A Predictive-Based Optimization Method and System for Desulfurization Tower Slurry Circulation Pump Combination

Abstract

This invention relates to the field of desulfurization tower slurry circulation pump optimization technology, and particularly to a desulfurization tower slurry circulation pump combination optimization method and system based on prediction technology. S1. Collect online operating parameters of the desulfurization system; S2. Calculate all possible pump-switching paths under the current operating conditions based on the slurry circulation pump combination using a logical algorithm. Compared with existing technologies, the advantages of this desulfurization tower slurry circulation pump combination optimization method and system based on prediction technology are: 1. Employing model prediction technology and introducing load prediction signals, it calculates and predicts the optimal combination of slurry circulation pumps and the SO2 concentration at the outlet after pump switching under different operating conditions, enabling operators to operate the wet desulfurization system under more economical conditions while ensuring emission standards are met.

Description

Technical Field

[0001] This invention relates to the field of desulfurization tower slurry circulation pump optimization technology, and in particular to a desulfurization tower slurry circulation pump combination optimization method and system based on prediction technology. Background Technology

[0002] Currently, most slurry circulation pumps in wet desulfurization towers of thermal power plants are industrial frequency pumps. Given the current large fluctuations in electricity market load, the desulfurization system lacks a predictive mechanism for the impact of different loads, pH levels, and the number of slurry circulation pumps on the outlet SO2 concentration. Even when the load trend of power units in the electricity market is predictable, operators still cannot make accurate decisions regarding pump start-up and shutdown. This leads to problems such as multiple slurry circulation pumps operating at excessive capacity simultaneously and liquid-to-gas ratios exceeding reasonable ranges, increasing the energy consumption of the desulfurization system. Summary of the Invention

[0003] The purpose of this invention is to address the above-mentioned problems by providing a method for optimizing the combination of desulfurization tower slurry circulation pumps based on predictive technology.

[0004] Another objective of this invention is to provide a desulfurization tower slurry circulation pump combination optimization system based on predictive technology.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: This desulfurization tower slurry circulation pump combination optimization method based on prediction technology is characterized by comprising the following steps:

[0006] S1. Collect online operating parameters of the desulfurization system;

[0007] S2. Based on the slurry circulation pump combination under the current operating conditions, calculate all possible pump switching paths under the current operating conditions using a logical algorithm;

[0008] S3. Based on the first-order inertial parameter model between the parameters of the wet desulfurization system, combined with all possible pump-cutting paths calculated in step S1 and the load forecast signal of the power market, calculate the peak SO2 concentration at the outlet after pump-cutting based on the model prediction method.

[0009] S4. Based on the possible pump switching paths and corresponding future outlet SO2 concentration data calculated in step S2, compare with the preset parameters set by the operators, and give the optimal combination scheme of slurry circulation pumps under the current operating conditions to meet the outlet SO2 concentration emission requirements.

[0010] In the above-mentioned optimization method for desulfurization tower slurry circulation pump combination based on prediction technology, the operating parameters include the flow rate of flue gas at the inlet and outlet of the desulfurization tower, the SO2 mass concentration and O2 volume concentration in the flue gas, the flow rate and pH value of the slurry, the current of each slurry circulation pump, and the unit load and total air volume.

[0011] In the aforementioned desulfurization tower slurry circulation pump combination optimization method based on prediction technology, a logical algorithm is used to analyze all possible pump switching paths for adding or subtracting a slurry circulation pump under the current operating conditions.

[0012] In the aforementioned desulfurization tower slurry circulation pump combination optimization method based on prediction technology, the parameter models include the unit load versus outlet SO2 concentration model, the inlet SO2 mass concentration versus outlet SO2 concentration model, the slurry supply rate versus slurry pH model, the slurry pH versus outlet SO2 concentration model, and the slurry circulation rate versus outlet SO2 concentration model. Combining all possible pump tripping paths of the slurry circulation pump calculated in step S1, by introducing the load prediction signal from the electricity market, the peak outlet SO2 concentration after pump tripping is calculated based on the model prediction method under the premise of fully utilizing the maximum influence of slurry pH and slurry supply flow rate on outlet SO2 concentration under the load change conditions and various pump tripping combination schemes in the next few hours.

[0013] In the above-mentioned desulfurization tower slurry circulation pump combination optimization method based on prediction technology, the preset parameters include the maximum allowable concentration of SO2 at the outlet, the allowable range of slurry pH, and the minimum slurry circulation pump switching interval.

