Comprehensive evaluation method for water treatment membrane product

Abstract

The invention discloses a comprehensive evaluation method for a water treatment membrane product. The method comprises the following steps: determining an environment dimension index and a resource dimension index; for each index, calculating a first weight through an improved fuzzy analytic hierarchy process, and calculating a second weight through an improved entropy weight method; calculating a final weight corresponding to each index according to the first weight and the second weight; and sorting the evaluation objects through an improved TOPSIS-gray correlation analysis sorting method based on the final weight corresponding to each index. According to the method, a full-dimension quantitative evaluation membrane product system covering resource consumption, carbon emission and environmental influence is constructed, so that the production process flow and the energy consumption structure can be systematically optimized, and the own membrane product is energized and remarkable added value is created.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of water drainage, more particularly, to a water treatment membrane product comprehensive evaluation method. BACKGROUND

[0002] By objectively analyzing multi-dimensional indexes such as resource consumption (indirectly reflecting material efficiency and economy), carbon emissions (indirectly reflecting energy consumption and energy structure), and comprehensive environmental impact (including human health damage, etc.), scientific decision support can be provided for product technology iteration, which is not only the basis for selecting environmentally friendly materials, but also the key means to drive production process route optimization, reduce environmental load, and ultimately improve product green competitiveness and market added value.

[0003] Limitations of existing evaluation methods: ① Traditional evaluation methods focus on a single dimension (such as environment or resources), and lack comprehensive quantification of the coordinated impact of environment and resources. ② Traditional AHP method uses 1-9 scale, and the consistency of the judgment matrix is poor, which easily leads to weight deviation. The weight of traditional entropy weight method decays sharply when the entropy value is close to 1, ignoring the actual data differences. ③ Evaluation model separation: traditional TOPSIS method only relies on Euclidean distance, without considering the shape characteristics of data distribution. Traditional grey correlation analysis is not combined with distance model, which reduces the reliability of the ranking results.

[0004] Therefore, it is necessary to develop a water treatment membrane product comprehensive evaluation method.

[0005] The information disclosed in the background section of this application is only intended to deepen the understanding of the general background of the application, and should not be considered as acknowledging or implying in any form that this information constitutes prior art known to those skilled in the art. SUMMARY

[0006] The present application proposes a water treatment membrane product comprehensive evaluation method, which can build a full-dimensional quantitative evaluation membrane product system covering resource consumption, carbon emissions and environmental impact, and can systematically optimize production process and energy consumption structure, enabling and creating significant added value for self-owned membrane products.

[0007] The present application provides a water treatment membrane product comprehensive evaluation method, which comprises: determining environmental dimension indexes and resource dimension indexes; For each index, the first weight is calculated by the improved fuzzy analytic hierarchy process, and the second weight is calculated by the improved entropy weight method; The maximum weight corresponding to each index is calculated according to the first weight and the second weight; The evaluation objects are sorted based on the maximum weight corresponding to each index by the improved TOPSIS-grey correlation analysis sorting method.

[0008] Preferably, the environmental dimension indicators include ozone depletion, ionizing radiation, fine particulate matter formation, formation of photochemical ozone, terrestrial acidification, freshwater eutrophication, marine eutrophication, human toxicity, terrestrial ecotoxicity, freshwater ecotoxicity, marine ecotoxicity.

[0009] Preferably, the resource dimension indicators include mineral resource scarcity indicators, fossil resource scarcity indicators, land use indicators, water resource use indicators.

[0010] Preferably, the first weight of each indicator is calculated by the improved fuzzy analytic hierarchy process, comprising: The index scale between each indicator is calculated, and a fuzzy complementary judgment matrix is constructed as:

[0011] wherein, is the index scale between the indicator e and the indicator f; The fuzzy complementary judgment matrix is subjected to consistency check, and a consistency indicator C1 and a consistency ratio C R :

[0012] wherein, is the maximum eigenvalue of the fuzzy complementary judgment matrix; is the average random consistency check indicator corresponding to the index scale, and n is the order of the judgment matrix; When <0.1, the fuzzy complementary judgment matrix passes the consistency check, and the corresponding eigenvector is calculated, and the first weight corresponding to the jth indicator is calculated.

[0013] Preferably, the first weight corresponding to the jth indicator is:

[0014] wherein, is the first weight corresponding to the jth indicator; is the jth element of the eigenvector X.

[0015] Preferably, the second weight of each indicator is calculated by the improved entropy weight method, comprising: For given m evaluation objects and n evaluation indicators, an original information matrix is established and subjected to standardization processing to obtain a standardized information matrix :

[0016] wherein, The data after the standardization processing, is the jth index of the ith evaluation object; The proportion of the jth index of the ith evaluation object is calculated :

[0017] In the formula, is the proportion of the jth index of the ith evaluation object; The information entropy of each index is calculated :

[0018] The second weight of each index is calculated.

