CN107247994B - Fuzzy modeling method for desulfurization efficiency of tray tower desulfurization device - Google Patents
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- 238000006477 desulfuration reaction Methods 0.000 title claims abstract description 91
- 230000023556 desulfurization Effects 0.000 title claims abstract description 91
- 238000000034 method Methods 0.000 title claims abstract description 17
- 239000003546 flue gas Substances 0.000 claims abstract description 31
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 claims abstract description 24
- 238000010521 absorption reaction Methods 0.000 claims abstract description 18
- 239000007789 gas Substances 0.000 claims abstract description 18
- 238000012360 testing method Methods 0.000 claims abstract description 14
- 238000004088 simulation Methods 0.000 claims abstract description 11
- 239000011159 matrix material Substances 0.000 claims abstract description 5
- 238000010835 comparative analysis Methods 0.000 claims abstract description 3
- 230000006870 function Effects 0.000 claims description 6
- 238000004422 calculation algorithm Methods 0.000 claims description 4
- 238000012544 monitoring process Methods 0.000 claims description 4
- 239000002002 slurry Substances 0.000 claims description 3
- 238000001308 synthesis method Methods 0.000 claims description 3
- 230000005484 gravity Effects 0.000 claims description 2
- 238000004364 calculation method Methods 0.000 abstract description 4
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- 238000013139 quantization Methods 0.000 description 2
- 238000005507 spraying Methods 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 230000002745 absorbent Effects 0.000 description 1
- 239000002250 absorbent Substances 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
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- 238000012795 verification Methods 0.000 description 1
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Abstract
The invention discloses a fuzzy modeling method for desulfurization efficiency of a tray tower wet desulfurization device, which comprises the following steps: firstly, selecting the amount of flue gas and SO in inlet flue gas2The concentration, the liquid-gas ratio and the pH value of the absorption tower are used as input variables of the fuzzy model, and the desulfurization efficiency of the tray tower is selected as an output variable; selecting a triangular membership function, determining linguistic variable domains of input and output variables and fuzzy rules of a system, calculating a fuzzy relation matrix, resolving the fuzzy, and establishing a fuzzy model of the desulfurization efficiency; secondly, according to the amount of flue gas and SO in inlet flue gas2Correcting fuzzy rules according to the influence relationship of the concentration, the liquid-gas ratio and the pH value of the absorption tower on the desulfurization efficiency; finally, selecting multiple groups of data under normal operation conditions as check samples and multiple groups of data at other time intervals as test samples, quantizing the samples, carrying out desulfurization efficiency simulation output, and presetting parameters through comparative analysis; the method has high calculation precision and small software load, and can effectively predict and regulate the desulfurization efficiency of the system.
Description
Technical Field
The invention relates to a control method of a desulfurization system, in particular to a fuzzy modeling method of desulfurization efficiency of a tray tower wet desulfurization device.
Background
The hot tray tower desulfurization process adds one or more through-flow pore plate trays below the spraying layer or between the spraying layers of the absorption tower, flue gas enters the absorption tower and is evenly distributed on the cross section of the whole absorption tower through tray rectification, mass transfer is strengthened, the utilization rate of an absorbent is improved, the liquid-gas ratio is reduced, the desulfurization efficiency can reach more than 99%, and the flow and the power consumption of a circulating slurry pump are reduced. The method has the advantages of high efficiency, low energy consumption, stable operation, convenient transformation and the like. At present, tray tower desulfurization devices in China are applied more and more, and the tray tower desulfurization device has a good application prospect in the future.
