Multi-index comprehensive evaluation method for performance comparison and selection of sewage treatment filler

Abstract

The invention relates to the technical field of sewage treatment, and discloses a multi-index comprehensive evaluation method for performance comparison and selection of a sewage treatment filler, a scoring system including physical properties, chemical properties, biological properties and economic indexes is established, the physical properties include particle size, porosity and mechanical strength, and the chemical properties include adsorbability and ion exchange capacity; the biological properties comprise microbial adhesiveness and biological membrane activity, and the economical efficiency comprises filler cost and operation and maintenance cost. According to the method, multi-dimensional index fusion is adopted, physical, porosity, mechanical strength and chemical Langmuir adsorption models are integrated, biological electron microscope observation biological membrane coverage rate and economical efficiency LCCA analysis indexes are achieved, and full life cycle evaluation is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of sewage treatment, and in particular to a multi-index comprehensive evaluation method for performance comparison of sewage treatment fillers. Background Art

[0002] In wastewater biofilm treatment processes, the filler is a core component. The filler is the biofilm's habitat and carrier, influencing microbial growth, reproduction, shedding, morphology, and spatial structure, while also trapping suspended matter. Furthermore, the filler acts as a barrier to air bubbles, increasing their residence time in the water and the surface area of ​​gas-liquid contact, thereby improving mass transfer efficiency. This demonstrates the crucial role of fillers, significantly impacting the operational performance and energy consumption of the biofilm process. Furthermore, the cost of carrier fillers accounts for a significant portion of the capital expenditure of a biofilm treatment system, making the filler crucial to the system's viability.

[0003] The performance evaluation of wastewater treatment fillers is a core step in the optimization of biofilm processes. Traditional evaluation methods have the following drawbacks: (1) Existing technologies often focus on a single performance parameter (such as specific surface area), ignoring the synergistic effects of physical, chemical, biological, and economic factors; (2) They rely on subjective weighting methods such as the analytic hierarchy process (AHP), resulting in a disconnect between weight allocation and actual engineering needs; and (3) They fail to dynamically adjust the evaluation system based on water quality changes. For example, when adding new fillers, the model needs to be manually reconstructed. For example, conventional bioaffinity evaluation requires testing the pollutant removal rate after the biofilm is activated, which is time-consuming and cannot quantify the dynamic changes in the adsorption layer quality.

[0004] Therefore, it is urgent to design a multi-index comprehensive evaluation method for sewage treatment filler performance comparison to solve the above problems. Summary of the Invention

[0005] The purpose of the present invention is to provide a multi-index comprehensive evaluation method for sewage treatment filler performance comparison and selection to solve the above-mentioned shortcomings in the prior art.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A multi-index comprehensive evaluation method for performance comparison of sewage treatment fillers includes the following steps:

[0008] (1) Establish a scoring system that includes physical properties, chemical properties, biological properties, and economic indicators. The physical properties include particle size, porosity, and mechanical strength; the chemical properties include adsorption and ion exchange capacity; the biological properties include microbial attachment and biofilm activity; and the economics include filler cost and operation and maintenance costs.

[0009] (2) The subjective weight wi′ of each indicator is determined by the analytic hierarchy process (AHP), and the objective weight wi″ is calculated by the entropy weight method. The comprehensive weight is corrected by combining the formula Wi = αwi′ + (1-α)wi″, where α is the adjustment coefficient 0≤α≤1;

[0010] (3) Calculate the comprehensive score of fillers through the fuzzy comprehensive evaluation model, where Ri is the normalized measured value of the index;

[0011] (4) Output the optimal filler solution based on the score ranking and generate a visual comparison map.

[0012] Preferably, the porosity of the physical properties in step (1) needs to be measured by mercury intrusion porosimetry, and the particle size distribution needs to be full d 10 / d 90 ≤0.5 to ensure hydraulic conductivity.

[0013] Preferably, the adsorption evaluation of the chemical properties is fitted by the Langmuir adsorption isotherm model, and the ion exchange capacity is expressed by the Ca content per unit mass of the filler. 2 + Exchange equivalent quantification.

