Boiler tail gas multi-pollutant collaborative control system based on linkage of PLC and frequency converter

Abstract

The present application relates to the technical field of boiler tail gas treatment, in particular to a boiler tail gas multi-pollutant collaborative control system based on linkage of PLC and frequency converter, which comprises a data acquisition module, a PLC control module and a collaborative optimization module. The present application acquires concentration data of pollutants in the boiler tail gas in real time through the data acquisition module, and introduces the boiler tail gas into a desulfurization and denitrification tower by using an inlet induced draft fan. The PLC control module sets the driving rules of the inlet induced draft fan by using the logic controller of the PLC, dynamically adjusts the operating frequency of the inlet induced draft fan according to the content of pollutants in the boiler tail gas. The collaborative optimization module returns the tail gas that does not meet the standard to the boiler for secondary desulfurization and denitrification, and the tail gas that meets the standard is introduced into a dust removal tower through an outlet induced draft fan. According to the content of ammonia in the tail gas, the voltage of the electric dust removal is adjusted, and the linkage control of desulfurization, denitrification and dust removal is realized by using the genetic algorithm with the goal of minimizing the operating cost.

Description

Technical Field

[0001] This invention relates to the field of boiler exhaust gas treatment technology, and more specifically, to a multi-pollutant collaborative control system for boiler exhaust gas based on the linkage of PLC and frequency converter. Background Technology

[0002] Industrial boiler exhaust gas treatment has become a key link in reducing air pollution. Traditional technologies for treating pollutants such as particulate matter (PM), sulfur oxides (SOx), and nitrogen oxides (NOx) in boiler exhaust gas have evolved from single-pollutant purification to multi-pollutant synergistic treatment. However, existing technologies have low levels of automation, which can easily lead to energy waste and unstable treatment effects, making it difficult to meet increasingly stringent environmental protection requirements.

[0003] Currently, electrostatic precipitators are used to purify boiler flue gas. Flue gas passes through a high-voltage electric field, and charged particles are adsorbed by the collecting electrodes. The particles are then removed by rapping and fall into the ash hopper. However, the voltage of the high-voltage electric field is fixed and cannot be dynamically adjusted according to the dust load, leading to excessive PM emissions and reduced ash removal efficiency. Wet desulfurization involves flue gas entering a desulfurization tower and countercurrently contacting sprayed limestone slurry. SO2 is absorbed to form calcium sulfite, which is further oxidized to gypsum. However, the slurry pump operates at a fixed frequency, and pH adjustment relies on manual intervention, resulting in excessive slurry circulation when the equipment is under low load. SCR denitrification involves flue gas passing through a catalyst layer. At a temperature of 280-400℃, injected ammonia reacts with NOx to produce N2 and H2O. However, the amount of ammonia injected is controlled by a fixed ratio and is not linked to the flue gas flow rate, leading to unstable NOx removal rates and high ammonia escape rates, affecting the overall denitrification effect.

[0004] To enable the use of a PLC controller to regulate the frequency of the induced draft fan based on real-time data acquired from sensors, including flue gas flow rate, pollutant concentration, and equipment status, and to adjust the frequency of the slurry pump in conjunction with SO2 concentration, while dynamically adjusting the operating parameters of the ammonia injection device based on NOx concentration and flue gas flow rate, thus improving treatment efficiency and ensuring the stability and compliance rate of exhaust gas emissions, we propose a multi-pollutant collaborative control system for boiler exhaust gas based on the linkage of PLC and frequency converter. Summary of the Invention

[0005] The purpose of this invention is to address the problem that when enterprises treat boiler exhaust gas, the control logic of the exhaust gas treatment system is simple, relying on threshold alarms or fixed parameters, which cannot adapt to complex operating conditions. Each treatment unit is controlled independently and lacks global optimization. In order to achieve precise regulation of boiler exhaust gas treatment equipment through the linkage control of PLC and frequency converter, the operating parameters of the equipment can be adjusted in a timely manner according to the real-time concentration changes of exhaust gas pollutants, thereby improving treatment efficiency and ensuring the stability and compliance rate of exhaust gas emissions.

[0006] To achieve the above objectives, the present invention provides a data acquisition module, a PLC control module, and a collaborative optimization module;

[0007] The data acquisition module collects real-time data on the concentration of pollutants in the boiler exhaust gas, flue gas flow rate, and equipment status, and uses the inlet induced draft fan to introduce the boiler exhaust gas into the desulfurization and denitrification tower.

[0008] The PLC control module uses the PLC's logic controller to set the drive rules for the inlet induced draft fan. Based on the pollutant content in the boiler exhaust gas, it dynamically adjusts the operating frequency of the inlet induced draft fan. Taking the error value between the concentration of sulfur dioxide and nitrogen oxides in the boiler exhaust gas and the target concentration as input, it uses a PID control algorithm to output the operating parameters of the slurry circulation pump and the ammonia injection pump. It uses a frequency converter to dynamically adjust the operating frequency of the slurry circulation pump and the ammonia injection pump to desulfurize and denitrify the boiler exhaust gas.

[0009] The collaborative optimization module detects the content of sulfur dioxide and nitrogen oxides in the exhaust gas after desulfurization and denitrification. The logic controller of the PLC determines the desulfurization and denitrification of the exhaust gas. The exhaust gas that does not meet the standards is returned to the boiler for secondary desulfurization and denitrification. The exhaust gas that meets the standards is introduced into the dust removal tower through the outlet induced draft fan. The voltage of the electrostatic precipitator is adjusted according to the ammonia content in the exhaust gas. With the goal of minimizing operating costs, a genetic algorithm is used to coordinate the linkage control of desulfurization, denitrification and dust removal.

