A converter energy consumption and carbon emission diagnosis and optimization method based on production rhythm

By combining long short-term memory networks and clustering algorithms with entropy weighting, the problem of accurately assessing energy consumption and carbon emissions in converter steelmaking was solved, achieving optimization of energy efficiency and carbon emissions in the converter process and providing intelligent diagnosis and optimization suggestions.

CN122198269BActive Publication Date: 2026-07-24NORTHEASTERN UNIV CHINA
2 Cites 0 Cited by

Patent Information

Application Number
CN202610668381.3
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-05-15
Publication Date
2026-07-24
Estimated Expiration
2046-05-15

AI Technical Summary

Technical Problem

Existing technologies struggle to accurately assess energy consumption and carbon emissions during converter steelmaking, making it difficult to identify energy efficiency bottlenecks and key carbon emission points. Furthermore, existing systems fail to deeply integrate production rhythm with the energy efficiency and carbon emissions of the smelting process for optimization.

Method used

The oxygen consumption, converter gas recovery, and steam recovery of the converter process are predicted by using a long short-term memory network. The baseline operating conditions are determined by combining a clustering algorithm and the contribution of influencing factors is constructed by using the entropy weight method, so as to realize intelligent diagnosis and optimization of converter operating conditions.

Benefits of technology

It enables precise diagnosis and optimization of energy consumption and carbon emissions in the converter process, improves energy efficiency and reduces carbon emissions, and provides scientific guidance for production optimization.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The present application provides a kind of converter energy consumption and carbon emission diagnosis and optimization method based on production rhythm, belong to steel metallurgical process intelligent diagnosis technical field.Input with converter process production rhythm parameter, operating parameter and material structure parameter, oxygen consumption, converter gas recovery and steam recovery are predicted by long short-term memory network, and energy consumption and carbon emission of current converter working condition are calculated accordingly;Based on multidimensional production data, the reference working condition is obtained by clustering algorithm;The deviation index between the current working condition and the reference working condition is constructed and the working condition is diagnosed, and the influencing factors are sorted according to the contribution degree;On this basis, the converter production is optimized combined with working condition deviation and influence weight.This method uses entropy weight method, integrates energy efficiency and carbon emission index, constructs working condition evaluation system, selects the reference working condition with optimal energy efficiency and carbon emission by clustering algorithm, and realizes converter production optimization.
Need to check novelty before this filing date? Find Prior Art