Work

An incremental data-driven approach for carbon emission prediction and optimization of heat treatment processes

Year: 2025

Type: article

Abstract: : In manufacturing enterprises, the variety of heat treatment processes, insufficient data in the early stages of production, and the continuous increase of samples over time make it difficult to predict and optimize carbon emissions dynamically. Therefore, this paper proposes a low-carbon optimization method for heat treatment processes based on incremental data-driven approaches. Using life cycle assessment (LCA) theory, carbon emission sources are accurately analyzed and quantified, and a full life cycle carbon emission model is established. The key process parameters affecting part performance and carbon emission were screened through mechanism analysis, and the incremental data were fused by the Elasticity Weight Consolidation (EWC) algorithm to establish an EWC-BPNN heat treatment carbon emission prediction model. Then the model is solved by the Multi-objective Seagull Optimization Algorithm (MOSOA), and the result achieves a good balance between the carbon emission and hardness of the heat treatment process. Finally, using real data collected in enterprises for four consecutive years, it is verified that the predictive model can be effectively applied to the working conditions of multi-variety, small batch, and incremental heat treatment data.

Source: Case Studies in Thermal Engineering

DOI: 10.1016/j.csite.2025.106320

Authors: Qian Yi, Xin Wu, Junkang Zhuo, Congbo Li, Chuanjiang Li, Huajun Cao

Institutions: Chongqing University, Guizhou University

Language: English

Topic: Impact of AI and Big Data on Business and Society

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDGs: Climate action, Responsible consumption and production, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,620