Spray cooling intelligent management and control system of 330MW cogeneration unit

Abstract

The invention discloses an intelligent spray cooling management and control system for a 330MW cogeneration unit, and relates to the technical field of automatic control of a thermal power generation system, and the system comprises a data collection module which collects the whole-plant economic operation parameters of the on-grid price and the water resource cost in real time, and carries out the control of the whole-plant economic operation parameters based on the economic operation parameters. Dynamically outputting an optimal exhaust gas temperature target value through an optimization decision model; the intelligent decision-making module is used for establishing a whole-plant water consumption model reflecting the water consumption relationship between the spray cooling system and the desulfurization system, and determining an energy-saving control dead zone surrounding the optimal exhaust gas temperature target value based on the whole-plant water consumption model and the water resource cost; and the cooperative control module calculates a feedforward water spraying amount according to the unit operation state and calculates a feedback water spraying amount correction value according to the exhaust gas temperature deviation. According to the method, the optimization decision model is continuously updated according to the actual exhaust gas temperature and resource consumption data, so that closed-loop intelligent management and control of the whole process are realized.

Description

Technical Field

[0001] This invention relates to the field of automatic control technology for thermal power generation systems, and in particular to a smart management and control system for spray cooling of a 330MW cogeneration unit. Background Technology

[0002] In the operation of a 330MW cogeneration unit, the flue gas spray cooling system is a key component to ensure the safe and efficient operation of downstream environmental protection facilities. It cools the high-temperature flue gas discharged from the boiler to within the temperature range allowed by the desulfurization process. The most widely used control strategy is a feedback control based on a fixed setpoint combined with a simple feedforward control strategy. Typically, a fixed target value for the flue gas temperature is preset. The actual flue gas temperature is detected by a temperature sensor installed downstream of the spray device, and a proportional-integral-derivative controller is used to adjust the spray water volume according to the deviation between the measured value and the fixed setpoint.

[0003] When dealing with complex operating scenarios in the power market environment, such as the need for units to deeply participate in peak shaving, the variability of fuel sources, and the increasing prominence of water resource costs and constraints, the static nature and single-objective limitations of existing technical solutions in their control logic gradually become apparent. Their shortcomings are mainly reflected in the lack of comprehensive economic considerations for unit operation. The control mode with fixed setpoints always aims to stabilize the flue gas temperature at a preset point, without incorporating key economic parameters that directly affect operating revenue, such as grid-connected electricity prices and water resource costs, into the control closed loop. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a smart control system for spray cooling of a 330MW cogeneration unit to solve the problem that existing technologies cannot respond to changes in market and resource prices due to static and singular control targets, thereby achieving optimal overall economic efficiency of unit operation.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: This invention provides a smart control system for spray cooling of a 330MW cogeneration unit, which includes a data acquisition module that collects the plant's economic operating parameters, such as grid-connected electricity price and water resource cost, in real time, and dynamically outputs the optimal flue gas temperature target value based on the economic operating parameters through an optimized decision-making model. The intelligent decision-making module establishes a plant-wide water consumption model that reflects the relationship between the water consumption of the spray cooling system and the desulfurization system. Based on the plant-wide water consumption model and water resource costs, it determines the energy-saving control dead zone around the optimal flue gas temperature target value. The collaborative control module calculates the feedforward water spray volume based on the unit's operating status and calculates the feedback water spray volume correction value based on the flue gas temperature deviation. The drive module synthesizes the feedforward water volume and the feedback water volume correction value, and outputs the final water volume command after safety constraint verification. The monitoring module executes the final water volume command to complete the spray cooling process; The update module monitors the actual exhaust temperature and resource consumption data after cooling, and updates the optimization decision model based on the monitored data.

[0007] As a preferred embodiment of the intelligent control system for spray cooling of the 330MW cogeneration unit described in this invention, the system includes the following steps: real-time collection of plant-wide economic operating parameters, including grid-connected electricity price and water resource cost: The on-grid electricity price data at the current moment is obtained from the electricity market data interface. The on-grid electricity price data and the current period industrial water unit price data provided by the water billing system are used together to form the basic cost data. The basic cost data is combined with real-time unit load data read from the plant-level monitoring information system, sulfur dioxide concentration monitoring value of flue gas at the desulfurization system inlet, and process water flow meter reading of the desulfurization tower. The collected real-time unit load data, sulfur dioxide concentration monitoring values ​​of flue gas at the desulfurization system inlet, process water flow meter readings of the desulfurization tower, grid electricity price data, and industrial water unit price data are standardized in format and validated in validity. The real-time unit load data, sulfur dioxide concentration monitoring values ​​of flue gas at the desulfurization system inlet, process water flow meter readings of the desulfurization tower, grid electricity price data, and industrial water unit price data, which have been standardized in format and verified for validity, are packaged to form the plant's economic operating parameters.

[0008] As a preferred embodiment of the intelligent control system for spray cooling of the 330MW cogeneration unit described in this invention, the system dynamically outputs the optimal flue gas temperature target value based on economic operating parameters through an optimized decision-making model, including the following steps: Extract grid-connected electricity price data and real-time unit load data from the plant’s economic operating parameters, and input the grid-connected electricity price data and real-time unit load data into the optimization decision model; The optimized decision-making model calculates the optimal target value of flue gas temperature by taking the maximization of comprehensive economic benefits as the objective function.

