Non-ferrous smelting industry hazardous waste whole process risk assessment system and method

Abstract

This invention relates to a system and method for comprehensive risk assessment of hazardous waste in the non-ferrous metal smelting industry, comprising a waste attribute identification module, a toxicity migration effect module, a process disposal difficulty module, a comprehensive assessment module, and an analysis module. The system quantifies the flammability, corrosivity, reactivity, and toxicity of the waste using the entropy weight method to obtain attribute risk scores. CER Leaching risk scores are calculated using leaching toxicity data. LH The analytic hierarchy process (AHP) was used to assess the resource recyclability, operational difficulty, and degradation cycle of the waste, resulting in a comprehensive score for the difficulty of the treatment process. RDD The entropy weight method is used to... CER , LH and RDD By comprehensively assigning weights and calculating the total risk score for the entire process, hazardous waste is classified according to risk, major risk sources are identified, and targeted measures are taken. This invention achieves a multi-dimensional quantitative assessment of the entire chain of hazardous waste from generation to disposal, improving the objectivity of risk identification and the accuracy of management decisions.

Description

Technical Field

[0001] This invention belongs to the field of environmental risk management and hazardous waste treatment technology, specifically relating to a comprehensive and quantitative risk assessment system and method for hazardous waste in the non-ferrous metal smelting industry. This system and method are particularly suitable for the full-process risk quantification, ranking, and hierarchical management of multi-source, complex-component hazardous waste generated during the smelting processes of copper, aluminum, lead, zinc, etc. Background Technology

[0002] The non-ferrous metals industry is a crucial foundation of the national economy, generating substantial amounts of solid waste, wastewater, and waste gas during its production processes. A significant portion of this waste is hazardous, including smelting slag, anode mud, and acid sludge. These hazardous wastes are complex in composition, often containing various toxic heavy metals such as arsenic, lead, cadmium, and mercury, as well as persistent organic pollutants. Without proper management, they pose a serious threat to the ecological environment and human health throughout their entire life cycle. Globally, the annual production of hazardous waste exceeds 400 million tons, with a cumulative storage of approximately 2.5 billion tons. As a major producer and exporter of hazardous waste, my country experiences an annual growth rate exceeding 4% of its economic growth rate. Its large population further exacerbates the severity of the problem, making environmental risk management particularly challenging.

[0003] At the level of risk assessment methodologies, existing technologies have significant limitations. Qualitative and semi-quantitative methods (such as expert assessment and risk matrix methods) are highly subjective and lack accuracy. While methods based on the analytic hierarchy process (AHP) are commonly used, they struggle to objectively quantify cross-process indicators and are easily affected by the consistency of expert judgments. Methods such as Bayesian networks have high requirements for data quality and model construction, limiting their practicality. Furthermore, existing assessments often focus on a single stage of hazardous waste (such as storage, disposal, or single pollutant attributes such as leaching toxicity and ecological risk), lacking a systematic consideration of the risks across the entire chain of generation, collection, storage, transportation, utilization, and disposal, especially neglecting the secondary risks arising from the disposal process itself. Therefore, there is an urgent need to establish a comprehensive risk assessment methodology that covers waste attributes, migration behavior, and disposal difficulty to support the precise, hierarchical, and classified management of hazardous waste in the non-ferrous metals industry. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the present invention aims to provide a method for comprehensive risk assessment of hazardous waste in the non-ferrous metal smelting industry. Focusing on the environmental risks of hazardous waste in China's non-ferrous metal smelting industry, this invention constructs a comprehensive risk assessment framework integrating waste attribute identification, toxicity migration effects, and process treatment difficulty. Empirical analysis is conducted using copper and aluminum smelting as core research objects, providing a scientific method and practical basis for the classification, categorization, and targeted management of hazardous waste in the industry.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: In a first aspect, the present invention provides a whole-process risk assessment system for hazardous waste in the non-ferrous metal smelting industry, characterized in that it includes a waste attribute identification module, a toxicity migration effect module, a process treatment difficulty module, a comprehensive assessment module, and an analysis module. The waste attribute identification module, based on four basic indicators of hazardous waste—combustibility (I), corrosivity (C), reactivity (R), and toxicity (T)—uses the entropy weight method to determine the weight of each indicator and calculates the waste attribute identification score. WMI and characteristic environmental risk score CER To quantify the inherent hazardous properties of waste; The toxicity migration effect module is used to calculate the absolute leaching toxicity based on the leaching toxicity data of hazardous waste. LH A and relative leaching toxicity LH R To assess the risk of pollutants migrating and being released into the environment; The process handling difficulty module is constructed based on the analytic hierarchy process (AHP), including resource recyclability. RET Operational difficulty DD and degradation cycle DC The evaluation system uses three secondary indicators to calculate a comprehensive score based on the difficulty of the processing technology. RDD ; The comprehensive evaluation module is used to... CER , LH and RDD The total score for the whole-process risk assessment is obtained by weighted summation using the entropy weight method. H This is further compared with the annual production of hazardous waste. N Combined, the annual total risk index is obtained. TAR Hazardous waste is classified into risk levels based on preset risk level thresholds; The analysis module, with TAR The classification results provide an overall assessment of the risks of hazardous waste from non-ferrous metal smelting throughout the entire process. Based on the outputs of the waste attribute identification module, toxicity migration effect module, and process treatment difficulty module, targeted risk tracing analysis and control strategy recommendations can be provided.

