Integrated intelligent control system for circulating desulfurization and dust removal furnace

Abstract

The invention discloses an integrated intelligent control system for a circulating desulfurization and dust removal furnace, which belongs to the technical field of desulfurization and dust removal and is characterized in that historical working data of the desulfurization and dust removal furnace are classified and analyzed to obtain influence trends of various influence factors on desulfurization efficiency of the desulfurization and dust removal furnace under different operating conditions; on the basis, which influence factor is specifically adjusted to adjust the desulfurization efficiency so as to meet the desulfurization requirement is judged, and the influence factor which has obvious influence on the existing working system, namely the desulfurization efficiency, is followed, so that the influence is as small as possible, and the large-range change of a certain influence factor is avoided; therefore, the continuous stability of the whole desulfurization system is ensured, and the influence of sudden change of a certain influence factor on the system stability is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of desulfurization and dust removal, and specifically relates to an integrated intelligent control system for a circulating desulfurization and dust removal furnace. Background Art

[0002] Traditional industrial furnace dust prevention and desulfurization purification devices usually use spraying, adsorption or catalytic methods to remove sulfur and dust. For example, the spraying method sprays desulfurizers (such as lime slurry) into the flue gas to absorb and convert sulfur dioxide and other sulfide gases. At the same time, the spraying process also has a certain dust removal effect. There are many factors that affect the desulfurization efficiency of the circulating desulfurization and dust removal furnace, including but not limited to flue gas temperature, flue gas flow rate, pH value, air oxidation amount, dust content, etc. The numerical change of any factor will affect the desulfurization efficiency. Therefore, an optimal working value is usually set for each parameter, and each parameter is adjusted as much as possible to work within the set optimal range to ensure the desulfurization efficiency during operation. However, due to the complexity of the actual working process, it is difficult to achieve this. Therefore, it is necessary to adjust each parameter in a floating manner to achieve stable regulation of the desulfurization efficiency. The prior art mainly relies on the experience of technicians for adjustment. This method has high requirements on personal experience and requires slow adjustment while observing, and may cause the parameters to gradually move away from the optimal working range set at the beginning. In order to solve this problem, a method for automatically and reasonably controlling the desulfurization efficiency of a circulating desulfurization and dust removal furnace is provided. The present invention provides the following technical solutions. Summary of the invention

[0003] The purpose of the present invention is to provide an integrated intelligent control system for a circulating desulfurization and dust removal furnace, so as to solve the problem that in the prior art, the desulfurization efficiency of the circulating desulfurization and dust removal furnace is adjusted mainly by relying on the personal experience of technicians, which has high requirements on personal experience and requires slow observation and adjustment, and may cause various parameters to gradually move away from the optimal working range set at the beginning, which is not conducive to the stable operation of the entire desulfurization system.

[0004] The purpose of the present invention can be achieved through the following technical solutions: An integrated intelligent control system for a circulating desulfurization and dust removal furnace, comprising: A sulfur dioxide concentration sensor is used to detect the sulfur dioxide concentration at the flue gas inlet and outlet of the desulfurization and dust removal furnace, and transmit the detection results to the controller; A database for storing data input by the sulfur dioxide concentration sensor and the controller; A controller, used for receiving real-time sulfur dioxide concentration information transmitted by the sulfur dioxide concentration sensor and data records in a memorial database; Record the changes of various factors affecting desulfurization efficiency over time; Then, the influence of each influencing factor on the desulfurization efficiency under different conditions is obtained; When the desulfurization efficiency of the circulating desulfurization dust removal furnace does not meet the requirements, obtain the real-time value of each influencing factor, and determine which influencing factor has the least impact on the overall stability of the circulating desulfurization dust removal furnace based on the real-time value of each influencing factor, the impact of each influencing factor on the desulfurization efficiency under current conditions and the current desulfurization efficiency, and adjust the influencing factor with the least impact on the overall stability of the circulating desulfurization dust removal furnace to make the desulfurization efficiency meet the requirements.