[0009] As a preferred embodiment of the intelligent control system for spray cooling of the 330MW cogeneration unit described in this invention, the following steps are included in establishing a plant-wide water consumption model reflecting the relationship between the water consumption of the spray cooling system and the desulfurization system: Analyze the water spray volume data of the spray cooling system under different flue gas temperatures, and analyze the process water makeup volume data of the desulfurization system under different flue gas temperatures; Add the water spray volume data of the spray cooling system at different flue gas temperatures to the process water makeup volume data of the desulfurization system at the corresponding flue gas temperatures to obtain the total water consumption data of the whole plant at different flue gas temperatures. Based on the total water consumption data of the whole plant corresponding to different flue gas temperatures, a functional relationship between flue gas temperature and total water consumption of the whole plant is established by regression fitting, and the functional relationship between flue gas temperature and total water consumption of the whole plant is defined as the whole plant water consumption model.

[0010] As a preferred embodiment of the intelligent control system for spray cooling of the 330MW cogeneration unit described in this invention, the following steps are taken to determine the energy-saving control dead zone around the optimal flue gas temperature target value based on the plant-wide water consumption model and water resource costs: Input the optimal flue gas temperature target value into the plant water consumption model, and output the total plant water consumption variation curve around the optimal flue gas temperature target value; Analyze the total water consumption variation curve of the entire plant and identify the flue gas temperature range where the total water consumption of the entire plant is lower than the preset threshold as candidate energy-saving control dead zones; The width of the candidate energy-saving control dead zone is adjusted according to the level of water resource costs, and the candidate energy-saving control dead zone with the adjusted width is officially determined as the energy-saving control dead zone around the optimal flue gas temperature target value.

[0011] As a preferred embodiment of the intelligent control system for spray cooling of the 330MW cogeneration unit described in this invention, the calculation of the feedforward spray water volume based on the unit's operating status includes the following steps: The flue gas mass flow rate is calculated based on real-time unit load data, fuel characteristic data, and flue gas oxygen content data. The isobaric specific heat capacity of the flue gas is calculated based on fuel characteristic data and flue gas oxygen content data. The flue gas heat capacity flow rate is obtained by multiplying the flue gas mass flow rate by the flue gas isobaric specific heat capacity. The difference between the flue gas temperature data before spraying and the optimal exhaust temperature target value is calculated. The feedforward water spray rate is calculated by multiplying the flue gas heat capacity flow rate by the difference between the flue gas temperature data before spraying and the optimal exhaust temperature target value, and then using a feedforward model.

[0012] As a preferred embodiment of the intelligent control system for spray cooling of the 330MW cogeneration unit described in this invention, the following steps are included in calculating the feedback spray water volume correction value based on the flue gas temperature deviation: Obtain the optimal target value of flue gas temperature and the real-time measured flue gas temperature data, calculate the difference between the optimal target value of flue gas temperature and the real-time measured flue gas temperature data, and obtain the flue gas temperature deviation; The flue gas temperature deviation is input into the proportional-integral-derivative control algorithm, which calculates the current value, historical cumulative value and trend of the flue gas temperature deviation, and outputs the feedback spray water volume correction value.

[0013] As a preferred embodiment of the intelligent control system for spray cooling of the 330MW cogeneration unit described in this invention, the process of synthesizing the feedforward spray volume and the feedback spray volume correction value, and outputting the final spray volume command after safety constraint verification, includes the following steps: The theoretical total water volume is obtained by algebraically adding the feedforward water volume and the feedback water volume correction value. The theoretical total water volume is then compared with the preset safe upper limit value of the water volume and the preset safe lower limit value of the water volume. When the theoretical total spray volume exceeds the upper limit of the safe spray volume, the upper limit of the safe spray volume is used as the spray volume to be verified. When the theoretical total spray volume is lower than the lower limit of the safe spray volume, the lower limit of the safe spray volume is used as the spray volume to be verified. When the theoretical total spray volume is between the upper limit of the safe spray volume and the lower limit of the safe spray volume, the theoretical total spray volume is used as the spray volume to be verified. The anti-corrosion safety logic verification is performed on the water volume to be verified, and the water volume to be verified that passes the anti-corrosion safety logic verification is determined as the final water volume command.

[0014] As a preferred embodiment of the intelligent control system for spray cooling of the 330MW cogeneration unit described in this invention, the process of executing the final spray volume command to complete spray cooling includes the following steps: The final water spray volume command is converted into a frequency control signal for the spray pump inverter, and the final water spray volume command is converted into an opening control signal for the spray regulating valve. The spray pump speed is adjusted according to the frequency control signal, and the spray regulating valve adjusts the flow area of ​​the valve according to the opening control signal; The spray pump with adjusted speed and the spray regulating valve with adjusted flow area work together to output cooling water flow that meets the final spray volume command. The water mist is formed through the atomizing nozzle and mixes with the hot flue gas in the flue and absorbs heat. The water mist evaporates after absorbing heat, reducing the temperature of the hot flue gas and completing the spray cooling process.