[0006] Furthermore, in the aforementioned analytic hierarchy process, the target layer is the difficulty of process handling, and the criterion layer is set as resource recyclability. RET Operational difficulty DD Degradation cycle DC The indicator layer consists of 10 sub-indicators: recyclability (L11), secondary environmental pollution (L21), complexity of emitted pollutants (L22), operational hazards (L23), complexity of treatment processes (L24), complexity of equipment maintenance (L25), transportation and logistics constraints (L26), refractory materials (L31), degradation difficulty (L32), and degree of cross-media contamination (L33). Resource recyclability score RET Calculations based on the quality and market value of recyclable metals: In the formula, m A,r The quality of recyclable valuable metals; P r Let d represent the recycling value of a certain valuable metal, and d represent the number of types of valuable metals. When applying the AHP method to conduct assessments, we should try to follow the principles of completeness and operability to establish an index system for the difficulty of hazardous waste treatment and disposal. Experts are required to evaluate the relative importance of different factors, construct a pairwise comparison matrix, calculate the maximum eigenvalue and corresponding eigenvector of the matrix, obtain the weight of each index, and conduct a consistency test. Comprehensive score of process handling difficulty in the assessment of process handling difficulty RDD The calculation is as follows: , In the formula, DD Score based on the difficulty of operation. DC The degradation cycle score was also determined by expert scoring using the analytic hierarchy process.

[0007] Secondly, this invention provides a method for risk assessment of hazardous waste throughout the entire process in the non-ferrous metal smelting industry, the method comprising the following: Based on four fundamental indicators of hazardous waste—combustibility (I), corrosivity (C), reactivity (R), and toxicity (T)—the entropy weight method is used to determine the weight of each indicator and calculate the waste attribute identification score. WMI and characteristic environmental risk score CER To quantify the inherent hazardous properties of waste; Calculate the absolute leaching toxicity based on the leaching toxicity data of hazardous waste. LH A and relative leaching toxicity LH R To assess the risk of pollutants migrating and being released into the environment; Based on the analytic hierarchy process, a system including resource recyclability is constructed. RET Operational difficulty DD and degradation cycle DC The evaluation system uses three secondary indicators to calculate a comprehensive score based on the difficulty of the processing technology. RDD ; Will CER , LH and RDD The total score for the whole-process risk assessment is obtained by weighted summation using the entropy weight method. H This is further compared with the annual production of hazardous waste. N Combined, the annual total risk index is obtained. TARHazardous waste is classified into risk levels based on preset risk level thresholds; by TAR The classification results provide an overall assessment of the risks associated with hazardous waste disposal throughout the entire non-ferrous metal smelting process, and can be based on... CER , LH A , LH R and RET, DD, DC It provides targeted risk tracing analysis and control strategy recommendations.

[0008] The risk assessment method for hazardous waste from non-ferrous metal smelting applied to the above system includes: data acquisition and input; calculation of waste attribute identification module operation. CER ; Run the toxicity migration effect module to calculate LH ; Calculation of the difficulty of handling the process during operation RDD The comprehensive evaluation module calculates the overall score. H and annual total risk index TAR The operation analysis and management module performs risk classification, source tracing, and provides control recommendations.

[0009] Compared with the prior art, the beneficial effects of the present invention are: This invention establishes a universal, end-to-end evaluation framework for the non-ferrous metals industry, considering aspects such as waste attribute identification, toxic migration effects, and process disposal difficulty. A novel risk assessment method is constructed by combining the entropy weight method and the analytic hierarchy process (AHP). Waste attribute identification is based on elemental and material composition, assessing inherent hazards such as flammability, corrosivity, reactivity, and toxicity. Toxic migration effects are assessed through leaching toxicity analysis, quantifying the potential for pollutant release and transport in the environment. At the macro level, process disposal difficulty is quantified by integrating factors such as resource recovery feasibility, operational difficulty, and environmental durability. Finally, the comprehensive end-to-end risk assessment provides ranking and classification for hazardous waste. This contributes to regulatory strategies, process optimization, and sustainable hazardous waste management in the non-ferrous metals industry.

