Performance evaluation method for limestone wet desulphurization slurry of coal-fired power plant

Abstract

The invention discloses a performance evaluation method for limestone wet desulphurization slurry of a coal-fired power plant, which comprises the following steps: (1) selecting proper evaluation indexes which can comprehensively reflect the performance of the desulphurization slurry and the desulphurization efficiency of the coal-fired power plant, and the core indexes comprise oxidation reduction potential (ORP), pH value, slurry density and desulphurization efficiency; (2) evaluation grades are determined, and evaluation results are divided into an excellent grade, a good grade, a medium grade and a poor grade; and (3) constructing a membership function, using a triangular membership function or a trapezoidal membership function for each evaluation index, and converting data into a membership value to facilitate comprehensive evaluation. And (4) calculating the weight of each index by using an entropy weight method, and calculating the comprehensive membership degree by using a weighted geometric averaging method. And (5) determining a final evaluation grade according to the comprehensive membership degree. The performance of the desulfurization slurry can be comprehensively evaluated, decision support is provided for stable operation of a desulfurization system of a coal-fired power plant, and field operators are helped to perform optimization operation of the desulfurization system.

Description

Technical Field

[0001] The invention relates to the technical field of coal-fired power plant desulfurization, in particular to a performance evaluation method for limestone wet desulfurization slurry in a coal-fired power plant. Technical Background

[0002] With the continuous improvement of environmental protection requirements, the desulfurization technology of coal-fired power plants has become particularly important. Limestone-gypsum wet desulfurization technology (FGD) is widely used in coal-fired power plants. This technology achieves desulfurization by reacting SO2 with limestone in the slurry to generate gypsum in the desulfurization tower. However, many problems will occur during the operation of the desulfurization system, such as the deterioration of the quality of the desulfurization slurry, which is manifested by the appearance of a large amount of foam in the slurry, the accumulation of suspended matter on the surface of the slurry, and the overflow of black liquor. These problems will cause the SO2 absorption reaction process to fail to proceed normally, affecting the desulfurization efficiency. At the same time, the impurities in the slurry cannot be discharged normally, and gradually accumulate, which will further deteriorate the quality of the slurry and form a vicious circle. Since the by-products (such as gypsum) generated during the desulfurization process may be deposited on the surface of the equipment, scaling and clogging will occur in the inner wall of the desulfurization tower, the spray layer nozzle, and the circulating pump filter. This will not only increase the maintenance cost of the system, but also lead to a decrease in the operating efficiency of the equipment, and even cause equipment failures, affecting the normal operation of the power plant. Problems with the desulfurization system will not only reduce desulfurization efficiency and pollute the environment, but also increase the operation and maintenance costs of the power plant. Scaling and blockage problems require frequent cleaning and repair, which increases maintenance costs and affects economic benefits.

[0003] In response to these problems, an intelligent evaluation method is urgently needed to comprehensively improve the performance and operating efficiency of the desulfurization system through comprehensive multivariable evaluation and real-time monitoring. Through intelligent analysis and dynamic adjustment, a comprehensive and accurate evaluation and optimization of the desulfurization slurry performance can be achieved, thereby ensuring the efficient and stable operation of the desulfurization system and further improving the environmental protection level and economic benefits of coal-fired power plants.

[0004] Patent No. 202210450760.7 provides a method and system for quantitatively evaluating the quality of desulfurization slurry in a wet desulfurization process. In the wet desulfurization process, this method uses the desulfurization slurry quality evaluation index determination model to determine the desulfurization slurry quality evaluation index in the wet desulfurization process based on the desulfurization efficiency of the desulfurization equipment and the desulfurization operation status parameters that can be obtained in real time. Patent No. 202210968827.6 provides a method for evaluating the quality of limestone-gypsum wet flue gas desulfurization slurry, classifies the quality indicators of desulfurization tower slurry, and realizes a dimensionality reduction description of the quality indicators of desulfurization tower slurry. According to the project needs, the slurry quality grade with a relative deviation range is set to evaluate the quality of the limestone-gypsum wet flue gas desulfurization slurry.