[0015] As a preferred embodiment of the intelligent control system for spray cooling of the 330MW cogeneration unit described in this invention, the system includes the following steps: monitoring the actual flue gas temperature and resource consumption data after cooling, and updating the optimization decision model based on the monitored data: After the spray cooling is completed, the actual exhaust temperature data is measured using a flue gas temperature sensor, and the actual power consumption data of the spray pump during the operating cycle is measured using an electricity meter. The actual process water consumption data of the desulfurization tower during the operation cycle is measured using a flow meter. The actual flue gas temperature data, actual power consumption data and actual process water consumption data are collected to form a resource consumption dataset. Extract the corresponding grid-connected electricity price data from the plant's overall economic operating parameters, input the resource consumption dataset and grid-connected electricity price data into the comprehensive economic benefit calculation function, and output the comprehensive economic benefit value of this control action; The real-time unit load data, on-grid electricity price data, optimal flue gas temperature target value, and comprehensive economic benefit value corresponding to this control action are used as a training sample to update the parameters of the optimization decision model.

[0016] The beneficial effects of this invention are as follows: The data acquisition module collects real-time economic operating parameters of the entire plant, such as grid-connected electricity price and water resource costs, and dynamically outputs the optimal flue gas temperature target value using an optimized decision-making model; the intelligent decision-making module establishes a plant-wide water consumption model reflecting the water consumption relationship between the spray cooling system and the desulfurization system, and determines the energy-saving control dead zone around the optimal flue gas temperature target value; the collaborative control module calculates the feedforward spray water volume based on the unit's operating status and calculates the feedback spray water volume correction value in conjunction with the flue gas temperature deviation; the drive module synthesizes the feedforward spray water volume and the feedback correction value, and outputs the final spray water volume command after safety constraint verification; the monitoring module executes this command to complete the spray cooling; and the update module continuously updates the optimized decision-making model based on actual flue gas temperature and resource consumption data, thereby achieving closed-loop intelligent control of the entire process. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A schematic diagram of the intelligent control system for spray cooling in a 330MW cogeneration unit. Detailed Implementation

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0020] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0022] Reference Figure 1 As one embodiment of the present invention, this embodiment provides a smart control system for spray cooling of a 330MW cogeneration unit, comprising the following steps: The data acquisition module collects real-time economic operating parameters of the entire plant, including grid-connected electricity price and water resource cost. Based on these economic operating parameters, it dynamically outputs the optimal target value for flue gas temperature through an optimized decision-making model.

[0023] The on-grid electricity price data at the current moment is obtained from the electricity market data interface. The on-grid electricity price data and the current period industrial water unit price data provided by the water billing system are used together to form the basic cost data.

[0024] Furthermore, the on-grid electricity price data at the current moment is obtained from the electricity market data interface. This on-grid electricity price data is combined with the current period industrial water unit price data provided by the water billing system to form basic cost data, thus combining two key external market parameters that reflect operating revenue and costs.

[0025] The basic cost data is combined with real-time unit load data read from the plant-level monitoring information system, sulfur dioxide concentration monitoring value of flue gas at the desulfurization system inlet, and process water flow meter reading of the desulfurization tower.

[0026] Furthermore, the basic cost data is combined with real-time unit load data read from the plant-level monitoring information system, sulfur dioxide concentration monitoring values ​​of flue gas at the desulfurization system inlet, and process water flow meter readings of the desulfurization tower, integrating key internal process parameters characterizing the unit's operating status and the environmental protection facility's operating status with the basic cost data.

[0027] The collected real-time unit load data, sulfur dioxide concentration monitoring values ​​of flue gas at the desulfurization system inlet, process water flow meter readings of the desulfurization tower, grid electricity price data, and industrial water unit price data are standardized in format and validated in terms of validity.

[0028] Furthermore, the collected real-time unit load data, sulfur dioxide concentration monitoring values ​​in the flue gas at the desulfurization system inlet, process water flow meter readings in the desulfurization tower, grid electricity price data, and industrial water unit price data are standardized in format and validated in validity to ensure that all data from different sources have a consistent format, unit, and are within a reasonable range.

[0029] The real-time unit load data, sulfur dioxide concentration monitoring values ​​of flue gas at the desulfurization system inlet, process water flow meter readings of the desulfurization tower, grid electricity price data, and industrial water unit price data, which have been standardized in format and verified for validity, are packaged to form the plant's economic operating parameters.

[0030] Furthermore, the real-time unit load data, sulfur dioxide concentration monitoring values ​​of flue gas at the desulfurization system inlet, process water flow meter readings of the desulfurization tower, grid electricity price data, and industrial water unit price data, which have been standardized and validated, are packaged together to form the plant's economic operating parameters, resulting in a standardized and usable comprehensive data package containing cost, load, and environmental information.

[0031] The on-grid electricity price data and real-time unit load data are extracted from the plant's economic operating parameters and then input into the optimization decision model.

[0032] Furthermore, the on-grid electricity price data and real-time unit load data are extracted from the plant's economic operating parameters. These data are then input into the optimization decision-making model, providing the model with two core driving variables that directly reflect operational economics and energy output levels.

[0033] The optimized decision-making model calculates the optimal target value of flue gas temperature by taking the maximization of comprehensive economic benefits as the objective function.

[0034] Furthermore, the optimization decision model calculates the optimal flue gas temperature target value by using the maximization of comprehensive economic benefits as the objective function. Based on grid-connected electricity price data and real-time unit load data, the optimization decision model solves for the flue gas temperature setpoint that maximizes comprehensive economic benefits, thus completing the conversion from economic signals to process control objectives.