[0010] In this invention, AHP is only used to assess the difficulty of process treatment. A three-level hierarchical assessment system including target layer, criterion layer, and indicator layer is constructed. The weight assignment and consistency test of 10 sub-indicators in this dimension are completed. Finally, the entropy weight method is used to assign global weights to the three dimensions of "waste attribute identification, toxic migration effect, and process treatment difficulty" to form a complete full-process risk assessment result. Attached Figure Description

[0011] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is a multi-faceted framework diagram of waste attribute identification, toxic migration effects, and process treatment difficulty described in this invention; Figure 2 Hazardous waste attribute identification results: (a) Various hazardous waste categories WMI (b) Various components in the nine hazardous waste categories of copper and aluminum. WMI (c) 9 categories of hazardous waste CER ; Figure 3 Results of toxic migration effects of hazardous waste in the copper and aluminum smelting industries: (a) Hazardous waste in copper LH A (b) Hazardous waste in aluminum LH A (c) Hazardous waste in copper LH R (d) Hazardous waste in aluminum LH R (e) Specific gravity of various leached metals; Figure 4 Results of process handling difficulty: (a) Recyclable metal value of hazardous waste category; (b) Resource recyclability assessment; (c) Operational difficulty of hazardous waste treatment; (d) Degradation cycle assessment; (e) Overall process handling difficulty assessment; Figure 5 For full-process risk assessment: (a) various categories H (b) All categories TAR (c) 2021-2023 categories TAR (d) Classification and grading management of hazardous waste in the copper and aluminum smelting industry. Detailed Implementation

[0012] The present invention will be further explained below with reference to the embodiments and accompanying drawings, but this is not intended to limit the scope of protection of this application.

[0013] This invention relates to a risk assessment system for hazardous waste in the non-ferrous metal smelting industry throughout the entire process. The system includes a waste attribute identification module, a toxicity migration effect module, a process disposal difficulty module, a comprehensive assessment module, and an analysis module. This invention pioneers a risk classification and control system for hazardous waste in the non-ferrous metal smelting industry throughout its entire life cycle, as follows: (1) Waste attribute identification module: Based on the composition of hazardous waste materials / elements, the entropy weight method is used to clarify the weights of four major indicators: flammability, corrosivity, reactivity, and toxicity. (2) Toxicity migration effect module: The absolute leaching (LHA) and relative leaching (LHR) are used for dual-dimensional quantification, covering both the inherent migration potential of pollutants and the risk of exceeding national standard limits, thus solving the one-sidedness of single-indicator assessment (i.e., only outputting absolute leaching concentration, or only making a binary judgment of "compliant / exceeding the standard"). (3) Process disposal difficulty module: The analytic hierarchy process is used to construct a three-level hierarchical assessment system, covering three secondary indicators (resource recyclability, operational difficulty, and degradation cycle) and ten tertiary quantitative indicators, achieving full coverage of the entire disposal process. (4) Comprehensive assessment module: The three dimensions of waste properties, toxicity migration and disposal difficulty are systematically coupled. The dimensional weights are determined by the entropy weight method to form a comprehensive risk score (H) for the whole process. The annual production scale is innovatively coupled to construct the total annual risk index (TAR). Based on the TAR, a hazardous waste classification and management system is established, realizing a closed loop from "methodology" to "implementation and control".

[0014] The waste attribute identification module is used to determine the weights of flammability, corrosivity, reactivity, and toxicity of hazardous waste based on its composition data using the entropy weight method, and to calculate a characteristic environmental risk score. CER Calculate the weights of I, C, R, and T using the entropy weight method. w1 j . Specifically: Step 1 is to construct the initial decision matrix, which is constructed as follows: In the formula, X Let be the initial decision matrix. m The number of indicators (in this embodiment of the invention, the number of indicators is 4, representing I, C, R, and T respectively). n This represents the number of objects (in this example, the number of objects is 35, representing the 35 substances or elements: Cr2O3, NiO, CuO, ZnO, As2O3, SeO, CdO, BaO, Hg, PbO, F, Fe2O3, SO3, SiO2, Al2O3, MgO, CaO, Na2O, K2O, CoO, TiO, MnO, MoO3, Ag, ZrO2, Bi2O3, Cl, Sb2O3, TeO2, CeO2, WO3, P2O5, SnO2, Tb4O7, and Er2O3). x ij These are the analytical values ​​for each sample parameter. i= 1,2,…, n , j =1,2,…, m ; Step 2 is the normalization of the initial decision matrix. Due to the inconsistency in the dimension and metric of the data, it is necessary to normalize the matrix. The normalized decision matrix Y can be expressed as: ; in, y ij Represents the elements in the normalized decision matrix Y, ( xij ) j max 、( xij ) j min These represent the maximum and minimum values ​​of the j-th indicator, respectively; Step 3 involves calculating the information entropy of each indicator, which can be done as follows: The proportion of the value of the i-th object under the j-th indicator to the total value of the indicator is:

[0015] The information entropy of the j-th indicator is, ; Step 4 involves calculating the weights of each indicator, which can be done as follows: ; Step 5 is to calculate the waste attribute identification score for each substance (element): , In the formula, m =4; y ij These are the values ​​of the four core indicators in the normalized matrix; The characteristic environmental risk score is: , In the formula, M i The mass fraction of each substance (element) in this hazardous waste category.

