A multisource data‐driven approach for carbon footprint analysis of remanufacturing systems

Abstract

Abstract The remanufacturing industry is experiencing significant growth, resulting in a substantial increase in its carbon footprint. Unfortunately, this issue is not receiving adequate attention at present. Moreover, existing methods for analyzing carbon footprints in remanufacturing systems are inefficient and inaccurate due to the complex relationship between carbon emissions and the dynamic nature of the systems. In this paper, we propose a multisource data‐driven approach for carbon footprint analysis of remanufacturing systems. First, we examine the relationship between carbon emissions and the uncertainty surrounding end‐of‐life (EOL) product components, as well as the corresponding resource recovery strategies, such as reuse, recycle, and remanufacture. This analysis allows us to define the carbon footprint boundary of remanufacturing systems. Next, we establish a carbon footprint model for recycling strategies using recycling data. Additionally, we propose a carbon footprint model for remanufacture strategies by utilizing the back propagation neural network based on particle swarm optimization algorithm and data from the materials flow, energy flow, and waste flow of these strategies. By combining these models, we can accurately calculate the carbon footprint of remanufacturing systems. To demonstrate the effectiveness of our approach, we present a case study on the remanufacturing of an EOL automobile engine. This case study showcases how our methodology can be utilized as a valuable tool for analyzing carbon footprints and identifying strategies to reduce carbon emissions in remanufacturing systems.

Keywords

• carbon footprint
• dynamic coupling relationship
• multisource data‐driven
• PSO–BP neural network
• remanufacturing system