[0014] In the aforementioned desulfurization tower slurry circulation pump combination optimization method based on prediction technology, the model prediction method includes:

[0015] At time k, the initial prediction value of the model is Where k+i|k represents the prediction of time k+i at time k. If we consider a control duration of M steps and a prediction duration of P steps, that is, under the action of Δu(k),...,Δu(k+M-1) control variables, the predicted output values ​​at each time within the future time P are:

[0016]

[0017] In the formula:

[0018]

[0019] In the formula, The initial predicted output value is A, where A is the dynamic matrix, P is the prediction time domain, and M is the control time domain. Typically, M ≤ P ≤ N.

[0020] In the aforementioned desulfurization tower slurry circulation pump combination optimization method based on prediction technology, the control quantity is optimized and solved at each time step to ensure that, under the action of the obtained M control increments Δu(k),...,Δu(k+M-1), the predicted output value of the controlled object at the next P time steps is achieved. To get as close as possible to the given expected value w(k+i), i=1,2,...,P, while minimizing the control variation, the optimization index at time k can be taken as follows:

[0021]

[0022] In the formula, q i and r j These are weighting coefficients;

[0023] Rewritten in vector form as follows:

[0024]

[0025] In the formula, Q = diag(q1, q2, ..., q P R = diag(r1, r2, ..., r) M ),

[0026] w P (k) = [w(k+1),...,w(k+P)] T Q and R are the error weight matrix and the control weight matrix, respectively;

[0027] The above equation can be solved by the necessary condition for extrema: the derivative must be zero. Find:

[0028]

[0029] The above formula gives the optimal values ​​of Δu(k),...,Δu(k+M-1) obtained at time k. However, DMC only calculates the first term as the actual control operation applied to the object in each rolling optimization:

[0030]

[0031] In the formula, c T =[1 0 ... 0], d T =c T (A T QA+R) -1 A T Q,d T This is called the control vector;

[0032] The values ​​of P, M, Q, and R can be determined through offline simulation and then the control matrix d is established. T The value can also be calculated offline; during actual online control, only d needs to be calculated. T and The dot product is sufficient;

[0033] The calculated Δu(k) is added to the control quantity u(k) of the previous step, that is, the control quantity u(k+1) = u(k) + Δu(k) will be applied to the object in the next step, and so on.

[0034] In the aforementioned desulfurization tower slurry circulation pump combination optimization method based on prediction technology, before calculating the optimized control quantity at time k+1, it is necessary to measure the actual output y(k+1) of the object and the output predicted by the model at that time given in Formula 1. Compare the results and calculate the output error:

[0035]

[0036] Output error is corrected using a weighted method:

[0037]

[0038] In the formula, The corrected output prediction vector is h = [h1,...,h] N ] T To correct the weight vector;

[0039] The corrected prediction value is used as the initial prediction value at time k+1, that is:

[0040]

[0041] Due to model truncation, the predicted value at time k is... can be Approximately; the initial prediction value at time k+1 can be expressed as follows using displacement:

[0042]

[0043] In the formula, Given the displacement matrix, we obtain Then we can perform rolling optimization calculations at time k+1.

[0044] In the above-mentioned optimization method for the desulfurization tower slurry circulation pump combination based on prediction technology, the prediction algorithm for the peak SO2 concentration at the outlet is as follows:

[0045]

[0046] In the formula, SO 2 now The current hourly average SO2 concentration at the outlet. 2pumpOff To predict the SO2 concentration at the outlet after pump disconnection, SO 2pH ΔAMP represents the maximum SO2 suppression capacity at the outlet after pump shutdown under the current slurry flow rate. pump This represents the change in current after switching the slurry circulation pump combination. These are the current slurry flow rate and the maximum allowable slurry flow rate, respectively. These represent the gains of the slurry circulation pump current on outlet SO2 and the slurry flow rate on outlet SO2 in the closed-loop control model, respectively.

[0047] This desulfurization tower slurry circulation pump combination optimization system based on predictive technology includes a desulfurization data acquisition module, an analysis and optimization module, and a display and interaction module.

[0048] The desulfurization data acquisition module is used to collect online operating parameters of the desulfurization system;

[0049] The analysis and optimization module includes a slurry circulation pump path calculation unit, a model prediction unit, and an optimal slurry circulation pump combination suggestion unit;

[0050] The slurry circulation pump path calculation unit calculates all feasible paths for adding or subtracting a slurry circulation pump under the current working conditions based on the current slurry circulation pump combination using logical algorithms.