[0016] The toxicity migration effect module is used to calculate the absolute leaching toxicity based on the leaching toxicity data of hazardous waste. LH A and relative leaching toxicity LH R Assess the risk of pollutant migration and release into the environment. Specifically: Absolute leaching toxicity LH A The calculation formula is: , , In the formula, W p The weight of each substance in different hazardous waste categories. CC p This represents the actual leaching concentration of each substance. RC p Let p = 1, ..., q, where q is the number of leached substances in the hazardous waste (in this example, q = 11, and the leached substances are Cr, Ni, Cu, Zn, As, Se, Cd, Ba, Hg, Pb, and F). - ); Relative leaching toxicity LH R The calculation formula is: If the leaching concentration does not exceed the standard, CC p - RC p Then set it to 0.

[0017] The process handling difficulty module is used to assess the resource recyclability, operational difficulty, and degradation cycle of hazardous waste based on the analytic hierarchy process (AHP), and to calculate a comprehensive score for process handling difficulty. RDD : In the Analytic Hierarchy Process (AHP), the objective layer is the difficulty of process handling, and the criterion layer is set as resource recyclability. RET Operational difficulty DD Degradation cycle DC The indicator layer consists of 10 sub-indicators: recyclability (L11), secondary environmental pollution (L21), complexity of emitted pollutants (L22), operational hazards (L23), complexity of treatment processes (L24), complexity of equipment maintenance (L25), transportation and logistics constraints (L26), refractory materials (L31), degradation difficulty (L32), and degree of cross-media contamination (L33). Resource recyclability score RET Calculations based on the quality and market value of recyclable metals: In the formula, m A,r The quality of recyclable valuable metals; P r The value of a certain valuable metal is denoted by d, which represents the number of valuable metal types (d=7 in this embodiment, which are seven metal types: Zn, Cu, Al, Fe, Pb, Au, and Ag). Considering the numerous influencing factors such as the recycling value and treatment technology of hazardous waste during the evaluation process, the AHP method should be applied to establish a hazardous waste treatment and disposal difficulty index system in accordance with the principles of completeness and operability. Experts are required to evaluate the relative importance of different factors, construct a pairwise comparison matrix, calculate the maximum eigenvalue and corresponding eigenvector of the matrix, obtain the weight of each index, and conduct a consistency test.

[0018] The eigenvectors, after normalization, become the weight vectors, which are then calculated using the geometric mean method as follows: Assume judgment matrix Weight vector , In the formula, z is the matrix order (i.e., the number of sub-indicators belonging to resource recyclability, operational difficulty, and degradation cycle in the third layer (index layer). In this embodiment of the invention, resource recyclability has 1 sub-indicator, operational difficulty has 6 sub-indicators, and degradation cycle has 3 sub-indicators, i.e., z is 1, 6, and 3 respectively). , All are elements in the judgment matrix A; based on the number of sub-indicators belonging to resource recyclability, operational difficulty, and degradation cycle, according to the weight vector The calculation formulas yielded the respective weights of resource recyclability, operational difficulty, and degradation cycle. , , .

[0019] During the process, the specified consistency ratio is used. CR To assess the potential inconsistencies arising from pairwise comparisons and ensure an acceptable deviation threshold: , In the formula, RI It is a random consistency index; CI It is a metric for measuring the deviation of the judgment matrix from consistency. λ is the largest eigenvalue of the judgment matrix. If the consistency ratio CR is less than 0.1, the numerical judgment is considered acceptable.

[0020] Comprehensive score of process handling difficulty in the assessment of process handling difficulty RDD The calculation is as follows: In the formula, DD Score based on the difficulty of operation. DC The degradation cycle score was also determined by expert scoring using the analytic hierarchy process.

[0021] The comprehensive evaluation module is used to utilize the CER , LH and RDD The scores were determined by using the entropy weight method to determine the weights of the three factors and calculating the total score for the entire risk assessment process. H Further combined with annual production volume N Calculate the annual total risk index TAR Risk classification: Total score of risk assessment throughout the entire process H The calculation formula is: , in, , , They represent the values ​​determined by the entropy weight method. CER , LH and RDD The corresponding weights.

[0022] Total Annual Risk Value of Hazardous Waste TAR The calculation is as follows: , In the formula, N The annual production of hazardous waste; here LH is defined as absolute leaching toxicity. LH A count.

[0023] like TAR If the value is ≥16, it is classified as Category I high-risk waste, and priority control and key monitoring measures are recommended; if 8≤ TAR If the value is less than 16, it is classified as Class II medium-risk waste, and routine supervision and process optimization are recommended; if TAR If the value is less than 8, it is classified as Class III low-risk waste and can be subject to simplified management.

[0024] The analysis and management module is used for... CER , LH , RDD The relative size of the risk source analysis and control recommendations are output.

[0025] The implementation of this invention relies on the collection and organization of basic data on the target hazardous waste, including its chemical composition, leaching toxicity data, valuable metal content and market price, typical treatment process routes, environmental degradation characteristic parameters, and annual production data. This data can be obtained from the national environmental statistics database, enterprise discharge permit implementation reports, published academic literature, or authoritative technical manuals.

