Prediction of arsenic removal in aqueous solutions with non‐neural network algorithms
Author:
Affiliation:
1. Department of Computer Science King Faisal University Al‐Ahsa Saudi Arabia
2. Department of Chemical Engineering King Faisal University Al‐Ahsa Saudi Arabia
Funder
Deanship of Scientific Research, King Faisal University
Publisher
Wiley
Subject
General Chemical Engineering
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1002/cjce.23966
Reference56 articles.
1. Carbon nanotubes as adsorbents in environmental pollution management: A review
2. Environmental Source of Arsenic Exposure
3. Arsenic Pollution
4. Arsenic: occurrence, toxicity and speciation techniques
5. A method for preparing silica-containing iron(III) oxide adsorbents for arsenic removal
Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Molecular simulations and deep neural networks‐based interpretable machine learning modelling of reverse adsorptive MOFs for ethane/ethylene separation;The Canadian Journal of Chemical Engineering;2024-08-20
2. Predicting bioavailability of potentially toxic elements (PTEs) in sediment using various machine learning (ML) models: A case study in Mahabad Dam and River-Iran;Journal of Environmental Management;2024-08
3. Predictive Modeling for Pollutant Removal: Machine Learning Algorithms for Predictive Analysis;Application of Artificial Intelligence in Wastewater Treatment;2024
4. Machine Learning for Heavy Metal Removal from Water: Recent Advances and Challenges;ACS ES&T Water;2023-10-18
5. Modeling, optimization and understanding of adsorption process for pollutant removal via machine learning: Recent progress and future perspectives;Chemosphere;2023-01
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3