[0005] However, the existing methods and technologies have the following disadvantages: Relying on a single indicator to evaluate the quality of desulfurization slurry may not fully reflect the dynamic changes and diversity of the system. Relying on laboratory analysis and offline data processing, it is difficult to reflect the state changes of desulfurization slurry in a timely manner. A large amount of historical operation data is required to establish and verify the model, which may cause problems when the data is incomplete or inaccurate. The classification and dimensionality reduction description is complex, and the desulfurization slurry quality indicators need to be classified and described in dimensionality reduction. The process is complicated and may affect the accuracy of the evaluation results. It is difficult to set the target value. It is necessary to set the various performance characterization parameters of the ideal desulfurization slurry as the target value. It may be difficult and inaccurate to determine these target values ​​in actual operation. There is a lack of effective data association and comprehensive evaluation methods between various monitoring indicators, and it is difficult to directly reflect the activity state of the slurry through existing monitoring data. The relative deviation calculation is complex. The calculation of relative deviation involves multiple complex parameters and formulas, which is difficult to operate in actual applications. Summary of the invention

[0006] The present invention provides a method for evaluating the performance of desulfurization slurry in a coal-fired power plant based on fuzzy mathematics evaluation and model construction using Python programming. The method adopts fuzzy mathematics and entropy weight method to comprehensively evaluate multiple key indicators, and can comprehensively reflect the performance of the desulfurization slurry. Combining the entropy weight method and the weighted geometric mean method, intelligent analysis and evaluation are performed through membership function and comprehensive membership, thereby improving the accuracy and reliability of the evaluation. Through real-time data acquisition and processing, real-time evaluation of the performance of the desulfurization slurry is achieved, and the system operation status is timely reflected and adjusted immediately. The method provides evaluation grades and optimization suggestions, and can dynamically adjust the operation of the desulfurization system according to the actual operation conditions, thereby improving the stability and efficiency of the system. Model construction and calculation are realized through Python programming, which is easy to operate and suitable for field application and real-time monitoring. Through the above-mentioned means, a comprehensive evaluation of the desulfurization performance is achieved by comprehensively analyzing multiple key indicators, providing a decision-making means for the stable operation of the desulfurization system of a coal-fired power plant.

[0007] A method for evaluating the performance of limestone wet desulfurization slurry in a coal-fired power plant comprises the following steps:

[0008] (1) selecting evaluation indicators, wherein the evaluation indicators include oxidation-reduction potential (ORP), pH value, slurry density and desulfurization efficiency;

[0009] (2) Evaluate each evaluation index by setting a membership function, wherein the membership function includes a triangular membership function, a trapezoidal membership function and a growing Sigmoid function to convert the data into a membership value;

[0010] (3) Use the entropy weight method to calculate the weight of each evaluation index;

[0011] (4) Apply the weighted geometric mean method to calculate the comprehensive membership based on the membership value and weight;

[0012] (5) The evaluation grade of the desulfurization slurry performance is determined based on the comprehensive membership degree, and the evaluation grades are divided into excellent, good, medium and poor.

[0013] The ORP and slurry density membership function uses a trapezoidal function, with left and right boundaries being the minimum and maximum values ​​of the data set, and the specific parameter setting interval of the optimal area being 20%-30% of the data; or being set based on historical operating data and theoretical analysis to ensure the accuracy and practicality of the evaluation results.

[0014] The entropy weight method comprises the following steps:

[0015] a. Standardize the data of each indicator to eliminate the impact of dimension and data range differences;

[0016] b. Calculate the entropy value of each indicator data to evaluate the amount of information it provides;

[0017] c. Calculate the weight of each index according to the entropy value, ORP = x1; pH = x2; slurry density = x3; desulfurization efficiency = x4;

[0018] The weighted geometric mean method determines the comprehensive membership by calculating the weighted geometric mean of the membership of each indicator. A comprehensive membership > 0.75 is "excellent"; 0.5 < comprehensive membership < 0.75 is "good"; 0.25 < comprehensive membership < 0.5 is "medium"; and a comprehensive membership < 0.25 is "poor".