[0026] Waste properties are physicochemical indicators, toxicity migration is an environmental indicator, and disposal difficulty is a technological indicator. Their evaluation logic and dimensions are completely different. Therefore, this invention performs 0-1 interval normalization on all indicators in the entire system before treatment to completely eliminate dimensional differences. At the same time, the global weights of the three modules are determined by the entropy weight method (CER 0.33, LH 0.26, RDD 0.41), and a unified risk quantification logic is constructed. The outputs of all modules are superimposed risk scores, realizing the unified quantification of indicators of different dimensions.

[0027] The risks of hazardous waste throughout its entire life cycle are transmitted in a chain (inherent properties determine migration potential, and migration potential determines disposal difficulty). The chain-closed-loop assessment framework for waste property identification, toxic migration effects, and treatment and disposal difficulty constructed in this invention follows the risk transmission logic of hazardous waste throughout its entire life cycle and fully reflects the risk transmission nature.

[0028] With a large number of indicators across the entire chain, purely subjective weighting is easily influenced by expert experience, while purely objective weighting cannot adapt to the practical characteristics of the process. This invention uses entropy weighting to objectively weight the waste attributes and toxicity migration modules, which have a large amount of objective data, to avoid subjective bias. For the process handling difficulty module, which relies on expert experience, AHP subjective weighting is used, while controlling deviation through a consistency check of CR < 0.1. Finally, the entropy weighting method is used to complete the global objective weight allocation of the three modules, achieving a balance between subjective and objective weights.

[0029] The toxic migration effect module of this application is designed to quantify the migration risk and degree of harm of pollutants in the environment after leaching. It utilizes a two-dimensional evaluation system of absolute leaching capacity (LHA) and relative leaching capacity (LHR): LHA quantifies the total release potential of pollutants and reflects the absolute migration risk; LHR quantifies the degree of exceedance based on the comparison of leaching concentration with the limit, and is used to distinguish between the two degrees of "leaching but no compliance risk" and "high risk of exceeding the limit".

[0030] This invention covers the entire disposal chain from resource utilization and harmless treatment to final environmental fate, which is broken down into three dimensions: resource recyclability, operational difficulty, and degradation cycle, rather than isolated end links.

[0031] Example: Taking nine typical hazardous wastes generated by China's copper and aluminum smelting industry as examples Nine categories of hazardous waste from non-ferrous metal smelting, generated in large quantities and posing significant environmental risks nationwide between 2021 and 2023, were selected as the assessment targets. These wastes have specific codes in the National Hazardous Waste List, such as 321-023-48 (denoted as NF23) and 321-002-48 (denoted as NF02). Details are as follows: S1, Waste attribute risk score ( CER )calculate: Based on the flammability (e.g., flash point), corrosiveness (e.g., corrosion rate), reactivity (e.g., electrode potential), and toxicity (e.g., median lethal dose, LD50) of each component (substance or element). 50 The parameters of all involved components are normalized to form an initial evaluation matrix. The entropy weight method is used to calculate the objective weights of the four attributes. In this embodiment, the calculated weights are: toxicity (0.79), reactivity (0.18), corrosivity (0.03), and flammability (0.00). Subsequently, the waste attribute identification score of each substance is calculated. WMI ,like Figure 2 As shown in (a), the nine types of hazardous waste selected from the copper and aluminum industries... WMI like Figure 2As shown in (b), the nine hazardous wastes are designated as NF02, NF27, NF31, NF32, NF23, NF24, NF25, NF26, and NF34. NF02 contains large amounts of Fe2O3 (116.03%) and As2O3 (101.18%). NF32 also has a high Fe2O3 content (267.48%). NF31 contains large amounts of Fe2O3 (84.40%), CdO (77.42%), and As2O3 (55.78%). NF27 contains large amounts of As2O3 (94.59%) and CdO (63.47%). The weighted summation based on the mass fraction of each component in the corresponding waste type yields the final value for each waste type. CER Value. The calculation result is as follows: Figure 2 As shown in (c), aluminum smelting waste NF23 has a high fluoride content and a high toxicity weight. CER The values ​​were significantly higher than those of other wastes: NF23 (708.66), NF25 (373.36), NF02 (358.93), NF32 (346.31), NF31 (247.29), NF27 (246.62), NF24 (66.23), NF34 (40.14), and NF26 (11.60), indicating that it had the greatest inherent hazard.

[0032] S2, Toxicity Migration Risk Score ( LH )calculate: The leaching concentration of each pollutant ( CC ) and the corresponding concentration limit in the "Standard for Identification of Hazardous Waste" ( RC Compare using... LH A ( Figure 3 (a) and (b) in the middle LH R ( Figure 3 (c) and (d) in the text illustrate the toxic migration effects of hazardous waste. For aluminum smelting waste, due to the limited mobility of heavy metals under typical leaching conditions, LH R Usually below LH A In contrast, fluorides frequently exceed regulatory thresholds, thus requiring the use of... LH A To more accurately reflect potential environmental risks. For copper smelting waste, LH R and LH A The high degree of consistency indicates a robust leaching risk profile. LH AThe results are as follows: NF31 (11413.05), NF27 (3865.37), NF02 (1858.48), NF32 (1649.83), NF23 (1155.96), NF25 (118.78), NF24 (74.49), NF34 (46.74), and NF26 (1.92). Overall, the leaching toxicity of copper smelting waste is significantly higher than that of aluminum smelting waste.