[0019] Preferably, the second weight corresponding to the jth index is:

[0020] In the formula, is the second weight corresponding to the jth index, is the average value of all entropy values that are not 1, is the entropy weight value of the traditional entropy weight method, , .

[0021] Preferably, the maximum weight corresponding to each index is calculated according to the first weight and the second weight:

[0022] In the formula, is the maximum weight of the jth index, is the correction coefficient of , is the correction coefficient of .

[0023] Preferably, the sorting of the evaluation objects based on the maximum weight corresponding to each index through the improved TOPSIS-gray correlation analysis sorting method includes: A normalized decision matrix is constructed, and the index values are normalized to obtain :

[0024] According to the maximum weight of each index and the observation value after the normalization processing , the weighted observation value is calculated:

[0025] The positive ideal solution is constructed according to the maximum value in the weighted observation value of each evaluation object The negative ideal solution is constructed according to the minimum value in the weighted observation value of each evaluation object :

[0026] The distance of each evaluation object to the positive ideal solution and the negative ideal solution is calculated 、 :

[0027] In the formula, is the distance of the ith evaluation object to the positive ideal solution, is the distance of the ith evaluation object to the negative ideal solution; The grey correlation coefficient matrix between each evaluation object and the positive ideal solution and the negative ideal solution is calculated, wherein the positive grey correlation coefficient matrix is:

[0028] The negative grey correlation coefficient matrix is:

[0029] In the formula is the resolution coefficient; The grey correlation degree of each evaluation object to the positive ideal solution and the negative ideal solution is calculated and :

[0030] The distance 、 and the grey correlation degree 、 are respectively non-dimensionalized:

[0031] According to 、 、 、 , the 、 are calculated:

[0032] In the formula The preference degree of the reaction decision maker to distance and grey correlation degree; According to , The closeness degree of each evaluation object is calculated, and the closeness degrees are sorted from large to small.

[0033] Preferably, the closeness degree of the i-th evaluation object is:

[0034] In the formula, The closeness degree of the i-th evaluation object.

[0035] The beneficial effects are: 1. Building a full-dimensional evaluation index system: integrating 14 environmental indicators (such as global warming (carbon footprint), ozone layer destruction, ozone formation-human health damage, particulate matter formation, etc.) and 4 resource indicators (such as land use, mineral resource depletion, etc.), realizing full-factor coverage through a unified characteristic model; 2. Developing a dynamic weighting model: improving the FAHP method: using exponential scaling to build a fuzzy consistency matrix, reducing consistency bias; improving the entropy weight method: introducing entropy mean value, solving the mutation problem of traditional methods when information entropy ≈ 1; 3. Designing a fusion sorting algorithm: coupling TOPSIS Euclidean distance and grey correlation coefficient, innovating a comprehensive sorting method.

[0036] The method and device of the present application have other characteristics and advantages, which will be apparent or will be described in detail in the accompanying drawings and subsequent specific embodiments incorporated herein, which together serve to explain the specific principles of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0037] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings, in which like reference characters refer to like parts throughout the figures, and wherein:

[0038] Figure 1 A flowchart showing the steps of a water treatment membrane product comprehensive evaluation method according to one embodiment of the present application is shown. DETAILED DESCRIPTION

[0039] Preferred embodiments of the present application will be described in more detail below. Although the following describes preferred embodiments of the present application, it is to be understood that the application can be carried out in various forms and should not be limited by the embodiments set forth herein.

[0040] Figure 1 A flow chart showing the steps of a water treatment membrane product comprehensive evaluation method according to an embodiment of the present application is shown.

[0041] As Figure 1 shown, the water treatment membrane product comprehensive evaluation method includes: Step 101, determining environmental dimension indicators and resource dimension indicators; Step 102, for each indicator, calculating a first weight by an improved fuzzy analytic hierarchy process and a second weight by an improved entropy weight method; Step 103, calculating a maximum weight corresponding to each indicator according to the first weight and the second weight; Step 104, sorting the evaluation objects based on the maximum weight corresponding to each indicator by an improved TOPSIS-gray correlation analysis sorting method.

[0042] In one example, the environmental dimension indicators include ozone depletion, ionizing radiation, fine particulate matter formation, formation of photochemical ozone, terrestrial acidification, freshwater eutrophication, marine eutrophication, human toxicity, terrestrial ecotoxicity, freshwater ecotoxicity, and marine ecotoxicity.

[0043] In one example, the resource dimension indicators include mineral resource scarcity indicators, fossil resource scarcity indicators, land use indicators, and water resource use indicators.

