Dynamic carbon emissions accounting in the mixed production process of multi-pressure die-castingproducts based on cyber physical production system

IF 12.2 1区 工程技术 Q1 ENGINEERING, INDUSTRIAL Journal of Manufacturing Systems Pub Date : 2024-11-26 DOI:10.1016/j.jmsy.2024.11.005
Hongcheng Li , Jian Peng , Yachao Jia , Rong Luo , Huajun Cao , Yunpeng Cao , Yu Zhang , Haihong Shi
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Abstract

Die-casting is an efficient and precise casting process, but it consumes significant energy and contributes to severe environmental pollution. The characteristic features of the die-casting process chain include high demand for energy and resources, dynamic synergy among multiple processing equipment, and mixed production of various products. These characteristics lead to challenges in carbon emission accounting, such as the problem of carbon emission data haze. To address this issue, this study analyzes the dynamic characteristics of carbon emissions in the die-casting process chain to identify the sources of carbon emissions. Subsequently, a multi-source carbon data collection scheme is developed based on these sources, and an information-physical fusion-based model for carbon source data collection and integration is established. Following this, the correlation between carbon sources in the die-casting process chain and the production process is elucidated, and a carbon emission accounting model for mixed production of multiple die-casting products is developed. For model parameterization, time-series power data are systematically integrated. Finally, using the dynamic characteristics of carbon emissions from typical die-casting production and the carbon source data model as a foundation, a case study is conducted on the carbon emissions from mixed production in the die-casting process chain. The results demonstrate the effectiveness, feasibility, and reliability of the proposed carbon emission accounting model. This study lays the foundation for optimizing carbon reduction in the die-casting process chain and supports the transition to a low-carbon die-casting workshop.
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基于网络物理生产系统的多压压铸产品混合生产过程的动态碳排放核算
压铸是一种高效、精密的铸造工艺,但能耗大,环境污染严重。压铸工艺链的特点包括对能源和资源的需求量大、多种加工设备动态协同、多种产品混合生产等。这些特点给碳排放核算带来了挑战,如碳排放数据雾霾问题。针对这一问题,本研究通过分析压铸工艺链中碳排放的动态特征,找出碳排放的源头。随后,根据这些碳排放源制定了多源碳数据收集方案,并建立了基于信息物理融合的碳源数据收集和整合模型。随后,阐明了压铸工艺链中碳源与生产过程的相关性,并建立了多种压铸产品混合生产的碳排放核算模型。在模型参数化方面,系统地整合了时间序列功率数据。最后,以典型压铸生产的碳排放动态特征和碳源数据模型为基础,对压铸工艺链中混合生产的碳排放进行了案例研究。研究结果证明了所提出的碳排放核算模型的有效性、可行性和可靠性。该研究为优化压铸工艺链的碳减排奠定了基础,并为压铸车间向低碳转型提供了支持。
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来源期刊
Journal of Manufacturing Systems
Journal of Manufacturing Systems 工程技术-工程:工业
CiteScore
23.30
自引率
13.20%
发文量
216
审稿时长
25 days
期刊介绍: The Journal of Manufacturing Systems is dedicated to showcasing cutting-edge fundamental and applied research in manufacturing at the systems level. Encompassing products, equipment, people, information, control, and support functions, manufacturing systems play a pivotal role in the economical and competitive development, production, delivery, and total lifecycle of products, meeting market and societal needs. With a commitment to publishing archival scholarly literature, the journal strives to advance the state of the art in manufacturing systems and foster innovation in crafting efficient, robust, and sustainable manufacturing systems. The focus extends from equipment-level considerations to the broader scope of the extended enterprise. The Journal welcomes research addressing challenges across various scales, including nano, micro, and macro-scale manufacturing, and spanning diverse sectors such as aerospace, automotive, energy, and medical device manufacturing.
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A dynamic artificial bee colony for fuzzy distributed energy-efficient hybrid flow shop scheduling with batch processing machines Assisted production system planning by means of complex robotic assembly line balancing Novel deep learning based soft sensor feature extraction for part weight prediction in injection molding processes Dynamic carbon emissions accounting in the mixed production process of multi-pressure die-castingproducts based on cyber physical production system Flexible robotic cell scheduling with graph neural network based deep reinforcement learning
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