Insights into the estimation of hydroxyl radical rate constant of water contaminants in AOP using new smart QSPR models-Reference-Cited by-同舟云学术

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Insights into the estimation of hydroxyl radical rate constant of water contaminants in AOP using new smart QSPR models

Published:2020-08-07 Issue:1 Volume:35 Page:312-321
ISSN:1747-6585
Container-title:Water and Environment Journal
language:en
Short-container-title:Water Environ. J.

Author:

Tangestani Ebrahim¹,Keivanimehr Farhad²,Ghadiri Mahdi³⁴^ORCID

Affiliation:

1. Chemical Engineering Department Tarbiat Modares University Tehran Iran

2. Chemical Engineering Department Amirkabir University of Technology Mahshahr Iran

3. Informetrics Research Group Ton Duc Thang University Ho Chi Minh City Vietnam

4. Faculty of Applied Sciences Ton Duc Thang University Ho Chi Minh City Vietnam

Publisher

Wiley

Subject

Management, Monitoring, Policy and Law,Pollution,Water Science and Technology,Environmental Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/wej.12628

Reference42 articles.

1. Predicting minor nutrient requirements of hazelnut shoot cultures using regression trees

2. Identifying Risk Factors for Drug Use in an Iranian Treatment Sample: A Prediction Approach Using Decision Trees

3. A new chemical structure-based model to estimate solid compound solubility in supercritical CO2

4. An insight into the modeling of sulfur content of sour gases in supercritical region

5. Estimation of adsorption capacity of CO2, CH4, and their binary mixtures in Quidam shale using LSSVM: Application in CO2 enhanced shale gas recovery and CO2 storage

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1. Unveiling the potential of machine learning in cost-effective degradation of pharmaceutically active compounds: A stirred photo-reactor study;Chemosphere;2024-06

2. A review on the lignocellulosic derived biochar-based catalyst in wastewater remediation: Advanced treatment technologies and machine learning tools;Bioresource Technology;2023-11

3. The state of art on the prediction of efficiency and modeling of the processes of pollutants removal based on machine learning;Science of The Total Environment;2022-02

4. Progress in QSPR modelling methods;Chinese Science Bulletin;2021-01-12

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