[0044] In one example, for each indicator, calculating a first weight by an improved fuzzy analytic hierarchy process includes: calculating an index scale between each pair of indicators, and constructing a fuzzy complementary judgment matrix as:

[0045] wherein, is the index scale between indicator e and indicator f; performing consistency check on the fuzzy complementary judgment matrix, and calculating a consistency indicator C1 and a consistency ratio C R :

[0046] wherein, is the maximum eigenvalue of the fuzzy complementary judgment matrix; is the average random consistency check indicator corresponding to the index scale, and n is the order of the judgment matrix. When <0.1, the fuzzy complementary judgment matrix passes the consistency test, and the first weight corresponding to the jth index is calculated The corresponding eigenvector , the first weight corresponding to the jth index is calculated.

[0047] In one example, the first weight corresponding to the jth index is:

[0048] In the formula: is the first weight corresponding to the jth index; is the jth element of the eigenvector X.

[0049] In one example, the second weight is calculated by improving the entropy weight method for each index, including: For a given m evaluation objects and n evaluation indexes, an original information matrix is established and standardized to obtain a standardized information matrix :

[0050] In the formula, is the data after standardization, is the jth index of the ith evaluation object; The proportion of the jth index of the ith evaluation object is calculated :

[0051] In the formula, is the proportion of the jth index of the ith evaluation object; The information entropy of each index is calculated :

[0052] The second weight of each index is calculated.

[0053] In one example, the second weight corresponding to the jth index is:

[0054] In the formula, is the second weight corresponding to the jth index, is the average value of all entropy values other than 1, is the entropy weight value of the traditional entropy weight method, , .

[0055] In one example, the maximum weight corresponding to each index is calculated according to the first weight and the second weight:

[0056] wherein, is the maximum weight of the jth index, is the correction coefficient of is the correction coefficient of

[0057] In one example, the ranking of the evaluation objects based on the maximum weight of each index by the improved TOPSIS-gray correlation analysis ranking method comprises: constructing a normalized decision matrix, normalizing the index values to obtain :

[0058] According to the maximum weight of each index and the normalized observation value , the weighted observation value is calculated:

[0059] According to the maximum value in the weighted observation value of each evaluation object, the positive ideal solution is constructed, and according to the minimum value in the weighted observation value of each evaluation object, the negative ideal solution is constructed:

[0060] The distance of each evaluation object to the positive ideal solution and the negative ideal solution is calculated , :

[0061] wherein, is the distance of the ith evaluation object to the positive ideal solution, is the distance of the ith evaluation object to the negative ideal solution; The gray correlation coefficient matrix between each evaluation object and the positive ideal solution and the negative ideal solution is calculated, wherein the positive gray correlation degree matrix is:

[0062] The negative gray correlation degree matrix is:

[0063] wherein is the resolution coefficient; ​​The grey correlation degrees of each evaluation object with the positive ideal solution and the negative ideal solution are calculated and :

[0064] The distances , and the grey correlation degrees , are respectively non-dimensionalized

[0065] According to , , , , the , are calculated

[0066] In the formula , the distance and the grey correlation degree are the preference degrees of the decision maker. According to , , the closeness degree of each evaluation object is calculated, and the evaluation objects are sorted in descending order according to the closeness degree.

[0067] In an example, the closeness degree of the ith evaluation object is

[0068] In the formula, is the closeness degree of the ith evaluation object.

[0069] Specifically, the environmental dimension evaluation index system of the present research is determined with reference to the ReCiPe 2016 method system, the environmental dimension adopts an equivalent model to perform environmental impact characterization calculation, and for the calculation of the considered environmental impact categories, the given in ReCiPe 2016 is adopted, as shown in Table 1, and the 13 intermediate point impact types of comprehensive evaluation constitute the evaluation indexes of the environmental dimension in multi-dimensional evaluation.

[0070] Table 1 Environmental dimension evaluation index system

[0071] Ozone depletion potential (ODP) (in kg CFC-11 equivalent) is:

[0072] where is ODP over an infinite time horizon; and are the EESC changes caused by emissions of 1 kg and 1 kg CFC-11, respectively; x is the name of each ozone-depleting substance. EESC can be referenced to WMO reports and the Base Atmospheric Model.

[0073] Ionizing radiation potential (IRP) is:

[0074] where is the ionizing radiation potential of 1 kBq of substance x emitted into compartment i, is the collective dose (in Man.Sv) caused by the emission of this substance into this compartment, is the collective dose caused by the emission of 1 kBq of Co-60 into air.

[0075] Fine particulate matter (PM10) with a diameter less than is a complex mixture of organic and inorganic substances. The fine particulate matter formation potential PMFP is the ratio of the inhaled fraction of precursor emissions to the emission-weighted world average inhaled fraction of PM2.5. The inhaled fraction (iF) of fine particulate matter emitted by region i is determined for each precursor x ( ). The particulate matter formation potential (PMFP) is expressed in primary PM2.5 equivalent by dividing by the emission-weighted world average iF of PM2.5:

[0076] The region-specific intake fraction is defined as the sum of the change in PM2.5 intake rate in each affected region j due to a change in the amount of precursor emissions in region i ( ). The intake rate is calculated by multiplying the change in PM2.5 concentration in each receptor region ( ) by the population in receptor region i (Nj) and the average breathing rate per person (BR):