[0051] The model prediction unit and module incorporate first-order inertial parameter models for various parameters of the wet desulfurization system. These parameter models include models of unit load versus outlet SO2 concentration, inlet SO2 mass concentration versus outlet SO2 concentration, slurry supply rate versus slurry pH, slurry pH versus outlet SO2 concentration, and slurry circulation rate versus outlet SO2 concentration. Combined with all possible pump tripping paths calculated in the slurry circulation pump path calculation unit, and by introducing load forecasting signals from the electricity market, the model prediction method calculates the peak outlet SO2 concentration after pump tripping under load variations and various pump tripping combinations, maximizing the influence of slurry pH and slurry supply flow rate on the outlet SO2 concentration over the next few hours.

[0052] The optimal slurry circulation pump combination suggestion unit, based on the various feasible slurry circulation pump switching schemes calculated in the model prediction unit and the corresponding future outlet SO2 concentration prediction data, provides the optimal combination scheme of slurry circulation pumps under the current working conditions to meet the outlet SO2 concentration emission requirements, according to the maximum allowable concentration of outlet SO2, the allowable pH range of slurry, and the minimum slurry circulation pump switching interval set by the operators.

[0053] The display interaction module is used for interaction between the system and the operators.

[0054] Compared with existing technologies, the advantages of this desulfurization tower slurry circulation pump combination optimization method and system based on prediction technology are as follows: 1. By adopting model prediction technology and introducing load prediction signals, the optimal combination of slurry circulation pumps and the SO2 concentration at the outlet after pump shutdown under different operating conditions are calculated and predicted, enabling operators to operate the wet desulfurization system under more economical conditions while ensuring that emission standards are met. 2. This system is applicable to most wet desulfurization systems, requiring no equipment-level modification to common power frequency slurry circulation pumps, resulting in low cost and quick results. Attached Figure Description

[0055] Figure 1 This is a system structure block diagram provided by the present invention. Detailed Implementation

[0056] like Figure 1 As shown, this desulfurization tower slurry circulation pump combination optimization system based on prediction technology includes a desulfurization data acquisition module, a model prediction module, and a display and interaction module.

[0057] The desulfurization data acquisition module collects online operating parameters of the desulfurization system, including the flow rate of flue gas at the inlet and outlet of the desulfurization tower, the SO2 mass concentration and O2 volume concentration in the flue gas, the flow rate and pH value of the slurry, the current of each slurry recirculation pump, and the unit load and total air volume.

[0058] The analysis and optimization module includes:

[0059] (1) Slurry circulation pump path calculation unit. Based on the slurry circulation pump combination under the current working conditions, the unit calculates all feasible paths for adding or subtracting a slurry circulation pump under the current working conditions using a logical algorithm.

[0060] The pump switching path algorithm takes into account the principle of evenly distributing the bus load. For example, if the slurry circulation pumps currently in operation are pumps A and C on bus A and pump D on bus B, then when calculating the combined path of stopping one pump, the algorithm will automatically exclude the possibility of stopping pump B to avoid uneven load distribution between buses.

[0061] (2) Model prediction unit. The model prediction module contains a first-order inertial parameter model between various parameters of the wet desulfurization system. The parameter models include the unit load to outlet SO2 concentration model, the inlet SO2 mass concentration to outlet SO2 concentration model, the slurry supply to slurry pH model, the slurry pH to outlet SO2 concentration model, and the slurry circulation to outlet SO2 concentration model.

[0062] Combining all possible pump-cutting paths calculated in the slurry circulation pump path calculation unit, and by introducing load forecast signals from the electricity market, the peak SO2 concentration at the outlet after pump-cutting is calculated based on the model prediction method under load change conditions in the next few hours, and under the premise that each pump-cutting combination scheme fully utilizes the maximum influence of slurry pH and slurry flow rate on the outlet SO2 concentration.

[0063] (3) Optimal Slurry Circulation Pump Combination Recommendation Unit. Based on the various feasible slurry circulation pump switching schemes calculated in the model prediction unit and the corresponding future outlet SO2 concentration prediction data, according to the maximum allowable outlet SO2 concentration, the allowable pH range of slurry, and the minimum slurry circulation pump switching interval set by the operators, the optimal combination scheme of slurry circulation pumps under the current operating conditions that meets the outlet SO2 concentration emission requirements is given.

