Application of machine learning for environmentally friendly advancement: exploring biomass-derived materials in wastewater treatment and agricultural sector − a review DOI Creative Commons
Banza Jean Claude, Linda L. Sibali

Journal of Environmental Science and Health Part A, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 16

Published: Feb. 2, 2025

There are several uses for biomass-derived materials (BDMs) in the irrigation and farming industries. To solve problems with material, process, supply chain design, BDM systems have started to use machine learning (ML), a new technique approach. This study examined articles published since 2015 understand better current status, future possibilities, capabilities of ML supporting environmentally friendly development applications. Previous applications were classified into three categories according their objectives: material process performance prediction sustainability evaluation. helps optimize BDMs systems, predict properties performance, reverse engineering, data difficulties evaluations. Ensemble models cutting-edge Neural Networks operate satisfactorily on these datasets easily generalized. neural network poor interpretability, there not been any studies assessment that consider geo-temporal dynamics; thus, building methods is currently practical. Future research should follow workflow. Investigating potential system optimization, evaluation sustainable requires further investigation.

Language: Английский

Machine learning-driven prediction of nitrate-N adsorption efficiency by Fe-modified biochar: Refined model tuning and identification of crucial features DOI
Chen Li,

Xie Guixian,

Jing Li

et al.

Journal of Water Process Engineering, Journal Year: 2025, Volume and Issue: 70, P. 107026 - 107026

Published: Jan. 22, 2025

Language: Английский

Citations

1

Application of machine learning for environmentally friendly advancement: exploring biomass-derived materials in wastewater treatment and agricultural sector − a review DOI Creative Commons
Banza Jean Claude, Linda L. Sibali

Journal of Environmental Science and Health Part A, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 16

Published: Feb. 2, 2025

There are several uses for biomass-derived materials (BDMs) in the irrigation and farming industries. To solve problems with material, process, supply chain design, BDM systems have started to use machine learning (ML), a new technique approach. This study examined articles published since 2015 understand better current status, future possibilities, capabilities of ML supporting environmentally friendly development applications. Previous applications were classified into three categories according their objectives: material process performance prediction sustainability evaluation. helps optimize BDMs systems, predict properties performance, reverse engineering, data difficulties evaluations. Ensemble models cutting-edge Neural Networks operate satisfactorily on these datasets easily generalized. neural network poor interpretability, there not been any studies assessment that consider geo-temporal dynamics; thus, building methods is currently practical. Future research should follow workflow. Investigating potential system optimization, evaluation sustainable requires further investigation.

Language: Английский

Citations

0