Optimizing the early-stage of composting process emissions – artificial intelligence primary tests DOI Creative Commons
Joanna Rosik, Maciej Karczewski, Sylwia Stegenta-Dąbrowska

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Nov. 8, 2024

Although composting has many advantages in treating organic waste, problems and challenges are still associated with emissions, like NH

Language: Английский

Selection Path for Energy-Efficient Food Waste Management in Urban Areas: Scenario Analysis and Insights from Poland DOI Creative Commons
Anna Rolewicz-Kalińska, Krystyna Lelicińska-Serafin, Piotr Manczarski

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(2), P. 385 - 385

Published: Jan. 17, 2025

The problem of food being wasted in households has become an essential challenge recent years. Food waste can be valorized accordance with the principles sustainable development, including as a source energy. This study analyses potential anaerobic fermentation, pyrolysis, ethanol incineration, and composting to treat waste, focusing on its energy yield. research considered two scenarios for generating Poland both near term (2030) long (2050). Scenarios were proposed regions different levels urbanization demographic trends. criteria selection technologies energy-efficient processing from identified, taking into account current state these technologies, their prospective changes, nature regions, trajectory generation, spatial generation rate, potential. Technologies like methane fermentation thermochemical methods should developed densely populated areas high rate. Among processes, fast pyrolysis will provide most significant benefits, followed by moderate biocarbonization—at similar levels. Incineration is placed between carbonization gasification. In less lower rates, combining substrates co-processing green considered. Biocarbonization systems integrated rural regions.

Language: Английский

Citations

0
Dynamic nitrogen metabolism in urban environments: Cross-sectoral and cross-media insights for sustainable management DOI

Yu Lin,

Haoyi Lv,

Jiachao Ke

et al.

Resources Environment and Sustainability, Journal Year: 2025, Volume and Issue: 19, P. 100193 - 100193

Published: Jan. 27, 2025

Language: Английский

Citations

0
Mechanisms of removing terdizolamide phosphate from water by the activation of potassium peroxymonosulfate with CeFe2O4 biochar DOI
Lei Zhang, Lei Zhang, Shujuan Dai

et al.

Environmental Science and Pollution Research, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 30, 2025

Language: Английский

Citations

0
Advances in innovative extraction techniques for polysaccharides, peptides, and polyphenols from distillery by-products: Common extraction techniques, emerging technologies, and AI-driven optimization DOI
Kouadio Jean Eric‐Parfait Kouamé, Ebenezer Ola Falade, Yanyun Zhu

et al.

Food Chemistry, Journal Year: 2025, Volume and Issue: 476, P. 143326 - 143326

Published: Feb. 18, 2025

Language: Английский

Citations

0
Leveraging Artificial Intelligence Models (GBR, SVR, and GA) for Efficient Chromium Reduction via UV/Trichlorophenol/Sulfite Reaction DOI Creative Commons
Amir H. Mohammadi,

Parsa Khakzad,

Tayebeh Rasolevandi

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104599 - 104599

Published: March 1, 2025

Language: Английский

Citations

0
Machine learning for enhancing prediction of biogas production and building a VFA/ALK soft sensor in full-scale dry anaerobic digestion of kitchen food waste DOI

Jinlin Zou,

Fan Lü, Long Chen

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 371, P. 123190 - 123190

Published: Nov. 5, 2024

Language: Английский

Citations

2
Development of Artificial Intelligence/Machine Learning (AI/ML) Models for Methane Emissions Forecasting in Seaweed DOI Creative Commons
Clifford Louime,

Tariq Asleem Raza

Methane, Journal Year: 2024, Volume and Issue: 3(3), P. 485 - 499

Published: Sept. 4, 2024

This research project aimed to address the growing concern about methane emissions from seaweed by developing a Convolutional Neural Network (CNN) model capable of accurately predicting these emissions. The study used PANDAS read and analyze dataset, incorporating statistical measures like mean, median, standard deviation understand dataset. CNN was trained using ReLU activation function mean absolute error as loss function. performance evaluated through MAPE graphs, comparing percentage (MAPE) between training validation sets true predicted emissions, analyzing trends in yearly greenhouse gas results demonstrated that achieved high level accuracy with low expected actual values. approach should enhance our understanding Sargassum, contributing more accurate environmental impact assessments effective mitigation strategies.

Language: Английский

Citations

0
Optimizing the early-stage of composting process emissions – artificial intelligence primary tests DOI Creative Commons
Joanna Rosik, Maciej Karczewski, Sylwia Stegenta-Dąbrowska

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Nov. 8, 2024

Although composting has many advantages in treating organic waste, problems and challenges are still associated with emissions, like NH

Language: Английский

Citations

0