Chemical process technology route optimization method based on multi-dimension feasibility evaluation

Abstract

This invention relates to a method for optimizing chemical process technology routes based on multi-dimensional feasibility assessment. It automatically collects full lifecycle data of each candidate chemical process route from multiple data sources to form a standard database of process routes and constructs a multi-dimensional assessment index system. The method calculates the quantified full lifecycle data values ​​of each candidate chemical process route on each assessment index within the multi-dimensional assessment index system. The dynamically adjusted index weights based on the multi-dimensional assessment index system are used as the latest index weights. The standardized index values ​​and their corresponding latest index weights are then fused to obtain a comprehensive feasibility assessment value for each candidate chemical process route. Finally, the candidate chemical process route with the highest comprehensive feasibility assessment value is selected as the optimal recommended chemical process route. This method achieves multi-dimensional correlation and improves the optimization effect of chemical process routes.

Description

Technical Field

[0001] This invention relates to the field of chemical processes, and in particular to a method for optimizing chemical process technology routes based on multi-dimensional feasibility assessment. Background Technology

[0002] In the current context of industrial transformation, the chemical industry, as a foundation and pillar of the national economy, is facing unprecedented opportunities and challenges. Promoting technological innovation and achieving industrial upgrading are essential for the sustainable development of chemical enterprises. In this process, chemical companies must continuously develop high-performance, high-value-added new products to optimize product structure, reduce energy consumption, minimize environmental pollution, and ultimately achieve a dual improvement in economic and social benefits.

[0003] However, the exploration and selection of new process routes is itself a highly complex systems engineering project. When chemical companies face multiple potential process routes emerging at different stages, from laboratory research and pilot-scale amplification to industrial production, how to construct a scientific, comprehensive, and efficient evaluation mechanism to comprehensively assess the technical feasibility, economic rationality, environmental friendliness, and strategic alignment of these process route schemes, and accurately select the implementation plan that best suits the current resource endowment and long-term strategic goals of the chemical company, becomes a key decision-making challenge that determines the success or failure of technology investment and even affects the future fate of the company.

[0004] However, existing methods for selecting chemical process technologies have shortcomings: traditional methods often focus on one or several independent dimensions, which are scattered and independent of each other. They neglect the economic cost of implementing the process solution or its environmental and social impact, and rely too much on the subjective judgment of experts. This results in the selected process technology routes exhibiting a tendency of "isolation and singularity," making it impossible for chemical companies to predict the potential risks of the final selected process technology routes. This makes them extremely passive when facing uncertainty. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method for optimizing chemical process technology routes based on multi-dimensional feasibility assessment, which is an improvement over the above-mentioned prior art.

[0006] The technical solution adopted by this invention to solve the above-mentioned technical problems is: a method for optimizing chemical process technology routes based on multi-dimensional feasibility assessment, characterized by comprising the following steps: Step 1: Predetermine a set of candidate process routes formed by multiple candidate chemical process routes; Step 2: Automatically collect the full lifecycle data of each candidate chemical process route in the candidate process route set from multiple data sources; Step 3: Preprocess all collected full lifecycle data to obtain preprocessed full lifecycle data, and form a process route standard database from all preprocessed full lifecycle data; Step 4: Construct a multi-dimensional evaluation index system based on the process route standard database, which covers multiple evaluation dimensions and indicators. The multi-dimensional evaluation index system includes technology maturity dimension, economic dimension, safety dimension, environmental protection dimension, resource adaptability dimension and strategic synergy dimension, and each dimension has multiple evaluation indicators. Step 5: Standardize each lifecycle data in the process route standard database to obtain the standardized index value for each lifecycle data; wherein, the standardized index value and the lifecycle data have a one-to-one correspondence. Step 6: Dynamically adjust the weights of each evaluation indicator in the multi-dimensional evaluation indicator system to obtain the corresponding dynamically adjusted indicator weight values, and use the obtained dynamically adjusted indicator weight values ​​as the latest comprehensive indicator weights of the corresponding evaluation indicators; wherein, each evaluation indicator in the multi-dimensional evaluation indicator system has a dynamically adjusted indicator weight value that corresponds to it one by one. Step 7: Process the obtained standardized index values ​​and the corresponding latest index comprehensive weights to calculate the comprehensive feasibility evaluation value for each candidate chemical process route. Step 8: Sort all the obtained comprehensive feasibility assessment values ​​in descending order, and select the candidate chemical process route corresponding to the comprehensive feasibility assessment value with the largest value as the recommended optimal chemical process route.