[0010] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0011] 1. This boiler exhaust gas multi-pollutant collaborative control system based on PLC and frequency converter linkage collects real-time data on pollutant concentration, flue gas flow, and equipment status in the boiler exhaust gas through a data acquisition module. The system uses an inlet induced draft fan to introduce the boiler exhaust gas into the desulfurization and denitrification tower. The PLC control module uses the PLC's logic controller to set the drive rules for the inlet induced draft fan and dynamically adjusts its operating frequency based on the pollutant content in the boiler exhaust gas. Using the error value between the concentration of sulfur dioxide and nitrogen oxides in the boiler exhaust gas and the target concentration as input, a PID control algorithm is used to output the operating parameters of the slurry circulation pump and the ammonia injection pump. The frequency converter dynamically adjusts the operating frequencies of the slurry circulation pump and the ammonia injection pump to desulfurize and denitrify the boiler exhaust gas. This achieves control of the flue gas flow in the desulfurization and denitrification tower, as well as the operating frequencies of the slurry circulation pump and the ammonia injection pump, based on real-time pollutant concentration data in the boiler exhaust gas, thereby improving the stability and compliance rate of boiler exhaust gas treatment.

[0012] 2. The collaborative optimization module detects the sulfur dioxide and nitrogen oxide content in the exhaust gas after desulfurization and denitrification. The PLC logic controller determines the desulfurization and denitrification status of the exhaust gas. Exhaust gas that does not meet the standards is returned to the boiler for secondary desulfurization and denitrification. Exhaust gas that meets the standards is introduced into the dust removal tower through the outlet induced draft fan. The electrostatic precipitator voltage is adjusted according to the ammonia content in the exhaust gas to improve the purification rate of pollutants in the boiler exhaust gas, ensure the quality of exhaust gas emissions, and aim to minimize operating costs. A genetic algorithm is used to coordinate the linkage control of desulfurization, denitrification and dust removal to purify pollutants in the exhaust gas while reducing energy consumption and saving resources.

[0013] As a further improvement to this technical solution, the PLC control module sets reference values ​​for boiler exhaust pollutants based on environmental protection requirements and the parameters of the desulfurization and denitrification tower. When the pollutant concentration or flue gas flow rate deviates from the reference value, the PLC logic controller adjusts the operating frequency of the induced draft fan proportionally according to the magnitude of the deviation.

[0014] As a further improvement to this technical solution, the PLC control module includes a parameter adjustment unit and a frequency conversion drive unit;

[0015] The parameter control unit defines a fuzzy set for the collected concentration data of sulfur dioxide and nitrogen oxides, the error value and error change rate between the flue gas flow rate and the target concentration value in the environmental emission standard, as well as the parameter adjustment amount of the output PID controller. The fuzzy set is: negative large NB, negative medium NM, negative small NS, zero ZO, positive small PS, positive medium PM, and positive large PB.

[0016] The variable frequency drive unit adjusts the proportional coefficient, derivative coefficient, and integral coefficient of the PID controller according to the parameter adjustment amount of the output PID controller, and uses the PID control algorithm to calculate the operating parameters of the fan, slurry circulation pump, and ammonia injection pump.

[0017] The beneficial effect of adopting the above-mentioned further improvements is that the definition of fuzzy set provides a basis for the formulation of fuzzy control rules. Based on the fuzzy set of error value and error change rate and the corresponding fuzzy rules, the system can realize intelligent decision-making and automatically adjust the parameters of PID controller according to different situations.

[0018] By adjusting the parameters of the PID controller online, fuzzy adaptive PID control can optimize the system based on its real-time status, improving control accuracy. Compared with traditional fixed-parameter PID control, fuzzy adaptive PID control can better track the target concentration value, reduce errors, and make exhaust emissions closer to environmental emission standards.

[0019] As a further improvement to this technical solution, the parameter control unit establishes a fuzzy rule base, which takes the following form:

[0020] IF e is Ai AND Δe is B j THEN ΔK p is C ij ;

[0021] Where e is the error value, Δe is the error rate of change, and A i Let B be the fuzzy set of error value e. j Let ΔK be the fuzzy set of the error change rate Δe. p C is the parameter adjustment value for the PID controller. ij ΔK is the adjustment value of the PID controller parameters. p A fuzzy set.

[0022] The beneficial effects of the above-mentioned further improvements are that the fuzzy rule base can formulate rules according to different operating conditions, allowing the system to automatically adjust the control strategy and ensure effective control of exhaust emissions under various operating conditions. With the help of the fuzzy rule base, the PID controller parameters can be adjusted in real time according to the error and the error change rate. The system can dynamically adjust the proportional, integral, and derivative coefficients of the PID controller according to the error between the exhaust pollutant concentration and the target value and the error change rate, thereby enhancing the control effect and ensuring that the exhaust emissions meet the standards.