[0077] The damaging pathway of photochemical ozone formation is initially the emission of nitrogen oxides (NOx) or NMVOCs; then, atmospheric fate and airborne chemicals, NOx and NMVOCs, are converted into ozone in the atmosphere; subsequently, ozone can be inhaled by humans or absorbed by plants; leading to increased human mortality and harmful effects on plant species; ultimately causing damage to human health and ecosystems. The terrestrial ecosystem ozone formation potential (EOFP) is the ratio of the emission fate factor of precursor substances to the emission-weighted world average fate factor of NOx. The ozone formation fate factor (FF) due to emissions from region i is determined by the precursor x (… Ecosystem ozone formation potential (EOFP), expressed in kilograms of NOx equivalent, is determined by... Divide by the world-weighted average FF of NOx emissions to calculate:

[0078] To determine the ecosystem fate factor, AOT40 (the sum of the differences between the hourly average ozone concentration during the relevant growing season and 40 ppb, expressed in ppm) was used. (in h) is used as an indicator of cumulative concentration change and is derived through the TM5-FASST model. The fate factor represents the amount of precursor x emitted from region i. The sum of changes in AOT40 in each receiver grid g due to the changes:

[0079] The monthly AOT40 concentration per unit of NOx and NMVOC emissions is based on hourly ozone concentrations derived from the 2000 TM5CTM reference operation. The AOT40 was calculated using the longest growing season (i.e., forest), which is April to September in the Northern Hemisphere and October to March in the Southern Hemisphere.

[0080] The following equation is used to calculate the EOFP of a specific hydrocarbon:

[0081] The human body's potential for ozone formation (HOFP) is as follows, and the HOFP for certain hydrocarbons is:

[0082] The acidification potential AP is:

[0083] In the formula, the fate factor is:

[0084] Freshwater eutrophication potential, FEP, is:

[0085] wherein FEPx,i,c is the freshwater eutrophication potential of x substance discharged to grid cell i to compartment c (in kg P freshwater equivalent to grid cell i per kg x substance to grid cell i per kg freshwater equivalent to grid cell i), FFx,i,c is the fate factor of x substance discharged to compartment c in grid cell i, FFP is the fate factor of world average freshwater P discharge (85 days).

[0086] Marine eutrophication indicator is:

[0087] wherein, FEMx,i is the marine eutrophication potential of x substance discharged to compartment i; FEMx,i,reg is the marine eutrophication regionally weighted fate factor of x substance discharged to compartment i; FEMN,i is the marine eutrophication globally weighted fate factor of N discharged to compartment i; reg is the desired region (e.g. continent, world); Ex,i is the emission of x substance discharged to compartment i of the desired region reg.

[0088] The characterization factors for human toxicity and ecotoxicity explain the persistence (fate) of a chemical in the environment and the accumulation (exposure) and toxicity (effect) in the human food chain. All these toxic potentials are aggregated into a total human population characteristic factor for substance x released to compartment i: represents the midpoint level of human characterization factor for carcinogenic or non-carcinogenic properties of substance x to discharge compartment i in cultural perspective c (kg 1,4-DCB to urban air equivalent per kg). is the proportion of the population in cultural perspective c in geographical scale g that ingests x substance via ingestion pathway r to compartment i. is the carcinogenic or non-carcinogenic effect factor of x substance in ingestion pathway r associated with cultural perspective c, reflecting the change in lifetime disease incidence due to ingestion of the substance of interest and the change in the ingestion pathway.

[0089] The region-specific ecotoxicological midpoint characterization factor is composed of a fate factor (FF) and an effect factor (EF): is the emission of chemical substance x from compartment i to receiving compartment j, transported to receiving compartment j, related to the cultural perspective c (kg 1,4dcb-eq for freshwater ecotoxicity to freshwater, marine ecotoxicity to marine (and marine) water and terrestrial ecotoxicity to industrial soil per kg). is the fate factor, defined as the marginal change in the steady-state mass of substance x in environmental compartment j at scale g, due to a marginal emission of compartment i in cultural perspective c (year). is the impact factor is the marine and freshwater ecotoxicity, is the terrestrial ecotoxicity), representing the change in the potential fraction of species that disappears due to a change in the environmental concentration of x in receiving compartment j in cultural perspective c.

[0090] The resource dimension assessment procedure is the same as the environmental dimension assessment procedure. The resource dimension mainly evaluates the consumption of products in mineral resources, fossil resources, land resources and water resources. The resource dimension evaluation index system of this study is determined by referring to the ReCiPe 2016 method system, as shown in Table 2, and the four intermediate point impact types of comprehensive evaluation constitute the evaluation index of the resource dimension in multi-dimensional evaluation.