[0005] As a further solution of the present invention, the method for the controller to adjust each influencing factor specifically includes the following steps: The first step is to obtain the relationship curve a1 between desulfurization efficiency and time; The second step is to obtain each influencing factor and the relationship curve ki of each influencing factor and time, where i ranges from 1 to n, and n is the number of influencing factors; The third step is to divide the normal working time period into several comparison units, and each comparison unit corresponds to an equal duration; Get n corresponding relationship curves ki in a comparison unit; Eliminate the contrast units in which the relationship curve ki is unstable, and mark the remaining relationship units as stable contrast units; Step 4: Obtain all steady-state contrast units and store all steady-state contrast units in a database; The controller analyzes and processes the steady-state comparison units in the database to obtain the influence relationship curves of various influencing factors on the desulfurization efficiency under different working conditions, and marks these influence relationship curves as reference curves; Step 5: When the desulfurization efficiency is less than a preset threshold value x1y, where x1y is a preset desulfurization efficiency alarm value, the controller analyzes and obtains a reference curve corresponding to each influencing factor in the current state; Step 6: According to the corresponding reference curve when the n influencing factors obtained in step 5 are used as core factors, the adjustment amount hi of each influencing factor and the adjusted value ci are obtained to adjust the desulfurization efficiency to the preset threshold value x2y under the current conditions; Where x2y is a preset safety value, which is greater than x1y; The adjustment coefficient Ri corresponding to each influencing factor is calculated according to the formula Ri=θ1×hi / hbi+θ2×|ci-hbi| / hbi; Where hbi is the optimal working value corresponding to the setting of the impact factor; θ1 and θ2 are both preset proportional coefficients, and θ1+θ2=1; Select the influencing factor with the smallest corresponding adjustment coefficient Ri, and mark the influencing factor as the influencing factor to be adjusted; In the seventh step, the controller sends an execution instruction to the execution unit, and the execution unit adjusts the regulator corresponding to the influence factor to be adjusted, and the adjustment amount of the influence factor to be adjusted is the corresponding hi.

[0006] As a further solution of the present invention, a method for obtaining a steady-state contrast unit is: For a comparison unit, when the change rate of one or more relationship curves in the comparison unit is greater than the preset proportional coefficient, the comparison unit will not be further processed. On the contrary, when the change rates of all relationship curves are less than or equal to the preset proportional coefficient, the corresponding comparison unit will be marked as a steady-state comparison unit.

[0007] As a further solution of the present invention, a method for calculating the rate of change of a relationship curve ki is: A number of coordinate points are obtained at equal time differences on the relationship curve ki, the ordinates corresponding to the coordinate points are obtained, and the standard deviation of the ordinates is further calculated, and the standard deviation is used as the change rate of the corresponding relationship curve ki.

[0008] As a further solution of the present invention, the method for obtaining the influence relationship of each influencing factor on the desulfurization efficiency under different working conditions in the fourth step is: Determine one impact factor as the calibration factor, and mark other impact factors except the calibration factor as positioning factors; Compare all positioning factors in each steady-state contrast unit. When the similarity between two steady-state contrast units is greater than a preset proportional coefficient α, the two steady-state contrast units are regarded as the same contrast group, that is, the similarity between any two steady-state contrast units in the same contrast group is greater than the preset proportional coefficient α. Obtain the calibration factor corresponding to each steady-state comparison unit in a comparison group and the desulfurization efficiency corresponding to the calibration factor, use the value of the calibration factor as the abscissa and the desulfurization efficiency as the ordinate, calibrate several points in the rectangular coordinate system, and fit to form a relationship curve between the calibration factor and the desulfurization efficiency corresponding to the comparison group; Then, the relationship curves between each calibration factor and the desulfurization efficiency are obtained in turn.

[0009] As a further solution of the present invention, the method for obtaining the relationship curve corresponding to each influencing factor in the current state in the fifth step is: Take one influencing factor as the core factor, obtain the actual collected values ​​of other influencing factors, and mark the combination of the core factor and the actual collected values ​​of other influencing factors as an actual parameter unit; Compare the other influencing factors in the actual parameter unit with the comparison groups in the database to obtain the comparison group whose similarity with the actual parameter unit is greater than a preset proportional coefficient α; The baseline curve corresponding to the determined comparison group is used as the baseline curve corresponding to the core factor; then the baseline curves corresponding to other influencing factors when they are used as core factors are obtained in turn.

[0010] As a further solution of the present invention, when obtaining the actual collected value, the average value of the influencing factor within the past preset time t is obtained as the actual collected value.

[0011] As a further solution of the present invention, when there are two or more comparison groups and the actual parameter unit have similarities greater than a preset proportional coefficient α, a comparison group with the greatest similarity is selected as the comparison group finally confirmed; When there is no comparison group with an actual parameter unit whose similarity is greater than a preset proportional coefficient α, a comparison group with the greatest similarity is selected as the final confirmed comparison group or an alarm is issued for manual adjustment.