[0023] As a further improvement to this technical solution, the variable frequency drive unit adopts a discrete PID control algorithm. Based on the collected concentration data of sulfur dioxide and nitrogen oxides and the error value of flue gas flow, the operating parameters of the corresponding equipment are calculated, and the formula is as follows:

[0024]

[0025] Where μ(t) represents the equipment operating parameters, and K p K is the proportionality coefficient. i K is the differential coefficient. d Let e(t) be the integral coefficient, e(t) be the error value at time t, and e(t-1) be the error value at time t-1.

[0026] The beneficial effect of the above-mentioned further improvements is that the PID control algorithm can accurately adjust the control quantity based on the error between the system output and the set target value through the combined action of proportional, integral, and derivative components. In boiler flue gas treatment systems, this enables the concentration of pollutants such as sulfur dioxide and nitrogen oxides in the flue gas, as well as the flue gas flow rate, to stably approach the target values ​​stipulated by environmental emission standards, achieving high-precision steady-state control.

[0027] As a further improvement to this technical solution, the collaborative optimization module includes a detection and judgment unit and a genetic optimization unit;

[0028] The detection and judgment unit collects the content data of sulfur dioxide and nitrogen oxides in the exhaust gas in real time through sulfur dioxide and nitrogen oxide sensors installed on the exhaust gas pipeline after desulfurization and denitrification. The PLC logic controller compares the received sulfur dioxide and nitrogen oxide content data with the set thresholds respectively to determine the desulfurization and denitrification of the exhaust gas.

[0029] The genetic optimization unit takes minimizing the cost of desulfurizer, denitrifier, and equipment energy consumption as its objective function. It uses real number encoding to encode the decision variables and selects the optimal combination of decision variables for each individual through selection, genetic, and mutation operations.

[0030] As a further improvement to this technical solution, the detection and judgment unit adopts a graded threshold setting method. The content thresholds of sulfur dioxide and nitrogen oxides are set in the logic controller of the PLC, and divided into normal zone, warning zone, first-level exceeding zone and second-level exceeding zone, and a graded response strategy is set.

[0031] As a further improvement to this technical solution, the detection and judgment unit sets corresponding response delays for different threshold ranges, and sets upper and lower limits for the corresponding device adjustment.

[0032] The beneficial effects of adopting the above-mentioned further improvements are that traditional single threshold control is prone to "black and white" decision-making, while graded thresholds can dynamically adjust the response intensity according to the degree of pollution, achieving precise control. Graded response can reduce unnecessary equipment start-ups and shutdowns (such as standby pumps), reduce equipment wear and tear, and form safety redundancy by setting first-level and second-level exceeding zones. When the first-level measures are ineffective, more stringent control logic is automatically triggered.

[0033] In exhaust gas detection, sensors may experience momentary fluctuations or errors due to various reasons. For example, brief changes in airflow or minor instrument malfunctions can cause momentary anomalies in the detection data. Without a response delay, the system might immediately adjust based on these momentary anomalies, leading to unnecessary and frequent equipment actions. However, by setting a response delay, the system continuously monitors the data for a period of time, and only triggers the corresponding operation when the data remains within the corresponding threshold range during that time period. This effectively avoids erroneous actions caused by momentary data fluctuations.

[0034] As a further improvement to this technical solution, when the genetic optimization unit uses a genetic algorithm to find the optimal combination of decision variables, it determines that pollutant emissions meet the standards and equipment parameters operate within the normal range as constraints.

[0035] The beneficial effect of adopting the above-mentioned further improvements is that setting the compliance of pollutant emissions as a constraint condition can ensure that the concentration of pollutants such as sulfur dioxide, nitrogen oxides, and particulate matter in boiler exhaust gas is always controlled within the range specified by environmental protection standards. This is the basic requirement and primary goal of system operation, and it is of vital importance for reducing environmental pollution, protecting the ecological environment and human health. By using a genetic algorithm to find the optimal combination of decision variables under the constraint condition, precise control of the exhaust gas treatment process can be achieved, ensuring that the system can stably and efficiently control pollutant emissions at compliant levels under various operating conditions.

[0036] As a further improvement to this technical solution, the genetic optimization unit runs the genetic algorithm multiple times, observes the change in fitness value with the number of iterations, analyzes the convergence speed of the algorithm, selects the convergence threshold of fitness value, and determines the termination condition of the genetic algorithm.

[0037] The beneficial effects of the above-mentioned further improvements are that, since the genetic algorithm is an iterative search algorithm, if a reasonable termination condition is not set, it may perform a large number of unnecessary iterations, wasting computational resources and time. By observing the changes in fitness values ​​and analyzing the convergence speed, it is possible to determine when the algorithm has approached the optimal solution. By selecting an appropriate convergence threshold as the termination condition, the iteration can be stopped in time after the algorithm has reached a certain degree of optimization, avoiding over-computation and improving the algorithm's running efficiency.

[0038] In addition to the objectives, features, and advantages described above, the present invention has other objectives, features, and advantages. The invention will now be described in further detail with reference to the figures. Attached Figure Description

[0039] Figure 1 This is a schematic diagram of the overall process of the present invention;

[0040] Figure 2 This is a schematic diagram illustrating the overall detailed process of the present invention;

[0041] Figure 3 This is a simplified diagram of the overall structure of the present invention.

