Product carbon emissions estimation method in the early design stage based on multi-perspective similarity matching of design scenarios

Kong, Lin Nie, Yanyan Wang, Liming Li, Fangyi Zhou, Lirong Wang, Geng

Lu, Haiyang Xiao, Xingyuan Liu, Weitong Ma, Yan

Department of Mechanical and Electronic Engineering, Shandong University of Science and Technology, 579 Qianwangang Road, Huangdao District, Qingdao, Shandong 266590 China

Journal：《Advanced Engineering Informatics》

Abstract：

Carbon emissions estimation of design schemes during the early design stage enables thorough consideration of environmental issues from the source, which holds critical significance for carbon reduction and emission mitigation. Nevertheless, the scarcity of life cycle inventory data, coupled with the intricacies involved in the collection, presents a formidable challenge to conducting precise carbon emissions assessment. To address this issue, this research proposes a product carbon emissions estimation method in the early design stage based on multi-perspective similarity matching of design scenarios, which utilizes the idea of knowledge reuse through case-based reasoning. Specifically, the case-based reasoning framework encompassing case base construction, case retrieval, reuse, and revision has been outlined, which standards the procedure for obtaining the most similar case. Moreover, the design scenario is defined to comprehensively describe all life cycle activities that influence product carbon emissions, and the design scenario-based multi-layer model is constructed that encompasses the product’s lifecycle-related design information pertinent to carbon emissions, along with its intricate interrelationships, serving as the input information for precise case retrieval. Subsequently, a multi-perspective similarity matching strategy that integrates both the attribute and correlation information of design scenarios is developed, which accurately identifies the most similar case in the case base, enabling the efficient reuse of historical data. An example of the wind turbine gearbox is given as an example, the results indicating that the proposed carbon emission estimation method aligns most closely with actual machining conditions, achieving a minimal error of 2.75%, thereby unequivocally validating its effectiveness and reliability. This work provides designers with a targeted strategy for obtaining carbon emissions during the early design stage, thereby facilitating optimized decision-making for low-carbon design.

Keywords：Low-carbon design；Case-based reasoning；Multi-perspective similarity matching；Design scenarios

Download: https://doi.org/10.1016/j.aei.2024.103094