The research of the existing tray tower desulfurization system mostly stays in the research of the tray device and the theoretical research of the desulfurization efficiency, for example, the flue gas flowing uniformity is enhanced by changing the opening area and the opening rate of the tray, and the research of online monitoring on the operation of the desulfurization system is less. In the actual operation process, the operation condition of the desulfurization system deteriorates frequently, and once the problem is not found in time, the desulfurization efficiency is not favorable for standard emission, and huge economic loss is caused to the whole plant, so the operation condition needs to be controlled within a certain range. In order to ensure the safe and continuous operation of the unit set, the establishment of an accurate wet flue gas desulfurization efficiency prediction model to guide the optimal operation of the desulfurization system has important significance. Meanwhile, the algorithm of the desulfurization efficiency simulation model is complex, and the mathematical modeling of a nonlinear system which is applied in practical engineering and is complex is difficult.
Disclosure of Invention
The purpose of the invention is as follows: in order to effectively predict the desulfurization efficiency of a tray tower desulfurization system, monitor the desulfurization efficiency in real time under the condition of complex change working conditions of a power plant and preset working condition parameters to prevent the deterioration of the operation conditions, the invention provides a fuzzy modeling method for the desulfurization efficiency of a tray tower wet desulfurization device.
The technical scheme is as follows: the fuzzy modeling method for the desulfurization efficiency of the tray tower wet desulfurization device comprises the following steps of:
(1) selecting the amount V of flue gas and SO in inlet flue gas according to the actual operation condition of a tray tower wet flue gas desulfurization device of a power plant2The concentration N, the liquid-gas ratio E and the pH value P of the absorption tower are used as input variables of the fuzzy model, and the desulfurization efficiency U of the tray tower is selected as an output variable;
(2) setting membership functions of input variables and output variables as triangular membership functions, and measuring the flue gas volume V and SO in inlet flue gas2The linguistic variables of the concentration N, the liquid-gas ratio E and the desulfurization efficiency U are defined as [ -N [)1，n1]The language variable domain of pH value of absorption tower is defined as [ -n [ ]2，n2]Wherein n is1And n2Are all positive integers; summarizing a control rule base of the fuzzy diagnosis system through long-term on-site tracking experiments and accumulation of a large amount of expert knowledge and desulfurization literature data(ii) a Carrying out fuzzy reasoning by adopting a Mamdani algorithm, selecting a centroid method to solve the fuzzy, and establishing a fuzzy model of the desulfurization efficiency;
(2a) flue gas volume V, inlet SO2The concentration N, the liquid-gas ratio E and the desulfurization efficiency U respectively take five fuzzy subsets of negative large, negative small, zero, positive small and positive large, and are represented as NB, NS, ZE, PS and PB; the pH value P of the absorption tower is represented as L, Z, H by taking three fuzzy subsets of small, medium and large respectively.
(2b) The fuzzy condition statement of the fuzzy rule is 'if A and B then C', each rule can establish a fuzzy relation RiThe fuzzy relation matrix R of the model is obtained by a CRI synthesis method according to the following calculation formula:
C＝(A×B)οR
μR(x，yz)＝m ax[μA(x)∩μB(y)∩μc(z)]
if the input e of the system is known0Corresponding to the fuzzy variable E, a fuzzy output variable U can be obtained:
U*＝E*οR
(3) on-line monitoring hour data is derived from a power plant tray tower desulfurization database, abnormal data caused by desulfurization shutdown and instrument fault reasons is eliminated, and the flue gas volume V in a tray tower desulfurization system and SO in inlet flue gas are explored2The influence relationship of the concentration N, the liquid-gas ratio E and the pH value P of the absorption tower on the desulfurization efficiency U.
(4) Selecting multiple groups of data under normal operation conditions as calibration samples, selecting multiple groups of data at other time intervals as test samples, quantizing the samples by using the fuzzy model in the step (2), respectively performing model test calibration on the calibration samples by writing a membership calculation program, and correcting the fuzzy rule base again to obtain the fuzzy rules of 125 desulfurization systems; and (4) carrying out desulfurization efficiency simulation output on the test sample by using the modified model, and presetting parameters through comparative analysis.