[0042] The meanings of the labels in the diagram are as follows:

[0043] 100. Data acquisition module; 200. PLC control module; 210. Parameter adjustment unit; 220. Variable frequency drive unit; 300. Collaborative optimization module; 310. Detection and judgment unit; 320. Genetic optimization unit. Detailed Implementation

[0044] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] Currently, when enterprises treat boiler exhaust gas, the control logic of the exhaust gas treatment system is simple, relying on threshold alarms or fixed parameters, which cannot adapt to complex working conditions. Each treatment unit is controlled independently, lacking global optimization. In order to achieve precise regulation of boiler exhaust gas treatment equipment through the linkage control of PLC and frequency converter, the operating parameters of the equipment can be adjusted in a timely manner according to the real-time concentration changes of exhaust gas pollutants, thereby improving treatment efficiency and ensuring the stability and compliance rate of exhaust gas emissions.

[0046] Therefore, this invention proposes a method to collect real-time data on pollutant concentrations, flue gas flow rates, and equipment status in boiler exhaust gas via a data acquisition module. The exhaust gas is then introduced into the desulfurization and denitrification tower using an inlet induced draft fan. A PLC control module uses its logic controller to set the drive rules for the inlet induced draft fan, dynamically adjusting its operating frequency based on the pollutant content in the exhaust gas. A collaborative optimization module detects the sulfur dioxide and nitrogen oxide content in the exhaust gas after desulfurization and denitrification. The PLC logic controller determines the desulfurization and denitrification status of the exhaust gas, returning any substandard exhaust gas to the boiler for secondary desulfurization and denitrification. The compliant exhaust gas is introduced into the dust removal tower via an outlet induced draft fan. The electrostatic precipitator voltage is adjusted based on the ammonia content in the exhaust gas. Furthermore, with the goal of minimizing operating costs, a genetic algorithm is used to coordinate the linkage control of desulfurization, denitrification, and dust removal.

[0047] Specifically as follows:

[0048] Please see Figure 1 As shown, the present invention provides a multi-pollutant collaborative control system for boiler exhaust gas based on the linkage of PLC and frequency converter, including a data acquisition module 100, a PLC control module 200 and a collaborative optimization module 300.

[0049] The data acquisition module 100 collects real-time data on the concentration of pollutants in the boiler exhaust gas, flue gas flow rate, and equipment status, and uses the inlet induced draft fan to introduce the boiler exhaust gas into the desulfurization and denitrification tower.

[0050] The concentration data of sulfur dioxide and nitrogen oxide pollutants in boiler exhaust gas, as well as flue gas flow rate, are collected using laser PM sensors, infrared SO2 / SOx analyzers, and temperature / pressure transmitters. The speed and frequency of equipment such as induced draft fans, slurry circulation pumps, and ammonia injection pumps are controlled by frequency converters.

[0051] In addition, the PLC control module 200 uses the PLC's logic controller to set the drive rules for the inlet induced draft fan. Based on the pollutant content in the boiler exhaust gas, it dynamically adjusts the operating frequency of the inlet induced draft fan. Taking the error value between the concentration of sulfur dioxide and nitrogen oxides in the boiler exhaust gas and the target concentration as input, it uses a PID control algorithm to output the operating parameters of the slurry circulation pump and the ammonia injection pump. It uses a frequency converter to dynamically adjust the operating frequency of the slurry circulation pump and the ammonia injection pump to desulfurize and denitrify the boiler exhaust gas.

[0052] In order to better set the driving rules of the inlet induced draft fan, the PLC control module 200 sets the reference value of boiler exhaust pollutants according to environmental protection requirements and the parameters of the desulfurization and denitrification tower. When the pollutant concentration or flue gas flow rate deviates from the reference value, the PLC logic controller adjusts the operating frequency of the induced draft fan proportionally according to the magnitude of the deviation.

[0053] When pollutant concentration or flue gas flow rate deviates from the reference value, the PLC adjusts the operating frequency of the induced draft fan proportionally according to the magnitude of the deviation. For example, if the sulfur dioxide concentration is higher than the reference value and the flue gas flow rate is lower, the PLC will increase the frequency of the induced draft fan by a certain proportion to increase the flue gas flow rate, allowing more exhaust gas to enter the desulfurization and denitrification tower for treatment, thereby reducing the sulfur dioxide concentration.

[0054] Reducing the frequency of the induced draft fan means a decrease in the amount of flue gas entering the desulfurization system. To maintain the residence time of flue gas in the desulfurization tower, automatically shortening the spray layer interval is an effective adjustment method. By shortening the spray layer interval, the flue gas can pass through more spray layers in the spray area, increasing the number of contact times and the contact time between the flue gas and the spray slurry. Thus, even with a reduced flue gas volume, the desulfurization reaction can still be fully carried out, maintaining a good desulfurization effect.

[0055] By adjusting the parameters of the PID controller (proportional coefficient, integral time constant, derivative time constant), the frequency adjustment of the induced draft fan can respond quickly and stably to changes in pollutant concentration, while ensuring that the system does not experience excessive oscillation or overshoot.

[0056] like Figure 2 As shown, the PLC control module 200 includes a parameter adjustment unit 210 and a frequency conversion drive unit 220;

[0057] The parameter control unit 210 defines a fuzzy set for the collected concentration data of sulfur dioxide and nitrogen oxides and flue gas flow rate and the error value and error change rate between the target concentration value in the environmental emission standard, as well as the parameter adjustment amount of the output PID controller. The fuzzy set is: negative large NB, negative medium NM, negative small NS, zero ZO, positive small PS, positive medium PM, and positive large PB.