[0064] The algorithm steps used in the above three units are as follows:

[0065] Step 1: Arrange all the reslurry circulation pumps in combination to obtain all possible combinations, and arrange them in ascending order of total current;

[0066] Step 2: Calculate the bus load deviation for each scheme and exclude combinations with deviations greater than the set threshold.

[0067] Step 3: Locate the current re-slurry circulation pump combination scheme. Based on the model prediction method, calculate the predicted value of the outlet SO2 concentration of each combination under the load prediction signal and the current maximum capacity of slurry pH. Select the combination that meets the limit range of slurry pH and the outlet SO2 concentration is lower than the set value, and display it to the operators.

[0068] The model prediction algorithm:

[0069] (i) Model prediction: At time k, the initial predicted value of the model is... Where k+i|k represents the prediction of time k+i at time k. If we consider a control duration of M steps and a prediction duration of P steps, that is, under the action of Δu(k),...,Δu(k+M-1) control variables, the predicted output values ​​at each time within the future time P are:

[0070]

[0071] In the formula:

[0072]

[0073] In the formula, The initial predicted output value is A, where A is the dynamic matrix, P is the prediction time domain, and M is the control time domain. Typically, M ≤ P ≤ N.

[0074] (II) Rolling Optimization

[0075] The model predictive control algorithm optimizes the control input at each time step to ensure that, under the action of the obtained M control increments Δu(k),...,Δu(k+M-1), the output prediction value of the controlled object at P future time steps is achieved. The goal is to obtain an optimization index as close as possible to the given desired value w(k+i), i = 1, 2, ..., P, while minimizing the control variation. Therefore, the optimization index at time k can be:

[0076]

[0077] In the formula, q i and r j It is the weighting coefficient.

[0078] Rewritten in vector form as follows:

[0079]

[0080] In the formula, Q = diag(q1, q2, ..., q P R = diag(r1, r2, ..., r) M ),

[0081] w P (k) = [w(k+1),...,w(k+P)] T Q and R are the error weight matrix and the control weight matrix, respectively.

[0082] The above equation can be solved by the necessary condition for extrema: the derivative must be zero. Find:

[0083]

[0084] The above formula gives the optimal values ​​of Δu(k),...,Δu(k+M-1) obtained at time k. However, DMC only calculates the first term as the actual control operation applied to the object in each rolling optimization:

[0085]

[0086] In the formula, c T =[1 0 … 0], d T =c T (A T QA+R) -1 A T Q,d T This is called the control vector.

[0087] In the rolling optimization step, the values ​​of P, M, Q, and R can be debugged offline through simulation, and the control matrix d is determined afterward. T The value can also be calculated offline; during actual online control, only d needs to be calculated. T and The dot product is sufficient.

[0088] The calculated Δu(k) is added to the control quantity u(k) of the previous step, that is, the control quantity u(k+1) = u(k) + Δu(k) will be applied to the object in the next step, and so on.

[0089] (III) Feedback Correction

[0090] The prediction results calculated by the prediction model may deviate from reality or have steady-state deviations, requiring feedback correction. Before calculating the optimal control quantity at time k+1, it is necessary to measure the actual output y(k+1) of the object and the output predicted by the model at that time given in Equation 1. Compare the results and calculate the output error:

[0091]

[0092] Output error is corrected using a weighted method:

[0093]

[0094] In the formula, The corrected output prediction vector is h = [h1,...,h] N ] T This is for correcting the weight vector.

[0095] The corrected prediction value is used as the initial prediction value at time k+1, that is:

[0096]

[0097] Due to model truncation, the predicted value at time k is... can be Approximately. The initial prediction value at time k+1 based on the displacement can be expressed as:

[0098]

[0099] In the formula, This is the displacement matrix. We obtain... Then we can perform rolling optimization calculations at time k+1.

[0100] The algorithm for predicting SO2 concentration at the outlet:

[0101]

[0102] In the formula, SO2 now The current hourly average SO2 concentration at the outlet. 2pumpOff To predict the SO2 concentration at the outlet after pump disconnection, SO 2pH ΔAMP represents the maximum SO2 suppression capacity at the outlet after pump shutdown under the current slurry flow rate. pump This represents the change in current after switching the slurry circulation pump combination. These are the current slurry flow rate and the maximum allowable slurry flow rate, respectively. These represent the gains of the slurry circulation pump current on outlet SO2 and the slurry flow rate on outlet SO2 in the closed-loop control model, respectively.

