Recycling-Oriented Characterization of Space Waste Through Clean Hyperspectral Imaging Technology in a Circular Economy Context DOI Creative Commons
Giuseppe Bonifazi, Idiano D’Adamo, Roberta Palmieri

и другие.

Clean Technologies, Год журнала: 2025, Номер 7(1), С. 26 - 26

Опубликована: Март 14, 2025

Waste management is one of the key areas where circular models should be promoted, as it plays a crucial role in minimizing environmental impact and conserving resources. Effective material identification classification are essential for optimizing recycling processes selecting appropriate production equipment. Proper sorting materials enhances both efficiency sustainability systems. The proposed study explores potential using cost-effective strategy based on hyperspectral imaging (HSI) to classify space waste products, an emerging challenge management. Specifically, investigates use HSI sensors operating near-infrared range detect identify classification. Analyses focused textile plastic materials. results show promising further research, suggesting that approach capable effectively identifying classifying various categories predicted images achieve exceptional sensitivity specificity, ranging from 0.989 1.000 0.995 1.000, respectively. Using cost-effective, non-invasive technology could offer significant improvement over traditional methods classification, particularly challenging context operations. implications this work how enables development geared toward sustainable hence proper distinction they allow better recovery end-of-life management, ultimately contributing more efficient recycling, valorization, practices.

Язык: Английский

Challenges and Benefits of Implementing AI in Timber Construction for Circular Economy Goals DOI Creative Commons

Mohsen Ghobadi,

Samad M. E. Sepasgozar

Buildings, Год журнала: 2025, Номер 15(7), С. 1073 - 1073

Опубликована: Март 26, 2025

Artificial intelligence (AI) is considered an essential enabler of a circular economy (CE) in the construction industry. AI can significantly enhance efficiency applying innovative CE practices other projects. However, it has not yet been fully integrated into application principles and explicitly overlooked context timber construction. This study aims to bridge this gap by examining potential contributions applications achieving construction, as well identifying associated benefits challenges. Through mixed-methods approach, research utilizes both qualitative data, collected through industry interviews, quantitative analysis explore professional perspectives uncover actionable insights. The findings highlight transformative sustainability operational Moreover, six 11 challenges for integrating are identified that act accelerator advancing circularity Based on results, reduction waste facilitating deconstruction reuse process emerge most important benefits. Data obstacles, technological integration, finance resources, organizational determined main makes novel field providing empirical evidence form addition practical recommendations integration promote goals improve sector.

Язык: Английский

Процитировано

0
Recycling-Oriented Characterization of Space Waste Through Clean Hyperspectral Imaging Technology in a Circular Economy Context DOI Creative Commons
Giuseppe Bonifazi, Idiano D’Adamo, Roberta Palmieri

и другие.

Clean Technologies, Год журнала: 2025, Номер 7(1), С. 26 - 26

Опубликована: Март 14, 2025

Waste management is one of the key areas where circular models should be promoted, as it plays a crucial role in minimizing environmental impact and conserving resources. Effective material identification classification are essential for optimizing recycling processes selecting appropriate production equipment. Proper sorting materials enhances both efficiency sustainability systems. The proposed study explores potential using cost-effective strategy based on hyperspectral imaging (HSI) to classify space waste products, an emerging challenge management. Specifically, investigates use HSI sensors operating near-infrared range detect identify classification. Analyses focused textile plastic materials. results show promising further research, suggesting that approach capable effectively identifying classifying various categories predicted images achieve exceptional sensitivity specificity, ranging from 0.989 1.000 0.995 1.000, respectively. Using cost-effective, non-invasive technology could offer significant improvement over traditional methods classification, particularly challenging context operations. implications this work how enables development geared toward sustainable hence proper distinction they allow better recovery end-of-life management, ultimately contributing more efficient recycling, valorization, practices.

Язык: Английский

Процитировано

0