Risk assessment method for circulating cooling water system of converter station

Abstract

The invention provides a converter station circulating cooling water system risk assessment method, and belongs to the field of green converter station cooling water. According to the method, a fuzzy comprehensive evaluation method and an analytic hierarchy process are mainly utilized, and different influence factors and evaluation systems are combined to carry out risk evaluation on the converter station circulating cooling water system. The method comprises the steps of firstly constructing a factor set and an evaluation set, then evaluating a matrix through a single factor, calculating a weight through an analytic hierarchy process, and finally realizing comprehensive evaluation. According to the judgment result, the invention also provides water quality risk control suggestions, such as strengthening of water quality on-line detection, medicament use management and supervision and the like. The method not only is scientific and objective in evaluation result, but also can provide a basis for factory operation management and risk control, and has wide application value.

Description

Technical Field

[0001] The present invention belongs to the field of green converter station cooling water, and more particularly relates to a risk assessment method for a converter station circulating cooling water system. Background Art

[0002] Converter station circulating cooling water systems are widely used in power systems and are a key component of power conversion equipment. Effective risk assessment of these systems is crucial for ensuring the proper operation of these systems and preventing major accidents caused by equipment failure. However, due to the complexity and uncertainty associated with the various physical and chemical parameters of circulating cooling water, traditional cooling water risk assessment methods struggle to meet the requirements for comprehensive and accurate risk assessment of cooling water systems.

[0003] Traditional cooling water risk assessment methods primarily rely on manual judgment or single-factor analysis, such as subjective manual experience judgment, physical observation, and single-factor statistical analysis. These methods have certain limitations. For example, manual experience judgment is easily influenced by personal experience and subjectivity, physical observation cannot accurately reflect the actual operating conditions of the cooling water system, and single-factor statistical analysis ignores the mutual influence between various parameters. To address these issues, researchers have proposed many new risk assessment methods, such as grey prediction models, neural network models, and fuzzy theory models. However, although these methods are superior to traditional methods, they also have some problems in application. For example, the grey prediction model ignores the internal dynamic variation relationship of the system, the neural network model requires a large amount of sample data, and the scientific nature of the fuzzy theory model is affected by the selection of the domain.

[0004] Furthermore, according to the circulating cooling water quality requirements outlined in GB50050-2007, "Design Specifications for Industrial Circulating Cooling Water Treatment," managers are required to monitor key parameters within the circulating cooling water system, such as turbidity, conductivity, pH, reagent concentration, and total iron. While existing monitoring technologies and equipment enable online monitoring, the data is subject to randomness and uncertainty due to instrument accuracy, stability, and interference immunity. Therefore, accurately assessing the risks posed by these factors has always been a major challenge in cooling water system risk assessment. Summary of the Invention

[0005] The present invention proposes a new risk assessment method for circulating cooling water in a converter station, which aims to solve the problems existing in the prior art in risk assessment of circulating cooling water systems in converter stations.

[0006] In order to achieve the above object, the present invention is implemented by adopting the following technical solutions: the method comprises:

[0007] Construct the factor set and the judgment set, establish the single factor evaluation matrix according to the steps of the fuzzy comprehensive evaluation method, and determine the membership degree of the judgment object to the elements of the judgment set;

[0008] The weights are calculated using the hierarchical analysis method, and different weight values are assigned to each element in the factor set according to its importance in the evaluation, and then a comprehensive evaluation is performed;

[0009] According to the maximum subordination principle, the evaluation results are analyzed and evaluated to determine the risk level of the converter station circulating cooling water system.

[0010] In one embodiment, the evaluation objects include turbidity, conductivity, pH, reagent concentration and total iron in the circulating cooling water system of the converter station.

[0011] In one embodiment, the weight calculation process specifically includes using a three-scaling method to compare each element in the factor set in pairs, establishing a judgment matrix, and then obtaining a weight vector by solving the eigenvector of the judgment matrix.

[0012] In one solution, the evaluation results often include the water quality risk level of the converter station's circulating cooling water system. By analyzing and evaluating the level, the system status can be judged.