[0058] The variable frequency drive unit 220 adjusts the proportional coefficient, derivative coefficient and integral coefficient of the PID controller according to the parameter adjustment amount of the output PID controller, and uses the PID control algorithm to calculate the operating parameters of the fan, slurry circulation pump and ammonia injection pump.

[0059] Fuzzy rules can reasonably adjust the parameters of the PID controller according to different errors and error change rates, so that the system can achieve better control performance under various operating conditions. By continuously adjusting the PID parameters based on real-time errors and error change rates, the system can track the target concentration more accurately and improve control accuracy and stability.

[0060] Fuzzy reasoning process:

[0061] First, calculate the membership degree of the input variables. Then, use the "smaller" operation to obtain the membership degree of the preconditions of the rules. Based on the membership degree of the preconditions, "truncate" the fuzzy set of the conclusions to obtain the membership degree of the conclusions of the rules. Then, use the "larger" operation on the membership degree of the conclusions of all rules to obtain the fuzzy membership degree of the final output variables. Finally, use the centroid method to convert the fuzzy membership degree of the output variables obtained by fuzzy inference into precise adjustment values.

[0062] In order to better establish the fuzzy rule base, the parameter control unit 210 establishes the fuzzy rule base in the following form:

[0063] IF e is A i AND Δe is B j THEN ΔK p is C ij ;

[0064] Where e is the error value, Δe is the error rate of change, and A i Let B be the fuzzy set of error value e. j Let ΔK be the fuzzy set of the error change rate Δe. p C is the parameter adjustment value for the PID controller. ij ΔK is the adjustment value of the PID controller parameters. p A fuzzy set.

[0065] The collected concentration data of sulfur dioxide and nitrogen oxides, as well as flue gas flow rate, are compared with the target concentration values ​​in the environmental emission standards. A fuzzy rule base is established using the error value and error change rate between the actual collected concentration data and the target concentration as input, as shown in the following example:

[0066] Error / rate of change NB NM NS ZO NB PB,NB,PB PB, NB, PM PM, NM, PM PM, NM, PS NM PB,NB,PB PB, NB, PM PM, NM, PM PM,NS,PS NS PM, NM, PB PM, NM, PM PM,NS,PM PS, NS, PS ZO PM, NM, PM PM,NS,PM PS, NS, PS ZO,ZO,ZO

[0067] Taking the rule "PB,NB,PB" in the first row and first column as an example:

[0068] Prerequisite: When the error e is "negatively large" (NB) and the rate of change of error Δe is "negatively large" (NB);

[0069] Conclusion: The proportional coefficient adjustment ΔKp is "positive large" (PB), the integral coefficient adjustment ΔKi is "negative large" (NB), and the differential coefficient adjustment ΔKd is "positive large" (PB).

[0070] Explanation: When the error between the actual collected concentration data value and the target concentration is a large negative value, and the error change rate is also a large negative value, in order to make the system respond quickly and reduce the error, it is necessary to significantly increase the proportional coefficient Kp (ΔKp is "positive and large"), while significantly decreasing the integral coefficient Ki (ΔKi is "negative and large"; excessive integral action may lead to slow system response or overshoot, so Ki is reduced), and significantly increasing the derivative coefficient Kd (ΔKd is "positive and large" to enhance the dynamic performance of the system and speed up the response).

[0071] To better calculate the operating parameters of the corresponding equipment, the variable frequency drive unit 220 employs a discrete PID control algorithm. Based on the collected concentration data of sulfur dioxide and nitrogen oxides and the error value of flue gas flow, it calculates the operating parameters of the corresponding equipment using the following formula:

[0072]

[0073] Where μ(t) represents the equipment operating parameters, and K p K is the proportionality coefficient. i K is the differential coefficient. d Let e(t) be the integral coefficient, e(t) be the error value at time t, and e(t-1) be the error value at time t-1.

[0074] By substituting the concentration data of sulfur dioxide and nitrogen oxides and the error value of flue gas flow rate into the above formula, the operating parameters of slurry circulation pump, ammonia injection pump and induced draft fan are calculated in sequence. The operating frequency of slurry circulation pump, ammonia injection pump and induced draft fan is dynamically adjusted by frequency converter to improve the efficiency of exhaust gas treatment.

[0075] For different equipment, specific control objectives are determined. For example, the induced draft fan mainly controls the flue gas flow rate or furnace negative pressure, the slurry circulation pump controls the circulation volume of slurry in the desulfurization tower to ensure desulfurization efficiency, and the ammonia injection pump controls the ammonia injection volume to ensure that nitrogen oxide emissions meet the standards during the denitrification process.

[0076] Based on process requirements and environmental standards, set reasonable setpoints for each control objective. For example, determine the setpoint for flue gas flow rate based on boiler load and environmental requirements, determine the setpoint for slurry circulation rate based on desulfurization efficiency and emission standards, and determine the setpoint for ammonia injection rate based on denitrification efficiency and nitrogen oxide emission standards.

[0077] The error between the setpoint and the actual measured value of the control target is calculated in real time. The calculated error is substituted into the discrete PID formula to calculate the control output at time k. The control output after limiting is converted into the control signal of the frequency converter. The operating frequency of the induced draft fan, slurry circulation pump and ammonia injection pump is dynamically adjusted by the frequency converter.