[0007] Improved, in the method for selecting the chemical process technology route based on multi-dimensional feasibility assessment, the multiple data sources include technical data sources, economic data sources, safety data sources, environmental data sources, and asset data sources; technical data sources include laboratory reports, intellectual property documents, and pilot-scale reports; economic data sources include enterprise resource planning systems; safety data sources include safety information databases; environmental data sources include environmental management databases and production execution systems; and asset data sources include production execution systems and equipment management systems. The full lifecycle data includes technical data, economic data, safety data, environmental data, and asset data for candidate chemical process routes.

[0008] In a further improvement, the preprocessing in the chemical process technology route selection method based on multi-dimensional feasibility assessment includes cleaning and normalizing the full life cycle data.

[0009] Furthermore, in the method for selecting the chemical process technology route based on multi-dimensional feasibility assessment, the cleaning and normalization process includes: filling missing data in the full life cycle data using linear interpolation or Lagrange interpolation; smoothing and denoising abnormal data using wavelet transform or median filtering algorithms; and establishing unified quantification rules and assigning corresponding numerical values ​​to qualitative descriptions.

[0010] Improved, in the aforementioned method for selecting chemical process technology routes based on multi-dimensional feasibility assessment, the technology maturity dimension includes technology readiness level indicators, process stability index indicators, technology reliability score indicators, and intellectual property strength index indicators; the economic dimension includes unit product raw material cost indicators, unit product energy consumption cost indicators, return on investment indicators, and cost sensitivity coefficient indicators; the safety dimension includes comprehensive safety index indicators, inherent safety level score indicators, risk index indicators, and emergency response capability score indicators; the environmental protection dimension includes unit product waste emission equivalent indicators, unit product carbon footprint indicators, environmental compliance score indicators, and ecological impact index indicators; the resource adaptability dimension includes equipment compatibility score indicators, raw material availability index indicators, energy matching degree score indicators, and human resource adaptability indicators; and the strategic synergy dimension includes policy compliance indicators, technology route consistency score indicators, market synergy effect index indicators, and innovation capability contribution indicators.

[0011] Further improvements are made to the chemical process technology route selection method based on multi-dimensional feasibility assessment. In step 6, the process of dynamically adjusting the weights of each assessment indicator within the multi-dimensional assessment indicator system includes the following steps a1 to a3: Step a1: Based on expert experience, initialize the weights of each evaluation indicator to obtain the first weight vector of the corresponding evaluation indicator. Step a2: Receive the weights assigned by the user to each evaluation indicator based on strategic preferences, and obtain the second weight vector of the corresponding evaluation indicator. Step a3: Perform vector fusion processing on the obtained first indicator weight vector and second indicator weight vector to obtain the intelligent fusion comprehensive weight vector; wherein, each weight in the intelligent fusion comprehensive weight vector is the latest comprehensive weight of the corresponding evaluation indicator.

[0012] Furthermore, in the method for selecting the optimal chemical process technology route based on multi-dimensional feasibility assessment, the process of initializing the weights of each assessment indicator based on expert experience is as follows: Step b1: Establish a hierarchical structure with the optimal chemical process route as the target layer, the multi-dimensional criteria layer as the criterion layer, and all evaluation indicators as the indicator layer. Step b2: Based on multiple experts in the field of chemical processes, and using a 1-9 degree scaling method, pairwise comparisons of the relative importance of each dimension in the multi-dimensional process are performed to construct a judgment matrix for the comparison of the importance of the multi-dimensional process. Step b3: The maximum eigenvalue of the constructed judgment matrix and the eigenvector corresponding to the maximum eigenvalue are calculated using the eigenvector method, and the eigenvector is normalized to obtain the initial weight vector; wherein, each weight in the initial weight vector corresponds to each evaluation index in the multi-dimensional evaluation index system.

