Remanufactured mechanical products with high-added value are generally claimed to gain environmental benefits. These claims were made based on different products and assessment methodologies. The variability of life cycle assessment (LCA) results precludes a meaningful comparison across products and studies. This paper aims to critically and systematically evaluate the lifecycle environmental performance of remanufactured products compared with their new counterparts and to identify the key factors, strengths, and limitations in the assessment procedure. Faced with the noteworthy variations, we closely examined and harmonized the unit function, allocation approach, system boundary, impact assessment method, and the underlying assumptions in screened 20 papers regarding 11 types of products. The environmental indicators adopted in this study were global warming potential (GWP) and primary energy consumption (PEC). In terms of these two indicators, the environmental burdens of remanufactured products relative to newly manufactured alternatives were harmonized to the comparison ratios. With these harmonized samples, descriptive statistics were calculated using Monte Carlo Simulation to disclose the variations of comparison results and identify the general tendency. Results of this meta-study showed that remanufacturing could contribute to over 50% reduction for GWP when usage and end-of-life stages were excluded from the life cycle. The GWP and PEC of remanufactured mechanical products account for 28.5% and 25.9% of the new counterparts, respectively, on average. This meta-analysis of comparative LCAs on new and remanufactured products would advance the understanding of the environmental advantages of remanufacturing.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.jenvman.2022.114479 | DOI Listing |
Sci Rep
January 2025
School of Mine Safety, North China Institute of Science and Technology, Langfang, 065201, China.
The soft-rock roadways in kilometer-deep coal mines are often damaged by large deformation and have to be periodically expanded and repaired, which seriously restricts the safe and efficient production of coal mines. A typical soft-rock roadway in a kilometer-deep coal mine is selected as the engineering, and the main reasons for roadway deformation are analyzed, and the ground stress and mechanical characteristics are obtained. The Flac numerical model, which can accurately reflect the deformation characteristics of surrounding rock in kilometer-deep soft-rock roadway, has been constructed, and the evolution laws of stress field and its damage mechanism have been analyzed with the vertical stress, vertical displacement and plastic zone.
View Article and Find Full Text PDFSci Rep
January 2025
School of Intelligent Manufacturing and Modern Industry (School of Mechanical Engineering), Xinjiang University, Ürümqi, 830017, China.
The rapid expansion of the coal mining industry has introduced significant safety risks, particularly within the harsh environments of open-pit coal mines. The safe and stable operation of belt conveyor idlers is crucial not only for ensuring efficient coal production but also for safeguarding the lives of coal mine workers. Therefore, this paper proposes a method based on deep learning for real-time detection of conveyor idler faults.
View Article and Find Full Text PDFSci Rep
January 2025
School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China.
This research presents a method based on deep learning for the reverse design of sound-absorbing structures. Traditional methods require time-consuming individual numerical simulations followed by cumbersome calculations, whereas the deep learning design method significantly simplifies the design process, achieving efficient and rapid design objectives. By utilizing deep neural networks, a mapping relationship between structural parameters and the sound absorption coefficient curve is established.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC, 29209, USA.
Accurately predicting the energy consumption plays a vital role in battery electric buses (BEBs) route planning and deployment. Based on the algebraic derivative estimation, we present a novel method to forecast the energy consumption in real time. In contrast to the mainstream machine-learning-based methods, the proposed method does not require access to the historical energy consumption data.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Neurosurgery, Kepler University Hospital and Johannes Kepler University Linz, Wagner-Jauregg Weg 15, 4020 Linz and Altenbergerstrasse 69, Linz, 4040, Austria.
Accurate rupture risk assessment is essential for optimizing treatment decisions in patients with cerebral aneurysms. While computational fluid dynamics (CFD) has provided critical insights into aneurysmal hemodynamics, most analyses focus on blood flow patterns, neglecting the biomechanical properties of the aneurysm wall. To address this limitation, we applied Fluid-Structure Interaction (FSI) analysis, an integrative approach that simulates the dynamic interplay between hemodynamics and wall mechanics, offering a more comprehensive risk assessment.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!