[0019] Specific:

[0020] 1. Select evaluation indicators;

[0021] The first step of the evaluation model is to select appropriate evaluation indicators. These indicators should be able to fully reflect the performance of the desulfurization slurry and the desulfurization efficiency of the coal-fired power plant. The core indicators include:

[0022] ORP (oxidation-reduction potential): This indicator measures the state of the redox reaction in the desulfurization system and is a key parameter in the desulfurization process. Changes in the ORP value directly affect the oxidation reaction rate and efficiency of SO2. A higher ORP value indicates that the redox reaction is more fully carried out, thereby improving the desulfurization efficiency. pH value: pH value is an important factor affecting the chemical balance of the desulfurization reaction. Limestone reacts with SO2 in an alkaline environment to produce gypsum. A low pH value will hinder the reaction process, and vice versa, it may lead to excessive desulfurization or incomplete reaction. Therefore, maintaining a suitable pH range is crucial to optimizing desulfurization efficiency.

[0023] Slurry density: Slurry density reflects the amount of solid content in the slurry. Too high a density will make the slurry viscous and have poor fluidity, affecting the desulfurization effect; too low a density may lead to a decrease in the adsorption and reaction capacity of the slurry. Therefore, slurry density is a key factor in evaluating the processing capacity and stability of the desulfurization system.

[0024] Desulfurization efficiency: Desulfurization efficiency is a direct indicator of the performance of the desulfurization system, usually evaluated by the removal rate of SO2 in the flue gas. High desulfurization efficiency means that the system can efficiently remove SO2, reflecting the good operation status of the desulfurization device. The desulfurization efficiency is generally calculated by the difference in SO2 concentration between the inlet and outlet.

[0025] 2. Determine the evaluation level;

[0026] Excellent: The desulfurization effect is the best, all evaluation indicators are in the best working state, the desulfurization system operates stably, the SO2 removal rate is high, the equipment load is moderate, and it can maintain efficient operation for a long time.

[0027] Good: The desulfurization effect is good, the desulfurization system operates stably, and some indicators fluctuate slightly, but it will not affect the overall efficiency, and there is still room for improvement.

[0028] Medium: The desulfurization effect is average. There are some unstable operating factors. It may be that the desulfurization efficiency does not meet expectations due to problems with equipment or slurry performance. It is necessary to improve operations or optimize management.

[0029] Poor: The desulfurization effect is poor, there are serious problems in the desulfurization system, and the SO2 removal rate is low. It may be due to poor slurry quality or equipment failure. Improvement and adjustment are urgently needed to avoid environmental pollution and equipment damage.

[0030] 3. Construct membership function;

[0031] ORP, pH, slurry density: These indicators use triangular membership function or trapezoidal membership function, set a moderate optimal value range, and the membership will be reduced if it is lower or higher. The higher the desulfurization efficiency, the better, and a monotonically increasing function should be used. This data selects the three-month operation data of a coal-fired power plant as an example.

[0032] (1) pH value of absorption tower

[0033] The ideal range for pH value setting is 4.5 to 5.5. The closer the value is to 5.0, the higher the membership. This function defines a range in which the membership increases from 0 to 1 and then decreases back to 0, forming a triangle. The calculation range is: a = 4.5, b = 5.0, c = 5.5, and the formula is (2).

[0034] Function type: Triangle.

[0035]

[0036] (2) ORP value and slurry density

[0037] The ideal ORP setting range is 100 to 100 mV, and the central range is 200 to 300 mV; the ideal slurry density is set to 1040 to 1180 kg / m 3 , of which 1070 to 1120 kg / m 3 is the most ideal range. In the ideal interval, the membership degree remains at 1, and gradually increases from or to 1 or decreases from 1 to 0 in the acceptance interval. The ORP setting calculation range is: a = 100, b = 200, c = 300, d = 400. The slurry density setting calculation range is: a = 1040, b = 1070, c = 1120, d = 1180, and the formula is (5).

[0038]

[0039] Function type: Trapezoidal.

[0040] (3) Desulfurization efficiency

[0041] S-shaped increasing membership function (smf): designed to describe parameters that gradually reach the optimal state, such as desulfurization efficiency, with the goal of being as close to 100% as possible. The function smoothly transitions from 0 to 1 within a specified range. The S-shaped function is determined by parameters a and c: the positive and negative signs of parameter a determine whether the S-shaped membership function opens to the left or right, which is used to express the concept of "positive large" or "negative large". Calculation range: X min =96,X max =100, the formula is (7).