Has the advantages that: compared with the prior art, the invention has the following remarkable advantages: the invention can effectively predict and monitor the desulfurization efficiency of the tray tower desulfurization system in real time under the condition of complex change working conditions of the power plant, has high calculation speed and high precision, can perform pre-adjustment on working condition parameters in time, and is favorable for optimizing operation, saving energy and reducing consumption of the tray tower desulfurization system.
Drawings
FIG. 1 is a flow chart of a fuzzy modeling method of the present invention;
FIG. 2 is a diagram of a fuzzy model of desulfurization efficiency of the tray tower wet desulfurization device according to the present invention;
FIG. 3 is a graph comparing the simulated output of the desulfurization efficiency of the calibration sample with the actual value according to the present invention;
FIG. 4 is a graph comparing the desulfurization efficiency simulation output of the test sample with the actual value according to the present invention.
Detailed Description
The technical solution of the present invention will be further described in detail with reference to the following examples and accompanying drawings.
Taking a tray tower desulfurization system after transformation of a certain thermal power plant as an example, as shown in fig. 1, the fuzzy modeling method for the desulfurization efficiency of the tray tower wet desulfurization device specifically comprises the following steps:
(1) collecting the operation parameters of a tray tower wet desulphurization system of a power plant, and selecting the amount V of flue gas and the SO in inlet flue gas2The concentration N, the liquid-gas ratio E and the pH value P of the absorption tower are used as input variables of the fuzzy model, and the desulfurization efficiency U of the tray tower is used as an output variable, as shown in FIG. 2;
fuzzifying the data collected by the power plant, fuzzifying the operation parameters by using quantization factors, and defining the continuous domain as [ xL，xH]Quantized to integer set { -n, -n +1, …, -1,0,1, …, n-1, n }, quantization factor k:
wherein xLAnd xHRespectively taking the maximum value and the minimum value of the continuous domain, wherein n is a set integer, and respectively taking n as 2 and n as 4;
the element X in the continuum domain is then converted to the element X in the discrete theory domain by:
in the formula, < > represents that rounding operation is adopted for X, and X is a value obtained by quantizing actual data and is used as input data.
(2) Setting a membership function of the operation parameters (input variables) and the desulfurization efficiency (output variables) as a triangular membership function, and measuring the flue gas volume V and the SO in the inlet flue gas2The linguistic variables of the concentration N, the liquid-gas ratio E and the desulfurization efficiency U are defined as [ -4, 4]The linguistic variable domain of the pH value P of the absorption tower is defined as [ -2, 2](ii) a Through long-term field tracking experiments including model testing and verification (comparing and modifying results with actual power plant data), and accumulating a large amount of expert knowledge and desulfurization literature data, a fuzzy diagnosis system control rule base comprising 125 fuzzy rules is summarized;
carrying out fuzzy reasoning by adopting a Mamdani algorithm, solving the fuzzy by adopting a centroid (gravity center method), and establishing a fuzzy model of the desulfurization efficiency;
(2a) flue gas volume V, inlet SO2The concentration N, the liquid-gas ratio E and the desulfurization efficiency U respectively adopt five fuzzy subsets of negative large (NB), Negative Small (NS), Zero (ZE), Positive Small (PS) and positive large (PB); the pH value P of the absorption tower is respectively selected from three fuzzy subsets of small (L), medium (Z) and large (H);
(2b) the fuzzy conditional statement "if A and B then C" of fuzzy rule, each rule can establish a fuzzy relation RiThe fuzzy relation matrix R of the model is obtained by a CRI synthesis method according to the following calculation formula:
C＝(A×B)οR
μR(x，y，z)＝m ax[μA(x)∩μB(y)∩μc(z)]
if the input e of the system is known0Corresponding to the fuzzy variable E, a fuzzy output variable U can be obtained:
U*＝E*οR
(3) abnormal data caused by desulfurization shutdown and instrument fault reasons are eliminated, and the flue gas volume in the tray tower desulfurization system and the SO in the inlet flue gas are explored2The influence relationship of the concentration, the liquid-gas ratio and the pH value of the absorption tower on the desulfurization efficiency is as follows: flue gas volume and inlet SO of tray tower desulfurization system2The influence relationship of the concentration, the liquid-gas ratio and the pH value of the absorption tower on the desulfurization efficiency of the system is as follows: under the condition that other parameters are not changed, when the flue gas volume and the concentration of the inlet SO2 are increased within a certain range, the early-stage increase of the desulfurization efficiency is slower, the desulfurization efficiency is basically kept unchanged, and the desulfurization efficiency is gradually increased in the later stage. However, when both of them exceed a certain range, the desulfurization efficiency will start to decrease. When the liquid-gas ratio is increased, the desulfurization efficiency is also increased, and when the liquid-gas ratio is increased to about 14, the desulfurization efficiency is not increased any more; the desulfurization efficiency is best when the pH value of the slurry is controlled to be between 5.0 and 6.0; the fuzzy rule base is modified accordingly.