[0012] As a further solution of the present invention, when obtaining a comparison group whose similarity with the actual parameter unit is greater than a preset proportional coefficient α, other influencing factors in the actual parameter unit are compared with the positioning parameters in the steady-state comparison unit in the comparison group to obtain a comparison group whose average similarity is greater than the preset proportional coefficient α.

[0013] Beneficial effects of the present invention: The present invention classifies and analyzes the historical data of the desulfurization dust removal furnace to obtain the influence trend of various influencing factors on the desulfurization efficiency of the desulfurization dust removal furnace under different operating conditions, and on this basis, determines which influencing factor to adjust to adjust the desulfurization efficiency so that it meets the desulfurization requirements, and follows the principle of minimizing the impact on the current working system (that is, the influencing factors that have a significant impact on the desulfurization efficiency) and avoiding large-scale changes in a certain influencing factor, thereby ensuring the continuous stability of the entire desulfurization system and reducing the impact of a sudden change in a certain influencing factor on the stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The present invention will be further described below in conjunction with the accompanying drawings.

[0015] Figure 1 It is a structural schematic diagram of an integrated intelligent control system for a circulating desulfurization and dust removal furnace of the present invention; Figure 2 It is a flow chart of a method for the controller to adjust various influencing factors. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] Embodiment 1 An integrated intelligent control system for a circulating desulfurization and dust removal furnace, such as Figure 1 As shown, including: A sulfur dioxide concentration sensor is used to detect the sulfur dioxide concentration at the flue gas inlet and outlet of the desulfurization and dust removal furnace, and transmit the detection results to the controller; A database for storing data input by the sulfur dioxide concentration sensor and the controller; A controller, used for receiving real-time sulfur dioxide concentration information transmitted by the sulfur dioxide concentration sensor and data records in a database; Record the changes of various factors affecting desulfurization efficiency over time; Then, the influence of each influencing factor on the desulfurization efficiency under different conditions is obtained; When the desulfurization efficiency of the circulating desulfurization dust removal furnace does not meet the requirements, obtain the real-time value of each influencing factor, and determine which influencing factor has the least impact on the overall stability of the circulating desulfurization dust removal furnace based on the real-time value of each influencing factor, the impact of each influencing factor on the desulfurization efficiency under current conditions and the current desulfurization efficiency, and adjust the influencing factor with the least impact on the overall stability of the circulating desulfurization dust removal furnace to make the desulfurization efficiency meet the requirements.

[0018] Embodiment 2 like Figure 2 As shown, based on the first embodiment, the method for the controller to adjust each influencing factor specifically includes the following steps: The first step is to monitor the desulfurization efficiency of the circulating desulfurization and dust removal furnace in real time and obtain the relationship curve a1 between the desulfurization efficiency and time during the normal working period; The normal working period refers to the sulfur dioxide concentration in the exhaust gas meeting the requirements; Specifically, the sulfur dioxide concentration S1 in the flue gas entering the circulating desulfurization and dust removal furnace and the sulfur dioxide concentration S2 in the flue gas discharged from the circulating desulfurization and dust removal furnace are detected in real time by a sulfur dioxide concentration sensor, and the desulfurization efficiency α of the circulating desulfurization and dust removal furnace is calculated by α=S2 / S1; The second step is to obtain the parameters that affect the desulfurization efficiency by statistics, and mark these parameters as influencing factors; When the sulfur dioxide concentration in the exhaust gas meets the requirements, the various influencing factors of the circulating desulfurization and dust removal furnace during operation are recorded to obtain the relationship curve ki between each influencing factor and time, where i ranges from 1 to n, and n is the number of influencing factors; The influencing factors include flue gas temperature, flue gas flow rate, pH value, air oxidation amount, dust content, etc.; The third step is to divide the normal working time period into several comparison units, and each comparison unit corresponds to an equal duration; Get n corresponding relationship curves ki in a comparison unit; For a comparison unit, the change rates of the corresponding n relationship curves ki are calculated. When the change rate of one or more relationship curves in the comparison unit is greater than the preset proportional coefficient, the comparison unit is not further processed. On the contrary, when the change rates of all relationship curves are less than or equal to the preset proportional coefficient, the corresponding comparison unit is marked as a steady-state comparison unit. The calculation method of the rate of change of one of the relationship curves ki is: A number of coordinate points are obtained at equal time differences on the relationship curve ki, the ordinates corresponding to the coordinate points are obtained, and the standard deviation of the ordinates is further calculated, and the standard deviation is used as the change rate of the corresponding relationship curve ki.