[0078] In addition, the collaborative optimization module 300 detects the content of sulfur dioxide and nitrogen oxides in the exhaust gas after desulfurization and denitrification. The logic controller of the PLC determines the desulfurization and denitrification of the exhaust gas. The exhaust gas that does not meet the standards is returned to the boiler for secondary desulfurization and denitrification. The exhaust gas that meets the standards is introduced into the dust removal tower through the outlet induced draft fan. The voltage of the electrostatic precipitator is adjusted according to the ammonia content in the exhaust gas. With the goal of minimizing operating costs, a genetic algorithm is used to coordinate the linkage control of desulfurization, denitrification and dust removal.

[0079] The collaborative optimization module 300 includes a detection and judgment unit 310 and a genetic optimization unit 320.

[0080] The detection and judgment unit 310 collects the content data of sulfur dioxide and nitrogen oxides in the exhaust gas in real time through the sulfur dioxide and nitrogen oxide sensors installed on the exhaust gas pipeline after desulfurization and denitrification. The logic controller of the PLC compares the received sulfur dioxide and nitrogen oxide content data with the set thresholds respectively to determine the desulfurization and denitrification of the exhaust gas.

[0081] The genetic optimization unit 320 takes minimizing the cost of desulfurizer, denitrifier, and equipment energy consumption as its objective function. It uses real number encoding to encode the decision variables and selects the optimal combination of decision variables corresponding to the individual through selection, genetic, and mutation operations.

[0082] Let the amount of desulfurizing agent added be x1, the amount of denitrifying agent added be x2, the fan speed be x3, the water pump speed be x4, and the ammonia injection frequency of the ammonia injection device be x5. Then the decision variable vector can be represented as X = (x1, x2, x3, x4, x5).

[0083] Using real-number encoding, the decision variable vector X is directly used as chromosomes, with each chromosome representing a possible combination of decision variables. N chromosomes are randomly generated to form an initial population P. The decision variable x in each chromosome takes a random value within its feasible range. The fitness function F is used to evaluate the quality of each chromosome. Since the objective is to minimize the operating cost, the fitness function can be defined as the reciprocal of the objective function. A roulette wheel selection algorithm is used to calculate the probability of each chromosome being selected, and crossover and mutation operations are performed. After the algorithm terminates, the optimal combination of decision variables corresponding to the resulting chromosome is the solution that minimizes the operating cost. Applying these decision variable values ​​to a practical system can achieve optimized control of the desulfurization and denitrification processes.

[0084] In order to better set the content thresholds of sulfur dioxide and nitrogen oxides, the detection and judgment unit 310 adopts a graded threshold setting method. The content thresholds of sulfur dioxide and nitrogen oxides are set in the logic controller of the PLC, and divided into normal zone, warning zone, first-level exceeding zone and second-level exceeding zone, and a graded response strategy is set.

[0085] Emission standards (SO2 ≤ 35 mg / m³) shall be set in accordance with environmental regulations or process requirements. 3 The parameter range is divided into normal zone, warning zone, first-level exceedance zone, and second-level exceedance zone, as follows:

[0086] Normal zone: 0-30 (green);

[0087] Warning zone: 30-35 (yellow);

[0088] Level 1 exceedance: 35-45 (orange);

[0089] Level 2 exceedance: >45 (red);

[0090] A hierarchical control strategy based on SO2 concentration:

[0091]

[0092] The hierarchical output control strategy forms a closed-loop system through threshold grading → stepped response → safety protection → adaptive optimization, which can ensure process stability and avoid resource waste.

[0093] To prevent malfunctions caused by instantaneous data fluctuations, the detection and judgment unit 310 sets corresponding response delays for different threshold ranges and sets upper and lower limits for corresponding device adjustments.

[0094] Different response delays are set for different levels to avoid accidental triggering due to sudden fluctuations:

[0095] Warning zone: Adjustments will be made after a 30-second delay upon confirmation;

[0096] Level 1 exceedance: Delay of 10 seconds;

[0097] Level 2 exceedance: Immediately;

[0098] Desulfurization and denitrification systems are complex dynamic systems, and equipment adjustments require time to reach a stable state. Short response delays may cause the system to become overly sensitive to minute changes, leading to frequent adjustments and an unstable, fluctuating state. By setting different response delays, the system can adjust at different rhythms according to different threshold ranges. For relatively small changes, such as those in the warning zone, appropriately extending the response delay allows the system more time to confirm the trend of the change, avoiding over-adjustment and achieving smoother system adjustment, thus contributing to maintaining system stability and reliability.

[0099] In order to better determine the termination conditions, the genetic optimization unit 320 uses a genetic algorithm to find the optimal combination of decision variables, and determines that pollutant emissions meet the standards and equipment parameters operate within the normal range as constraints.

[0100] Operating equipment parameters within normal ranges is a prerequisite for safe and stable operation. Equipment in boiler flue gas treatment systems, such as fans, pumps, and agitators, all have specific operating parameter ranges, including motor speed, pressure, and temperature. Limiting these parameters within normal ranges prevents equipment damage due to overload, overheating, overpressure, or other abnormal conditions, extends equipment lifespan, and reduces maintenance and replacement costs.

[0101] The stable operation of equipment is crucial to the reliability of the entire boiler flue gas treatment system. When equipment parameters exceed the normal range, it may lead to decreased system performance, unstable treatment results, or even system failure. By using genetic algorithms to find the optimal combination of decision variables while satisfying the constraints of normal equipment parameter ranges, it is possible to ensure the coordinated operation of various devices in the system, improve the overall reliability and stability of the system, and reduce the risk of production interruptions and environmental pollution caused by equipment failures.

