Advancements in machine learning modelling for energy and emissions optimization in wastewater treatment plants: A systematic review DOI
Taher Abunama,

Antoine Dellieu,

S. Nonet

et al.

Water and Environment Journal, Journal Year: 2024, Volume and Issue: 38(4), P. 554 - 572

Published: July 8, 2024

Abstract Wastewater treatment plants (WWTPs) are high‐energy consumers and major Greenhouse Gas (GHG) emitters. This review offers a comprehensive global overview of the current utilization machine learning (ML) to optimize energy usage reduce emissions in WWTPs. It compiles analyses findings from over hundred studies primarily conducted within last decade. These organized into five primary areas: consumption (EC), aeration (AE), pumping (PE), sludge (STE) greenhouse gas (GHG). Additionally, they further categorized based on type, scale application, geographic location, year, performance metrics, software, etc. ANNs emerged as most prevalent, closely trailed by FL RF. While GA PSO predominant metaheuristic approaches. Despite increasing complexity, researchers inclined towards employing hybrid models enhance performance. Reported reductions or GHG spanned various ranges, falling 0–10%, 10–20% >20% brackets.

Language: Английский

The Influence of Sewage Sludge Composts on the Enzymatic Activity of Reclaimed Post-Mining Soil DOI Open Access
Magdalena Myszura-Dymek,

Grażyna Żukowska

Sustainability, Journal Year: 2023, Volume and Issue: 15(6), P. 4749 - 4749

Published: March 7, 2023

Mining leads to serious degradation of the ecological values landscape. After mining is completed, degraded areas should be reclamated in order mitigate destructive effects activities. Effective reclamation aims initiate soil-forming processes. The paper evaluates land post-mining 12 14 years after process. assessment was based on a determination activity selected enzymes. Municipal sewage sludge compost (SSC) and with composition 70% municipal + 30% fly ash (SSFAC) were used as an external source organic matter reclamation. dehydrogenases, phosphatases, urease determined. fertilization reclaimed soil caused significant increase assessed Significantly higher dehydrogenase found treated SSC. SSFAC characterized by phosphatase urease. one-time application composts from ash, introduction mixture grasses, allow for permanent effect. An additional advantage this model waste management, which part circular economy strategy.

Language: Английский

Citations

9

A fatigue crack growth prediction method on small datasets based on optimized deep neural network and Delaunay data augmentation DOI

Weixing Liang,

Min Lou, Yu Wang

et al.

Theoretical and Applied Fracture Mechanics, Journal Year: 2023, Volume and Issue: 129, P. 104218 - 104218

Published: Nov. 30, 2023

Language: Английский

Citations

9

Properties of Organic Matter in Composts Based on Sewage Sludge DOI Creative Commons
Magdalena Myszura-Dymek

Journal of Ecological Engineering, Journal Year: 2024, Volume and Issue: 25(7), P. 70 - 81

Published: May 20, 2024

The aim of the research was to assess quality organic matter contained in sewage sludge composting products and their co-composting with fly ash mineral wool.The object were composts produced using stabilized from municipal treatment plant (SS_1C) addition 20% (SSF_2C) 30% (SSF_3C) 5% (SSW_4C) 10% (SSW_5C) wool.Selected physicochemical properties, fractional composition humic compounds, degree rate humification determined compost samples taken after 180 days composting.The reaction evaluated close optimal for mature composts.Co-composting wool increased sorption capacity compared SS_1C.Due content available P Mg, discussed formed SS_1C>SSF_2C SSF_3C>SSW_4C SSW_5C series.However, terms K content: SSF_2C SSW_5C>SS_1C.In SS_1C carbon (TOC) slightly higher, but no statistically significant effect on TOC confirmed.The significantly total nitrogen content.Due index, series: SSW_4C > SSF_3C.The values C-KH/C-KF ratio typical good soils, while remaining lower.The assessed characterized by poorly humified materials, highest this indicator found indicators indicate that 100% quality.

