AIMC Topic: Sewage
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Understanding the low-temperature drying process of sludge with machine learning in a sewage-source heat pump drying system.
Journal of environmental management
Jan 23, 2025
Heat pump drying technology based on sewage heat source is an eco-friendly sludge drying method. It can effectively reduce the pollution of natural water bodies by waste heat while reducing energy consumption. However, the drying characteristics of s...
Integrating machine learning, suspect and nontarget screening reveal the interpretable fates of micropollutants and their transformation products in sludge.
Journal of hazardous materials
Jan 11, 2025
Activated sludge enriches vast amounts of micropollutants (MPs) when wastewater is treated, posing potential environmental risks. While standard methods typically focus on target analysis of known compounds, the identity, structure, and concentration...
Optimizing carbon source addition to control surplus sludge yield via machine learning-based interpretable ensemble model.
Environmental research
Dec 17, 2024
Appropriate carbon source addition can save operational costs and reduce surplus sludge yield in the wastewater treatment plant (WWTP). However, the link between carbon source and surplus sludge yield remains neglected although machine learning (ML) ...
Machine learning prediction of fundamental sewage sludge biochar properties based on sludge characteristics and pyrolysis conditions.
Chemosphere
Nov 29, 2024
Sewage sludge biochar (SSBC) has significant potential for resource recovery from sewage sludge (SS) and has been widely studied and applied across various fields. However, the variability in SSBC properties, resulting from the diverse nature of SS a...
Feasibility study of machine learning to explore relationships between antimicrobial resistance and microbial community structure in global wastewater treatment plant sludges.
Bioresource technology
Nov 26, 2024
Wastewater sludges (WSs) are major reservoirs and emission sources of antibiotic resistance genes (ARGs) in cities. Identifying antimicrobial resistance (AMR) host bacteria in WSs is crucial for understanding AMR formation and mitigating biological a...
Interpretable causal machine learning optimization tool for improving efficiency of internal carbon source-biological denitrification.
Bioresource technology
Nov 8, 2024
Interpretable causal machine learning (ICML) was used to predict the performance of denitrification and clarify the relationships between influencing factors and denitrification. Multiple models were examined, and XG-Boost model provided the best pre...
Microbial-Guided prediction of methane and sulfide production in Sewers: Integrating mechanistic models with Machine learning.
Bioresource technology
Oct 15, 2024
Accurate modeling of methane (CH) and sulfide (HS) production in sewer systems was constrained by insufficient consideration of microbial processes under dynamic environmental conditions. This study introduces a microbial-guided machine learning (ML)...
Application of machine learning in ultrasonic pretreatment of sewage sludge: Prediction and optimization.
Environmental research
Oct 5, 2024
In this research, typical industrial scenarios were analyzed optimized by machine learning algorithms, which fills the gap of massive data and industrial requirements in ultrasonic sludge treatment. Principal component analysis showed that the ultras...
Intelligent optimal control model of selection pressure for rapid culture of aerobic granular sludge based on machine learning and simulated annealing algorithm.
Bioresource technology
Sep 24, 2024
Aerobic Granular Sludge (AGS) has advantages over Activated sludge (AS) but faces challenges with long granulation periods. In this study, a novel grey-box model is devised to optimize the cultivation of AGS to shorten the formation time. This model ...
Thermodynamics and explainable machine learning assist in interpreting biodegradability of dissolved organic matter in sludge anaerobic digestion with thermal hydrolysis.
Bioresource technology
Aug 28, 2024
Dissolved organic matter (DOM) is essential in biological treatment, yet its specific roles remain incompletely understood. This study introduces a machine learning (ML) framework to interpret DOM biodegradability in the anaerobic digestion (AD) of s...
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