Deep learning for water quality

Nature Water, Journal Year: 2024, Volume and Issue: 2(3), P. 228 - 241

Published: March 12, 2024

Language: Английский

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Guideline for modeling solid-liquid adsorption: Kinetics, isotherm, fixed bed, and thermodynamics

Chemosphere, Journal Year: 2023, Volume and Issue: 349, P. 140736 - 140736

Published: Nov. 21, 2023

Language: Английский

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A review of machine learning for modeling air quality: Overlooked but important issues

Atmospheric Research, Journal Year: 2024, Volume and Issue: 300, P. 107261 - 107261

Published: Jan. 21, 2024

Language: Английский

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Deciphering carbon emissions in urban sewer networks: Bridging urban sewer networks with city-wide environmental dynamics

Water Research, Journal Year: 2024, Volume and Issue: 256, P. 121576 - 121576

Published: April 6, 2024

Language: Английский

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How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences

Earth s Future, Journal Year: 2024, Volume and Issue: 12(7)

Published: July 1, 2024

Abstract Interpretable Machine Learning (IML) has rapidly advanced in recent years, offering new opportunities to improve our understanding of the complex Earth system. IML goes beyond conventional machine learning by not only making predictions but also seeking elucidate reasoning behind those predictions. The combination predictive power and enhanced transparency makes a promising approach for uncovering relationships data that may be overlooked traditional analysis. Despite its potential, broader implications field have yet fully appreciated. Meanwhile, rapid proliferation IML, still early stages, been accompanied instances careless application. In response these challenges, this paper focuses on how can effectively appropriately aid geoscientists advancing process understanding—areas are often underexplored more technical discussions IML. Specifically, we identify pragmatic application scenarios typical geoscientific studies, such as quantifying specific contexts, generating hypotheses about potential mechanisms, evaluating process‐based models. Moreover, present general practical workflow using address research questions. particular, several critical common pitfalls use lead misleading conclusions, propose corresponding good practices. Our goal is facilitate broader, careful thoughtful integration into science research, positioning it valuable tool capable enhancing current

Language: Английский

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Prediction of effluent total nitrogen and energy consumption in wastewater treatment plants: Bayesian optimization machine learning methods

Bioresource Technology, Journal Year: 2024, Volume and Issue: 395, P. 130361 - 130361

Published: Jan. 28, 2024

Language: Английский

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Machine learning-driven prediction of phosphorus removal performance of metal-modified biochar and optimization of preparation processes considering water quality management objectives

Bioresource Technology, Journal Year: 2024, Volume and Issue: 403, P. 130861 - 130861

Published: May 18, 2024

Language: Английский

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Optimization of a Novel Engineered Ecosystem Integrating Carbon, Nitrogen, Phosphorus, and Sulfur Biotransformation for Saline Wastewater Treatment Using an Interpretable Machine Learning Approach

Environmental Science & Technology, Journal Year: 2024, Volume and Issue: 58(29), P. 12989 - 12999

Published: July 10, 2024

The denitrifying sulfur (S) conversion-associated enhanced biological phosphorus removal (DS-EBPR) process for treating saline wastewater is characterized by its unique microbial ecology that integrates carbon (C), nitrogen (N), (P), and S biotransformation. However, operational instability arises due to the numerous parameters intricates bacterial interactions. This study introduces a two-stage interpretable machine learning approach predict conversion-driven P efficiency optimize DS-EBPR process. Stage one utilized XGBoost regression model, achieving an

Language: Английский

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Additive Manufacturing Modification by Artificial Intelligence, Machine Learning, and Deep Learning: A Review

Deleted Journal, Journal Year: 2025, Volume and Issue: unknown, P. 200198 - 200198

Published: Feb. 1, 2025

Language: Английский

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Machine Learning Accelerated Discovery of Covalent Organic Frameworks for Environmental and Energy Applications

Environmental Science & Technology, Journal Year: 2025, Volume and Issue: unknown

Published: March 30, 2025

Covalent organic frameworks (COFs) are porous crystalline materials obtained by linking ligands covalently. Their high surface area and adjustable pore sizes make them ideal for a range of applications, including CO2 capture, CH4 storage, gas separation, catalysis, etc. Traditional methods material research, which mainly rely on manual experimentation, not particularly efficient, while with advancements in computer science, high-throughput computational screening based molecular simulation have become crucial discovery, yet they face limitations terms resources time. Currently, machine learning (ML) has emerged as transformative tool many fields, capable analyzing large data sets, identifying underlying patterns, predicting performance efficiently accurately. This approach, termed "materials genomics", combines ML to predict design high-performance materials, significantly speeding up the discovery process compared traditional methods. review discusses functions screening, design, prediction COFs highlights their applications across various domains like thereby providing new research directions enhancing understanding COF applications.

Language: Английский

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