[0013] In one embodiment, the method further includes selecting appropriate water quality risk assessment indicators and testing and evaluating the water quality of circulating cooling water according to industrial circulating cooling water treatment design specifications.

[0014] In one solution, during the comprehensive evaluation, a composite operation is first performed on the weight matrix and the membership fuzzy relationship matrix to obtain a fuzzy comprehensive evaluation result.

[0015] In one scenario, after determining the risk level of the converter station circulating cooling water system, appropriate risk control recommendations are also needed based on the evaluation results, including strengthening online water quality testing, strengthening the management and supervision of chemical use, and strictly controlling the pretreatment process.

[0016] Beneficial effects of the present invention:

[0017] The present invention's converter station circulating cooling water system risk assessment method utilizes fuzzy comprehensive evaluation and the analytic hierarchy process to assess water quality risk levels based on various water quality indicators, resulting in more objective and scientific evaluation results. This assessment method can clearly define the risk level of water quality in the circulating cooling water system, providing a basis for plant operations management and risk control. Furthermore, water quality risk control recommendations based on the assessment results can help plants improve the accuracy of water quality monitoring, reduce water quality risks, and ensure the normal operation of the cooling water system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 Flow chart of the method of the present invention;

[0019] Figure 2 Schematic diagram of fuzzy comprehensive evaluation of water quality indicators. DETAILED DESCRIPTION

[0020] To facilitate understanding of the present invention, the present invention will be described more fully below with reference to the accompanying drawings. The drawings illustrate exemplary embodiments of the present invention. However, the present invention may be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and comprehensive understanding of the present invention.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as those understood by those skilled in the art to which the present invention pertains. The terms used in the present specification are for the purpose of describing specific embodiments only and are not intended to limit the present invention. To facilitate understanding of the present invention, a more comprehensive description of the present invention will be provided below with reference to the accompanying drawings. Typical embodiments of the present invention are shown in the drawings. However, the present invention may be embodied in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and comprehensive understanding of the present invention.

[0022] like Figure 1 As shown, a risk assessment method for a converter station circulating cooling water system includes the following steps:

[0023] S1. Construct factor set and judgment set

[0024] The factor set is a collection of factors that affect the evaluation object, U = {u l ,u2,u3,...,u n},u i Represents various influencing factors. The evaluation set V is a set of possible evaluation results of the evaluation object, V={v1,v2,...,v m}, where element v j (j=1,2,...,m) are various possible evaluation results.

[0025] S2. Single factor evaluation matrix

[0026] The single factor evaluation matrix is formed by the single factor fuzzy evaluation set, from a factor u i To evaluate the evaluation object, determine the evaluation set element v j The membership degree r ij , if the total number of measurements of a certain evaluation item is n, then define r ij =(n j / n), and the single factor evaluation matrix R is composed of each single factor evaluation set.

[0027] S3. Calculate weights using the AHP method

[0028] The analytic hierarchy process (AHP) is based on the theory of fuzzy cluster analysis and pattern recognition. It decomposes complex problems that are difficult to quantify into several ordered levels, simplifies the problem, and then analyzes it step by step from the simplified level according to the method of system analysis. Each element in the factor set has different degrees of importance in the evaluation, and each element needs to be given a certain level of importance. i It is called giving different weight values a according to their importance ij , by a ij Combine them into a weight set A. Use the three-scaling method to compare each element pairwise to establish a judgment matrix. The three-scaling method comparison method is as follows:

[0029]

[0030] Arrange the comparison results to obtain the judgment matrix A:

[0031]

[0032] Professor Saaty suggested constructing a consistency matrix in the hierarchical analysis method. The matrix A must satisfy: a ji =1 / a ij ,a ii =1; when a ij ×a jk =a ik (i, j, k = 1, 2, ..., n), then the matrix A is a consistency matrix, and the weight vector w is calculated by the matrix eigenvector solution formula.

[0033] S4. Comprehensive evaluation

[0034] like Figure 2 As shown, the weight matrix w and the membership fuzzy relationship matrix R are compounded:

[0035] B=W·R (1)

[0036] Obtain the fuzzy comprehensive evaluation result B={b l ,b2,...,b k}

[0037] Analyze and evaluate the results, where each b i , should be a water quality grade, and the maximum value of b is b max =b k The corresponding level is the water quality risk level obtained by fuzzy comprehensive evaluation.