[0102] In order to better determine the convergence threshold of the fitness value, the genetic optimization unit 320 runs the genetic algorithm multiple times, observes the change of the fitness value with the number of iterations, analyzes the convergence speed of the algorithm, selects the convergence threshold of the fitness value, and determines the termination condition of the genetic algorithm.

[0103] After multiple tests, it was found that the fitness value of the algorithm changed by less than 10⁻³ within 50 iterations, so the convergence threshold can be set to 10⁻³. Conversely, if the algorithm converges slowly and the fitness value fluctuates significantly over a long period of time, a larger convergence threshold needs to be set to avoid excessive iteration leading to inefficiency.

[0104] During algorithm execution, population diversity should be monitored. A rapid decrease in population diversity may cause the algorithm to converge prematurely to a local optimum. In this case, the convergence threshold can be appropriately increased to allow the algorithm more opportunities to search in different regions, thus maintaining population diversity. Population diversity can be assessed by calculating the similarity between individuals in the population; when the similarity exceeds a certain proportion, the convergence threshold should be increased.

[0105] like Figure 3 As shown, boilers 1 and 2 generate exhaust gas. By detecting the pollutant content in boilers 1 and 2, the operating frequency of the inlet induced draft fan is dynamically adjusted to introduce the boiler exhaust gas into the desulfurization and denitrification tower. Using the PLC control module 200, based on the error value between the concentration of sulfur dioxide and nitrogen oxides in the boiler exhaust gas and the target concentration, the operating parameters of the slurry circulation pump and the ammonia injection pump are output. The operating frequency of the slurry circulation pump and the ammonia injection pump is dynamically adjusted using the frequency converter to desulfurize and denitrify the exhaust gas. The results of desulfurization and denitrification are detected. For boiler exhaust gas that does not meet the standards, it is guided to boilers 1 and 2 through outlet induced draft fan 1 for secondary desulfurization and denitrification until the exhaust gas emission standards are met. For exhaust gas that meets the standards, it is guided to the dust removal tower through outlet induced draft fan 2 and outlet induced draft fan 3 for electrostatic dust removal. The impurities in the desulfurization and denitrification tower and the dust adsorbed by the dust removal tower enter the ash silo below for centralized collection and treatment. The boiler exhaust gas that has undergone dust removal is discharged from the top of the dust removal tower.

[0106] In summary, the working principle of this solution is as follows:

[0107] This boiler exhaust gas multi-pollutant collaborative control system, based on PLC and frequency converter linkage, collects real-time data on pollutant concentration, flue gas flow, and equipment status in the boiler exhaust gas through the data acquisition module 100. By detecting the pollutant content in the boiler, the frequency of the inlet induced draft fan is dynamically adjusted. When the pollutant content is high, the frequency converter is used to increase the operating frequency of the inlet induced draft fan, and vice versa. This achieves dynamic adjustment of the boiler exhaust gas treatment rate based on real-time pollutant concentration data.

[0108] The PLC control module 200 utilizes the PLC's logic controller to calculate the operating parameters of the slurry circulation pump and ammonia injection pump based on the error value between the detected concentrations of sulfur dioxide and nitrogen oxides in the boiler exhaust gas and the target concentrations. It uses a PID control algorithm to dynamically adjust the operating frequencies of the slurry circulation pump and ammonia injection pump using a frequency converter. When the SO2 concentration is higher than the target value, it indicates that the desulfurization effect needs to be enhanced. The PLC control module 200 increases the operating frequency of the slurry pump, increases the circulation volume of the slurry in the desulfurization tower, and increases the contact area and reaction time between the slurry and SO2 in the flue gas, thereby improving the desulfurization efficiency. Conversely, when the SO2 concentration is lower than the target value, the slurry pump frequency can be appropriately reduced to decrease the slurry circulation volume, thereby reducing energy consumption and equipment wear while ensuring the desulfurization effect.

[0109] The collaborative optimization module 300 uses a PLC logic controller to determine the desulfurization and denitrification of the exhaust gas. Exhaust gas that does not meet the standards is returned to the boiler for secondary desulfurization and denitrification. Exhaust gas that meets the standards is introduced into the dust removal tower through the outlet induced draft fan. The electrostatic precipitator voltage is adjusted according to the ammonia content in the exhaust gas to improve the purification rate of pollutants in the boiler exhaust gas, ensure the emission quality of the exhaust gas, and aim to minimize operating costs. It adopts a genetic algorithm to coordinate the linkage control of desulfurization, denitrification and dust removal to purify pollutants in the exhaust gas, while reducing energy consumption and saving resources.