[0033] The relative magnitude of leaching toxicity between different substances is expressed by the specific gravity of the leachate in the hazardous waste, such as... Figure 3 As shown in (e), copper exhibits significantly higher leaching toxicity, which can be attributed to the better fluidity of arsenic under typical leaching conditions: NF31 (11412.89), NF27 (3652.17), NF32 (1649.39), and NF02 (1196.04). Arsenic in copper smelting residues is typically present in more soluble forms such as arsenite and arsenate. Aluminum exhibits relatively high leaching toxicity, primarily due to higher fluoride content in the leachate: NF23 (1155.94), NF25 (117.77), NF24 (74.47), NF34 (46.66), and NF26 (1.92). Therefore, targeted protection measures should be implemented to control leaching conditions for arsenic and fluorides during storage. Furthermore, the development of leaching schemes that better simulate real-world environmental conditions is necessary, particularly important for anionic contaminants such as fluorides.

[0034] S3, Processing Difficulty Score ( RDD )calculate: An evaluation model was constructed using the analytic hierarchy process (AHP). The model includes an objective layer (process handling difficulty) and a criterion layer (resource recyclability). RET Operational difficulty DD Degradation cycle DC The evaluation criteria were determined using an indicator layer (10 sub-indicators and their weights as follows: L11: Recyclability (0.11), L21: Secondary Environmental Pollution (0.07), L22: Complexity of Emitted Pollutants (0.04), L23: Operational Hazards (0.03), L24: Complexity of Processing Workflow (0.07), L25: Complexity of Equipment Maintenance (0.02), L26: Transportation and Logistics Restrictions (0.01), L31: Refractory Materials (0.02), L32: Degradation Difficulty (0.02), L33: Degree of Cross-Media Pollution (0.02)). A judgment matrix was constructed through an expert questionnaire survey, and the weights of each indicator were calculated and subjected to a consistency test (consistency ratio). CR <0.1).

[0035] Then, the resource value data, expert scores, and degradation characteristic data are substituted into the evaluation model to calculate... RET , DD, DC Then, a comprehensive score for the difficulty of the process is obtained by weighting and summing the results according to the weights. RDD Points.

[0036] The recyclable metal value resources of each hazardous waste category, such as Figure 4 As shown in (a) above, recyclability is calculated from this. RET like Figure 4 As shown in (b), NF02 (0.57) RET The smallest, while NF27, NF23 and NF25 RET The highest concentration (2.86) reflects their relatively high economic value in terms of metal content. Therefore, it is necessary to improve the treatment and recycling processes for these three types of hazardous waste to achieve greater economic benefits. Copper smelting waste typically contains multiple recyclable metals, including zinc (7.95), copper (4.92), and lead (2.43). In contrast, aluminum smelting waste, due to the electrolytic production route, is primarily composed of recyclable aluminum, NF24 (14.25), NF34 (8.55), and NF26 (8.14). These differences highlight the varying economic incentives for recycling within the smelting industry. Figure 4 (c) shows the operational difficulty scores for each category: NF32 (5.38), NF31 (5.14), NF02 (3.90), NF27 (3.81), NF23 (2.99), NF25 (2.96), NF34 (2.61), NF24 (2.19), and NF26 (2.19). High disposal difficulty scores are generally associated with complex contaminant mixtures and the need for multi-stage treatment processes. The relatively high values ​​for L24 (1.05) and L21 (0.99) indicate complex treatment processes and significant environmental pollution risks, a problem of considerable importance in the copper smelting industry. The lower scores for L25 (0.21) and L26 (0.13) indicate relatively simple contaminants and minimal risk to operational safety, suggesting lower operational hazards in aluminum smelting compared to copper smelting. Degradation cycle analysis further indicates that copper smelting waste has higher persistence and cross-media pollution potential than aluminum smelting waste. The degradation cycle analysis scores are shown below. Figure 4 As shown in (d), NF31 (1.43), NF32 (1.43), NF27 (1.14), NF23 (0.86), NF02 (0.84), NF25 (0.69), NF24 (0.67), NF26 (0.67), and NF34 (0.67) have relatively balanced risk values ​​associated with recalcitrant substances, degradation difficulty, and cross-media contamination levels (0.24, 0.38, 0.31, respectively). NF31, NF32, and NF27 score highly in each aspect, leading to serious contamination problems related to degradation in copper smelting processes. The scores for process handling difficulty assessment are as follows: Figure 4As shown in (e), NF32 (9.10), NF27 (7.81), NF31 (7.71), NF23 (6.70), NF25 (6.50), NF34 (5.56), NF02 (5.31), NF26 (5.14), and NF24 (4.57) scored the highest. Special attention should be paid to the copper smelting industry, particularly for NF31, NF27, and NF32, when considering treatment and disposal. Among the nine categories, the treatment difficulty score (3.46) is very high, followed by resource recoverability (2.10) and degradation cycle (0.93). Recovering valuable metals can significantly improve the assessment of the difficulty of hazardous waste disposal by improving treatment processes and adopting environmentally sustainable methods.

