A cross-regional metabolic prediction method for industrial solid waste based on a combined model

By constructing a physical input-output matrix and a combined prediction model, the key sectors and interactions of industrial solid waste are identified, solving the challenges of full life-cycle analysis and cross-regional collaborative governance of industrial solid waste, and improving the efficiency and safety of industrial solid waste management.

CN122311548APending Publication Date: 2026-06-30HEFEI UNIV OF TECH +1
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Patent Information

Application Number
CN202610436790.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-04-03
Publication Date
2026-06-30

AI Technical Summary

Technical Problem

Existing research has failed to effectively incorporate industrial solid waste into the urban material flow cycle system, making it difficult to achieve full life cycle analysis and optimization. Cross-regional collaborative governance lacks quantitative characterization, hazardous waste disposal risk assessment is weak, and industrial solid waste utilization efficiency is low.

Method used

A combined model-based approach is adopted. By constructing a physical input-output matrix, key sectors and their interactions are identified. Combining grey prediction and autoregressive differential moving average models, a combined prediction model is established to simulate the dynamic trends of cross-regional industrial solid waste generation and metabolism, and to output an optimized path.

Benefits of technology

It enables the analysis and optimization of industrial solid waste from a full life cycle perspective, improves the accuracy and safety of cross-regional collaborative governance, identifies key sectors and their interactions, and provides decision-making basis for management departments.

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Abstract

This application discloses a cross-regional metabolic prediction method for industrial solid waste based on a combined model, applicable to the field of industrial solid waste treatment. The method includes: converting monetary flows into physical flows to calculate the total flow of industrial solid waste; determining the contribution rate of each sector by calculating the total flow matrix through structural path analysis; using network control analysis to calculate the difference between driving force weights and pulling force weights to identify key sectors and their interactions; constructing a grey prediction model and an autoregressive differential moving average model, and determining weighting based on the residual variance of the grey prediction model and the autoregressive differential moving average model to establish a combined prediction model; inputting key control sectors and their interactions as constraints into the combined prediction model to obtain the optimized path for collaborative governance. This application reflects the inherent connection between industrial production sectors and industrial solid waste generation, obtaining the source path of solid waste generation.
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