[0013] Improved, in the method for selecting the chemical process technology route based on multi-dimensional feasibility assessment, in step a3, the vector fusion method for performing vector fusion processing on the obtained first index weight vector and second index weight vector is: Z j =a×X j +(1-a) ×Y j , 0≤a≤1, 1≤j≤J; where a is an adjustable fusion coefficient, X j Y is the weight vector of the first indicator corresponding to the j-th evaluation dimension within the multi-dimensional evaluation indicator system. j Z is the weight vector of the second indicator corresponding to the j-th evaluation dimension within the multi-dimensional evaluation indicator system. j Let J be the intelligent fusion comprehensive weight vector corresponding to the j-th evaluation dimension, and J be the total number of evaluation dimensions in the multi-dimensional evaluation index system.

[0014] Compared with existing technologies, the advantages of this invention are as follows: The method for optimizing chemical process routes based on multi-dimensional feasibility assessment automatically collects full lifecycle data of each candidate chemical process route from multiple data sources after a pre-determined set of candidate routes is established. After preprocessing, a standard database of process routes is formed using all the preprocessed full lifecycle data. Then, a multi-dimensional assessment index system covering multiple assessment dimensions and indicators is constructed based on this standard database, along with another standard database of process routes. The dynamically adjusted index weights within the multi-dimensional assessment index system are used as the latest comprehensive weights for the corresponding assessment indicators. The standardized index values ​​and their corresponding latest comprehensive weights are then fused to obtain a comprehensive feasibility assessment value for each candidate chemical process route. Finally, the candidate chemical process route with the highest comprehensive feasibility assessment value is selected as the optimal recommended chemical process route. This approach considers multiple assessment dimensions and corresponding assessment indicators that influence the selection of chemical process routes, while also achieving interrelationships between these dimensions. It avoids the shortcomings of independence between different dimensions, achieving multi-dimensional assessment of chemical process routes, reducing the potential decision-making risks of the final selected process route, and improving the optimization effect of chemical process routes. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the chemical process technology route optimization method based on multi-dimensional feasibility assessment in an embodiment of the present invention. Detailed Implementation

[0016] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0017] This embodiment provides a method for optimizing chemical process technology routes based on multi-dimensional feasibility assessment. See also... Figure 1 As shown, the chemical process technology route optimization method based on multi-dimensional feasibility assessment in this embodiment includes the following steps: Step 1: Predetermine a set of candidate process routes formed by multiple candidate chemical process routes; Step 2: Automatically collect the full lifecycle data of each candidate chemical process route within the candidate process route set from multiple data sources. In this embodiment, the multiple data sources include technical data sources, economic data sources, safety data sources, environmental data sources, and asset data sources. Technical data sources include laboratory reports, intellectual property documents, and pilot-scale reports; economic data sources include enterprise resource planning systems; safety data sources include safety information databases; environmental data sources include environmental management databases and production execution systems; and asset data sources include production execution systems and equipment management systems. The full lifecycle data includes technical data, economic data, safety data, environmental data, and asset data of the candidate chemical process routes. Step 3: Preprocess all collected full lifecycle data to obtain preprocessed full lifecycle data, and form a process route standard database from all preprocessed full lifecycle data; for example, in this embodiment, the preprocessing here includes cleaning and normalizing the full lifecycle data. Specifically, the cleaning and normalizing process involves filling missing data in the full lifecycle data with linear interpolation or Lagrange interpolation; smoothing and denoising outlier data with wavelet transform or median filtering algorithm; and establishing unified quantization rules and assigning corresponding values ​​to qualitative descriptions. Step 4: Construct a multi-dimensional evaluation index system based on the process route standard database, which covers multiple evaluation dimensions and multiple evaluation indicators. The multi-dimensional evaluation index system includes six dimensions: technology maturity, economic efficiency, safety, environmental protection, resource adaptability, and strategic synergy. Each dimension has multiple evaluation indicators. Step 5: Standardize each lifecycle data in the process route standard database to obtain the standardized index value for each lifecycle data; wherein, the standardized index value and the lifecycle data have a one-to-one correspondence. Step 6: Dynamically adjust the weights of each evaluation indicator in the multi-dimensional evaluation indicator system to obtain the corresponding dynamically adjusted indicator weight values, and use the obtained dynamically adjusted indicator weight values ​​as the latest comprehensive indicator weights of the corresponding evaluation indicators; wherein, each evaluation indicator in the multi-dimensional evaluation indicator system has a dynamically adjusted indicator weight value that corresponds to it one by one. Step 7: Based on the obtained standardized index values ​​and the corresponding latest index weights, perform fusion processing to calculate the comprehensive feasibility evaluation value for each candidate chemical process route. Step 8: Sort all the obtained comprehensive feasibility assessment values ​​in descending order, and select the candidate chemical process route corresponding to the comprehensive feasibility assessment value with the largest value as the recommended optimal chemical process route.