[0037] S4, Comprehensive Assessment and Risk Classification Throughout the Process: First, calculate the overall risk score. H Nine types of waste CER , LH , RDD The three scores form a decision matrix. The entropy weight method is then applied to this matrix to determine the weights of the three scores in this comprehensive evaluation: 0.41 (…). RDD ), 0.33 ( CER ), 0.26 ( LH The total risk score for each type of waste is obtained by weighted summation. H ,like Figure 5 As shown in (a), NF31 (0.34), NF32 (0.39), NF23 (0.45), NF27 (0.51), NF25 (0.65), NF02 (0.73), NF34 (0.90), NF26 (0.95), and NF24 (0.97). The copper smelting industry typically generates high waste risks, primarily due to the large quantities of ore and metal involved in pyrometallurgical processes, compared to the smaller quantities of raw materials and hazardous waste involved in electrolytic aluminum production. Combining the average annual production from 2021 to 2023, TAR Calculation as Figure 5 As shown in (b), NF23 (21.50), NF26 (13.01), NF32 (8.13), NF02 (7.21), NF27 (6.00), NF25 (5.69), NF24 (5.35), NF34 (1.26), and NF31 (1.21) are high in aluminum smelting. The high concentrations of NF23 and NF26 in aluminum smelting are also significant. TAR This is mainly due to the higher average annual production of hazardous waste. In contrast, the production of NF32, NF02, and NF27 in copper smelting... TAR The increase stems from the generation of high-risk waste. To accurately analyze annual hazardous waste... TAR And compare the severity of the harm, 2021-2023 TAR like Figure 5 As shown in (c), NF23 has shown high performance every year. TAR (21.12, 21.57, 21.81), worth noting. NF26's... TAR The number of hazardous wastes has been increasing annually (8.16, 13.08, 17.80), therefore it is necessary to investigate the root causes of this increase and take effective prevention and control measures. Hazardous wastes are classified by integrating the average annual risk level, hazardous waste generation, and full-process risk assessment results. The results are shown in […]. Figure 5 In section (d), the assessment classified NF23 as a Category I hazardous waste, indicating a consistently high risk across multiple dimensions and requiring priority for regulatory oversight. This classification provides a practical basis for differentiated regulation and risk-based decision-making in the non-ferrous metal smelting industry.

[0038] Any aspects not covered in this invention are applicable to existing technologies.

Claims

1. A whole-process risk assessment system for hazardous waste in the non-ferrous metal smelting industry, characterized in that, It includes modules for waste property identification, toxicity migration effects, process handling difficulty, comprehensive assessment, and analysis. The waste attribute identification module, based on four basic indicators of hazardous waste—combustibility (I), corrosivity (C), reactivity (R), and toxicity (T)—uses the entropy weight method to determine the weight of each indicator and calculates the waste attribute identification score. WMI and characteristic environmental risk score CER To quantify the inherent hazardous properties of waste; The toxicity migration effect module is used to calculate the absolute leaching toxicity based on the leaching toxicity data of hazardous waste. LH A and relative leaching toxicity LH R To assess the risk of pollutants migrating and being released into the environment; The process handling difficulty module is constructed based on the analytic hierarchy process (AHP), including resource recyclability. RET Operational difficulty DD and degradation cycle DC The evaluation system uses three secondary indicators to calculate a comprehensive score based on the difficulty of the processing technology. RDD ; The comprehensive evaluation module is used to... CER , LH and RDD The total score for the whole-process risk assessment is obtained by weighted summation using the entropy weight method. H This is further compared with the annual production of hazardous waste. N Combined, the annual total risk index is obtained. TAR Hazardous waste is classified into risk levels based on preset risk level thresholds; The analysis module, with TAR The classification results provide an overall assessment of the risks of hazardous waste from non-ferrous metal smelting throughout the entire process. Based on the outputs of the waste attribute identification module, toxicity migration effect module, and process treatment difficulty module, targeted risk tracing analysis and control strategy recommendations can be provided.