[0038] S5. Water quality risk assessment of circulating cooling water system

[0039] Selection of water quality risk assessment indicators

[0040] Establishing a water quality evaluation system requires determining water quality parameters based on the intended use of the water. Based on the circulating cooling water quality requirements in GB 50050-2007, "Design Specifications for Industrial Circulating Cooling Water Treatment," and in conjunction with on-site water quality testing reports, the key evaluation parameters selected are turbidity, conductivity, pH, reagent concentration, and total iron. Turbidity reflects the amount of suspended solids in the water. Suspended solids in the cooling water system can deposit on pipes and heat exchange equipment due to changes in flow rate, leading to pipe blockage and reduced heat transfer efficiency. Conductivity reflects the salt content in the water. High conductivity indicates high concentrations of various salts, particularly hardness (calcium and magnesium ions) and alkalinity. According to solubility product theory, the water is prone to scaling. pH reflects the acidity and alkalinity of the water. When the water is acidic, an increase in total iron content indicates a potential system leak. In circulating cooling water systems, chemicals can be added to control corrosion, scaling, and microbial growth. Testing chemical concentrations provides guidance for on-site operators on how to dosing.

[0041] Water Quality Risk Actual Case Analysis

[0042] Water quality is a complex, multi-layered system comprised of multiple factors. The parameters of evaluation factors are subject to instability and monitoring data is subject to randomness. This paper employs a fuzzy comprehensive evaluation method to assess water quality risks in water treatment systems, constructing a quantitative evaluation system suitable for the intrinsic safety evaluation index system of circulating cooling water treatment systems. Fuzzy comprehensive evaluation involves three elements: a factor set, a judgment set, and a single-factor judgment. Based on the single-factor judgment, a multi-factor comprehensive evaluation is then conducted. Because the traditional method of calculating weights for exceeding standards lacks objectivity, this paper introduces the analytic hierarchy process (AHP) into the weight calculation.

[0043] Membership calculation

[0044] The circulating cooling water systems of factories B and C were selected for water quality risk analysis. Water quality testing was conducted once a month, with 12 data records for each circulating cooling water system. Five water quality indicators, turbidity, conductivity, pH, reagent concentration, and total iron, were selected. Based on the requirements for circulating cooling water system water quality in GB50050-2007 "Design Specifications for Industrial Circulating Cooling Water Treatment" and related specifications, the risk level of water quality indicators was graded as shown in the Water Quality Indicator Risk Level Grade.

[0045]

[0046] The application of the water quality evaluation method is detailed using the circulating cooling water quality of Plant B as an example. Based on Table 1, the frequency of the corresponding indicators within the grading standard range is statistically analyzed, and the results are shown in Table 2. The membership degree of each evaluation indicator is calculated according to the membership degree calculation formula in (1) to establish the fuzzy relationship matrix R.

[0047] Water quality risk assessment table

[0048]

[0049]

[0050] According to the calculation method of membership degree, the fuzzy relationship matrix R is constructed. The matrix is as follows:

[0051]

[0052] Weight calculation

[0053] The three-scale method was used to establish a judgment matrix for the evaluation indicators (turbidity, conductivity, pH, reagent concentration, and total iron). Based on the expert opinions, the pairwise comparison matrix A for the above indicators was obtained:

[0054]

[0055] Through the matrix eigenvector solution operation, the weight calculation results are shown in the table Water quality index M calculation results

[0056]

[0057] The weight vector w = (0.170, 0.296, 0.195, 0.170, 0.170) is obtained.

[0058] Evaluation results

[0059] According to the calculation and evaluation results, the comprehensive evaluation vector B = (0.417, 0.533, 0.049)

[0060] According to the maximum membership principle, the membership of the evaluation index to the risk level safety is 0.417; the membership to the moderate risk is 0.533; and the membership to the high risk is 0.049. The corresponding level of 0.533 is selected as the evaluation level for comprehensive evaluation.