[0018] More specifically, the evaluation dimensions of the constructed multi-dimensional evaluation indicator system are explained as follows: The technology maturity dimension includes the technology readiness level index, the process stability index, the technology reliability score, and the intellectual property strength index. The economic dimension includes unit product raw material cost, unit product energy consumption cost, return on investment, and cost sensitivity coefficient. The safety dimension includes comprehensive safety index indicators, intrinsic safety level scoring indicators, risk index indicators, and emergency response capability scoring indicators; The environmental protection dimension includes the unit product waste emission equivalent index, unit product carbon footprint index, environmental compliance score index, and ecological impact index index; The resource adaptability dimension includes equipment compatibility scoring indicators, raw material availability index indicators, energy matching degree scoring indicators, and human resource adaptability indicators. The strategic synergy dimension includes policy compliance indicators, technology roadmap consistency scoring indicators, market synergy index indicators, and innovation capability contribution indicators.

[0019] Specifically, in step 5 of this embodiment, when standardizing each lifecycle data in the process route standard database, Min-Max standardization or Z-score standardization methods are used to sequentially perform dimensionless processing on each lifecycle data to eliminate the influence of dimensions and obtain the corresponding standardized data. For example, the standardized index value of any lifecycle data obtained is G. ij , i represents the sequence number of the technology route, j represents a specific dimension, 1≤i≤I, 1≤j≤J; I is the total number of technologies, and J is the total number of the aforementioned multi-evaluation dimensions.

[0020] Additionally, it should be noted that in step 6 of this embodiment, the process of dynamically adjusting the weights of each evaluation indicator within the multi-dimensional evaluation indicator system includes the following steps a1 to a3: Step a1: Based on expert experience, initialize the weights of each evaluation indicator to obtain the first weight vector of the corresponding evaluation indicator; where the first weight vector is the initial vector. For example, by constructing a matrix (representing the importance of the i-th dimension relative to the j-th dimension), the initial weight vector X is obtained. j (That is, the first indicator weight vector), such as the initial weight vector X. j =(0.15, 0.2, 0.25, 0.2, 0.1, 0.1), representing the weight percentage of the six evaluation indicators in the evaluation of this technical route; Step a2: Receive the weights assigned by the user to each evaluation indicator based on strategic preferences, and obtain the second indicator weight vector for each evaluation indicator; where the second indicator weight vector here is a dynamic vector. For example, when a user selects their strategic preference, such as choosing "cost-first mode" as their strategic preference, the system will accordingly amplify the importance of cost indicators to other indicators when constructing the judgment matrix, resulting in a dynamic weight vector, such as the dynamic weight vector (i.e., the second indicator weight vector) Y. j =(0.08, 0.1, 0.33, 0.33, 0.07, 0.09); Step a3: Perform vector fusion processing on the obtained first indicator weight vector and second indicator weight vector to obtain the intelligent fusion comprehensive weight vector; wherein, each weight in the intelligent fusion comprehensive weight vector is the latest comprehensive weight of the corresponding evaluation indicator.

[0021] It should be noted that during the dynamic adjustment of the weights of each evaluation indicator within the multi-dimensional evaluation indicator system, the weight of a particular evaluation indicator is related to the "current scenario" or "mode" selected by the user (i.e., "strategic preference"). For example, if the user selects the "cost-first" mode, the dynamic weight vector will be the weight vector that increases the weight of cost priority; if the user selects the "safety-first" mode, the dynamic weight vector will also be changed accordingly to increase the weight of safety priority, and it is variable.