2. The system according to claim 1, characterized in that, In the waste attribute identification module, the characteristic environmental risk score CER The calculation process is as follows: Step 1 is to construct the initial decision matrix, which is constructed as follows: ， In the formula, X Let be the initial decision matrix. m For the number of indicators, n For the number of objects, x ij These are the analytical values ​​for each sample parameter. i =1,2,…, n , j = 1,2,…, m ; Step 2 is to normalize the initial decision matrix. The normalized decision matrix Y is: ； in, y ij Represents the elements in the normalized decision matrix Y, ( xij ) j max 、( xij ) j min These represent the maximum and minimum values ​​of the j-th indicator, respectively; Step 3 is to calculate the information entropy of each indicator: The proportion of the value of the i-th object under the j-th indicator to the total value of the indicator is: , The information entropy of the j-th indicator is, ; Step 4 is to calculate the weight of each indicator. ; Step 5 is to calculate the waste attribute identification score for each substance / element: , In the formula, m =4; y ij These are the values ​​of the four core indicators in the normalized matrix; The characteristic environmental risk score is: , In the formula, M i The mass fraction of each substance / element in this hazardous waste category.

3. The system according to claim 1, characterized in that, In the toxicity migration effect module, the absolute leaching toxicity LH A The calculation formula is: Absolute leaching toxicity LH A The calculation formula is: , , In the formula, W p The weight of each substance in different hazardous waste categories. CC p This represents the actual leaching concentration of each substance. RC p Let p be the standard leaching concentration of each substance, p = 1, ..., q, where q is the number of leached substances contained in the hazardous waste.

4. The system according to claim 1, characterized in that, In the aforementioned analytic hierarchy process (AHP), the target layer is the difficulty of process handling, and the criterion layer is set as resource recyclability. RET Operational difficulty DD Degradation cycle DC The indicator layer consists of 10 sub-indicators: recyclability (L11), secondary environmental pollution (L21), complexity of emitted pollutants (L22), operational hazards (L23), complexity of treatment processes (L24), complexity of equipment maintenance (L25), transportation and logistics constraints (L26), refractory materials (L31), degradation difficulty (L32), and degree of cross-media contamination (L33). Resource recyclability score RET Calculations based on the quality and market value of recyclable metals: In the formula, m A,r The quality of recyclable valuable metals; P r Let d represent the recycling value of a certain valuable metal, and d represent the number of types of valuable metals. When applying the AHP method to conduct assessments, we should try to follow the principles of completeness and operability to establish an index system for the difficulty of hazardous waste treatment and disposal. Experts are required to evaluate the relative importance of different factors, construct a pairwise comparison matrix, calculate the maximum eigenvalue and corresponding eigenvector of the matrix, obtain the weight of each index, and conduct a consistency test. Comprehensive score of process handling difficulty in the assessment of process handling difficulty RDD The calculation is as follows: ， In the formula, DD Score based on the difficulty of operation. DC The degradation cycle score was also determined by expert scoring using the analytic hierarchy process.

5. The system according to claim 1, characterized in that, In the comprehensive evaluation module, the total score for the whole process risk assessment H The calculation formula is: in, , , They represent the values ​​determined by the entropy weight method. CER , LH and RDD The corresponding weights; The annual total risk value of hazardous waste is calculated as follows: ， In the formula, N Annual production of hazardous waste; like TAR If the value is ≥16, it is classified as Category I high-risk waste, and priority control and key monitoring measures are recommended; if 8≤ TAR If the value is less than 16, it is classified as Class II medium-risk waste, and routine supervision and process optimization are recommended; if TAR If the value is less than 8, it is classified as Class III low-risk waste and simplified management is implemented.

6. A method for risk assessment of hazardous waste throughout the non-ferrous metal smelting industry, characterized in that, The method includes the following: Based on four fundamental indicators of hazardous waste—combustibility (I), corrosivity (C), reactivity (R), and toxicity (T)—the entropy weight method is used to determine the weight of each indicator and calculate the waste attribute identification score. WMI and characteristic environmental risk score CER To quantify the inherent hazardous properties of waste; Calculate the absolute leaching toxicity based on the leaching toxicity data of hazardous waste. LH A and relative leaching toxicity LH R To assess the risk of pollutants migrating and being released into the environment; Based on the analytic hierarchy process, a system including resource recyclability is constructed. RET Operational difficulty DD and degradation cycle DC The evaluation system uses three secondary indicators to calculate a comprehensive score based on the difficulty of the processing technology. RDD ; Will CER , LH and RDD The total score for the whole-process risk assessment is obtained by weighted summation using the entropy weight method. H This is further compared with the annual production of hazardous waste. N Combined, the annual total risk index is obtained. TAR Hazardous waste is classified into risk levels based on preset risk level thresholds; by TAR The classification results provide an overall assessment of the risks associated with hazardous waste disposal throughout the entire non-ferrous metal smelting process, and can be based on... CER , LH A , LH R and RET, DD, DC It provides targeted risk tracing analysis and control strategy recommendations.

7. The method according to claim 6, characterized in that, The method employs the system described in any one of claims 1-5, comprising: data acquisition and input; calculation of CER by running the waste attribute identification module; calculation of LH by running the toxicity migration effect module; calculation of RDD by running the process disposal difficulty module; calculation of the comprehensive total score H and annual total risk index TAR by running the comprehensive assessment module; and risk classification, source tracing, and control recommendations by running the analysis and management module.

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