[0061] Because maxb j =b2

[0062] Therefore, the water quality risk level of circulating cooling water in Factory B is medium risk.

[0063] Similarly, the circulating cooling water quality risk level at Plant C is safe. Tables 2.2 and 2.3 show that the circulating cooling water system at Plant B exhibits high conductivity, with significant fluctuations in indicators such as pH and total hardness. The system operates at high concentrations for a long period of time. Calculations confirm that the water quality risk level at System B is moderate. Meanwhile, the circulating cooling water quality at Plant C is relatively stable across all indicators. Although conductivity has exceeded the standard, this has only occurred twice. Therefore, the calculated circulating cooling water quality at Plant C is safe. This analysis demonstrates the feasibility of applying the fuzzy evaluation method to circulating cooling water quality risk assessment.

[0064] To improve cooling water utilization, all indicators of circulating cooling water must be strictly controlled within standard ranges. Currently, instrumented testing of basic water quality indicators has certain drawbacks. Manual testing, while reliable, is labor-intensive. It is recommended that factories use online testing equipment to monitor water quality parameters such as pH, hardness, and dissolved oxygen, and that these equipment be regularly calibrated and maintained.

[0065] The water treatment chemicals used in the circulating cooling water system should be provided by a single manufacturer. Due to the varying compatibility of different chemical formulas, their synergistic effects have not been experimentally verified, impacting the slow-release scale and bactericidal efficacy and water quality stability. A single entity should adopt an overall responsibility model, with the corrosion inhibitors, scale inhibitors, and bactericides all sourced from a single supplier. Out of environmental responsibility, chemicals with minimal environmental impact should be prioritized. The brand, origin, model, main ingredients, quantity, and product specifications of all chemicals should be clearly provided.

[0066] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware through a computer program. The program can be stored in a computer-readable storage medium, and when executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0067] It should be understood that the detailed description of the technical solutions of the present invention using the preferred embodiments above is illustrative and not restrictive. A person skilled in the art, after reading the present specification, may modify the technical solutions described in the embodiments or replace some of the technical features therein with equivalents; such modifications or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A risk assessment method for a converter station circulating cooling water system, characterized by: The method includes: Construct the factor set and the judgment set, establish the single factor evaluation matrix according to the steps of the fuzzy comprehensive evaluation method, and determine the membership degree of the judgment object to the elements of the judgment set; The weights are calculated using the hierarchical analysis method, and different weight values are assigned to each element in the factor set according to its importance in the evaluation, and then a comprehensive evaluation is performed; According to the maximum subordination principle, the evaluation results are analyzed and evaluated to determine the risk level of the converter station circulating cooling water system.

2. The risk assessment method for a circulating cooling water system in a converter station according to claim 1, characterized in that: The evaluation objects include turbidity, conductivity, pH, reagent concentration and total iron in the circulating cooling water system of the converter station.

3. The risk assessment method for a circulating cooling water system in a converter station according to claim 1, characterized in that: The weight calculation process specifically includes using the three-scaling method to compare each element in the factor set in pairs, establishing a judgment matrix, and then obtaining the weight vector by solving the eigenvector of the judgment matrix.

4. The risk assessment method for a converter station circulating cooling water system according to claim 1, characterized in that: The evaluation results include the water quality risk level of the converter station's circulating cooling water system. Through analysis and evaluation of this level, the system status is judged.

5. The risk assessment method for a circulating cooling water system in a converter station according to claim 1, characterized in that: The method also includes selecting appropriate water quality risk assessment indicators and testing and evaluating the water quality of circulating cooling water according to industrial circulating cooling water treatment design specifications.

6. The risk assessment method for a circulating cooling water system in a converter station according to claim 1, characterized in that: During the comprehensive evaluation, a composite operation is first performed on the weight matrix and the membership fuzzy relationship matrix to obtain a fuzzy comprehensive evaluation result.

7. The risk assessment method for a circulating cooling water system in a converter station according to claim 1, characterized in that: After determining the risk level of the converter station circulating cooling water system, appropriate risk control recommendations need to be made based on the evaluation results, including strengthening online water quality testing, strengthening the management and supervision of chemical use, and strictly controlling the pretreatment process.

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