[0022] Specifically, in step a1 mentioned above, the process of initializing the weights of each evaluation indicator based on expert experience is as follows: Step b1: Establish a hierarchical structure with the optimal chemical process route as the target layer, the multi-dimensional criteria layer as the criterion layer, and all evaluation indicators as the indicator layer. Step b2: Based on multiple experts in the field of chemical processes, and using a 1-9 degree scaling method, pairwise comparisons of the relative importance of each dimension in the multi-dimensional process are performed to construct a judgment matrix for the comparison of the importance of the multi-dimensional process. For example, the judgment matrix constructed here is labeled A=[a ij ] 6×6 ;a ij This indicates the importance of the i-th evaluation dimension relative to the j-th evaluation dimension; in the embodiment, the row and column order of the judgment matrix is: technical feasibility, economic efficiency, safety, environmental friendliness, resource adaptability, and strategic synergy. Step b3 involves calculating the maximum eigenvalue and corresponding eigenvector of the constructed judgment matrix using the eigenvector method, and then normalizing the eigenvector to obtain the initial weight vector. Each weight in the initial weight vector corresponds to one of the evaluation indicators within the multi-dimensional evaluation index system. For example, the maximum eigenvalue of the judgment matrix is ​​calculated and labeled as λ. max The corresponding largest eigenvalue λ max The initial weight vector obtained after normalizing the feature vector is labeled as X. j Of course, this initial weight vector X j That is, the first indicator weight vector.

[0023] Regarding step 7 above, the method for fusion processing based on the obtained standardized index values ​​and their corresponding latest index weights is as follows: Z j =a×X j +(1-a) ×Y j , 0≤a≤1, 1≤j≤J; where a is an adjustable fusion coefficient, for example, the fusion coefficient a can be 0.3; X j Y is the initial weight vector corresponding to the j-th evaluation dimension within the multi-dimensional evaluation index system. j The dynamic weight prediction model predicts the index weight value corresponding to the j-th evaluation dimension; Z j Let J be the dynamically adjusted indicator weight value corresponding to the j-th evaluation dimension, where J is the total number of evaluation dimensions in the aforementioned multi-dimensional evaluation indicator system.

[0024] Specifically, in step 7, for any candidate chemical process route i, the comprehensive feasibility evaluation value corresponding to candidate chemical process route i is marked as follows: S i , .

[0025] For example, a chemical company plans to build a new production line for "Chemical X" with an annual capacity of 100,000 tons. There are five candidate process routes (hereinafter referred to as "routes"): Route A: Traditional catalytic process, with high technological maturity, but high energy consumption and poor environmental performance; that is, Route A is mature but energy-intensive. Route B: A novel biocatalytic process with excellent safety and environmental performance, but the technology is immature and the cost is high; that is, Route B is an emerging but costly process. Route C: Improved thermochemical process, with balanced performance across all dimensions; that is, Route C is a balanced approach. Route D: Highly efficient catalytic oxidation process with good economic efficiency, but inherent safety risks exist; that is, Route D offers high performance but requires high safety standards. Route E: Resource recycling process, with excellent safety and environmental performance, but low technological maturity and poor economic efficiency. In other words, Route E is environmentally friendly but technologically immature.

[0026] Then, the entire lifecycle data was automatically collected from multiple data sources. The collected raw lifecycle data covers six dimensions and their sub-indicators (taking the first two core indicators of each dimension as an example), as shown in Table 1: Table 1 .

[0027] Specifically, the process of standardizing each lifecycle data in the process route standard database using Min-Max standardization or Z-score standardization methods is as follows: The standardization formula is: Standardized value of an evaluation indicator of benefit type = (XX) min ) / (X max -X min ); Standardized value of cost-type evaluation indicators = (X max -X) / ( X max -X min ).

[0028] The standardization process for the core indicators of each assessment dimension is as follows (taking technology readiness level, unit product cost, comprehensive safety index, carbon footprint, equipment compatibility score, and policy compliance as examples): (1) Technology maturity (technology readiness level, benefit type): X min =1,X max =4: Route A: G A1 =(4-1) / 3=1; Route B: G B1 =(2-1) / 3=0.333; Route C: G C1 =(3-1) / 3=0.667; Route D: G D1 =(3-1) / 3=0.667; Route E: G E1 =(1-1) / 3=0.