[0014] Preferably, the microbial adhesion of the biological performance is characterized by observing the biofilm coverage by scanning electron microscopy, and the biofilm activity is characterized by the COD removal rate per unit time.

[0015] Preferably, the adjustment coefficient α in step (2) is optimized by a genetic algorithm, and the input parameters include filler type, sewage quality and treatment scale.

[0016] Preferably, the normalization process in step (3) adopts the range method, and the positive index and the negative index are respectively and calculate

[0017] Preferably, the visual comparison map includes a radar map and a heat map, which dynamically displays the advantages and disadvantages of different fillers in various indicator dimensions. max

[0018] Preferably, a dynamic update module is also included to automatically expand the scoring system and recalculate the weight when a new filler type is added.

[0019] Preferably, the operation and maintenance costs in the economic indicators include backwash frequency, packing replacement cycle and energy consumption cost, which are quantified by life cycle cost analysis (LCCA).

[0020] Preferably, the method is integrated into a cloud-based decision-making platform, supporting multiple users to collaboratively fill in data and call a historical case library for similarity matching.

[0021] In the above technical solution, the present invention provides a multi-index comprehensive evaluation method for the performance comparison of sewage treatment fillers, (1) using multi-dimensional index fusion, integrating physical, porosity, mechanical strength, chemical Langmuir adsorption model, biological electron microscopy observation of biofilm coverage and economic LCCA analysis indicators to achieve full life cycle evaluation; (2) using subjective and objective weight dynamic optimization using AHP-entropy weight method combined with genetic algorithm adjustment coefficient α (0≤α≤1) to improve the scientific nature of the weight. For example, by inputting the sewage COD concentration to dynamically correct the α value, the model adaptability is enhanced; (3) through real-time visual decision support, radar charts and thermal maps are generated to compare the porosity distribution of different fillers, such as d 10 / d 90 ≤0.5 and COD removal rate difference, assisting in quick selection. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0023] Figure 1 A schematic diagram of the steps provided in an embodiment of a multi-index comprehensive evaluation method for comparing and selecting performance of sewage treatment fillers according to the present invention.

[0024] Figure 2 A positive indicator formula diagram is provided for an embodiment of a multi-index comprehensive evaluation method for comparing and selecting performance of sewage treatment fillers according to the present invention.

[0025] Figure 3 A schematic diagram of a negative indicator formula is provided for an embodiment of a multi-index comprehensive evaluation method for comparing and selecting performance of sewage treatment fillers according to the present invention.

[0026] Figure 4 A schematic diagram of a weight correction formula provided for an embodiment of a multi-index comprehensive evaluation method for comparing and selecting performance of sewage treatment fillers according to the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0028] like Figure 1-4 As shown, the embodiment of the present invention provides a multi-index comprehensive evaluation method for comparing and selecting the performance of sewage treatment fillers, including the following steps:

[0029] (1) Establish a scoring system that includes physical properties, chemical properties, biological properties, and economic indicators. The physical properties include particle size, porosity, and mechanical strength; the chemical properties include adsorption and ion exchange capacity; the biological properties include microbial attachment and biofilm activity; and the economics include filler cost and operation and maintenance costs.

[0030] (2) The subjective weight wi′ of each indicator is determined by the analytic hierarchy process (AHP), and the objective weight wi″ is calculated by the entropy weight method. The comprehensive weight is corrected by combining the formula Wi = αwi′ + (1-α)wi″, where α is the adjustment coefficient 0≤α≤1;

[0031] (3) Calculate the comprehensive score of fillers through the fuzzy comprehensive evaluation model, where Ri is the normalized measured value of the index;

[0032] (4) Output the optimal filler solution based on the score ranking and generate a visual comparison map.

[0033] Preferably, the porosity of the physical properties in step (1) needs to be measured by mercury intrusion porosimetry, and the particle size distribution needs to be full d 10 / d 90 ≤0.5 to ensure hydraulic conductivity.