[0042]

[0043] Function type: S-type or exponential type with monotonically increasing right side.

[0044] 4. Calculation of weight and comprehensive membership;

[0045] Weight determination method:

[0046] Step 1: Calculate weights (entropy weight method)

[0047] The entropy weight method is an objective data-driven method used to determine the weight of each indicator (or variable) based on data variability. In multi-indicator decision analysis, the entropy weight method evaluates the amount of effective information provided by each indicator by calculating the information entropy of each indicator. The lower the uncertainty of the indicator (i.e., the greater the data variability), the greater its weight.

[0048] The following are the specific steps and usage of the entropy weight method:

[0049] (1) Data standardization

[0050] First, the data of each indicator needs to be standardized to eliminate the impact of the dimensions and the differences in data ranges between different indicators. There are usually two methods for standardization:

[0051] Minimum-maximum normalization: linearly transform the data to the [0, 1] interval. The formula is as follows:

[0052]

[0053] Among them, xij′ is the standardized value of the i-th sample on the j-th index, min(xj) and max(xj) are the minimum and maximum values ​​of the index respectively.

[0054] Ratio normalization: Divide the value of each indicator by its sum so that the sum of all values ​​of each indicator is 1. The formula is as follows:

[0055]

[0056] (2) Calculate entropy value

[0057] For each indicator, use the following formula to calculate its entropy value E j :

[0058]

[0059] Among them, p ij It is the proportion of the i-th sample on the j-th index.

[0060] k is a constant, generally 1 / log(n), where n is the number of samples.

[0061] If p ij =0, then p ij log(p ij )=0.

[0062] This formula reflects the uncertainty of the ,th indicator. The larger the entropy value, the higher the uncertainty and the less effective information.

[0063] (3) Calculate weight

[0064] Calculate the weight w of each indicator j :

[0065]

[0066] Where m is the total number of indicators. This formula is obtained by 1-E j Transform the entropy value so that indicators with low entropy values ​​(large amount of information) receive higher weights.

[0067] Calculate the weight of each index according to the entropy value: ORP = x1; pH = x2; slurry density = x3; desulfurization efficiency = x4

[0068] Step 2: Calculate the comprehensive membership (weighted geometric mean method)

[0069] Using the previously defined weights and membership values, apply the weighted geometric mean method to calculate the composite membership:

[0070] Comprehensive membership = (ORP membership x1 ×pH membership x2 × Slurry density x3 ×Desulfurization efficiency membership x4 ) 1 / (∑Weights) (12)

[0071] 5. Evaluation level;

[0072] According to the calculated comprehensive membership, the desulfurization slurry performance evaluation level is determined according to the following rules:

[0073] If total_membership>0.75, the evaluation level is "excellent".

[0074] If 0.5<total_membership≤0.75, the evaluation level is "good".

[0075] If 0.25<total_membership≤0.5, the evaluation level is "medium".

[0076] If total_membership≤0.25, the rating is "poor".

[0077] This method provides a comprehensive evaluation framework, using fuzzy logic combined with machine language programming, which not only improves the accuracy of the evaluation, but also enhances the support for dynamic adjustment of the desulfurization process, helping on-site operation and maintenance personnel to optimize operations in real time and improve the overall performance of the desulfurization system.

[0078] The beneficial effects of the present invention are:

[0079] The present invention provides a scientific and accurate desulfurization efficiency evaluation method by comprehensively evaluating the key performance indicators of limestone wet desulfurization slurry in coal-fired power plants. The method uses fuzzy mathematics and entropy weight method, combined with real-time data monitoring, which not only improves desulfurization efficiency and system stability, but also reduces operating costs and environmental pollution. It is applicable to all coal-fired power plants using limestone wet desulfurization technology, and has a wide range of practical application value and significant environmental and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1It is a flow chart of the implementation of the present invention. DETAILED DESCRIPTION

[0081] The specific technical solution of the present invention is described in conjunction with the embodiments:

[0082] like Figure 1 As shown, the steps include:

[0084] The specific technical solution of the present invention is described in conjunction with the embodiments:

[0085] like Figure 1 As shown, the following steps are included:

[0086] 1. Import data processing and mathematical calculation libraries:

[0087] Prepare the required data processing and mathematical calculation tools.