[0035] The optimal target value for flue gas temperature is expressed as follows: ; in, The target value for the optimal flue gas temperature. This is the actual grid connection price. For unit load, This is the unit price for industrial water. This refers to the water volume sprayed by the spray system. For the process water makeup volume of the desulfurization system, The price per unit of standard coal. To slightly increase coal consumption, For operation and maintenance costs, For comprehensive economic benefits.

[0036] The intelligent decision-making module establishes a plant-wide water consumption model that reflects the relationship between the water consumption of the spray cooling system and the desulfurization system. Based on the plant-wide water consumption model and water resource costs, it determines the energy-saving control dead zone around the optimal flue gas temperature target value.

[0037] Analyze the water spray volume data of the spray cooling system under different flue gas temperatures, and analyze the process water makeup volume data of the desulfurization system under different flue gas temperatures.

[0038] Furthermore, based on actual operation records or historical data collected by monitoring systems, the operating conditions are classified according to the range of flue gas temperature variation. The water consumption of the spray cooling system under each flue gas temperature condition, as well as the process water replenishment required by the desulfurization system due to water evaporation, gypsum carryover, and flushing, are extracted. Through statistical and trend analysis, the response patterns of these two types of water consumption with flue gas temperature changes are clarified, and their sensitive areas and nonlinear characteristics are identified.

[0039] The total water consumption data of the spray cooling system at different flue gas temperatures is obtained by adding the process water makeup data of the desulfurization system at the corresponding flue gas temperatures.

[0040] Furthermore, under the same flue gas temperature conditions, the water consumption of the spray cooling system and the desulfurization system are synchronously superimposed to form the total comprehensive water consumption directly related to water consumption at that temperature point. By traversing all typical flue gas temperature ranges, a discrete dataset is constructed with flue gas temperature as the independent variable and the relevant water consumption of the entire plant as the dependent variable.

[0041] Based on the total water consumption data of the whole plant corresponding to different flue gas temperatures, a functional relationship between flue gas temperature and total water consumption of the whole plant is established by regression fitting, and the functional relationship between flue gas temperature and total water consumption of the whole plant is defined as the whole plant water consumption model.

[0042] Furthermore, mathematical regression methods are used to model the aforementioned discrete data, and a function form with high goodness of fit, reasonable physical meaning, and good generalization ability is selected. Finally, an analytical or numerical model that can accurately describe the quantitative relationship between flue gas temperature and total plant water consumption is established, namely the total plant water consumption model, which is used to predict the system water demand under any flue gas temperature.

[0043] Input the optimal flue gas temperature target value into the plant-wide water consumption model, and output the total plant water consumption variation curve around the optimal flue gas temperature target value.

[0044] Furthermore, after comprehensively considering factors such as thermal efficiency, equipment safety, and environmental compliance, the optimal flue gas temperature target value is determined as the center. Within its vicinity, the plant-wide water consumption model is invoked to generate a continuous curve reflecting the change in water consumption with small fluctuations in flue gas temperature. This curve can intuitively present the local sensitivity of water consumption near the target value.

[0045] By analyzing the total water consumption variation curve of the entire plant, the flue gas temperature range where the total water consumption of the entire plant is lower than the preset threshold is identified as a candidate energy-saving control dead zone.

[0046] Furthermore, based on water-saving targets or operational economic requirements, an acceptable upper limit threshold for water consumption is pre-set. The range of flue gas temperatures that meet the condition of total water consumption not exceeding this threshold is then identified on the water consumption variation curve. This temperature range is the candidate energy-saving control dead zone. Within this range, the flue gas temperature is allowed to fluctuate naturally without initiating active regulation, thereby avoiding unnecessary control actions and resource consumption.

[0047] The width of the candidate energy-saving control dead zone is adjusted according to the level of water resource costs, and the candidate energy-saving control dead zone with the adjusted width is officially determined as the energy-saving control dead zone around the optimal flue gas temperature target value.

[0048] Furthermore, the economic value of water resources is introduced as a regulating factor: when the cost of water resources is high, the dead zone width tends to be narrowed to pursue lower water consumption; when the cost is low or the marginal benefit of water saving weakens, the dead zone can be appropriately widened to improve the stability of system operation. By combining cost-benefit analysis to dynamically optimize the dead zone boundary, an energy-saving control dead zone that takes into account economy, energy saving and control robustness is finally formed as the core parameter of intelligent operation and regulation.

[0049] The collaborative control module calculates the feedforward water spray volume based on the unit's operating status and calculates the feedback water spray volume correction value based on the flue gas temperature deviation.

[0050] The flue gas mass flow rate is calculated based on real-time unit load data, fuel characteristic data, and flue gas oxygen content data. Furthermore, by combining the real-time load signal of the current unit, the chemical composition of the fuel used, and the measured oxygen content of the flue gas in the flue, the actual amount of air participating in the reaction during the combustion process is derived, and the total mass of flue gas generated per unit time is calculated accordingly. The boiler thermal efficiency, theoretical air demand, and actual excess air ratio need to be comprehensively considered to form a dynamic estimate of the flue gas mass flow rate.

[0051] The expression for flue gas mass flow rate is: ; in, For flue gas mass flow rate, For the higher heating value of fuel, For boiler efficiency, This is the actual excess air coefficient. It is the sum of the ash and moisture content of the fuel. This is the coefficient corresponding to the theoretical air volume.