[0019] Step 4: Obtain all steady-state contrast units and store all steady-state contrast units in a database; The controller analyzes and processes the steady-state comparison unit in the database to obtain the influence relationship of each influencing factor on the desulfurization efficiency under different working conditions; Specifically, the method for obtaining the influence relationship of each influencing factor on the desulfurization efficiency under different working conditions is: Determine one impact factor as the calibration factor, and mark other impact factors except the calibration factor as positioning factors; Compare all positioning factors in each steady-state contrast unit. When the similarity between two steady-state contrast units is greater than the preset proportional coefficient α, the two steady-state contrast units are regarded as the same contrast group and the contrast groups are divided according to the rule, that is, the similarity between any two steady-state contrast units in the same contrast group is greater than the preset proportional coefficient α. The calibration factors corresponding to each steady-state comparison unit in a comparison group and the desulfurization efficiency corresponding to the calibration factors are obtained. With the value of the calibration factor as the horizontal coordinate and the desulfurization efficiency as the vertical coordinate, several points are calibrated in the rectangular coordinate system, and the relationship curve between the calibration factor and the desulfurization efficiency corresponding to the comparison group is formed by fitting, and these relationship curves are marked as reference curves.

[0020] The fifth step is to monitor the desulfurization efficiency in real time through the sulfur dioxide concentration sensor during actual operation; When the desulfurization efficiency is less than the preset threshold value x1y, where x1y is the preset desulfurization efficiency alarm value, that is, when the desulfurization efficiency is already less than the threshold value, it means that the desulfurization efficiency is about to or has not met the corresponding requirements, then the controller analyzes and obtains the reference curve obtained in the fourth step corresponding to each influencing factor in the current state: Take one influencing factor as the core factor, obtain the actual collected values ​​of other influencing factors, and mark the combination of the core factor and the actual collected values ​​of other influencing factors as an actual parameter unit; To ensure the accuracy of the calculation results, when obtaining the actual collected value, the average value of the influencing factor within the past preset time t is obtained as the actual collected value; Compare the other influencing factors in the actual parameter unit with the comparison groups in the database to obtain the comparison group whose similarity with the actual parameter unit is greater than a preset proportionality coefficient α; In addition, when there are two or more comparison groups with the actual parameter unit whose similarity is greater than the preset proportional coefficient α, the comparison group with the greatest similarity is selected as the comparison group finally confirmed; When there is no comparison group with an actual parameter unit whose similarity is greater than the preset proportional coefficient α, a comparison group with the greatest similarity is selected as the final confirmed comparison group or an alarm is issued for manual adjustment; Specifically, the other influencing factors in the actual parameter unit are compared with the positioning parameters in the steady-state comparison unit in the comparison group to obtain a comparison group whose average similarity is greater than a preset proportional coefficient α; The baseline curve corresponding to the determined comparison group is used as the baseline curve corresponding to the core factor; Then, according to the above method, the corresponding benchmark curves of each influencing factor when it is used as a core factor are obtained in turn; Step 6: According to the corresponding reference curve when the n influencing factors obtained in step 5 are used as core factors, the adjustment amount hi of each influencing factor and the adjusted value ci are obtained to adjust the desulfurization efficiency to the preset threshold value x2y under the current conditions; Where x2y is a preset safety value, which is greater than x1y.

[0021] The adjustment coefficient Ri corresponding to each influencing factor is calculated according to the formula Ri=θ1×hi / hbi+θ2×|ci-hbi| / hbi; Where hbi is the standard value corresponding to the influencing factor, that is, the set optimal working value; θ1 and θ2 are both preset proportional coefficients, and θ1+θ2=1; The influencing factor with the smallest corresponding adjustment coefficient Ri is selected, and the influencing factor is marked as the influencing factor to be adjusted.

[0022] In the seventh step, the controller sends an execution instruction to the execution unit, and the execution unit adjusts the regulator corresponding to the influence factor to be adjusted, and the adjustment amount of the influence factor to be adjusted is the corresponding hi.