[0034] Preferably, the adsorption evaluation of the chemical properties is fitted by the Langmuir adsorption isotherm model, and the ion exchange capacity is expressed by the Ca content per unit mass of the filler. 2 + Exchange equivalent quantification.

[0035] Preferably, the microbial adhesion of the biological performance is characterized by observing the biofilm coverage by scanning electron microscopy, and the biofilm activity is characterized by the COD removal rate per unit time.

[0036] Preferably, the adjustment coefficient α in step (2) is optimized by a genetic algorithm, and the input parameters include filler type, sewage quality and treatment scale.

[0037] Preferably, the normalization process in step (3) adopts the range method, and the positive index and the negative index are respectively and calculate

[0038] Preferably, the visual comparison map includes a radar map and a heat map, which dynamically displays the advantages and disadvantages of different fillers in various indicator dimensions. max

[0039] Preferably, a dynamic update module is also included to automatically expand the scoring system and recalculate the weight when a new filler type is added.

[0040] Preferably, the operation and maintenance costs in the economic indicators include backwash frequency, packing replacement cycle and energy consumption cost, which are quantified by life cycle cost analysis (LCCA).

[0041] Preferably, the method is integrated into a cloud-based decision-making platform, supporting multiple users to collaboratively fill in data and call historical case libraries for similarity matching.

[0042] Example 1

[0043] Step 1: Establish a multi-indicator scoring system

[0044] Physical properties: Porosity was determined by mercury intrusion method and particle size distribution was verified by sieving method (d 10 / d 90 ≤0.5);

[0045] Chemical properties: Adsorption capacity (qe) fitted by Langmuir model, Ca 2 +Exchange equivalents quantify ion exchange capacity;

[0046] Biological performance: Scanning electron microscopy (SEM) was used to observe biofilm coverage, and COD removal rate per unit time was used to characterize activity;

[0047] Economical: Life cycle cost analysis (LCCA) is used to calculate the packing replacement cycle and backwash energy consumption;

[0048] Step 2: Weight calculation and optimization

[0049] Subjective weight (wi'): The judgment matrix is ​​constructed through AHP, and the priority is determined by expert scoring;

[0050] Objective weight (wi”): Entropy weight method is used to analyze the data dispersion. The formula is wi″=(1-Ei) / ∑(1-Ei)

[0051] Where E_i is information entropy;

[0052] Dynamic adjustment: Genetic algorithm optimizes the α value, and the input parameters include sewage BOD5 load and filler cost threshold;

[0053] Step 3: Fuzzy comprehensive evaluation and visualization

[0054] Normalization: Positive indicators (such as specific surface area) are used Negative indicators (such as wear rate) are inversely normalized;

[0055] Comprehensive score calculation: S = ∑(Wi·Ri), output ranking results;

[0056] Graph generation: Python draws a three-dimensional radar graph to show the biofilm activity (≥80% coverage) and economic efficiency (cost ≤500 yuan / m3 ) balance;

[0057] Step 4: Cloud Platform Integration

[0058] Data entry: Multiple users upload filler parameters (such as the hydroxyl content of polyurethane modified fillers) through the API interface;

[0059] Historical case matching: Recommend the optimal filler solution for similar water quality (such as TN ≥ 30mg / L) based on similarity algorithm.

[0060] Example 2: Selection of filler for municipal sewage treatment plants

[0061] Sewage characteristics: COD = 300 mg / L, NH3-N = 40 mg / L, TP = 5 mg / L;

[0062] Candidate fillers: A (ceramsite), B (activated carbon), C (modified polyethylene);

[0063] Evaluation results:

[0064] Weight distribution: biological performance has the highest weight (W=0.4), followed by economic performance (W=0.3);

[0065] Comprehensive score: Filler B ranked first due to its high adsorption capacity (qe=45.6mg / g) and low backwash frequency (3 times / month);

[0066] Visualization output: The heat map shows the difference between filler B in TP removal rate (90%) and cost (480 yuan / m 3 )Dual dimensions are optimal.