Language: Английский

Citations

3

Development and optimization of a neural network model using genetic algorithm to predict the performance of a packed bed reactor treating sulphate-rich wastewater DOI Creative Commons
Manoj Kumar,

Rohil Saraf,

Shishir Kumar Behera

et al.

Case Studies in Chemical and Environmental Engineering, Journal Year: 2024, Volume and Issue: 10, P. 100793 - 100793

Published: June 10, 2024

Language: Английский

Citations

3

Application of Neural Networks and Genetic Algorithms in Optimization Model for Flower Base Layout DOI
Yao Tong, Shangyi Yang, Lei Zhong

et al.

Sustainable civil infrastructures, Journal Year: 2025, Volume and Issue: unknown, P. 711 - 724

Published: Jan. 1, 2025

Language: Английский

Citations

0

Stochastic State-Space Modeling for Sludge Concentration Height at the Ucubamba Guangarcucho Wastewater Treatment Plant DOI Open Access
Cristian Luis Inca Balseca, Cristian Salazar, Jesús Rodríguez-Flores

et al.

Water, Journal Year: 2025, Volume and Issue: 17(6), P. 793 - 793

Published: March 10, 2025

Wastewater treatment plants consist of many biological reactors and a settler, representing an example large-scale, nonlinear systems. The wastewater plant in this study operates using activated sludge system, which relies on processes to treat effectively. It is for reason that iterative process modeling was used through the implementation Extended Kalman Filter (EKF) predict height layer secondary clarifiers, where accumulation occurs during sedimentation process. This technique consists maximum likelihood estimation works more consistently various noise scenarios. As result evaluation model estimated by (EKF), suitability tends be concluded on. In sense, prediction sewage systems represents complicated heteroscedastic process, can understood as phenomenon influenced variety factors. Therefore, does not identify problems estimates thorough examination residuals. state-space increases adaptability adjustability achieve structural optimization plant. approach viable effective solution efficient management polluting levels minimizing possible environmental impact out-of-control situations plants.

Language: Английский

Citations

0

Coupled Deep Neural Network Model with CFD for Predicting the Heat Transfer Coefficient in Fluidized Beds DOI
Chhotelal Prajapati,

Mahesh Nadda,

Kushagra Singh

et al.

Industrial & Engineering Chemistry Research, Journal Year: 2025, Volume and Issue: unknown

Published: April 16, 2025

Language: Английский

Citations

0

Composting Modelling: State of the Art DOI
A. Trémier

Royal Society of Chemistry eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 117 - 134

Published: April 25, 2025

This chapter on composting process modelling offers a comprehensive overview of the current state modelling, highlighting two primary approaches: mechanistic and probabilistic. Mechanistic models focus biological, mass, heat transfer processes within composting, considering factors like organic matter biodegradability, microbial biomass, environmental conditions such as temperature humidity. These typically employ deterministic methods to simulate process, though some stochastic approaches also exist account for variability. explores role artificial intelligence (AI) machine learning (ML) in modelling. techniques, particularly neural networks genetic algorithms, are increasingly used predict outcomes optimize processes, complementing by providing insights into complex, non-linear relationships. However, limitations AI ML, data dependency interpretability challenges, discussed. emphasizes need further research areas maturation phase, passive aeration nutrient cycling, well integration with enhance accuracy applicability simulations.

Language: Английский

Citations

0

Optimizing swine manure composting parameters with integrated CatBoost and XGBoost models: nitrogen loss mitigation and mechanism DOI
Xuan Wu, Ying Ren,

Weilong Wu

et al.

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 388, P. 125995 - 125995

Published: May 30, 2025

Language: Английский

Citations

0

AI-Powered Machine Learning Models for Monitoring and Optimization of Biodrying Process DOI Creative Commons
Abhisit Bhatsada,

Panida Payomthip,

Tanik Itsarathorn

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 105584 - 105584

Published: June 1, 2025

Language: Английский

Citations

0