[0023] The present invention classifies and analyzes the historical data of the desulfurization dust removal furnace to obtain the influence trend of various influencing factors on the desulfurization efficiency of the desulfurization dust removal furnace under different operating conditions, and on this basis, determines which influencing factor to adjust to adjust the desulfurization efficiency so that it meets the desulfurization requirements, and follows the principle of minimizing the impact on the current working system (that is, the influencing factors that have a significant impact on the desulfurization efficiency) and avoiding large-scale changes in a certain influencing factor, thereby ensuring the continuous stability of the entire desulfurization system and reducing the impact of a sudden change in a certain influencing factor on the stability of the system.

[0024] Embodiment 3 On the basis of the second embodiment, the influencing factors involved in the subsequent adjustment are also preliminarily screened in this embodiment, so as to avoid that some influencing factors with little influence on the desulfurization efficiency within a certain range are also involved in the subsequent processing, thereby increasing the data processing volume and having limited influence on the overall adjustment effect. Specifically, the following steps are included: In the fourth step, after obtaining the influence relationship curves of various influencing factors on desulfurization efficiency under different working conditions, each influence relationship curve is analyzed, specifically: For an influence relationship curve, obtain a curve with a corresponding calibration factor value within the range of d±γ×d, where γ is preferably between 0.05 and 0.15; In the curve within the range, several points are selected along the horizontal axis with equal differences, the vertical coordinates of these points are obtained, and then the standard deviation of the vertical coordinates of these points is calculated. When the standard deviation is less than or equal to a preset standard deviation threshold, it is considered that the corresponding influence relationship curve has a small impact on the desulfurization efficiency within the corresponding range. When the desulfurization efficiency is adjusted later, the influence factor corresponding to the influence relationship curve is not processed and pre-adjusted. This can reduce the pressure of data processing.

[0025] The above contents are merely examples and explanations of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the invention or exceed the scope defined by the claims, they shall all fall within the protection scope of the present invention.

Claims

1. An integrated intelligent control system for a circulating desulfurization and dust removal furnace, characterized in that: include: A sulfur dioxide concentration sensor is used to detect the sulfur dioxide concentration at the flue gas inlet and outlet of the desulfurization and dust removal furnace, and transmit the detection results to the controller; A database for storing data input by the sulfur dioxide concentration sensor and the controller; A controller, used for receiving real-time sulfur dioxide concentration information transmitted by the sulfur dioxide concentration sensor and data records in a memorial database; Record the changes of various factors affecting desulfurization efficiency over time; Then, the influence of each influencing factor on the desulfurization efficiency under different conditions is obtained; When the desulfurization efficiency of the circulating desulfurization dust removal furnace does not meet the requirements, obtain the real-time value of each influencing factor, and determine which influencing factor has the least impact on the overall stability of the circulating desulfurization dust removal furnace based on the real-time value of each influencing factor, the impact of each influencing factor on the desulfurization efficiency under current conditions and the current desulfurization efficiency, and adjust the influencing factor with the least impact on the overall stability of the circulating desulfurization dust removal furnace to make the desulfurization efficiency meet the requirements.

2. The integrated intelligent control system for a circulating desulfurization and dust removal furnace according to claim 1 is characterized in that: The method for the controller to adjust each influencing factor specifically comprises the following steps: The first step is to obtain the relationship curve a1 between desulfurization efficiency and time; The second step is to obtain each influencing factor and the relationship curve ki of each influencing factor and time, where i ranges from 1 to n, and n is the number of influencing factors; The third step is to divide the normal working time period into several comparison units, and each comparison unit corresponds to an equal duration; Get n corresponding relationship curves ki in a comparison unit; Eliminate the contrast units in which the relationship curve ki is unstable, and mark the remaining relationship units as stable contrast units; Step 4: Obtain all steady-state contrast units and store all steady-state contrast units in a database; The controller analyzes and processes the steady-state comparison units in the database to obtain the influence relationship curves of various influencing factors on the desulfurization efficiency under different working conditions, and marks these influence relationship curves as reference curves; Step 5: When the desulfurization efficiency is less than a preset threshold value x1y, where x1y is a preset desulfurization efficiency alarm value, the controller analyzes and obtains a reference curve corresponding to each influencing factor in the current state; Step 6: According to the corresponding reference curve when the n influencing factors obtained in step 5 are used as core factors, the adjustment amount hi of each influencing factor and the adjusted value ci are obtained to adjust the desulfurization efficiency to the preset threshold value x2y under the current conditions; Where x2y is a preset safety value, which is greater than x1y; The adjustment coefficient Ri corresponding to each influencing factor is calculated according to the formula Ri=θ1×hi / hbi+θ2×|ci-hbi| / hbi; Where hbi is the optimal working value corresponding to the setting of the impact factor; θ1 and θ2 are both preset proportional coefficients, and θ1+θ2=1; Select the influencing factor with the smallest corresponding adjustment coefficient Ri, and mark the influencing factor as the influencing factor to be adjusted; In the seventh step, the controller sends an execution instruction to the execution unit, and the execution unit adjusts the regulator corresponding to the influence factor to be adjusted, and the adjustment amount of the influence factor to be adjusted is the corresponding hi.