[0110] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A boiler tail gas multi-pollutant collaborative control system based on PLC and frequency converter linkage, characterized in that: It comprises a data acquisition module (100), a PLC control module (200) and a collaborative optimization module (300); The data acquisition module (100) collects the concentration data of pollutants in the boiler tail gas, the flue gas flow and the equipment state data in real time, and uses the inlet induced draft fan to introduce the boiler tail gas into the desulfurization and denitrification tower; The PLC control module (200) sets the driving rules of the inlet induced draft fan by using the logic controller of the PLC, dynamically adjusts the operating frequency of the inlet induced draft fan according to the content of pollutants in the boiler tail gas, takes the error value of the concentration of sulfur dioxide and nitrogen oxides in the boiler tail gas and the target concentration as the input, uses the PID control algorithm to output the operating parameters of the slurry circulating pump and the ammonia injection pump, and dynamically adjusts the operating frequency of the slurry circulating pump and the ammonia injection pump by using the frequency converter, so as to desulfurize and denitrate the boiler tail gas; The collaborative optimization module (300) detects the content of sulfur dioxide and nitrogen oxides in the tail gas after desulfurization and denitrification, judges the desulfurization and denitrification of the tail gas by the logic controller of the PLC, returns the tail gas that does not meet the standard to the boiler for secondary desulfurization and denitrification, and introduces the tail gas that meets the standard into the dust removal tower through the outlet induced draft fan, adjusts the electric dust removal voltage according to the content of ammonia in the tail gas, and adopts the genetic algorithm to coordinate the linkage control of desulfurization, denitrification and dust removal with the goal of minimizing the operating cost. The PLC control module (200) sets the reference value of the pollutants in the boiler tail gas according to the environmental protection requirements and the parameters of the desulfurization and denitrification tower, and the logic controller of the PLC adjusts the operating frequency of the induced draft fan in proportion according to the deviation when the concentration of pollutants or the flue gas flow deviates from the reference value.

2. The PLC-based and frequency converter-linked boiler tail gas multi-pollutant synergic control system according to claim 1, characterized in that: The PLC control module (200) comprises a parameter regulation unit (210) and a frequency conversion driving unit (220); The parameter regulation unit (210) defines the error value and error change rate between the collected concentration data of sulfur dioxide and nitrogen oxides and the target concentration value in the environmental protection emission standard, and the parameter adjustment amount of the output PID controller as fuzzy sets, and the fuzzy sets are: negative large NB, negative medium NM, negative small NS, zero ZO, positive small PS, positive medium PM and positive large PB. The frequency conversion driving unit (220) adjusts the proportional coefficient, differential coefficient and integral coefficient of the PID controller according to the parameter adjustment amount of the output PID controller, adopts the PID control algorithm, and calculates the operating parameters of the fan, the slurry circulating pump and the ammonia injection pump.

3. The PLC-based and frequency converter-linked boiler tail gas multi-pollutant synergic control system according to claim 2, characterized in that: The parameter regulation unit (210) establishes a fuzzy rule base, which has the following form: ； wherein, is an error value, is a rate of change of error, is an error value is a fuzzy set of error values, is a fuzzy set of rates of change of error is a fuzzy set of rates of change of error, is a parameter adjustment of a PID controller, is a fuzzy set of PID controller parameter adjustments is a fuzzy set of PID controller parameter adjustments.

4. The PLC-based and frequency converter-linked boiler tail gas multi-pollutant synergic control system according to claim 2, characterized in that: The frequency conversion driving unit (220) adopts the discrete form of the PID control algorithm, calculates the operating parameters of the corresponding equipment according to the error value of the collected concentration data of sulfur dioxide and nitrogen oxides and the flue gas flow, and the formula is: ； wherein is a device operating parameter, is a proportional coefficient, is a differential coefficient, is an integral coefficient, is an error value at a time instant, is an error value at a time instant.

5. The PLC-based and frequency converter-linked boiler tail gas multi-pollutant synergic control system according to claim 1, characterized in that: The collaborative optimization module (300) comprises a detection and judgment unit (310) and a genetic optimization unit (320). The detection and determination unit (310) collects the content data of the two pollutants in the tail gas in real time through the sulfur dioxide and nitrogen oxide sensors installed on the tail gas pipeline after desulfurization and denitrification, compares the received sulfur dioxide and nitrogen oxide content data with the set threshold values respectively by using the logic controller of the PLC, and determines the desulfurization and denitrification of the tail gas. The genetic optimization unit (320) takes the minimization of the desulfurizer cost, the denitrifier cost and the equipment energy consumption cost as the objective function, adopts real number coding, encodes the decision variables, and selects the optimal individual corresponding to the decision variable combination through selection, genetic and mutation operations.

6. The PLC-based and frequency converter-linked boiler tail gas multi-pollutant synergic control system according to claim 5, characterized in that: The detection and determination unit (310) adopts the hierarchical threshold setting method, sets the content threshold values of sulfur dioxide and nitrogen oxide in the logic controller of the PLC, divides them into normal zone, early warning zone, first-level over-standard zone and second-level over-standard zone, and sets the hierarchical response strategy.

7. The PLC-based and frequency converter-linked boiler tail gas multi-pollutant synergic control system according to claim 6, characterized in that: The detection and determination unit (310) sets the corresponding response delay for different threshold intervals, and sets the upper limit and lower limit of the corresponding equipment adjustment.

8. The PLC-based and frequency converter-linked boiler tail gas multi-pollutant synergic control system according to claim 5, characterized in that: When the genetic optimization unit (320) adopts the genetic algorithm to find the optimal decision variable combination, the constraint conditions are determined as the pollutant emission standard and the equipment parameter operation in the normal range.

9. The PLC-based and frequency converter-linked boiler tail gas multi-pollutant synergic control system according to claim 8, characterized in that: The genetic optimization unit (320) observes the change of the fitness value with the iteration number by running the genetic algorithm multiple times, analyzes the convergence speed of the algorithm, selects the convergence threshold value of the fitness value, and determines the termination condition of the genetic algorithm.

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