Artificial intelligence for waste management in smart cities: a review

B Fang, J Yu, Z Chen, AI Osman, M Farghali… - Environmental …, 2023 - Springer
The rising amount of waste generated worldwide is inducing issues of pollution, waste
management, and recycling, calling for new strategies to improve the waste ecosystem, such …

Applicability and limitation of compost maturity evaluation indicators: A review

Y Kong, J Zhang, X Zhang, X Gao, J Yin… - Chemical Engineering …, 2024 - Elsevier
Composting is a resource treatment method that uses aerobic microorganisms to convert
organic solid waste into stable humus and applied as organic fertilizer. The maturity …

Machine-learning intervention progress in the field of organic waste composting: Simulation, prediction, optimization, and challenges

L Huang, J Hou, H Liu - Waste Management, 2024 - Elsevier
Aerobic composting stands as a widely-adopted method for treating organic solid waste
(OSW), simultaneously producing organic fertilizers and soil amendments. This biologically …

Artificial intelligence and machine learning for smart bioprocesses

SK Khanal, A Tarafdar, S You - Bioresource Technology, 2023 - Elsevier
In recent years, the digital transformation of bioprocesses, which focuses on
interconnectivity, online monitoring, process automation, artificial intelligence (AI) and …

From waste to wealth: Innovations in organic solid waste composting

M Xu, H Sun, E Chen, M Yang, C Wu, X Sun… - Environmental …, 2023 - Elsevier
Organic solid waste (OSW) is not only a major source of environmental contamination, but
also a vast store of useful materials due to its high concentration of biodegradable …

Machine learning applications for biochar studies: A mini-review

W Wang, JS Chang, DJ Lee - Bioresource technology, 2024 - Elsevier
Biochar is a promising carbon sink whose application can assist in reducing carbon
emissions. Development of this technology currently relies on experimental trials, which are …

Prediction models for bioavailability of Cu and Zn during composting: Insights into machine learning

B Bai, L Wang, F Guan, Y Cui, M Bao, S Gong - Journal of Hazardous …, 2024 - Elsevier
Bioavailability assessment of heavy metals in compost products is crucial for evaluating
associated environmental risks. However, existing experimental methods are time …

[HTML][HTML] An artificial intelligence approach for identification of microalgae cultures

P Otálora, JL Guzmán, FG Acién, M Berenguel… - New Biotechnology, 2023 - Elsevier
In this work, a model for the characterization of microalgae cultures based on artificial neural
networks has been developed. The characterization of microalgae cultures is essential to …

Machine learning for sustainable organic waste treatment: a critical review

R Gupta, ZH Ouderji, Uzma, Z Yu, WT Sloan… - npj Materials …, 2024 - nature.com
Data-driven modeling is being increasingly applied in designing and optimizing organic
waste management toward greater resource circularity. This study investigates a spectrum of …

Synergistic improvement of humus formation in compost residue by fenton-like and effective microorganism composite agents

JZ Cai, YL Yu, ZB Yang, XX Xu, GC Lv, CL Xu… - Bioresource …, 2024 - Elsevier
Improving the humification of compost through a synergistic approach of biotic and abiotic
methods is of great significance. This study employed a composite reagent, comprising …