3. The integrated intelligent control system for a circulating desulfurization and dust removal furnace according to claim 2 is characterized in that: The method to obtain the steady-state contrast unit is: For a comparison unit, when the change rate of one or more relationship curves in the comparison unit is greater than the preset proportional coefficient, the comparison unit will not be further processed. On the contrary, when the change rates of all relationship curves are less than or equal to the preset proportional coefficient, the corresponding comparison unit will be marked as a steady-state comparison unit.

4. The integrated intelligent control system for a circulating desulfurization and dust removal furnace according to claim 3 is characterized in that: The calculation method of the rate of change of one of the relationship curves ki is: A number of coordinate points are obtained at equal time differences on the relationship curve ki, the ordinates corresponding to the coordinate points are obtained, and the standard deviation of the ordinates is further calculated, and the standard deviation is used as the change rate of the corresponding relationship curve ki.

5. The integrated intelligent control system for a circulating desulfurization and dust removal furnace according to claim 2 is characterized in that: In the fourth step, the method for obtaining the influence relationship of each influencing factor on the desulfurization efficiency under different working conditions is: Determine one impact factor as the calibration factor, and mark other impact factors except the calibration factor as positioning factors; Compare all positioning factors in each steady-state contrast unit. When the similarity between two steady-state contrast units is greater than a preset proportional coefficient α, the two steady-state contrast units are regarded as the same contrast group, that is, the similarity between any two steady-state contrast units in the same contrast group is greater than the preset proportional coefficient α. Obtain the calibration factor corresponding to each steady-state comparison unit in a comparison group and the desulfurization efficiency corresponding to the calibration factor, use the value of the calibration factor as the abscissa and the desulfurization efficiency as the ordinate, calibrate several points in the rectangular coordinate system, and fit to form a relationship curve between the calibration factor and the desulfurization efficiency corresponding to the comparison group; Then, the relationship curves between each calibration factor and the desulfurization efficiency are obtained in turn.

6. The integrated intelligent control system for a circulating desulfurization and dust removal furnace according to claim 2 is characterized in that: In the fifth step, the method for obtaining the relationship curve corresponding to each influencing factor in the current state is: Take one influencing factor as the core factor, obtain the actual collected values ​​of other influencing factors, and mark the combination of the core factor and the actual collected values ​​of other influencing factors as an actual parameter unit; Compare the other influencing factors in the actual parameter unit with the comparison groups in the database to obtain the comparison group whose similarity with the actual parameter unit is greater than a preset proportionality coefficient α; The baseline curve corresponding to the determined comparison group is used as the baseline curve corresponding to the core factor; then the baseline curves corresponding to other influencing factors when they are used as core factors are obtained in turn.

7. The integrated intelligent control system for a circulating desulfurization and dust removal furnace according to claim 6 is characterized in that: When obtaining the actual collected value, the average value of the influencing factor within the past preset time t is obtained as the actual collected value.

8. The integrated intelligent control system for a circulating desulfurization and dust removal furnace according to claim 6 is characterized in that: When there are two or more comparison groups with the actual parameter unit whose similarity is greater than the preset proportional coefficient α, the comparison group with the greatest similarity is selected as the comparison group finally confirmed; When there is no comparison group with an actual parameter unit whose similarity is greater than a preset proportional coefficient α, a comparison group with the greatest similarity is selected as the comparison group to be finally confirmed or an alarm is issued for manual adjustment.

9. The integrated intelligent control system for a circulating desulfurization and dust removal furnace according to claim 6 is characterized in that: When obtaining a comparison group whose similarity with the actual parameter unit is greater than a preset proportional coefficient α, the other influencing factors in the actual parameter unit are compared with the positioning parameters in the steady-state comparison unit in the comparison group to obtain a comparison group whose average similarity is greater than the preset proportional coefficient α.

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