[0052] The isobaric specific heat capacity of the flue gas is calculated based on fuel characteristic data and flue gas oxygen content data. The flue gas heat capacity flow rate is obtained by multiplying the flue gas mass flow rate by the isobaric specific heat capacity of the flue gas. The difference between the flue gas temperature data before spraying and the optimal exhaust temperature target value is calculated.

[0053] Furthermore, the volume fractions of major components in the flue gas (such as carbon dioxide, water vapor, oxygen, and nitrogen) are inferred from the fuel composition and oxygen content. Combined with the isobaric specific heat capacity characteristics of each component within the current temperature range, a weighted sum is obtained to determine the overall isobaric specific heat capacity of the flue gas. This specific heat capacity is then multiplied by the aforementioned flue gas mass flow rate to obtain the heat capacity flow rate, characterizing the flue gas's ability to carry heat energy. Simultaneously, the actual flue gas temperature before spraying is determined by the average value of multiple temperature sensors and compared with the set optimal exhaust temperature target value to obtain the temperature difference signal used for subsequent control.

[0054] The expression for the isobaric specific heat capacity of flue gas is: ; in, The specific heat capacity of flue gas at constant pressure. This represents the volume fraction of carbon dioxide. This represents the volume fraction of water vapor. The specific heat capacity at constant pressure of water vapor. This represents the volume fraction of oxygen. The specific heat capacity at constant pressure of oxygen. This represents the volume fraction of nitrogen. The specific heat capacity at constant pressure of nitrogen. This is the specific heat capacity of carbon dioxide at constant pressure.

[0055] The expression for the flue gas temperature data before spraying is: ; in, This is the flue gas temperature data before spraying. For the first The measured values ​​of a temperature sensor, The number of effective temperature sensors, This is an index for temperature sensing.

[0056] The expression for the difference between the optimal flue gas temperature and the target value is: ; in, The difference between the target value and the target value. This is the flue gas temperature data before spraying. Optimal target value for flue gas temperature.

[0057] The feedforward water spray rate is calculated by multiplying the flue gas heat capacity flow rate by the difference between the flue gas temperature data before spraying and the optimal exhaust temperature target value, and then using a feedforward model.

[0058] Furthermore, based on the principle of energy balance, the required amount of cooling water injected should be sufficient to absorb the sensible heat released as the flue gas temperature drops from its current temperature to the target exhaust temperature. Therefore, by multiplying the heat capacity flow rate by the temperature difference to obtain the heat power to be removed, and then combining the latent heat of vaporization and the heat absorption capacity of the atomized water, the theoretical amount of water injected to maintain the target exhaust temperature can be directly calculated using a feedforward model, thus achieving a rapid response to load and fuel changes.

[0059] Obtain the optimal target value of flue gas temperature and the real-time measured flue gas temperature data, calculate the difference between the optimal target value of flue gas temperature and the real-time measured flue gas temperature data, and obtain the flue gas temperature deviation.

[0060] Furthermore, the actual flue gas temperature is continuously collected at the system outlet and compared in real time with the dynamically set optimal target value to generate a deviation signal reflecting the control effect. This deviation not only reflects the current control accuracy but also includes the impact of external disturbances (such as coal quality fluctuations and changes in ambient temperature) on the system. The expression for flue gas temperature deviance is: ; in, For a moment Flue gas temperature deviation For a moment Optimal target value for flue gas temperature. For a moment Real-time measured flue gas temperature data.

[0061] The flue gas temperature deviation is input into the proportional-integral-derivative control algorithm, which calculates the current value, historical cumulative value and trend of the flue gas temperature deviation, and outputs the feedback spray water volume correction value.

[0062] Furthermore, a classic PID control structure is adopted to perform triple processing on the flue gas temperature deviation: the proportional term responds to the current error magnitude, the integral term eliminates steady-state deviation, and the derivative term suppresses overshoot and improves dynamic response speed. These three components work together to generate a dynamically adjusted water spray volume correction, used to compensate for disturbances or modeling errors not covered by the feedforward model.

[0063] The expression for the feedback spray volume correction value is: ; in, For a moment Feedback on water spray volume correction value For proportional gain, For integral gain, For differential gain, For the time derivative, This is the differential of the deviation.

[0064] The drive module synthesizes the feedforward water volume and the feedback water volume correction value, and outputs the final water volume command after safety constraint verification.

[0065] The theoretical total water volume is obtained by algebraically adding the feedforward water volume and the feedback water volume correction value. The theoretical total water volume is then compared with the preset safe upper limit value and the theoretical total water volume is compared with the preset safe lower limit value.

[0066] Furthermore, the feedforward water spray volume calculated based on energy balance is algebraically superimposed with the correction value output by the closed-loop feedback controller to form the theoretical total water spray volume required under the current operating conditions. Subsequently, this theoretical value is compared in real time with the system's preset safe upper and lower limits for water spray volume to determine whether it is within the safe operating range allowed by the equipment.

[0067] When the theoretical total water volume exceeds the upper limit of the safe water volume, the upper limit of the safe water volume is used as the water volume to be verified. When the theoretical total water volume is lower than the lower limit of the safe water volume, the lower limit of the safe water volume is used as the water volume to be verified. When the theoretical total water volume is between the upper limit of the safe water volume and the lower limit of the safe water volume, the theoretical total water volume is used as the water volume to be verified.

