CN107247994B - Fuzzy modeling method for desulfurization efficiency of tray tower desulfurization device - Google Patents

Abstract

_The invention discloses a fuzzy modeling method for the desulfurization efficiency of a tray tower wet desulfurization device. Select the input variable of the tray tower as the output variable; select the triangular membership function to determine the linguistic variable universe of each input and output variable and the fuzzy rules of the system, calculate the fuzzy relationship matrix, solve the fuzzy and establish the fuzzy model of the desulfurization efficiency; Secondly, according to the influence of flue gas volume, SO ₂ concentration in inlet flue gas, liquid-gas ratio, and pH value of absorption tower on desulfurization efficiency, the fuzzy rules are revised; Multiple sets of data in a period are used as test samples, the samples are quantified and desulfurization efficiency simulation output is performed, and parameters are pre-adjusted through comparative analysis; the invention has high calculation accuracy and small software load, and can effectively predict and control the desulfurization efficiency of the system.

Description

A fuzzy modeling method for desulfurization efficiency of tray tower desulfurization device

technical field

The invention relates to a desulfurization system control method, in particular to a fuzzy modeling method for desulfurization efficiency of a tray tower wet desulfurization device.

Background technique

In the hot tray tower desulfurization process, one or more through-flow orifice trays are added under the spray layer of the absorption tower or between the spray layers. Improve the quality of the absorbent and improve the utilization rate of the absorbent, reduce the liquid-gas ratio, the desulfurization efficiency can reach more than 99%, and at the same time reduce the flow rate and power consumption of the circulating slurry pump. It has the advantages of high efficiency, low energy consumption, stable operation, and convenient transformation. At present, there are more and more applications of domestic tray tower desulfurization devices, and it has a good application prospect in the future.

Most of the existing research on tray tower desulfurization systems remain in the study of tray devices and theoretical research on desulfurization efficiency. Less research. In the actual operation process, the operating conditions of the desulfurization system often deteriorate. Once the problem is not found in time, it will be unfavorable for the desulfurization efficiency to reach the standard emission and cause huge economic losses to the whole plant. Therefore, the operating conditions must be controlled within a certain range. In order to ensure the safe and continuous operation of the unit unit, it is of great significance to establish an accurate wet flue gas desulfurization efficiency prediction model to guide the optimal operation of the desulfurization system. At the same time, the algorithm of the simulation model of desulfurization efficiency is complex, and it is also difficult to mathematically model complex nonlinear systems in practical engineering applications.

SUMMARY OF THE INVENTION

Purpose of the invention: In order to effectively predict the desulfurization efficiency of the tray tower desulfurization system, monitor the desulfurization efficiency in real time under the complex changing working conditions of the power plant, and pre-adjust the working condition parameters to prevent the deterioration of the operating conditions, the present invention provides a tray tower. Fuzzy modeling method for desulfurization efficiency of wet desulfurization unit.

Technical solution: The fuzzy modeling method for the desulfurization efficiency of a tray tower wet desulfurization device according to the present invention includes the following steps:

(1) According to the actual operation of the tray tower wet flue gas desulfurization device in the power plant, the flue gas volume V, the SO ₂ concentration N in the inlet flue gas, the liquid-gas ratio E, and the pH value P of the absorption tower are selected as the input variables of the fuzzy model , select the desulfurization efficiency U of the tray tower as the output variable;

(2) The membership functions of the input variables and output variables are set as triangular membership functions, and the linguistic variables of the flue gas volume V, the SO ₂ concentration N in the inlet flue gas, the liquid-gas ratio E and the desulfurization efficiency U are set as [- n ₁ , n ₁ ], the language variable domain of pH value of the absorption tower is set as [-n ₂ , n ₂ ], where n ₁ and n ₂ are both positive integers; through long-term field tracking experiments and accumulation of a large number of expert knowledge And desulfurization literature, summed up the control rule base of the fuzzy diagnosis system; used Mamdani algorithm to carry out fuzzy reasoning, selected the centroid method to defuzzify, and established the fuzzy model of desulfurization efficiency;

(2a) Five fuzzy subsets of flue gas volume V, inlet SO ₂ concentration N, liquid-gas ratio E and desulfurization efficiency U are taken as negative large, negative small, zero, positive small, and positive large, respectively, denoted as NB, NS, ZE , PS, PB; the pH value P of the absorption tower is taken as three fuzzy subsets of small, medium and large respectively, expressed as L, Z, H.