[0029] (2) Economic efficiency (unit product cost, cost type): X min =1.2, X max =2.0: Route A: G A2 =(2-1.2) / 0.8=1; Route B: G B2 =(2-1.8) / 0.8=0.25; Route C: G C2 =(2-1.5) / 0.8=0.625; Route D: G D2 =(2-1.4) / 0.8=0.75; Route E: G E2 =(2-2) / 0.8=0.

[0030] The standardized values ​​for the other evaluation indicators can be found in the calculation formulas for the different types of indicators mentioned above, and will not be repeated here.

[0031] Through the calculations described in the preceding steps, a judgment matrix for multi-dimensional importance comparison is constructed. A The situation is as follows: .

[0032] Based on the obtained judgment matrix A Its largest eigenvalue is calculated to be λ. max =6, the largest eigenvalue λ max The normalized vector of the corresponding eigenvector is X. j =[0.15, 0.2, 0.25, 0.2, 0.1, 0.1], where the normalized vector X is... j It is the initial weight vector (i.e., the first index weight vector).

[0033] Based on this, for example, if a user selects their strategic preference, such as choosing "cost-first mode" as their strategic preference, the system will accordingly amplify the importance of cost indicators to other indicators when constructing the judgment matrix, resulting in a dynamic weight vector, such as the dynamic weight vector (i.e., the second indicator weight vector) Y. j =[0.08, 0.1, 0.33, 0.33, 0.07, 0.09].

[0034] Based on the aforementioned first indicator weight vector X j Second index weight vector Y j After vector fusion processing, the resulting intelligent fusion comprehensive weight vector is Z.j =[0.101, 0.130, 0.306, 0.291, 0.079, 0.093].

[0035] After executing the formula in step 7, the comprehensive feasibility evaluation values ​​for each candidate chemical process route are as follows: The overall feasibility assessment value S of Route A A =0.463; The overall feasibility assessment value S of Route B B =0.697; The overall feasibility assessment value S for route C C =0.577; The overall feasibility assessment value S of route D D =0.368; The overall feasibility assessment value S for route E E =0.671.

[0036] Based on the overall ranking, if the "safety and environmental protection priority" mode is selected, route B is the recommended route.

[0037] To conduct a more comprehensive performance analysis of the recommended optimal chemical process route, the chemical process technology route selection method based on multi-dimensional feasibility assessment in this embodiment also includes a process of performing multi-dimensional in-depth analysis of the optimal chemical process route. Specifically, the process of performing multi-dimensional in-depth analysis of the optimal chemical process route includes the following steps: Step c1: Based on the core indicators of each chemical process route across various evaluation dimensions, obtain the route distribution characteristics of each chemical process route; for example: The route distribution characteristics of chemical process route A are as follows: G Aj =[1.0, 1.0, 0.5, 0.0, 1.0, 0] → It has outstanding technical feasibility, economic efficiency and resource adaptability, but has shortcomings in safety, environmental protection and strategic synergy; The route distribution characteristics of chemical process route B are as follows: G Bj =[0.333, 0.25, 1.0, 0.75, 0.167, 1.0]→ It has significant advantages in safety, environmental protection, and strategic synergy, but its technical feasibility and economic viability are relatively weak; The route distribution characteristics of chemical process route C are as follows: G Cj =[0.667, 0.625, 0.75, 0.375, 0.667,0.4] → Relatively balanced across dimensions, with no prominent weaknesses; The route distribution characteristics of chemical process route D are: G Dj=[0.667, 0.75, 0.0, 0.5, 0.5, 0.2] → This method has good economic and technical feasibility, but extremely poor security. The route distribution characteristics of chemical process route E are as follows: G Ej =[0.0, 0.0, 1.0, 1.0, 0.0, 0.8] → Extremely safe and environmentally friendly, but poor in technical feasibility and economics; Step c2 involves performing preliminary sensitivity analysis on each evaluation indicator to obtain preliminary sensitivity analysis results. This preliminary sensitivity analysis is conducted as follows: receiving reassignment values ​​to the indicator weights based on user strategic preferences, and using all reassignments to form the latest second indicator weight vector for each evaluation indicator; then, performing vector fusion processing on the original first indicator weight vector and the latest second indicator weight vector to obtain the latest intelligent fusion comprehensive weight vector; where each weight in the latest intelligent fusion comprehensive weight vector represents the latest comprehensive weight of the corresponding evaluation indicator. For example, when a user's strategic preference is slightly adjusted from "safety and environmental protection first" to "cost first", the dynamic weight vector Y... j The values ​​were adjusted from [0.08, 0.1, 0.33, 0.33, 0.07, 0.09] to [0.09, 0.5, 0.11, 0.09, 0.08, 0.13]. The calculated latest intelligent fusion comprehensive weight vector Z... j Adjusted to [0.108, 0.41, 0.152, 0.123, 0.086, 0.121].