[0068] Furthermore, a limiting process is implemented: if the theoretical water spray volume exceeds the upper limit, the safe upper limit value is used as the candidate instruction to prevent excessive water spraying from causing water accumulation in the flue or moisture damage to downstream equipment; if it is below the lower limit, the safe lower limit value is used to avoid losing basic cooling capacity due to insufficient water spraying; if it is between the upper and lower limits, the original theoretical value is retained. The water spray volume after this processing is called the water spray volume to be verified, which serves as the input for entering the next safety verification stage.

[0069] The anti-corrosion safety logic verification is performed on the water volume to be verified, and the water volume to be verified that passes the anti-corrosion safety logic verification is determined as the final water volume command.

[0070] Furthermore, based on the limited water spray volume to be verified, a corrosion prevention safety logic judgment is executed. This logic comprehensively considers factors such as the current flue gas temperature, flue gas composition (e.g., SO3 concentration), wall metal temperature, and historical operating conditions to assess whether water spraying may trigger low-temperature corrosion risks. Only when the verification results confirm that the water spraying operation will not lead to increased corrosion of the heated surface or flue material will the water spray volume to be verified be officially confirmed as the final water spray volume instruction; otherwise, the system will trigger protective adjustments to ensure the long-term safe operation of the equipment.

[0071] The monitoring module executes the final water spray volume command to complete the spray cooling process.

[0072] The final water spray volume command is converted into a frequency control signal for the spray pump inverter, and the final water spray volume command is converted into an opening control signal for the spray regulating valve.

[0073] Furthermore, based on the determined final water volume command, the control system synchronously generates two execution signals: one is converted into a frequency control signal suitable for the spray pump drive inverter through a preset flow-frequency mapping relationship; the other is mapped to the same water volume command as a corresponding regulating valve opening control signal based on the valve flow characteristic curve, ensuring that the pump and valve respond to the flow demand together under coordinated logic.

[0074] The spray pump speed is adjusted according to the frequency control signal, and the spray regulating valve adjusts the flow area of ​​the valve according to the opening control signal.

[0075] Furthermore, after receiving the frequency signal, the frequency converter of the spray pump dynamically adjusts the motor speed, thereby changing the pump's output head and flow capacity. At the same time, the spray regulating valve precisely adjusts its internal flow cross-sectional area according to the received opening command. Both of these mechanisms work together to regulate the cooling water delivery capacity from the perspectives of the power source and the throttling element, forming a redundant and complementary flow regulation mechanism.

[0076] The spray pump, after adjusting its speed, works in conjunction with the spray regulating valve, after adjusting its flow area, to output a cooling water flow rate that meets the final spray volume command. The water mist is formed through the atomizing nozzle, and the water mist mixes with the hot flue gas in the flue and absorbs heat.

[0077] Furthermore, under the combined control of the pump and valves, cooling water is stably delivered to the atomizing nozzle at a flow rate matching the command requirements. The nozzle uses the pressure difference to break the water flow into fine droplets, forming a water mist with a high specific surface area; this water mist is then introduced into the high-temperature flue, where it comes into full contact with the mainstream flue gas and rapidly transfers heat, achieving a highly efficient heat exchange process.

[0078] After absorbing heat, the water mist evaporates, lowering the temperature of the hot flue gas and completing the spray cooling process. Furthermore, during the heat absorption process, the water mist undergoes a temperature rise and vaporization phase change, eventually evaporating completely into water vapor. This process consumes a large amount of sensible heat from the flue gas, resulting in a significant decrease in the overall temperature of the flue gas. The cooled flue gas meets the inlet temperature requirements of subsequent equipment (such as desulfurization towers or chimneys), thus achieving the thermal objectives of the entire spray cooling process while avoiding the risks of corrosion or blockage caused by residual liquid water.

[0079] The update module monitors the actual exhaust temperature and resource consumption data after cooling, and updates the optimization decision model based on the monitored data.

[0080] After the spray cooling is completed, the actual exhaust temperature data is measured using a flue gas temperature sensor, and the actual power consumption data of the spray pump during the operating cycle is measured using an electricity meter.

[0081] Furthermore, after the spray cooling process is completed, the actual exhaust temperature under the current operating conditions is obtained by the flue gas temperature sensor installed at the flue outlet, which serves as direct feedback on the system control effect. A high-precision power metering device is used to record the power consumption of the spray pump throughout the entire operating cycle, which is used to quantify the power resource input of this cooling operation.

[0082] The actual process water consumption data of the desulfurization tower during its operation cycle is measured using a flow meter. The actual flue gas temperature data, actual power consumption data, and actual process water consumption data are then collected to form a resource consumption dataset.

[0083] Furthermore, during the synchronization period, the actual process water consumption of the desulfurization tower is collected by the flow meter at the inlet of the desulfurization system or on the water supply pipeline, reflecting the subsequent water consumption response caused by changes in flue gas temperature. The above three types of measured data—flue gas temperature, spray pump power consumption, and desulfurization process water consumption—are aligned and integrated by time to construct a structured resource consumption dataset, which comprehensively depicts the multi-dimensional resource usage caused by a single control action.

[0084] Extract the corresponding on-grid electricity price data from the plant's overall economic operating parameters, input the resource consumption dataset and on-grid electricity price data into the comprehensive economic benefit calculation function, and output the comprehensive economic benefit value of this control action.