[0103] The interactive display module is based on editable web page configuration technology. It presents the current operating parameters of the wet desulfurization system, all feasible pump switching schemes obtained from the analysis and optimization module, the corresponding outlet SO2 concentration prediction, and the optimal pump switching suggestion with audio and visual prompts to the operators in the form of charts and graphical interfaces.

[0104] Given that most slurry circulation pumps in the desulfurization systems of thermal power plants are industrial frequency pumps, this paper combines the unit load forecast signals from the power market and uses a prediction model to predict the outlet SO2 concentration for the next few hours under different slurry circulation pump combinations. This provides an optimal switching scheme for slurry circulation pumps while ensuring compliance with environmental emission requirements.

[0105] The specific embodiments described herein are merely illustrative of the spirit of the invention. Those skilled in the art to which this invention pertains may make various modifications or additions to the described specific embodiments or use similar methods to substitute them, without departing from the spirit of the invention or exceeding the scope defined by the appended claims.

Claims

1. A method for optimizing the combination of desulfurization tower slurry circulation pumps based on predictive technology, characterized in that, Includes the following steps: S1. Collect online operating parameters of the desulfurization system; S2. Based on the slurry circulation pump combination under the current operating conditions, calculate all possible pump switching paths under the current operating conditions using a logical algorithm; S3. Based on the first-order inertial parameter model between the parameters of the wet desulfurization system, combined with all possible pump-cutting paths calculated in step S1 and the load forecast signal of the power market, calculate the peak SO2 concentration at the outlet after pump-cutting based on the model prediction method. S4. Based on the possible pump switching paths and corresponding future outlet SO2 concentration data calculated in step S2, compare with the preset parameters set by the operators, and give the optimal combination scheme of slurry circulation pumps under the current operating conditions to meet the outlet SO2 concentration emission requirements. In step S3, the parameter models include the unit load model on the outlet SO2 concentration, the inlet SO2 mass concentration model on the outlet SO2 concentration, the slurry supply rate model on the slurry pH model, the slurry pH model on the outlet SO2 concentration, and the slurry circulation rate model on the outlet SO2 concentration. Combining all possible pump tripping paths of the slurry circulation pump calculated in step S1, and by introducing the load forecast signal from the electricity market, the peak outlet SO2 concentration after pump tripping is calculated based on the model prediction method under the load change conditions and various pump tripping combinations in the next few hours, under the premise of giving full play to the maximum influence of slurry pH and slurry supply flow on the outlet SO2 concentration. The model prediction method includes: At time k, the initial prediction value of the model is Where k+i|k represents the prediction of time k+i at time k. If we consider a control duration of M steps and a prediction duration of P steps, that is, under the action of Δu(k),...,Δu(k+M-1) control variables, the predicted output values ​​at each time within the future time P are: In the formula: In the formula, The initial predicted output value is A, where A is the dynamic matrix, P is the prediction time domain, and M is the control time domain. Typically, M ≤ P ≤ N is defined. The algorithm for predicting SO2 concentration at the outlet: In the formula, SO 2now The current hourly average SO2 concentration at the outlet. 2pumpOff To predict the SO2 concentration at the outlet after pump disconnection, SO 2pH ΔAMP represents the maximum SO2 suppression capacity at the outlet after pump shutdown under the current slurry flow rate. pump This represents the change in current after switching the slurry circulation pump combination. These are the current slurry flow rate and the maximum allowable slurry flow rate, respectively. These represent the gains of the slurry circulation pump current on outlet SO2 and the slurry flow rate on the outlet SO2 closed-loop control model, respectively.

2. The desulfurization tower slurry circulation pump combination optimization method based on prediction technology according to claim 1, characterized in that, In step S1, the operating parameters include the flow rate of flue gas at the inlet and outlet of the desulfurization tower, the mass concentration of SO2 and the volume concentration of O2 in the flue gas, the flow rate and pH value of the slurry, the current of each slurry circulation pump, and the load and total air volume of the unit.

3. The desulfurization tower slurry circulation pump combination optimization method based on prediction technology according to claim 1, characterized in that, In step S2, a logical algorithm is used to add or subtract all possible pump switching paths for a slurry circulation pump under the current operating conditions.