[0091] Table 2 Resource dimension evaluation index system

[0092] The midpoint characteristic factor of the scarcity of mineral resources is the surplus ore potential (SOP). SOP represents the average additional ore quantity produced in the future due to the exploitation of 1 kg of mineral resource x, considering all future production of this mineral resource (R) relative to the average additional ore production due to the exploitation of 1 kg of copper (Cu), considering all future copper production. The surplus ore potential considering the future exploitation of mineral resources is called absolute surplus ore potential (ASOP) and is expressed in units of kg ore / kg x. The midpoint characteristic factor of any mineral resource x and any reserve estimate (Rx) can be calculated as follows:

[0093] The specific SOP of future production is expressed in units of kg Cu-eq / kg x.

[0094] ASOP consists of two calculation steps. In the first step, a cumulative grade-tonnage relationship is derived. In the second step, the average cost increase due to all future exploitation of mineral resources is calculated, resulting in the absolute surplus cost per unit of exploited mineral resources. The cumulative grade-tonnage regression reflects the relationship between the cumulative exploitation of a mineral resource and its ore grade. The ore grade of mineral resource x can be derived as:

[0095] where, is the ore grade of resource x (in kg x / kg of ore), is the total quantity of resource x that has been extracted, is the cumulative quantity of resource x that has been extracted, and are the scale and shape parameters, respectively, of the log-logistic distribution of the cumulative grade-tonnage relationship of mineral resource.

[0096] Mineral x The absolute remaining ore potential of mineral x (kg Ore / kg x) is defined as the additional quantity of ore that will be produced per unit of mineral resource x that is extracted in the future:

[0097] where (kg ore) is the quantity of ore x that is produced for a certain quantity of mineral resource, (kg x), (kg x) is the actual reserve of this mineral resource x, (kg x) is the maximum total quantity that can be extracted of this mineral resource, is the cumulative tonnage of this mineral resource x that has been extracted so far.

[0098] Values of were available for 18 mineral resources, namely aluminium, antimony, chromium, cobalt, copper, gold, iron, lead, lithium, manganese, molybdenum, nickel, niobium, phosphorus, silver, tin, uranium and zinc. For minerals for which values could not be obtained from empirical cumulative grade-tonnage relationships, the price of the mineral resource was used to estimate its value.

[0099] The price of U3O8 spot data published by the European Space Agency (2015) was used to calculate the price of uranium.

[0100] The midpoint indicator for fossil resource use is the fossil fuel potential (FFP) of fossil resource x, defined as the energy content of fossil resource x divided by the energy content of crude oil, in kg oil-eq / unit of resource, and is calculated as:

[0101] The fossil fuel potential (FFP) is calculated based on the higher heating value (HHV) of each fossil resource, and characteristic factors for crude oil, natural gas, hard coal, lignite and peat are given in the report.

[0102] Land conversion / occupation Midpoint characteristic factor (in year crop equivalents) based on relative species loss caused by land use type x Proportional to relative species loss caused by annual crop production: The calculation method is to compare the field data of local species richness in specific types of natural and artificial land cover using the linear relationship described by Köllner et al.:

[0103] where and are the observed species richness (number of species) under land use type x and the observed species richness of the reference land cover in region i, respectively. The result from equation 2 ranges between -∞ and +1, where negative values imply a positive effect of land occupation (i.e. greater species richness), while the maximum value of 1 represents a 100% loss of species richness. Second, the midpoint characteristic factor (in year crop equivalents·yr) is directly related to using the following equation by Köllner et al.:

[0104] where is the recovery time of species richness (years).

[0105] The characteristic factor (CF) at the midpoint level is the amount of water consumed per cubic meter of water extracted. Water extraction refers to the withdrawal of water from surface water or the pumping of groundwater from aquifers. It is the total amount of water withdrawn, without considering the amount of water that returns to water bodies or the efficiency of water use. On the other hand, water consumption is the amount of water lost from the water source watershed.

[0106]

[0107] Therefore, for flows that are already given as water consumption flows, the midpoint indicator aligns with the inventory. For water flows that are simply reported as withdrawn water or extracted water, a factor needs to be applied to account for the efficiency of water use. The higher the efficiency, the more water per cubic meter actually reaches the factory and is consumed; whereas if the efficiency is low, more water needs to be extracted to achieve the same result; while a large portion of the water withdrawn is not consumed but returned to the environment.

[0108] The specific process of the proposed fuzzy analytic hierarchy process - improved entropy weight - TOPSIS weighted method is as follows: The improved fuzzy analytic hierarchy process includes: The fuzzy complementary judgment matrix is established for an evaluation object:

[0109] wherein the matrix , n represents the order of the matrix, the improved analytic hierarchy process improves the weight scale method, and an index scale as shown in Table 3 is adopted.

[0110] Table 3 AHP index scale

[0111] Compared with the traditional analytic hierarchy process, the improved analytic hierarchy process based on the index scale has the optimal consistency of the judgment matrix, can improve the weight calculation accuracy, and reduces the subjective influence to a certain extent.