(2b) The fuzzy conditional statement of the fuzzy rule is "if A and B then C", each rule can establish a fuzzy relation R _i , and the CRI synthesis method is used to obtain the fuzzy relation matrix R of the model. The calculation formula is as follows:

C=(A×B)οR

μ _R (x, yz)=m ax[μ _A (x)∩μ _B (y)∩μ _c (z)]

If the input e ₀ of the known system corresponds to the fuzzy variable E*, the fuzzy output variable U* can be obtained:

U*=E*οR

(3) Export the online monitoring hourly data from the tray tower desulfurization database of the power plant, exclude abnormal data caused by desulfurization outages and instrument failures, and explore the flue gas volume V in the tray tower desulfurization system, the SO ₂ concentration N in the inlet flue gas, liquid concentration The relationship between the gas ratio E and the pH value P of the absorption tower on the desulfurization efficiency U.

(4) Select multiple sets of data in normal operating conditions as verification samples, and multiple sets of data in other periods as test samples, using the fuzzy model described in step (2) to quantify the samples by writing a membership calculation program, respectively. Model test and verify the verification samples, and amend the fuzzy rule base again to obtain 125 fuzzy rules of the desulfurization system; use the modified model to simulate the desulfurization efficiency of the test samples, and pre-adjust the parameters through comparative analysis.

Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: the present invention can effectively predict and monitor the desulfurization efficiency of the tray tower desulfurization system under the complex changing working conditions of the power plant, with fast calculation speed and high precision , which can pre-adjust the working parameters in time, which is helpful to optimize the operation of the tray tower desulfurization system and save energy and reduce consumption.

Description of drawings

Fig. 1 is the flow chart of the fuzzy modeling method of the present invention;

Fig. 2 is the fuzzy model structure diagram of the desulfurization efficiency of the tray tower wet desulfurization device of the present invention;

Fig. 3 is the comparison diagram of the simulation output of the desulfurization efficiency of the verification sample of the present invention and the actual value;

FIG. 4 is a comparison diagram of the simulation output of the desulfurization efficiency of the test sample of the present invention and the actual value.

Detailed ways

The technical solutions of the present invention will be described in further detail below with reference to the embodiments and the accompanying drawings.

Taking the reformed tray tower desulfurization system of a thermal power plant as an example, as shown in Figure 1, a fuzzy modeling method for the desulfurization efficiency of a tray tower wet desulfurization device of the present invention specifically includes the following steps:

(1) Collect the operating parameters of the wet desulfurization system of the tray tower in the power plant, select the flue gas volume V, the SO ₂ concentration N in the inlet flue gas, the liquid-gas ratio E, and the pH value P of the absorption tower as the input variables of the fuzzy model. The desulfurization efficiency U of the tower is the output variable, as shown in Figure 2;

The data collected by the power plant is fuzzified, the operating parameters are fuzzified by the quantization factor, and the continuous universe [x _L , x _H ] is quantized into a set of integers {-n,-n+1,…,-1 ,0,1,…,n-1,n}, then the quantization factor k:

Wherein x _L and x _H are the maximum and minimum values of the continuous domain respectively, n is a set integer, and the present invention takes n=2 and n=4 respectively;

Then convert the element x in the continuous domain to the element X in the discrete universe by the following formula:

In the formula, <> represents the rounding method for X, and X is the quantized value of the actual data, which is used as the input data.

( ₂ ) Set the membership functions of operating parameters (input variables) and desulfurization efficiency (output variables) as triangular membership functions. The linguistic variable universe is set as [-4, 4], and the linguistic variable universe of the pH value P of the absorption tower is set as [-2, 2]; Power plant data comparison and modification), and accumulated a lot of expert knowledge and desulfurization literature, summed up a fuzzy diagnosis system control rule base including 125 fuzzy rules;

Mamdani algorithm is used for fuzzy reasoning, centroid (center of gravity method) is used to solve the fuzzy, and the fuzzy model of desulfurization efficiency is established;

(2a) The flue gas volume V, the inlet SO ₂ concentration N, the liquid-gas ratio E and the desulfurization efficiency U are taken as negative large (NB), negative small (NS), zero (ZE), positive small (PS), and positive large (PB), respectively. ) five fuzzy subsets; the pH value P of the absorption tower takes three fuzzy subsets of small (L), medium (Z) and large (H) respectively;

(2b) The fuzzy conditional statement "if A and B then C" of the fuzzy rules, each rule can establish a fuzzy relationship R _i , and the CRI synthesis method is used to obtain the fuzzy relationship matrix R of the model. The calculation formula is as follows:

C=(A×B)οR

μ _R (x, y, z)=m ax[μ _A (x)∩μ _B (y)∩μ _c (z)]

If the input e ₀ of the known system corresponds to the fuzzy variable E*, the fuzzy output variable U* can be obtained:

U*=E*οR

(3) Exclude the abnormal data caused by desulfurization shutdown and instrument failure, and explore the influence of flue gas volume in the tray tower desulfurization system, SO ₂ concentration in the inlet flue gas, liquid-gas ratio, and pH value of the absorption tower on the desulfurization efficiency: The effect of the flue gas volume, inlet SO ₂ concentration, liquid-gas ratio, and absorption tower pH on the desulfurization efficiency of the tray tower desulfurization system is as follows: when other parameters remain unchanged, the flue gas volume and the inlet SO 2 concentration are When it increases within a certain range, the desulfurization efficiency increases slowly in the early stage, basically remains unchanged, and gradually increases in the later stage. But when the two exceed a certain range, the desulfurization efficiency will begin to decline. When the liquid-gas ratio increases, the desulfurization efficiency also increases. When the liquid-gas ratio increases to about 14, the desulfurization efficiency no longer increases; the desulfurization efficiency is the best when the pH of the slurry is controlled between 5.0 and 6.0; accordingly, the fuzzy rule library is corrected. .

(4) The monitoring data of tray tower desulfurization are used as inspection samples and test samples for simulation test;

(4a) Select 395 sets of data in normal operating conditions as verification samples, use the fuzzy model established in step (2) to quantify the continuous domain of the samples, write a membership calculation program, and simulate the desulfurization efficiency output for the verification samples , revise the fuzzy rule base again to further improve the accuracy of the model, and obtain 125 fuzzy rules, as shown in Table 1; Figure 3 is the comparison between the simulated output of the corrected sample desulfurization efficiency and the actual value;

Table 1

(4b) Using the established fuzzy model, the simulation output of desulfurization efficiency is carried out for 336 groups of test samples in other periods, and the operating parameters are pre-adjusted through the analysis of the simulation results. The results show that the error is small and the prediction accuracy is high.

As mentioned above, although the present invention has been described and illustrated with reference to specific preferred embodiments, this should not be construed as limiting the invention itself. Various changes in form and details may be made therein without departing from the spirit and scope of the present invention as defined by the appended claims.

Claims (1)

1. the fuzzy modeling method of the desulfurization efficiency of a tray tower wet desulfurization device, is characterized in that, comprises the steps: (1) According to the actual operation of the tray tower wet desulfurization device in the power plant, the flue gas volume V, the SO ₂ concentration N in the inlet flue gas, the liquid-gas ratio E, and the pH value P of the absorption tower are selected as the input variables of the fuzzy model. The desulfurization efficiency U of the tray tower is used as the output variable; (2) The membership functions of the input variables and output variables are set as triangular membership functions, and the linguistic variables of the flue gas volume V, the SO ₂ concentration N in the inlet flue gas, the liquid-gas ratio E and the desulfurization efficiency U are set as [- n ₁ , n ₁ ], the language variable domain of the pH value P of the absorption tower is set as [-n ₂ , n ₂ ], n ₁ and n ₂ are both positive integers; determine the fuzzy rules of the tray tower desulfurization system; calculate the fuzzy Relation matrix; Mamdani algorithm is used for fuzzy reasoning, gravity center method is used for defuzzification and a fuzzy model of desulfurization efficiency is established; The flue gas volume V, the inlet SO ₂ 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, which are expressed as NB, NS, ZE, PS, PB; the pH value P of the absorption tower is taken as three fuzzy subsets of small, medium and large respectively, expressed as L, Z, H; The fuzzy conditional statement of the fuzzy rule is "if A and B then C", and the CRI synthesis method is adopted to obtain the fuzzy relation matrix R of the model:

μR(x,y,z)=max[ _μA (x) _∩μB (y) _∩μC ( _Z )

If the input e ₀ of the known system corresponds to the fuzzy variable E*, E*=A*×B*, then the fuzzy output variable U* is obtained:

(3) Export the online monitoring hour data from the tray tower desulfurization database of the power plant, according to the flue gas volume V in the tray tower desulfurization system, the SO ₂ concentration N in the inlet flue gas, the liquid-gas ratio E, and the pH value P of the absorption tower to the desulfurization efficiency. The influence relationship of the above-mentioned fuzzy rules is corrected; the pH of the slurry in the tray tower desulfurization system is controlled at 5.0-6.0; (4) Select multiple sets of data in normal operating conditions as verification samples, and multiple sets of data in other periods as test samples, using the fuzzy model described in step (2) to quantify the samples by writing a membership calculation program, respectively. Model test and verify the verification samples, and amend the fuzzy rule base again to obtain 125 fuzzy rules of the desulfurization system; use the modified model to simulate the desulfurization efficiency of the test samples, and pre-adjust the parameters through comparative analysis.

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