 + |NH 3逃逸 - 2|)] x 0.2 + [1 - min(l, |dP / dt| / 0.5)] x 0.1; Where, η 实 For the actual denitrification efficiency, η 理 To achieve the theoretically expected denitrification efficiency, NH 3逃逸 dP / dt represents the actual ammonia slip concentration, and dP / dt represents the rate of change of bed pressure difference.

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