[0067] Example 3 Dynamic Evaluation of Industrial Wastewater

[0068] Scenario: Adding D filler (magnetic effect composite filler) to the phenol-containing wastewater treatment system;

[0069] Dynamic update: The platform automatically expands the indicator system, adds the "magnetic field strength (≥10mT)" indicator, and readjusts the genetic algorithm to α = 0.6;

[0070] Results: Filler D jumped to the first place due to its stimulation of bacterial metabolism (COD removal rate increased by 2 times), and the system recommended it to replace the original zeolite filler.

[0071] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

Claims

1. A multi-index comprehensive evaluation method for sewage treatment filler performance comparison, characterized in that: The following steps are involved: (1) Establish a multi-index scoring system; establish a scoring system that includes physical properties, chemical properties, biological properties, and economic indicators. The physical properties include particle size, porosity, and mechanical strength; the chemical properties include adsorption and ion exchange capacity; the biological properties include microbial attachment and biofilm activity; and the economics include filler cost and operation and maintenance costs. (2) Weight calculation and optimization: The subjective weight wi′ of each indicator is determined by the analytic hierarchy process (AHP), and the objective weight wi″ is calculated by the entropy weight method. The comprehensive weight is corrected by combining the formula Wi = αwi′ + (1-α)wi″, where α is the adjustment coefficient 0≤α≤1; (3) Fuzzy comprehensive evaluation and visualization: The comprehensive score of the filler is calculated through the fuzzy comprehensive evaluation model, where Ri is the normalized measured value of the index; (4) Cloud platform integration; output the optimal filler solution based on the score ranking and generate a visual comparison map.

2. A multi-index comprehensive evaluation method for sewage treatment filler performance comparison and selection according to claim 1, characterized in that: The porosity of the physical properties in step (1) must be determined by mercury intrusion porosimetry, and the particle size distribution must be full d 10 / d 90 ≤0.5 to ensure hydraulic conductivity.

3. A multi-index comprehensive evaluation method for sewage treatment filler performance comparison and selection according to claim 1, characterized in that: The adsorption evaluation of the chemical properties was performed by fitting the Langmuir adsorption isotherm model, and the ion exchange capacity was calculated by the Ca content per unit mass of filler. 2 + Exchange equivalent quantification.

4. A multi-index comprehensive evaluation method for sewage treatment filler performance comparison and selection according to claim 1, characterized in that: The microbial adhesion of the biological performance was measured by observing the biofilm coverage using a scanning electron microscope, and the biofilm activity was characterized by the COD removal rate per unit time.

5. The multi-index comprehensive evaluation method for sewage treatment filler performance comparison according to claim 1 is characterized in that: The adjustment coefficient α in step (2) is optimized by a genetic algorithm, and the input parameters include filler type, sewage quality and treatment scale.

6. A multi-index comprehensive evaluation method for sewage treatment filler performance comparison and selection according to claim 1, characterized in that: The normalization process of step (3) adopts the range method, and the positive index and the negative index are respectively and calculate.

7. A multi-index comprehensive evaluation method for sewage treatment filler performance comparison and selection according to claim 1, characterized in that: The visual comparison chart includes a radar chart and a heat map, which dynamically displays the advantages and disadvantages of different fillers in various indicator dimensions.

8. A multi-index comprehensive evaluation method for sewage treatment filler performance comparison and selection according to claim 1, characterized in that: It also includes a dynamic update module that automatically expands the scoring system and recalculates weights when new filler types are added.

9. A multi-index comprehensive evaluation method for sewage treatment filler performance comparison and selection according to claim 1, characterized in that: The operating and maintenance costs in the economic indicators include backwash frequency, packing replacement cycle and energy consumption cost, which are quantified through life cycle cost analysis (LCCA).

10. A multi-index comprehensive evaluation method for sewage treatment filler performance comparison and selection according to claim 1, characterized in that: The method is integrated into a cloud-based decision-making platform, supporting multiple users to collaboratively fill in data and call a historical case library for similarity matching.

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