[0112] The consistency of the judgment matrix A is checked, and the consistency index C1 and the consistency ratio C R :

[0113] wherein is the maximum eigenvalue of the judgment matrix A, n is the order of the judgment matrix; let As the average random consistency check index corresponding to the index scale, the value is shown in Table 4.

[0114] Table 4

[0115] When <0.1, the judgment matrix passes the consistency check, and the calculated weight can be used, otherwise the judgment matrix needs to be restructured. Based on the fuzzy consistent judgment matrix , the first weight of each index is calculated.

[0116] The “characteristic matrix” is constructed A - λI ( I is the n-order unit matrix); the characteristic polynomial f ( λ )=det( A - λI ) (determinant) is calculated; the characteristic equation f ( λ )=0 is solved, and all eigenvalues λ 1, λ 2,..., λ n are obtained; for each eigenvalue λ j , the homogeneous linear equation group is solvedA - λ j I ) x =0, its nonzero solution is the eigenvector corresponding to λ j Calculate the corresponding eigenvector , the first weight of each index is calculated as:

[0117] In the formula: is the first weight corresponding to the jth index; is the jth element of the eigenvector X.

[0118] The improved entropy weight method includes: Entropy weight method (EWM) is a method for calculating objective weight, which can eliminate the subjectivity of index weight and is conducive to obtaining evaluation results consistent with objective facts.

[0119] Data standardization, for given m evaluation objects and n evaluation indexes, the original information matrix is established , where is the evaluation value of the ith evaluation object under the jth evaluation index. The original matrix is standardized to obtain the standardized information matrix , the data is standardized:

[0120] In the formula, is the standardized data, is the jth index value of the ith evaluation object.

[0121] Define the characteristic weight, calculate the proportion of the jth index value of the ith evaluation object :

[0122] In the formula, is the proportion of the jth index value of the ith evaluation object.

[0123] Calculate the information entropy, calculate the information entropy of each evaluation index :

[0124] Calculate the second weight of each index:

[0125] In the formula, is the second weight corresponding to the jth index, is the average value of all entropy values that are not 1, is the entropy weight value of the traditional entropy weight method, , .

[0126] By introducing and , the sharp decay of the weight when the entropy value is close to 1 in the traditional entropy weight method is weakened, and the weight distribution is more in line with the actual difference of the index entropy value. The gap between the index weights distributed by the improved entropy weight method can better correspond to the gap between the entropy values.

[0127] The FAHP method has certain subjective limitations and ignores actual data information. The EWM method has certain objective limitations and ignores subjective bias, so the subjective and objective evaluation methods need to be combined for weighting. According to the first weight and the second weight, the maximum weight corresponding to each index is calculated as:

[0128] In the formula, is the maximum weight of the jth index, is the correction coefficient of is the correction coefficient of In this scheme , , .

[0129] The improved TOPSIS-gray correlation analysis ranking method includes: Construct a normalized decision matrix to obtain by normalizing the index values:

[0130] In the formula, is the jth index value of the ith evaluation object, is the normalized index value.

[0131] Construct a weighted decision matrix so that the weight of each evaluation index is multiplied by the observation value of all evaluation indexes under each evaluation object to obtain the weighted observation value :

[0132] According to the maximum value in the weighted observation value of each evaluation object, construct the positive ideal solution , and according to the minimum value in the weighted observation value of each evaluation object, construct the negative ideal solution:

[0133] wherein, is the maximum value of the weighted observation value corresponding to each index of the i-th evaluation object, is the minimum value of the weighted observation value corresponding to each index of the i-th evaluation object.

[0134] Calculate the distance of each evaluation object to the positive ideal solution and the negative ideal solution 、 :

[0135] wherein, is the distance of the i-th evaluation object to the positive ideal solution, is the distance of the i-th evaluation object to the negative ideal solution.

[0136] Calculate the grey correlation coefficient matrix between each evaluation object and the positive ideal solution and the negative ideal solution, the positive grey correlation degree matrix:

[0137] the negative grey correlation degree matrix:

[0138] wherein is the resolution coefficient, generally taking the value of 0.5.

[0139] Calculate the grey correlation degree of each evaluation object to the positive ideal solution and the negative ideal solution and :

[0140] Carry out non-dimensionalization processing on the Euclidean distance 、 and the grey correlation degree 、 :

[0141] Combine the Euclidean distance and the grey correlation degree to design a new distance formula:

[0142] wherein The preference degree of the reaction decision maker to distance and grey correlation degree, Here, the .

[0143] The proximity degree is calculated, and the proximity degree of each evaluation object to the optimal scheme is calculated. The proximity degree is calculated using the optimal vector and the worst vector, so that the advantages and disadvantages of each evaluation object can be better compared. According to , the values are arranged from large to small, The greater the value, the closer the relative distance to the optimal scheme, that is, the better the scheme:

[0144] In the formula, The greater the value, the better the evaluation object. After obtaining the comprehensive of all evaluation items, the order is sorted from high to low, and the final ranking of advantages and disadvantages can be obtained.