[0038] Correspondingly, after re-executing the formula in step 7, the comprehensive feasibility evaluation values ​​for each candidate chemical process route are as follows: The overall feasibility assessment value S of Route A A =0.680; The overall feasibility assessment value S of Route B B =0.518; The overall feasibility assessment value S for route C C =0.594; The overall feasibility assessment value S of route D D =0.508; The overall feasibility assessment value S for route E E =0.372.

[0039] Based on the aforementioned calculations, the scores and ranking changes for each route, according to the cost priority weighting, are shown in Table 2: Table 2

[0040] Step c3 involves performing a deep sensitivity analysis on each route based on the preliminary sensitivity analysis results of each evaluation index, yielding the deep sensitivity analysis results for each route. The deep sensitivity analysis results include: identifying the route with the largest variation in the preliminary sensitivity analysis results as the extremely sensitive route, and the route with the smallest variation in the preliminary sensitivity analysis results as the most robust route; and generating a decision recommendation matrix for all routes, including the preliminary sensitivity analysis results. For example, the decision recommendation matrix is ​​shown in Table 3. Table 3 .

[0041] This embodiment also provides a readable storage medium. Specifically, the readable storage medium stores a computer program, which, when executed by a processor, implements the aforementioned method for optimizing chemical process technology routes based on multi-dimensional feasibility assessment.

[0042] Although preferred embodiments of the present invention have been described in detail above, it should be clearly understood that various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing chemical process technology routes based on multi-dimensional feasibility assessment, characterized in that, Includes the following steps: Step 1: Predetermine a set of candidate process routes formed by multiple candidate chemical process routes; Step 2: Automatically collect the full lifecycle data of each candidate chemical process route in the candidate process route set from multiple data sources; Step 3: Preprocess all collected full lifecycle data to obtain preprocessed full lifecycle data, and form a process route standard database from all preprocessed full lifecycle data; Step 4: Construct a multi-dimensional evaluation index system based on the process route standard database, which covers multiple evaluation dimensions and indicators. The multi-dimensional evaluation index system includes technology maturity dimension, economic dimension, safety dimension, environmental protection dimension, resource adaptability dimension and strategic synergy dimension, and each dimension has multiple evaluation indicators. Step 5: Standardize each lifecycle data in the process route standard database to obtain the standardized index value for each lifecycle data; wherein, the standardized index value and the lifecycle data have a one-to-one correspondence. Step 6: Dynamically adjust the weights of each evaluation indicator in the multi-dimensional evaluation indicator system to obtain the corresponding dynamically adjusted indicator weight values, and use the obtained dynamically adjusted indicator weight values ​​as the latest comprehensive indicator weights of the corresponding evaluation indicators; wherein, each evaluation indicator in the multi-dimensional evaluation indicator system has a dynamically adjusted indicator weight value that corresponds to it one by one. Step 7: Process the obtained standardized index values ​​and the corresponding latest index comprehensive weights to calculate the comprehensive feasibility evaluation value for each candidate chemical process route. Step 8: Sort all the obtained comprehensive feasibility assessment values ​​in descending order, and select the candidate chemical process route corresponding to the comprehensive feasibility assessment value with the largest value as the recommended optimal chemical process route.

2. The method for selecting the optimal chemical process technology route based on multi-dimensional feasibility assessment according to claim 1, characterized in that, The multiple data sources include technical data sources, economic data sources, security data sources, environmental data sources, and asset data sources; Technical data sources include laboratory reports, intellectual property documents, and pilot-scale reports; economic data sources include enterprise resource planning systems; safety data sources include safety information databases; and environmental data sources include environmental management databases and production execution systems. Asset data sources include production execution systems and equipment management systems; The full lifecycle data includes technical data, economic data, safety data, environmental data, and asset data for candidate chemical process routes.

