Biomimicry-Inspired Automated Machine Learning Fit-for-Purpose Wastewater Treatment for Sustainable Water Reuse DOI Open Access
Vasileios Alevizos, Zongliang Yue,

Sabrina Edralin

et al.

Water, Journal Year: 2025, Volume and Issue: 17(9), P. 1395 - 1395

Published: May 6, 2025

The growing global freshwater scarcity urgently requires innovative wastewater treatment technologies. This study hypothesized that biomimicry-inspired automated machine learning (AML) could effectively manage variability through adaptive processing techniques. Utilizing decentralized swarm intelligence, specifically the Respected Parametric Insecta Swarm (RPIS), system demonstrated robust adaptability to fluctuating influent conditions, maintaining stable effluent quality without centralized control. Bio-inspired oscillatory control algorithms maintained stability under dynamic scenarios, while sensor feedback enhanced real-time responsiveness. Machine (ML) methods inspired by biological morphological evolution accurately classified characteristics (F1 score of 0.91), optimizing resource allocation dynamically. Significant reductions were observed, with chemical consumption decreasing approximately 11% and additional energy usage declining 14%. Furthermore, bio-inspired membranes selective permeability substantially reduced fouling, minimal fouling for up 30 days. Polynomial chaos expansions efficiently approximated complex nonlinear interactions, reducing computational overhead 35% parallel processing. Decentralized allowed rapid recalibration parameters, achieving pathogen removal turbidity near 3.2 NTU (Nephelometric Turbidity Units), total suspended solids consistently below 8 mg/L. Integrating biomimicry AML thus significantly advances sustainable reclamation practices, offering quantifiable improvements critical resource-efficient water management.

Language: Английский

Impact of Boehmite and Gibbsite on the supercapacitor performances of Polypyrrole: γ-AlO(OH)/PPy/CF and γ-Al(OH)3/PPy/CF flexible and wearable nanocomposites DOI
Melih Beşir Arvas,

Sultan Yaylagül,

Kardelen Uzbiçen

et al.

Journal of Alloys and Compounds, Journal Year: 2025, Volume and Issue: unknown, P. 178879 - 178879

Published: Jan. 1, 2025

Language: Английский

Citations

0
Advancing in Situ synthesis of Zn3(OH)2V2O7·2H2O/Betalains nanocomposite for simultaneous enhancement of electrochemical performance and green energy storage in high-performance Li-Ion batteries and supercapacitors DOI

T. L. Soundarya,

M. Jayachandran,

T. Maiyalagan

et al.

Materials Science and Engineering B, Journal Year: 2025, Volume and Issue: 317, P. 118189 - 118189

Published: March 4, 2025

Language: Английский

Citations

0
Biomimicry-Inspired Automated Machine Learning Fit-for-Purpose Wastewater Treatment for Sustainable Water Reuse DOI Open Access
Vasileios Alevizos, Zongliang Yue,

Sabrina Edralin

et al.

Water, Journal Year: 2025, Volume and Issue: 17(9), P. 1395 - 1395

Published: May 6, 2025

The growing global freshwater scarcity urgently requires innovative wastewater treatment technologies. This study hypothesized that biomimicry-inspired automated machine learning (AML) could effectively manage variability through adaptive processing techniques. Utilizing decentralized swarm intelligence, specifically the Respected Parametric Insecta Swarm (RPIS), system demonstrated robust adaptability to fluctuating influent conditions, maintaining stable effluent quality without centralized control. Bio-inspired oscillatory control algorithms maintained stability under dynamic scenarios, while sensor feedback enhanced real-time responsiveness. Machine (ML) methods inspired by biological morphological evolution accurately classified characteristics (F1 score of 0.91), optimizing resource allocation dynamically. Significant reductions were observed, with chemical consumption decreasing approximately 11% and additional energy usage declining 14%. Furthermore, bio-inspired membranes selective permeability substantially reduced fouling, minimal fouling for up 30 days. Polynomial chaos expansions efficiently approximated complex nonlinear interactions, reducing computational overhead 35% parallel processing. Decentralized allowed rapid recalibration parameters, achieving pathogen removal turbidity near 3.2 NTU (Nephelometric Turbidity Units), total suspended solids consistently below 8 mg/L. Integrating biomimicry AML thus significantly advances sustainable reclamation practices, offering quantifiable improvements critical resource-efficient water management.

Language: Английский

Citations

0