[0145] In order to understand the scheme and effect of the embodiment of the present application, a specific application example is given below. Those skilled in the art should understand that the example is only for the purpose of facilitating understanding of the present application, and any specific details are not intended to limit the present application in any way.

[0146] Example 1

[0147] In this embodiment, for the comprehensive environmental impact assessment of water treatment membrane products, a functional unit (F.U.) is first defined. The functional unit is set as "meeting the application demand of 10,000 m³ / day domestic sewage treatment capacity and serving for 10 years continuously", to ensure the comparability of the evaluation results. The data is derived from inventory analysis in the actual production and use stage, including raw material input, energy consumption and waste discharge. The LCI results are converted to a unified unit, and the converted results are combined within the same impact type. The specific input data is shown in Table 5, including: Organic membrane module: including polyvinylidene fluoride resin, organic pore-forming agent (dioctyl phthalate and dibutyl phthalate), active nano zinc oxide and other raw material inputs, and electric power energy consumption. The solid waste is mainly incineration treatment of organic membrane material; Ceramic membrane module: including α-alumina, pore-forming agent (starch), binder (carboxymethyl cellulose), nano silicon dioxide and other raw material inputs, and natural gas energy consumption.

[0148] Table 5 Raw materials and energy inputs for production of organic membrane and ceramic membrane modules

[0149] All data were obtained from field research and industry databases, ensuring compliance with the requirements of ISO 14040 standards. The normalization of functional units eliminated the impact of scale differences on the evaluation results.

[0150] Based on the ReCiPe 2016 methodology, 18 environmental impact indicators (14 environmental dimensions and 4 resource dimensions) were calculated, as shown in Table 6, which shows the environmental impact values of organic and ceramic membranes at different scales. For example, the global warming indicator shows that the carbon footprint of ceramic membranes (2,820,000 kg C eq) is significantly higher than that of organic membranes (809,000 kg C eq), mainly due to its high energy consumption production process.

[0151] Table 6. Membrane module environmental impact indicator results

[0152] To reduce subjective bias and improve the objectivity of weights, a combined weighting model (Combined Weighting Model) was used: Step 1: Improved Fuzzy Analytic Hierarchy Process (FAHP) In the improved fuzzy analytic hierarchy process, a fuzzy complementary judgment matrix was constructed, using the AHP index scale shown in Table 3 instead of the traditional 1-9 scale. For example, for the "human toxicity - non-carcinogenic damage" indicator, the judgment matrix was constructed through expert consultation, with a consistency test satisfying <0.1, and the weight calculation results are shown in the "Improved Analytic Hierarchy Process Calculation Results" column of Table 7.

[0153] Step 2: Improved Entropy Weight Method

[0154] In the improved entropy weight method, the original data was standardized (for positive indicators) or reversed (for negative indicators), and the information entropy was calculated. To overcome the weight mutation problem of traditional entropy weight method when entropy value ≈ 1, the entropy value mean correction was introduced, and the weight results are shown in the "Improved Entropy Weight Method" column of Table 7.

[0155] Step 3: Combined Weighting

[0156] In the improved entropy weight method, the FAHP weight and the entropy weight were combined to calculate the final weight. In this example, a = b = 1 to ensure the balance of subjective and objective weights. The "Combined Weight" column of Table 7 is the final application value, for example, the "human toxicity - non-carcinogenic damage" weight is the highest (0.18), reflecting its environmental importance.

[0157] Table 7. Improved Fuzzy Analytic Hierarchy Process and Entropy Weight Method Calculation Results

[0158] The improved TOPSIS-gray correlation analysis ranking method is adopted, the fusion algorithm is used to calculate the environmental performance ranking of each membrane group, and the Euclidean distance (position difference) and gray correlation degree (distribution similarity) are combined, as shown in Table 8.

[0159] Table 8 Euclidean distance and gray correlation degree

[0160] Step 1: Construct a weighted decision matrix; Step 2: Determine the positive and negative ideal solutions; Step 3: Calculate the Euclidean distance and gray correlation degree; Step 4: Design the relative closeness degree, as shown in Table 9.

[0161] Table 9 Relative closeness degree

[0162] Through the calculation of the FAHP-entropy weight-TOPSIS evaluation model, it is obtained from Table 8 that in the field of domestic sewage treatment, the organic membrane group-10000m 3 / d and 22.5m 3 / d has a lower impact on the environment than the ceramic membrane group-10000m 3 / d and 22.5m 3 / d for 10 consecutive years. In the field of municipal sewage treatment, the organic membrane group exhibits more significant low-carbon environmental protection characteristics in the production and use process, and its pollutant emission and environmental load are lower than those of the ceramic membrane group.