3. The method for selecting the optimal chemical process technology route based on multi-dimensional feasibility assessment according to claim 1, characterized in that, In step 3, the preprocessing includes cleaning and normalizing the data throughout its entire lifecycle.

4. The method for selecting the optimal chemical process technology route based on multi-dimensional feasibility assessment according to claim 3, characterized in that, The cleaning and normalization process includes: filling missing data in the full lifecycle data using linear interpolation or Lagrange interpolation; smoothing and denoising abnormal data using wavelet transform or median filtering algorithms; and establishing unified quantization rules and assigning corresponding numerical values ​​to qualitative descriptions.

5. The method for selecting the optimal chemical process technology route based on multi-dimensional feasibility assessment according to claim 1, characterized in that, The technology maturity dimension includes technology readiness level indicators, process stability index indicators, technology reliability score indicators, and intellectual property strength index indicators; the economic dimension includes unit product raw material cost indicators, unit product energy consumption cost indicators, return on investment indicators, and cost sensitivity coefficient indicators; the safety dimension includes comprehensive safety index indicators, inherent safety level score indicators, risk index indicators, and emergency response capability score indicators; the environmental protection dimension includes unit product waste emission equivalent indicators, unit product carbon footprint indicators, environmental compliance score indicators, and ecological impact index indicators; the resource adaptability dimension includes equipment compatibility score indicators, raw material availability index indicators, energy matching degree score indicators, and human resource adaptability index indicators; the strategic synergy dimension includes policy compliance indicators, technology route consistency score indicators, market synergy effect index indicators, and innovation capability contribution indicators.

6. The method for optimizing chemical process technology routes based on multi-dimensional feasibility assessment according to claim 1, characterized in that, In step 6, the process of dynamically adjusting the weights of each evaluation indicator within the multi-dimensional evaluation indicator system includes the following steps a1~a3: Step a1: Based on expert experience, initialize the weights of each evaluation indicator to obtain the first weight vector of the corresponding evaluation indicator. Step a2: Receive the weights assigned by the user to each evaluation indicator based on strategic preferences, and obtain the second weight vector of the corresponding evaluation indicator. Step a3: Perform vector fusion processing on the obtained first indicator weight vector and second indicator weight vector to obtain the intelligent fusion comprehensive weight vector; wherein, each weight in the intelligent fusion comprehensive weight vector is the latest comprehensive weight of the corresponding evaluation indicator.

7. The method for selecting the optimal chemical process technology route based on multi-dimensional feasibility assessment according to claim 6, characterized in that, The process of initializing the weights of each evaluation indicator based on expert experience includes the following steps b1~b3: Step b1: Establish a hierarchical structure with the optimal chemical process route as the target layer, the multi-dimensional criteria layer as the criterion layer, and all evaluation indicators as the indicator layer. Step b2: Based on multiple experts in the field of chemical processes and using a 1-9 degree scale method, pairwise comparisons of the relative importance of each dimension in the multi-dimensional process are performed to construct a judgment matrix for the comparison of the importance of the multi-dimensional process. Step b3: The maximum eigenvalue of the constructed judgment matrix and the eigenvector corresponding to the maximum eigenvalue are calculated using the eigenvector method, and the eigenvector is normalized to obtain the initial weight vector; wherein, each weight in the initial weight vector corresponds to each evaluation index in the multi-dimensional evaluation index system.

8. The method for selecting the optimal chemical process technology route based on multi-dimensional feasibility assessment according to claim 6, characterized in that, In step a3, the vector fusion method for performing vector fusion processing on the obtained first indicator weight vector and second indicator weight vector is: Z j =a×X j +(1-a) ×Y j , 0≤a≤1, 1≤j≤J; where a is an adjustable fusion coefficient, X j Y is the weight vector of the first indicator corresponding to the j-th evaluation dimension within the multi-dimensional evaluation indicator system. j Z is the weight vector of the second indicator corresponding to the j-th evaluation dimension within the multi-dimensional evaluation indicator system. j Let J be the intelligent fusion comprehensive weight vector corresponding to the j-th evaluation dimension, and J be the total number of evaluation dimensions in the multi-dimensional evaluation index system.

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