[0085] Furthermore, by combining the real-time or time-of-use electricity price information provided by the power plant dispatch system or market trading platform, this information, along with the aforementioned resource consumption dataset, is input into a preset comprehensive economic benefit calculation function. This function comprehensively considers the operating cost savings brought about by water and electricity conservation, the potential impact on power generation revenue, and environmental compliance benefits. Through a unified economic value scale, it quantifies the net economic benefits achieved by this flue gas temperature regulation.

[0086] The real-time unit load data, on-grid electricity price data, optimal flue gas temperature target value, and comprehensive economic benefit value corresponding to this control action are used as a training sample to update the parameters of the optimization decision model.

[0087] Furthermore, the key input variables involved in this closed-loop control process (including the current load level of the unit, electricity price signal, and the set optimal flue gas temperature target) and the corresponding output results (i.e. the calculated comprehensive economic benefits) are combined into a complete training sample, which is used to update and optimize the decision-making model (such as reinforcement learning strategy network, regression model or rule engine) online or offline. During operation, it can more accurately predict the economic performance of different control strategies, thereby gradually improving the plant's autonomous optimization capability under complex operating conditions.

[0088] In summary, this invention uses a data acquisition module to collect real-time economic operating parameters of the entire plant, such as grid-connected electricity price and water resource cost, and uses an optimized decision-making model to dynamically output the optimal flue gas temperature target value. The intelligent decision-making module establishes a plant-wide water consumption model reflecting the water consumption relationship between the spray cooling system and the desulfurization system, and determines the energy-saving control dead zone around the optimal flue gas temperature target value. The collaborative control module calculates the feedforward spray water volume based on the unit's operating status and calculates the feedback spray water volume correction value based on the flue gas temperature deviation. The drive module synthesizes the feedforward spray water volume and the feedback correction value, and outputs the final spray water volume command after safety constraint verification. The monitoring module executes this command to complete the spray cooling. The update module continuously updates the optimized decision-making model based on actual flue gas temperature and resource consumption data, thereby achieving closed-loop intelligent management and control of the entire process.

[0089] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A smart control system for spray cooling in a 330MW combined heat and power unit, characterized in that: include, The data acquisition module collects real-time economic operating parameters of the entire plant, including grid-connected electricity price and water resource cost. Based on these economic operating parameters, it dynamically outputs the optimal target value for flue gas temperature through an optimized decision-making model. The intelligent decision-making module establishes a plant-wide water consumption model that reflects the relationship between the water consumption of the spray cooling system and the desulfurization system. Based on the plant-wide water consumption model and water resource costs, it determines the energy-saving control dead zone around the optimal flue gas temperature target value. The collaborative control module calculates the feedforward water spray volume based on the unit's operating status and calculates the feedback water spray volume correction value based on the flue gas temperature deviation. The drive module synthesizes the feedforward water volume and the feedback water volume correction value, and outputs the final water volume command after safety constraint verification. The monitoring module executes the final water volume command to complete the spray cooling process; The update module monitors the actual exhaust temperature and resource consumption data after cooling, and updates the optimization decision model based on the monitored data.

2. The intelligent control system for spray cooling of a 330MW cogeneration unit as described in claim 1, characterized in that: Real-time collection of plant-wide economic operating parameters, including grid-connected electricity prices and water resource costs, includes the following steps: The on-grid electricity price data at the current moment is obtained from the electricity market data interface. The on-grid electricity price data and the current period industrial water unit price data provided by the water billing system are used together to form the basic cost data. The basic cost data is combined with real-time unit load data read from the plant-level monitoring information system, sulfur dioxide concentration monitoring value of flue gas at the desulfurization system inlet, and process water flow meter reading of the desulfurization tower. The collected real-time unit load data, sulfur dioxide concentration monitoring values ​​of flue gas at the desulfurization system inlet, process water flow meter readings of the desulfurization tower, grid electricity price data, and industrial water unit price data are standardized in format and validated in validity. The real-time unit load data, sulfur dioxide concentration monitoring values ​​of flue gas at the desulfurization system inlet, process water flow meter readings of the desulfurization tower, grid electricity price data, and industrial water unit price data, which have been standardized in format and verified for validity, are packaged to form the plant's economic operating parameters.

3. The intelligent control system for spray cooling of a 330MW cogeneration unit as described in claim 2, characterized in that: Based on economic operating parameters, the optimal flue gas temperature target value is dynamically output through an optimized decision-making model, including the following steps: Extract grid-connected electricity price data and real-time unit load data from the plant’s economic operating parameters, and input the grid-connected electricity price data and real-time unit load data into the optimization decision model; The optimized decision-making model calculates the optimal target value of flue gas temperature by taking the maximization of comprehensive economic benefits as the objective function.

4. The intelligent control system for spray cooling of a 330MW cogeneration unit as described in claim 3, characterized in that: Establish a plant-wide water consumption model reflecting the relationship between the water consumption of the spray cooling system and the desulfurization system, including the following steps: Analyze the water spray volume data of the spray cooling system under different flue gas temperatures, and analyze the process water makeup volume data of the desulfurization system under different flue gas temperatures; Add the water spray volume data of the spray cooling system at different flue gas temperatures to the process water makeup volume data of the desulfurization system at the corresponding flue gas temperatures to obtain the total water consumption data of the whole plant at different flue gas temperatures. Based on the total water consumption data of the whole plant corresponding to different flue gas temperatures, a functional relationship between flue gas temperature and total water consumption of the whole plant is established by regression fitting, and the functional relationship between flue gas temperature and total water consumption of the whole plant is defined as the whole plant water consumption model.