[0163] Those skilled in the art will understand that the purpose of the above description of the embodiments of the present application is only to exemplarily illustrate the beneficial effects of the embodiments of the present application, and is not intended to limit the embodiments of the present application to any examples given.

[0164] The above has described various embodiments of the present application, and the above description is exemplary and is not exhaustive, and is also not limited to the disclosed embodiments. Many modifications and changes will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A comprehensive evaluation method of a water treatment membrane product, characterized by, The method comprises the following steps: determining environment dimension indexes and resource dimension indexes; for each index, calculating a first weight by using an improved fuzzy analytic hierarchy process and calculating a second weight by using an improved entropy weight method; calculating a final weight corresponding to each index according to the first weight and the second weight; ranking the evaluation objects based on the final weight corresponding to each index by using an improved TOPSIS-gray correlation analysis ranking method.

2. The comprehensive evaluation method of a water treatment membrane product according to claim 1, wherein, The environment dimension indexes include ozone depletion, ionizing radiation, fine particulate matter formation, photochemical ozone formation, land acidification, freshwater eutrophication, marine eutrophication, human toxicity, land ecological toxicity, freshwater ecological toxicity and marine ecological toxicity.

3. The comprehensive evaluation method of a water treatment membrane product according to claim 1, wherein, The resource dimension indexes include mineral resource scarcity indexes, fossil resource scarcity indexes, land use indexes and water resource use indexes.

4. The comprehensive evaluation method of a water treatment membrane product according to claim 1, wherein, For each index, the first weight is calculated by using the improved fuzzy analytic hierarchy process, which comprises the following steps: calculate the index scale between each two indexes to construct a fuzzy complementary judgment matrix as follows: wherein, is the index scale between index e and index f; The consistency index C1 and the consistency ratio C are calculated for the fuzzy complementary judgment matrix. R : In the formula, is the maximum eigenvalue of the fuzzy complementary judgment matrix; is the average random consistency test index corresponding to the exponential scale, and n is the order of the judgment matrix. When When the fuzzy complementary judgment matrix passes the consistency test, calculate The corresponding characteristic vector Calculate the first weight corresponding to the jth index.

5. The comprehensive evaluation method of a water treatment membrane product according to claim 4, wherein, the first weight corresponding to the jth index is as follows: In the formula: is the first weight corresponding to the jth index; is the jth element of the feature vector X.

6. The water treatment membrane product integrated evaluation method according to claim 5, wherein, For each index, the second weight is calculated by using the improved entropy weight method, which comprises the following steps: For a given m evaluation objects and n evaluation indexes, the original information matrix is established and normalized to obtain the normalized information matrix : In the formula, is the data after standardization processing, is the jth index of the ith evaluation object; calculating a proportion of the jth index of the ith evaluation object : In the formula, a proportion of the jth index of the ith evaluation object; calculating the information entropy of each index : calculate the second weight of each index.

7. The comprehensive evaluation method of a water treatment membrane product according to claim 6, wherein, the second weight corresponding to the jth index is as follows: In the formula, is the second weight corresponding to the jth index, is the average of all entropy values that are not 1, is the entropy weight value of the traditional entropy weight method, , .

8. The comprehensive evaluation method of a water treatment membrane product according to claim 7, wherein, According to the first weight and the second weight, the final weight corresponding to each index is calculated, which comprises the following steps: wherein is the maximum weight of the jth index, is is the correction coefficient of is is the correction coefficient of 9. The water treatment membrane product integrated evaluation method according to claim 8, wherein, Based on the final weight corresponding to each index, the evaluation objects are ranked by using the improved TOPSIS-gray correlation analysis ranking method, which comprises the following steps: The normalized decision matrix is constructed, and the index value is normalized to obtain : According to the maximum weight of each index with the normalized observation value , the weighted observation value is calculated constructing a positive ideal solution from the maximum value among the weighted observation values of each evaluation object constructing a negative ideal solution from the minimum value among the weighted observation values of each evaluation object : calculating the distance of each evaluation object to the positive ideal solution and the negative ideal solution , : wherein is the distance of the ith evaluation object from the positive ideal solution, is the distance of the ith evaluation object from the negative ideal solution; calculate the gray correlation coefficient matrix between each evaluation object and the positive ideal solution and the negative ideal solution, wherein the positive gray correlation degree matrix is as follows: the negative gray correlation degree matrix is as follows: In the formula are the resolution coefficients; The grey correlation degree of each evaluation object with the positive ideal solution and the negative ideal solution is calculated and : The distance , and the grey correlation degree , are respectively non-dimensionalized According to , , , calculate , : In the formula the decision maker's preference degree for distance and grey correlation degree; According to , The degree of similarity of each evaluation object is calculated, and the degrees of similarity are ranked from large to small.

10. The comprehensive evaluation method of a water treatment membrane product according to claim 9, wherein, the close fitting degree of the ith evaluation object is as follows: In the formula, is the similar progress of the i-th evaluation object.

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