4. The desulfurization tower slurry circulation pump combination optimization method based on prediction technology according to claim 1, characterized in that, In step S4, the preset parameters include the maximum allowable concentration of SO2 at the outlet, the allowable pH range of the slurry, and the minimum slurry circulation pump switching interval.

5. The desulfurization tower slurry circulation pump combination optimization method based on prediction technology according to claim 1, characterized in that, At each time step, the control input is optimized to ensure that, under the action of the obtained M control increments Δu(k),...,Δu(k+M-1), the predicted output value of the controlled object at P future time steps is achieved. To get as close as possible to the given expected value w(k+i), i=1,2,...,P, while minimizing the control variation, the optimization index at time k can be taken as follows: In the formula, q i and r j These are weighting coefficients; Rewritten in vector form as follows: In the formula, Q = diag(q1, q2, ..., q P R = diag(r1, r2, ..., r) M ), w P (k) = [w(k+1),...,w(k+P)] T Q and R are the error weight matrix and the control weight matrix, respectively; The above equation can be solved by the necessary condition for extrema: the derivative must be zero. Find: The above formula gives the optimal values ​​of Δu(k),...,Δu(k+M-1) obtained at time k. However, DMC only calculates the first term as the actual control operation applied to the object in each rolling optimization: In the formula, c T =[10…0], d T =c T (A T QA+R) -1 A T Q,d T This is called the control vector; The values ​​of P, M, Q, and R can be determined through offline simulation and then the control matrix d is established. T The value can also be calculated offline; during actual online control, only d needs to be calculated. T and The dot product is sufficient; The calculated Δu(k) is added to the control quantity u(k) of the previous step, that is, the control quantity u(k+1) = u(k) + Δu(k) will be applied to the object in the next step, and so on.

6. The desulfurization tower slurry circulation pump combination optimization method based on prediction technology according to claim 5, characterized in that, Before calculating the optimal control quantity at time k+1, it is necessary to measure the actual output y(k+1) of the object and the output predicted by the model at that time given in Equation 1. Compare the results and calculate the output error: Output error is corrected using a weighted method: In the formula, The corrected output prediction vector is h = [h1,...,h] N ] T To correct the weight vector; The corrected prediction value is used as the initial prediction value at time k+1, that is: Due to model truncation, the predicted value at time k is... can be Approximately; the initial prediction value at time k+1 can be expressed as follows using displacement: In the formula, Given the displacement matrix, we obtain Then we can perform rolling optimization calculations at time k+1.

7. A desulfurization tower slurry circulation pump combination optimization system based on predictive technology, characterized in that, The method for optimizing the combination of desulfurization tower slurry circulation pumps based on prediction technology, as described in any one of claims 1-6, is applicable. It includes a desulfurization data acquisition module, an analysis and optimization module, and a display and interaction module; The desulfurization data acquisition module is used to collect online operating parameters of the desulfurization system; The analysis and optimization module includes a slurry circulation pump path calculation unit, a model prediction unit, and an optimal slurry circulation pump combination suggestion unit; The slurry circulation pump path calculation unit calculates all feasible paths for adding or subtracting a slurry circulation pump under the current working conditions based on the current slurry circulation pump combination using logical algorithms. The model prediction unit and module incorporate first-order inertial parameter models for various parameters of the wet desulfurization system. These parameter models include models of unit load versus outlet SO2 concentration, inlet SO2 mass concentration versus outlet SO2 concentration, slurry supply rate versus slurry pH, slurry pH versus outlet SO2 concentration, and slurry circulation rate versus outlet SO2 concentration. Combined with all possible pump tripping paths calculated in the slurry circulation pump path calculation unit, and by introducing load forecasting signals from the electricity market, the model prediction method calculates the peak outlet SO2 concentration after pump tripping under load variations and various pump tripping combinations, maximizing the influence of slurry pH and slurry supply flow rate on the outlet SO2 concentration over the next few hours. The optimal slurry circulation pump combination suggestion unit, based on the various feasible slurry circulation pump switching schemes calculated in the model prediction unit and the corresponding future outlet SO2 concentration prediction data, provides the optimal combination scheme of slurry circulation pumps under the current working conditions to meet the outlet SO2 concentration emission requirements, according to the maximum allowable concentration of outlet SO2, the allowable pH range of slurry, and the minimum slurry circulation pump switching interval set by the operators. The display interaction module is used for interaction between the system and the operators.

Images