Optimization of process parameters for remediation performance on mass removal of tetrachloroethylene source zones by using Taguchi design of experiment and artificial neural network-Reference-Cited by-同舟云学术

Optimization of process parameters for remediation performance on mass removal of tetrachloroethylene source zones by using Taguchi design of experiment and artificial neural network

Published:2022-03-23 Issue:3 Volume:36 Page:564-574
ISSN:1747-6585
Container-title:Water and Environment Journal
language:en
Short-container-title:Water & Environment J

Author:

Şahin Yıldız¹^ORCID,Akyol Nihat Hakan²^ORCID,Kasap Sedanur Selay¹^ORCID

Affiliation:

1. Department of Industrial Engineering Kocaeli University Kocaeli Turkey

2. Department of Geological Engineering Kocaeli University Kocaeli Turkey

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.12787

Reference44 articles.

1. Surfactant-Enhanced Permanganate Oxidation on Mass-Flux Reduction and Mass Removal (MFR-MR) Relationship for Pool-Dominated TCE Source Zones in Heterogeneous Porous Media

2. Impact of Enhanced-Flushing Reagents and Organic Liquid Distribution on Mass Removal and Mass Discharge Reduction

3. Effect of Cyclodextrin-Enhanced Dissolution on Mass Removal and Mass-Flux Reduction Relationships for Non-uniformly Organic Liquid Distribution in Heterogeneous Porous Media

4. EMG Sinyallerinin Kısa Zamanlı Fourier Dönüşüm Özellikleri Kullanılarak Yapay Sinir Ağları ile Sınıflandırılması

5. Çok Yanıtlı Kalite Karakteristiklerinin Eşzamanlı Eniyilemesinde Taguchi Yöntemi ve Otomotiv Endüstrisinde Bir Uygulama;Baynal K.;Journal of Industrial Engineering,2005

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2. Multi‐response optimization of process parameters for remediation of tetrachloroethylene pools by surfactants: Application of Taguchi design of experiment and Artificial Neural Network;Water and Environment Journal;2023-01-31