(4) Tray tower desulfurization monitoring data are used as a test sample and a test sample to carry out simulation test;
(4a) selecting 395 groups of data in normal operation conditions as a check sample, quantizing the continuous domain of the sample by using the fuzzy model established in the step (2), writing a membership calculation program, carrying out desulfurization efficiency simulation output on the check sample, revising the fuzzy rule base again, further improving the accuracy of the model, and obtaining 125 fuzzy rules as shown in table 1; FIG. 3 is a diagram comparing the corrected desulfurization efficiency simulation output of the calibration sample with the actual value;
TABLE 1
(4b) And carrying out desulfurization efficiency simulation output on 336 groups of test samples in other time periods by using the established fuzzy model, and presetting operating parameters by analyzing simulation results, wherein FIG. 4 is a comparison graph of desulfurization efficiency simulation output and actual values of the test samples. The result shows that the error is small and the prediction accuracy is high.
As noted above, while the present invention has been described and illustrated with reference to certain preferred embodiments, it is not to be construed as limited thereto. Various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (1)
1. A fuzzy modeling method for desulfurization efficiency of a tray tower wet desulfurization device is characterized by comprising the following steps:
(1) selecting the amount V of flue gas and SO in inlet flue gas according to the actual operation condition of a tray tower wet desulphurization device of a power plant2The concentration N, the liquid-gas ratio E and the pH value P of the absorption tower are used as input variables of the fuzzy model, and the desulfurization efficiency U of the tray tower is selected as an output variable;
(2) setting membership functions of input variables and output variables as triangular membership functions, and measuring the flue gas volume V and SO in inlet flue gas2The linguistic variables of the concentration N, the liquid-gas ratio E and the desulfurization efficiency U are defined as [ -N [)1，n1]Linguistic variables of pH value P of absorption towerIs defined as [ -n ]2，n2]，n1And n2Are all positive integers; determining fuzzy rules of a tray tower desulfurization system; calculating a fuzzy relation matrix; carrying out fuzzy reasoning by adopting a Mamdani algorithm, carrying out ambiguity resolution by adopting a gravity center method and establishing a fuzzy model of desulfurization efficiency;
the flue gas volume V and the inlet SO2The concentration N, the liquid-gas ratio E and the desulfurization efficiency U respectively take five fuzzy subsets of negative large, negative small, zero, positive small and positive large, and are represented as NB, NS, ZE, PS and PB; the pH value P of the absorption tower is represented as L, Z, H by taking three fuzzy subsets of small, medium and large respectively;
the fuzzy condition statement of the fuzzy rule is 'if A and B then C', and a fuzzy relation matrix R of the model is obtained by adopting a CRI synthesis method:
μR(x，y，z)＝max[μA(x)∩μB(y)∩μC(Z)
if the input e of the system is known0Corresponding to the fuzzy variable E, E ═ a × B, the fuzzy output variable U is obtained:
(3) on-line monitoring hour data are derived from a tray tower desulfurization database of the power plant according to the flue gas volume V in a tray tower desulfurization system and the SO in inlet flue gas2Concentration N, liquid-gas ratio E, pH value P of absorption towerCorrecting the fuzzy rule according to the influence relation of the desulfurization efficiency; the pH value of the slurry of the tray tower desulfurization system is controlled to be 5.0-6.0;
(4) selecting multiple groups of data under normal operation conditions as calibration samples, selecting multiple groups of data at other time intervals as test samples, quantizing the samples by using the fuzzy model in the step (2), respectively performing model test calibration on the calibration samples by writing a membership calculation program, and correcting the fuzzy rule base again to obtain the fuzzy rules of 125 desulfurization systems; and (4) carrying out desulfurization efficiency simulation output on the test sample by using the modified model, and presetting parameters through comparative analysis.
