Monitoring of Municipal Solid Waste Operations in Urban Areas: A Review DOI
Ashwani Kumar,

Yash Choudhary,

Amit Kumar

et al.

Lecture notes in civil engineering, Journal Year: 2023, Volume and Issue: unknown, P. 295 - 310

Published: Sept. 7, 2023

Language: Английский

Artificial intelligence and machine learning approaches in composting process: A review DOI
Fulya Aydın Temel, Özge Cağcağ Yolcu, Nurdan Gamze Turan

et al.

Bioresource Technology, Journal Year: 2023, Volume and Issue: 370, P. 128539 - 128539

Published: Jan. 3, 2023

Language: Английский

Citations

49
Machine learning and circular bioeconomy: Building new resource efficiency from diverse waste streams DOI

To‐Hung Tsui,

Mark C.M. van Loosdrecht, Yanjun Dai

et al.

Bioresource Technology, Journal Year: 2022, Volume and Issue: 369, P. 128445 - 128445

Published: Dec. 5, 2022

Language: Английский

Citations

54
Artificial intelligence technologies in bioprocess: Opportunities and challenges DOI

Yang Cheng,

Xinyu Bi,

Yameng Xu

et al.

Bioresource Technology, Journal Year: 2022, Volume and Issue: 369, P. 128451 - 128451

Published: Dec. 9, 2022

Language: Английский

Citations

39
Machine-learning intervention progress in the field of organic waste composting: Simulation, prediction, optimization, and challenges DOI

Li-ting Huang,

Jia-yi Hou,

Hongtao Liu

et al.

Waste Management, Journal Year: 2024, Volume and Issue: 178, P. 155 - 167

Published: Feb. 24, 2024

Language: Английский

Citations

12
Biodegradation of high di-(2-Ethylhexyl) phthalate (DEHP) concentration by food waste composting and its toxicity assessment using seed germination test DOI
‬Huu-Tuan Tran, Chitsan Lin, Su Shiung Lam

et al.

Environmental Pollution, Journal Year: 2022, Volume and Issue: 316, P. 120640 - 120640

Published: Nov. 17, 2022

Language: Английский

Citations

36
MachIne learning for nutrient recovery in the smart city circular economy – A review DOI
Allan Soo, Li Wang, Chen Wang

et al.

Process Safety and Environmental Protection, Journal Year: 2023, Volume and Issue: 173, P. 529 - 557

Published: March 16, 2023

Language: Английский

Citations

21
Applications of machine learning tools for biological treatment of organic wastes: Perspectives and challenges DOI Creative Commons
Long Chen, Pinjing He, Hua Zhang

et al.

Circular Economy, Journal Year: 2024, Volume and Issue: 3(2), P. 100088 - 100088

Published: May 31, 2024

Biological treatment technologies (such as anaerobic digestion, composting, and insect farming) have been extensively employed to handle various degradable organic wastes. However, the inherent complexity instability of biological processes adversely affect production renewable energy nutrient-rich products. To ensure stable consistent product quality, researchers invested heavily in control strategies for treatment, with machine learning (ML) recently proving effective optimizing predicting parameters, detecting disturbances, enabling real-time monitoring. This review critically assesses application ML providing an in-depth evaluation key algorithms. study reveals that artificial neural networks, tree-based models, support vector machines, genetic algorithms are leading treatment. A thorough investigation applications farming underscores its remarkable capacity predict products, optimize processes, perform monitoring, mitigate pollution emissions. Furthermore, this outlines challenges prospects encountered applying highlighting crucial directions future research area.

Language: Английский

Citations

8
Review: Biotic and abiotic approaches to artificial humic acids production DOI
Ming Wang, Yunting Li, Hao Peng

et al.

Renewable and Sustainable Energy Reviews, Journal Year: 2023, Volume and Issue: 187, P. 113771 - 113771

Published: Sept. 23, 2023

Language: Английский

Citations

16
Effect of different bulking agents on fed-batch composting and microbial community profile DOI
Fei Wang, Jingyao Wang,

Yuheng He

et al.

Environmental Research, Journal Year: 2024, Volume and Issue: 249, P. 118449 - 118449

Published: Feb. 13, 2024

Language: Английский

Citations

5
Cotton Irrigation Scheduling: Which Approach is the Best Fit for Georgia? DOI Open Access
Michael J. Hayes,

Wesley Porter,

John L. Snider

et al.

˜The œjournal of cotton science/Journal of cotton science, Journal Year: 2025, Volume and Issue: 28(3), P. 136 - 144

Published: Jan. 27, 2025

Cotton (Gossypium hirsutum) is one of the most difficult crops to manage irrigation effectively due crop’s perennial physiology. In recent years, many new technologies have been developed help improve management. The main objective this study was evaluate various management tools and assist farmers in determining which method best for their operation. Other objectives included monitoring soil moisture optimal application point each by logging total rainfall distribution throughout growing season. A three-year conducted at University Georgia (UGA) Stripling Irrigation Research Park near Camilla, GA where cotton grown on loamy sand soil. lateral movement, overhead sprinkler system equipped with a variable rate allowed plots be irrigated independently based treatment. treatments 20- 45-kPa weighted average water tension (SWT) measurements made using three Watermark SWT sensors placed two replicates. UGA SmartIrrigation app (SI app), Checkbook method, rainfed check were trial. Each evaluated crop yield, water-use efficiency, profitability. analysis revealed significant variations several metrics between validates threshold SI are top-performing advanced scheduling showed importance strengths weaknesses method.

Language: Английский

Citations

0