[0088] 2. Define the membership function:

[0089] Define a triangular membership function for pH values, where the membership values ​​increase linearly from 0 to 1 and then decrease linearly to 0 between pH values ​​4.5 and 5.5.

[0090] A trapezoidal membership function is defined for ORP and slurry density, which is proportional to the ORP value between 100mV and 400mV and the slurry density between 1040kg / m 3 Up to 1120kg / m 3 When , the membership value increases linearly from 0 to 1, then remains at 1 within the optimal interval, and finally decreases linearly to 0.

[0091] An S-shaped increasing membership function is defined for the desulfurization efficiency, which increases smoothly from 0 to 1 as the desulfurization efficiency increases.

[0092] 3. Calculate the weight based on the entropy weight method:

[0093] The data is standardized, and then the information entropy of each indicator is calculated. Finally, the weight of each indicator is calculated based on the information entropy.

[0094] 4. Read data:

[0095] Read the desulfurization slurry data from the data file.

[0096] 5. Calculate the degree of membership:

[0097] The membership value is calculated for the actual value of each indicator.

[0098] 6. Output the intermediate results of membership calculation:

[0099] Display the membership value of each indicator to check whether there is a membership of 0.

[0100] 7. Define the evaluation function:

[0101] According to the membership value and weight, the comprehensive membership is calculated, and the evaluation level is determined according to the comprehensive membership value.

[0102] 8. Apply the evaluation function and add the comprehensive membership:

[0103] Apply the evaluation function to each data record, calculate the comprehensive membership and evaluation level, and add the results to the data record.

[0104] 9. Output the final result:

[0105] Display the final comprehensive membership and evaluation level of each indicator.

[0106] 10. Save the results to CSV:

[0107] Save the evaluation results to the output result file.

Claims

1. A performance evaluation method for limestone wet desulfurization slurry in coal-fired power plants, characterized in that: The following steps are involved: (1) selecting evaluation indicators, wherein the evaluation indicators include oxidation-reduction potential (ORP), pH value, slurry density and desulfurization efficiency; (2) Evaluate each evaluation index by setting a membership function, wherein the membership function includes a triangular membership function, a trapezoidal membership function and a growing Sigmoid function to convert the data into a membership value; (3) Use the entropy weight method to calculate the weight of each evaluation index; (4) Apply the weighted geometric mean method to calculate the comprehensive membership based on the membership value and weight; (5) The evaluation grade of the desulfurization slurry performance is determined based on the comprehensive membership degree, and the evaluation grades are divided into excellent, good, medium and poor.

2. The performance evaluation method of a limestone wet desulfurization slurry for a coal-fired power plant according to claim 1, characterized in that: The ORP and slurry density membership function uses a trapezoidal function, with left and right boundaries being the minimum and maximum values ​​of the data set, and the specific parameter setting interval of the optimal area being 20%-30% of the data; or being set based on historical operating data and theoretical analysis to ensure the accuracy and practicality of the evaluation results.

3. The performance evaluation method of desulfurization slurry in a coal-fired power plant according to claim 1, wherein the entropy weight method comprises the following steps: a. Standardize the data of each indicator to eliminate the impact of dimension and data range differences; b. Calculate the entropy value of each indicator data to evaluate the amount of information it provides; c. Calculate the weight of each index according to the entropy value: ORP = x1; pH = x2; slurry density = x3; desulfurization efficiency = x4.

4. The performance evaluation method of a limestone wet desulfurization slurry for a coal-fired power plant according to claim 1, characterized in that: The weighted geometric mean method determines the comprehensive membership by calculating the weighted geometric mean of the membership of each indicator. A comprehensive membership > 0.75 is "excellent"; 0.5 < comprehensive membership < 0.75 is "good"; 0.25 < comprehensive membership < 0.5 is "medium"; and a comprehensive membership < 0.25 is "poor".

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