5. The intelligent control system for spray cooling of a 330MW cogeneration unit as described in claim 4, characterized in that: Based on the plant-wide water consumption model and water resource costs, the energy-saving control dead zone around the optimal flue gas temperature target value is determined, including the following steps: Input the optimal flue gas temperature target value into the plant water consumption model, and output the total plant water consumption variation curve around the optimal flue gas temperature target value; Analyze the total water consumption variation curve of the entire plant and identify the flue gas temperature range where the total water consumption of the entire plant is lower than the preset threshold as candidate energy-saving control dead zones; The width of the candidate energy-saving control dead zone is adjusted according to the level of water resource costs, and the candidate energy-saving control dead zone with the adjusted width is officially determined as the energy-saving control dead zone around the optimal flue gas temperature target value.

6. The intelligent control system for spray cooling of a 330MW cogeneration unit as described in claim 5, characterized in that: Calculating the feedforward water volume based on the unit's operating status includes the following steps: The flue gas mass flow rate is calculated based on real-time unit load data, fuel characteristic data, and flue gas oxygen content data. The isobaric specific heat capacity of the flue gas is calculated based on fuel characteristic data and flue gas oxygen content data. The flue gas heat capacity flow rate is obtained by multiplying the flue gas mass flow rate by the flue gas isobaric specific heat capacity. The difference between the flue gas temperature data before spraying and the optimal exhaust temperature target value is calculated. The feedforward water spray rate is calculated by multiplying the flue gas heat capacity flow rate by the difference between the flue gas temperature data before spraying and the optimal exhaust temperature target value, and then using a feedforward model.

7. The intelligent control system for spray cooling of a 330MW cogeneration unit as described in claim 6, characterized in that: The calculation of the feedback water spray volume correction value based on the flue gas temperature deviation includes the following steps: Obtain the optimal target value of flue gas temperature and the real-time measured flue gas temperature data, calculate the difference between the optimal target value of flue gas temperature and the real-time measured flue gas temperature data, and obtain the flue gas temperature deviation; The flue gas temperature deviation is input into the proportional-integral-derivative control algorithm, which calculates the current value, historical cumulative value and trend of the flue gas temperature deviation, and outputs the feedback spray water volume correction value.

8. The intelligent control system for spray cooling of a 330MW cogeneration unit as described in claim 7, characterized in that: The process of synthesizing the feedforward water volume and the feedback water volume correction value, and then outputting the final water volume command after safety constraint verification, includes the following steps: The theoretical total water volume is obtained by algebraically adding the feedforward water volume and the feedback water volume correction value. The theoretical total water volume is then compared with the preset safe upper limit value of the water volume and the preset safe lower limit value of the water volume. When the theoretical total spray volume exceeds the upper limit of the safe spray volume, the upper limit of the safe spray volume is used as the spray volume to be verified. When the theoretical total spray volume is lower than the lower limit of the safe spray volume, the lower limit of the safe spray volume is used as the spray volume to be verified. When the theoretical total spray volume is between the upper limit of the safe spray volume and the lower limit of the safe spray volume, the theoretical total spray volume is used as the spray volume to be verified. The anti-corrosion safety logic verification is performed on the water volume to be verified, and the water volume to be verified that passes the anti-corrosion safety logic verification is determined as the final water volume command.

9. The intelligent control system for spray cooling of a 330MW cogeneration unit as described in claim 8, characterized in that: Executing the final water volume command to complete the spray cooling process includes the following steps: The final water spray volume command is converted into a frequency control signal for the spray pump inverter, and the final water spray volume command is converted into an opening control signal for the spray regulating valve. The spray pump speed is adjusted according to the frequency control signal, and the spray regulating valve adjusts the flow area of ​​the valve according to the opening control signal; The spray pump with adjusted speed and the spray regulating valve with adjusted flow area work together to output cooling water flow that meets the final spray volume command. The water mist is formed through the atomizing nozzle and mixes with the hot flue gas in the flue and absorbs heat. The water mist evaporates after absorbing heat, reducing the temperature of the hot flue gas and completing the spray cooling process.

10. The intelligent control system for spray cooling of a 330MW cogeneration unit as described in claim 9, characterized in that: Monitor the actual flue gas temperature and resource consumption data after cooling, and update the optimization decision-making model based on the monitored data, including the following steps: After the spray cooling is completed, the actual exhaust temperature data is measured using a flue gas temperature sensor, and the actual power consumption data of the spray pump during the operating cycle is measured using an electricity meter. The actual process water consumption data of the desulfurization tower during the operation cycle is measured using a flow meter. The actual flue gas temperature data, actual power consumption data and actual process water consumption data are collected to form a resource consumption dataset. Extract the corresponding grid-connected electricity price data from the plant's overall economic operating parameters, input the resource consumption dataset and grid-connected electricity price data into the comprehensive economic benefit calculation function, and output the comprehensive economic benefit value of this control action; The real-time unit load data, on-grid electricity price data, optimal flue gas temperature target value, and comprehensive economic benefit value corresponding to this control action are used as a training sample to update the parameters of the optimization decision model.

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