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| CN113505497B (en) * | 2021-08-18 | 2023-05-12 | 山东建筑大学 | Method and system for monitoring slurry quality of wet flue gas desulfurization absorption tower |
| CN114911169A (en) * | 2022-06-13 | 2022-08-16 | 大唐环境产业集团股份有限公司 | Method, system, equipment and medium for optimizing desulfurization synergistic device |
| CN115970476B (en) * | 2023-01-16 | 2024-06-11 | 西安热工研究院有限公司 | Automatic slurry supply control method for desulfurization island based on DCS control system |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0822831A (en) * | 1994-07-08 | 1996-01-23 | Mitsubishi Electric Corp | Fuel cell power generating device |
| EP1190758A1 (en) * | 2000-09-25 | 2002-03-27 | Siemens Westinghouse Power Corporation | Desulphurization of fuel gas for fuel cell systems using sulphur separating membranes |
| CN102269973A (en) * | 2011-04-06 | 2011-12-07 | 中南大学 | Method for vaguely predicting, compensating and controlling tower top temperature during desulfurization of sintering flue gas |
| CN103771553A (en) * | 2014-01-08 | 2014-05-07 | 大连森雅能源环保设备有限公司 | Self-adaptive fuzzy control method for evaporation treatment of wet-method smoke desulphurization waste water |
| CN105116855A (en) * | 2015-07-22 | 2015-12-02 | 华北电力大学 | Optimal control method for flue gas circulating fluidized bed desulphurization |
| CN106390703A (en) * | 2016-05-26 | 2017-02-15 | 河北工程大学 | System for controlling desulphurization of fire coal by using wireless sensor network and red mud |
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Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0822831A (en) * | 1994-07-08 | 1996-01-23 | Mitsubishi Electric Corp | Fuel cell power generating device |
| EP1190758A1 (en) * | 2000-09-25 | 2002-03-27 | Siemens Westinghouse Power Corporation | Desulphurization of fuel gas for fuel cell systems using sulphur separating membranes |
| CN102269973A (en) * | 2011-04-06 | 2011-12-07 | 中南大学 | Method for vaguely predicting, compensating and controlling tower top temperature during desulfurization of sintering flue gas |
| CN103771553A (en) * | 2014-01-08 | 2014-05-07 | 大连森雅能源环保设备有限公司 | Self-adaptive fuzzy control method for evaporation treatment of wet-method smoke desulphurization waste water |
| CN105116855A (en) * | 2015-07-22 | 2015-12-02 | 华北电力大学 | Optimal control method for flue gas circulating fluidized bed desulphurization |
| CN106390703A (en) * | 2016-05-26 | 2017-02-15 | 河北工程大学 | System for controlling desulphurization of fire coal by using wireless sensor network and red mud |
Non-Patent Citations (2)
| Title |
|---|
| 基于模糊控制的火电厂湿法烟气脱硫系统;李栋等;《测控技术》;20131231;第32卷(第1期);第1，3部分 * |
| 火力发电厂湿法烟气脱硫自动控制系统的设计;丁德群;《中国优秀硕士论文全文数据库》;20130715;第21-26页 * |
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Granted publication date: 20201225 |
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| CF01 | Termination of patent right due to non-payment of annual fee |